System

Abstract

An object of a system according to an embodiment is to generate an individually customized picture book by utilizing a photograph of a user.SOLUTION: A system according to an embodiment includes a photograph capturing unit, a scenario selecting unit, a scenario customizing unit, and a generating unit. The photograph capturing unit captures a photograph of a user. The scenario selecting unit selects a scenario based on the photograph captured by the photograph capturing unit. The scenario customization unit customizes the scenario selected by the scenario selection unit. The generation unit generates a picture book based on the scenario customized by the scenario customization unit and the photograph.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to generate individually customized picture books using a user's photographs.

[0005] The system according to the embodiment aims to generate an individually customized picture book using a user's photographs. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo capture unit, a scenario selection unit, a scenario customization unit, and a generation unit. The photo capture unit captures a user's photos. The scenario selection unit selects a scenario based on the photos captured by the photo capture unit. The scenario customization unit customizes the scenario selected by the scenario selection unit. The generation unit generates a picture book based on the scenario and photos customized by the scenario customization unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate an individually customized picture book using a user's photographs. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The picture book creation system according to the embodiment of the present invention is a system that takes in a user's photos, customizes a scenario using a generation AI, and creates a picture book. This allows the picture book creation system to create an original picture book based on the user's photos and the customized scenario.

[0029] A picture book generation system according to an embodiment includes a photo import unit, a scenario selection unit, a scenario customization unit, and a generation unit. The photo import unit imports a user's photos. For example, photos taken with a smartphone or digital camera can be uploaded. The photo import unit can also import photos in JPEG or PNG format. The scenario selection unit selects a scenario based on the imported photos. For example, the system provides multiple original scenarios, allowing the user to select a preferred scenario. The scenario customization unit customizes the selected scenario. For example, the user can change the names and lines of characters in the scenario, or add or delete specific scenes. The generation unit generates a picture book based on the customized scenario and the imported photos. For example, the generation AI appropriately positions the imported photos as characters in the picture book and develops a story consistent with the scenario. This allows the picture book generation system according to an embodiment to generate an original picture book based on the user's photos and customized scenario. For example, a user can create their own original picture book and give it as a gift to family or friends.

[0030] The photo capture unit can use generative AI to automatically remove the background from the captured photo, optimizing its use as a character. The photo capture unit uses generative AI to automatically remove the background from the captured photo. For example, it can extract people from photos uploaded by users and make the background transparent, optimizing its use as a character. The generative AI uses a deep learning model to remove the background. For example, it removes the background by combining techniques such as edge detection, hue separation, and the use of depth information. This allows the background to be automatically removed from the captured photo, optimizing its use as a character.

[0031] The photo capture unit can automatically analyze facial expressions using facial recognition technology when capturing a photo and suggest facial expressions that match the scene in the picture book. The photo capture unit, for example, uses facial recognition technology to analyze a person's facial expression when capturing a photo. For example, facial expressions such as smiling or surprised are automatically detected and a facial expression that matches the scene in the picture book is suggested. The facial recognition technology uses a Haar feature classifier or a deep learning-based facial recognition model. For example, facial expressions are analyzed using facial landmark detection and facial expression classification algorithms. This allows the unit to analyze facial expressions when capturing a photo and suggest facial expressions that match the scene.

[0032] The photo capture unit can simultaneously capture an audio message when capturing a photo, and a character in the picture book can play the audio. The photo capture unit, for example, can simultaneously upload an audio message when capturing a photo. For example, a user-recorded audio is captured, and a character in the picture book plays the audio. Audio messages can be subject to restrictions on the audio file format and recording time. For example, the audio is analyzed using voice recognition technology, and the character plays the audio. This allows photos and audio messages to be captured simultaneously, and the character can play the audio.

[0033] The photo capture unit captures photos in real time, allowing a picture book to be generated instantly during a live event. The photo capture unit, for example, builds a system that captures photos in real time and generates a picture book instantly during a live event. For example, photos taken during an event are captured instantly to generate a picture book. The definition and criteria of real time can be set to include the allowable delay time and data processing speed. This allows photos to be captured in real time and a picture book to be generated instantly during a live event.

