System

Abstract

An object of a system according to an embodiment is to automatically generate and provide a preview moving image according to a user's preference.SOLUTION: A system according to an embodiment includes a user preference analysis unit, a preview moving image generation unit, and a provision unit. The user's preference analysis unit analyzes data such as the user's past viewing history, evaluation, and favorite genre to grasp the user's preference. The preview moving image generation unit automatically generates a preview moving image based on the user's preference grasped by the user's preference analysis unit. The provision unit provides the user with the preview moving image generated by the preview moving image generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, preview videos are created uniformly, and there is a problem in that they are not sufficiently personalized to suit the preferences of individual users.

[0005] The system according to the embodiment aims to automatically generate and provide preview videos that suit the preferences of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a user preference analysis unit, a preview video generation unit, and a providing unit. The user preference analysis unit analyzes data such as the user's past viewing history, ratings, and preferred genres to understand the user's preferences. The preview video generation unit automatically generates a preview video based on the user's preferences understood by the user preference analysis unit. The providing unit provides the preview video generated by the preview video generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate and provide a preview video according to the user's preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The preview video generation system according to an embodiment of the present invention is a system that automatically generates and provides preview videos according to user preferences. As a result, the preview video generation system can provide preview videos according to user preferences and maximize the appeal of entertainment.

[0029] A preview video generation system according to an embodiment includes a user preference analysis unit, a preview video generation unit, and a providing unit. The user preference analysis unit analyzes data such as the user's past viewing history, ratings, and preferred genres to understand the user's preferences. For example, the user's viewing history may be analyzed to identify the titles, rating scores, and genres of movies viewed. User rating data may also be analyzed to understand the level of ratings and bias in genres. Furthermore, analysis may be performed based on the frequency of viewing history and the level of ratings to identify the user's preferred genres. The preview video generation unit automatically generates a preview video based on the user's preferences as understood by the user preference analysis unit. For example, the generation AI generates a preview video based on scene selection criteria, editing techniques, and music selection. The generation AI may also select and edit scenes according to the user's preferences to generate a preview video. The generation AI may also select music based on the user's preferences to generate a preview video. The providing unit provides the preview video generated by the preview video generation unit to the user. For example, the preview video may be provided via streaming, download, or notification. The providing unit can also provide the trailer video in a format optimal for the user's device. Furthermore, the providing unit can optimize the image quality and sound quality of the trailer video according to the user's viewing environment to provide the best viewing experience. This allows the trailer video generation system according to the embodiment to provide trailer videos tailored to the user's preferences and maximize the appeal of entertainment. For example, if a user likes action movies, the generation AI generates a trailer video that includes many action scenes and provides it to the user. If a user likes moving stories, the generation AI generates a trailer video that includes many moving scenes and provides it to the user. If a user likes horror movies, the generation AI generates a trailer video that includes many horror scenes and provides it to the user. If a user likes comedy movies, the generation AI generates a trailer video that includes many comedy scenes and provides it to the user.

[0030] The user preference analysis unit can analyze social media activity and extract preferred genres and themes from the content of posts and reactions. The user preference analysis unit, for example, analyzes the user's social media posts and extracts frequently mentioned genres and themes. For example, it analyzes the content of posts related to movies and TV dramas to identify preference trends. It can also analyze the user's social media activity and identify preferred genres and themes based on the number of likes and comments. For example, it can analyze the level of engagement with specific genres and themes. It can also analyze the user's social media activity and identify preferred genres and themes based on the platform used and the frequency of posts. In this way, analyzing social media activity can identify user preferences more broadly.

[0031] The user preference analysis unit can analyze the viewing environment and identify changes in preferences based on the viewing time period and device. The user preference analysis unit, for example, analyzes the user's viewing time period and identifies the tendency of content viewed during specific times. For example, it analyzes the tendency of users to prefer horror movies at night. It can also analyze the user's viewing device and identify differences in viewing trends depending on the device. For example, it analyzes changes in preferences when viewing on a smartphone and when viewing on a television. It can also analyze the user's viewing environment and identify changes in preferences based on the network environment and viewing location. This allows it to identify changes in preferences depending on the viewing environment and provide more appropriate preview videos.

