system

Abstract

The system according to the embodiment aims to enable users to experience a sporting event in a realistic manner in virtual reality and receive advice based on their movements. [Solution] A system according to an embodiment includes a reception unit, a generation unit, an analysis unit, and a competition unit. The reception unit selects a sporting event that a user wants to experience. The generation unit realistically recreates the sporting event selected by the reception unit. The analysis unit analyzes the user's movements in real time within the sporting event recreated by the generation unit and provides advice based on the user's movements. The competition unit simulates a match against a virtual opponent based on the information analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for users to experience a sporting event realistically in virtual reality and receive advice based on their movements.

[0005] The system according to the embodiment aims to enable users to experience a sporting event in a realistic manner in virtual reality and receive advice based on their movements. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, an analysis unit, and a competition unit. The reception unit selects a sporting event that the user wants to experience. The generation unit realistically reproduces the sporting event selected by the reception unit. The analysis unit analyzes the user's movements in real time within the sporting event reproduced by the generation unit and provides advice based on the user's movements. The competition unit simulates a match against a virtual opponent based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to experience sporting events in a realistic virtual reality environment and receive advice based on their movements. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A virtual reality sports experience system according to an embodiment of the present invention utilizes cutting-edge VR technology and a generation AI to realistically recreate a sporting event selected by the user and offer matches against virtual opponents. The system allows users to select the sporting event they wish to experience, and the generation AI then realistically recreates the event. The user wears a VR headset and experiences the sport in virtual reality. The generation AI analyzes the user's movements in real time and provides appropriate advice. The user can also enjoy matches against virtual opponents. This allows users to enjoy the fascinating and exciting experience of sports in an immersive environment. For example, a user selects a sporting event they wish to experience. To do so, the user simply inputs their starting point and destination. For example, they might input "I want to go from my home to the train station." This information is then input into the generation AI. The generation AI then analyzes the input information and creates a video showing how to get from their current location to their destination. The generation AI calculates the optimal route based on map data and generates a video that follows that route. For example, if a user inputs a route from their home to the train station, a video that follows that route is generated. The generated video begins navigation based on the orientation of the user's smartphone. For example, if a user points their smartphone north, the video will also begin navigating north. This allows the user to navigate in the direction they are facing. Furthermore, the video on the screen moves in sync with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows the user to navigate at their own pace. This mechanism creates a simple structure that is easy to use for both children and the elderly, making it appealing to everyone. Users can intuitively navigate without complex operations. Furthermore, since the viewpoint of the smartphone is the axis of all directions, they will not get lost, and since the smartphone is pointed horizontally, walking safety is ensured. For example, if a user walks with their smartphone pointed horizontally, the video will also be displayed horizontally, allowing the user to walk safely. This allows the virtual reality sports experience system to realistically recreate a sporting event selected by the user and provide matches against virtual opponents.

[0029] A virtual reality sports experience system according to an embodiment includes a reception unit, a generation unit, an analysis unit, and a competition unit. The reception unit selects a sports event that the user wants to experience. Examples of sports events that the user wants to experience include, but are not limited to, soccer, basketball, and tennis. For example, if the user is relaxed, the reception unit may prioritize displaying relaxing sports events (such as yoga or golf). Furthermore, if the user is excited, the reception unit may prioritize displaying active sports events (such as soccer or basketball). Furthermore, if the user is stressed, the reception unit may prioritize displaying sports events that are effective for stress relief (such as boxing or running). The generation unit uses a generation AI to realistically reproduce the sports event selected by the reception unit. For example, 3D modeling or virtual reality technology may be used for the reproduction, but is not limited to these examples. For example, the generation unit realistically reproduces the field or stadium of the sports event selected by the user. Furthermore, the generation unit may estimate the user's emotions and adjust the degree of reproduction of the sports event based on the estimated user emotions. For example, if the user is relaxed, a sporting event progressing at a leisurely pace is reproduced. The analysis unit uses a generation AI to analyze the user's movements in real time within the sporting event reproduced by the generation unit and provides appropriate advice. For example, a motion analysis algorithm or sensor technology may be used for the analysis, but is not limited to such examples. For example, the analysis unit analyzes the user's movements in real time and immediately identifies areas for improvement. The analysis unit can also provide more accurate advice by referring to the user's past performance data. The match unit uses the generation AI to simulate a match against a virtual opponent based on the information analyzed by the analysis unit. For example, an AI opponent or past player data may be used for the match, but is not limited to such examples. For example, the match unit estimates the user's emotions and adjusts the opponent's strength based on the estimated user emotions. The match unit can also provide a more realistic match by referring to the user's past match data.As a result, the virtual reality sports experience system according to the embodiment can realistically reproduce a sports event selected by the user and provide a match with a virtual opponent.

[0030] The generation unit can realistically recreate a field or stadium for a sporting event selected by a user. The generation unit, for example, uses 3D modeling technology to realistically recreate a field or stadium for a sporting event selected by a user. For example, the generation unit recreates a soccer field using 3D modeling technology, allowing the user to play therein. The generation unit can also realistically recreate a stadium for a sporting event selected by a user using virtual reality technology. For example, the generation unit recreates a basketball stadium using virtual reality technology, allowing the user to play therein. This realistically recreates the field or stadium for a sporting event selected by a user, providing a more immersive experience. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data about the field or stadium for a sporting event selected by a user into the generation AI, which can then recreate the field or stadium based on that data.

[0031] The reception unit can analyze the user's past selection history and recommend sporting events suitable for the user. The reception unit can analyze the user's past selection history using, for example, data mining technology. For example, the reception unit can preferentially display sporting events that the user has frequently selected in the past. The reception unit can also recommend similar sporting events based on the user's past selection history. Furthermore, the reception unit can analyze the user's past selection history and recommend optimal sporting events according to the season or time of day. In this way, by analyzing the user's past selection history, more suitable sporting events can be recommended. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past selection history data into the generation AI, and the generation AI can recommend sporting events based on that data.