[0034] The scenario selection unit can implement an algorithm that recommends the optimal scenario based on the user's past selection history when selecting a scenario. For example, the scenario selection unit implements an algorithm that analyzes the user's past selection history and recommends the optimal scenario when selecting a scenario. For example, it recommends a scenario that the user prefers based on trends in scenarios selected in the past. As the specific content and storage method of the selection history, the storage period and data format of past selection data can be set. As the specific type and implementation method of the algorithm, a recommendation algorithm or a machine learning model can be used. This makes it possible to recommend the optimal scenario based on the user's past selection history.

[0035] The scenario selection unit displays a preview of each scenario on the scenario selection screen, making it easier for the user to visually select. The scenario selection unit, for example, adds a function to display a preview of each scenario on the scenario selection screen. For example, a portion of the scenario may be visually displayed to make it easier for the user to select. The specific content and display method of the preview may be a thumbnail display, a video preview, or an interactive preview. This allows the preview to be displayed on the scenario selection screen, making it easier for the user to visually select.

[0036] The scenario selection unit can share customized scenarios created by other users when selecting a scenario, allowing scenario selection on a community basis. The scenario selection unit, for example, adds a function for sharing customized scenarios created by other users when selecting a scenario. For example, a scenario created by a user can be shared within a community and selected by other users. As the specific content and sharing method of the customized scenario, editing authority for the scenario and a sharing platform can be set. This allows customized scenarios created by other users to be shared, allowing scenario selection on a community basis.

[0037] The scenario selection unit can enable scenario selection by voice command, allowing the user to select a scenario without using their hands. The scenario selection unit, for example, builds a system that enables scenario selection by voice command. For example, the system allows the user to select a scenario by voice and operate without using their hands. As the specific type and implementation method of the voice command, voice recognition technology and a list of commands can be set. This allows scenario selection by voice command, allowing the user to select a scenario without using their hands.

[0038] The scenario customization unit can use a generation AI to automatically generate character lines, allowing users to easily customize the scenario. For example, when customizing a scenario, the scenario customization unit automatically generates character lines using the generation AI. For example, the user simply inputs the general flow of the scenario, and the generation AI automatically generates appropriate lines. The generation AI uses a text generation model or natural language processing technology. For example, the generation AI generates lines based on the character's personality and the context of the scenario. This allows the generation AI to automatically generate character lines, allowing users to easily customize the scenario.

[0039] The scenario customization unit can add an interactive preview function to the scenario customization screen, allowing the user to check changes in real time. The scenario customization unit, for example, adds an interactive preview function to the scenario customization screen, allowing the user to check changes in real time. For example, when a user changes a line of dialogue, the change is immediately reflected on the preview screen. Specific details and implementation methods of interactivity can be set by designing a user interface or setting a real-time feedback function. This allows the interactive preview function to be added to the scenario customization screen, allowing the user to check changes in real time.

[0040] The scenario customization unit can add a function that allows a user to refer to customization examples created by other users. For example, when customizing a scenario, the scenario customization unit adds a function that allows a user to refer to customization examples created by other users. For example, popular customization examples can be displayed so that the user can refer to them. As the specific content of the customization examples and the method for sharing them, a list of scenarios created by other users and a sharing platform can be set. This adds a function that allows a user to refer to customization examples created by other users.

[0041] The scenario customization unit can implement a collaboration function that allows multiple users to customize a scenario simultaneously. The scenario customization unit implements, for example, a collaboration function that allows multiple users to customize a scenario simultaneously. For example, it allows multiple users to edit a scenario in real time. The specific method and scope of collaboration can be set as a simultaneous editing function or a real-time communication function. This implements a collaboration function that allows multiple users to customize a scenario simultaneously.

[0042] The generation unit automatically generates character movements based on the imported photos, making it possible to create a more dynamic picture book. For example, the generation unit uses a generation AI to automatically generate character movements based on the imported photos. For example, it generates movements such as walking or jumping of the person in the photo. The specific type of movement and generation method can be set by specifying the type of animation and movement pattern. This allows for the automatic generation of character movements based on the imported photos, making it possible to create a more dynamic picture book.

[0043] The generation unit can automatically generate backgrounds and props according to the scenario, enriching the visuals of picture books. For example, the generation unit uses a generation AI to automatically generate backgrounds and props according to the scenario. For example, it can generate forest or castle backgrounds to match the scenario, and automatically generate props held by characters. Specific types and generation methods of backgrounds and props can be set to landscape backgrounds, indoor backgrounds, items held by characters, and objects placed in scenes. This allows backgrounds and props to be automatically generated according to the scenario, enriching the visuals of picture books.

