system

Abstract

The system according to this embodiment aims to automatically generate trailer videos tailored to each user's preferences. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs the user's status information. The analysis unit analyzes the main feature film based on the status information input by the reception unit. The generation unit generates a trailer video based on the information analyzed by the analysis unit. The provision unit provides the trailer video generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes 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, there is a problem that a preview video tailored to each user's preference cannot be automatically generated, and the diversity of preview videos is low.

[0005] The system according to the embodiment aims to automatically generate a preview video tailored to each user's preference.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user status information. The analysis unit analyzes the main feature film based on the status information received by the reception unit. The generation unit generates a trailer video based on the information analyzed by the analysis unit. The provision unit provides the trailer video generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate trailer videos tailored to each user's preferences. [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, etc. The communication I / F controls communication between multiple 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 the data processing system 10 according to the first embodiment.

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The trailer video generation system according to an embodiment of the present invention is a system in which a generating AI automatically generates trailer videos from movies according to each user's preferences. The trailer video generation system works by having the user input their status information, which the generating AI then analyzes to input the movie itself, and generates a trailer video tailored to the user's preferences. For example, the user inputs information such as "No spoilers," "I don't need much explanation of the world setting," or "I want to be told it's a horror movie in advance." This information is input to the generating AI. Next, the generating AI analyzes the input user status information and inputs the movie itself. The generating AI analyzes the content of the movie and generates a trailer video tailored to the user's preferences. For example, if the user inputs "More excitement than story details," the generating AI extracts exciting scenes from the movie and generates a trailer video based on them. The generated trailer video is then provided to the user. The user can then watch the trailer video tailored to their preferences. This allows the user to watch a trailer video that matches the amount of information they want to input, thereby improving their entertainment satisfaction. For example, if a user inputs "I want character descriptions," the AI ​​will extract scenes from the movie where characters are introduced and generate a trailer based on those scenes. This allows users to learn about the characters beforehand, enhancing their enjoyment of the film. Similarly, if a user inputs "No spoilers," the AI ​​will generate a trailer that avoids spoiler scenes. This allows users to experience the film's appeal while avoiding spoilers. This mechanism generates trailers tailored to each user's preferences, improving entertainment satisfaction. By watching trailers that match their preferences, users can enjoy movies, games, and other forms of entertainment more effectively. Thus, the trailer generation system can automatically generate and provide trailers tailored to user preferences.

[0029] The trailer video generation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs the user's status information. The user's status information includes, but is not limited to, examples such as "No spoilers," "I don't need much explanation of the world setting," and "I want to be told in advance if it's a horror film." The reception unit stores the status information entered by the user in a database. The reception unit can also refer to status information entered by the user in the past. Furthermore, the reception unit transmits the user's status information to the analysis unit. The analysis unit uses a generation AI to analyze the main film based on the status information entered by the reception unit. The analysis unit analyzes the content of the main film and extracts scenes that match the user's preferences. The analysis unit can use a generation AI to identify particularly important scenes in the main film and scenes that match the user's preferences. For example, the analysis unit can extract exciting scenes from the main film. The analysis unit can also extract scenes in the main film where characters are introduced. Furthermore, the analysis unit can analyze the main film while avoiding scenes that contain spoilers. The generation unit generates a trailer video using generation AI based on information analyzed by the analysis unit. For example, the generation unit generates a trailer video based on scenes extracted by the analysis unit. The generation unit can use generation AI to combine particularly important scenes from the main film or scenes that match the user's preferences to generate a trailer video. For example, the generation unit can combine exciting scenes to generate a trailer video. The generation unit can also combine scenes introducing characters to generate a trailer video. Furthermore, the generation unit can generate a trailer video while avoiding scenes that would spoil the film. The provision unit provides the trailer video generated by the generation unit to the user. For example, the provision unit sends the generated trailer video to the user's device. The provision unit can also provide a platform or application that allows the user to view the generated trailer video. Furthermore, the provision unit can customize the generated trailer video to match the user's preferences. As a result, the trailer video generation system according to this embodiment can automatically generate and provide trailer videos tailored to the user's preferences.

[0030] The reception desk inputs the user's status information. This status information may include, but is not limited to, examples such as "no spoilers," "don't need much explanation of the world setting," or "please tell me beforehand if it's horror." The reception desk saves the status information entered by the user to a database. Specifically, information entered by the user through web forms or applications is recorded in the database in real time. The database can create individual profiles for each user and manage them, including past status information and viewing history. The reception desk can also refer to the status information that the user has entered in the past. This allows past preferences and settings to be automatically reflected when the user uses the system again. Furthermore, the reception desk transmits the user's status information to the analysis department. Transmission is performed using a secure communication protocol, and encryption technology is applied to prevent data leakage and tampering. This allows the reception desk to accurately and safely convey the user's preferences and requests to the analysis department.

[0031] The analysis unit uses a generative AI to analyze the film based on status information entered by the reception unit. For example, the analysis unit analyzes the content of the film and extracts scenes that match the user's preferences. Specifically, the generative AI tags each scene in the film and analyzes the content, emotions, and relationships between characters in detail. The generative AI uses natural language processing and image recognition technologies to extract important information from the film's dialogue and visuals. For example, to extract exciting scenes from the film, the analysis unit analyzes the music, character expressions, and frequency of action in each scene. The analysis unit can also extract scenes in the film where characters are introduced. This includes scenes where characters first appear and scenes that reveal important character relationships. Furthermore, the analysis unit can analyze the film while avoiding scenes that contain spoilers. The generative AI understands the film's storyline, identifies scenes containing climaxes and important plot twists, and excludes them from the trailer. This allows the analysis unit to accurately extract scenes that match the user's preferences and requests and provide them to the generative unit.