[0032] The user preference analysis unit can integrate data from different platforms to analyze overall entertainment preferences. For example, the user preference analysis unit can analyze the listening history of a music streaming service to identify the user's musical preferences. For example, it can analyze the user's tendency to like rock music. It can also analyze the reading history of an e-book service to identify the user's reading preferences. For example, it can analyze the user's tendency to like mystery novels. It can also integrate data from different platforms to identify common areas of interest. For example, it can integrate data from a music streaming service and an e-book service to analyze the user's overall entertainment preferences. In this way, it is possible to analyze overall entertainment preferences by integrating data from different platforms.

[0033] The trailer video generation unit can analyze the tempo of a scene and the rhythm of the music for scene data of a movie, and generate a trailer video with a tempo and rhythm that suits the user's preferences. The trailer video generation unit, for example, analyzes the scene data of a movie and identifies the tempo and rhythm of the music for each scene. For example, it analyzes the fast tempo of an action scene and the slow rhythm of an emotional scene. It can also analyze the user's past viewing history and identify the user's preferred tempo and rhythm. For example, it analyzes the rhythm and tempo of the music that the user prefers. It can also analyze the tempo of a scene and the rhythm of the music and generate a trailer video with a tempo and rhythm that suits the user's preferences. In this way, by analyzing the tempo of a scene and the rhythm of the music, it is possible to generate a trailer video that suits the user's preferences.

[0034] The trailer video generation unit can analyze the facial expressions and movements of characters included in movie scene data and generate a trailer video centered on characters that the user likes. The trailer video generation unit, for example, analyzes movie scene data and identifies the facial expressions and movements of each character. For example, it analyzes smiles, tears, and specific movements. It can also analyze the user's past viewing history and identify characters that the user likes. For example, it can analyze the characteristics of characters that the user likes and identify similar characters. It can also analyze the facial expressions and movements of characters and generate a trailer video centered on characters that the user likes. In this way, by analyzing the facial expressions and movements of characters, it is possible to generate a trailer video centered on characters that the user likes.

[0035] The trailer video generation unit can generate a trailer video that combines behind-the-scenes footage and interview footage in addition to movie scene data. The trailer video generation unit, for example, analyzes behind-the-scenes footage in addition to movie scene data and incorporates it into the trailer video. For example, it adds footage of filming scenes and interviews with the cast. It can also analyze interview footage and incorporate it into the trailer video. For example, it can add comments from the director and cast. It can also generate a trailer video by combining behind-the-scenes footage and interview footage. In this way, by combining behind-the-scenes footage and interview footage, a wider variety of trailer videos can be generated.

[0036] The providing unit can automatically suggest recommended movies and related content after viewing a trailer video based on the user's viewing history. The providing unit, for example, analyzes the user's viewing history and automatically suggests recommended movies after viewing a trailer video. For example, the latest action movies can be suggested to a user who watches a lot of action movies. Related content can also be suggested based on the user's viewing history. For example, movies by the same director or movies starring the same actors can be suggested. It is also possible to analyze the viewing history and develop a recommendation algorithm for suggesting content that matches the user's interests. This makes it possible to provide content that matches the user's interests by suggesting recommended movies and related content based on the viewing history.

[0037] The providing unit can optimize the image quality and sound quality of the preview video according to the user's viewing environment, providing the best viewing experience. The providing unit, for example, analyzes the user's viewing device and automatically sets the optimal image quality and sound quality for the device. For example, high-resolution image quality is provided on a smartphone. The providing unit can also analyze the user's network environment and provide the optimal image quality and sound quality. For example, in a slow network environment, the image quality is adjusted to achieve smooth playback. The sound quality can also be optimized according to the viewing environment. For example, high-quality audio is provided when headphones are used. This makes it possible to provide the best viewing experience by optimizing the image quality and sound quality according to the viewing environment.

[0038] The providing unit can insert a message based on the user's name or past viewing history into the personalized preview video, thereby providing a more personalized experience. The providing unit, for example, inserts the user's name into the preview video and displays a personalized message. For example, it displays a message such as "Mr. / Ms. XX, this is a movie recommended for you." It can also insert a message based on the user's past viewing history. For example, it displays a message such as "This is a movie related to the last movie you watched." It can also develop a template for generating a message based on the user's name or viewing history and inserting it into the preview video. This allows a more personalized experience to be provided by inserting a message based on the user's name or viewing history.