[0044] The generation unit can enable picture books to be generated in different art styles. For example, the generation unit enables the generation AI to generate picture books in different art styles. For example, a picture book is generated in an art style selected by the user, such as a watercolor painting style or a comic book style. Specific types of art styles and generation methods can be set to watercolor painting style, comic book style, realistic style, etc. This allows picture books to be generated in different art styles.

[0045] The generation unit can introduce a function to automatically generate audio narration and add audio to a picture book. For example, the generation unit introduces a function in which a generation AI automatically generates audio narration and adds audio to a picture book. For example, character lines and narration are automatically generated and played as audio. The type of narration voice and audio file format can be set as the specific content and generation method of the audio narration. This allows for automatic generation of audio narration and addition of audio to a picture book.

[0046] The generation unit can adapt the output format of the picture book to not only digital but also interactive e-book formats. For example, the generation unit can adapt the output format of the picture book to not only digital but also interactive e-book formats. For example, the generation unit can make characters move with a touch. As specific content and implementation methods of the interactive e-book format, characters that move with a touch and interactive page transitions can be set. By adapting the output format of the picture book to an interactive e-book format, the user can enjoy characters that move with a touch.

[0047] The generation unit can introduce a function that allows the user to add comments and notes to the output picture book later. The generation unit, for example, introduces a function that allows the user to add comments and notes to the output picture book later. For example, the generation unit allows the user to add comments to the pages of the picture book. As the specific content and addition method of the comments and notes, text comments, voice comments, handwritten notes, and text notes can be set. This allows the user to add comments and notes to the output picture book later.

[0048] The generation unit can add a function that allows the output picture book to be shared on the cloud and edited collaboratively with other users. The generation unit adds, for example, a function that allows the output picture book to be shared on the cloud and edited collaboratively with other users. For example, a user uploads a picture book to the cloud and edits it collaboratively with other users. As specific services and implementation methods of the cloud, cloud storage services and data sharing methods can be set. This allows the output picture book to be shared on the cloud and edited collaboratively with other users.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The scenario selection unit can implement an algorithm that recommends the optimal scenario based on the user's past selection history. For example, it can recommend a scenario that the user prefers based on the trends of scenarios selected in the past. As the specific content and storage method of the selection history, the storage period and data format of past selection data can be set. As the specific type and implementation method of the algorithm, a recommendation algorithm or a machine learning model can be used. This makes it possible to recommend the optimal scenario based on the user's past selection history.

[0051] The scenario selection unit displays a preview of each scenario on the scenario selection screen, making it easier for the user to visually select. For example, a portion of the scenario may be visually displayed to make it easier for the user to select. Specific preview content and display methods may include thumbnail display, video preview, and interactive preview. This allows the preview to be displayed on the scenario selection screen, making it easier for the user to visually select.

[0052] The scenario selection unit can share customized scenarios created by other users when selecting a scenario, allowing scenario selection on a community basis. For example, a scenario created by a user can be shared within the community and selected by other users. Specific details of the customized scenario and the sharing method can be set, such as editing authority for the scenario and a sharing platform. This allows customized scenarios created by other users to be shared, allowing scenario selection on a community basis.

[0053] The scenario selection unit can enable scenario selection by voice command, allowing the user to select a scenario without using their hands. For example, the user can select a scenario by voice, allowing for hands-free operation. Specific types and implementation methods of voice commands can be set to voice recognition technology or a list of commands. This allows scenario selection by voice command, allowing the user to select a scenario without using their hands.

[0054] The scenario customization unit can use generation AI to automatically generate character lines, allowing users to easily customize them. For example, a user can simply input the general flow of a scenario, and the generation AI will automatically generate appropriate lines. The generation AI uses text generation models and natural language processing technology. For example, the generation AI generates lines based on the character's personality and the context of the scenario. This allows the generation AI to automatically generate character lines, allowing users to easily customize them.

[0055] The scenario customization unit can add an interactive preview function to the scenario customization screen, allowing the user to check changes in real time. For example, when a user changes a line of dialogue, the change is immediately reflected on the preview screen. Specific details and implementation methods of interactivity can include user interface design and real-time feedback functions. This allows the interactive preview function to be added to the scenario customization screen, allowing the user to check changes in real time.

