system

Abstract

The system according to the embodiment aims to efficiently provide sports videos that match the preferences of the user. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives information from a user, such as specific player names and play details. The generation unit analyzes the information received by the reception unit and customizes and generates related video. The provision unit provides the video generated by the generation unit in an editing style that matches the user's preferences.

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] With conventional technology, it is difficult to efficiently provide sports videos that match the preferences of users, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently provide sports videos that match the preferences of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives information from a user, such as specific player names and play details. The generation unit analyzes the information received by the reception unit and customizes and generates related video. The provision unit provides the video generated by the generation unit in an editing style that matches the user's preferences. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide sports videos that match the preferences of the user. [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 sports video generation system according to an embodiment of the present invention utilizes a generation AI to automatically generate sports videos for each player and shot. In this sports video generation system, a user inputs information about a specific player or play that interests them through an application. The generation AI analyzes the information and generates customized, relevant videos. The generated videos are provided in an edited format tailored to the user's preferences. This opens up a new dimension of sports entertainment, allowing users to become more immersed in sports. For example, a user may input a request to watch a specific player's goal. This information is input into the generation AI. The generation AI then analyzes the input information and generates customized, relevant videos. The generation AI extracts relevant videos from a sports video database and edits them based on the user's request. For example, a video that includes a collection of goal scenes featuring a specific player is generated. The generated videos are provided in an edited format tailored to the user's preferences. For example, the videos can be customized to meet user requests, such as a collection of goal scenes featuring a specific player or a video focusing on a specific play. This system allows users to easily watch sports videos tailored to their interests. This opens up a new dimension of sports entertainment, allowing users to become more immersed in sports. For example, if a user is a fan of a particular player, they can get a greater sense of satisfaction by watching a collection of videos of that player's plays. This allows the sports video generation system to provide customized videos based on information about the user's specific players and plays, thereby improving the user's sports viewing experience.

[0029] A sports video generation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives information from a user, such as specific player names and play details. For example, a user may input information about a specific player or play through an application. The user may input a request such as, "I want to see a specific player's goal." The generation unit analyzes the information received by the reception unit and generates customized relevant video. The generation unit uses a generation AI to extract relevant video from a sports video database and edit it based on the user's request. For example, the generation AI generates a video that includes a collection of goal scenes by a specific player. The provision unit provides the video generated by the generation unit in an editing style tailored to the user's preferences. In response to the user's request, the provision unit provides a video that includes only goal scenes by a specific player or a video that focuses on a specific play. For example, the provision unit provides the video so that the user can view it on a smartphone or tablet. As a result, the sports video generation system according to an embodiment can improve the user's sports viewing experience by providing customized video based on the user's information about a specific player or play.

[0030] The reception unit can analyze the user's past request history and select a reception method based on the user's past request history. For example, the reception unit prioritizes receiving players and plays that the user has frequently requested in the past. For example, if the user has frequently requested goal scenes of a specific player in the past, the reception unit prioritizes receiving information related to that player. The reception unit can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, if the user has previously made requests using voice input, the reception unit prioritizes suggesting voice input. The reception unit can also predict and suggest players and plays to be used in a specific time period based on the user's past request history. For example, if the user has previously requested a specific player in a specific time period, the reception unit prioritizes suggesting information related to that time period. This allows the user's convenience to be improved by selecting the optimal reception method based on the user's past request history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past request history data into a generation AI and have the generation AI select the optimal reception method.

[0031] When receiving information, the reception unit can filter the information based on the user's current areas of interest and sporting events. For example, the reception unit preferentially receives information related to sporting events in which the user is currently interested. For example, if the user is interested in a soccer tournament currently being held, the reception unit preferentially receives information related to the tournament. Furthermore, if the user is interested in a specific player, the reception unit can preferentially receive information related to the player. For example, if the user is a fan of a specific player, the reception unit preferentially receives information related to the player. Furthermore, if the user is interested in a specific play, the reception unit can preferentially receive information related to the play. For example, if the user is interested in a specific play (e.g., a goal shot), the reception unit preferentially receives information related to the play. This allows for filtering information based on the user's current areas of interest and sporting events, thereby providing highly relevant information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's areas of interest data into a generation AI and cause the generation AI to perform filtering.

[0032] When receiving information, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. For example, the reception unit prioritizes receiving information about sporting events related to the user's current location. For example, the reception unit prioritizes receiving information related to sporting events being held in the user's current city. The reception unit can also prioritize receiving information related to places the user has visited in the past. For example, the reception unit prioritizes receiving information related to sporting events in cities the user has visited in the past. The reception unit can also prioritize receiving information related to places the user plans to visit in the future. For example, the reception unit prioritizes receiving information related to sporting events in cities the user plans to visit in the future. This makes it possible to prioritize receiving highly relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize receiving highly relevant information.