[0039] The providing unit generates preview videos optimized for different devices, thereby providing different viewing experiences for each device. The providing unit, for example, builds a system for generating preview videos optimized for different devices. For example, a short preview video can be generated for smartphones. A high-resolution preview video can also be generated for tablets. Furthermore, a preview video optimized for a large screen can be generated for smart TVs. The UI and interface can also be adjusted to provide different viewing experiences for each device. In this way, by generating preview videos optimized for different devices, different viewing experiences can be provided for each device.

[0040] The trailer video generation unit can analyze the facial expressions and movements of characters included in movie scene data and generate a trailer video centered on characters that the user likes. The trailer video generation unit, for example, analyzes movie scene data and identifies the facial expressions and movements of each character. For example, it analyzes smiles, tears, and specific movements. It can also analyze the user's past viewing history and identify characters that the user likes. For example, it can analyze the characteristics of characters that the user likes and identify similar characters. It can also analyze the facial expressions and movements of characters and generate a trailer video centered on characters that the user likes. In this way, by analyzing the facial expressions and movements of characters, it is possible to generate a trailer video centered on characters that the user likes.

[0041] The trailer video generation unit can generate a trailer video that combines behind-the-scenes footage and interview footage in addition to movie scene data. The trailer video generation unit, for example, analyzes behind-the-scenes footage in addition to movie scene data and incorporates it into the trailer video. For example, it adds footage of filming scenes and interviews with the cast. It can also analyze interview footage and incorporate it into the trailer video. For example, it can add comments from the director and cast. It can also generate a trailer video by combining behind-the-scenes footage and interview footage. In this way, by combining behind-the-scenes footage and interview footage, a wider variety of trailer videos can be generated.

[0042] The trailer generation unit can combine scenes from different movies to generate a crossover trailer, providing a new viewing experience. The trailer generation unit, for example, analyzes scene data from different movies to identify common themes and characters. For example, it extracts scenes featuring the same actor. It can also combine scenes from different movies to generate a crossover trailer. For example, it can combine scenes based on genre agreement or story relevance. It can also identify scene editing techniques and story structures for generating a crossover trailer. This allows for a new viewing experience to be provided by combining scenes from different movies.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The trailer generation system can also analyze the user's geographic location information and customize trailers based on local culture and events. For example, it can generate trailers that include scenes related to movie genres or events that are popular in a particular region. It can also provide special trailers tailored to local festivals and events. It can also generate trailers that correspond to local languages ​​and dialects and provide them to users. This makes it easier to attract users' interest by providing trailers based on local culture and events.

[0045] The trailer video generation system can also analyze a user's purchasing history and incorporate advertisements for related products and services into the trailer video. For example, advertisements for related products can be inserted into the trailer video based on movie-related goods and services that the user has previously purchased. It can also select advertisements for products that are likely to interest the user based on the user's purchasing history. Furthermore, it can combine the user's purchasing history and viewing history to provide more personalized advertisements. This can increase the effectiveness of advertisements by providing advertisements based on the user's purchasing history.

[0046] The preview video generation system can also analyze the user's health data and provide preview videos tailored to the user's health condition. For example, the system can analyze the user's heart rate and stress level to generate preview videos that include many relaxing scenes. It can also provide preview videos that include many energetic scenes based on the user's exercise data. It can also analyze the user's sleep data to provide relaxing preview videos at night. This allows for a more comfortable viewing experience by providing preview videos tailored to the user's health condition.

[0047] The preview video generation system can also analyze a user's learning history and provide preview videos that include educational elements. For example, it can generate preview videos that include scenes related to learning topics that interest the user. It can also select educational content that is likely to interest the user based on the user's learning history. It can also customize the content of the preview video according to the user's learning progress. This can increase motivation to study by providing educational preview videos based on the user's learning history.

[0048] The preview video generation system can also customize the preview video based on the user's hobbies and skills. For example, if the user's hobby is music, the system can generate a preview video that includes many scenes related to music. Also, if the user's hobby is sports, the system can provide a preview video that includes many sports scenes. Furthermore, the system can generate a preview video that focuses on a specific theme according to the user's skills and interests. This allows the system to provide a more personalized viewing experience by providing a preview video based on the user's hobbies and skills.

