system

Abstract

The system according to the embodiment aims to automatically generate animations based on memorable episodes and photos, and to enable users to customize them. [Solution] A system according to an embodiment includes a natural language processing unit, an image recognition unit, a generation unit, and a customization unit. The natural language processing unit analyzes the text of an episode. The image recognition unit analyzes photos. The generation unit generates animations based on information analyzed by the natural language processing unit and the image recognition unit. The customization unit allows a user to customize the animations generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult and time-consuming to automatically generate animations based on memorable episodes and photographs.

[0005] The system according to the embodiment aims to automatically generate animations based on memorable episodes and photos, and to enable users to customize them. [Means for solving the problem]

[0006] The system according to the embodiment includes a natural language processing unit, an image recognition unit, a generation unit, and a customization unit. The natural language processing unit analyzes the text of the episode. The image recognition unit analyzes the photos. The generation unit generates animations based on the information analyzed by the natural language processing unit and the image recognition unit. The customization unit allows a user to customize the animations generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate animations based on memorable episodes and photos, and allow users to customize them. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An animation generation system according to an embodiment of the present invention analyzes episode text and multiple photos entered by a user and automatically generates animations. The animation generation system includes a natural language processing unit that analyzes the episode text, an image recognition unit that analyzes photos, a generation unit that generates animations based on information analyzed by the natural language processing unit and the image recognition unit, and a customization unit that allows the user to customize the animations generated by the generation unit. For example, when a user enters photos of an episode from a family trip or a party with friends, the animation generation system analyzes the information and generates an animation scenario. Next, an animation is created based on the generated scenario, and the user can customize it by selecting the animation style and music. This allows the user to preserve memories of family and friends as animations. The animation generation system thus vividly recreates and preserves the user's memories. For example, recreating memories of a family trip as an animation can help preserve the memories more vividly. Also, preserving memories of a party with friends as an animation allows the user to relive fun times at any time.

[0029] An animation generation system according to an embodiment includes a natural language processing unit, an image recognition unit, a generation unit, and a customization unit. The natural language processing unit analyzes episode text. For example, the natural language processing unit receives episode text as input and identifies important events and emotions. The natural language processing unit can also analyze the episode text and detect changes in context. When analyzing the episode text, the natural language processing unit can automatically translate and analyze episodes in different languages. When analyzing the episode text, the natural language processing unit can also improve analysis accuracy by referring to past episode data. The image recognition unit analyzes photos. For example, the image recognition unit receives photos as input and recognizes people and backgrounds. When analyzing photos, the image recognition unit can also automatically correct image resolution and quality to improve analysis accuracy. When analyzing photos, the image recognition unit can also integrate photos taken under different lighting conditions and angles. When analyzing photos, the image recognition unit can also improve analysis accuracy by referring to past photo data. The generation unit generates an animation scenario based on the information analyzed by the natural language processing unit and the image recognition unit. For example, the generation unit creates a storyboard based on the analyzed information and constructs an animation scenario. The generation unit also creates animation based on the generated scenario. For example, the generation unit moves characters based on the scenario and recreates the content of an episode. The customization unit provides a function that allows the user to select the style and music of the animation. For example, the customization unit allows the user to adjust the color and character movement of the animation. The customization unit also allows the user to select the music to be used in the animation. In this way, the animation generation system according to the embodiment can vividly recreate and preserve the user's memories. For example, recreating memories of a family trip as an animation makes the memory more vivid. Also, preserving memories of a party with friends as an animation allows the user to look back on fun times at any time.

[0030] The natural language processing unit can analyze the episode text and identify important events and emotions. For example, the natural language processing unit can analyze the episode text and identify important events based on frequently occurring keywords or specific events. The natural language processing unit can also analyze the episode text and identify emotions such as joy, sadness, and anger. The natural language processing unit can also analyze the episode text and learn criteria for and methods of identifying important events. For example, the natural language processing unit can refer to past episode data and learn patterns of important events. This improves the accuracy of analysis by identifying important events and emotions in an episode.

[0031] The image recognition unit can analyze a photo and recognize people and backgrounds. For example, the image recognition unit can analyze a photo and identify people using face recognition technology. The image recognition unit can also analyze a photo and recognize backgrounds such as landscapes and buildings. The image recognition unit can also analyze a photo and learn the characteristics of people and backgrounds. For example, the image recognition unit can refer to past photo data and learn patterns of people and backgrounds. This improves the accuracy of analysis by recognizing people and backgrounds in photos.

[0032] The generation unit can generate an animation scenario based on the information analyzed by the natural language processing unit and the image recognition unit. For example, the generation unit creates a storyboard based on the information analyzed by the natural language processing unit and the image recognition unit, and constructs an animation scenario. The generation unit can also compose a plot based on the analyzed information. The generation unit can also learn scenario generation methods and standards. For example, the generation unit refers to past scenario data and learns scenario patterns. This improves the accuracy of the scenario by generating an animation scenario based on the analyzed information.

[0033] The generation unit can create animation based on the generated scenario. For example, the generation unit moves characters based on the generated scenario and recreates the content of an episode. The generation unit can also set backgrounds and music based on the scenario. The generation unit can also learn animation creation methods and standards. For example, the generation unit can refer to past animation data and learn animation patterns. This improves the accuracy of the animation by creating animation based on the generated scenario.