[0032] The generation unit uses a generation AI to generate trailer videos based on information analyzed by the analysis unit. For example, the generation unit generates trailer videos based on scenes extracted by the analysis unit. Specifically, the generation AI optimizes the order of scenes and transitions to create visually appealing trailer videos. The generation AI automatically handles scene switching, music selection, and narration addition to produce professional-quality trailer videos. For example, the generation unit generates trailer videos by combining exciting scenes. This includes action scenes and emotional scenes, structured to capture the viewer's interest. The generation unit can also generate trailer videos by combining scenes that introduce characters. This allows viewers to learn about the main characters and their relationships in advance. Furthermore, the generation unit can generate trailer videos while avoiding scenes that would contain spoilers. Based on the information provided by the analysis unit, the generation AI creates trailer videos that maximize the appeal of the film while avoiding the core of the story. This allows the generation unit to automatically generate high-quality trailer videos that meet the user's preferences and requests.

[0033] The distribution unit provides users with trailers generated by the generation unit. For example, the distribution unit sends the generated trailers to the user's device. Specifically, it provides trailers to the user's smartphone, tablet, or PC in streaming or download format. The distribution unit can also provide a platform or application that allows users to view the generated trailers. This includes dedicated apps and websites, allowing users to easily watch the trailers. Furthermore, the distribution unit can customize the generated trailers to the user's preferences. For example, it can provide individually optimized trailers based on the user's past viewing history and status information. This allows the distribution unit to provide users with a personalized viewing experience and increase their interest in the film. Additionally, the distribution unit can collect user feedback and continuously improve the quality and content of the trailers. This allows the distribution unit to always provide users with the latest and best trailers, maximizing the promotional effect of the film.

[0034] The reception desk can analyze the user's past status information and suggest the optimal input method. For example, the reception desk can automatically display status information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest status information to be used during specific time periods based on the user's past status information. This makes user input more efficient by suggesting the optimal input method based on past status information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past status information into a generating AI and have the generating AI suggest the optimal input method.

[0035] The reception desk can customize input fields based on the user's current interests when they enter status information. For example, the reception desk can suggest relevant status information based on the genres of movies and TV shows the user has recently watched. It can also suggest relevant status information based on keywords the user has recently searched for. Furthermore, it can suggest relevant status information based on events and activities the user has recently participated in. This improves user satisfaction by providing input fields that match the user's interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data about the user's interests into a generating AI and have the generating AI customize the input fields.

[0036] The reception desk can prioritize displaying highly relevant input fields when users enter status information, taking into account their geographical location. For example, if a user is in a specific region, the reception desk can prioritize displaying status information related to that region. Furthermore, if a user is traveling, the reception desk can prioritize displaying status information related to their travel destination. Additionally, if a user is at home, the reception desk can prioritize displaying status information related to their home. This allows the reception desk to provide users with highly relevant information by considering their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI display highly relevant input fields.

[0037] The reception desk can analyze the user's social media activity and suggest relevant input fields when status information is entered. For example, the reception desk can suggest relevant status information based on posts the user has recently "liked." It can also suggest relevant status information based on comments the user has recently made. Furthermore, it can suggest relevant status information based on content the user has recently shared. This allows the system to provide relevant information to the user by analyzing their social media activity. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generative AI and have the generative AI suggest relevant input fields.

[0038] The analysis unit can improve the accuracy of its analysis by considering the interrelationships within the film during the analysis process. For example, the analysis unit can consider the temporal continuity between scenes in the film. It can also consider the relationships between characters in the film during the analysis. Furthermore, the analysis unit can consider the consistency of the film's storyline during the analysis. This improves the accuracy of the analysis by considering the interrelationships within the film. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input scene data from the film into a generating AI and have the generating AI perform the analysis of interrelationships.

[0039] The analysis unit can apply different analysis algorithms depending on the genre and theme of the film during the analysis. For example, in the case of a horror film, the analysis unit will focus on scenes that enhance the sense of fear. In the case of a comedy film, the analysis unit can also focus on humorous scenes. Furthermore, in the case of a drama film, the analysis unit can also focus on emotional scenes. By applying an analysis algorithm appropriate to the genre and theme, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input film genre data into a generating AI and have the generating AI perform an analysis according to the genre and theme.

[0040] The analysis unit can perform its analysis while considering the film's geographical background. For example, the analysis unit can consider the cultural background of the city or country where the film is set. It can also consider the geographical features of the locations where the film scenes were shot. Furthermore, the analysis unit can consider geographical elements related to the film's storyline. This improves the accuracy of the analysis by considering the geographical background. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the film's geographical background data into a generating AI and have the generating AI perform the geographical background analysis.

[0041] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the film during the analysis process. For example, the analysis unit may refer to the novel on which the film is based. It can also refer to interviews and articles related to the film's production. Furthermore, it can refer to film reviews and critiques. By referring to relevant literature, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on relevant literature on the film into a generating AI and have the generating AI perform the analysis of the relevant literature.