[0056] The scenario customization unit can add a function that allows users to refer to customization examples created by other users. For example, popular customization examples can be displayed so that users can refer to them. As the specific content of the customization examples and the method for sharing them, a list of scenarios created by other users and a sharing platform can be set. This adds a function that allows users to refer to customization examples created by other users.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The photo capture unit captures a user's photo. For example, a user can upload a photo taken with a smartphone or digital camera. The photo capture unit can also capture photos in JPEG or PNG format. Step 2: The scenario selection unit selects a scenario based on the captured photo. For example, the system may have multiple original scenarios prepared in advance, and the user can select the scenario they like. Step 3: The scenario customization section allows you to customize the selected scenario. For example, you can change the names and lines of characters in the scenario, or add or delete specific scenes. Step 4: The generator generates a picture book based on the customized scenario and the imported photos. For example, the generator AI appropriately positions the imported photos as characters in the picture book and develops a story that follows the scenario.

[0059] (Example 2) The picture book creation system according to the embodiment of the present invention is a system that takes in a user's photos, customizes a scenario using a generation AI, and creates a picture book. This allows the picture book creation system to create an original picture book based on the user's photos and the customized scenario.

[0060] A picture book generation system according to an embodiment includes a photo import unit, a scenario selection unit, a scenario customization unit, and a generation unit. The photo import unit imports a user's photos. For example, photos taken with a smartphone or digital camera can be uploaded. The photo import unit can also import photos in JPEG or PNG format. The scenario selection unit selects a scenario based on the imported photos. For example, the system provides multiple original scenarios, allowing the user to select a preferred scenario. The scenario customization unit customizes the selected scenario. For example, the user can change the names and lines of characters in the scenario, or add or delete specific scenes. The generation unit generates a picture book based on the customized scenario and the imported photos. For example, the generation AI appropriately positions the imported photos as characters in the picture book and develops a story consistent with the scenario. This allows the picture book generation system according to an embodiment to generate an original picture book based on the user's photos and customized scenario. For example, a user can create their own original picture book and give it as a gift to family or friends.

[0061] The photo capture unit can use generative AI to automatically remove the background from the captured photo, optimizing its use as a character. The photo capture unit uses generative AI to automatically remove the background from the captured photo. For example, it can extract people from photos uploaded by users and make the background transparent, optimizing its use as a character. The generative AI uses a deep learning model to remove the background. For example, it removes the background by combining techniques such as edge detection, hue separation, and the use of depth information. This allows the background to be automatically removed from the captured photo, optimizing its use as a character.

[0062] The photo capture unit can automatically analyze facial expressions using facial recognition technology when capturing a photo and suggest facial expressions that match the scene in the picture book. The photo capture unit, for example, uses facial recognition technology to analyze a person's facial expression when capturing a photo. For example, facial expressions such as smiling or surprised are automatically detected and a facial expression that matches the scene in the picture book is suggested. The facial recognition technology uses a Haar feature classifier or a deep learning-based facial recognition model. For example, facial expressions are analyzed using facial landmark detection and facial expression classification algorithms. This allows the unit to analyze facial expressions when capturing a photo and suggest facial expressions that match the scene.

[0063] The photo capture unit can use the emotion estimation function to estimate the emotion of a person appearing in a photo and automatically generate a scene for a picture book based on that emotion. The photo capture unit, for example, uses the emotion estimation function to analyze the emotion of a person appearing in a captured photo. For example, emotions such as smiling or anger are estimated, and a scene for a picture book is automatically generated based on that emotion. The emotion estimation function uses technologies such as facial expression recognition, voice analysis, and text analysis. For example, emotions are estimated using types of emotion labels and estimation algorithms. This makes it possible to estimate the emotion of a person appearing in a photo and automatically generate a scene based on that emotion.

[0064] The photo capture unit can simultaneously capture an audio message when capturing a photo, and a character in the picture book can play the audio. The photo capture unit, for example, can simultaneously upload an audio message when capturing a photo. For example, a user-recorded audio is captured, and a character in the picture book plays the audio. Audio messages can be subject to restrictions on the audio file format and recording time. For example, the audio is analyzed using voice recognition technology, and the character plays the audio. This allows photos and audio messages to be captured simultaneously, and the character can play the audio.