[0034] The customization unit can provide a function that allows the user to select the style and music of the animation. For example, the customization unit allows the user to adjust the color of the animation or the movement of the character. The customization unit can also allow the user to select the music to be used in the animation. The customization unit can also learn customization methods and standards. For example, the customization unit refers to past customization data and learns customization patterns. This allows the user to select the style and music of the animation, thereby increasing the degree of freedom in customization.

[0035] When analyzing the text of an episode, the natural language processor can detect changes in context and prioritize important events. For example, the natural language processor can detect points in an episode where the context changes and identify important events. The natural language processor can also automatically prioritize events based on the changes in context. The natural language processor can also detect changes in context and adjust the analysis method to emphasize important events. This improves analysis accuracy by detecting changes in context and prioritizing important events.

[0036] When analyzing the text of an episode, the natural language processing unit can automatically translate episodes in different languages ​​and perform analysis. For example, the natural language processing unit can automatically translate an episode written in English into Japanese and perform analysis. The natural language processing unit can also automatically translate an episode written in French into English and perform analysis. The natural language processing unit can also automatically translate an episode written in Spanish into Japanese and perform analysis. In this way, by automatically translating and analyzing episodes in different languages, the accuracy of analysis is improved.

[0037] When analyzing episode text, the natural language processing unit can improve analysis accuracy by referring to past episode data. For example, the natural language processing unit improves analysis accuracy by referring to past episode data and identifying similar events. The natural language processing unit can also improve analysis accuracy by learning patterns of important events based on past episode data. The natural language processing unit can also improve analysis accuracy by referring to past episode data and identifying changes in emotions. In this way, improving analysis accuracy by referring to past episode data improves analysis accuracy.

[0038] When analyzing episode text, the natural language processing unit can learn the user's writing habits and improve analysis accuracy. The natural language processing unit can, for example, learn expressions and phrases frequently used by the user and improve analysis accuracy. The natural language processing unit can also learn the user's writing habits and improve the accuracy of identifying important events. The natural language processing unit can also learn the user's writing patterns and accurately detect changes in context. In this way, learning the user's writing habits and improving analysis accuracy improves analysis accuracy.

[0039] When analyzing the text of an episode, the natural language processor can customize the analysis method by reflecting the user's past feedback. The natural language processor adjusts the analysis method based on, for example, feedback provided by the user in the past. The natural language processor can also improve the accuracy of identifying important events by reflecting the user's past feedback. The natural language processor can also accurately detect changes in context based on the user's past feedback. In this way, customizing the analysis method by reflecting the user's past feedback improves the analysis accuracy.

[0040] When analyzing the text of an episode, the natural language processing unit can perform the analysis while taking into account the geographical background of the user. For example, the natural language processing unit identifies important locations in an episode by taking into account the geographical background of the user. The natural language processing unit can also accurately detect changes in context based on the geographical background of the user. The natural language processing unit can also improve the accuracy of identifying important events by taking into account the geographical background of the user. As a result, by performing the analysis while taking into account the geographical background of the user, the accuracy of the analysis is improved.

[0041] When analyzing a photo, the image recognition unit can automatically correct the resolution and quality of the image to improve the analysis accuracy. For example, the image recognition unit automatically corrects low-resolution photos to improve the analysis accuracy. The image recognition unit can also automatically correct the quality of the photo to improve the recognition accuracy of people and backgrounds. The image recognition unit can also automatically remove noise from the image to improve the analysis accuracy. In this way, the analysis accuracy is improved by automatically correcting the resolution and quality of the image.

[0042] When analyzing a photo, the image recognition unit can integrate photos taken under different lighting conditions and from different angles. For example, the image recognition unit integrates photos taken under different lighting conditions to improve the accuracy of analysis. The image recognition unit can also integrate photos taken from different angles to improve the accuracy of recognizing people and backgrounds. The image recognition unit can also integrate photos taken under different lighting conditions and from different angles to identify important elements. As a result, the analysis accuracy is improved by integrating photos taken under different lighting conditions and from different angles.

[0043] When analyzing a photo, the image recognition unit can improve the analysis accuracy by referring to past photo data. For example, the image recognition unit improves the analysis accuracy by referring to past photo data and identifying similar people and backgrounds. The image recognition unit can also improve the analysis accuracy by learning patterns of important elements based on past photo data. The image recognition unit can also improve the analysis accuracy by referring to past photo data and identifying changes in emotions. In this way, the analysis accuracy is improved by improving the analysis accuracy by referring to past photo data.

[0044] When analyzing photos, the image recognition unit can learn the user's shooting style and improve the analysis accuracy. The image recognition unit can learn, for example, the angles and compositions that the user often uses to take photos and improve the analysis accuracy. The image recognition unit can also learn the user's shooting style and improve the accuracy of identifying important elements. The image recognition unit can also learn the patterns of the user's shooting style and improve the analysis accuracy of photos. In this way, by learning the user's shooting style and improving the analysis accuracy, the analysis accuracy is improved.

[0045] When analyzing a photo, the image recognition unit can customize the analysis method by reflecting the user's past feedback. The image recognition unit adjusts the analysis method based on, for example, feedback provided by the user in the past. The image recognition unit can also reflect the user's past feedback to improve the accuracy of identifying important elements. The image recognition unit can also improve the accuracy of photo analysis based on the user's past feedback. In this way, customizing the analysis method by reflecting the user's past feedback improves the accuracy of analysis.