[0032] The reception unit can suggest sporting events suitable for the user based on the user's current fitness level and health condition. The reception unit can evaluate the user's fitness level and health condition based on, for example, the user's heart rate and exercise history. For example, the reception unit can analyze the user's heart rate data to evaluate the user's fitness level. The reception unit can also evaluate the user's health condition based on the user's exercise history data. The reception unit can also suggest appropriate sporting events based on the user's fitness level and health condition. For example, the reception unit can suggest challenging sporting events (such as marathons and triathlons) if the user's fitness level is high. The reception unit can also suggest light exercise (such as walking and stretching) if the user's health condition is not good. The reception unit can also suggest customized training programs based on the user's fitness level and health condition. This can provide a safer and more effective experience by suggesting appropriate sporting events based on the user's fitness level and health condition. Some or all of the above-described processing by the reception unit can be performed using or without a generation AI. For example, the reception unit can input the user's heart rate data and exercise history data into the generation AI, which can then use that data to evaluate their fitness level and health condition and suggest sports events.

[0033] The reception unit can prioritize displaying sporting events specific to a region by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information using GPS data. For example, the reception unit identifies the user's current location by using GPS data from the user's smartphone. The reception unit can also acquire the user's geographical location information using a location information service. For example, the reception unit identifies the user's current location by using a location information service. The reception unit also prioritizes displaying sporting events specific to a region by taking into account the user's geographical location information. For example, the reception unit can prioritize displaying sporting events such as beach volleyball and surfing when the user is near the coast. The reception unit can also prioritize displaying sporting events such as hiking and rock climbing when the user is in a mountainous area. The reception unit can also prioritize displaying sporting events such as running and cycling when the user is in an urban area. This makes it possible to provide sporting events specific to a region by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then display sporting events specific to the region based on that data.

[0034] The reception unit can analyze the user's social media activity and suggest related sporting events. The reception unit, for example, analyzes the content of social media posts to identify the user's interests. For example, the reception unit can suggest sporting events that the user frequently mentions on social media. The reception unit can also suggest sporting events in which the user's friends are participating. Furthermore, the reception unit can suggest events related to sports teams or athletes that the user follows. In this way, related sporting events can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's social media data into the generation AI, and the generation AI can suggest sporting events based on that data.

[0035] The generation unit can provide a more realistic experience when recreating a sporting event by referring to the user's past performance data. The generation unit, for example, acquires the user's past performance data from a database. For example, the generation unit can recreate a sporting event of an appropriate level of difficulty based on the user's past performance data. The generation unit can also recreate a sporting event that emphasizes specific techniques or skills by referring to the user's past performance data. Furthermore, the generation unit can analyze the user's past performance data and recreate an individually customized sporting event. This makes it possible to provide a more realistic experience by referring to the user's past performance data. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the user's past performance data into the generation AI, which can then recreate the sporting event based on that data.

[0036] The generation unit can reflect user feedback in real time when reproducing a sporting event. The generation unit, for example, adjusts the progress of the sporting event based on feedback provided by the user in real time. For example, the generation unit can reflect user feedback in real time and adjust the difficulty of the sporting event. The generation unit can also change the content and scenario of the sporting event in real time based on user feedback. This makes it possible to provide a more appropriate experience by reflecting user feedback in real time. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user feedback data into the generation AI, and the generation AI can adjust the sporting event based on that data.

[0037] When recreating a sporting event, the generation unit can incorporate region-specific elements by taking into account the user's geographical location information. The generation unit, for example, acquires the user's geographical location information using GPS data. For example, the generation unit identifies the user's current location using GPS data from the user's smartphone. The generation unit can also acquire the user's geographical location information using a location information service. For example, the generation unit identifies the user's current location using a location information service. The generation unit further incorporates region-specific elements by taking into account the user's geographical location information. For example, the generation unit can recreate region-specific sporting events such as beach volleyball and surfing when the user is near the coast. The generation unit can also recreate region-specific sporting events such as hiking and rock climbing when the user is in a mountainous area. The generation unit can also recreate region-specific sporting events such as running and cycling when the user is in an urban area. In this way, a sporting event incorporating region-specific elements can be provided by taking into account the user's geographical location information. Some or all of the above-described processing by the generation unit may be performed using a generation AI or without a generation AI. For example, the generation unit can input the user's geographical location data into the generation AI, which can then use that data to recreate a sporting event that incorporates elements unique to the region.

[0038] The generation unit can analyze the user's social media activity and incorporate related elements when recreating a sporting event. The generation unit, for example, analyzes social media posts to identify the user's interests. For example, the generation unit recreates sporting events that the user frequently mentions on social media. The generation unit can also recreate sporting events in which the user's friends are participating. Furthermore, the generation unit can incorporate elements related to sports teams and athletes that the user follows. In this way, by analyzing the user's social media activity, it is possible to provide sporting events that incorporate related elements. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the user's social media data into the generation AI, and the generation AI can recreate the sporting event based on that data.

[0039] When analyzing a user's movements, the analysis unit can provide more accurate advice by referring to past performance data. The analysis unit, for example, acquires the user's past performance data from a database. For example, the analysis unit can provide appropriate advice based on the user's past performance data. The analysis unit can also provide advice on specific techniques or skills by referring to the user's past performance data. Furthermore, the analysis unit can analyze the user's past performance data and provide individually customized advice. In this way, more accurate advice can be provided by referring to the past performance data. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's past performance data into the generation AI, and the generation AI can provide advice based on that data.

[0040] When analyzing the user's movements, the analysis unit can provide feedback in real time and immediately point out areas for improvement. The analysis unit, for example, analyzes the user's movements in real time and immediately point out areas for improvement. For example, the analysis unit can analyze the user's movements in real time and suggest appropriate training methods. The analysis unit can also analyze the user's movements in real time and provide feedback that is useful for improving techniques and skills. In this way, by providing feedback in real time, areas for improvement can be immediately pointed out. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input user movement data into a generation AI, and the generation AI can provide feedback based on that data.

[0041] When analyzing the user's movements, the analysis unit can provide region-specific advice by taking geographical location information into consideration. The analysis unit, for example, acquires the user's geographical location information using GPS data. For example, the analysis unit identifies the user's current location using GPS data from the user's smartphone. The analysis unit can also acquire the user's geographical location information using a location information service. For example, the analysis unit identifies the user's current location using a location information service. The analysis unit then provides region-specific advice by taking the user's geographical location information into consideration. For example, the analysis unit can provide advice about beach volleyball or surfing if the user is near the beach. The analysis unit can also provide advice about hiking or rock climbing if the user is in a mountainous area. The analysis unit can also provide advice about running or cycling if the user is in an urban area. In this way, region-specific advice can be provided by taking geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's geographical location information data into the generation AI, and the generation AI can provide region-specific advice based on that data.

