system

Abstract

We provide the system. [Solution] A means of inputting multiple image data, Means for recognizing objects and backgrounds based on the aforementioned image data and input related information, A means for generating narrative data based on the aforementioned recognition results, A means for automatically generating animation data based on the aforementioned story data, Means for encoding and outputting the aforementioned animation data, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order for a user to clearly record memories, it is usually necessary to go through a video production process that requires specialized techniques and time, but many users are not accustomed to this, and as a result, they may miss the opportunity to save precious memories. To solve such problems, there is a need for a means for anyone to easily animate and individually save memories.

Means for Solving the Problems

[0005] This invention provides a system that automatically generates a story based on the user's input of multiple image data and related information, and further creates animation data based on that story. This allows users to easily record and save memories in the form of animation without having to learn complex techniques. This system improves the efficiency of animation generation by combining image recognition technology using machine learning models with computer graphics technology.

[0006] "Image data" refers to multiple still images input by the user, and is what the system recognizes.

[0007] "Related information" refers to text-based descriptions and episode-related information that are entered alongside image data.

[0008] "Object" refers to the specific object, person, animal, etc., identified within the image data.

[0009] "Background" refers to the elements within image data that form the environment or scene surrounding an object.

[0010] "Narrative data" refers to data that contains narrative content generated based on image data and related information.

[0011] "Animation data" refers to data generated as dynamic video based on narrative data.

[0012] "Encoding" refers to the process of converting animation data into a specific format.

[0013] "Computer graphics technology" refers to digital technology used for animation generation, enabling character motion and scene construction.

[0014] A "machine learning model" refers to an algorithm or network that learns from data and makes predictions or recognitions about unknown data.

Brief Description of the Drawings

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0016] Next, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0019] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is an automated generation system for users to record memories as animations, and is primarily implemented via devices such as smartphones and tablets. The embodiments thereof are described below.

[0037] The user launches the application using their device. This application includes a function that allows the user to select and upload multiple images that represent memories. The user can also input text descriptions of the episodes associated with the images.

[0038] After the terminal inputs this information, it sends the data to the server. The server uses a high-performance machine learning model to analyze the received image data. This analysis identifies objects and backgrounds within the image, and based on this, generates narrative data associated with the episode.

[0039] The generated story data is used on the server as the source for generating animation data. Computer graphics technology is employed to create character motions and scenes based on the story. The generated animation data is then encoded into an appropriate video format to enable user viewing.

[0040] The encoded animation data is sent to the device. Users can then play the animation on their device and view their memories in animated form. In this way, users can save their memories through automatically generated animations without having to learn any special skills.

[0041] As a concrete example, when a user wants to preserve travel memories, they upload a photo of the beach taken during their trip to the app and input an episode describing what happened with their family there. The system then automatically recognizes elements in the photo and generates an animation based on the travel story. The user can watch this animation within the app and save their family memories in vivid visual form.

[0042] Thus, the present invention makes it possible for users to preserve their memories as animations that they can visually enjoy.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user launches the application on their device, selects multiple images that represent memories, and uploads them. They then enter related anecdotes and supplementary information in text format for each image.

[0046] Step 2:

[0047] The device converts the image data and text information entered by the user into JSON or XML format and sends it to the server using an HTTP request. The data is transmitted through a secure connection, and the system ensures that user information is properly protected.

[0048] Step 3:

[0049] The server analyzes the received data and carefully preprocesses it. Image data is normalized and resized to an appropriate size through image processing algorithms. Meanwhile, text data is evaluated by a natural language processing engine, and keywords and contextual information are extracted.

[0050] Step 4:

[0051] The server uses machine learning models to recognize objects, people, and backgrounds from image data. This identifies each element in the image and generates associated tags. This process utilizes techniques such as convolutional neural networks (CNNs).

[0052] Step 5:

[0053] Based on the recognized elements and the user's episode information, the server generates narrative data. Here, a narrative structure algorithm works to determine the story's progression, naturally merging the information obtained from the images with the user's intentions.

[0054] Step 6:

[0055] The server initiates the process of creating animation based on the generated story data. The animation generation engine uses computer graphics technology to design the dynamic representation of characters and scenes, and renders it as a sequence of frames.

[0056] Step 7:

[0057] The server encodes the generated animation data into the specified video format (e.g., MP4, WebM). This makes the data suitable for streaming and local storage, allowing users to easily access it.

[0058] Step 8:

[0059] The server sends the encoded animation data to the device. The device receives the data, makes it playable within the application, and provides it to the user. The user can then view and save the animation to enjoy as a memory.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] Traditionally, users were limited to using photos and videos to visually record their memories and special events, creating a need for new ways to express experiences more richly. Furthermore, there was a lack of easy-to-use methods for visualizing memories without requiring technical knowledge.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for inputting multiple image information, means for recognizing features and the environment based on the image information and the input related information, and means for generating narrative information to be stored based on the recognition results. This makes it possible for users to automatically and effectively generate their memories as animations and enjoy them visually, without requiring any special technical knowledge.