[0046] When analyzing a photo, the image recognition unit can perform the analysis while taking into account the geographical background of the user. For example, the image recognition unit identifies important locations in the photo while taking into account the geographical background of the user. The image recognition unit can also improve the accuracy of analyzing the photo based on the geographical background of the user. The image recognition unit can also improve the accuracy of identifying important elements while taking into account the geographical background of the user. As a result, by performing the analysis while taking into account the geographical background of the user, the accuracy of the analysis is improved.

[0047] When generating an animation scenario, the generation unit can construct the scenario taking into account the timeline of the episodes. For example, the generation unit constructs a scenario by sequencing important events along the timeline of the episodes. The generation unit can also construct a scenario that accurately reflects the chronological relationship of events by taking the timeline into account. The generation unit can also construct a scenario that naturally reproduces the flow of episodes based on the timeline. In this way, constructing a scenario taking into account the timeline of the episodes improves the accuracy of the scenario.

[0048] When generating an animation scenario, the generation unit can automatically generate different scenario patterns and provide the user with options. For example, the generation unit can automatically generate multiple scenario patterns and provide the user with options. The generation unit can also generate scenario patterns with different perspectives or styles and provide the user with options. The generation unit can also generate scenario patterns according to the user's preferences and provide the user with options. In this way, by automatically generating different scenario patterns and providing the user with options, user satisfaction is improved.

[0049] When generating an animation scenario, the generation unit can improve generation accuracy by referring to past scenario data. For example, the generation unit can improve generation accuracy by referring to past scenario data and identifying similar events. The generation unit can also improve generation accuracy by learning patterns of important events based on past scenario data. The generation unit can also improve generation accuracy by referring to past scenario data and identifying changes in emotions. In this way, improving generation accuracy by referring to past scenario data improves generation accuracy.

[0050] When generating an animation scenario, the generation unit can learn the user's preferences and style and customize the scenario. For example, the generation unit can learn the user's preferred storytelling style and customize the scenario. The generation unit can also select characters and backgrounds according to the user's preferences and customize the scenario. The generation unit can also customize the optimal scenario based on the user's past selection history. In this way, the generation unit can learn the user's preferences and style and customize the scenario, thereby improving generation accuracy.

[0051] When generating an animation scenario, the generation unit can adjust the generation method by reflecting the user's past feedback. For example, the generation unit adjusts the scenario generation method based on feedback provided by the user in the past. The generation unit can also improve the accuracy of identifying important events by reflecting the user's past feedback. The generation unit can also reproduce the natural flow of the scenario based on the user's past feedback. In this way, adjusting the generation method by reflecting the user's past feedback improves the generation accuracy.

[0052] When generating an animation scenario, the generation unit can construct the scenario taking into account the user's geographical background. For example, the generation unit can identify important locations in the scenario taking into account the user's geographical background. The generation unit can also reproduce a natural flow of the scenario based on the user's geographical background. The generation unit can also improve the accuracy of identifying important events by taking into account the user's geographical background. As a result, constructing a scenario taking into account the user's geographical background improves the generation accuracy.

[0053] During customization, the customization unit can suggest optimal options by referring to the user's past customization history. For example, the customization unit can suggest optimal options based on customization options previously selected by the user. The customization unit can also suggest options that match the user's preferences by referring to the user's past customization history. The customization unit can also suggest optimal styles and music based on the user's past customization history. This improves the accuracy of customization by suggesting optimal options by referring to the user's past customization history.

[0054] During customization, the customization unit can adjust the customization content according to the user's current mood and situation. For example, if the user is relaxed, the customization unit can suggest a calm style or music. Furthermore, if the user is excited, the customization unit can also suggest a dynamic style or music. Furthermore, if the user is feeling sad, the customization unit can also suggest customization content that emphasizes emotional elements. In this way, the accuracy of customization is improved by adjusting the customization content according to the user's current mood and situation.

[0055] During customization, the customization unit can improve the customization options by reflecting user feedback. For example, the customization unit improves the customization options based on feedback provided by the user. The customization unit can also reflect the user feedback and suggest optimal styles and music. The customization unit can also adjust the customization content based on the user feedback. In this way, the accuracy of customization is improved by improving the customization options by reflecting the user feedback.

[0056] During customization, the customization unit can provide optimal customization options by taking into account the user's device information. For example, if the user is using a smartphone, the customization unit can provide customization options that match the screen size. Furthermore, if the user is using a tablet, the customization unit can provide customization options that are optimized for a large screen. Furthermore, if the user is using a smartwatch, the customization unit can provide concise and highly visible customization options. This improves the accuracy of customization by providing optimal customization options by taking into account the user's device information.

[0057] During customization, the customization unit can make the customization content multilingual in accordance with the user's language setting. The customization unit automatically sets the customization content based on, for example, the language setting of the user's device. The customization unit can also provide a language switching function when the user uses multiple languages. The customization unit can also provide the customization content in a specific language when the user selects that language. This improves the accuracy of customization by making the customization content multilingual in accordance with the user's language setting.

[0058] During customization, the customization unit can suggest customization content based on the user's occupation and lifestyle. For example, the customization unit suggests customization content according to the user's occupation. The customization unit can also suggest optimal customization content based on the user's lifestyle. The customization unit can also adjust the customization content taking the user's occupation and lifestyle into consideration. This improves the accuracy of customization by suggesting customization content based on the user's occupation and lifestyle.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The animation generation system can further include a location information acquisition unit. The location information acquisition unit can acquire location information from the user's device and use it to analyze episodes. For example, an animation that recreates a travel route can be generated based on the location information of photos taken by the user during a trip. The location information acquisition unit can also suggest related episodes based on the user's current location. This makes it possible to generate more realistic animations by utilizing location information.

