system

Abstract

The system according to this embodiment aims to quickly provide video footage that selects important scenes during a match in real time and integrates them with the company's concept. [Solution] The system according to the embodiment comprises a selection unit, a fusion unit, a broadcasting unit, and a payment unit. The selection unit selects important scenes during a match in real time. The fusion unit generates a video that fuses the highlight scenes selected by the selection unit with the company's concept. The broadcasting unit broadcasts the video generated by the fusion unit as a commercial in real time. The payment unit pays appearance fees to the players featured in the commercial.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to incorporate important scenes during a game into a commercial in real time, and there is room for improvement in quickly providing commercials that attract viewers' interest.

[0005] The system according to the embodiment aims to select important scenes during a game in real time and quickly provide a video integrated with a company's concept.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a selection unit, a fusion unit, a broadcasting unit, and a payment unit. The selection unit selects important scenes during a match in real time. The fusion unit generates a video that fuses the highlight scenes selected by the selection unit with the company's concept. The broadcasting unit broadcasts the video generated by the fusion unit as a commercial in real time. The payment unit pays appearance fees to the athletes featured in the commercial. [Effects of the Invention]

[0007] The system according to this embodiment can quickly select important scenes during a match in real time and provide video footage that integrates with the company's concept. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The CM generation system according to an embodiment of the present invention is a system in which AI instantly generates and immediately broadcasts commercials that combine sports highlight scenes with a company's concept and story. This CM generation system allows viewers to look forward to the commercials, advertisers to increase their publicity by having their commercials seen, and TV stations to improve their viewership ratings. Specifically, when a highlight occurs during a match, such as a dramatic comeback, the CM generation system receives real-time input to the AI. The AI ​​analyzes this scene to generate a video that combines it with the company's concept and story. For example, a video is generated in which the company's products or services appear in accordance with the flow of the match. The generated video is instantly broadcast as a commercial. Viewers look forward to the commercials because they can watch them while the excitement of the match is still fresh. In addition, athletes featured in the commercials are paid a fee afterward, and if they perform well they can receive a bonus, which also improves their motivation. Through this mechanism, viewers want to watch the commercials, advertisers can increase their publicity by having their commercials seen, and TV stations can improve their viewership ratings. In this way, the CM generation system can attract viewers' interest and enhance the effectiveness of advertising.

[0029] The CM generation system according to this embodiment comprises a selection unit, a fusion unit, a broadcasting unit, and a payment unit. The selection unit selects important scenes during a match in real time. The selection unit selects important scenes based on, for example, scoring scenes, foul scenes, and audience reactions. The selection unit can use AI to analyze the progress of the match and viewer reactions in real time and select important scenes. For example, the selection unit can detect scoring scenes during a match in real time and select those scenes as important scenes. The selection unit can also analyze audience reactions and select scenes where the audience is excited as important scenes. The fusion unit generates a video that fuses the highlight scenes selected by the selection unit with the company's concept. The fusion unit generates a video in which, for example, the company's products or services appear in accordance with the flow of the match. The fusion unit can use AI to generate a video that fuses the company's concept or story with the highlight scenes. For example, the fusion unit generates a video in which the company's products appear in accordance with scoring scenes in the match. The fusion unit can also generate a video in which the company's services are introduced in accordance with the flow of the match. The broadcasting unit broadcasts the video generated by the fusion unit as a commercial in real time. The broadcasting unit can, for example, broadcast a commercial immediately after a highlight scene of a match occurs. The broadcasting unit can use AI to analyze viewer reactions and broadcast commercials at the optimal timing. For example, the broadcasting unit can broadcast a commercial when viewers are excited. The broadcasting unit can also broadcast a commercial when viewers are relaxed. The payment unit pays the players featured in the commercials after the fact. The payment unit can, for example, pay bonuses based on the players' performance. The payment unit can use AI to analyze the players' performance and pay appropriate appearance fees and bonuses. For example, the payment unit can pay a bonus if a player performs well in a scoring scene. The payment unit can also pay a bonus if a player performs well throughout the entire match. As a result, the commercial generation system according to this embodiment can attract viewer interest and enhance advertising effectiveness.

[0030] The selection unit selects important scenes from a match in real time. For example, it selects important scenes based on factors such as scoring, fouls, and crowd reactions. Using AI, the selection unit can analyze the match's progress and viewer reactions in real time to select important scenes. Specifically, the AI ​​analyzes match video data in real time and automatically detects specific events such as scoring and fouls. For example, in scoring scenes, it analyzes the movement of the goal net, the players' movements, and the crowd's cheers to identify the moment a goal is scored. Similarly, in foul scenes, it analyzes contact between players, the referee's movements, and the crowd's reactions to identify the moment a foul occurs. Furthermore, to analyze crowd reactions, the AI ​​detects their facial expressions and movements and evaluates their emotions, such as excitement, joy, and surprise, in real time. This allows the selection unit to accurately select the moments that excite viewers the most. When selecting these important scenes, the selection unit utilizes not only the match's progress and viewer reactions, but also historical data and statistical information. For example, based on past match data, the system predicts scenes in which specific players or teams are likely to score and selects those scenes as important. Furthermore, it evaluates how specific scenes affect viewers based on viewer reaction data and incorporates the results into the selection process. This allows the selection team to choose the optimal scenes to maximize viewer interest.

[0031] The fusion unit generates videos that combine highlight scenes selected by the selection unit with the company's concept. For example, the fusion unit generates videos in which the company's products or services appear in accordance with the flow of the game. Specifically, it uses AI to generate videos that combine the company's concept and story with highlight scenes. For example, when generating videos in which the company's products appear in accordance with scoring scenes in a game, the AI ​​analyzes the video data of the scoring scenes and inserts the company's products at the appropriate timing. Also, when generating videos in which the company's services are introduced in accordance with the flow of the game, the AI ​​analyzes the progress of the game and identifies the timing when the service introduction is most effective. Furthermore, the fusion unit adjusts the color tone, effects, and text of the video to visually emphasize the company's brand image and message. For example, to naturally incorporate the company's logo or slogan into the highlight scenes of the game, the AI ​​analyzes the background and movement of the video and displays the logo or slogan at the optimal position and timing. It also adjusts the color tone and effects of the video so that the company's products or services harmonize with the game scenes, creating a sense of visual unity. In this way, the fusion unit can generate videos that effectively convey the company's message to the viewer.

