System

Abstract

An object of the system according to the embodiment is to enable a general user to easily create a wedding movie with rich originality.SOLUTION: A system includes a comment input unit, a photograph input unit, a prompt specification unit, and a generation unit. The comment input unit receives a comment from a user. The photograph input unit receives a photograph from a user. The prompt specification unit receives a prompt from a user. The generation unit generates the wedding movie on the basis of the information received by the comment input unit, the photograph input unit, and the prompt designation 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, creating wedding movies requires specialized knowledge and skills, making it difficult for ordinary users to easily create original movies.

[0005] The system according to the embodiment aims to enable general users to easily create original wedding movies. [Means for solving the problem]

[0006] The system according to the embodiment includes a comment input unit, a photo input unit, a prompt designation unit, and a generation unit. The comment input unit accepts comments from users. The photo input unit accepts photos from users. The prompt designation unit accepts prompts from users. The generation unit generates a wedding movie based on the information accepted by the comment input unit, the photo input unit, and the prompt designation unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable general users to easily create highly original wedding movies. [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 AI ​​generation system according to an embodiment of the present invention generates a wedding video based on comments, photos, and prompts entered by a user. This allows even couples who are unfamiliar with general video production to easily create original and moving wedding videos.

[0029] A generative AI system according to an embodiment includes a comment input unit, a photo input unit, a prompt designation unit, and a generation unit. The comment input unit accepts comments from a user. For example, the user inputs comments for each scene and provides explanations and episodes for each scene in the wedding movie. The photo input unit accepts photos from a user. For example, the user uploads photos for each scene and uses them as material for the wedding movie. The prompt designation unit accepts prompts from a user. For example, the user designates video movements and effects for each scene as prompts. The generation unit generates a wedding movie based on the information accepted by the comment input unit, photo input unit, and prompt designation unit. For example, the generative AI generates video for each scene based on the comments, photos, and prompts entered by the user, and applies the designated movements and effects. This allows the generative AI system to generate a wedding movie based on the information entered by the user.

[0030] The generator can automatically suggest related music and sound effects based on comments and photos and add them to movies. For example, the generator analyzes comments and photos entered by the user, and the generation AI automatically suggests music and sound effects that match the content. For example, romantic music can be selected for a meeting scene, and moving sound effects for a proposal scene. This allows related music and sound effects to be automatically suggested and added to movies based on the information entered by the user.

[0031] The generation unit can analyze a photo and automatically generate a scene focusing on a specific person using face recognition technology. For example, the generation unit analyzes an input photo using face recognition technology and automatically generates a scene focusing on a specific person. For example, a scene is created that zooms in on the faces of a bride and groom. This allows the input photo to be analyzed and a scene focusing on a specific person to be automatically generated.

[0032] The generation unit can automatically suggest related video clips and animations based on comments and photos and add them to movies. For example, the generation unit analyzes comments and photos entered by the user, and the generation AI automatically suggests related video clips and animations. For example, romantic animations can be added to dating scenes. This allows related video clips and animations to be automatically suggested and added to movies based on the information entered by the user.

[0033] The generation unit can provide templates that match wedding styles of different cultures and regions, allowing the user to select from them. For example, the generation unit provides templates that match wedding styles of different cultures and regions, allowing the user to select from them. For example, Japanese-style, Western-style, Indian-style, and other templates are prepared. This allows the user to select from templates that match wedding styles of different cultures and regions.

[0034] The generation unit can automatically suggest optimal camera angles and zoom effects based on prompts. For example, the generation unit uses a generation AI to automatically suggest optimal camera angles based on prompts specified by the user. For example, it suggests close-ups for important scenes. This allows the generation unit to automatically suggest optimal camera angles and zoom effects based on prompts specified by the user.

[0035] The generation unit automatically selects transition effects between scenes according to the content of the prompt, achieving a smooth video flow. For example, the generation unit analyzes the content of the prompt, and the generation AI automatically selects the optimal transition effect. For example, it suggests a fade-in effect from an emotional scene to the next scene. This allows the automatic selection of transition effects between scenes according to the content of the prompt, achieving a smooth video flow.

[0036] The generation unit can automatically suggest relevant 3D animations and special effects based on the content of the prompt and add them to the movie. For example, the generation unit analyzes the content of the prompt, and the generation AI automatically suggests relevant 3D animations. For example, a 3D animation of a ring can be added to a marriage proposal scene. This allows relevant 3D animations and special effects to be automatically suggested and added to the movie based on the content of the prompt.