[0065] "Image information" is a general term for image data that users provide as objects to be visually recorded.

[0066] "Features" refer to identifiable elements such as objects, people, and backgrounds contained within image information.

[0067] "Environment" refers to elements that describe the background and surrounding settings in image information.

[0068] "Narrative information" refers to story and episode data constructed based on recognized features and environments.

[0069] "Video information" refers to animation data generated based on narrative information.

[0070] "Digital image technology" is a general term for computer-based image processing techniques used to construct human movements and scenes.

[0071] An "artificial intelligence model" refers to an intelligent processing system that includes machine learning algorithms used to recognize features and environments.

[0072] A description of embodiments for carrying out this invention will be given.

[0073] Users launch the application using devices such as smartphones and tablets. This application includes a function that allows users to select and upload image information that they want to use as a memento. Users can also enter episodes and descriptions related to each image.

[0074] The device sends image and text information entered by the user to the server. To protect data privacy, this transmission is conducted via a secure protocol.

[0075] The server performs analysis using a high-performance artificial intelligence model. Image information is broken down into features and environment by the AI, and each is recognized in detail. Based on the recognition results, narrative information is generated. This narrative information associates the image features with the episode entered by the user.

[0076] Next, the server uses digital image technology to generate video information based on the generated narrative information. During this process, character motion and scene composition are automatically handled and shaped into video data. The generated video information is then encoded so that users can easily view it.

[0077] Ultimately, the device receives encoded video information from the server, allowing the user to play back animated memories on their device. For example, if a user wants to preserve memories of a family trip, they input photos from the trip and anecdotes about it. The system then automatically recognizes elements such as the sea and sky in the photos and generates an animation based on the story of the trip.

[0078] An example of a prompt might be, "I want to animate memories of a family trip. Please create a story based on a photo of the beach and the events that took place there." Based on this prompt, the system will create an animation that incorporates the user's intent.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] Users launch the application on their device and select and upload image information to animate their memories. They also enter episodes and descriptions related to each image in text format. The input data consists of multiple image files and text information. This allows the system to concretely convey the user's intentions.

[0082] Step 2:

[0083] The terminal sends image and text information entered by the user to the server. The input here consists of image and text data, which are converted and formatted in a format suitable for transmission to the server. During this process, the data is encrypted and securely transmitted to the server over the network.

[0084] Step 3:

[0085] The server analyzes the received image information using a high-performance artificial intelligence model. This analysis involves a process of recognizing features and the environment within the image. The input image data is broken down into objects and background elements by the AI. As output, metadata for each recognized element is generated.

[0086] Step 4:

[0087] The server generates narrative information associated with text information based on recognized features and environmental data. The input here is the metadata and text information of the recognition results, and the output is narrative data based on these. The AI ​​model determines the relevance and weaves the story.

[0088] Step 5:

[0089] The server generates video information by utilizing narrative information. It uses digital image technology to automate character motion and scene composition. The input is narrative data, and the output is video information, i.e., animation data. In this process, visually appealing images are constructed.

[0090] Step 6:

[0091] The server encodes the generated animation data and converts it into a viewable format. This is to ensure that the user can play it smoothly on their device. The input is animation data, and the output is an encoded video file.

[0092] Step 7:

[0093] The device receives an encoded video file sent from the server. The user can play the video using an application on the device and visualize their memories as an animation. The final output is a moving video provided as the user's viewing experience.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] In modern times, it has become common to preserve personal memories as digital content and share them with others. However, the process of visually enjoying memories as animation is technically difficult and requires a lot of knowledge and effort. Therefore, there is a need for a system that allows even users without special skills to easily and intuitively animate their memories and share them with others.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for inputting multiple image data and text information; means for recognizing objects and backgrounds based on the image data and text information and automatically generating narrative data; and means for generating animation data based on the narrative data and converting it into a format shareable among users via a platform including visual display and information communication. This makes it possible to easily generate personal memories as animations and share them with others without requiring any special skills or knowledge.

[0099] "Image data" refers to visual information digitally represented as a series of pixel pieces, and includes photographs and image files provided by users.

[0100] "Text information" refers to a collection of sentences and words entered by the user, and is textual information used as a description of image data or as an element of a story.

[0101] "Recognition means" refers to the process of identifying objects and backgrounds from image data and text information using machine learning models, and extracting related information.

[0102] "Narrative data" refers to data that represents the structure and content of a story, generated based on recognition results obtained from image data and text information.

[0103] "Animation data" refers to dynamic visual content generated based on narrative data, and is video data composed of multiple frames.

[0104] A "platform" is the infrastructure for information and communication that users access to view and share animation data, and includes devices and networks.