[0033] When receiving information, the reception unit can analyze the user's social media activity and receive related information. For example, the reception unit prioritizes receiving information related to players the user follows on social media. For example, if the user follows a specific player on social media, the reception unit prioritizes receiving information related to the player. The reception unit can also prioritize receiving information related to plays the user shared on social media. For example, if the user shares a specific play on social media, the reception unit prioritizes receiving information related to the play. The reception unit can also prioritize receiving information related to sporting events the user is participating in on social media. For example, if the user is participating in a specific sporting event on social media, the reception unit prioritizes receiving information related to the event. In this way, by analyzing the user's social media activity, related information can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to receive related information.

[0034] When generating video, the generation unit can adjust the level of detail of the generation based on the importance of the request. For example, in the case of an important request, the generation unit generates detailed video. For example, if a user requests important game scenes of a specific player, the generation unit generates detailed video. The generation unit can also generate video with a standard level of detail in the case of a general request. For example, if a user requests highlights of a general game, the generation unit generates video with a standard level of detail. The generation unit can also generate simplified video in the case of a low-priority request. For example, if a user requests practice scenes of a specific player, the generation unit generates simplified video. In this way, by adjusting the level of detail of the generation based on the importance of the request, video with an appropriate level of detail can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input request importance data to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0035] When generating video, the generation unit can apply different generation algorithms depending on the category of the request. For example, for a request for a goal scene, the generation unit applies a generation algorithm specialized for goal scenes. For example, if a user requests a goal scene featuring a specific player, the generation unit applies a generation algorithm specialized for goal scenes. Furthermore, the generation unit can also apply a generation algorithm specialized for dribbling scenes to a request for a dribbling scene. For example, if a user requests a dribbling scene featuring a specific player, the generation unit applies a generation algorithm specialized for dribbling scenes. Furthermore, the generation unit can also apply a generation algorithm specialized for defense scenes to a request for a defensive scene. For example, if a user requests a defensive scene featuring a specific player, the generation unit applies a generation algorithm specialized for defense scenes. In this way, by applying different generation algorithms depending on the category of the request, more appropriate video can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input request category data into a generation AI and cause the generation AI to apply a generation algorithm.

[0036] When generating videos, the generation unit can determine the generation priority based on the time of request submission. The generation unit, for example, prioritizes processing the most recent request and generates the video. For example, if a user has submitted a request most recently, the generation unit prioritizes processing that request and generates the video. The generation unit can also sequentially process past requests and generate the video. For example, it sequentially processes requests submitted by the user in the past and generates the video. The generation unit can also prioritize processing requests related to a specific event and generate the video. For example, if a user submits a request related to a specific sporting event, the generation unit prioritizes processing that request and generates the video. In this way, by determining the generation priority based on the time of request submission, videos can be generated in an appropriate order. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input request submission time data to the generation AI and have the generation AI determine the generation priority.

[0037] When generating videos, the generation unit can adjust the order of generation based on the relevance of the requests. For example, the generation unit prioritizes generating videos related to the user's request. For example, if the user requests a goal scene featuring a specific player, the generation unit prioritizes generating videos related to the goal scene. The generation unit can also prioritize generating plays related to the user's request. For example, if the user requests a specific play (e.g., a dribbling scene), the generation unit prioritizes generating videos related to the play. The generation unit can also prioritize generating players related to the user's request. For example, if the user requests a defensive scene featuring a specific player, the generation unit prioritizes generating videos related to the player. In this way, by adjusting the order of generation based on the relevance of the requests, highly related videos can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input request relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0038] When providing video, the providing unit can select the optimal video providing method by referring to the user's past viewing history. The providing unit, for example, provides related video based on video previously viewed by the user. For example, if the user previously viewed a goal scene of a specific player, the providing unit provides video related to that player. The providing unit can also provide video related to a specific player or play based on the user's past viewing history. For example, if the user previously viewed a specific play (e.g., a dribbling scene), the providing unit provides video related to that play. The providing unit can also analyze the user's past viewing history to provide video that is likely to be of most interest to the user. For example, the providing unit analyzes patterns of videos previously viewed by the user and provides video that is likely to be of most interest to the user. In this way, the optimal video providing method can be selected by referring to the user's past viewing history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's viewing history data to a generation AI and cause the generation AI to select the optimal video providing method.