[0032] The broadcasting unit airs the video generated by the fusion unit as commercials in real time. For example, the broadcasting unit might air a commercial immediately after a highlight scene in a match. Specifically, it uses AI to analyze viewer reactions and air commercials at the optimal timing. For example, when airing a commercial when viewers are excited, the AI ​​analyzes their facial expressions and movements to identify the peak of their excitement. When airing a commercial when viewers are relaxed, the AI ​​analyzes their posture and movements to identify their relaxed state. Furthermore, the broadcasting unit evaluates the effectiveness of the commercials in real time based on viewer reaction data and adjusts the content and timing as needed. For example, if viewer reaction is lower than expected, the AI ​​re-evaluates the content and timing of the commercial and reflects this in the next airing. Also, if viewer reaction is very high, the AI ​​uses that data to recommend airing a commercial at a similar timing and with similar content. This allows the broadcasting unit to achieve optimal commercial airing to maximize viewer interest.

[0033] The payment department pays appearance fees to players who appear in commercials after the fact. The payment department also pays bonuses based on the players' performance, for example. Specifically, it can use AI to analyze players' performance and pay appropriate appearance fees and bonuses. For example, if a bonus is paid for a player's performance in a scoring play, the AI ​​analyzes the video data of the scoring play and evaluates the player's contribution. Also, if a bonus is paid for a player's outstanding performance throughout a match, the AI ​​analyzes the data of the entire match and comprehensively evaluates the player's performance. Furthermore, the payment department determines the amount of appearance fees and bonuses considering the player's contract details and past performance. For example, it pays additional bonuses if certain conditions are met based on the player's contract. It also adjusts compensation for specific performances based on the player's past performance. In this way, the payment department can provide appropriate compensation to increase players' motivation.

[0034] The selection unit can improve the accuracy of selecting important scenes based on past match data. For example, the selection unit can select scenes that received a good response from viewers in past matches. The selection unit can improve the accuracy of selecting important scenes by referring to past match data. For example, the selection unit can prioritize selecting scenes in which a particular player performs well. The selection unit can also analyze past match data and select scenes that viewers paid particular attention to. Past match data includes, but is not limited to, scoring scenes, foul scenes, and match results. This improves the accuracy of selecting important scenes by referring to past match data. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not using AI. For example, the selection unit can input past match data into AI and have the AI ​​perform analysis to improve the accuracy of selecting important scenes.

[0035] The selection unit can dynamically change its selection criteria according to the progress of the match. For example, in the early stages of the match, the selection unit selects scenes that make it easy to grasp the flow of the game. The selection unit can dynamically change its selection criteria according to the progress of the match. For example, in the middle stages of the match, the selection unit selects scenes that convey a sense of tension. Furthermore, in the later stages of the match, the selection unit can also select decisive scenes. The progress of the match includes, but is not limited to, the score, match time, and player movements. By dynamically changing the selection criteria according to the progress of the match, more appropriate scenes can be selected. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not using AI. For example, the selection unit can input the progress of the match into AI and have the AI ​​dynamically change the selection criteria.

[0036] The fusion unit can customize the style of the video based on the company's brand image. For example, the fusion unit can reflect the company's brand colors in the video. The fusion unit can customize the style of the video based on the company's brand image. For example, the fusion unit can incorporate the company's logo and slogan into the video. The fusion unit can also add music and narration that match the company's brand image to the video. Brand image includes, but is not limited to, the company's logo, slogan, and marketing strategy. By customizing the style of the video based on the company's brand image, the company can effectively convey its message. Some or all of the above processes in the fusion unit may be performed using AI, for example, or not using AI. For example, the fusion unit can input the company's brand image into AI and have the AI ​​perform the customization of the video style.

[0037] The fusion unit can apply different video generation algorithms depending on the category of the match. For example, in a soccer match, the fusion unit can apply an algorithm that emphasizes goal scenes. The fusion unit can apply different video generation algorithms depending on the category of the match. For example, in a basketball match, the fusion unit can apply an algorithm that emphasizes dunk scenes. Also, in a baseball match, the fusion unit can apply an algorithm that emphasizes home run scenes. The categories of matches include, but are not limited to, soccer, basketball, and tennis. This allows for the generation of more appropriate videos by applying different video generation algorithms depending on the category of the match. Some or all of the above processing in the fusion unit may be performed using AI, for example, or without AI. For example, the fusion unit can input the category of the match into the AI ​​and have the AI ​​perform the application of the video generation algorithm.

[0038] The broadcasting department can select the optimal broadcasting method based on the viewer's past viewing history. For example, the broadcasting department may broadcast commercials based on the style of commercials the viewer has previously enjoyed watching. The broadcasting department can select the optimal broadcasting method by referring to the viewer's past viewing history. For example, the broadcasting department may select a broadcasting method tailored to a specific time slot based on the viewer's past viewing history. The broadcasting department can also analyze the viewer's past viewing history and select the most effective broadcasting method. Viewing history includes, but is not limited to, previously watched matches, viewing time, and viewing frequency. This allows the broadcasting department to select the optimal broadcasting method by referring to the viewer's past viewing history. Some or all of the above processing in the broadcasting department may be performed using, for example, AI, or not using AI. For example, the broadcasting department can input the viewer's past viewing history into AI and have the AI ​​select the optimal broadcasting method.