[0042] When analyzing the user's movements, the analysis unit can analyze social media activity and provide relevant advice. For example, the analysis unit can analyze social media postings to identify the user's interests. For example, the analysis unit can provide advice related to sports that the user frequently mentions on social media. The analysis unit can also provide advice related to sports in which the user's friends participate. Furthermore, the analysis unit can provide advice related to sports teams or athletes that the user follows. In this way, relevant advice can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's social media data into the generation AI, and the generation AI can provide advice based on that data.

[0043] When simulating a match with a virtual opponent, the match unit can provide a more realistic match by referring to the user's past match data. The match unit, for example, acquires the user's past match data from a database. For example, the match unit can set an opponent of appropriate difficulty based on the user's past match data. The match unit can also set an opponent that emphasizes specific techniques or skills by referring to the user's past match data. Furthermore, the match unit can analyze the user's past match data and set an opponent that is individually customized. In this way, by referring to the user's past match data, a more realistic match can be provided. Some or all of the above-described processing in the match unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the match unit can input the user's past match data into the generation AI, and the generation AI can set an opponent based on that data.

[0044] The match unit can reflect user feedback in real time when simulating a match with a virtual opponent. The match unit, for example, adjusts the strength of the opponent based on feedback provided by the user in real time. For example, the match unit can reflect user feedback in real time and adjust the opponent's strategy. The match unit can also change the opponent's movements and actions in real time based on user feedback. This makes it possible to provide a more appropriate match experience by reflecting user feedback in real time. Some or all of the above-described processing in the match unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the match unit can input user feedback data into the generation AI, and the generation AI can adjust the opponent based on that data.

[0045] When simulating a match with a virtual opponent, the match unit can set a region-specific opponent by taking geographical location information into consideration. The match unit, for example, acquires the user's geographical location information using GPS data. For example, the match unit identifies the user's current location by using GPS data from the user's smartphone. The match unit can also acquire the user's geographical location information by using a location information service. For example, the match unit identifies the user's current location by using a location information service. Furthermore, the match unit sets a region-specific opponent by taking the user's geographical location information into consideration. For example, the match unit can set a beach volleyball or surfing opponent if the user is near the coast. Furthermore, the match unit can set a hiking or rock climbing opponent if the user is in a mountainous area. Furthermore, the match unit can set a running or cycling opponent if the user is in an urban area. In this way, region-specific opponents can be set by taking geographical location information into consideration. Some or all of the above-described processing in the match unit may be performed using a generation AI or without using a generation AI. For example, the competition unit can input the user's geographical location information data into the generation AI, and the generation AI can set an opponent based on that data.

[0046] When simulating a match with a virtual opponent, the match unit can analyze social media activity and set a relevant opponent. The match unit, for example, analyzes social media postings to identify the user's interests. For example, the match unit can set an opponent in a sport that the user frequently mentions on social media. The match unit can also set an opponent in a sport in which the user's friends participate. Furthermore, the match unit can set an opponent related to a sports team or athlete that the user follows. In this way, relevant opponents can be set by analyzing social media activity. Some or all of the above-mentioned processing in the match unit may be performed using or without the generation AI. For example, the match unit can input the user's social media data into the generation AI, and the generation AI can set an opponent based on that data.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reception unit can analyze the user's past selection history and recommend sporting events suitable for the user. For example, the reception unit can analyze the user's past selection history using data mining technology and prioritize displaying sporting events that the user has frequently selected in the past. The reception unit can also recommend similar sporting events based on the user's past selection history. Furthermore, the reception unit can analyze the user's past selection history and recommend optimal sporting events according to the season or time of day. In this way, by analyzing the user's past selection history, more suitable sporting events can be recommended. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past selection history data into the generation AI, and the generation AI can recommend sporting events based on that data.

[0049] The generation unit can suggest sporting events suitable for the user based on the user's current fitness level and health condition. For example, the generation unit evaluates the user's fitness level and health condition based on the user's heart rate and exercise history. For example, the generation unit analyzes the user's heart rate data to evaluate the user's fitness level. The generation unit can also evaluate the user's health condition based on the user's exercise history data. Furthermore, the generation unit suggests appropriate sporting events based on the user's fitness level and health condition. For example, the generation unit can suggest challenging sporting events (such as marathons and triathlons) if the user's fitness level is high. Furthermore, the generation unit can suggest light exercise (such as walking and stretching) if the user's health condition is not good. Furthermore, the generation unit can suggest customized training programs based on the user's fitness level and health condition. This allows for a safer and more effective experience by suggesting appropriate sporting events based on the user's fitness level and health condition. Some or all of the above-described processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input the user's heart rate data and exercise history data into the generation AI, which can then use that data to evaluate their fitness level and health condition and suggest sports events.

[0050] When analyzing a user's movements, the analysis unit can provide more accurate advice by referring to past performance data. For example, the analysis unit retrieves the user's past performance data from a database. For example, the analysis unit can provide appropriate advice based on the user's past performance data. The analysis unit can also provide advice on specific techniques or skills by referring to the user's past performance data. Furthermore, the analysis unit can analyze the user's past performance data and provide individually customized advice. In this way, more accurate advice can be provided by referring to the past performance data. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's past performance data into the generation AI, and the generation AI can provide advice based on that data.

[0051] When simulating a match with a virtual opponent, the match unit can provide a more realistic match by referring to the user's past match data. For example, the match unit retrieves the user's past match data from a database. For example, the match unit can set an opponent of appropriate difficulty based on the user's past match data. The match unit can also set an opponent that emphasizes specific techniques or skills by referring to the user's past match data. Furthermore, the match unit can analyze the user's past match data and set an opponent that is individually customized. In this way, by referring to the user's past match data, a more realistic match can be provided. Some or all of the above-described processing in the match unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the match unit can input the user's past match data into the generation AI, and the generation AI can set an opponent based on that data.

[0052] The generation unit can reflect user feedback in real time when reproducing a sporting event. For example, the generation unit adjusts the progress of the sporting event based on feedback provided by the user in real time. For example, the generation unit can reflect user feedback in real time and adjust the difficulty of the sporting event. The generation unit can also change the content and scenario of the sporting event in real time based on user feedback. This makes it possible to provide a more appropriate experience by reflecting user feedback in real time. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user feedback data into the generation AI, and the generation AI can adjust the sporting event based on that data.