[0039] When providing video, the providing unit can customize the means of providing video based on the user's current device status. For example, if the user is using a smartphone, the providing unit provides video that matches the screen size. For example, if the user is using a smartphone, the providing unit provides video that is optimized for the smartphone's screen size. Furthermore, if the user is using a tablet, the providing unit can also provide video that is optimized for a larger screen. For example, if the user is using a tablet, the providing unit provides video that is optimized for the tablet's large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide video that is concise and highly visible. For example, if the user is using a smartwatch, the providing unit provides video that is concise and highly visible. This enables optimal video provision by customizing the means of providing video based on the user's device status. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device status data into the generation AI and cause the generation AI to customize the means of providing video.

[0040] When providing video, the providing unit can select the optimal providing method by taking into account the user's geographical location information. For example, the providing unit can prioritize providing video of a sporting event related to the user's current location. For example, the providing unit can prioritize providing video related to a sporting event being held in the user's current city. The providing unit can also prioritize providing video related to places the user has visited in the past. For example, the providing unit can prioritize providing video related to a sporting event in a city the user has visited in the past. The providing unit can also prioritize providing video related to places the user plans to visit in the future. For example, the providing unit can prioritize providing video related to a sporting event in a city the user plans to visit in the future. In this way, the optimal video providing method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data to the generation AI and cause the generation AI to select the optimal providing method.

[0041] When providing video, the providing unit can analyze the user's social media activity and suggest a means of providing the video. For example, the providing unit can prioritize providing video related to players the user follows on social media. For example, if the user follows a specific player on social media, the providing unit can prioritize providing video related to the player. The providing unit can also prioritize providing video related to plays the user shared on social media. For example, if the user shares a specific play on social media, the providing unit can prioritize providing video related to that play. The providing unit can also prioritize providing video related to sporting events the user is participating in on social media. For example, if the user is participating in a specific sporting event on social media, the providing unit can prioritize providing video related to that event. This allows the user's social media activity to be analyzed to suggest the optimal means of providing the video. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a means of providing the video.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The reception unit can acquire the user's health data and adjust the method of receiving information based on the user's health condition. For example, if the user is tired, the reception unit can provide a simple interface to make it easier for the user to input a request. Alternatively, if the user is healthy and active, the reception unit can provide a detailed interface to allow the user to input more information. Furthermore, if the user is ill or injured, the reception unit can simplify the request to reduce the burden on the user. In this way, by adjusting the method of receiving information according to the user's health condition, it is possible to receive information at a more appropriate time.

[0044] The reception unit can analyze the user's past request history and suggest new requests based on changes in the user's interests. For example, if a user frequently requested a specific player in the past but has recently become interested in a different player, the reception unit can suggest a request related to the new player. Also, if a user was interested in a specific play in the past but has recently become interested in a different play, the reception unit can suggest a request related to that play. Furthermore, based on changes in the user's interests, requests related to new sporting events or trends can be suggested. This allows the system to provide more appropriate videos by suggesting new requests in response to changes in the user's interests.

[0045] When receiving information, the reception unit can filter the information based on the user's current activity status. For example, if the user is exercising, the reception unit can preferentially receive information related to exercise. Also, if the user is taking a break, the reception unit can preferentially receive information that helps the user relax. Furthermore, if the user is working, the reception unit can preferentially receive information that can be viewed in a short time. In this way, by filtering information based on the user's current activity status, highly relevant information can be provided.

[0046] When receiving information, the reception unit can adjust the method of receiving information based on the remaining battery power of the user's device. For example, when the battery power of the user's device is low, the reception unit can provide a simplified interface to quickly receive information. When the battery power is sufficient, the reception unit can provide a detailed interface to allow more information to be input. Furthermore, when the battery power is very low, the reception unit can prioritize receiving only the most important information. In this way, by adjusting the method of receiving information according to the remaining battery power of the user's device, information can be received at a more appropriate time.

[0047] When generating a video, the generation unit can adjust the style of the video based on the user's past viewing history. For example, the generation unit can analyze the style of videos the user has viewed in the past and generate a video with a similar style. The generation unit can also preferentially apply a style that the user particularly liked among the videos they have viewed in the past. Furthermore, the generation unit can suggest a new style based on the user's viewing history and generate the video. In this way, by adjusting the style of the video based on the user's past viewing history, more appropriate video can be provided.