[0042] The generation unit can analyze important scenes from the main film during the generation process to create the optimal trailer. For example, the generation unit can generate a trailer that emphasizes the climax scene of the film. It can also generate a trailer that emphasizes scenes in which the main characters of the film appear. Furthermore, it can generate a trailer that emphasizes scenes that mark turning points in the film's story. In this way, the optimal trailer is generated by analyzing important scenes. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input scene data from the main film into a generation AI and have the generation AI perform an analysis of important scenes.

[0043] The generation unit can customize the content of the trailer video by referring to the user's past viewing history during generation. For example, the generation unit can customize the trailer video based on the genre of movies the user has watched in the past. It can also customize the trailer video based on the director and actors of movies the user has watched in the past. Furthermore, the generation unit can customize the trailer video based on the user's ratings of movies they have watched in the past. In this way, by referring to past viewing history, the most suitable trailer video for the user is generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past viewing history data into a generation AI and have the generation AI perform the trailer video customization.

[0044] The generation unit can determine the priority of trailers based on the film's release date during the generation process. For example, the generation unit can generate a trailer containing the most important scenes just before the film's release. It can also generate a trailer containing intriguing scenes several months before the film's release. Furthermore, the generation unit can generate a trailer that reflects reviews and ratings after the film's release. This makes it easier to attract user interest by providing trailers based on the release date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input film release date data into a generation AI and have the generation AI determine the priority of the trailers.

[0045] The generation unit can adjust the order of trailers based on the relevance of the films during generation. For example, the generation unit can adjust the order of trailers based on previous films or related works. It can also adjust the order of trailers based on the genre or theme of the films. Furthermore, the generation unit can adjust the order of trailers based on the progression of the film's story. This allows for a deeper understanding by providing trailers in a relevance-based order. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input film relevance data into a generation AI and have the generation AI perform the adjustment of the trailer order.

[0046] The delivery unit can select the optimal delivery method by referring to the user's past viewing history at the time of delivery. For example, the delivery unit can provide trailers based on the genres of movies the user has watched in the past. It can also provide trailers based on the directors and actors of movies the user has watched in the past. Furthermore, it can provide trailers based on the ratings the user has given to movies they have watched in the past. In this way, by referring to past viewing history, the delivery unit can provide the user with the most suitable delivery method. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's past viewing history data into a generating AI and have the generating AI select the optimal delivery method.

[0047] The delivery unit can customize how the trailer is delivered based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the delivery unit can provide a trailer optimized for the screen size. It can also provide a trailer optimized for a larger screen if the user is using a tablet. Furthermore, if the user is using a smartwatch, the delivery unit can provide a concise and highly visible trailer. This improves the user's viewing experience by providing a delivery method based on device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into a generating AI and have the generating AI perform the customization of the delivery method.

[0048] The delivery unit can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the delivery unit can provide a trailer related to that region. Furthermore, if the user is traveling, the delivery unit can provide a trailer related to their travel destination. In addition, if the user is at home, the delivery unit can provide a trailer related to their home. This allows the delivery unit to provide information that is highly relevant to the user by considering their geographical location information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0049] The service provider can analyze the user's social media activity at the time of delivery and propose a method for delivering the teaser video. For example, the service provider can provide relevant teaser videos based on posts the user has recently "liked." It can also provide relevant teaser videos based on comments the user has recently made. Furthermore, it can provide relevant teaser videos based on content the user has recently shared. This allows the service provider to provide information relevant to the user by analyzing their social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI propose a method for delivering the teaser video.

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

[0051] The trailer generation system can also include a history analysis unit that analyzes the user's viewing history. The history analysis unit collects data on movies and dramas the user has watched in the past and estimates the user's preferences based on that viewing history. For example, if a user watches many action movies, the history analysis unit estimates that the user likes action scenes and provides this information to the generation AI. This allows the generation AI to generate trailers that include many action scenes based on the user's viewing history. The history analysis unit can also collect rating data for movies the user has watched and generate trailers that prioritize elements of highly-rated movies. Furthermore, the history analysis unit can analyze data on the genre, director, and actors of movies the user has watched and customize the trailers considering these elements. This results in trailers being generated that are based on the user's viewing history, improving user satisfaction.

[0052] The trailer generation system can also include a social analytics unit that analyzes the user's social media activity. The social analytics unit collects content shared and liked by the user on social media to estimate the user's interests and preferences. For example, if a user frequently likes posts related to horror movies, the social analytics unit estimates that the user likes horror movies and provides this information to the generation AI. This allows the generation AI to generate trailers with a high proportion of horror elements based on the user's social media activity. The social analytics unit can also analyze the genre and theme of the content shared by the user and customize the trailers considering these elements. Furthermore, the social analytics unit can analyze the content of user comments and generate trailers that reflect the user's preferences and opinions. This results in trailers being generated based on the user's social media activity, improving user satisfaction.

[0053] The trailer generation system may also include a geographic analysis unit that takes into account the user's geographical location. The geographic analysis unit collects data on the user's current location and places they have visited in the past, and estimates the user's geographical interests. For example, if a user frequently visits a particular city, the geographic analysis unit can generate trailers that emphasize movie scenes related to that city. Furthermore, if the user is traveling, the geographic analysis unit can generate trailers that include movie scenes related to their travel destination. Additionally, if the user is at home, the geographic analysis unit can generate trailers that emphasize movie scenes related to their home. This results in trailers that are based on the user's geographical location, making them more likely to capture the user's interest.

[0054] The trailer video generation system may also include a device analysis unit that takes into account the user's device information. The device analysis unit collects information about the type and settings of the device the user is using and generates an optimal trailer video. For example, if the user is using a smartphone, the device analysis unit can generate a trailer video optimized for the smartphone's screen size. If the user is using a tablet, it can also generate a trailer video optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can generate a concise and highly visible trailer video. This results in trailer videos being generated based on the user's device information, improving the viewing experience.