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

[0050] Step 1: The user preference analysis unit analyzes data such as the user's past viewing history, ratings, and preferred genres to understand the user's preferences. For example, it analyzes the user's viewing history to identify the titles of movies viewed, their rating scores, and the genres they have. It can also analyze the user's rating data to understand the level of ratings and bias in genres. Furthermore, to identify the user's preferred genres, it can perform analysis based on the frequency of viewing history and the level of ratings. Step 2: The preview video generation unit automatically generates a preview video based on the user's preferences ascertained by the user preference analysis unit. For example, the generation AI generates a preview video based on scene selection criteria, editing techniques, and music selection. The generation AI can also select and edit scenes according to the user's preferences to generate a preview video. Furthermore, the generation AI can also select music based on the user's preferences to generate a preview video. Step 3: The providing unit provides the preview video generated by the preview video generating unit to the user. For example, the preview video may be provided by streaming, downloading, or notification. The providing unit may also provide the preview video in a format optimal for the user's device. Furthermore, the providing unit may optimize the image quality and sound quality of the preview video according to the user's viewing environment to provide the best viewing experience.

[0051] (Example 2) The preview video generation system according to an embodiment of the present invention is a system that automatically generates and provides preview videos according to user preferences. As a result, the preview video generation system can provide preview videos according to user preferences and maximize the appeal of entertainment.

[0052] A preview video generation system according to an embodiment includes a user preference analysis unit, a preview video generation unit, and a providing unit. The user preference analysis unit analyzes data such as the user's past viewing history, ratings, and preferred genres to understand the user's preferences. For example, the user's viewing history may be analyzed to identify the titles, rating scores, and genres of movies viewed. User rating data may also be analyzed to understand the level of ratings and bias in genres. Furthermore, analysis may be performed based on the frequency of viewing history and the level of ratings to identify the user's preferred genres. The preview video generation unit automatically generates a preview video based on the user's preferences as understood by the user preference analysis unit. For example, the generation AI generates a preview video based on scene selection criteria, editing techniques, and music selection. The generation AI may also select and edit scenes according to the user's preferences to generate a preview video. The generation AI may also select music based on the user's preferences to generate a preview video. The providing unit provides the preview video generated by the preview video generation unit to the user. For example, the preview video may be provided via streaming, download, or notification. The providing unit can also provide the trailer video in a format optimal for the user's device. Furthermore, the providing unit can optimize the image quality and sound quality of the trailer video according to the user's viewing environment to provide the best viewing experience. This allows the trailer video generation system according to the embodiment to provide trailer videos tailored to the user's preferences and maximize the appeal of entertainment. For example, if a user likes action movies, the generation AI generates a trailer video that includes many action scenes and provides it to the user. If a user likes moving stories, the generation AI generates a trailer video that includes many moving scenes and provides it to the user. If a user likes horror movies, the generation AI generates a trailer video that includes many horror scenes and provides it to the user. If a user likes comedy movies, the generation AI generates a trailer video that includes many comedy scenes and provides it to the user.

[0053] The user preference analysis unit can analyze emotional changes from viewing history and rating data and identify preferences based on emotional patterns. The user preference analysis unit, for example, analyzes emotional responses to each viewing content in real time based on the user's viewing history. For example, it analyzes facial expressions and tone of voice while viewing and quantifies emotional changes. It can also analyze user rating data to identify emotional changes. For example, it analyzes emotional changes based on the level of ratings and genre bias. It can also analyze changes over time and reactions to specific events to identify user emotional patterns. In this way, more detailed preferences can be identified by analyzing the user's emotional changes.

[0054] The user preference analysis unit can analyze social media activity and extract preferred genres and themes from the content of posts and reactions. The user preference analysis unit, for example, analyzes the user's social media posts and extracts frequently mentioned genres and themes. For example, it analyzes the content of posts related to movies and TV dramas to identify preference trends. It can also analyze the user's social media activity and identify preferred genres and themes based on the number of likes and comments. For example, it can analyze the level of engagement with specific genres and themes. It can also analyze the user's social media activity and identify preferred genres and themes based on the platform used and the frequency of posts. In this way, analyzing social media activity can identify user preferences more broadly.

[0055] The user preference analysis unit can analyze the viewing environment and identify changes in preferences based on the viewing time period and device. The user preference analysis unit, for example, analyzes the user's viewing time period and identifies the tendency of content viewed during specific times. For example, it analyzes the tendency of users to prefer horror movies at night. It can also analyze the user's viewing device and identify differences in viewing trends depending on the device. For example, it analyzes changes in preferences when viewing on a smartphone and when viewing on a television. It can also analyze the user's viewing environment and identify changes in preferences based on the network environment and viewing location. This allows it to identify changes in preferences depending on the viewing environment and provide more appropriate preview videos.