[0053] The match unit can reflect user feedback in real time when simulating a match with a virtual opponent. For example, the match unit adjusts the strength of the opponent based on feedback provided by the user in real time. For example, the match unit can reflect user feedback in real time and adjust the opponent's strategy. The match unit can also change the opponent's movements and actions in real time based on user feedback. This makes it possible to provide a more appropriate match experience by reflecting user feedback in real time. Some or all of the above-mentioned processing in the match unit may be performed using a generation AI or may be performed without using a generation AI. For example, the match unit can input user feedback data into the generation AI, and the generation AI can adjust the opponent based on that data.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit selects the sporting event that the user wants to experience. Sporting events that the user wants to experience include soccer, basketball, tennis, etc. The reception unit prioritizes and displays relaxing sporting events (such as yoga or golf), active sporting events (such as soccer or basketball), and sporting events that are effective for relieving stress (such as boxing or running) according to the user's condition. Step 2: The generation unit uses the generation AI to realistically recreate the sporting event selected by the reception unit. 3D modeling and virtual reality technology are used for the recreation. The generation unit realistically recreates the field and stadium of the sporting event selected by the user and estimates the user's emotions to adjust the degree of recreation. For example, if the user is relaxed, the generation unit recreates a sporting event proceeding at a leisurely pace. Step 3: The analysis unit uses the generation AI to analyze the user's movements in real time within the sporting event recreated by the generation unit and provide appropriate advice. The analysis uses motion analysis algorithms and sensor technology. The analysis unit analyzes the user's movements in real time and immediately identifies areas for improvement. It also references the user's past performance data to provide more accurate advice. Step 4: The match unit uses the generation AI to simulate a match against a virtual opponent based on the information analyzed by the analysis unit. The match uses AI opponents and past player data. The match unit estimates the user's emotions and adjusts the strength of the opponent based on the estimated user emotions. It also references the user's past match data to provide a more realistic match.

[0056] (Example 2) A virtual reality sports experience system according to an embodiment of the present invention utilizes cutting-edge VR technology and a generation AI to realistically recreate a sporting event selected by the user and offer matches against virtual opponents. The system allows users to select the sporting event they wish to experience, and the generation AI then realistically recreates the event. The user wears a VR headset and experiences the sport in virtual reality. The generation AI analyzes the user's movements in real time and provides appropriate advice. The user can also enjoy matches against virtual opponents. This allows users to enjoy the fascinating and exciting experience of sports in an immersive environment. For example, a user selects a sporting event they wish to experience. To do so, the user simply inputs their starting point and destination. For example, they might input "I want to go from my home to the train station." This information is then input into the generation AI. The generation AI then analyzes the input information and creates a video showing how to get from their current location to their destination. The generation AI calculates the optimal route based on map data and generates a video that follows that route. For example, if a user inputs a route from their home to the train station, a video that follows that route is generated. The generated video begins navigation based on the orientation of the user's smartphone. For example, if a user points their smartphone north, the video will also begin navigating north. This allows the user to navigate in the direction they are facing. Furthermore, the video on the screen moves in sync with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows the user to navigate at their own pace. This mechanism creates a simple structure that is easy to use for both children and the elderly, making it appealing to everyone. Users can intuitively navigate without complex operations. Furthermore, since the viewpoint of the smartphone is the axis of all directions, they will not get lost, and since the smartphone is pointed horizontally, walking safety is ensured. For example, if a user walks with their smartphone pointed horizontally, the video will also be displayed horizontally, allowing the user to walk safely. This allows the virtual reality sports experience system to realistically recreate a sporting event selected by the user and provide matches against virtual opponents.

[0057] A virtual reality sports experience system according to an embodiment includes a reception unit, a generation unit, an analysis unit, and a competition unit. The reception unit selects a sports event that the user wants to experience. Examples of sports events that the user wants to experience include, but are not limited to, soccer, basketball, and tennis. For example, if the user is relaxed, the reception unit may prioritize displaying relaxing sports events (such as yoga or golf). Furthermore, if the user is excited, the reception unit may prioritize displaying active sports events (such as soccer or basketball). Furthermore, if the user is stressed, the reception unit may prioritize displaying sports events that are effective for stress relief (such as boxing or running). The generation unit uses a generation AI to realistically reproduce the sports event selected by the reception unit. For example, 3D modeling or virtual reality technology may be used for the reproduction, but is not limited to these examples. For example, the generation unit realistically reproduces the field or stadium of the sports event selected by the user. Furthermore, the generation unit may estimate the user's emotions and adjust the degree of reproduction of the sports event based on the estimated user emotions. For example, if the user is relaxed, a sporting event progressing at a leisurely pace is reproduced. The analysis unit uses a generation AI to analyze the user's movements in real time within the sporting event reproduced by the generation unit and provides appropriate advice. For example, a motion analysis algorithm or sensor technology may be used for the analysis, but is not limited to such examples. For example, the analysis unit analyzes the user's movements in real time and immediately identifies areas for improvement. The analysis unit can also provide more accurate advice by referring to the user's past performance data. The match unit uses the generation AI to simulate a match against a virtual opponent based on the information analyzed by the analysis unit. For example, an AI opponent or past player data may be used for the match, but is not limited to such examples. For example, the match unit estimates the user's emotions and adjusts the opponent's strength based on the estimated user emotions. The match unit can also provide a more realistic match by referring to the user's past match data.As a result, the virtual reality sports experience system according to the embodiment can realistically reproduce a sports event selected by the user and provide a match with a virtual opponent.

[0058] The generation unit can realistically recreate a field or stadium for a sporting event selected by a user. The generation unit, for example, uses 3D modeling technology to realistically recreate a field or stadium for a sporting event selected by a user. For example, the generation unit recreates a soccer field using 3D modeling technology, allowing the user to play therein. The generation unit can also realistically recreate a stadium for a sporting event selected by a user using virtual reality technology. For example, the generation unit recreates a basketball stadium using virtual reality technology, allowing the user to play therein. This realistically recreates the field or stadium for a sporting event selected by a user, providing a more immersive experience. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data about the field or stadium for a sporting event selected by a user into the generation AI, which can then recreate the field or stadium based on that data.

[0059] The reception unit can estimate the user's emotions and customize sporting event options based on the estimated user's emotions. The reception unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the reception unit captures the user's facial expression with a camera and estimates the user's emotions using facial expression recognition technology. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the user's emotions. The reception unit further customizes sporting event options based on the user's emotions. For example, if the user is relaxed, the reception unit can prioritize displaying relaxing sporting events (such as yoga or golf). If the user is excited, the reception unit can prioritize displaying active sporting events (such as soccer or basketball). If the user is stressed, the reception unit can prioritize displaying sporting events that are effective for stress relief (such as boxing or running). This allows the user to customize sporting event options based on the user's emotions, thereby providing more appropriate events. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's facial expression data into the generation AI, which may then infer emotions based on the data and customize sporting event options.