[0055] The trailer generation system can also include a purchase analysis unit that analyzes the user's past purchase history. The purchase analysis unit collects movies and related products that the user has purchased in the past and estimates the user's preferences. For example, if a user has purchased many related products for a particular movie series, the purchase analysis unit estimates that the user likes that movie series and provides this information to the generation AI. This allows the generation AI to generate trailers for relevant movies based on the user's purchase history. The purchase analysis unit can also collect rating data for products purchased by the user and generate trailers that emphasize elements of highly rated products. Furthermore, the purchase analysis unit can analyze the genre and theme of products purchased by the user and customize the trailers considering these elements. This results in trailers being generated based on the user's purchase history, improving user satisfaction.

[0056] The trailer generation system can also include a feedback collection unit to gather user feedback. This unit collects user ratings and comments on trailers they have watched and provides them to the generation AI. For example, if a user gives a trailer a high rating, the feedback collection unit can prioritize elements of that trailer when generating the next trailer. Furthermore, if a user leaves specific comments on a trailer, the feedback collection unit can analyze those comments and generate a trailer that reflects the user's preferences and opinions. The feedback collection unit can also collect the number of views and viewing time of trailers watched by users and adjust the content of the trailers based on this data. This results in trailers being generated based on user feedback, improving user satisfaction.

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

[0058] Step 1: The reception desk enters the user's status information. This status information includes things like "No spoilers," "Do not need much explanation of the world setting," and "Please tell me in advance if it's horror." The reception desk saves the status information entered by the user to a database and can also refer to previously entered status information. Furthermore, the reception desk sends the user's status information to the analysis department. Step 2: The analysis unit uses a generation AI to analyze the main feature film based on the status information entered by the reception unit. The analysis unit analyzes the content of the main feature film and extracts scenes that match the user's preferences. For example, it identifies exciting scenes, scenes introducing characters, and scenes that avoid spoilers. Step 3: The generation unit uses generation AI to generate a trailer video based on the information analyzed by the analysis unit. The generation unit generates a trailer video based on the scenes extracted by the analysis unit, combining particularly important scenes and scenes that match the user's preferences. For example, it combines exciting scenes and scenes introducing characters, while avoiding scenes that would spoil the movie. Step 4: The provider unit provides the user with the trailer video generated by the generator unit. The provider unit sends the generated trailer video to the user's device and provides a platform or application where the user can view it. Furthermore, the provider unit can also customize the generated trailer video to the user's preferences.

[0059] (Example of form 2) The trailer video generation system according to an embodiment of the present invention is a system in which a generating AI automatically generates trailer videos from movies according to each user's preferences. The trailer video generation system works by having the user input their status information, which the generating AI then analyzes to input the movie itself, and generates a trailer video tailored to the user's preferences. For example, the user inputs information such as "No spoilers," "I don't need much explanation of the world setting," or "I want to be told it's a horror movie in advance." This information is input to the generating AI. Next, the generating AI analyzes the input user status information and inputs the movie itself. The generating AI analyzes the content of the movie and generates a trailer video tailored to the user's preferences. For example, if the user inputs "More excitement than story details," the generating AI extracts exciting scenes from the movie and generates a trailer video based on them. The generated trailer video is then provided to the user. The user can then watch the trailer video tailored to their preferences. This allows the user to watch a trailer video that matches the amount of information they want to input, thereby improving their entertainment satisfaction. For example, if a user inputs "I want character descriptions," the AI ​​will extract scenes from the movie where characters are introduced and generate a trailer based on those scenes. This allows users to learn about the characters beforehand, enhancing their enjoyment of the film. Similarly, if a user inputs "No spoilers," the AI ​​will generate a trailer that avoids spoiler scenes. This allows users to experience the film's appeal while avoiding spoilers. This mechanism generates trailers tailored to each user's preferences, improving entertainment satisfaction. By watching trailers that match their preferences, users can enjoy movies, games, and other forms of entertainment more effectively. Thus, the trailer generation system can automatically generate and provide trailers tailored to user preferences.

[0060] The trailer video generation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs the user's status information. The user's status information includes, but is not limited to, examples such as "No spoilers," "I don't need much explanation of the world setting," and "I want to be told in advance if it's a horror film." The reception unit stores the status information entered by the user in a database. The reception unit can also refer to status information entered by the user in the past. Furthermore, the reception unit transmits the user's status information to the analysis unit. The analysis unit uses a generation AI to analyze the main film based on the status information entered by the reception unit. The analysis unit analyzes the content of the main film and extracts scenes that match the user's preferences. The analysis unit can use a generation AI to identify particularly important scenes in the main film and scenes that match the user's preferences. For example, the analysis unit can extract exciting scenes from the main film. The analysis unit can also extract scenes in the main film where characters are introduced. Furthermore, the analysis unit can analyze the main film while avoiding scenes that contain spoilers. The generation unit generates a trailer video using generation AI based on information analyzed by the analysis unit. For example, the generation unit generates a trailer video based on scenes extracted by the analysis unit. The generation unit can use generation AI to combine particularly important scenes from the main film or scenes that match the user's preferences to generate a trailer video. For example, the generation unit can combine exciting scenes to generate a trailer video. The generation unit can also combine scenes introducing characters to generate a trailer video. Furthermore, the generation unit can generate a trailer video while avoiding scenes that would spoil the film. The provision unit provides the trailer video generated by the generation unit to the user. For example, the provision unit sends the generated trailer video to the user's device. The provision unit can also provide a platform or application that allows the user to view the generated trailer video. Furthermore, the provision unit can customize the generated trailer video to match the user's preferences. As a result, the trailer video generation system according to this embodiment can automatically generate and provide trailer videos tailored to the user's preferences.