[0065] The photo capture unit captures photos in real time, allowing a picture book to be generated instantly during a live event. The photo capture unit, for example, builds a system that captures photos in real time and generates a picture book instantly during a live event. For example, photos taken during an event are captured instantly to generate a picture book. The definition and criteria of real time can be set to include the allowable delay time and data processing speed. This allows photos to be captured in real time and a picture book to be generated instantly during a live event.

[0066] The photo capture unit can use the emotion estimation function to analyze the user's emotion in real time when capturing a photo and provide feedback to elicit positive emotions. The photo capture unit, for example, uses the emotion estimation function to analyze the user's emotion in real time when capturing a photo. For example, the photo capture unit can analyze the user's facial expression in front of the camera and provide feedback to elicit positive emotions. Specific content and methods of feedback can include voice feedback, text feedback, and real-time feedback. This allows the user's emotion to be analyzed in real time when capturing a photo and provide feedback to elicit positive emotions.

[0067] The scenario selection unit can implement an algorithm that recommends the optimal scenario based on the user's past selection history when selecting a scenario. For example, the scenario selection unit implements an algorithm that analyzes the user's past selection history and recommends the optimal scenario when selecting a scenario. For example, it recommends a scenario that the user prefers based on trends in scenarios selected in the past. As the specific content and storage method of the selection history, the storage period and data format of past selection data can be set. As the specific type and implementation method of the algorithm, a recommendation algorithm or a machine learning model can be used. This makes it possible to recommend the optimal scenario based on the user's past selection history.

[0068] The scenario selection unit displays a preview of each scenario on the scenario selection screen, making it easier for the user to visually select. The scenario selection unit, for example, adds a function to display a preview of each scenario on the scenario selection screen. For example, a portion of the scenario may be visually displayed to make it easier for the user to select. The specific content and display method of the preview may be a thumbnail display, a video preview, or an interactive preview. This allows the preview to be displayed on the scenario selection screen, making it easier for the user to visually select.

[0069] The scenario selection unit can use the emotion estimation function to automatically recommend a scenario that is most suitable for the user's current emotional state. For example, the scenario selection unit uses the emotion estimation function to analyze the user's current emotional state and automatically recommend the most suitable scenario. For example, if the user is relaxed, a calm scenario is recommended. As specific types and estimation methods of emotional states, emotion labels such as joy, sadness, and anger, and estimation algorithms can be used. This makes it possible to automatically recommend a scenario that is most suitable for the user's current emotional state.

[0070] The scenario selection unit can share customized scenarios created by other users when selecting a scenario, allowing scenario selection on a community basis. The scenario selection unit, for example, adds a function for sharing customized scenarios created by other users when selecting a scenario. For example, a scenario created by a user can be shared within a community and selected by other users. As the specific content and sharing method of the customized scenario, editing authority for the scenario and a sharing platform can be set. This allows customized scenarios created by other users to be shared, allowing scenario selection on a community basis.

[0071] The scenario selection unit can enable scenario selection by voice command, allowing the user to select a scenario without using their hands. The scenario selection unit, for example, builds a system that enables scenario selection by voice command. For example, the system allows the user to select a scenario by voice and operate without using their hands. As the specific type and implementation method of the voice command, voice recognition technology and a list of commands can be set. This allows scenario selection by voice command, allowing the user to select a scenario without using their hands.

[0072] The scenario selection unit can use the emotion estimation function to analyze the user's emotions when selecting a scenario and recommend the scenario that the user can most emotionally empathize with. The scenario selection unit can, for example, use the emotion estimation function to analyze the user's emotions when selecting a scenario and recommend the scenario that the user can most emotionally empathize with. For example, if the user is moved, an emotional scenario is recommended. As specific methods and criteria for emotion analysis, types of emotion labels and analysis algorithms can be used. This makes it possible to analyze the user's emotions when selecting a scenario and recommend the scenario that the user can most emotionally empathize with.

[0073] The scenario customization unit can use a generation AI to automatically generate character lines, allowing users to easily customize the scenario. For example, when customizing a scenario, the scenario customization unit automatically generates character lines using the generation AI. For example, the user simply inputs the general flow of the scenario, and the generation AI automatically generates appropriate lines. The generation AI uses a text generation model or natural language processing technology. For example, the generation AI generates lines based on the character's personality and the context of the scenario. This allows the generation AI to automatically generate character lines, allowing users to easily customize the scenario.