[0105] "Encoding" is the process of converting data into a specific format, making it easier to store or transmit.

[0106] "A format that can be shared among users" refers to a data format or method that makes the generated animation data easily accessible to other users.

[0107] This invention is designed as a system for users to animate and share their memories. Its implementation primarily involves devices such as smartphones and tablets, cloud servers, and machine learning models.

[0108] 1. Device Usage and User Interface

[0109] Users launch a dedicated application using their smartphone or tablet. This application provides an interface for users to input image data they have taken and text information related to their memories.

[0110] 2. Data transmission and analysis processing

[0111] The device sends the input image data and text information to a server in the cloud. The server analyzes the received data using a pre-trained machine learning model. This model is implemented using a framework such as TENSORFLOW® and identifies objects and backgrounds in the image and analyzes the associated text information.

[0112] 3. Generating the story and animation

[0113] The server generates narrative data based on the analysis results, and then creates animation data based on that. Computer graphics technologies such as Unity are used for animation creation, and specific character motions and scene construction are carried out.

[0114] 4. Encoding and Distribution

[0115] The generated animation data is encoded in an appropriate format so that users can view it. This data is then stored using AWS® S3, etc., and configured for efficient delivery via CloudFront.

[0116] As a concrete example, a user uploads a birthday photo to the app and enters a text description of the events as an episode. Based on this information, the system automatically generates and animates a story that includes scenes such as blowing out candles on a cake. An example of a prompt sentence for the generating AI model would be: "The uploaded photo contains elements of a birthday party. Please check the cake and balloons and generate an animated story based on the following episode: 'Everyone gathers to sing Happy Birthday, and the birthday person blows out the candles on the cake.'"

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The user launches an application on their device and inputs image data and text information. They select photos they have taken and use an interface to input stories about those memories as text. The entered data is temporarily stored on the device.

[0120] Step 2:

[0121] The terminal sends the input image data and text information to a server in the cloud. The input consists of image files and text data, while the output is raw data received on the server side. HTTPS is used as the transmission protocol, ensuring secure data transfer.

[0122] Step 3:

[0123] The server analyzes the received data. First, it uses a machine learning model to identify objects and the background on the image data. The input is the image data, and the output is a list of identified objects and their characteristics. TensorFlow is used to perform this calculation.

[0124] Step 4:

[0125] The server generates narrative data by associating text information with identified objects based on the analysis results. This generation uses prompt statements, and the input includes text information and a list of objects. The output is narrative data, structured in JSON format.

[0126] Step 5:

[0127] The server creates animation data based on the generated story data. In this step, character motion and background scenes are created using a graphics engine such as Unity. The input is story data, and the output is a rendered video file.

[0128] Step 6:

[0129] Animation data is encoded and converted into a format suitable for streaming. This conversion is performed using video compression algorithms, with a video file as input and an encoded file as output.

[0130] Step 7:

[0131] The server uploads the encoded animation data to AWS S3 and prepares it for delivery using CloudFront. The input is the encoded file, and the output is a streamable URL. Users can view the animation via this URL and share it with other users.

[0132] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0133] This invention is a technology that animates users' memories along with the emotions they felt at the time, and is implemented using smartphones and tablets. Specific embodiments of the invention are described below.

[0134] Users launch an application on their device, select multiple images that make up a memory, and upload them. They can also enter text describing episodes and feelings associated with each image. The device then works with an emotion engine to recognize emotions from the user's input and facial expressions in the images.

[0135] Based on the information collected by the device, the server receives the data and uses a machine learning model to analyze objects and backgrounds within the image. Furthermore, an emotion engine dynamically detects emotions and incorporates them into the generation of narrative data. In this case, for example, if the user expresses joy, that emotion influences the narrative data, generating a storyline that evokes a sense of happiness.

[0136] The server produces animation data based on the generated story data. Animation generation includes character motion and scene direction that responds to emotional expression, and utilizes computer graphics technology. The created animations are dynamically and interactively expressed, reflecting emotional information.

[0137] For example, if a user wants to preserve memories of a summer camping trip, they would input a photo from the campsite and the text, "It was fun sitting around a campfire for the first time." The emotion engine then detects joyful emotions from the user's text and facial recognition in the photo. Based on these emotions, the server constructs a story and creates a joyful animation for the user. The user can then view and enjoy the generated animation through the application.

[0138] Thus, by generating animations that take emotions into account, the present invention goes beyond mere video recording and makes it possible to express the user's memories in a rich emotional way.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The user launches the application on their device, selects multiple images, and uploads them. In addition, the user enters text describing the episode or emotions associated with each image.

[0142] Step 2:

[0143] The device formats the uploaded image data and text information and sends it to the server. The information is sent in a format suitable for processing by the emotion engine.