[0061] The reception desk inputs the user's status information. This status information may include, but is not limited to, examples such as "no spoilers," "don't need much explanation of the world setting," or "please tell me beforehand if it's horror." The reception desk saves the status information entered by the user to a database. Specifically, information entered by the user through web forms or applications is recorded in the database in real time. The database can create individual profiles for each user and manage them, including past status information and viewing history. The reception desk can also refer to the status information that the user has entered in the past. This allows past preferences and settings to be automatically reflected when the user uses the system again. Furthermore, the reception desk transmits the user's status information to the analysis department. Transmission is performed using a secure communication protocol, and encryption technology is applied to prevent data leakage and tampering. This allows the reception desk to accurately and safely convey the user's preferences and requests to the analysis department.

[0062] The analysis unit uses a generative AI to analyze the film based on status information entered by the reception unit. For example, the analysis unit analyzes the content of the film and extracts scenes that match the user's preferences. Specifically, the generative AI tags each scene in the film and analyzes the content, emotions, and relationships between characters in detail. The generative AI uses natural language processing and image recognition technologies to extract important information from the film's dialogue and visuals. For example, to extract exciting scenes from the film, the analysis unit analyzes the music, character expressions, and frequency of action in each scene. The analysis unit can also extract scenes in the film where characters are introduced. This includes scenes where characters first appear and scenes that reveal important character relationships. Furthermore, the analysis unit can analyze the film while avoiding scenes that contain spoilers. The generative AI understands the film's storyline, identifies scenes containing climaxes and important plot twists, and excludes them from the trailer. This allows the analysis unit to accurately extract scenes that match the user's preferences and requests and provide them to the generative unit.

[0063] The generation unit uses a generation AI to generate trailer videos based on information analyzed by the analysis unit. For example, the generation unit generates trailer videos based on scenes extracted by the analysis unit. Specifically, the generation AI optimizes the order of scenes and transitions to create visually appealing trailer videos. The generation AI automatically handles scene switching, music selection, and narration addition to produce professional-quality trailer videos. For example, the generation unit generates trailer videos by combining exciting scenes. This includes action scenes and emotional scenes, structured to capture the viewer's interest. The generation unit can also generate trailer videos by combining scenes that introduce characters. This allows viewers to learn about the main characters and their relationships in advance. Furthermore, the generation unit can generate trailer videos while avoiding scenes that would contain spoilers. Based on the information provided by the analysis unit, the generation AI creates trailer videos that maximize the appeal of the film while avoiding the core of the story. This allows the generation unit to automatically generate high-quality trailer videos that meet the user's preferences and requests.

[0064] The distribution unit provides users with trailers generated by the generation unit. For example, the distribution unit sends the generated trailers to the user's device. Specifically, it provides trailers to the user's smartphone, tablet, or PC in streaming or download format. The distribution unit can also provide a platform or application that allows users to view the generated trailers. This includes dedicated apps and websites, allowing users to easily watch the trailers. Furthermore, the distribution unit can customize the generated trailers to the user's preferences. For example, it can provide individually optimized trailers based on the user's past viewing history and status information. This allows the distribution unit to provide users with a personalized viewing experience and increase their interest in the film. Additionally, the distribution unit can collect user feedback and continuously improve the quality and content of the trailers. This allows the distribution unit to always provide users with the latest and best trailers, maximizing the promotional effect of the film.

[0065] The reception desk can estimate the user's emotions and adjust the input method for status information based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of status information. This improves user convenience by providing an input method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0066] The reception desk can analyze the user's past status information and suggest the optimal input method. For example, the reception desk can automatically display status information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest status information to be used during specific time periods based on the user's past status information. This makes user input more efficient by suggesting the optimal input method based on past status information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past status information into a generating AI and have the generating AI suggest the optimal input method.

[0067] The reception desk can customize input fields based on the user's current interests when they enter status information. For example, the reception desk can suggest relevant status information based on the genres of movies and TV shows the user has recently watched. It can also suggest relevant status information based on keywords the user has recently searched for. Furthermore, it can suggest relevant status information based on events and activities the user has recently participated in. This improves user satisfaction by providing input fields that match the user's interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data about the user's interests into a generating AI and have the generating AI customize the input fields.

[0068] The reception unit can estimate the user's emotions and determine the priority of status information to be entered based on the estimated emotions. For example, if the user is excited, the reception unit may prioritize the input of important status information. If the user is relaxed, the reception unit may also prioritize the input of detailed status information. Furthermore, if the user is tired, the reception unit may also prioritize the input of minimal status information. This allows for the priority of important information to be entered by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit may input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0069] The reception desk can prioritize displaying highly relevant input fields when users enter status information, taking into account their geographical location. For example, if a user is in a specific region, the reception desk can prioritize displaying status information related to that region. Furthermore, if a user is traveling, the reception desk can prioritize displaying status information related to their travel destination. Additionally, if a user is at home, the reception desk can prioritize displaying status information related to their home. This allows the reception desk to provide users with highly relevant information by considering their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI display highly relevant input fields.