[0037] The generation unit can automatically select the color tone and filter effect of a scene based on the prompt and adjust the atmosphere of the video. For example, the generation unit analyzes the content of the prompt, and the generation AI automatically selects the optimal color tone and filter effect. For example, a sepia filter can be applied to a nostalgic scene. This allows the color tone and filter effect of a scene to be automatically selected based on the prompt specified by the user, adjusting the atmosphere of the video.

[0038] The generation unit can optimize the order of scenes to maximize the effectiveness of the storytelling. For example, the generation AI analyzes the comments and photos entered and automatically optimizes the order of scenes. For example, it can create a natural flow from the meeting scene to the proposal and wedding day. This optimizes the order of scenes and maximizes the effectiveness of the storytelling.

[0039] The generation unit can automatically generate audio narration for each scene and add it to the movie. For example, the generation unit automatically generates audio narration for each scene based on comments entered by the generation AI. For example, in a meeting scene, it adds a narration such as "This is where the two met for the first time." This allows audio narration for each scene to be automatically generated and added to the movie.

[0040] The generation unit can automatically generate subtitles for each scene and support display in different languages. For example, the generation unit can automatically generate subtitles for each scene based on comments entered by the generation AI and support display in different languages. For example, it can provide subtitles in English, Japanese, French, etc. This allows subtitles to be automatically generated for each scene and support display in different languages.

[0041] The generation unit can automatically suggest effects for each scene and allow the user to select from them. For example, the generation unit may have a generation AI automatically suggest effects for each scene and allow the user to select from them. For example, effects such as slow motion, zoom in, and fade in may be suggested. This allows effects for each scene to be automatically suggested and allowed to be selected by the user.

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

[0043] The generator can automatically suggest related historical events and cultural backgrounds to add to a movie based on comments and photos entered by the user. For example, if a user describes an encounter at a specific location, a scene introducing the history and cultural background of that location can be added. Information about wedding traditions and customs can also be provided. This allows related historical events and cultural backgrounds to be automatically suggested and added to a movie based on the information entered by the user.

[0044] The generation unit can automatically suggest related travel destinations and tourist attractions based on comments and photos entered by the user and add them to the movie. For example, if a user describes their honeymoon plans, scenes introducing the tourist attractions and famous places at the travel destination can be added. Information such as activities at the travel destination and recommended restaurants can also be provided. This allows related travel destinations and tourist attractions to be automatically suggested and added to the movie based on the information entered by the user.

[0045] The generation unit can automatically suggest related fashions and styles based on comments and photos entered by the user and add them to the movie. For example, if a user describes a wedding outfit, a scene can be added that provides information about fashions and styles related to the outfit. It can also suggest decoration and design ideas that match the wedding theme. This allows related fashions and styles to be automatically suggested and added to the movie based on the information entered by the user.

[0046] The generation unit can automatically suggest related dishes and recipes based on comments and photos entered by the user and add them to the movie. For example, if a user describes a wedding menu, a scene can be added that provides information about dishes and recipes related to that menu. It can also suggest ideas for special dishes and desserts that match the wedding theme. This allows related dishes and recipes to be automatically suggested and added to the movie based on the information entered by the user.

[0047] The generation unit can automatically suggest related sports and activities to add to the movie based on comments and photos entered by the user. For example, if a user describes wedding entertainment and activities, a scene providing information about sports and games related to the activities can be added. It can also suggest ideas for special activities and events that match the theme of the wedding. This allows related sports and activities to be automatically suggested and added to the movie based on the information entered by the user.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The comment input unit accepts comments from the user. For example, the user inputs a comment for each scene, providing an explanation or anecdote for each scene in the wedding movie. Step 2: The photo input unit accepts photos from the user. For example, the user uploads photos for each scene and uses them as material for the wedding movie. Step 3: The prompt specification unit receives a prompt from the user. For example, the user specifies the movement or effect of the video for each scene as a prompt. Step 4: The generation unit generates a wedding video based on the information received by the comment input unit, photo input unit, and prompt specification unit. For example, the generation AI generates a video for each scene based on the comments, photos, and prompts entered by the user, and applies the specified movements and effects.

[0050] (Example 2) The AI ​​generation system according to an embodiment of the present invention generates a wedding video based on comments, photos, and prompts entered by a user. This allows even couples who are unfamiliar with general video production to easily create original and moving wedding videos.