[0039] The broadcasting unit can dynamically change the content broadcast according to the progress of the match. For example, in the early stages of the match, the broadcasting unit can broadcast commercials that make it easy to understand the flow of the match. The broadcasting unit can dynamically change the content broadcast according to the progress of the match. For example, in the middle stages of the match, the broadcasting unit can broadcast commercials that create a sense of tension. Furthermore, in the later stages of the match, the broadcasting unit can broadcast commercials that emphasize decisive scenes. The progress of the match includes, but is not limited to, the score, match time, and player movements. By dynamically changing the content broadcast according to the progress of the match, more appropriate commercials can be broadcast. Some or all of the above processing in the broadcasting unit may be performed using AI, for example, or not using AI. For example, the broadcasting unit can input the progress of the match into AI and have AI perform dynamic changes to the broadcasting content.

[0040] The payment department can determine bonus amounts based on a player's past performance data. For example, the payment department may increase a player's bonus if they have performed exceptionally well in the past. The payment department can also determine bonus amounts by referring to a player's past performance data. For example, the payment department may analyze a player's past performance data and pay a bonus if certain criteria are met. The payment department may also pay a bonus based on a player's past performance data if specific conditions are met. Performance data includes, but is not limited to, goals scored, assists, and playing time. This allows for the appropriate determination of bonus amounts by referring to a player's past performance data. Some or all of the above processes in the payment department may be performed using, for example, AI, or not using AI. For example, the payment department may input a player's past performance data into an AI and have the AI ​​determine the bonus amount.

[0041] The payment unit can dynamically change the criteria for paying appearance fees according to the importance of the match. For example, the payment unit can increase appearance fees if an actor performs well in an important match. The payment unit can dynamically change the criteria for paying appearance fees according to the importance of the match. For example, the payment unit can adjust the criteria for paying appearance fees according to the importance of the match. The payment unit can also evaluate the importance of a match in real time and change the criteria for paying appearance fees. The importance of a match includes, but is not limited to, league matches, cup matches, and friendly matches. This allows for increased motivation among players by dynamically changing the criteria for paying appearance fees according to the importance of the match. Some or all of the above processes in the payment unit may be performed using, for example, AI, or not using AI. For example, the payment unit can input the importance of a match into an AI and have the AI ​​dynamically change the criteria for paying appearance fees.

[0042] The payment unit can select the optimal payment method based on the player's contract information. For example, the payment unit can select the optimal payment method based on the player's contract details. The payment unit can select the optimal payment method by considering the player's contract information. For example, the payment unit can refer to the player's contract information and customize the payment method. The payment unit can also adjust the payment method according to the player's contract terms. Contract information includes, but is not limited to, contract period, contract amount, and bonus conditions. This allows the payment unit to select the optimal payment method by considering the player's contract information. Some or all of the above processing in the payment unit may be performed using, for example, AI, or not using AI. For example, the payment unit can input the player's contract information into AI and have the AI ​​select the optimal payment method.

[0043] The payment department can determine the bonus amount based on the performance data of the player's team. For example, the payment department may increase the bonus if the team performs exceptionally well. The payment department can also determine the bonus amount by referring to the performance data of the player's team. For example, the payment department may analyze the team's performance data and pay a bonus if certain criteria are met. The payment department may also pay a bonus based on the team's performance data if specific conditions are met. Performance data includes, but is not limited to, goals scored, assists, and playing time. This allows for the appropriate determination of the bonus amount by referring to the player's team's performance data. Some or all of the above processes in the payment department may be performed using, for example, AI, or not. For example, the payment department may input the team's performance data into an AI and have the AI ​​determine the bonus amount.

[0044] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0045] The fusion unit can customize the style of the video based on the viewer's past viewing history. For example, it can generate video by referencing the style of commercials the viewer has enjoyed watching in the past. The fusion unit can also analyze the viewer's past viewing history and generate video tailored to specific viewer groups. Furthermore, based on the viewer's past viewing history, the fusion unit can incorporate products and services that are likely to interest the viewer into the video. This makes it possible to generate video that reflects the viewer's past viewing history, making it easier to capture the viewer's interest.

[0046] The broadcasting department can customize the content of commercials based on viewers' geographical location information. For example, they can broadcast commercials for products or services related to a specific region to viewers in that region. The broadcasting department can also analyze viewers' geographical location information and broadcast commercials tailored to the interests of viewers in each region. Furthermore, the broadcasting department can broadcast commercials related to local events and campaigns based on viewers' geographical location information. This makes it possible to broadcast commercials that reflect viewers' geographical location, making them more likely to attract viewers' interest.

[0047] The payment department can customize the payment method for appearance fees based on the players' performance data. For example, if a player meets certain performance criteria, they can be paid immediately. The payment department can also analyze players' performance data and pay bonuses when they perform exceptionally well. Furthermore, the payment department can adjust the timing of appearance fee payments based on players' performance data. This allows for payment methods that reflect players' performance data, thereby increasing player motivation.

[0048] The selection unit can dynamically change the criteria for selecting important scenes depending on the specific situation of the match. For example, if the match is close, it will prioritize selecting tense scenes. It can also select humorous scenes if the match is one-sided. Furthermore, if the match goes into overtime, it can select decisive scenes. This allows for scene selection tailored to the match situation, making it easier to capture viewers' interest.

[0049] The Fusion Unit can customize video content based on a company's marketing campaigns. For example, if a company launches a new product, it can generate a video highlighting that new product. The Fusion Unit can also generate seasonal videos to match a company's seasonal campaigns. Furthermore, it can generate videos related to specific company events. This allows for the creation of videos that reflect a company's marketing campaigns, effectively conveying the company's message.

[0050] The following briefly describes the processing flow for example form 1.