[0060] The reception unit can analyze the user's past selection history and recommend sporting events suitable for the user. The reception unit can analyze the user's past selection history using, for example, data mining technology. For example, the reception unit can preferentially display sporting events that the user has frequently selected in the past. The reception unit can also recommend similar sporting events based on the user's past selection history. Furthermore, the reception unit can analyze the user's past selection history and recommend optimal sporting events according to the season or time of day. In this way, by analyzing the user's past selection history, more suitable sporting events can be recommended. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past selection history data into the generation AI, and the generation AI can recommend sporting events based on that data.

[0061] The reception unit can suggest sporting events suitable for the user based on the user's current fitness level and health condition. The reception unit can evaluate the user's fitness level and health condition based on, for example, the user's heart rate and exercise history. For example, the reception unit can analyze the user's heart rate data to evaluate the user's fitness level. The reception unit can also evaluate the user's health condition based on the user's exercise history data. The reception unit can also suggest appropriate sporting events based on the user's fitness level and health condition. For example, the reception unit can suggest challenging sporting events (such as marathons and triathlons) if the user's fitness level is high. The reception unit can also suggest light exercise (such as walking and stretching) if the user's health condition is not good. The reception unit can also suggest customized training programs based on the user's fitness level and health condition. This can provide a safer and more effective experience by suggesting appropriate sporting events based on the user's fitness level and health condition. Some or all of the above-described processing by the reception unit can be performed using or without a generation AI. For example, the reception unit can input the user's heart rate data and exercise history data into the generation AI, which can then use that data to evaluate their fitness level and health condition and suggest sports events.

[0062] The reception unit can estimate the user's emotion and adjust the interface of the selection screen based on the estimated user's emotion. The reception unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit can also estimate the user's emotion using voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotion. The reception unit can also adjust the interface of the selection screen based on the user's emotion. For example, if the user is nervous, the reception unit can provide an interface with calm colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This allows for a more comfortable operating environment by adjusting the interface based on the user's emotion. Emotion estimation is achieved using, for example, an emotion estimation function using an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's facial expression data into the generation AI, which may then estimate the user's emotion based on the data and adjust the interface.

[0063] The reception unit can prioritize displaying sporting events specific to a region by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information using GPS data. For example, the reception unit identifies the user's current location by using GPS data from the user's smartphone. The reception unit can also acquire the user's geographical location information using a location information service. For example, the reception unit identifies the user's current location by using a location information service. The reception unit also prioritizes displaying sporting events specific to a region by taking into account the user's geographical location information. For example, the reception unit can prioritize displaying sporting events such as beach volleyball and surfing when the user is near the coast. The reception unit can also prioritize displaying sporting events such as hiking and rock climbing when the user is in a mountainous area. The reception unit can also prioritize displaying sporting events such as running and cycling when the user is in an urban area. This makes it possible to provide sporting events specific to a region by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then display sporting events specific to the region based on that data.

[0064] The reception unit can analyze the user's social media activity and suggest related sporting events. The reception unit, for example, analyzes the content of social media posts to identify the user's interests. For example, the reception unit can suggest sporting events that the user frequently mentions on social media. The reception unit can also suggest sporting events in which the user's friends are participating. Furthermore, the reception unit can suggest events related to sports teams or athletes that the user follows. In this way, related sporting events can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's social media data into the generation AI, and the generation AI can suggest sporting events based on that data.

[0065] The generation unit can estimate the user's emotions and adjust the reproducibility of the sporting event based on the estimated user's emotions. The generation unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotions. The generation unit can also adjust the reproducibility of the sporting event based on the user's emotions. For example, if the user is relaxed, the generation unit can reproducibly reproduce a sporting event that proceeds at a leisurely pace. If the user is excited, the generation unit can also reproducibly reproduce an active and dynamic sporting event. Furthermore, if the user is stressed, the generation unit can reproducibly reproduce a sporting event that is effective for stress relief. This allows for a more appropriate experience by adjusting the reproducibility of the sporting event based on the user's emotions. Emotion estimation is achieved using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input data of a user's facial expression into the generation AI, which may then estimate emotions based on the data and adjust the reproduction level of a sporting event.

[0066] The generation unit can provide a more realistic experience when recreating a sporting event by referring to the user's past performance data. The generation unit, for example, acquires the user's past performance data from a database. For example, the generation unit can recreate a sporting event of an appropriate level of difficulty based on the user's past performance data. The generation unit can also recreate a sporting event that emphasizes specific techniques or skills by referring to the user's past performance data. Furthermore, the generation unit can analyze the user's past performance data and recreate an individually customized sporting event. This makes it possible to provide a more realistic experience by referring to the user's past performance data. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the user's past performance data into the generation AI, which can then recreate the sporting event based on that data.

[0067] The generation unit can reflect user feedback in real time when reproducing a sporting event. The generation unit, for example, adjusts the progress of the sporting event based on feedback provided by the user in real time. For example, the generation unit can reflect user feedback in real time and adjust the difficulty of the sporting event. The generation unit can also change the content and scenario of the sporting event in real time based on user feedback. This makes it possible to provide a more appropriate experience by reflecting user feedback in real time. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user feedback data into the generation AI, and the generation AI can adjust the sporting event based on that data.

[0068] The generation unit can estimate the user's emotions and adjust the difficulty of the sporting event to be reproduced based on the estimated user's emotions. The generation unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotions. The generation unit can also adjust the difficulty of the sporting event to be reproduced based on the user's emotions. For example, the generation unit can reproduce a sporting event with a low level of difficulty when the user is relaxed. The generation unit can also reproduce a sporting event with a high level of difficulty when the user is excited. Furthermore, the generation unit can reproduce a sporting event with a level of difficulty that is effective for stress relief when the user is feeling stressed. This allows for a more appropriate experience by adjusting the difficulty of the sporting event based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input data of a user's facial expression into the generation AI, which may then estimate emotions based on the data and adjust the difficulty level of a sporting event.