[0056] The user preference analysis unit can identify preferences in more detail using audio data and biometric data. The user preference analysis unit, for example, analyzes audio data while the user is watching to identify changes in emotions. For example, it analyzes laughter and sounds of surprise to identify favorite scenes. It can also analyze the user's biometric data to identify preferences based on changes in heart rate and facial expression. For example, it analyzes fluctuations in heart rate and changes in facial expression to identify the intensity of emotions. It can also combine the user's audio data and biometric data to identify preferences in more detail. In this way, it is possible to identify preferences in more detail by using audio data and biometric data.

[0057] The user preference analysis unit can integrate data from different platforms to analyze overall entertainment preferences. For example, the user preference analysis unit can analyze the listening history of a music streaming service to identify the user's musical preferences. For example, it can analyze the user's tendency to like rock music. It can also analyze the reading history of an e-book service to identify the user's reading preferences. For example, it can analyze the user's tendency to like mystery novels. It can also integrate data from different platforms to identify common areas of interest. For example, it can integrate data from a music streaming service and an e-book service to analyze the user's overall entertainment preferences. In this way, it is possible to analyze overall entertainment preferences by integrating data from different platforms.

[0058] The preview video generation unit can use the emotion estimation function to identify the scene that moves the user the most and generate a preview video centered around that scene. The preview video generation unit, for example, analyzes scene data of a movie to identify moving scenes. For example, it extracts tear-jerking scenes or scenes with moving music. It can also analyze the user's past viewing history to identify trends in moving scenes. For example, it can analyze the characteristics of scenes that moved the user in the past and identify similar scenes. It can also use the emotion estimation function to analyze the intensity of the user's emotion and identify the most moving scene. This allows the user to identify the most moving scene and generate a preview video centered around that scene, providing a more moving preview experience.

[0059] The trailer video generation unit can analyze the tempo of a scene and the rhythm of the music for scene data of a movie, and generate a trailer video with a tempo and rhythm that suits the user's preferences. The trailer video generation unit, for example, analyzes the scene data of a movie and identifies the tempo and rhythm of the music for each scene. For example, it analyzes the fast tempo of an action scene and the slow rhythm of an emotional scene. It can also analyze the user's past viewing history and identify the user's preferred tempo and rhythm. For example, it analyzes the rhythm and tempo of the music that the user prefers. It can also analyze the tempo of a scene and the rhythm of the music and generate a trailer video with a tempo and rhythm that suits the user's preferences. In this way, by analyzing the tempo of a scene and the rhythm of the music, it is possible to generate a trailer video that suits the user's preferences.

[0060] The trailer video generation unit can analyze the facial expressions and movements of characters included in movie scene data and generate a trailer video centered on characters that the user likes. The trailer video generation unit, for example, analyzes movie scene data and identifies the facial expressions and movements of each character. For example, it analyzes smiles, tears, and specific movements. It can also analyze the user's past viewing history and identify characters that the user likes. For example, it can analyze the characteristics of characters that the user likes and identify similar characters. It can also analyze the facial expressions and movements of characters and generate a trailer video centered on characters that the user likes. In this way, by analyzing the facial expressions and movements of characters, it is possible to generate a trailer video centered on characters that the user likes.

[0061] The trailer video generation unit can generate a trailer video that combines behind-the-scenes footage and interview footage in addition to movie scene data. The trailer video generation unit, for example, analyzes behind-the-scenes footage in addition to movie scene data and incorporates it into the trailer video. For example, it adds footage of filming scenes and interviews with the cast. It can also analyze interview footage and incorporate it into the trailer video. For example, it can add comments from the director and cast. It can also generate a trailer video by combining behind-the-scenes footage and interview footage. In this way, by combining behind-the-scenes footage and interview footage, a wider variety of trailer videos can be generated.

[0062] The preview video generation unit can use the emotion estimation function to dynamically change the content of the preview video based on the user's real-time emotional reaction when watching the preview video. The preview video generation unit, for example, analyzes the user's emotional reaction while watching the preview video in real time and dynamically changes the content of the preview video. For example, it can add a moving scene. It can also switch or edit scenes in real time based on the user's emotional reaction. For example, it can add a horror scene when the user is surprised. It can also use the emotion estimation function to analyze the user's emotional reaction and develop an algorithm for dynamically changing the content of the preview video. This makes it possible to provide a more personalized preview experience by dynamically changing the content of the preview video based on the user's real-time emotional reaction.