[0061] The animation generation system may further include a data sharing unit. The data sharing unit may provide a function for a user to share animations generated by the user with other users. For example, a user may share animations with family and friends and enjoy them together. The data sharing unit may also collect feedback on the shared animations and improve the accuracy of analysis. This may promote interaction between users and improve the quality of animations.

[0062] The animation generation system may further include a story template unit. The story template unit may provide a plurality of story templates from which the user can select. For example, if the user inputs an episode about a family trip, the story template unit may suggest a template suitable for the trip. Alternatively, if the user inputs an episode about a party with friends, the story template unit may suggest a template suitable for the party. This allows the user to easily create an animation story.

[0063] The animation generation system can further include a user interface unit. The user interface unit can provide an interface that allows users to operate intuitively. For example, it can provide a function for placing photos and text by drag and drop. The user interface unit can also learn the user's operation history and suggest the optimal operation method. This allows users to easily generate and customize animations.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The natural language processor analyzes the episode text. For example, it receives the episode text as input and identifies key events and emotions. It can also detect contextual changes and automatically translate episodes in different languages ​​for analysis. It can also refer to past episode data to improve the accuracy of the analysis. Step 2: The image recognition unit analyzes the photo. For example, it receives a photo as input and recognizes people and backgrounds. It can also automatically correct the image resolution and quality, and integrate photos taken under different lighting conditions and angles for analysis. It can also improve the accuracy of the analysis by referencing past photo data. Step 3: The generator generates an animation scenario based on the information analyzed by the natural language processor and image recognition unit. For example, it creates a storyboard based on the analyzed information, moves the characters based on the scenario, and recreates the content of the episode. Step 4: The customization section allows users to select the animation style and music, for example, adjust the animation color and character movement, and select the music to be used.

[0066] (Example 2) An animation generation system according to an embodiment of the present invention analyzes episode text and multiple photos entered by a user and automatically generates animations. The animation generation system includes a natural language processing unit that analyzes the episode text, an image recognition unit that analyzes photos, a generation unit that generates animations based on information analyzed by the natural language processing unit and the image recognition unit, and a customization unit that allows the user to customize the animations generated by the generation unit. For example, when a user enters photos of an episode from a family trip or a party with friends, the animation generation system analyzes the information and generates an animation scenario. Next, an animation is created based on the generated scenario, and the user can customize it by selecting the animation style and music. This allows the user to preserve memories of family and friends as animations. The animation generation system thus vividly recreates and preserves the user's memories. For example, recreating memories of a family trip as an animation can help preserve the memories more vividly. Also, preserving memories of a party with friends as an animation allows the user to relive fun times at any time.

[0067] An animation generation system according to an embodiment includes a natural language processing unit, an image recognition unit, a generation unit, and a customization unit. The natural language processing unit analyzes episode text. For example, the natural language processing unit receives episode text as input and identifies important events and emotions. The natural language processing unit can also analyze the episode text and detect changes in context. When analyzing the episode text, the natural language processing unit can automatically translate and analyze episodes in different languages. When analyzing the episode text, the natural language processing unit can also improve analysis accuracy by referring to past episode data. The image recognition unit analyzes photos. For example, the image recognition unit receives photos as input and recognizes people and backgrounds. When analyzing photos, the image recognition unit can also automatically correct image resolution and quality to improve analysis accuracy. When analyzing photos, the image recognition unit can also integrate photos taken under different lighting conditions and angles. When analyzing photos, the image recognition unit can also improve analysis accuracy by referring to past photo data. The generation unit generates an animation scenario based on the information analyzed by the natural language processing unit and the image recognition unit. For example, the generation unit creates a storyboard based on the analyzed information and constructs an animation scenario. The generation unit also creates animation based on the generated scenario. For example, the generation unit moves characters based on the scenario and recreates the content of an episode. The customization unit provides a function that allows the user to select the style and music of the animation. For example, the customization unit allows the user to adjust the color and character movement of the animation. The customization unit also allows the user to select the music to be used in the animation. In this way, the animation generation system according to the embodiment can vividly recreate and preserve the user's memories. For example, recreating memories of a family trip as an animation makes the memory more vivid. Also, preserving memories of a party with friends as an animation allows the user to look back on fun times at any time.

[0068] The natural language processing unit can analyze the episode text and identify important events and emotions. For example, the natural language processing unit can analyze the episode text and identify important events based on frequently occurring keywords or specific events. The natural language processing unit can also analyze the episode text and identify emotions such as joy, sadness, and anger. The natural language processing unit can also analyze the episode text and learn criteria for and methods of identifying important events. For example, the natural language processing unit can refer to past episode data and learn patterns of important events. This improves the accuracy of analysis by identifying important events and emotions in an episode.

[0069] The image recognition unit can analyze a photo and recognize people and backgrounds. For example, the image recognition unit can analyze a photo and identify people using face recognition technology. The image recognition unit can also analyze a photo and recognize backgrounds such as landscapes and buildings. The image recognition unit can also analyze a photo and learn the characteristics of people and backgrounds. For example, the image recognition unit can refer to past photo data and learn patterns of people and backgrounds. This improves the accuracy of analysis by recognizing people and backgrounds in photos.