[0070] The reception desk can analyze the user's social media activity and suggest relevant input fields when status information is entered. For example, the reception desk can suggest relevant status information based on posts the user has recently "liked." It can also suggest relevant status information based on comments the user has recently made. Furthermore, it can suggest relevant status information based on content the user has recently shared. This allows the system to provide relevant information to the user by analyzing their social media activity. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generative AI and have the generative AI suggest relevant input fields.

[0071] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is excited, the analysis unit will prioritize emotional scenes in its analysis. If the user is relaxed, the analysis unit can also prioritize calm scenes in its analysis. Furthermore, if the user is tense, the analysis unit can prioritize scenes that alleviate tension in its analysis. This allows for more appropriate analysis by setting analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0072] The analysis unit can improve the accuracy of its analysis by considering the interrelationships within the film during the analysis process. For example, the analysis unit can consider the temporal continuity between scenes in the film. It can also consider the relationships between characters in the film during the analysis. Furthermore, the analysis unit can consider the consistency of the film's storyline during the analysis. This improves the accuracy of the analysis by considering the interrelationships within the film. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input scene data from the film into a generating AI and have the generating AI perform the analysis of interrelationships.

[0073] The analysis unit can apply different analysis algorithms depending on the genre and theme of the film during the analysis. For example, in the case of a horror film, the analysis unit will focus on scenes that enhance the sense of fear. In the case of a comedy film, the analysis unit can also focus on humorous scenes. Furthermore, in the case of a drama film, the analysis unit can also focus on emotional scenes. By applying an analysis algorithm appropriate to the genre and theme, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input film genre data into a generating AI and have the generating AI perform an analysis according to the genre and theme.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit can provide a visually stimulating display method. It can also provide a calm display method if the user is relaxed. Furthermore, if the user is tense, the analysis unit can provide a simple and highly visible display method. This deepens the user's understanding by providing a display method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0075] The analysis unit can perform its analysis while considering the film's geographical background. For example, the analysis unit can consider the cultural background of the city or country where the film is set. It can also consider the geographical features of the locations where the film scenes were shot. Furthermore, the analysis unit can consider geographical elements related to the film's storyline. This improves the accuracy of the analysis by considering the geographical background. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the film's geographical background data into a generating AI and have the generating AI perform the geographical background analysis.

[0076] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the film during the analysis process. For example, the analysis unit may refer to the novel on which the film is based. It can also refer to interviews and articles related to the film's production. Furthermore, it can refer to film reviews and critiques. By referring to relevant literature, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on relevant literature on the film into a generating AI and have the generating AI perform the analysis of the relevant literature.

[0077] The generation unit can estimate the user's emotions and adjust the method of generating the trailer video based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a trailer video that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a trailer video that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a trailer video with visually stimulating effects. By providing a generation method that responds to the user's emotions, a more appropriate trailer video can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0078] The generation unit can analyze important scenes from the main film during the generation process to create the optimal trailer. For example, the generation unit can generate a trailer that emphasizes the climax scene of the film. It can also generate a trailer that emphasizes scenes in which the main characters of the film appear. Furthermore, it can generate a trailer that emphasizes scenes that mark turning points in the film's story. In this way, the optimal trailer is generated by analyzing important scenes. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input scene data from the main film into a generation AI and have the generation AI perform an analysis of important scenes.

[0079] The generation unit can customize the content of the trailer video by referring to the user's past viewing history during generation. For example, the generation unit can customize the trailer video based on the genre of movies the user has watched in the past. It can also customize the trailer video based on the director and actors of movies the user has watched in the past. Furthermore, the generation unit can customize the trailer video based on the user's ratings of movies they have watched in the past. In this way, by referring to past viewing history, the most suitable trailer video for the user is generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past viewing history data into a generation AI and have the generation AI perform the trailer video customization.

[0080] The generation unit can estimate the user's emotions and adjust the length of the trailer video based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise trailer video. If the user is relaxed, the generation unit can also generate a longer trailer video with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a trailer video with visually stimulating effects. This improves user satisfaction by providing trailer videos of appropriate lengths according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the user's emotion estimation.

[0081] The generation unit can determine the priority of trailers based on the film's release date during the generation process. For example, the generation unit can generate a trailer containing the most important scenes just before the film's release. It can also generate a trailer containing intriguing scenes several months before the film's release. Furthermore, the generation unit can generate a trailer that reflects reviews and ratings after the film's release. This makes it easier to attract user interest by providing trailers based on the release date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input film release date data into a generation AI and have the generation AI determine the priority of the trailers.

[0082] The generation unit can adjust the order of trailers based on the relevance of the films during generation. For example, the generation unit can adjust the order of trailers based on previous films or related works. It can also adjust the order of trailers based on the genre or theme of the films. Furthermore, the generation unit can adjust the order of trailers based on the progression of the film's story. This allows for a deeper understanding by providing trailers in a relevance-based order. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input film relevance data into a generation AI and have the generation AI perform the adjustment of the trailer order.