[0144] Step 3:

[0145] The server analyzes the received data and normalizes the image data using image processing algorithms. Additionally, a natural language processing engine processes the text data, and a sentiment engine identifies the sentiment of the text.

[0146] Step 4:

[0147] The emotion engine extracts emotional information from text data and the facial expressions of people in images. This information is categorized into basic emotions such as happy, anxious, and sad.

[0148] Step 5:

[0149] The server uses a machine learning model to recognize objects and backgrounds within an image. This recognition result, along with data from the emotion engine, is incorporated into narrative data generation to form a storyline based on the user's experience.

[0150] Step 6:

[0151] The server generates animation data based on narrative data that corresponds to emotions. Here, emotional expression is particularly emphasized in character motion and scene direction, and computer graphics technology is utilized.

[0152] Step 7:

[0153] The generated animation data is encoded and converted into a video file that can be saved in a format suitable for the user's device. The server then sends this encoded data to the device.

[0154] Step 8:

[0155] The device receives the transmitted animation data and enables playback within the application. Users can watch this and enjoy their memories as animations that reflect their emotions.

[0156] (Example 2)

[0157] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0158] Many modern visual content generation systems face the challenge of generating animations that take into account the individual emotions and input information of users. This limits the creation of moving images that resonate with users' memories and feelings, resulting in a problem where richer expression cannot be achieved.

[0159] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0160] In this invention, the server includes means for inputting multiple visual data; means for recognizing the environment and background based on the visual data and associated information; means for identifying emotions from the input information and visual data and generating narrative data that reflects the emotional information in the recognition results; means for automatically generating video data including emotional expressions; and means for encoding and presenting the video data. This makes it possible to generate more personalized visual content that takes into account the individual emotions of the user.

[0161] "Visual data" refers to data that includes visual information such as images and photographs.

[0162] "Incidental information" refers to additional information related to visual data, and consists of text, audio, and other elements.

[0163] "Environment" refers to the surrounding context of objects and elements within visual data.

[0164] "Background" refers to the area of ​​visual data other than the main object, and the part that extends behind it.

[0165] "Means of identifying emotions" refers to methods and techniques for analyzing visual data and associated information to identify the emotions contained in that data.

[0166] "Narrative data" refers to data that includes the content of a story generated based on visual data and emotional information.

[0167] "Motion data" refers to data in animation or video format generated based on narrative data.

[0168] "Encoding" refers to the process of converting data into a specific format.

[0169] This invention is a system that animates memories in an emotionally rich way via a user's device. To implement this system, the user first uses an application on their device to select and upload images of memories. In addition, they can input text related to the images, such as episodes or feelings. The system can utilize the latest smart devices and tablets.

[0170] The device interacts with a software module called the emotion engine. The emotion engine uses natural language processing and image recognition technologies to extract emotions from the user's input text and facial expressions in images. In particular, machine learning models utilizing deep learning technology are used in this process.

[0171] The obtained emotional information and image data are sent to a server. The server uses more advanced computing resources to generate narrative data based on this data. This generation process includes AI-based image analysis and referencing similar past storylines from a database. The generated narrative data is then used to generate video image data that includes emotional expressions.

[0172] As a concrete example, consider a scenario where a user enters "a fun memory from a summer camping trip." The user enters a photo of the campsite and the text, "I had so much fun making a campfire for the first time." Based on this information, the emotion engine senses the enjoyment from the photo and sends it to the server. The server generates narrative data that reflects this enjoyment and creates an animation of a campfire accompanied by cheerful music.

[0173] Users can receive and view the generated animations through the application. This goes beyond simply recording images, allowing them to experience a rich and emotional expression of memories.

[0174] An example of a prompt message might be, "Please create a fun animation based on your camping memories." This prompt message allows the system to understand the user's intent and provide the desired animation.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The user launches the application from their device, selects multiple images of memories, and uploads them. They also enter text describing the episodes and feelings associated with each image. The input data consists of image files and text information. This data forms the basis for the subsequent sentiment analysis.

[0178] Step 2:

[0179] The device sends the uploaded image and entered text to the emotion engine. The emotion engine then performs facial expression analysis in the image and natural language processing on the text to identify the user's emotions. This process utilizes machine learning algorithms to generate emotion tags such as "happy" or "sad" from the image and text information. The output is data with these emotion tags attached.

[0180] Step 3:

[0181] The device transfers the generated emotion-tagged data to the server. Based on the received data, the server uses a generative AI model to perform detailed image analysis, recognizing objects and backgrounds within the image. This analysis extracts elements necessary for the story's context and generates the constituent elements of the narrative data. The output is data with identified objects and backgrounds.

[0182] Step 4:

[0183] The server generates narrative data by combining emotion tags with object and background data. This process develops a storyline that reflects the emotional expression. For example, if a user expresses joy, a positive story matching that emotion is constructed. The output is narrative data.