[0070] The generation unit can generate an animation scenario based on the information analyzed by the natural language processing unit and the image recognition unit. For example, the generation unit creates a storyboard based on the information analyzed by the natural language processing unit and the image recognition unit, and constructs an animation scenario. The generation unit can also compose a plot based on the analyzed information. The generation unit can also learn scenario generation methods and standards. For example, the generation unit refers to past scenario data and learns scenario patterns. This improves the accuracy of the scenario by generating an animation scenario based on the analyzed information.

[0071] The generation unit can create animation based on the generated scenario. For example, the generation unit moves characters based on the generated scenario and recreates the content of an episode. The generation unit can also set backgrounds and music based on the scenario. The generation unit can also learn animation creation methods and standards. For example, the generation unit can refer to past animation data and learn animation patterns. This improves the accuracy of the animation by creating animation based on the generated scenario.

[0072] The customization unit can provide a function that allows the user to select the style and music of the animation. For example, the customization unit allows the user to adjust the color of the animation or the movement of the character. The customization unit can also allow the user to select the music to be used in the animation. The customization unit can also learn customization methods and standards. For example, the customization unit refers to past customization data and learns customization patterns. This allows the user to select the style and music of the animation, thereby increasing the degree of freedom in customization.

[0073] The natural language processing unit can estimate the user's emotions and adjust the episode analysis method based on the estimated user's emotions. For example, if the user is feeling sad, the natural language processing unit can analyze the episode by focusing on emotional elements. Also, if the user is happy, the natural language processing unit can analyze the episode by focusing on positive events. Also, if the user is excited, the natural language processing unit can analyze the episode by focusing on dynamic events. In this way, by adjusting the episode analysis method based on the user's emotions, analysis accuracy is improved.

[0074] When analyzing the text of an episode, the natural language processor can detect changes in context and prioritize important events. For example, the natural language processor can detect points in an episode where the context changes and identify important events. The natural language processor can also automatically prioritize events based on the changes in context. The natural language processor can also detect changes in context and adjust the analysis method to emphasize important events. This improves analysis accuracy by detecting changes in context and prioritizing important events.

[0075] When analyzing the text of an episode, the natural language processing unit can automatically translate episodes in different languages ​​and perform analysis. For example, the natural language processing unit can automatically translate an episode written in English into Japanese and perform analysis. The natural language processing unit can also automatically translate an episode written in French into English and perform analysis. The natural language processing unit can also automatically translate an episode written in Spanish into Japanese and perform analysis. In this way, by automatically translating and analyzing episodes in different languages, the accuracy of analysis is improved.

[0076] When analyzing episode text, the natural language processing unit can improve analysis accuracy by referring to past episode data. For example, the natural language processing unit improves analysis accuracy by referring to past episode data and identifying similar events. The natural language processing unit can also improve analysis accuracy by learning patterns of important events based on past episode data. The natural language processing unit can also improve analysis accuracy by referring to past episode data and identifying changes in emotions. In this way, improving analysis accuracy by referring to past episode data improves analysis accuracy.

[0077] The natural language processing unit can estimate the user's emotions and adjust the importance of episodes based on the estimated user's emotions. For example, if the user is feeling sad, the natural language processing unit analyzes emotional events as being more important. The natural language processing unit can also analyze positive events as being more important if the user is happy. The natural language processing unit can also analyze dynamic events as being more important if the user is excited. In this way, adjusting the importance of episodes based on the user's emotions improves the accuracy of analysis.

[0078] When analyzing episode text, the natural language processing unit can learn the user's writing habits and improve analysis accuracy. The natural language processing unit can, for example, learn expressions and phrases frequently used by the user and improve analysis accuracy. The natural language processing unit can also learn the user's writing habits and improve the accuracy of identifying important events. The natural language processing unit can also learn the user's writing patterns and accurately detect changes in context. In this way, learning the user's writing habits and improving analysis accuracy improves analysis accuracy.

[0079] When analyzing the text of an episode, the natural language processor can customize the analysis method by reflecting the user's past feedback. The natural language processor adjusts the analysis method based on, for example, feedback provided by the user in the past. The natural language processor can also improve the accuracy of identifying important events by reflecting the user's past feedback. The natural language processor can also accurately detect changes in context based on the user's past feedback. In this way, customizing the analysis method by reflecting the user's past feedback improves the analysis accuracy.

[0080] When analyzing the text of an episode, the natural language processing unit can perform the analysis while taking into account the geographical background of the user. For example, the natural language processing unit identifies important locations in an episode by taking into account the geographical background of the user. The natural language processing unit can also accurately detect changes in context based on the geographical background of the user. The natural language processing unit can also improve the accuracy of identifying important events by taking into account the geographical background of the user. As a result, by performing the analysis while taking into account the geographical background of the user, the accuracy of the analysis is improved.

[0081] The image recognition unit can estimate the user's emotions and adjust the photo analysis method based on the estimated user's emotions. For example, if the user is feeling sad, the image recognition unit can analyze the photo by emphasizing emotional elements. Also, if the user is happy, the image recognition unit can analyze the photo by emphasizing positive elements. Also, if the user is excited, the image recognition unit can analyze the photo by emphasizing dynamic elements. In this way, by adjusting the photo analysis method based on the user's emotions, the analysis accuracy is improved.