[0051] Step 1: The selection unit selects important scenes during the match in real time. The selection unit selects important scenes based on factors such as scoring plays, fouls, and audience reactions. The selection unit can use AI to analyze the progress of the match and viewer reactions in real time and select important scenes. For example, the selection unit can detect scoring plays during the match in real time and select those scenes as important. The selection unit can also analyze audience reactions and select scenes where the audience is excited as important scenes. Step 2: The fusion unit generates a video that combines the highlight scenes selected by the selection unit with the company's concept. The fusion unit can generate a video in which, for example, the company's products or services appear in accordance with the flow of the game. The fusion unit can use AI to generate a video that combines the company's concept and story with highlight scenes. For example, the fusion unit can generate a video in which the company's products appear in accordance with scoring scenes in the game. The fusion unit can also generate a video in which the company's services are introduced in accordance with the flow of the game. Step 3: The broadcasting unit broadcasts the video generated by the fusion unit as a commercial in real time. For example, the broadcasting unit might broadcast a commercial immediately after a highlight scene of a match occurs. The broadcasting unit can use AI to analyze viewer reactions and broadcast commercials at the optimal timing. For example, the broadcasting unit might broadcast a commercial when viewers are excited. Alternatively, the broadcasting unit could broadcast a commercial when viewers are relaxed. Step 4: The payment department pays the players featured in the commercials their appearance fees afterward. The payment department may also pay bonuses based on the players' performance, for example. The payment department can use AI to analyze players' performance and pay appropriate appearance fees and bonuses. For example, the payment department may pay a bonus if a player performs well in scoring situations. The payment department may also pay a bonus if a player performs exceptionally well throughout the entire match.

[0052] (Example of form 2) The CM generation system according to an embodiment of the present invention is a system in which AI instantly generates and immediately broadcasts commercials that combine sports highlight scenes with a company's concept and story. This CM generation system allows viewers to look forward to the commercials, advertisers to increase their publicity by having their commercials seen, and TV stations to improve their viewership ratings. Specifically, when a highlight occurs during a match, such as a dramatic comeback, the CM generation system receives real-time input to the AI. The AI ​​analyzes this scene to generate a video that combines it with the company's concept and story. For example, a video is generated in which the company's products or services appear in accordance with the flow of the match. The generated video is instantly broadcast as a commercial. Viewers look forward to the commercials because they can watch them while the excitement of the match is still fresh. In addition, athletes featured in the commercials are paid a fee afterward, and if they perform well they can receive a bonus, which also improves their motivation. Through this mechanism, viewers want to watch the commercials, advertisers can increase their publicity by having their commercials seen, and TV stations can improve their viewership ratings. In this way, the CM generation system can attract viewers' interest and enhance the effectiveness of advertising.

[0053] The CM generation system according to this embodiment comprises a selection unit, a fusion unit, a broadcasting unit, and a payment unit. The selection unit selects important scenes during a match in real time. The selection unit selects important scenes based on, for example, scoring scenes, foul scenes, and audience reactions. The selection unit can use AI to analyze the progress of the match and viewer reactions in real time and select important scenes. For example, the selection unit can detect scoring scenes during a match in real time and select those scenes as important scenes. The selection unit can also analyze audience reactions and select scenes where the audience is excited as important scenes. The fusion unit generates a video that fuses the highlight scenes selected by the selection unit with the company's concept. The fusion unit generates a video in which, for example, the company's products or services appear in accordance with the flow of the match. The fusion unit can use AI to generate a video that fuses the company's concept or story with the highlight scenes. For example, the fusion unit generates a video in which the company's products appear in accordance with scoring scenes in the match. The fusion unit can also generate a video in which the company's services are introduced in accordance with the flow of the match. The broadcasting unit broadcasts the video generated by the fusion unit as a commercial in real time. The broadcasting unit can, for example, broadcast a commercial immediately after a highlight scene of a match occurs. The broadcasting unit can use AI to analyze viewer reactions and broadcast commercials at the optimal timing. For example, the broadcasting unit can broadcast a commercial when viewers are excited. The broadcasting unit can also broadcast a commercial when viewers are relaxed. The payment unit pays the players featured in the commercials after the fact. The payment unit can, for example, pay bonuses based on the players' performance. The payment unit can use AI to analyze the players' performance and pay appropriate appearance fees and bonuses. For example, the payment unit can pay a bonus if a player performs well in a scoring scene. The payment unit can also pay a bonus if a player performs well throughout the entire match. As a result, the commercial generation system according to this embodiment can attract viewer interest and enhance advertising effectiveness.

[0054] The selection unit selects important scenes from a match in real time. For example, it selects important scenes based on factors such as scoring, fouls, and crowd reactions. Using AI, the selection unit can analyze the match's progress and viewer reactions in real time to select important scenes. Specifically, the AI ​​analyzes match video data in real time and automatically detects specific events such as scoring and fouls. For example, in scoring scenes, it analyzes the movement of the goal net, the players' movements, and the crowd's cheers to identify the moment a goal is scored. Similarly, in foul scenes, it analyzes contact between players, the referee's movements, and the crowd's reactions to identify the moment a foul occurs. Furthermore, to analyze crowd reactions, the AI ​​detects their facial expressions and movements and evaluates their emotions, such as excitement, joy, and surprise, in real time. This allows the selection unit to accurately select the moments that excite viewers the most. When selecting these important scenes, the selection unit utilizes not only the match's progress and viewer reactions, but also historical data and statistical information. For example, based on past match data, the system predicts scenes in which specific players or teams are likely to score and selects those scenes as important. Furthermore, it evaluates how specific scenes affect viewers based on viewer reaction data and incorporates the results into the selection process. This allows the selection team to choose the optimal scenes to maximize viewer interest.

[0055] The fusion unit generates videos that combine highlight scenes selected by the selection unit with the company's concept. For example, the fusion unit generates videos in which the company's products or services appear in accordance with the flow of the game. Specifically, it uses AI to generate videos that combine the company's concept and story with highlight scenes. For example, when generating videos in which the company's products appear in accordance with scoring scenes in a game, the AI ​​analyzes the video data of the scoring scenes and inserts the company's products at the appropriate timing. Also, when generating videos in which the company's services are introduced in accordance with the flow of the game, the AI ​​analyzes the progress of the game and identifies the timing when the service introduction is most effective. Furthermore, the fusion unit adjusts the color tone, effects, and text of the video to visually emphasize the company's brand image and message. For example, to naturally incorporate the company's logo or slogan into the highlight scenes of the game, the AI ​​analyzes the background and movement of the video and displays the logo or slogan at the optimal position and timing. It also adjusts the color tone and effects of the video so that the company's products or services harmonize with the game scenes, creating a sense of visual unity. In this way, the fusion unit can generate videos that effectively convey the company's message to the viewer.