[0063] The providing unit can automatically suggest recommended movies and related content after viewing a trailer video based on the user's viewing history. The providing unit, for example, analyzes the user's viewing history and automatically suggests recommended movies after viewing a trailer video. For example, the latest action movies can be suggested to a user who watches a lot of action movies. Related content can also be suggested based on the user's viewing history. For example, movies by the same director or movies starring the same actors can be suggested. It is also possible to analyze the viewing history and develop a recommendation algorithm for suggesting content that matches the user's interests. This makes it possible to provide content that matches the user's interests by suggesting recommended movies and related content based on the viewing history.

[0064] The providing unit can optimize the image quality and sound quality of the preview video according to the user's viewing environment, providing the best viewing experience. The providing unit, for example, analyzes the user's viewing device and automatically sets the optimal image quality and sound quality for the device. For example, high-resolution image quality is provided on a smartphone. The providing unit can also analyze the user's network environment and provide the optimal image quality and sound quality. For example, in a slow network environment, the image quality is adjusted to achieve smooth playback. The sound quality can also be optimized according to the viewing environment. For example, high-quality audio is provided when headphones are used. This makes it possible to provide the best viewing experience by optimizing the image quality and sound quality according to the viewing environment.

[0065] The providing unit can insert a message based on the user's name or past viewing history into the personalized preview video, thereby providing a more personalized experience. The providing unit, for example, inserts the user's name into the preview video and displays a personalized message. For example, it displays a message such as "Mr. / Ms. XX, this is a movie recommended for you." It can also insert a message based on the user's past viewing history. For example, it displays a message such as "This is a movie related to the last movie you watched." It can also develop a template for generating a message based on the user's name or viewing history and inserting it into the preview video. This allows a more personalized experience to be provided by inserting a message based on the user's name or viewing history.

[0066] The providing unit generates preview videos optimized for different devices, thereby providing different viewing experiences for each device. The providing unit, for example, builds a system for generating preview videos optimized for different devices. For example, a short preview video can be generated for smartphones. A high-resolution preview video can also be generated for tablets. Furthermore, a preview video optimized for a large screen can be generated for smart TVs. The UI and interface can also be adjusted to provide different viewing experiences for each device. In this way, by generating preview videos optimized for different devices, different viewing experiences can be provided for each device.

[0067] The providing unit can use the emotion estimation function to collect post-viewing feedback based on the user's emotional reactions when watching the preview video and reflect this in generating the next preview video. The providing unit, for example, analyzes the user's emotional reactions while watching the preview video in real time and collects post-viewing feedback. For example, it analyzes the emotional reactions to moving scenes. It can also reflect this in generating the next preview video based on the user's emotional reactions. For example, it selects and edits scenes for the next preview video based on the analysis results of the emotional reactions. It can also use the emotion estimation function to develop an algorithm for analyzing the user's emotional reactions and reflecting this in generating the next preview video. This allows for a more personalized preview experience to be provided by collecting feedback based on the emotional reactions and reflecting this in generating the next preview video.

[0068] The preview video generation unit can use the emotion estimation function to identify the scene that moves the user the most and generate a preview video centered around that scene. The preview video generation unit, for example, analyzes scene data of a movie to identify moving scenes. For example, it extracts tear-jerking scenes or scenes with moving music. It can also analyze the user's past viewing history to identify trends in moving scenes. For example, it can analyze the characteristics of scenes that moved the user in the past and identify similar scenes. It can also use the emotion estimation function to analyze the intensity of the user's emotion and identify the most moving scene. This allows the user to identify the most moving scene and generate a preview video centered around that scene, providing a more moving preview experience.

[0069] The trailer video generation unit can analyze the facial expressions and movements of characters included in movie scene data and generate a trailer video centered on characters that the user likes. The trailer video generation unit, for example, analyzes movie scene data and identifies the facial expressions and movements of each character. For example, it analyzes smiles, tears, and specific movements. It can also analyze the user's past viewing history and identify characters that the user likes. For example, it can analyze the characteristics of characters that the user likes and identify similar characters. It can also analyze the facial expressions and movements of characters and generate a trailer video centered on characters that the user likes. In this way, by analyzing the facial expressions and movements of characters, it is possible to generate a trailer video centered on characters that the user likes.