[0048] The generation unit can adjust the quality of the video based on the performance of the user's device when generating the video. For example, if the user is using a high-performance device, high-quality video can be generated. On the other hand, if the user is using a low-performance device, lightweight video can be generated. Furthermore, video of an appropriate size can be generated based on the storage capacity of the user's device. This makes it possible to provide optimal video by adjusting the quality of the video based on the performance of the user's device.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The reception unit receives information from the user about specific player names and play details. For example, the user inputs information about a specific player or play through the application. The user can input a request such as, "I want to see a goal scored by a specific player." Step 2: The generation unit analyzes the information received by the reception unit and generates customized related videos. The generation unit uses a generation AI to extract relevant videos from a sports video database and edit them based on the user's request. For example, the generation AI generates a collection of videos of goal scenes scored by a specific player. Step 3: The providing unit provides the video generated by the generating unit in an edited format that matches the user's preferences. In response to a user request, the providing unit provides video that includes only goal scenes of a specific player or video that focuses on a specific play. For example, the providing unit provides the video so that the user can watch it on a smartphone or tablet.

[0051] (Example 2) A sports video generation system according to an embodiment of the present invention utilizes a generation AI to automatically generate sports videos for each player and shot. In this sports video generation system, a user inputs information about a specific player or play that interests them through an application. The generation AI analyzes the information and generates customized, relevant videos. The generated videos are provided in an edited format tailored to the user's preferences. This opens up a new dimension of sports entertainment, allowing users to become more immersed in sports. For example, a user may input a request to watch a specific player's goal. This information is input into the generation AI. The generation AI then analyzes the input information and generates customized, relevant videos. The generation AI extracts relevant videos from a sports video database and edits them based on the user's request. For example, a video that includes a collection of goal scenes featuring a specific player is generated. The generated videos are provided in an edited format tailored to the user's preferences. For example, the videos can be customized to meet user requests, such as a collection of goal scenes featuring a specific player or a video focusing on a specific play. This system allows users to easily watch sports videos tailored to their interests. This opens up a new dimension of sports entertainment, allowing users to become more immersed in sports. For example, if a user is a fan of a particular player, they can get a greater sense of satisfaction by watching a collection of videos of that player's plays. This allows the sports video generation system to provide customized videos based on information about the user's specific players and plays, thereby improving the user's sports viewing experience.

[0052] A sports video generation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives information from a user, such as specific player names and play details. For example, a user may input information about a specific player or play through an application. The user may input a request such as, "I want to see a specific player's goal." The generation unit analyzes the information received by the reception unit and generates customized relevant video. The generation unit uses a generation AI to extract relevant video from a sports video database and edit it based on the user's request. For example, the generation AI generates a video that includes a collection of goal scenes of a specific player. The provision unit provides the video generated by the generation unit in an editing style tailored to the user's preferences. In response to the user's request, the provision unit provides a video that includes only goal scenes of a specific player or a video that focuses on a specific play. For example, the provision unit provides the video so that the user can view it on a smartphone or tablet. As a result, the sports video generation system according to an embodiment can improve the user's sports viewing experience by providing customized video based on the user's information about a specific player or play.

[0053] The reception unit can estimate the user's emotions and adjust the timing of receiving information based on the estimated user emotions. For example, if the user is excited, the reception unit immediately receives information. For example, if the user is excited during a sporting event, the reception unit immediately receives a request and transmits the information to the generation unit. The reception unit can also receive information at a slower pace if the user is relaxed. For example, if the user inputs a request at night when they are relaxed, the reception unit receives information at a slower pace. The reception unit can also receive information using a simple interface if the user is stressed. For example, if the user is stressed, the reception unit provides a simple interface to make it easier for the user to input a request. This allows the timing of receiving information to be adjusted according to the user's emotions, thereby receiving information at a more appropriate time. Emotion estimation is realized using an emotion estimation function, such as 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0054] The reception unit can analyze the user's past request history and select a reception method based on the user's past request history. For example, the reception unit prioritizes receiving players and plays that the user has frequently requested in the past. For example, if the user has frequently requested goal scenes of a specific player in the past, the reception unit prioritizes receiving information related to that player. The reception unit can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, if the user has previously made requests using voice input, the reception unit prioritizes suggesting voice input. The reception unit can also predict and suggest players and plays to be used in a specific time period based on the user's past request history. For example, if the user has previously requested a specific player in a specific time period, the reception unit prioritizes suggesting information related to that time period. This allows the user's convenience to be improved by selecting the optimal reception method based on the user's past request history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past request history data into a generation AI and have the generation AI select the optimal reception method.