[0056] The broadcasting unit airs the video generated by the fusion unit as commercials in real time. For example, the broadcasting unit might air a commercial immediately after a highlight scene in a match. Specifically, it uses AI to analyze viewer reactions and air commercials at the optimal timing. For example, when airing a commercial when viewers are excited, the AI ​​analyzes their facial expressions and movements to identify the peak of their excitement. When airing a commercial when viewers are relaxed, the AI ​​analyzes their posture and movements to identify their relaxed state. Furthermore, the broadcasting unit evaluates the effectiveness of the commercials in real time based on viewer reaction data and adjusts the content and timing as needed. For example, if viewer reaction is lower than expected, the AI ​​re-evaluates the content and timing of the commercial and reflects this in the next airing. Also, if viewer reaction is very high, the AI ​​uses that data to recommend airing a commercial at a similar timing and with similar content. This allows the broadcasting unit to achieve optimal commercial airing to maximize viewer interest.

[0057] The payment department pays appearance fees to players who appear in commercials after the fact. The payment department also pays bonuses based on the players' performance, for example. Specifically, it can use AI to analyze players' performance and pay appropriate appearance fees and bonuses. For example, if a bonus is paid for a player's performance in a scoring play, the AI ​​analyzes the video data of the scoring play and evaluates the player's contribution. Also, if a bonus is paid for a player's outstanding performance throughout a match, the AI ​​analyzes the data of the entire match and comprehensively evaluates the player's performance. Furthermore, the payment department determines the amount of appearance fees and bonuses considering the player's contract details and past performance. For example, it pays additional bonuses if certain conditions are met based on the player's contract. It also adjusts compensation for specific performances based on the player's past performance. In this way, the payment department can provide appropriate compensation to increase players' motivation.

[0058] The selection unit can adjust the criteria for selecting important scenes based on the viewer's emotions. For example, if the viewer is excited, the selection unit will prioritize more dramatic scenes. The selection unit can also estimate the viewer's emotions and adjust the criteria for selecting important scenes based on the estimated emotions. For example, if the viewer is relaxed, the selection unit will select calm scenes. Alternatively, if the viewer is tense, the selection unit can select scenes that alleviate tension. The estimation of the viewer's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This makes it easier to capture the viewer's interest by adjusting the criteria for selecting important scenes based on their emotions.

[0059] The selection unit can improve the accuracy of selecting important scenes based on past match data. For example, the selection unit can select scenes that received a good response from viewers in past matches. The selection unit can improve the accuracy of selecting important scenes by referring to past match data. For example, the selection unit can prioritize selecting scenes in which a particular player performs well. The selection unit can also analyze past match data and select scenes that viewers paid particular attention to. Past match data includes, but is not limited to, scoring scenes, foul scenes, and match results. This improves the accuracy of selecting important scenes by referring to past match data. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not using AI. For example, the selection unit can input past match data into AI and have the AI ​​perform analysis to improve the accuracy of selecting important scenes.

[0060] The selection unit can dynamically change its selection criteria according to the progress of the match. For example, in the early stages of the match, the selection unit selects scenes that make it easy to grasp the flow of the game. The selection unit can dynamically change its selection criteria according to the progress of the match. For example, in the middle stages of the match, the selection unit selects scenes that convey a sense of tension. Furthermore, in the later stages of the match, the selection unit can also select decisive scenes. The progress of the match includes, but is not limited to, the score, match time, and player movements. By dynamically changing the selection criteria according to the progress of the match, more appropriate scenes can be selected. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not using AI. For example, the selection unit can input the progress of the match into AI and have the AI ​​dynamically change the selection criteria.

[0061] The fusion unit can adjust the way the video is presented based on the viewer's emotions. For example, if the viewer is excited, the fusion unit will generate a video with visually stimulating effects. The fusion unit can also estimate the viewer's emotions and adjust the way the video is presented based on those estimated emotions. For example, if the viewer is relaxed, the fusion unit will generate a video with calming effects. Furthermore, if the viewer is tense, the fusion unit can generate a video with tension-relieving effects. The estimation of the viewer's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for adjustment of the video's presentation based on the viewer's emotions, making it easier to capture the viewer's interest.

[0062] The fusion unit can customize the style of the video based on the company's brand image. For example, the fusion unit can reflect the company's brand colors in the video. The fusion unit can customize the style of the video based on the company's brand image. For example, the fusion unit can incorporate the company's logo and slogan into the video. The fusion unit can also add music and narration that match the company's brand image to the video. Brand image includes, but is not limited to, the company's logo, slogan, and marketing strategy. By customizing the style of the video based on the company's brand image, the company can effectively convey its message. Some or all of the above processes in the fusion unit may be performed using AI, for example, or not using AI. For example, the fusion unit can input the company's brand image into AI and have the AI ​​perform the customization of the video style.

[0063] The fusion unit can apply different video generation algorithms depending on the category of the match. For example, in a soccer match, the fusion unit can apply an algorithm that emphasizes goal scenes. The fusion unit can apply different video generation algorithms depending on the category of the match. For example, in a basketball match, the fusion unit can apply an algorithm that emphasizes dunk scenes. Also, in a baseball match, the fusion unit can apply an algorithm that emphasizes home run scenes. The categories of matches include, but are not limited to, soccer, basketball, and tennis. This allows for the generation of more appropriate videos by applying different video generation algorithms depending on the category of the match. Some or all of the above processing in the fusion unit may be performed using AI, for example, or without AI. For example, the fusion unit can input the category of the match into the AI ​​and have the AI ​​perform the application of the video generation algorithm.

[0064] The broadcasting department can adjust the timing of commercials based on the audience's emotions. For example, if the audience is excited, the broadcasting department might air commercials immediately after match highlights. The broadcasting department can also estimate the audience's emotions and adjust the timing of commercials based on those estimates. For example, if the audience is relaxed, the broadcasting department might air commercials during breaks in the match. Alternatively, if the audience is tense, the broadcasting department might air commercials at times that help to alleviate their tension. The estimation of audience emotions is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. By adjusting the timing of commercials based on the audience's emotions, it becomes easier to capture the audience's interest.