[0069] When recreating a sporting event, the generation unit can incorporate region-specific elements by taking into account the user's geographical location information. The generation unit, for example, acquires the user's geographical location information using GPS data. For example, the generation unit identifies the user's current location using GPS data from the user's smartphone. The generation unit can also acquire the user's geographical location information using a location information service. For example, the generation unit identifies the user's current location using a location information service. The generation unit further incorporates region-specific elements by taking into account the user's geographical location information. For example, the generation unit can recreate region-specific sporting events such as beach volleyball and surfing when the user is near the coast. The generation unit can also recreate region-specific sporting events such as hiking and rock climbing when the user is in a mountainous area. The generation unit can also recreate region-specific sporting events such as running and cycling when the user is in an urban area. In this way, a sporting event incorporating region-specific elements can be provided by taking into account the user's geographical location information. Some or all of the above-described processing by the generation unit may be performed using a generation AI or without a generation AI. For example, the generation unit can input the user's geographical location data into the generation AI, which can then use that data to recreate a sporting event that incorporates elements unique to the region.

[0070] The generation unit can analyze the user's social media activity and incorporate related elements when recreating a sporting event. The generation unit, for example, analyzes social media posts to identify the user's interests. For example, the generation unit recreates sporting events that the user frequently mentions on social media. The generation unit can also recreate sporting events in which the user's friends are participating. Furthermore, the generation unit can incorporate elements related to sports teams and athletes that the user follows. In this way, by analyzing the user's social media activity, it is possible to provide sporting events that incorporate related elements. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the user's social media data into the generation AI, and the generation AI can recreate the sporting event based on that data.

[0071] The analysis unit can estimate the user's emotions and adjust the content of advice based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The analysis unit then adjusts the content of advice based on the user's emotions. For example, the analysis unit provides relaxation advice when the user is relaxed. The analysis unit can also provide active advice when the user is excited. The analysis unit can also provide effective stress relief advice when the user is stressed. This allows the provision of more appropriate advice by adjusting the content of advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit may input the user's facial expression data into the generation AI, which may then estimate the user's emotions based on the data and adjust the content of the advice.

[0072] When analyzing a user's movements, the analysis unit can provide more accurate advice by referring to past performance data. The analysis unit, for example, acquires the user's past performance data from a database. For example, the analysis unit can provide appropriate advice based on the user's past performance data. The analysis unit can also provide advice on specific techniques or skills by referring to the user's past performance data. Furthermore, the analysis unit can analyze the user's past performance data and provide individually customized advice. In this way, more accurate advice can be provided by referring to the past performance data. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's past performance data into the generation AI, and the generation AI can provide advice based on that data.

[0073] When analyzing the user's movements, the analysis unit can provide feedback in real time and immediately point out areas for improvement. The analysis unit, for example, analyzes the user's movements in real time and immediately point out areas for improvement. For example, the analysis unit can analyze the user's movements in real time and suggest appropriate training methods. The analysis unit can also analyze the user's movements in real time and provide feedback that is useful for improving techniques and skills. In this way, by providing feedback in real time, areas for improvement can be immediately pointed out. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input user movement data into a generation AI, and the generation AI can provide feedback based on that data.

[0074] The analysis unit can estimate the user's emotions and adjust the timing of advice based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The analysis unit also adjusts the timing of advice based on the user's emotions. For example, if the user is relaxed, the analysis unit provides advice at a timing that allows the user to relax. If the user is excited, the analysis unit can provide advice at a timing that is effective for stress relief. In this way, by adjusting the timing of advice based on the user's emotions, it is possible to provide advice at a more appropriate timing. Emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit may input the user's facial expression data into the generation AI, which may then estimate the user's emotions based on the data and adjust the timing of advice.

[0075] When analyzing the user's movements, the analysis unit can provide region-specific advice by taking geographical location information into consideration. The analysis unit, for example, acquires the user's geographical location information using GPS data. For example, the analysis unit identifies the user's current location using GPS data from the user's smartphone. The analysis unit can also acquire the user's geographical location information using a location information service. For example, the analysis unit identifies the user's current location using a location information service. The analysis unit then provides region-specific advice by taking the user's geographical location information into consideration. For example, the analysis unit can provide advice about beach volleyball or surfing if the user is near the beach. The analysis unit can also provide advice about hiking or rock climbing if the user is in a mountainous area. The analysis unit can also provide advice about running or cycling if the user is in an urban area. In this way, region-specific advice can be provided by taking geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's geographical location information data into the generation AI, and the generation AI can provide region-specific advice based on that data.

[0076] When analyzing the user's movements, the analysis unit can analyze social media activity and provide relevant advice. For example, the analysis unit can analyze social media postings to identify the user's interests. For example, the analysis unit can provide advice related to sports that the user frequently mentions on social media. The analysis unit can also provide advice related to sports in which the user's friends participate. Furthermore, the analysis unit can provide advice related to sports teams or athletes that the user follows. In this way, relevant advice can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's social media data into the generation AI, and the generation AI can provide advice based on that data.

[0077] The match unit can estimate the user's emotions and adjust the strength of the opponent based on the estimated user's emotions. The match unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the match unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The match unit can also estimate the user's emotions using voice analysis technology. For example, the match unit can analyze the tone and speed of the user's voice to estimate the emotions. The match unit can also adjust the strength of the opponent based on the user's emotions. For example, the match unit can set an opponent with a low level of difficulty if the user is relaxed. The match unit can also set an opponent with a high level of difficulty if the user is excited. Furthermore, the match unit can also set an opponent that is effective in relieving stress if the user is feeling stressed. This allows for a more appropriate match experience by adjusting the strength of the opponent based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the battle section may be performed using or without the generation AI. For example, the battle section may input the user's facial expression data into the generation AI, which may then estimate the user's emotions based on the data and adjust the strength of the opponent.

[0078] When simulating a match with a virtual opponent, the match unit can provide a more realistic match by referring to the user's past match data. The match unit, for example, acquires the user's past match data from a database. For example, the match unit can set an opponent of appropriate difficulty based on the user's past match data. The match unit can also set an opponent that emphasizes specific techniques or skills by referring to the user's past match data. Furthermore, the match unit can analyze the user's past match data and set an opponent that is individually customized. In this way, by referring to the user's past match data, a more realistic match can be provided. Some or all of the above-described processing in the match unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the match unit can input the user's past match data into the generation AI, and the generation AI can set an opponent based on that data.