[0055] When receiving information, the reception unit can filter the information based on the user's current areas of interest and sporting events. For example, the reception unit preferentially receives information related to sporting events in which the user is currently interested. For example, if the user is interested in a soccer tournament currently being held, the reception unit preferentially receives information related to the tournament. Furthermore, if the user is interested in a specific player, the reception unit can preferentially receive information related to the player. For example, if the user is a fan of a specific player, the reception unit preferentially receives information related to the player. Furthermore, if the user is interested in a specific play, the reception unit can preferentially receive information related to the play. For example, if the user is interested in a specific play (e.g., a goal shot), the reception unit preferentially receives information related to the play. This allows for filtering information based on the user's current areas of interest and sporting events, thereby providing highly relevant information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's areas of interest data into a generation AI and cause the generation AI to perform filtering.

[0056] The reception unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions. For example, if the user is excited, the reception unit prioritizes receiving important information. For example, if the user is excited during a sporting event, the reception unit prioritizes receiving important requests. The reception unit can also prioritize receiving detailed information if the user is relaxed. For example, if the user inputs a request at night when they are relaxed, the reception unit prioritizes receiving detailed information. The reception unit can also prioritize receiving simple information if the user is stressed. For example, if the user is stressed, the reception unit provides a simple interface to make it easier for the user to input requests. This allows the information priority to be determined according to the user's emotions, thereby allowing more appropriate information to be received preferentially. Emotion estimation is achieved using an emotion estimation function, such as 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0057] When receiving information, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. For example, the reception unit prioritizes receiving information about sporting events related to the user's current location. For example, the reception unit prioritizes receiving information related to sporting events being held in the user's current city. The reception unit can also prioritize receiving information related to places the user has visited in the past. For example, the reception unit prioritizes receiving information related to sporting events in cities the user has visited in the past. The reception unit can also prioritize receiving information related to places the user plans to visit in the future. For example, the reception unit prioritizes receiving information related to sporting events in cities the user plans to visit in the future. This makes it possible to prioritize receiving highly relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize receiving highly relevant information.

[0058] When receiving information, the reception unit can analyze the user's social media activity and receive related information. For example, the reception unit prioritizes receiving information related to players the user follows on social media. For example, if the user follows a specific player on social media, the reception unit prioritizes receiving information related to the player. The reception unit can also prioritize receiving information related to plays the user shared on social media. For example, if the user shares a specific play on social media, the reception unit prioritizes receiving information related to the play. The reception unit can also prioritize receiving information related to sporting events the user is participating in on social media. For example, if the user is participating in a specific sporting event on social media, the reception unit prioritizes receiving information related to the event. In this way, by analyzing the user's social media activity, related information can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to receive related information.

[0059] The generation unit can estimate the user's emotions and adjust the image generation method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates an image that progresses at a leisurely pace. For example, if the user inputs a request at night when they are relaxed, the generation unit generates an image that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate an image that emphasizes the shortest route. For example, if the user is in a hurry, the generation unit generates an image that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate an image that adds visually stimulating effects. For example, if the user is excited during a sporting event, the generation unit generates an image that adds visually stimulating effects. In this way, by adjusting the image generation method according to the user's emotions, more appropriate images can be generated. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0060] When generating video, the generation unit can adjust the level of detail of the generation based on the importance of the request. For example, in the case of an important request, the generation unit generates detailed video. For example, if a user requests important game scenes of a specific player, the generation unit generates detailed video. The generation unit can also generate video with a standard level of detail in the case of a general request. For example, if a user requests highlights of a general game, the generation unit generates video with a standard level of detail. The generation unit can also generate simplified video in the case of a low-priority request. For example, if a user requests practice scenes of a specific player, the generation unit generates simplified video. In this way, by adjusting the level of detail of the generation based on the importance of the request, video with an appropriate level of detail can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input request importance data to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0061] When generating video, the generation unit can apply different generation algorithms depending on the category of the request. For example, for a request for a goal scene, the generation unit applies a generation algorithm specialized for goal scenes. For example, if a user requests a goal scene featuring a specific player, the generation unit applies a generation algorithm specialized for goal scenes. Furthermore, the generation unit can also apply a generation algorithm specialized for dribbling scenes to a request for a dribbling scene. For example, if a user requests a dribbling scene featuring a specific player, the generation unit applies a generation algorithm specialized for dribbling scenes. Furthermore, the generation unit can also apply a generation algorithm specialized for defense scenes to a request for a defensive scene. For example, if a user requests a defensive scene featuring a specific player, the generation unit applies a generation algorithm specialized for defense scenes. In this way, by applying different generation algorithms depending on the category of the request, more appropriate video can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input request category data into a generation AI and cause the generation AI to apply a generation algorithm.