[0082] When analyzing a photo, the image recognition unit can automatically correct the resolution and quality of the image to improve the analysis accuracy. For example, the image recognition unit automatically corrects low-resolution photos to improve the analysis accuracy. The image recognition unit can also automatically correct the quality of the photo to improve the recognition accuracy of people and backgrounds. The image recognition unit can also automatically remove noise from the image to improve the analysis accuracy. In this way, the analysis accuracy is improved by automatically correcting the resolution and quality of the image.

[0083] When analyzing a photo, the image recognition unit can integrate photos taken under different lighting conditions and from different angles. For example, the image recognition unit integrates photos taken under different lighting conditions to improve the accuracy of analysis. The image recognition unit can also integrate photos taken from different angles to improve the accuracy of recognizing people and backgrounds. The image recognition unit can also integrate photos taken under different lighting conditions and from different angles to identify important elements. As a result, the analysis accuracy is improved by integrating photos taken under different lighting conditions and from different angles.

[0084] When analyzing a photo, the image recognition unit can improve the analysis accuracy by referring to past photo data. For example, the image recognition unit improves the analysis accuracy by referring to past photo data and identifying similar people and backgrounds. The image recognition unit can also improve the analysis accuracy by learning patterns of important elements based on past photo data. The image recognition unit can also improve the analysis accuracy by referring to past photo data and identifying changes in emotions. In this way, the analysis accuracy is improved by improving the analysis accuracy by referring to past photo data.

[0085] The image recognition unit can estimate the user's emotions and adjust the importance of photos based on the estimated user emotions. For example, if the user is feeling sad, the image recognition unit can adjust the importance of photos by emphasizing emotional elements. Also, if the user is happy, the image recognition unit can adjust the importance of photos by emphasizing positive elements. Also, if the user is excited, the image recognition unit can adjust the importance of photos by emphasizing dynamic elements. In this way, adjusting the importance of photos based on the user's emotions improves analysis accuracy.

[0086] When analyzing photos, the image recognition unit can learn the user's shooting style and improve the analysis accuracy. The image recognition unit can learn, for example, the angles and compositions that the user often uses to take photos and improve the analysis accuracy. The image recognition unit can also learn the user's shooting style and improve the accuracy of identifying important elements. The image recognition unit can also learn the patterns of the user's shooting style and improve the analysis accuracy of photos. In this way, by learning the user's shooting style and improving the analysis accuracy, the analysis accuracy is improved.

[0087] When analyzing a photo, the image recognition unit can customize the analysis method by reflecting the user's past feedback. The image recognition unit adjusts the analysis method based on, for example, feedback provided by the user in the past. The image recognition unit can also reflect the user's past feedback to improve the accuracy of identifying important elements. The image recognition unit can also improve the accuracy of photo analysis based on the user's past feedback. In this way, customizing the analysis method by reflecting the user's past feedback improves the accuracy of analysis.

[0088] When analyzing a photo, the image recognition unit can perform the analysis while taking into account the geographical background of the user. For example, the image recognition unit identifies important locations in the photo while taking into account the geographical background of the user. The image recognition unit can also improve the accuracy of analyzing the photo based on the geographical background of the user. The image recognition unit can also improve the accuracy of identifying important elements while taking into account the geographical background of the user. As a result, by performing the analysis while taking into account the geographical background of the user, the accuracy of the analysis is improved.

[0089] The generation unit can estimate the user's emotions and adjust the animation scenario generation method based on the estimated user's emotions. For example, if the user is feeling sad, the generation unit generates a scenario by emphasizing emotional elements. Furthermore, if the user is happy, the generation unit can generate a scenario by emphasizing positive elements. Furthermore, if the user is excited, the generation unit can generate a scenario by emphasizing dynamic elements. In this way, by adjusting the animation scenario generation method based on the user's emotions, generation accuracy is improved.

[0090] When generating an animation scenario, the generation unit can construct the scenario taking into account the timeline of the episodes. For example, the generation unit constructs a scenario by sequencing important events along the timeline of the episodes. The generation unit can also construct a scenario that accurately reflects the chronological relationship of events by taking the timeline into account. The generation unit can also construct a scenario that naturally reproduces the flow of episodes based on the timeline. In this way, constructing a scenario taking into account the timeline of the episodes improves the accuracy of the scenario.

[0091] When generating an animation scenario, the generation unit can automatically generate different scenario patterns and provide the user with options. For example, the generation unit can automatically generate multiple scenario patterns and provide the user with options. The generation unit can also generate scenario patterns with different perspectives or styles and provide the user with options. The generation unit can also generate scenario patterns according to the user's preferences and provide the user with options. In this way, by automatically generating different scenario patterns and providing the user with options, user satisfaction is improved.

[0092] When generating an animation scenario, the generation unit can improve generation accuracy by referring to past scenario data. For example, the generation unit can improve generation accuracy by referring to past scenario data and identifying similar events. The generation unit can also improve generation accuracy by learning patterns of important events based on past scenario data. The generation unit can also improve generation accuracy by referring to past scenario data and identifying changes in emotions. In this way, improving generation accuracy by referring to past scenario data improves generation accuracy.

[0093] The generation unit can estimate the user's emotions and adjust the importance of the animation scenario based on the estimated user's emotions. For example, if the user is feeling sad, the generation unit can adjust the importance of the scenario by emphasizing emotional elements. Also, if the user is happy, the generation unit can adjust the importance of the scenario by emphasizing positive elements. Also, if the user is excited, the generation unit can adjust the importance of the scenario by emphasizing dynamic elements. In this way, adjusting the importance of the animation scenario based on the user's emotions improves generation accuracy.