[0074] The scenario customization unit can add an interactive preview function to the scenario customization screen, allowing the user to check changes in real time. The scenario customization unit, for example, adds an interactive preview function to the scenario customization screen, allowing the user to check changes in real time. For example, when a user changes a line of dialogue, the change is immediately reflected on the preview screen. Specific details and implementation methods of interactivity can be set by designing a user interface or setting a real-time feedback function. This allows the interactive preview function to be added to the scenario customization screen, allowing the user to check changes in real time.

[0075] The scenario customization unit uses the emotion estimation function to analyze the emotional reaction of the user to the scenario customized by the user, and can make optimal customization suggestions. The scenario customization unit, for example, uses the emotion estimation function to analyze the emotional reaction of the user to the scenario customized by the user. For example, it analyzes the emotions felt when the user changes the scenario and makes optimal customization suggestions. Specific types and analysis methods of emotional reactions that can be used include facial expression analysis, voice analysis, and text analysis. This allows the scenario customization unit to analyze the emotional reaction of the user to the scenario customized by the user, and make optimal customization suggestions.

[0076] The scenario customization unit can add a function that allows a user to refer to customization examples created by other users. For example, when customizing a scenario, the scenario customization unit adds a function that allows a user to refer to customization examples created by other users. For example, popular customization examples can be displayed so that the user can refer to them. As the specific content of the customization examples and the method for sharing them, a list of scenarios created by other users and a sharing platform can be set. This adds a function that allows a user to refer to customization examples created by other users.

[0077] The scenario customization unit can implement a collaboration function that allows multiple users to customize a scenario simultaneously. The scenario customization unit implements, for example, a collaboration function that allows multiple users to customize a scenario simultaneously. For example, it allows multiple users to edit a scenario in real time. The specific method and scope of collaboration can be set as a simultaneous editing function or a real-time communication function. This implements a collaboration function that allows multiple users to customize a scenario simultaneously.

[0078] The scenario customization unit can use the emotion estimation function to analyze the user's emotions in real time during scenario customization and provide feedback to elicit positive emotions. The scenario customization unit, for example, uses the emotion estimation function to analyze the user's emotions in real time during scenario customization. For example, it analyzes the emotions when the user changes the scenario and provides feedback to elicit positive emotions. Specific content and methods of feedback that can be used include voice feedback, text feedback, and real-time feedback. This makes it possible to analyze the user's emotions in real time during scenario customization and provide feedback to elicit positive emotions.

[0079] The generation unit automatically generates character movements based on the imported photos, making it possible to create a more dynamic picture book. For example, the generation unit uses a generation AI to automatically generate character movements based on the imported photos. For example, it generates movements such as walking or jumping of the person in the photo. The specific type of movement and generation method can be set by specifying the type of animation and movement pattern. This allows for the automatic generation of character movements based on the imported photos, making it possible to create a more dynamic picture book.

[0080] The generation unit can automatically generate backgrounds and props according to the scenario, enriching the visuals of picture books. For example, the generation unit uses a generation AI to automatically generate backgrounds and props according to the scenario. For example, it can generate forest or castle backgrounds to match the scenario, and automatically generate props held by characters. Specific types and generation methods of backgrounds and props can be set to landscape backgrounds, indoor backgrounds, items held by characters, and objects placed in scenes. This allows backgrounds and props to be automatically generated according to the scenario, enriching the visuals of picture books.

[0081] The generation unit can automatically generate a story development based on the user's emotions using the emotion estimation function. For example, the generation unit uses the emotion estimation function so that the generation AI automatically generates a story development based on the user's emotions. For example, if the user is moved, an emotional story is generated. The specific content of the story and the generation method can be set, such as the plot of the story, character settings, and scene details. This allows the emotion estimation function to automatically generate a story development based on the user's emotions.

[0082] The generation unit can enable picture books to be generated in different art styles. For example, the generation unit enables the generation AI to generate picture books in different art styles. For example, a picture book is generated in an art style selected by the user, such as a watercolor painting style or a comic book style. Specific types of art styles and generation methods can be set to watercolor painting style, comic book style, realistic style, etc. This allows picture books to be generated in different art styles.