[0065] The broadcasting department can select the optimal broadcasting method based on the viewer's past viewing history. For example, the broadcasting department may broadcast commercials based on the style of commercials the viewer has previously enjoyed watching. The broadcasting department can select the optimal broadcasting method by referring to the viewer's past viewing history. For example, the broadcasting department may select a broadcasting method tailored to a specific time slot based on the viewer's past viewing history. The broadcasting department can also analyze the viewer's past viewing history and select the most effective broadcasting method. Viewing history includes, but is not limited to, previously watched matches, viewing time, and viewing frequency. This allows the broadcasting department to select the optimal broadcasting method by referring to the viewer's past viewing history. Some or all of the above processing in the broadcasting department may be performed using, for example, AI, or not using AI. For example, the broadcasting department can input the viewer's past viewing history into AI and have the AI ​​select the optimal broadcasting method.

[0066] The broadcasting unit can dynamically change the content broadcast according to the progress of the match. For example, in the early stages of the match, the broadcasting unit can broadcast commercials that make it easy to understand the flow of the match. The broadcasting unit can dynamically change the content broadcast according to the progress of the match. For example, in the middle stages of the match, the broadcasting unit can broadcast commercials that create a sense of tension. Furthermore, in the later stages of the match, the broadcasting unit can broadcast commercials that emphasize decisive scenes. The progress of the match includes, but is not limited to, the score, match time, and player movements. By dynamically changing the content broadcast according to the progress of the match, more appropriate commercials can be broadcast. Some or all of the above processing in the broadcasting unit may be performed using AI, for example, or not using AI. For example, the broadcasting unit can input the progress of the match into AI and have AI perform dynamic changes to the broadcasting content.

[0067] The payment department can adjust the payment method for appearance fees based on the athlete's emotions. For example, if an athlete is excited, the payment department will pay the appearance fee immediately. The payment department can also estimate the athlete's emotions and adjust the payment method based on the estimated emotions. For example, if an athlete is relaxed, the payment department will pay the appearance fee in a lump sum at a later date. Alternatively, if an athlete is nervous, the payment department can pay the appearance fee in installments. The estimation of an athlete's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for increased athlete motivation by adjusting the payment method for appearance fees based on the athlete's emotions.

[0068] The payment department can determine bonus amounts based on a player's past performance data. For example, the payment department may increase a player's bonus if they have performed exceptionally well in the past. The payment department can also determine bonus amounts by referring to a player's past performance data. For example, the payment department may analyze a player's past performance data and pay a bonus if certain criteria are met. The payment department may also pay a bonus based on a player's past performance data if specific conditions are met. Performance data includes, but is not limited to, goals scored, assists, and playing time. This allows for the appropriate determination of bonus amounts by referring to a player's past performance data. Some or all of the above processes in the payment department may be performed using, for example, AI, or not using AI. For example, the payment department may input a player's past performance data into an AI and have the AI ​​determine the bonus amount.

[0069] The payment unit can dynamically change the criteria for paying appearance fees according to the importance of the match. For example, the payment unit can increase appearance fees if an actor performs well in an important match. The payment unit can dynamically change the criteria for paying appearance fees according to the importance of the match. For example, the payment unit can adjust the criteria for paying appearance fees according to the importance of the match. The payment unit can also evaluate the importance of a match in real time and change the criteria for paying appearance fees. The importance of a match includes, but is not limited to, league matches, cup matches, and friendly matches. This allows for increased motivation among players by dynamically changing the criteria for paying appearance fees according to the importance of the match. Some or all of the above processes in the payment unit may be performed using, for example, AI, or not using AI. For example, the payment unit can input the importance of a match into an AI and have the AI ​​dynamically change the criteria for paying appearance fees.

[0070] The payment department can adjust the timing of appearance fee payments based on the athlete's emotions. For example, if the athlete is excited, the payment department will pay the appearance fee immediately. The payment department can also estimate the athlete's emotions and adjust the timing of appearance fee payments based on the estimated emotions. For example, if the athlete is relaxed, the payment department will pay the appearance fee in a lump sum at a later date. Alternatively, if the athlete is nervous, the payment department can pay the appearance fee in installments. The estimation of the athlete's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for increased athlete motivation by adjusting the timing of appearance fee payments based on the athlete's emotions.

[0071] The payment unit can select the optimal payment method based on the player's contract information. For example, the payment unit can select the optimal payment method based on the player's contract details. The payment unit can select the optimal payment method by considering the player's contract information. For example, the payment unit can refer to the player's contract information and customize the payment method. The payment unit can also adjust the payment method according to the player's contract terms. Contract information includes, but is not limited to, contract period, contract amount, and bonus conditions. This allows the payment unit to select the optimal payment method by considering the player's contract information. Some or all of the above processing in the payment unit may be performed using, for example, AI, or not using AI. For example, the payment unit can input the player's contract information into AI and have the AI ​​select the optimal payment method.

[0072] The payment department can determine the bonus amount based on the performance data of the player's team. For example, the payment department may increase the bonus if the team performs exceptionally well. The payment department can also determine the bonus amount by referring to the performance data of the player's team. For example, the payment department may analyze the team's performance data and pay a bonus if certain criteria are met. The payment department may also pay a bonus based on the team's performance data if specific conditions are met. Performance data includes, but is not limited to, goals scored, assists, and playing time. This allows for the appropriate determination of the bonus amount by referring to the player's team's performance data. Some or all of the above processes in the payment department may be performed using, for example, AI, or not. For example, the payment department may input the team's performance data into an AI and have the AI ​​determine the bonus amount.