[0062] The generation unit can estimate the user's emotions and adjust the length of the video to be generated based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit generates a short, to-the-point video. For example, if the user is in a hurry, the generation unit generates a short, to-the-point video. Furthermore, if the user is relaxed, the generation unit can generate a longer video with detailed explanations. For example, if the user inputs a request at night when they are relaxing, the generation unit generates a longer video with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a video with visually stimulating effects. For example, if the user is excited during a sporting event, the generation unit generates a video with visually stimulating effects. This allows for the generation of more appropriate videos by adjusting the length of the video according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or 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 generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0063] When generating videos, the generation unit can determine the generation priority based on the time of request submission. The generation unit, for example, prioritizes processing the most recent request and generates the video. For example, if a user has submitted a request most recently, the generation unit prioritizes processing that request and generates the video. The generation unit can also sequentially process past requests and generate the video. For example, it sequentially processes requests submitted by the user in the past and generates the video. The generation unit can also prioritize processing requests related to a specific event and generate the video. For example, if a user submits a request related to a specific sporting event, the generation unit prioritizes processing that request and generates the video. In this way, by determining the generation priority based on the time of request submission, videos can be generated in an appropriate order. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input request submission time data to the generation AI and have the generation AI determine the generation priority.

[0064] When generating videos, the generation unit can adjust the order of generation based on the relevance of the requests. For example, the generation unit prioritizes generating videos related to the user's request. For example, if the user requests a goal scene featuring a specific player, the generation unit prioritizes generating videos related to the goal scene. The generation unit can also prioritize generating plays related to the user's request. For example, if the user requests a specific play (e.g., a dribbling scene), the generation unit prioritizes generating videos related to the play. The generation unit can also prioritize generating players related to the user's request. For example, if the user requests a defensive scene featuring a specific player, the generation unit prioritizes generating videos related to the player. In this way, by adjusting the order of generation based on the relevance of the requests, highly related videos can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input request relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0065] The providing unit can estimate the user's emotions and adjust the video presentation method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit provides the video at a leisurely pace. For example, if the user watches the video at night when they are relaxed, the providing unit provides the video at a leisurely pace. Furthermore, if the user is in a hurry, the providing unit can provide a short video that is concise and to the point. For example, if the user is in a hurry, the providing unit can provide a video that is concise and to the point. Furthermore, if the user is excited, the providing unit can provide a video that adds visually stimulating effects. For example, if the user is excited during a sporting event, the providing unit can provide a video that adds visually stimulating effects. This allows the video presentation method to be adjusted according to the user's emotions, thereby providing more appropriate video. Emotion estimation is realized 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-described processing in the providing unit can be performed, for example, using AI, or without AI. For example, the providing unit can input the user's facial expression data into the generating AI and cause the generating AI to estimate emotions.

[0066] When providing video, the providing unit can select the optimal video providing method by referring to the user's past viewing history. The providing unit, for example, provides related video based on video previously viewed by the user. For example, if the user previously viewed a goal scene of a specific player, the providing unit provides video related to that player. The providing unit can also provide video related to a specific player or play based on the user's past viewing history. For example, if the user previously viewed a specific play (e.g., a dribbling scene), the providing unit provides video related to that play. The providing unit can also analyze the user's past viewing history to provide video that is likely to be of most interest to the user. For example, the providing unit analyzes patterns of videos previously viewed by the user and provides video that is likely to be of most interest to the user. In this way, the optimal video providing method can be selected by referring to the user's past viewing history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's viewing history data to a generation AI and cause the generation AI to select the optimal video providing method.

[0067] When providing video, the providing unit can customize the means of providing video based on the user's current device status. For example, if the user is using a smartphone, the providing unit provides video that matches the screen size. For example, if the user is using a smartphone, the providing unit provides video that is optimized for the smartphone's screen size. Furthermore, if the user is using a tablet, the providing unit can also provide video that is optimized for a larger screen. For example, if the user is using a tablet, the providing unit provides video that is optimized for the tablet's large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide video that is concise and highly visible. For example, if the user is using a smartwatch, the providing unit provides video that is concise and highly visible. This enables optimal video provision by customizing the means of providing video based on the user's device status. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device status data into the generation AI and cause the generation AI to customize the means of providing video.