[0094] When generating an animation scenario, the generation unit can learn the user's preferences and style and customize the scenario. For example, the generation unit can learn the user's preferred storytelling style and customize the scenario. The generation unit can also select characters and backgrounds according to the user's preferences and customize the scenario. The generation unit can also customize the optimal scenario based on the user's past selection history. In this way, the generation unit can learn the user's preferences and style and customize the scenario, thereby improving generation accuracy.

[0095] When generating an animation scenario, the generation unit can adjust the generation method by reflecting the user's past feedback. For example, the generation unit adjusts the scenario generation method based on feedback provided by the user in the past. The generation unit can also improve the accuracy of identifying important events by reflecting the user's past feedback. The generation unit can also reproduce the natural flow of the scenario based on the user's past feedback. In this way, adjusting the generation method by reflecting the user's past feedback improves the generation accuracy.

[0096] When generating an animation scenario, the generation unit can construct the scenario taking into account the user's geographical background. For example, the generation unit can identify important locations in the scenario taking into account the user's geographical background. The generation unit can also reproduce a natural flow of the scenario based on the user's geographical background. The generation unit can also improve the accuracy of identifying important events by taking into account the user's geographical background. As a result, constructing a scenario taking into account the user's geographical background improves the generation accuracy.

[0097] The customization unit can estimate the user's emotion and provide customization options based on the estimated user's emotion. For example, if the user is sad, the customization unit can provide customization options that emphasize emotional elements. Furthermore, if the user is happy, the customization unit can provide customization options that emphasize positive elements. Furthermore, if the user is excited, the customization unit can provide customization options that emphasize dynamic elements. This improves the accuracy of customization by providing customization options based on the user's emotion.

[0098] During customization, the customization unit can suggest optimal options by referring to the user's past customization history. For example, the customization unit can suggest optimal options based on customization options previously selected by the user. The customization unit can also suggest options that match the user's preferences by referring to the user's past customization history. The customization unit can also suggest optimal styles and music based on the user's past customization history. This improves the accuracy of customization by suggesting optimal options by referring to the user's past customization history.

[0099] During customization, the customization unit can adjust the customization content according to the user's current mood and situation. For example, if the user is relaxed, the customization unit can suggest a calm style or music. Furthermore, if the user is excited, the customization unit can also suggest a dynamic style or music. Furthermore, if the user is feeling sad, the customization unit can also suggest customization content that emphasizes emotional elements. In this way, the accuracy of customization is improved by adjusting the customization content according to the user's current mood and situation.

[0100] During customization, the customization unit can improve the customization options by reflecting user feedback. For example, the customization unit improves the customization options based on feedback provided by the user. The customization unit can also reflect the user feedback and suggest optimal styles and music. The customization unit can also adjust the customization content based on the user feedback. In this way, the accuracy of customization is improved by improving the customization options by reflecting the user feedback.

[0101] The customization unit can estimate the user's emotions and determine a customization priority based on the estimated user's emotions. For example, if the user is sad, the customization unit can prioritize emotional elements in customization. Also, if the user is happy, the customization unit can prioritize positive elements in customization. Also, if the user is excited, the customization unit can prioritize dynamic elements in customization. In this way, by determining the customization priority based on the user's emotions, the accuracy of customization is improved.

[0102] During customization, the customization unit can provide optimal customization options by taking into account the user's device information. For example, if the user is using a smartphone, the customization unit can provide customization options that match the screen size. Furthermore, if the user is using a tablet, the customization unit can provide customization options that are optimized for a large screen. Furthermore, if the user is using a smartwatch, the customization unit can provide concise and highly visible customization options. This improves the accuracy of customization by providing optimal customization options by taking into account the user's device information.

[0103] During customization, the customization unit can make the customization content multilingual in accordance with the user's language setting. The customization unit automatically sets the customization content based on, for example, the language setting of the user's device. The customization unit can also provide a language switching function when the user uses multiple languages. The customization unit can also provide the customization content in a specific language when the user selects that language. This improves the accuracy of customization by making the customization content multilingual in accordance with the user's language setting.

[0104] During customization, the customization unit can suggest customization content based on the user's occupation and lifestyle. For example, the customization unit suggests customization content according to the user's occupation. The customization unit can also suggest optimal customization content based on the user's lifestyle. The customization unit can also adjust the customization content taking the user's occupation and lifestyle into consideration. This improves the accuracy of customization by suggesting customization content based on the user's occupation and lifestyle. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned natural language processing unit, image recognition unit, generation unit, and customization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the natural language processing unit is realized by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12. The image recognition unit is realized by the camera 42 and the processor 46 of the smart device 14 or the processor 28 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12. The customization unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned natural language processing unit, image recognition unit, generation unit, and customization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the natural language processing unit is realized by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12. The image recognition unit is realized by the camera 42 and the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12. The customization unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned natural language processing unit, image recognition unit, generation unit, and customization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the natural language processing unit is realized by the processor 46 of the headset type terminal 314 or the processor 28 of the data processing device 12. The image recognition unit is realized by the camera 42 and processor 46 of the headset type terminal 314 or the processor 28 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12. The customization unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the natural language processing unit, image recognition unit, generation unit, and customization unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the natural language processing unit is realized by the processor 46 of the robot 414 or the processor 28 of the data processing device 12. The image recognition unit is realized by the camera 42 and the processor 46 of the robot 414 or the processor 28 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12. The customization unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The animation generation system can further include a voice recognition unit. The voice recognition unit can analyze voice data provided by the user and convert it into episode text. For example, when a user speaks about their travel memories, the voice recognition unit converts the voice into text and analyzes it in the natural language processing unit. The voice recognition unit can also estimate the user's emotions from the voice and adjust the episode analysis method based on the emotions. This allows the user to input episodes by voice, allowing for more intuitive animation generation.