[0073] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0074] The selection unit can also analyze viewers' real-time social media posts and select important scenes. For example, if viewers frequently mention a particular scene on social media, that scene can be selected as an important scene. The selection unit can also perform sentiment analysis of viewers' social media posts and prioritize scenes with many positive reactions. Furthermore, the selection unit can track social media trends and select scenes related to those trends. This makes it possible to select scenes that reflect viewers' real-time reactions, making it easier to capture viewers' interest.

[0075] The fusion unit can customize the style of the video based on the viewer's past viewing history. For example, it can generate video by referencing the style of commercials the viewer has enjoyed watching in the past. The fusion unit can also analyze the viewer's past viewing history and generate video tailored to specific viewer groups. Furthermore, based on the viewer's past viewing history, the fusion unit can incorporate products and services that are likely to interest the viewer into the video. This makes it possible to generate video that reflects the viewer's past viewing history, making it easier to capture the viewer's interest.

[0076] The broadcasting department can customize the content of commercials based on viewers' geographical location information. For example, they can broadcast commercials for products or services related to a specific region to viewers in that region. The broadcasting department can also analyze viewers' geographical location information and broadcast commercials tailored to the interests of viewers in each region. Furthermore, the broadcasting department can broadcast commercials related to local events and campaigns based on viewers' geographical location information. This makes it possible to broadcast commercials that reflect viewers' geographical location, making them more likely to attract viewers' interest.

[0077] The payment department can customize the payment method for appearance fees based on the players' performance data. For example, if a player meets certain performance criteria, they can be paid immediately. The payment department can also analyze players' performance data and pay bonuses when they perform exceptionally well. Furthermore, the payment department can adjust the timing of appearance fee payments based on players' performance data. This allows for payment methods that reflect players' performance data, thereby increasing player motivation.

[0078] The selection unit can predict viewer reactions to specific scenes in a match based on viewer emotions and select important scenes based on those predictions. For example, it can prioritize scenes that are likely to excite viewers. The selection unit can also estimate viewer emotions and select scenes that are likely to move viewers based on those estimates. Furthermore, the selection unit can select scenes that are likely to make viewers laugh based on their emotions. This makes it possible to select scenes that reflect viewer emotions, making it easier to capture viewers' interest.

[0079] The fusion unit can adjust the audio expression of the video based on the viewer's emotions. For example, if the viewer is excited, it can generate a video that emphasizes music and sound effects. The fusion unit can also estimate the viewer's emotions and, based on those estimates, generate a video with calming music if the viewer is relaxed. Furthermore, based on the viewer's emotions, the fusion unit can generate a video with emotional music if the viewer is moved. This allows for audio expression that reflects the viewer's emotions, making it easier to capture the viewer's interest.

[0080] The broadcasting department can adjust the airtime of commercials based on the audience's emotions. For example, if the audience is excited, they can air a short, impactful commercial. They can also estimate the audience's emotions and, based on that estimation, air a longer, more detailed commercial if the audience is relaxed. Furthermore, based on the audience's emotions, they can air a commercial with an emotional story if the audience is moved. This allows for adjustments to airtime that reflect the audience's emotions, making it easier to capture their interest.

[0081] The payment department can adjust bonus payment methods based on the player's emotions. For example, if a player is excited, the bonus can be paid immediately. The payment department can also estimate a player's emotions and, based on that estimate, pay the bonus in a lump sum at a later date if the player is relaxed. Furthermore, based on the player's emotions, the payment department can pay the bonus with a special message if the player is moved. This allows for bonus payment methods that reflect the player's emotions, thereby increasing player motivation.

[0082] The selection unit can dynamically change the criteria for selecting important scenes depending on the specific situation of the match. For example, if the match is close, it will prioritize selecting tense scenes. It can also select humorous scenes if the match is one-sided. Furthermore, if the match goes into overtime, it can select decisive scenes. This allows for scene selection tailored to the match situation, making it easier to capture viewers' interest.

[0083] The Fusion Unit can customize video content based on a company's marketing campaigns. For example, if a company launches a new product, it can generate a video highlighting that new product. The Fusion Unit can also generate seasonal videos to match a company's seasonal campaigns. Furthermore, it can generate videos related to specific company events. This allows for the creation of videos that reflect a company's marketing campaigns, effectively conveying the company's message.

[0084] The following briefly describes the processing flow for example form 2.

[0085] Step 1: The selection unit selects important scenes during the match in real time. The selection unit selects important scenes based on factors such as scoring plays, fouls, and audience reactions. The selection unit can use AI to analyze the progress of the match and viewer reactions in real time and select important scenes. For example, the selection unit can detect scoring plays during the match in real time and select those scenes as important. The selection unit can also analyze audience reactions and select scenes where the audience is excited as important scenes. Step 2: The fusion unit generates a video that combines the highlight scenes selected by the selection unit with the company's concept. The fusion unit can generate a video in which, for example, the company's products or services appear in accordance with the flow of the game. The fusion unit can use AI to generate a video that combines the company's concept and story with highlight scenes. For example, the fusion unit can generate a video in which the company's products appear in accordance with scoring scenes in the game. The fusion unit can also generate a video in which the company's services are introduced in accordance with the flow of the game. Step 3: The broadcasting unit broadcasts the video generated by the fusion unit as a commercial in real time. For example, the broadcasting unit might broadcast a commercial immediately after a highlight scene of a match occurs. The broadcasting unit can use AI to analyze viewer reactions and broadcast commercials at the optimal timing. For example, the broadcasting unit might broadcast a commercial when viewers are excited. Alternatively, the broadcasting unit could broadcast a commercial when viewers are relaxed. Step 4: The payment department pays the players featured in the commercials their appearance fees afterward. The payment department may also pay bonuses based on the players' performance, for example. The payment department can use AI to analyze players' performance and pay appropriate appearance fees and bonuses. For example, the payment department may pay a bonus if a player performs well in scoring situations. The payment department may also pay a bonus if a player performs exceptionally well throughout the entire match.