[0184] Step 5:

[0185] The server creates motion data based on the generated story data. At this stage, computer graphics technology is used to design character motions and scene presentations, generating emotionally expressive animations. The output is animation data that users can view.

[0186] Step 6:

[0187] The server sends the completed animation data to the terminal. The user can then view this animation through an application on their terminal, enjoying a visual experience that richly expresses their memories. The output is the user's emotionally rich animation viewing experience.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0190] There is a growing demand to make users' memories viewable not merely as records, but as dynamic visual expressions that richly convey emotions. However, conventional technologies struggle to systematize narratives that accurately reflect users' emotions. Furthermore, there is a lack of methods to easily generate such visual expressions and provide them as interactive experiences. This necessitates a method for expressing individual experiences in a more moving and shareable way.

[0191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0192] In this invention, the server includes means for inputting multiple visual data, means for recognizing physical elements and the environment based on the visual data and related input information, and means for analyzing emotional information and generating narrative data based on the recognition results. This enables an interactive narrative experience that responds to the user's emotions.

[0193] "Visual data" refers to all image information, such as photos and videos, that users input.

[0194] "Physical elements" refer to specific objects, people, and their characteristics included within the visual data.

[0195] "Environment" refers to the elements that make up the background and overall situation of a scene within visual data.

[0196] "Emotional information" refers to the results of analyzing the feelings and sensations that users express based on the original data.

[0197] "Narrative data" refers to storylines and narrative structures generated based on visual data and emotional information.

[0198] "Visual representation data" refers to the entirety of animations and visual content generated based on narrative data.

[0199] An "interactive narrative experience" refers to a narrative expression that allows for interaction with the user and provides feedback that responds to the user's emotional state.

[0200] To implement this invention, the user first uses an application on their device. The user selects visual data, uploads images and video data, and inputs associated emotions and episodes as text. The device sends this data to an emotion engine, preparing it for emotion analysis.

[0201] The emotion engine utilizes recognition technologies such as Amazon Rekognition and Google Cloud Vision API to analyze physical elements and the environment within an image and extract the emotional information contained therein. The emotional information thus obtained is further analyzed on the server using machine learning models (e.g., using TensorFlow or PyTorch) to generate narrative data.

[0202] The server uses this narrative data to generate visual representation data using information technologies such as Unity and Blender. The generated visual representation data provides a narrative experience that dynamically and interactively reflects the user's emotions.

[0203] As a concrete example, suppose a user uploads a wedding photo and enters the emotion "tears of emotion." The server uses this data to generate an animation that includes the emotional scene, and mixes it with emotional music, enabling the sharing of the prompted emotion. An example of a prompt message would be: "Upload a wedding photo and enter the text 'emotionally moved.' Our emotion engine will analyze your memory and generate a special animated story."

[0204] As described above, through the collaboration of terminals, servers, and users, the present invention realizes dynamic and interactive visual representations that richly express the user's emotions.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The user launches the application on their device and selects and uploads visual data such as images and videos. The input consists of visual data and text information expressing emotions, which the device stores in the application's data storage.

[0208] Step 2:

[0209] The terminal sends stored visual and textual information to the server. The input here is the visual and textual information, and the output is the completion of data transmission via the connection to the server. The terminal uses a network protocol to transmit this data.

[0210] Step 3:

[0211] The server analyzes the received visual data using image recognition software such as Amazon Rekognition or Google Cloud Vision API to recognize physical elements and the environment. The input is visual data, and the output is recognized physical element and environment data. The server uses these analysis results for sentiment information analysis.

[0212] Step 4:

[0213] The server analyzes sentiment information using recognition results and text information. This process applies machine learning models (e.g., using TensorFlow or PyTorch). The input is physical element and environmental data and text information, and the output is sentiment information.

[0214] Step 5:

[0215] Based on emotional information, the server generates narrative data. The input is analyzed emotional information, and the output is narrative data. This process is carried out by a generative AI model, which determines the structure of the story.

[0216] Step 6:

[0217] The server utilizes information technologies such as Unity and Blender to generate visual representation data from narrative data. The input is narrative data, and the output is completed animation data. This animation data is designed to express the user's emotions.

[0218] Step 7:

[0219] The server sends the generated visual representation data to the terminal. The input is the visual representation data, and the output is the completion of the data transfer to the terminal. The server uses stable network communication to transfer this data.

[0220] Step 8:

[0221] The device decodes the received visual data and makes it viewable by the user within the application. The input is visual data, and the output is an interactive animation viewed by the user. The application provides the user with an emotionally rich narrative experience.

[0222] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0223] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0224] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0228] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0229] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0230] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0231] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0232] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0233] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0234] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0235] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0236] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0237] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0238] This invention is an automated generation system for users to record memories as animations, and is primarily implemented via devices such as smartphones and tablets. The embodiments thereof are described below.