[0070] The trailer video generation unit can generate a trailer video that combines behind-the-scenes footage and interview footage in addition to movie scene data. The trailer video generation unit, for example, analyzes behind-the-scenes footage in addition to movie scene data and incorporates it into the trailer video. For example, it adds footage of filming scenes and interviews with the cast. It can also analyze interview footage and incorporate it into the trailer video. For example, it can add comments from the director and cast. It can also generate a trailer video by combining behind-the-scenes footage and interview footage. In this way, by combining behind-the-scenes footage and interview footage, a wider variety of trailer videos can be generated.

[0071] The trailer generation unit can combine scenes from different movies to generate a crossover trailer, providing a new viewing experience. The trailer generation unit, for example, analyzes scene data from different movies to identify common themes and characters. For example, it extracts scenes featuring the same actor. It can also combine scenes from different movies to generate a crossover trailer. For example, it can combine scenes based on genre agreement or story relevance. It can also identify scene editing techniques and story structures for generating a crossover trailer. This allows for a new viewing experience to be provided by combining scenes from different movies.

[0072] The preview video generation unit can use the emotion estimation function to dynamically change the content of the preview video based on the user's real-time emotional reaction when watching the preview video. The preview video generation unit, for example, analyzes the user's emotional reaction while watching the preview video in real time and dynamically changes the content of the preview video. For example, it can add a moving scene. It can also switch or edit scenes in real time based on the user's emotional reaction. For example, it can add a horror scene when the user is surprised. It can also use the emotion estimation function to analyze the user's emotional reaction and develop an algorithm for dynamically changing the content of the preview video. This makes it possible to provide a more personalized preview experience by dynamically changing the content of the preview video based on the user's real-time emotional reaction.

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

[0074] The trailer generation system can also analyze the user's geographic location information and customize trailers based on local culture and events. For example, it can generate trailers that include scenes related to movie genres or events that are popular in a particular region. It can also provide special trailers tailored to local festivals and events. It can also generate trailers that correspond to local languages ​​and dialects and provide them to users. This makes it easier to attract users' interest by providing trailers based on local culture and events.

[0075] The trailer video generation system can also analyze a user's purchasing history and incorporate advertisements for related products and services into the trailer video. For example, advertisements for related products can be inserted into the trailer video based on movie-related goods and services that the user has previously purchased. It can also select advertisements for products that are likely to interest the user based on the user's purchasing history. Furthermore, it can combine the user's purchasing history and viewing history to provide more personalized advertisements. This can increase the effectiveness of advertisements by providing advertisements based on the user's purchasing history.

[0076] The preview video generation system can also analyze the user's health data and provide preview videos tailored to the user's health condition. For example, the system can analyze the user's heart rate and stress level to generate preview videos that include many relaxing scenes. It can also provide preview videos that include many energetic scenes based on the user's exercise data. It can also analyze the user's sleep data to provide relaxing preview videos at night. This allows for a more comfortable viewing experience by providing preview videos tailored to the user's health condition.

[0077] The preview video generation system can also analyze a user's learning history and provide preview videos that include educational elements. For example, it can generate preview videos that include scenes related to learning topics that interest the user. It can also select educational content that is likely to interest the user based on the user's learning history. It can also customize the content of the preview video according to the user's learning progress. This can increase motivation to study by providing educational preview videos based on the user's learning history.

[0078] The preview video generation system can also customize the preview video based on the user's hobbies and skills. For example, if the user's hobby is music, the system can generate a preview video that includes many scenes related to music. Also, if the user's hobby is sports, the system can provide a preview video that includes many sports scenes. Furthermore, the system can generate a preview video that focuses on a specific theme according to the user's skills and interests. This allows the system to provide a more personalized viewing experience by providing a preview video based on the user's hobbies and skills.

[0079] The preview video generation system can estimate the user's emotions and select scenes for the preview video based on the estimated emotions. For example, if the user is expressing joy, the system can generate a preview video that includes many happy scenes. Alternatively, if the user is expressing sadness, the system can provide a preview video that includes many moving scenes. Furthermore, the system can dynamically change the content of the preview video according to changes in the user's emotions. This allows the system to provide a more emotional viewing experience by providing a preview video based on the user's emotions.