[0083] The generation unit can introduce a function to automatically generate audio narration and add audio to a picture book. For example, the generation unit introduces a function in which a generation AI automatically generates audio narration and adds audio to a picture book. For example, character lines and narration are automatically generated and played as audio. The type of narration voice and audio file format can be set as the specific content and generation method of the audio narration. This allows for automatic generation of audio narration and addition of audio to a picture book.

[0084] The generation unit can automatically generate visual effects based on the user's emotions using the emotion estimation function. For example, the generation unit uses the emotion estimation function to have the generation AI automatically generate visual effects based on the user's emotions. For example, if the user is moved, an emotional effect is generated. As specific types and generation methods of visual effects, light effects, movement effects, and color effects can be set. This makes it possible to automatically generate visual effects based on the user's emotions using the emotion estimation function.

[0085] The generation unit can adapt the output format of the picture book to not only digital but also interactive e-book formats. For example, the generation unit can adapt the output format of the picture book to not only digital but also interactive e-book formats. For example, the generation unit can make characters move with a touch. As specific content and implementation methods of the interactive e-book format, characters that move with a touch and interactive page transitions can be set. By adapting the output format of the picture book to an interactive e-book format, the user can enjoy characters that move with a touch.

[0086] The generation unit can introduce a function that allows the user to add comments and notes to the output picture book later. The generation unit, for example, introduces a function that allows the user to add comments and notes to the output picture book later. For example, the generation unit allows the user to add comments to the pages of the picture book. As the specific content and addition method of the comments and notes, text comments, voice comments, handwritten notes, and text notes can be set. This allows the user to add comments and notes to the output picture book later.

[0087] The generation unit can analyze the user's emotional response to the output picture book using the emotion estimation function and provide feedback to the generation of the next picture book. The generation unit, for example, uses the emotion estimation function to analyze the user's emotional response to the output picture book. For example, the generation unit analyzes the user's facial expression while reading the picture book and provides feedback on the results to the generation of the next picture book. As specific content and implementation method of the feedback function, it is possible to set a method for analyzing the user's emotional response and a method for reflecting the results in the generation of the next picture book. This allows the user's emotional response to the output picture book to be analyzed and provided feedback on the generation of the next picture book.

[0088] The generation unit can add a function that allows the output picture book to be shared on the cloud and edited collaboratively with other users. The generation unit adds, for example, a function that allows the output picture book to be shared on the cloud and edited collaboratively with other users. For example, a user uploads a picture book to the cloud and edits it collaboratively with other users. As specific services and implementation methods of the cloud, cloud storage services and data sharing methods can be set. This allows the output picture book to be shared on the cloud and edited collaboratively with other users.

[0089] The generation unit can monitor the user's emotional response to the output picture book in real time using the emotion estimation function and propose the optimal output format. The generation unit, for example, uses the emotion estimation function to monitor the user's emotional response to the output picture book in real time. For example, it analyzes the user's facial expressions while reading the picture book and proposes the optimal output format. As specific methods and standards for real-time monitoring, the monitoring frequency and data collection method can be set. This makes it possible to monitor the user's emotional response to the output picture book in real time and propose the optimal output format.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The scenario selection unit can implement an algorithm that recommends the optimal scenario based on the user's past selection history. For example, it can recommend a scenario that the user prefers based on the trends of scenarios selected in the past. As the specific content and storage method of the selection history, the storage period and data format of past selection data can be set. As the specific type and implementation method of the algorithm, a recommendation algorithm or a machine learning model can be used. This makes it possible to recommend the optimal scenario based on the user's past selection history.

[0092] The scenario selection unit displays a preview of each scenario on the scenario selection screen, making it easier for the user to visually select. For example, a portion of the scenario may be visually displayed to make it easier for the user to select. Specific preview content and display methods may include thumbnail display, video preview, and interactive preview. This allows the preview to be displayed on the scenario selection screen, making it easier for the user to visually select.

[0093] The scenario selection unit can use the emotion estimation function to automatically recommend a scenario that best suits the user's current emotional state. For example, if the user is relaxed, a calm scenario is recommended. Specific types of emotional states and estimation methods can include emotion labels such as joy, sadness, and anger, as well as estimation algorithms. This allows the scenario that best suits the user's current emotional state to be automatically recommended.

[0094] The scenario selection unit can share customized scenarios created by other users when selecting a scenario, allowing scenario selection on a community basis. For example, a scenario created by a user can be shared within the community and selected by other users. Specific details of the customized scenario and the sharing method can be set, such as editing authority for the scenario and a sharing platform. This allows customized scenarios created by other users to be shared, allowing scenario selection on a community basis.