[0239] The user launches the application using their device. This application includes a function that allows the user to select and upload multiple images that represent memories. The user can also input text descriptions of the episodes associated with the images.

[0240] After the terminal inputs this information, it sends the data to the server. The server uses a high-performance machine learning model to analyze the received image data. This analysis identifies objects and backgrounds within the image, and based on this, generates narrative data associated with the episode.

[0241] The generated story data is used on the server as the source for generating animation data. Computer graphics technology is employed to create character motions and scenes based on the story. The generated animation data is then encoded into an appropriate video format to enable user viewing.

[0242] The encoded animation data is sent to the device. Users can then play the animation on their device and view their memories in animated form. In this way, users can save their memories through automatically generated animations without having to learn any special skills.

[0243] As a concrete example, when a user wants to preserve travel memories, they upload a photo of the beach taken during their trip to the app and input an episode describing what happened with their family there. The system then automatically recognizes elements in the photo and generates an animation based on the travel story. The user can watch this animation within the app and save their family memories in vivid visual form.

[0244] Thus, the present invention makes it possible for users to preserve their memories as animations that they can visually enjoy.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The user launches the application on their device, selects multiple images that represent memories, and uploads them. They then enter related anecdotes and supplementary information in text format for each image.

[0248] Step 2:

[0249] The device converts the image data and text information entered by the user into JSON or XML format and sends it to the server using an HTTP request. The data is transmitted through a secure connection, and the system ensures that user information is properly protected.

[0250] Step 3:

[0251] The server analyzes the received data and carefully preprocesses it. Image data is normalized and resized to an appropriate size through image processing algorithms. Meanwhile, text data is evaluated by a natural language processing engine, and keywords and contextual information are extracted.

[0252] Step 4:

[0253] The server uses machine learning models to recognize objects, people, and backgrounds from image data. This identifies each element in the image and generates associated tags. This process utilizes techniques such as convolutional neural networks (CNNs).

[0254] Step 5:

[0255] Based on the recognized elements and the user's episode information, the server generates narrative data. Here, a narrative structure algorithm works to determine the story's progression, naturally merging the information obtained from the images with the user's intentions.

[0256] Step 6:

[0257] The server initiates the process of creating animation based on the generated story data. The animation generation engine uses computer graphics technology to design the dynamic representation of characters and scenes, and renders it as a sequence of frames.

[0258] Step 7:

[0259] The server encodes the generated animation data into the specified video format (e.g., MP4, WebM). This makes the data suitable for streaming and local storage, allowing users to easily access it.

[0260] Step 8:

[0261] The server sends the encoded animation data to the device. The device receives the data, makes it playable within the application, and provides it to the user. The user can then view and save the animation to enjoy as a memory.

[0262] (Example 1)

[0263] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0264] Traditionally, users were limited to using photos and videos to visually record their memories and special events, creating a need for new ways to express experiences more richly. Furthermore, there was a lack of easy-to-use methods for visualizing memories without requiring technical knowledge.

[0265] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0266] In this invention, the server includes means for inputting multiple image information, means for recognizing features and the environment based on the image information and the input related information, and means for generating narrative information to be stored based on the recognition results. This makes it possible for users to automatically and effectively generate their memories as animations and enjoy them visually, without requiring any special technical knowledge.

[0267] "Image information" is a general term for image data that users provide as objects to be visually recorded.

[0268] "Features" refer to identifiable elements such as objects, people, and backgrounds contained within image information.

[0269] "Environment" refers to elements that describe the background and surrounding settings in image information.

[0270] "Narrative information" refers to story and episode data constructed based on recognized features and environments.

[0271] "Video information" refers to animation data generated based on narrative information.

[0272] "Digital image technology" is a general term for computer-based image processing techniques used to construct human movements and scenes.

[0273] An "artificial intelligence model" refers to an intelligent processing system that includes machine learning algorithms used to recognize features and environments.

[0274] A description of embodiments for carrying out this invention will be given.

[0275] Users launch the application using devices such as smartphones and tablets. This application includes a function that allows users to select and upload image information that they want to use as a memento. Users can also enter episodes and descriptions related to each image.

[0276] The device sends image and text information entered by the user to the server. To protect data privacy, this transmission is conducted via a secure protocol.

[0277] The server performs analysis using a high-performance artificial intelligence model. Image information is broken down into features and environment by the AI, and each is recognized in detail. Based on the recognition results, narrative information is generated. This narrative information associates the image features with the episode entered by the user.

[0278] Next, the server uses digital image technology to generate video information based on the generated narrative information. During this process, character motion and scene composition are automatically handled and shaped into video data. The generated video information is then encoded so that users can easily view it.

[0279] Ultimately, the device receives encoded video information from the server, allowing the user to play back animated memories on their device. For example, if a user wants to preserve memories of a family trip, they input photos from the trip and anecdotes about it. The system then automatically recognizes elements such as the sea and sky in the photos and generates an animation based on the story of the trip.