[0079] The match unit can reflect user feedback in real time when simulating a match with a virtual opponent. The match unit, for example, adjusts the strength of the opponent based on feedback provided by the user in real time. For example, the match unit can reflect user feedback in real time and adjust the opponent's strategy. The match unit can also change the opponent's movements and actions in real time based on user feedback. This makes it possible to provide a more appropriate match experience by reflecting user feedback in real time. Some or all of the above-described processing in the match unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the match unit can input user feedback data into the generation AI, and the generation AI can adjust the opponent based on that data.

[0080] The match unit can estimate the user's emotions and adjust the opponent's strategy based on the estimated user's emotions. The match unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the match unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The match unit can also estimate the user's emotions using voice analysis technology. For example, the match unit can analyze the tone and speed of the user's voice to estimate the emotions. The match unit can also adjust the opponent's strategy based on the user's emotions. For example, if the user is relaxed, the match unit can set an opponent with a relaxing strategy. If the user is excited, the match unit can set an opponent with an active strategy. Furthermore, if the user is stressed, the match unit can set an opponent with a strategy effective for relieving stress. This allows the opponent's strategy to be adjusted based on the user's emotions, providing a more appropriate match experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the match unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the match unit may input the user's facial expression data into the generation AI, which may then infer emotions based on the data and adjust the opponent's strategy.

[0081] When simulating a match with a virtual opponent, the match unit can set a region-specific opponent by taking geographical location information into consideration. The match unit, for example, acquires the user's geographical location information using GPS data. For example, the match unit identifies the user's current location by using GPS data from the user's smartphone. The match unit can also acquire the user's geographical location information by using a location information service. For example, the match unit identifies the user's current location by using a location information service. Furthermore, the match unit sets a region-specific opponent by taking the user's geographical location information into consideration. For example, the match unit can set a beach volleyball or surfing opponent if the user is near the coast. Furthermore, the match unit can set a hiking or rock climbing opponent if the user is in a mountainous area. Furthermore, the match unit can set a running or cycling opponent if the user is in an urban area. In this way, region-specific opponents can be set by taking geographical location information into consideration. Some or all of the above-described processing in the match unit may be performed using a generation AI or without using a generation AI. For example, the competition unit can input the user's geographical location information data into the generation AI, and the generation AI can set an opponent based on that data.

[0082] When simulating a match with a virtual opponent, the match unit can analyze social media activity and set a relevant opponent. The match unit, for example, analyzes social media postings to identify the user's interests. For example, the match unit can set an opponent in a sport that the user frequently mentions on social media. The match unit can also set an opponent in a sport in which the user's friends participate. Furthermore, the match unit can set an opponent related to a sports team or athlete that the user follows. In this way, relevant opponents can be set by analyzing social media activity. Some or all of the above-mentioned processing in the match unit may be performed using or without the generation AI. For example, the match unit can input the user's social media data into the generation AI, and the generation AI can set an opponent based on that data. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, generation unit, analysis unit, and competition unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and selects a sporting event that the user wants to experience. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and realistically reproduces the sporting event using a generation AI. The analysis unit is realized, for example, by the control unit 46A of the smart device 14 and analyzes the user's movements in real time and provides appropriate advice. The competition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and simulates a match against a virtual opponent. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, analysis unit, and competition unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and selects a sporting event that the user wants to experience. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and realistically reproduces the sporting event using a generation AI. The analysis unit is realized, for example, by the control unit 46A of the smart glasses 214 and analyzes the user's movements in real time and provides appropriate advice. The competition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and simulates a match against a virtual opponent. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, generation unit, analysis unit, and competition unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and selects a sporting event that the user wants to experience. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and realistically reproduces the sporting event using a generation AI. The analysis unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and analyzes the user's movements in real time and provides appropriate advice. The competition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and simulates a match against a virtual opponent. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, analysis unit, and competition unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and selects a sporting event that the user wants to experience. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and realistically reproduces the sporting event using a generation AI. The analysis unit is realized, for example, by the control unit 46A of the robot 414 and analyzes the user's movements in real time and provides appropriate advice. The competition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and simulates a match against a virtual opponent.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The reception unit can analyze the user's past selection history and recommend sporting events suitable for the user. For example, the reception unit can analyze the user's past selection history using data mining technology and prioritize displaying sporting events that the user has frequently selected in the past. The reception unit can also recommend similar sporting events based on the user's past selection history. Furthermore, the reception unit can analyze the user's past selection history and recommend optimal sporting events according to the season or time of day. In this way, by analyzing the user's past selection history, more suitable sporting events can be recommended. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past selection history data into the generation AI, and the generation AI can recommend sporting events based on that data.

[0085] The generation unit can suggest sporting events suitable for the user based on the user's current fitness level and health condition. For example, the generation unit evaluates the user's fitness level and health condition based on the user's heart rate and exercise history. For example, the generation unit analyzes the user's heart rate data to evaluate the user's fitness level. The generation unit can also evaluate the user's health condition based on the user's exercise history data. Furthermore, the generation unit suggests appropriate sporting events based on the user's fitness level and health condition. For example, the generation unit can suggest challenging sporting events (such as marathons and triathlons) if the user's fitness level is high. Furthermore, the generation unit can suggest light exercise (such as walking and stretching) if the user's health condition is not good. Furthermore, the generation unit can suggest customized training programs based on the user's fitness level and health condition. This allows for a safer and more effective experience by suggesting appropriate sporting events based on the user's fitness level and health condition. Some or all of the above-described processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input the user's heart rate data and exercise history data into the generation AI, which can then use that data to evaluate their fitness level and health condition and suggest sports events.

[0086] When analyzing a user's movements, the analysis unit can provide more accurate advice by referring to past performance data. For example, the analysis unit retrieves the user's past performance data from a database. For example, the analysis unit can provide appropriate advice based on the user's past performance data. The analysis unit can also provide advice on specific techniques or skills by referring to the user's past performance data. Furthermore, the analysis unit can analyze the user's past performance data and provide individually customized advice. In this way, more accurate advice can be provided by referring to the past performance data. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's past performance data into the generation AI, and the generation AI can provide advice based on that data.

[0087] When simulating a match with a virtual opponent, the match unit can provide a more realistic match by referring to the user's past match data. For example, the match unit retrieves the user's past match data from a database. For example, the match unit can set an opponent of appropriate difficulty based on the user's past match data. The match unit can also set an opponent that emphasizes specific techniques or skills by referring to the user's past match data. Furthermore, the match unit can analyze the user's past match data and set an opponent that is individually customized. In this way, by referring to the user's past match data, a more realistic match can be provided. Some or all of the above-described processing in the match unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the match unit can input the user's past match data into the generation AI, and the generation AI can set an opponent based on that data.