[0051] A generative AI system according to an embodiment includes a comment input unit, a photo input unit, a prompt designation unit, and a generation unit. The comment input unit accepts comments from a user. For example, the user inputs comments for each scene and provides explanations and episodes for each scene in the wedding movie. The photo input unit accepts photos from a user. For example, the user uploads photos for each scene and uses them as material for the wedding movie. The prompt designation unit accepts prompts from a user. For example, the user designates video movements and effects for each scene as prompts. The generation unit generates a wedding movie based on the information accepted by the comment input unit, photo input unit, and prompt designation unit. For example, the generative AI generates video for each scene based on the comments, photos, and prompts entered by the user, and applies the designated movements and effects. This allows the generative AI system to generate a wedding movie based on the information entered by the user.

[0052] The generator can automatically suggest related music and sound effects based on comments and photos and add them to movies. For example, the generator analyzes comments and photos entered by the user, and the generation AI automatically suggests music and sound effects that match the content. For example, romantic music can be selected for a meeting scene, and moving sound effects for a proposal scene. This allows related music and sound effects to be automatically suggested and added to movies based on the information entered by the user.

[0053] The generation unit can analyze a photo and automatically generate a scene focusing on a specific person using face recognition technology. For example, the generation unit analyzes an input photo using face recognition technology and automatically generates a scene focusing on a specific person. For example, a scene is created that zooms in on the faces of a bride and groom. This allows the input photo to be analyzed and a scene focusing on a specific person to be automatically generated.

[0054] The generation unit can use the emotion estimation function to analyze the emotion of the comment and automatically select visual effects and music that match that emotion. For example, the generation unit analyzes the emotion of the comment entered by the user and automatically selects visual effects and music that match that emotion. For example, bright visual effects and up-tempo music are selected for the emotion of joy. In this way, visual effects and music that match the emotion of the comment entered by the user can be automatically selected.

[0055] The generation unit can automatically suggest related video clips and animations based on comments and photos and add them to movies. For example, the generation unit analyzes comments and photos entered by the user, and the generation AI automatically suggests related video clips and animations. For example, romantic animations can be added to dating scenes. This allows related video clips and animations to be automatically suggested and added to movies based on the information entered by the user.

[0056] The generation unit can provide templates that match wedding styles of different cultures and regions, allowing the user to select from them. For example, the generation unit provides templates that match wedding styles of different cultures and regions, allowing the user to select from them. For example, Japanese-style, Western-style, Indian-style, and other templates are prepared. This allows the user to select from templates that match wedding styles of different cultures and regions.

[0057] The generation unit can use the emotion estimation function to collect other users' emotional reactions to comments and photos, and propose an optimal scene composition based on the collected emotional reactions. For example, the generation unit can use the emotion estimation function to collect other users' emotional reactions to comments and photos entered by the user, and propose an optimal scene composition based on the collected emotional reactions. For example, scenes with many positive reactions are preferentially arranged. This allows the generation unit to collect other users' emotional reactions to information entered by the user, and propose an optimal scene composition based on the collected emotional reactions.

[0058] The generation unit can automatically suggest optimal camera angles and zoom effects based on prompts. For example, the generation unit uses a generation AI to automatically suggest optimal camera angles based on prompts specified by the user. For example, it suggests close-ups for important scenes. This allows the generation unit to automatically suggest optimal camera angles and zoom effects based on prompts specified by the user.

[0059] The generation unit automatically selects transition effects between scenes according to the content of the prompt, achieving a smooth video flow. For example, the generation unit analyzes the content of the prompt, and the generation AI automatically selects the optimal transition effect. For example, it suggests a fade-in effect from an emotional scene to the next scene. This allows the automatic selection of transition effects between scenes according to the content of the prompt, achieving a smooth video flow.

[0060] The generation unit can use the emotion estimation function to analyze the emotion of the prompt and automatically select visual effects and transitions that match that emotion. For example, the generation unit can use the emotion estimation function to analyze the emotion of the prompt specified by the user and automatically select visual effects that match that emotion. For example, a bright visual effect can be selected for the emotion of joy. This makes it possible to automatically select visual effects and transitions that match the emotion of the prompt specified by the user.

[0061] The generation unit can automatically suggest relevant 3D animations and special effects based on the content of the prompt and add them to the movie. For example, the generation unit analyzes the content of the prompt, and the generation AI automatically suggests relevant 3D animations. For example, a 3D animation of a ring can be added to a marriage proposal scene. This allows relevant 3D animations and special effects to be automatically suggested and added to the movie based on the content of the prompt.