[0280] An example of a prompt might be, "I want to animate memories of a family trip. Please create a story based on a photo of the beach and the events that took place there." Based on this prompt, the system will create an animation that incorporates the user's intent.

[0281] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0282] Step 1:

[0283] Users launch the application on their device and select and upload image information to animate their memories. They also enter episodes and descriptions related to each image in text format. The input data consists of multiple image files and text information. This allows the system to concretely convey the user's intentions.

[0284] Step 2:

[0285] The terminal sends the image information and text information input by the user to the server. The input here is image and text data, which are converted and formatted into the form to be sent to the server. At this time, the data is encrypted and sent to the server securely via the network.

[0286] Step 3:

[0287] The server analyzes the received image information using a high-performance artificial intelligence model. In this analysis, a process of recognizing features and the environment in the image is carried out. The input image data is decomposed by the AI into objects and background elements. As output, metadata for each recognized element is generated.

[0288] Step 4:

[0289] The server generates story information associated with the text information based on the recognized features and environmental data. The input here is the metadata of the recognition result and the text information, and the output is the story data based on these. The AI model judges the relevance and weaves a story.

[0290] Step 5:

[0291] The server utilizes the story information to generate video information. Using digital image technology, it automates character motion and scene composition. The input is the story data, and the output is video information, that is, animation data. In this process, a visually appealing video is constructed.

[0292] Step 6:

[0293] The server encodes the generated animation data and converts it into a viewable format. This is to enable the user to play it smoothly on the terminal. The input is the animation data, and the output is the encoded video file.

[0294] Step 7:

[0295] The device receives an encoded video file sent from the server. The user can play the video using an application on the device and visualize their memories as an animation. The final output is a moving video provided as the user's viewing experience.

[0296] (Application Example 1)

[0297] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0298] In modern times, it has become common to preserve personal memories as digital content and share them with others. However, the process of visually enjoying memories as animation is technically difficult and requires a lot of knowledge and effort. Therefore, there is a need for a system that allows even users without special skills to easily and intuitively animate their memories and share them with others.

[0299] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0300] In this invention, the server includes means for inputting multiple image data and text information; means for recognizing objects and backgrounds based on the image data and text information and automatically generating narrative data; and means for generating animation data based on the narrative data and converting it into a format shareable among users via a platform including visual display and information communication. This makes it possible to easily generate personal memories as animations and share them with others without requiring any special skills or knowledge.

[0301] "Image data" refers to visual information digitally represented as a series of pixel pieces, and includes photographs and image files provided by users.

[0302] "Text information" refers to a collection of sentences and words input by users, and is character information used as an explanation for image data or as an element of a story.

[0303] "Recognition means" is a process for identifying objects and backgrounds from image data and text information using a machine learning model, and extracting information related to them.

[0304] "Story data" is data that expresses the structure and content of a story, generated based on the recognition results obtained from image data and text information.

[0305] "Animation data" is dynamic visual content generated based on story data, and is video data composed of multiple frames.

[0306] "Platform" is an information communication infrastructure that users access to view and share animation data, and includes devices and networks.

[0307] "Encoding" is a process of converting data into a specific format to make it easier to store or transmit.

[0308] "A format sharable among users" refers to a data format or method in which the generated animation data is arranged so that it can be easily accessed by other users.

[0309] This invention is designed as a system for users to animate and share their memories. In implementation, mainly terminals such as smartphones and tablets, cloud servers, and machine learning models are involved.

[0310] 1. Use of Devices and User Interface

[0311] Users launch a dedicated application using their smartphone or tablet. This application provides an interface for users to input image data they have taken and text information related to their memories.

[0312] 2. Data transmission and analysis processing

[0313] The device sends the input image data and text information to a server in the cloud. The server analyzes the received data using a pre-trained machine learning model. This model is implemented using a framework such as TensorFlow and identifies objects and backgrounds in the image and analyzes the associated text information.

[0314] 3. Generating the story and animation

[0315] The server generates narrative data based on the analysis results, and then creates animation data based on that. Computer graphics technologies such as Unity are used for animation creation, and specific character motions and scene construction are carried out.

[0316] 4. Encoding and Distribution

[0317] The generated animation data is encoded in an appropriate format so that users can view it. This data is then stored using AWS S3 or similar services and configured for efficient delivery via CloudFront.

[0318] As a concrete example, a user uploads a birthday photo to the app and enters a text description of the events as an episode. Based on this information, the system automatically generates and animates a story that includes scenes such as blowing out candles on a cake. An example of a prompt sentence for the generating AI model would be: "The uploaded photo contains elements of a birthday party. Please check the cake and balloons and generate an animated story based on the following episode: 'Everyone gathers to sing Happy Birthday, and the birthday person blows out the candles on the cake.'"