[0083] The delivery unit can estimate the user's emotions and adjust the way the trailer is delivered based on the estimated emotions. For example, if the user is relaxed, the delivery unit can provide a trailer that progresses at a leisurely pace. If the user is in a hurry, the delivery unit can also provide a short, to-the-point trailer. Furthermore, if the user is excited, the delivery unit can provide a trailer with visually stimulating effects. By providing a delivery method that matches the user's emotions, user satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0084] The delivery unit can select the optimal delivery method by referring to the user's past viewing history at the time of delivery. For example, the delivery unit can provide trailers based on the genres of movies the user has watched in the past. It can also provide trailers based on the directors and actors of movies the user has watched in the past. Furthermore, it can provide trailers based on the ratings the user has given to movies they have watched in the past. In this way, by referring to past viewing history, the delivery unit can provide the user with the most suitable delivery method. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's past viewing history data into a generating AI and have the generating AI select the optimal delivery method.

[0085] The delivery unit can customize how the trailer is delivered based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the delivery unit can provide a trailer optimized for the screen size. It can also provide a trailer optimized for a larger screen if the user is using a tablet. Furthermore, if the user is using a smartwatch, the delivery unit can provide a concise and highly visible trailer. This improves the user's viewing experience by providing a delivery method based on device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into a generating AI and have the generating AI perform the customization of the delivery method.

[0086] The delivery unit can estimate the user's emotions and adjust the order in which the trailers are presented based on the estimated emotions. For example, if the user is excited, the delivery unit may first present a visually stimulating trailer. If the user is relaxed, the delivery unit may first present a calm trailer. Furthermore, if the user is tense, the delivery unit may first present a simple and highly visible trailer. By providing a delivery order that matches the user's emotions, user satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0087] The delivery unit can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the delivery unit can provide a trailer related to that region. Furthermore, if the user is traveling, the delivery unit can provide a trailer related to their travel destination. In addition, if the user is at home, the delivery unit can provide a trailer related to their home. This allows the delivery unit to provide information that is highly relevant to the user by considering their geographical location information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0088] The service provider can analyze the user's social media activity at the time of delivery and propose a method for delivering the teaser video. For example, the service provider can provide relevant teaser videos based on posts the user has recently "liked." It can also provide relevant teaser videos based on comments the user has recently made. Furthermore, it can provide relevant teaser videos based on content the user has recently shared. This allows the service provider to provide information relevant to the user by analyzing their social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI propose a method for delivering the teaser video.

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

[0090] The trailer generation system can also include a history analysis unit that analyzes the user's viewing history. The history analysis unit collects data on movies and dramas the user has watched in the past and estimates the user's preferences based on that viewing history. For example, if a user watches many action movies, the history analysis unit estimates that the user likes action scenes and provides this information to the generation AI. This allows the generation AI to generate trailers that include many action scenes based on the user's viewing history. The history analysis unit can also collect rating data for movies the user has watched and generate trailers that prioritize elements of highly-rated movies. Furthermore, the history analysis unit can analyze data on the genre, director, and actors of movies the user has watched and customize the trailers considering these elements. This results in trailers being generated that are based on the user's viewing history, improving user satisfaction.

[0091] The trailer generation system can also include an emotion monitoring unit that monitors the user's emotions in real time. The emotion monitoring unit collects facial and audio data while the user is watching the trailer and estimates their emotions in real time. For example, if the user smiles while watching the trailer, the emotion monitoring unit estimates that the user is enjoying it and provides this information to the generation AI. This allows the generation AI to adjust the content of the trailer based on the user's real-time emotions. The emotion monitoring unit can also generate a trailer that emphasizes the element of surprise if the user shows a surprised expression. Furthermore, if the emotion monitoring unit shows a bored expression, it can generate a trailer that includes many fast-paced scenes. This provides a trailer that responds to the user's real-time emotions, improving the viewing experience.

[0092] The trailer generation system can also include a social analytics unit that analyzes the user's social media activity. The social analytics unit collects content shared and liked by the user on social media to estimate the user's interests and preferences. For example, if a user frequently likes posts related to horror movies, the social analytics unit estimates that the user likes horror movies and provides this information to the generation AI. This allows the generation AI to generate trailers with a high proportion of horror elements based on the user's social media activity. The social analytics unit can also analyze the genre and theme of the content shared by the user and customize the trailers considering these elements. Furthermore, the social analytics unit can analyze the content of user comments and generate trailers that reflect the user's preferences and opinions. This results in trailers being generated based on the user's social media activity, improving user satisfaction.

[0093] The trailer generation system may also include a geographic analysis unit that takes into account the user's geographical location. The geographic analysis unit collects data on the user's current location and places they have visited in the past, and estimates the user's geographical interests. For example, if a user frequently visits a particular city, the geographic analysis unit can generate trailers that emphasize movie scenes related to that city. Furthermore, if the user is traveling, the geographic analysis unit can generate trailers that include movie scenes related to their travel destination. Additionally, if the user is at home, the geographic analysis unit can generate trailers that emphasize movie scenes related to their home. This results in trailers that are based on the user's geographical location, making them more likely to capture the user's interest.

[0094] The trailer generation system can further estimate the user's emotions and adjust the audio of the trailer based on the estimated emotions. For example, if the user is relaxed, it can generate a trailer with calm music in the background. If the user is excited, it can generate a trailer with fast-paced music in the background. Furthermore, if the user is sad, it can generate a trailer with emotional music in the background. This improves the trailer viewing experience by providing audio that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0095] The trailer video generation system may also include a device analysis unit that takes into account the user's device information. The device analysis unit collects information about the type and settings of the device the user is using and generates an optimal trailer video. For example, if the user is using a smartphone, the device analysis unit can generate a trailer video optimized for the smartphone's screen size. If the user is using a tablet, it can also generate a trailer video optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can generate a concise and highly visible trailer video. This results in trailer videos being generated based on the user's device information, improving the viewing experience.