[0062] The generation unit can automatically select the color tone and filter effect of a scene based on the prompt and adjust the atmosphere of the video. For example, the generation unit analyzes the content of the prompt, and the generation AI automatically selects the optimal color tone and filter effect. For example, a sepia filter can be applied to a nostalgic scene. This allows the color tone and filter effect of a scene to be automatically selected based on the prompt specified by the user, adjusting the atmosphere of the video.

[0063] The generation unit can use the emotion estimation function to collect other users' emotional reactions to the prompt and suggest optimal video effects based on the collected emotional reactions. For example, the generation unit can use the emotion estimation function to collect other users' emotional reactions to the prompt specified by the user and suggest optimal video effects based on the collected emotional reactions. For example, effects with a high number of positive reactions are preferentially suggested. This allows the generation unit to collect other users' emotional reactions to the prompt specified by the user and suggest optimal video effects based on the collected emotional reactions.

[0064] The generation unit can optimize the order of scenes to maximize the effectiveness of the storytelling. For example, the generation AI analyzes the comments and photos entered and automatically optimizes the order of scenes. For example, it can create a natural flow from the meeting scene to the proposal and wedding day. This optimizes the order of scenes and maximizes the effectiveness of the storytelling.

[0065] The generation unit can automatically generate audio narration for each scene and add it to the movie. For example, the generation unit automatically generates audio narration for each scene based on comments entered by the generation AI. For example, in a meeting scene, it adds a narration such as "This is where the two met for the first time." This allows audio narration for each scene to be automatically generated and added to the movie.

[0066] The generation unit can use the emotion estimation function to analyze the emotional impact of each scene in the movie and emphasize the most moving scenes. For example, the generation unit can use the emotion estimation function to analyze the emotional impact of each scene in the generated movie and emphasize the most moving scenes. For example, a special effect can be added to scenes with high emotion scores. This allows the generation unit to analyze the emotional impact of each scene in the generated movie and emphasize the most moving scenes.

[0067] The generation unit can automatically generate subtitles for each scene and support display in different languages. For example, the generation unit can automatically generate subtitles for each scene based on comments entered by the generation AI and support display in different languages. For example, it can provide subtitles in English, Japanese, French, etc. This allows subtitles to be automatically generated for each scene and support display in different languages.

[0068] The generation unit can automatically suggest effects for each scene and allow the user to select from them. For example, the generation unit may have a generation AI automatically suggest effects for each scene and allow the user to select from them. For example, effects such as slow motion, zoom in, and fade in may be suggested. This allows effects for each scene to be automatically suggested and allowed to be selected by the user.

[0069] The generation unit can collect other users' emotional reactions to the generated movie using the emotion estimation function and propose an optimal scene configuration based on the collected emotional reactions. The generation unit can, for example, use the emotion estimation function to collect other users' emotional reactions to the generated movie and propose an optimal scene configuration based on the collected emotional reactions. For example, scenes with many positive reactions are preferentially arranged. This allows the generation unit to collect other users' emotional reactions to the generated movie and propose an optimal scene configuration based on the collected emotional reactions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The generator can automatically suggest related historical events and cultural backgrounds to add to a movie based on comments and photos entered by the user. For example, if a user describes an encounter at a specific location, a scene introducing the history and cultural background of that location can be added. Information about wedding traditions and customs can also be provided. This allows related historical events and cultural backgrounds to be automatically suggested and added to a movie based on the information entered by the user.

[0072] The generation unit can automatically suggest related travel destinations and tourist attractions based on comments and photos entered by the user and add them to the movie. For example, if a user describes their honeymoon plans, scenes introducing the tourist attractions and famous places at the travel destination can be added. Information such as activities at the travel destination and recommended restaurants can also be provided. This allows related travel destinations and tourist attractions to be automatically suggested and added to the movie based on the information entered by the user.

[0073] The generation unit can automatically suggest related fashions and styles based on comments and photos entered by the user and add them to the movie. For example, if a user describes a wedding outfit, a scene can be added that provides information about fashions and styles related to the outfit. It can also suggest decoration and design ideas that match the wedding theme. This allows related fashions and styles to be automatically suggested and added to the movie based on the information entered by the user.