[0086] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0087] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0088] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0089] For example, the selection unit can use the camera 42 and microphone 38B of the smart device 14 to select important scenes during a match in real time. The selection unit is also realized by the specific processing unit 290 of the data processing device 12, which analyzes the progress of the match and viewer reactions. The fusion unit can, for example, use the control unit 46A of the smart device 14 to generate a video that fuses the company's concept with highlight scenes. The fusion unit is also realized by the specific processing unit 290 of the data processing device 12. The broadcasting unit can, for example, use the output device 40 of the smart device 14 to broadcast the generated video as a commercial in real time. The broadcasting unit is also realized by the specific processing unit 290 of the data processing device 12. The payment unit can, for example, use the specific processing unit 290 of the data processing device 12 to pay players appearance fees and bonuses. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0090] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0091] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0092] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0093] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0094] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0095] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0096] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0097] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0098] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0099] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0100] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0101] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0104] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] For example, the selection unit can use the camera 42 and microphone 238 of the smart glasses 214 to select important scenes during a match in real time. The selection unit is also realized by the specific processing unit 290 of the data processing device 12, which analyzes the progress of the match and viewer reactions. The fusion unit can, for example, use the control unit 46A of the smart glasses 214 to generate a video that fuses the company's concept with highlight scenes. The fusion unit is also realized by the specific processing unit 290 of the data processing device 12. The broadcasting unit can, for example, use the speaker 240 of the smart glasses 214 to broadcast the generated video as a commercial in real time. The broadcasting unit is also realized by the specific processing unit 290 of the data processing device 12. The payment unit can, for example, use the specific processing unit 290 of the data processing device 12 to pay players appearance fees and bonuses. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0106] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0107] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0120] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] For example, the selection unit can use the camera 42 and microphone 238 of the headset terminal 314 to select important scenes during a match in real time. The selection unit is also realized by the specific processing unit 290 of the data processing device 12, which analyzes the progress of the match and viewer reactions. The fusion unit can, for example, use the control unit 46A of the headset terminal 314 to generate a video that fuses the company's concept with highlight scenes. The fusion unit is also realized by the specific processing unit 290 of the data processing device 12. The broadcasting unit can, for example, use the display 343 of the headset terminal 314 to broadcast the generated video as a commercial in real time. The broadcasting unit is also realized by the specific processing unit 290 of the data processing device 12. The payment unit can, for example, use the specific processing unit 290 of the data processing device 12 to pay players appearance fees and bonuses. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0122] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0123] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0130] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] For example, the selection unit can use the camera 42 and microphone 238 of the robot 414 to select important scenes during a match in real time. The selection unit is also realized by the specific processing unit 290 of the data processing device 12, which analyzes the progress of the match and viewer reactions. The fusion unit can, for example, use the control unit 46A of the robot 414 to generate a video that fuses the company's concept with highlight scenes. The fusion unit is also realized by the specific processing unit 290 of the data processing device 12. The broadcasting unit can, for example, use the speaker 240 of the robot 414 to broadcast the generated video as a commercial in real time. The broadcasting unit is also realized by the specific processing unit 290 of the data processing device 12. The payment unit can, for example, use the specific processing unit 290 of the data processing device 12 to pay appearance fees and bonuses to the players. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0139] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0140] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0141] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0142] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0143] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0144] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0145] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0146] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0147] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0148] 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.

[0149] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0150] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0151] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0152] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0153] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0154] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0155] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0156] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0157] (Note 1) The selection team selects important scenes from the match in real time, A fusion unit generates a video that combines the highlight scenes selected by the aforementioned selection unit with the company's concept, A broadcasting unit that broadcasts the video generated by the aforementioned fusion unit as a commercial in real time, It includes a payment section that pays appearance fees to athletes who are featured in commercials. A system characterized by the following features. (Note 2) The aforementioned selection unit is We adjust the criteria for selecting important scenes based on the audience's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned selection unit is Improve the accuracy of selecting important scenes based on past match data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is The selection criteria will be changed depending on the progress of the match. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned fusion part is Adjust the way the video is presented based on the viewer's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned fusion part is Customize the video style based on the company's brand image. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned fusion part is Different video generation algorithms are applied depending on the category of the match. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned broadcasting unit is Adjusting the timing of commercials based on viewer sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned broadcasting unit is The optimal broadcasting method is selected based on the viewer's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned broadcasting unit is The broadcast content will be dynamically changed according to the progress of the match. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned payment unit is, We adjust the payment method for appearance fees based on the players' feelings. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned payment unit is, The bonus amount will be determined by referencing the player's past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned payment unit is, The criteria for paying appearance fees will be dynamically changed according to the importance of the match. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned payment unit is, We adjust the timing of appearance fee payments based on the players' feelings. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned payment unit is, Select the optimal payment method based on the player's contract information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned payment unit is, The bonus amount is determined by referencing the performance data of the player's team. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The selection team selects important scenes from the match in real time, A fusion unit generates a video that combines the highlight scenes selected by the aforementioned selection unit with the company's concept, A broadcasting unit that broadcasts the video generated by the aforementioned fusion unit as a commercial in real time, It includes a payment section that pays appearance fees to athletes who are featured in commercials. A system characterized by the following features.

2. The aforementioned selection unit is We adjust the criteria for selecting important scenes based on the audience's emotions. The system according to feature 1.

3. The aforementioned selection unit is Improve the accuracy of selecting important scenes based on past match data. The system according to feature 1.

4. The aforementioned selection unit is The selection criteria will be changed depending on the progress of the match. The system according to feature 1.

5. The aforementioned fusion part is Adjust the way the video is presented based on the viewer's emotions. The system according to feature 1.

6. The aforementioned fusion part is Customize the video style based on the company's brand image. The system according to feature 1.

7. The aforementioned fusion part is Different video generation algorithms are applied depending on the category of the match. The system according to feature 1.

8. The aforementioned broadcasting unit is Adjusting the timing of commercials based on viewer sentiment. The system according to feature 1.

9. The aforementioned broadcasting unit is The optimal broadcasting method is selected based on the viewer's past viewing history. The system according to feature 1.

10. The aforementioned broadcasting unit is The broadcast content will be dynamically changed according to the progress of the match. The system according to feature 1.

Images