[0095] The scenario selection unit can enable scenario selection by voice command, allowing the user to select a scenario without using their hands. For example, the user can select a scenario by voice, allowing for hands-free operation. Specific types and implementation methods of voice commands can be set to voice recognition technology or a list of commands. This allows scenario selection by voice command, allowing the user to select a scenario without using their hands.

[0096] The scenario customization unit can use generation AI to automatically generate character lines, allowing users to easily customize them. For example, a user can simply input the general flow of a scenario, and the generation AI will automatically generate appropriate lines. The generation AI uses text generation models and natural language processing technology. For example, the generation AI generates lines based on the character's personality and the context of the scenario. This allows the generation AI to automatically generate character lines, allowing users to easily customize them.

[0097] The scenario customization unit can add an interactive preview function to the scenario customization screen, allowing the user to check changes in real time. For example, when a user changes a line of dialogue, the change is immediately reflected on the preview screen. Specific details and implementation methods of interactivity can include user interface design and real-time feedback functions. This allows the interactive preview function to be added to the scenario customization screen, allowing the user to check changes in real time.

[0098] The scenario customization unit uses the emotion estimation function to analyze the emotional response of the user to the customized scenario and can make optimal customization suggestions. For example, it analyzes the emotions felt when the user changes the scenario and makes optimal customization suggestions. Specific types and analysis methods of emotional responses include facial expression analysis, voice analysis, and text analysis. This allows the scenario customization unit to analyze the emotional response of the user to the customized scenario and make optimal customization suggestions.

[0099] The scenario customization unit can add a function that allows users to refer to customization examples created by other users. For example, popular customization examples can be displayed so that users can refer to them. As the specific content of the customization examples and the method for sharing them, a list of scenarios created by other users and a sharing platform can be set. This adds a function that allows users to refer to customization examples created by other users.

[0100] The scenario customization unit can use the emotion estimation function to analyze the user's emotions in real time during scenario customization and provide feedback to elicit positive emotions. For example, the scenario customization unit can analyze the user's emotions when changing a scenario and provide feedback to elicit positive emotions. Specific feedback content and methods can include voice feedback, text feedback, and real-time feedback. This allows the user's emotions to be analyzed in real time during scenario customization and provide feedback to elicit positive emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The photo capture unit captures a user's photo. For example, a user can upload a photo taken with a smartphone or digital camera. The photo capture unit can also capture photos in JPEG or PNG format. Step 2: The scenario selection unit selects a scenario based on the captured photo. For example, the system may have multiple original scenarios prepared in advance, and the user can select the scenario they like. Step 3: The scenario customization section allows you to customize the selected scenario. For example, you can change the names and lines of characters in the scenario, or add or delete specific scenes. Step 4: The generator generates a picture book based on the customized scenario and the imported photos. For example, the generator AI appropriately positions the imported photos as characters in the picture book and develops a story that follows the scenario.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

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

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

[0108] The data processing device 12 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

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

[0119] 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.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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 photo capture unit for capturing a user's photo; a scenario selection unit that selects a scenario based on the photographs captured by the photograph capture unit; a scenario customization unit that customizes the scenario selected by the scenario selection unit; a generation unit that generates a picture book based on the scenario customized by the scenario customization unit and the photos. A system characterized by:

2. The photo capture unit Using generative AI, the app automatically removes backgrounds from imported photos, optimizing their use as characters.

2. The system of claim 1.

3. The scenario selection unit When selecting a scenario, an algorithm is introduced that recommends the optimal scenario based on the user's past selection history.

2. The system of claim 1.

4. The scenario customization unit Using generative AI to automatically generate character dialogue and allow users to easily customize it 2. The system of claim 1.

5. The generation unit Automatically generate character movements based on imported photos to create more dynamic picture books 2. The system of claim 1.

6. The photo capture unit Using emotion estimation functionality, the emotions of people in photos are estimated, and picture book scenes are automatically generated based on those emotions.

2. The system of claim 1.

7. The scenario selection unit Automatically recommends scenarios that best fit the user's current emotional state using emotion estimation 2. The system of claim 1.

8. The generation unit Automatically generate storylines based on user emotions using emotion estimation 2. The system of claim 1.

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