[0107] The animation generation system can further include a location information acquisition unit. The location information acquisition unit can acquire location information from the user's device and use it to analyze episodes. For example, an animation that recreates a travel route can be generated based on the location information of photos taken by the user during a trip. The location information acquisition unit can also suggest related episodes based on the user's current location. This makes it possible to generate more realistic animations by utilizing location information.

[0108] The animation generation system may further include a weather information acquisition unit. The weather information acquisition unit can acquire weather information at the time based on the date, time, and location of an episode and reflect that information in the animation. For example, if a user inputs a photo taken on a rainy day, a rain effect can be added to the animation. The weather information acquisition unit can also adjust the weather effect based on the user's emotions. This makes it possible to utilize weather information to generate more realistic animation.

[0109] The animation generation system may further include a music recommendation unit. The music recommendation unit can recommend appropriate music based on the user's episode and emotions. For example, if the user inputs a happy episode, the music recommendation unit can suggest cheerful music. Also, if the user inputs a moving episode, the music recommendation unit can suggest moving music. This makes it easy to select music suitable for animation by utilizing the music recommendation unit.

[0110] The animation generation system may further include a character generation unit. The character generation unit can generate an original character based on a user's photograph or episode. For example, a character in an animation may be generated based on a photograph of the user's face. The character generation unit may also adjust the character's facial expressions and movements based on the user's emotions. This allows for the generation of animation that reflects the user's individuality.

[0111] The animation generation system may further include a data sharing unit. The data sharing unit may provide a function for a user to share animations generated by the user with other users. For example, a user may share animations with family and friends and enjoy them together. The data sharing unit may also collect feedback on the shared animations and improve the accuracy of analysis. This may promote interaction between users and improve the quality of animations.

[0112] The animation generation system can further include an episode recommendation unit. The episode recommendation unit can recommend new episodes based on the user's past episodes and emotions. For example, if the user has previously input a fun episode, the episode recommendation unit can suggest similar fun episodes. Also, if the user has input a moving episode, the episode recommendation unit can suggest moving episodes. This makes it easier for the user to discover new episodes.

[0113] The animation generation system may further include an effect adding unit. The effect adding unit may provide a function for adding various effects to the animation generated by the user. For example, light effects and sound effects may be added to the animation. The effect adding unit may also adjust the type and intensity of the effect based on the user's emotions. This may enhance the visual and auditory appeal of the animation.

[0114] The animation generation system may further include a story template unit. The story template unit may provide a plurality of story templates from which the user can select. For example, if the user inputs an episode about a family trip, the story template unit may suggest a template suitable for the trip. Alternatively, if the user inputs an episode about a party with friends, the story template unit may suggest a template suitable for the party. This allows the user to easily create an animation story.

[0115] The animation generation system can further include a user interface unit. The user interface unit can provide an interface that allows users to operate intuitively. For example, it can provide a function for placing photos and text by drag and drop. The user interface unit can also learn the user's operation history and suggest the optimal operation method. This allows users to easily generate and customize animations.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The natural language processor analyzes the episode text. For example, it receives the episode text as input and identifies key events and emotions. It can also detect contextual changes and automatically translate episodes in different languages ​​for analysis. It can also refer to past episode data to improve the accuracy of the analysis. Step 2: The image recognition unit analyzes the photo. For example, it receives a photo as input and recognizes people and backgrounds. It can also automatically correct the image resolution and quality, and integrate photos taken under different lighting conditions and angles for analysis. It can also improve the accuracy of the analysis by referencing past photo data. Step 3: The generator generates an animation scenario based on the information analyzed by the natural language processor and image recognition unit. For example, it creates a storyboard based on the analyzed information, moves the characters based on the scenario, and recreates the content of the episode. Step 4: The customization section allows users to select the animation style and music, for example, adjust the animation color and character movement, and select the music to be used.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

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

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 natural language processor that analyzes the episode text; An image recognition unit that analyzes photos; a generation unit that generates an animation based on the information analyzed by the natural language processing unit and the image recognition unit; a customization unit that allows a user to customize the animation generated by the generation unit; Equipped with A system characterized by:

2. The natural language processing unit Analyze the text of the episode to identify key events and emotions 2. The system of claim 1.

3. The image recognition unit Analyze photos and recognize people and backgrounds 2. The system of claim 1.

4. The generation unit An animation scenario is generated based on the information analyzed by the natural language processing unit and the image recognition unit.

2. The system of claim 1.

5. The generation unit Create animations based on generated scenarios 2. The system of claim 1.

6. The customization unit Providing users with the ability to choose animation style and music 2. The system of claim 1.

7. The natural language processing unit Estimate the user's emotions and adjust the episode analysis method based on the estimated user emotions.

2. The system of claim 1.

8. The natural language processing unit Detecting contextual changes and prioritizing important events when analyzing episode text 2. The system of claim 1.

Images