[0088] The generation unit can reflect user feedback in real time when reproducing a sporting event. For example, the generation unit adjusts the progress of the sporting event based on feedback provided by the user in real time. For example, the generation unit can reflect user feedback in real time and adjust the difficulty of the sporting event. The generation unit can also change the content and scenario of the sporting event in real time based on user feedback. This makes it possible to provide a more appropriate experience by reflecting user feedback in real time. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user feedback data into the generation AI, and the generation AI can adjust the sporting event based on that data.

[0089] The reception unit can estimate the user's emotions and adjust the interface of the selection screen based on the estimated user emotions. For example, the reception unit can estimate the user's emotions using facial expression recognition technology. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the reception unit can adjust the interface of the selection screen based on the user's emotions. For example, if the user is nervous, the reception unit can provide an interface with calm colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This allows for a more comfortable operating environment by adjusting the interface based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's facial expression data into the generation AI, which may then estimate the user's emotion based on the data and adjust the interface.

[0090] The generation unit can estimate the user's emotions and adjust the reproducibility of the sporting event based on the estimated user's emotions. For example, the generation unit can estimate the user's emotions using facial expression recognition technology. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the generation unit can adjust the reproducibility of the sporting event based on the user's emotions. For example, if the user is relaxed, the generation unit can reproducibly reproduce a sporting event that proceeds at a leisurely pace. Furthermore, if the user is excited, the generation unit can reproducibly reproduce an active and dynamic sporting event. Furthermore, if the user is stressed, the generation unit can reproducibly reproduce a sporting event that is effective for stress relief. This allows for a more appropriate experience by adjusting the reproducibility of the sporting event based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input data of a user's facial expression into the generation AI, which may then estimate emotions based on the data and adjust the reproduction level of a sporting event.

[0091] The analysis unit can estimate the user's emotions and adjust the content of advice based on the estimated user emotions. For example, the analysis unit estimates the user's emotions using facial expression recognition technology. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The analysis unit then adjusts the content of advice based on the user's emotions. For example, the analysis unit provides relaxation advice when the user is relaxed. The analysis unit can also provide active advice when the user is excited. The analysis unit can also provide effective stress relief advice when the user is stressed. This allows for more appropriate advice to be provided by adjusting the content of advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit may input the user's facial expression data into the generation AI, which may then estimate the user's emotions based on the data and adjust the content of the advice.

[0092] The match unit can estimate the user's emotions and adjust the strength of the opponent based on the estimated user's emotions. For example, the match unit estimates the user's emotions using facial expression recognition technology. For example, the match unit captures the user's facial expressions with a camera and estimates the emotions using facial expression recognition technology. In addition, the match unit can use voice analysis technology to estimate the user's emotions. The game unit can also estimate the user's emotions. For example, the game unit analyzes the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the game unit adjusts the strength of the opponent based on the user's emotions. For example, if the user is relaxed, the game unit can set an opponent with a low level of difficulty. Furthermore, if the user is excited, the game unit can set an opponent with a high level of difficulty. Furthermore, if the user is feeling stressed, the game unit can set an opponent that is effective in relieving stress. This allows for adjusting the strength of the opponent based on the user's emotions to provide a more appropriate game experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the game unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the game unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions based on the data and adjust the strength of the opponent.

[0093] The match unit can reflect user feedback in real time when simulating a match with a virtual opponent. For example, the match unit adjusts the strength of the opponent based on feedback provided by the user in real time. For example, the match unit can reflect user feedback in real time and adjust the opponent's strategy. The match unit can also change the opponent's movements and actions in real time based on user feedback. This makes it possible to provide a more appropriate match experience by reflecting user feedback in real time. Some or all of the above-mentioned processing in the match unit may be performed using a generation AI or may be performed without using a generation AI. For example, the match unit can input user feedback data into the generation AI, and the generation AI can adjust the opponent based on that data.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The reception unit selects the sporting event that the user wants to experience. Sporting events that the user wants to experience include soccer, basketball, tennis, etc. The reception unit prioritizes and displays relaxing sporting events (such as yoga or golf), active sporting events (such as soccer or basketball), and sporting events that are effective for relieving stress (such as boxing or running) according to the user's condition. Step 2: The generation unit uses the generation AI to realistically recreate the sporting event selected by the reception unit. 3D modeling and virtual reality technology are used for the recreation. The generation unit realistically recreates the field and stadium of the sporting event selected by the user and estimates the user's emotions to adjust the degree of recreation. For example, if the user is relaxed, the generation unit recreates a sporting event proceeding at a leisurely pace. Step 3: The analysis unit uses the generation AI to analyze the user's movements in real time within the sporting event recreated by the generation unit and provide appropriate advice. The analysis uses motion analysis algorithms and sensor technology. The analysis unit analyzes the user's movements in real time and immediately identifies areas for improvement. It also references the user's past performance data to provide more accurate advice. Step 4: The match unit uses the generation AI to simulate a match against a virtual opponent based on the information analyzed by the analysis unit. The match uses AI opponents and past player data. The match unit estimates the user's emotions and adjusts the strength of the opponent based on the estimated user emotions. It also references the user's past match data to provide a more realistic match.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0098] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0167] [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for allowing a user to select a sporting event that the user wishes to experience; a generating unit that realistically reproduces the sporting event selected by the receiving unit; an analysis unit that analyzes a user's movements in real time during the sporting event reproduced by the generation unit and provides advice based on the user's movements; a match unit that simulates a match with a virtual opponent based on the information analyzed by the analysis unit. A system characterized by:

2. The generation unit Realistically recreate the fields and stadiums of your chosen sporting event The system of claim 1 .

3. The reception unit Inferring user emotions and customizing sporting event choices based on the inferred user emotions The system of claim 1 .

4. The reception unit Analyze the user's past selection history and recommend suitable sporting events to the user The system of claim 1 .

5. The reception unit Recommend suitable sporting events to users based on their current fitness level and health status The system of claim 1 .

6. The reception unit Estimate the user's emotion and adjust the selection screen interface based on the estimated user emotion. The system of claim 1 .

7. The reception unit Taking into account the user's geographic location, the app prioritizes displaying local sporting events. The system of claim 1 .

8. The reception unit Analyze users' social media activity to suggest relevant sporting events The system of claim 1 .

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