[0068] The providing unit can estimate the user's emotions and determine the priority of videos to be provided based on the estimated user's emotions. For example, if the user is excited, the providing unit can prioritize providing important videos. For example, if the user is excited during a sporting event, the providing unit can prioritize providing important videos. Furthermore, if the user is relaxed, the providing unit can prioritize providing detailed videos. For example, if the user watches videos at night when they are relaxed, the providing unit can prioritize providing detailed videos. Furthermore, if the user is stressed, the providing unit can prioritize providing simple videos. For example, if the user is stressed, the providing unit can provide a simple interface and prioritize videos that are easy for the user to watch. In this way, by determining the priority of videos according to the user's emotions, more appropriate videos can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, 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 providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's facial expression data into the generating AI and cause the generating AI to estimate emotions.

[0069] When providing video, the providing unit can select the optimal providing method by taking into account the user's geographical location information. For example, the providing unit can prioritize providing video of a sporting event related to the user's current location. For example, the providing unit can prioritize providing video related to a sporting event being held in the user's current city. The providing unit can also prioritize providing video related to places the user has visited in the past. For example, the providing unit can prioritize providing video related to a sporting event in a city the user has visited in the past. The providing unit can also prioritize providing video related to places the user plans to visit in the future. For example, the providing unit can prioritize providing video related to a sporting event in a city the user plans to visit in the future. In this way, the optimal video providing method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data to the generation AI and cause the generation AI to select the optimal providing method.

[0070] When providing video, the providing unit can analyze the user's social media activity and suggest a means of providing the video. For example, the providing unit can prioritize providing video related to players the user follows on social media. For example, if the user follows a specific player on social media, the providing unit can prioritize providing video related to the player. The providing unit can also prioritize providing video related to plays the user shared on social media. For example, if the user shares a specific play on social media, the providing unit can prioritize providing video related to that play. The providing unit can also prioritize providing video related to sporting events the user is participating in on social media. For example, if the user is participating in a specific sporting event on social media, the providing unit can prioritize providing video related to that event. This allows the user's social media activity to be analyzed to suggest the optimal means of providing the video. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a means of providing the video. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives information from the user regarding specific player names and play details. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to extract relevant video from a sports video database and edit it based on the user's request. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated video in an editing style tailored to the user's preferences. The reception unit can estimate the user's emotions and adjust the timing of receiving information based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the method of generating video based on the estimated user emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives information from the user regarding specific player names and play details. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to extract relevant video from a sports video database and edit it based on the user's request. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated video in an editing style tailored to the user's preferences. The reception unit can estimate the user's emotions and adjust the timing of receiving information based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the method of generating video based on the estimated user emotions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision 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 receives information from the user regarding specific player names and play details. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to extract relevant video from a sports video database and edit it based on the user's request. The provision unit is realized by the display 343 of the headset-type terminal 314 and provides the generated video in an editing style tailored to the user's preferences. The reception unit can estimate the user's emotions and adjust the timing of receiving information based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the method of generating video based on the estimated user emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives information from the user regarding specific player names and play details. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to extract relevant video from a sports video database and edit it based on the user's request. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated video in an editing style tailored to the user's preferences. The reception unit can estimate the user's emotions and adjust the timing of receiving information based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the method of generating video based on the estimated user emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The reception unit can acquire the user's health data and adjust the method of receiving information based on the user's health condition. For example, if the user is tired, the reception unit can provide a simple interface to make it easier for the user to input a request. Alternatively, if the user is healthy and active, the reception unit can provide a detailed interface to allow the user to input more information. Furthermore, if the user is ill or injured, the reception unit can simplify the request to reduce the burden on the user. In this way, by adjusting the method of receiving information according to the user's health condition, it is possible to receive information at a more appropriate time.

[0073] The reception unit can estimate the user's emotions and suggest the content of the request based on the estimated user's emotions. For example, if the user is excited, the reception unit can suggest a request for a play or player that will increase the user's excitement. Also, if the user is relaxed, the reception unit can suggest a request for a video that will help the user relax. Furthermore, if the user is feeling stressed, the reception unit can suggest a request for a video that will help the user relieve stress. In this way, by suggesting the content of the request according to the user's emotions, more appropriate videos can be provided.

[0074] The reception unit can analyze the user's past request history and suggest new requests based on changes in the user's interests. For example, if a user frequently requested a specific player in the past but has recently become interested in a different player, the reception unit can suggest a request related to the new player. Also, if a user was interested in a specific play in the past but has recently become interested in a different play, the reception unit can suggest a request related to that play. Furthermore, based on changes in the user's interests, requests related to new sporting events or trends can be suggested. This allows the system to provide more appropriate videos by suggesting new requests in response to changes in the user's interests.