[0074] The generation unit can automatically suggest related dishes and recipes based on comments and photos entered by the user and add them to the movie. For example, if a user describes a wedding menu, a scene can be added that provides information about dishes and recipes related to that menu. It can also suggest ideas for special dishes and desserts that match the wedding theme. This allows related dishes and recipes to be automatically suggested and added to the movie based on the information entered by the user.

[0075] The generation unit can automatically suggest related sports and activities to add to the movie based on comments and photos entered by the user. For example, if a user describes wedding entertainment and activities, a scene providing information about sports and games related to the activities can be added. It can also suggest ideas for special activities and events that match the theme of the wedding. This allows related sports and activities to be automatically suggested and added to the movie based on the information entered by the user.

[0076] The generation unit can use the emotion estimation function to automatically suggest poems and quotes that match the user's emotions based on their comments and photos, and add them to the movie. For example, famous poems and quotes can be added to moving comments to further enhance the impact of the movie. Also, cheerful poems and quotes can be selected for emotions of joy. This allows poems and quotes that match the user's emotions to be automatically suggested and added to the movie.

[0077] The generation unit can use the emotion estimation function to automatically suggest colors and designs that match the user's emotions based on their comments and photos, and add them to the movie. For example, warm colors and soft designs can be added to moving comments to further enhance the impact of the movie. Bright colors and vibrant designs can also be selected for emotions of joy. In this way, colors and designs that match the user's emotions can be automatically suggested and added to the movie.

[0078] The generation unit can use the emotion estimation function to automatically suggest background sounds and environmental sounds that match the user's emotions based on their comments and photos, and add them to the movie. For example, quiet background sounds or natural sounds can be added to moving comments to further enhance the impact of the movie. Also, bright background sounds or lively environmental sounds can be selected for emotions of joy. In this way, background sounds and environmental sounds that match the user's emotions can be automatically suggested and added to the movie.

[0079] The generation unit can use the emotion estimation function to automatically suggest animations and visual effects that match the user's emotions based on their comments and photos, and add them to the movie. For example, soft animations and effects can be added to moving comments to further enhance the impact of the movie. Also, lively animations and effects can be selected for emotions of joy. In this way, animations and visual effects that match the user's emotions can be automatically suggested and added to the movie.

[0080] The generation unit can use the emotion estimation function to automatically suggest narration and audio effects that match the user's emotions based on their comments and photos, and add them to the movie. For example, an emotional narration can be added to a moving comment to further enhance the impact of the movie. Alternatively, a cheerful audio effect can be selected for an emotion of joy. This allows narration and audio effects that match the user's emotions to be automatically suggested and added to the movie.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The comment input unit accepts comments from the user. For example, the user inputs a comment for each scene, providing an explanation or anecdote for each scene in the wedding movie. Step 2: The photo input unit accepts photos from the user. For example, the user uploads photos for each scene and uses them as material for the wedding movie. Step 3: The prompt specification unit receives a prompt from the user. For example, the user specifies the movement or effect of the video for each scene as a prompt. Step 4: The generation unit generates a wedding video based on the information received by the comment input unit, photo input unit, and prompt specification unit. For example, the generation AI generates a video for each scene based on the comments, photos, and prompts entered by the user, and applies the specified movements and effects.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 AI 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 AI 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

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

[0119] The 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.

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

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 AI 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 comment input unit for accepting comments from users; a photo input unit that accepts a photo from a user; a prompt specification unit that accepts a prompt from a user; a generation unit that generates a wedding movie based on the information received by the comment input unit, the photo input unit, and the prompt designation unit. A system characterized by:

2. The generation unit Using an emotion estimation function, the emotion of the comment is analyzed and visual effects and music that match the emotion are automatically selected. The system of claim 1 .

3. The generation unit Based on the comments and the photos, automatically suggest relevant video clips and animations to add to the movie. The system of claim 1 .

4. The generation unit Automatically suggest the best camera angles and zoom effects based on the prompts The system of claim 1 .

5. The generation unit Using an emotion estimation function to analyze the emotion of the prompt and automatically select visual effects and transitions that match the emotion. The system of claim 1 .

6. The generation unit Optimize scene order for maximum storytelling effectiveness The system of claim 1 .

7. The generation unit Use emotion estimation to analyze the emotional impact of each scene in a movie and highlight the most moving scenes The system of claim 1 .

8. The generation unit Using emotion estimation, we collect other users' emotional reactions to the movie and suggest optimal scene composition based on that. The system of claim 1 .

Images