[0080] The preview video generation system can estimate the user's emotions and select music for the preview video based on the estimated emotions. For example, if the user is relaxed, calm music can be selected. Alternatively, if the user is excited, energetic music can be selected. Furthermore, the music for the preview video can be dynamically changed according to changes in the user's emotions. This allows for a more emotional viewing experience by providing music based on the user's emotions.

[0081] The trailer video generation system can estimate the user's emotions and customize the narration of the trailer video based on the estimated emotions. For example, if the user is moved, an inspiring narration can be added. Alternatively, if the user is excited, an energetic narration can be added. Furthermore, the content of the narration can be dynamically changed according to changes in the user's emotions. This allows for a more emotional viewing experience by providing narration based on the user's emotions.

[0082] The trailer video generation system can estimate the user's emotions and customize the trailer video effects based on the estimated emotions. For example, if the user is surprised, a surprise effect can be added. Alternatively, if the user is moved, an emotional effect can be added. Furthermore, the effects can be dynamically changed according to changes in the user's emotions. This allows for a more emotional viewing experience by providing effects based on the user's emotions.

[0083] The trailer video generation system can estimate the user's emotions and change the order of scenes in the trailer video based on the estimated emotions. For example, if the user is excited, an action scene can be placed first. Alternatively, if the user is relaxed, a calm scene can be placed first. Furthermore, the order of scenes can be dynamically changed according to changes in the user's emotions. This allows for a more emotional viewing experience by providing a scene order based on the user's emotions.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The user preference analysis unit analyzes data such as the user's past viewing history, ratings, and preferred genres to understand the user's preferences. For example, it analyzes the user's viewing history to identify the titles of movies viewed, their rating scores, and the genres they have. It can also analyze the user's rating data to understand the level of ratings and bias in genres. Furthermore, to identify the user's preferred genres, it can perform analysis based on the frequency of viewing history and the level of ratings. Step 2: The preview video generation unit automatically generates a preview video based on the user's preferences ascertained by the user preference analysis unit. For example, the generation AI generates a preview video based on scene selection criteria, editing techniques, and music selection. The generation AI can also select and edit scenes according to the user's preferences to generate a preview video. Furthermore, the generation AI can also select music based on the user's preferences to generate a preview video. Step 3: The providing unit provides the preview video generated by the preview video generating unit to the user. For example, the preview video may be provided by streaming, downloading, or notification. The providing unit may also provide the preview video in a format optimal for the user's device. Furthermore, the providing unit may optimize the image quality and sound quality of the preview video according to the user's viewing environment to provide the best viewing experience.

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

[0087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0091] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0092] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0093] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0094] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0096] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0097] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0127] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0136] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0137] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0138] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0139] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0140] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0141] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0142] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0143] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0144] 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.

[0145] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0146] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0147] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0148] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0149] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0150] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0151] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0152] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0153] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user preference analysis unit that analyzes data such as the user's past viewing history, ratings, and preferred genres to understand the user's preferences; A preview video generation unit that automatically generates a preview video based on the user's preferences ascertained by the user's preference analysis unit; a providing unit that provides the preview video generated by the preview video generating unit to a user. A system characterized by:

2. The user preference analysis unit Analyzing changes in emotions from the viewing history and evaluation data, and identifying the preferences based on the patterns of the emotions.

2. The system of claim 1.

3. The user preference analysis unit Use voice data and biometric data to identify those preferences in more detail 2. The system of claim 1.

4. The preview video generation unit The tempo and rhythm of the music are analyzed for the scene data of the movie, and the preview video is generated with a tempo and rhythm that suits the user's preferences.

2. The system of claim 1.

5. The preview video generation unit Scenes from the different films are combined to create a crossover trailer, providing a new viewing experience.

2. The system of claim 1.

6. a user preference analysis unit that analyzes data such as the user's past viewing history, ratings, and preferred genres to understand the user's preferences; A preview video generation unit that automatically generates a preview video based on the user's preferences ascertained by the user's preference analysis unit; a providing unit that provides the preview video generated by the preview video generating unit to a user. A system characterized by:

7. The providing unit The image and sound quality of the trailer video will be optimized according to the viewing environment to provide the best viewing experience.

2. The system of claim 1.

8. The providing unit Based on the emotional response when watching the preview video, feedback after the viewing is collected and reflected in the generation of the next preview video.

2. The system of claim 1.

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