[0075] When receiving information, the reception unit can filter the information based on the user's current activity status. For example, if the user is exercising, the reception unit can preferentially receive information related to exercise. Also, if the user is taking a break, the reception unit can preferentially receive information that helps the user relax. Furthermore, if the user is working, the reception unit can preferentially receive information that can be viewed in a short time. In this way, by filtering information based on the user's current activity status, highly relevant information can be provided.

[0076] The reception unit can estimate the user's emotions and dynamically change the priority of requests based on the estimated user emotions. For example, if the user is very excited, the reception unit can prioritize requests related to maintaining that excitement. Also, if the user is calm, requests containing detailed information can be prioritized. Furthermore, if the user is feeling stressed, requests that will help relieve stress can be prioritized. In this way, by dynamically changing the priority of requests according to the user's emotions, more appropriate information can be provided.

[0077] When receiving information, the reception unit can adjust the method of receiving information based on the remaining battery power of the user's device. For example, when the battery power of the user's device is low, the reception unit can provide a simplified interface to quickly receive information. When the battery power is sufficient, the reception unit can provide a detailed interface to allow more information to be input. Furthermore, when the battery power is very low, the reception unit can prioritize receiving only the most important information. In this way, by adjusting the method of receiving information according to the remaining battery power of the user's device, information can be received at a more appropriate time.

[0078] When receiving information, the reception unit can analyze the user's social media activity, infer the user's emotions, and suggest the content of the request. For example, if the user posts on social media that they are excited, the reception unit can suggest requests for plays or players that will increase their excitement. Also, if the user posts that they are relaxed, the reception unit can suggest requests for videos that will help them relax. Furthermore, if the user posts that they are stressed, the reception unit can suggest requests for videos that will help them relieve stress. In this way, by analyzing the user's social media activity, inferring their emotions, and suggesting the content of the request, more appropriate videos can be provided.

[0079] The generation unit can estimate the user's emotions and adjust the audio of the video based on the estimated user's emotions. For example, if the user is relaxed, a video can be generated with calm music playing in the background. If the user is excited, a video can be generated with energetic music playing in the background. Furthermore, if the user is stressed, a video can be generated with relaxing music playing in the background. In this way, by adjusting the audio of the video according to the user's emotions, more appropriate videos can be provided.

[0080] When generating a video, the generation unit can adjust the style of the video based on the user's past viewing history. For example, the generation unit can analyze the style of videos the user has viewed in the past and generate a video with a similar style. The generation unit can also preferentially apply a style that the user particularly liked among the videos they have viewed in the past. Furthermore, the generation unit can suggest a new style based on the user's viewing history and generate the video. In this way, by adjusting the style of the video based on the user's past viewing history, more appropriate video can be provided.

[0081] The generation unit can adjust the quality of the video based on the performance of the user's device when generating the video. For example, if the user is using a high-performance device, high-quality video can be generated. On the other hand, if the user is using a low-performance device, lightweight video can be generated. Furthermore, video of an appropriate size can be generated based on the storage capacity of the user's device. This makes it possible to provide optimal video by adjusting the quality of the video based on the performance of the user's device.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The reception unit receives information from the user about specific player names and play details. For example, the user inputs information about a specific player or play through the application. The user can input a request such as, "I want to see a goal scored by a specific player." Step 2: The generation unit analyzes the information received by the reception unit and generates customized related videos. The generation unit uses a generation AI to extract relevant videos from a sports video database and edit them based on the user's request. For example, the generation AI generates a collection of videos of goal scenes scored by a specific player. Step 3: The providing unit provides the video generated by the generating unit in an edited format that matches the user's preferences. In response to a user request, the providing unit provides video that includes only goal scenes of a specific player or video that focuses on a specific play. For example, the providing unit provides the video so that the user can watch it on a smartphone or tablet.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] [Explanation of symbols]

[0156] 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 that receives information about specific player names and play details from a user; a generation unit that analyzes the information received by the reception unit and customizes and generates a related video; a providing unit that provides the video generated by the generating unit in an editing aspect that matches the user's preferences. A system characterized by:

2. The reception unit Estimates user emotions and adjusts the timing of information reception based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past request history and select a reception method based on the user's past request history.

2. The system of claim 1.

4. The reception unit Filtering information as it arrives based on the user's current interests and sporting events 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit When receiving information, prioritize receiving information that is highly relevant based on the user's geographic location information.

2. The system of claim 1.

7. The reception unit When receiving information, analyze the user's social media activity and receive related information.

2. The system of claim 1.

8. The generation unit Estimate the user's emotions and adjust the video generation method based on the estimated user emotions.

2. The system of claim 1.

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