[0096] The trailer generation system can further estimate the user's emotions and adjust the visual effects of the trailer based on the estimated emotions. For example, if the user is relaxed, it can generate a trailer with calm colors and slow-motion effects. If the user is excited, it can generate a trailer with vibrant colors and fast cuts. Furthermore, if the user is tense, it can generate a trailer with simple and highly visible visual effects. This improves the trailer viewing experience by providing visual effects that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0097] The trailer generation system can also include a purchase analysis unit that analyzes the user's past purchase history. The purchase analysis unit collects movies and related products that the user has purchased in the past and estimates the user's preferences. For example, if a user has purchased many related products for a particular movie series, the purchase analysis unit estimates that the user likes that movie series and provides this information to the generation AI. This allows the generation AI to generate trailers for relevant movies based on the user's purchase history. The purchase analysis unit can also collect rating data for products purchased by the user and generate trailers that emphasize elements of highly rated products. Furthermore, the purchase analysis unit can analyze the genre and theme of products purchased by the user and customize the trailers considering these elements. This results in trailers being generated based on the user's purchase history, improving user satisfaction.

[0098] The trailer generation system can further estimate the user's emotions and adjust the trailer narration based on the estimated emotions. For example, if the user is relaxed, the system can generate a trailer with a calm narration. If the user is excited, it can generate a trailer with an energetic narration. Furthermore, if the user is sad, it can generate a trailer with an emotional narration. This improves the trailer viewing experience by providing narration that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0099] The trailer generation system can also include a feedback collection unit to gather user feedback. This unit collects user ratings and comments on trailers they have watched and provides them to the generation AI. For example, if a user gives a trailer a high rating, the feedback collection unit can prioritize elements of that trailer when generating the next trailer. Furthermore, if a user leaves specific comments on a trailer, the feedback collection unit can analyze those comments and generate a trailer that reflects the user's preferences and opinions. The feedback collection unit can also collect the number of views and viewing time of trailers watched by users and adjust the content of the trailers based on this data. This results in trailers being generated based on user feedback, improving user satisfaction.

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

[0101] Step 1: The reception desk enters the user's status information. This status information includes things like "No spoilers," "Do not need much explanation of the world setting," and "Please tell me in advance if it's horror." The reception desk saves the status information entered by the user to a database and can also refer to previously entered status information. Furthermore, the reception desk sends the user's status information to the analysis department. Step 2: The analysis unit uses a generation AI to analyze the main feature film based on the status information entered by the reception unit. The analysis unit analyzes the content of the main feature film and extracts scenes that match the user's preferences. For example, it identifies exciting scenes, scenes introducing characters, and scenes that avoid spoilers. Step 3: The generation unit uses generation AI to generate a trailer video based on the information analyzed by the analysis unit. The generation unit generates a trailer video based on the scenes extracted by the analysis unit, combining particularly important scenes and scenes that match the user's preferences. For example, it combines exciting scenes and scenes introducing characters, while avoiding scenes that would spoil the movie. Step 4: The provider unit provides the user with the trailer video generated by the generator unit. The provider unit sends the generated trailer video to the user's device and provides a platform or application where the user can view it. Furthermore, the provider unit can also customize the generated trailer video to the user's preferences.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include 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.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and inputs the user's status information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the main feature film using a generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a trailer video based on the analyzed information. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated trailer video to the user. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

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

[0107] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

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

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and inputs the user's status information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the main feature film using generation AI. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a trailer video based on the analyzed information. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides the generated trailer video to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and inputs the user's status information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the main feature film using a generation AI. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a trailer video based on the analyzed information. The provision unit is implemented by, for example, the display 343 of the headset terminal 314 and provides the generated trailer video to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0139] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and inputs the user's status information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the main feature film using a generation AI. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a trailer video based on the analyzed information. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated trailer video to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0165] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] (Note 1) A reception area where user status information is entered, An analysis unit analyzes the main feature of the film based on the status information entered by the reception unit, A generation unit generates a preview video based on the information analyzed by the analysis unit, The system includes a providing unit that provides the user with the preview video generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for status information based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past status information and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When entering status information, the input fields are customized based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the status information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When entering status information, the system prioritizes displaying the most relevant input fields, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users enter status information, the system analyzes their social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by considering the interrelationships within the film itself. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the genre and theme of the film. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, the geographical context of the film will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, we refer to relevant literature on the film to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is We estimate the user's emotions and adjust the method of generating the trailer video based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the system analyzes key scenes from the main film to create the most suitable trailer. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the content of the preview video is customized by referencing the user's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the length of the trailer video based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the priority of trailers is determined based on the film's release date. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the order of trailers is adjusted based on the relevance of the films. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, We estimate the user's emotions and adjust how the trailer is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing content, the system will refer to the user's past viewing history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the content, the method of delivering the preview video will be customized based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the order in which trailer videos are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and suggest the best way to deliver the preview video. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where user status information is entered, An analysis unit analyzes the main feature of the film based on the status information entered by the reception unit, A generation unit generates a preview video based on the information analyzed by the analysis unit, The system includes a providing unit that provides the user with the preview video generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for status information based on those emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past status information and suggests the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is When entering status information, the input fields are customized based on the user's current interests and preferences. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the status information to be entered based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When entering status information, the system prioritizes displaying the most relevant input fields, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users enter status information, the system analyzes their social media activity and suggests relevant input fields. The system according to feature 1.

8. The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system according to feature 1.

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