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The user launches an application on their device and inputs image data and text information. They select photos they have taken and use an interface to input stories about those memories as text. The entered data is temporarily stored on the device.

[0322] Step 2:

[0323] The terminal sends the input image data and text information to a server in the cloud. The input consists of image files and text data, while the output is raw data received on the server side. HTTPS is used as the transmission protocol, ensuring secure data transfer.

[0324] Step 3:

[0325] The server analyzes the received data. First, it uses a machine learning model to identify objects and the background on the image data. The input is the image data, and the output is a list of identified objects and their characteristics. TensorFlow is used to perform this calculation.

[0326] Step 4:

[0327] The server generates narrative data by associating text information with identified objects based on the analysis results. This generation uses prompt statements, and the input includes text information and a list of objects. The output is narrative data, structured in JSON format.

[0328] Step 5:

[0329] The server creates animation data based on the generated story data. In this step, character motion and background scenes are created using a graphics engine such as Unity. The input is story data, and the output is a rendered video file.

[0330] Step 6:

[0331] Animation data is encoded and converted into a format suitable for streaming. This conversion is performed using video compression algorithms, with a video file as input and an encoded file as output.

[0332] Step 7:

[0333] The server uploads the encoded animation data to AWS S3 and prepares it for delivery using CloudFront. The input is the encoded file, and the output is a streamable URL. Users can view the animation via this URL and share it with other users.

[0334] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0335] This invention is a technology that animates users' memories along with the emotions they felt at the time, and is implemented using smartphones and tablets. Specific embodiments of the invention are described below.

[0336] Users launch an application on their device, select multiple images that make up a memory, and upload them. They can also enter text describing episodes and feelings associated with each image. The device then works with an emotion engine to recognize emotions from the user's input and facial expressions in the images.

[0337] Based on the information collected by the device, the server receives the data and uses a machine learning model to analyze objects and backgrounds within the image. Furthermore, an emotion engine dynamically detects emotions and incorporates them into the generation of narrative data. In this case, for example, if the user expresses joy, that emotion influences the narrative data, generating a storyline that evokes a sense of happiness.

[0338] The server produces animation data based on the generated story data. Animation generation includes character motion and scene direction that responds to emotional expression, and utilizes computer graphics technology. The created animations are dynamically and interactively expressed, reflecting emotional information.

[0339] For example, if a user wants to preserve memories of a summer camping trip, they would input a photo from the campsite and the text, "It was fun sitting around a campfire for the first time." The emotion engine then detects joyful emotions from the user's text and facial recognition in the photo. Based on these emotions, the server constructs a story and creates a joyful animation for the user. The user can then view and enjoy the generated animation through the application.

[0340] Thus, by generating animations that take emotions into account, the present invention goes beyond mere video recording and makes it possible to express the user's memories in a rich emotional way.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The user launches the application on their device, selects multiple images, and uploads them. In addition, the user enters text describing the episode or emotions associated with each image.

[0344] Step 2:

[0345] The device formats the uploaded image data and text information and sends it to the server. The information is sent in a format suitable for processing by the emotion engine.

[0346] Step 3:

[0347] The server analyzes the received data and normalizes the image data using image processing algorithms. Additionally, a natural language processing engine processes the text data, and a sentiment engine identifies the sentiment of the text.

[0348] Step 4:

[0349] The emotion engine extracts emotional information from text data and the facial expressions of people in images. This information is categorized into basic emotions such as happy, anxious, and sad.

[0350] Step 5:

[0351] The server uses a machine learning model to recognize objects and backgrounds within an image. This recognition result, along with data from the emotion engine, is incorporated into narrative data generation to form a storyline based on the user's experience.

[0352] Step 6:

[0353] The server generates animation data based on narrative data that corresponds to emotions. Here, emotional expression is particularly emphasized in character motion and scene direction, and computer graphics technology is utilized.

[0354] Step 7:

[0355] The generated animation data is encoded and converted into a video file that can be saved in a format suitable for the user's device. The server then sends this encoded data to the device.

[0356] Step 8:

[0357] The device receives the transmitted animation data and enables playback within the application. Users can watch this and enjoy their memories as animations that reflect their emotions.

[0358] (Example 2)

[0359] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0360] Many modern visual content generation systems face the challenge of generating animations that take into account the individual emotions and input information of users. This limits the creation of moving images that resonate with users' memories and feelings, resulting in a problem where richer expression cannot be achieved.

[0361] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0362] In this invention, the server includes means for inputting multiple visual data; means for recognizing the environment and background based on the visual data and associated information; means for identifying emotions from the input information and visual data and generating narrative data that reflects the emotional information in the recognition results; means for automatically generating video data including emotional expressions; and means for encoding and presenting the video data. This makes it possible to generate more personalized visual content that takes into account the individual emotions of the user.

[0363] "Visual data" refers to data that includes visual information such as images and photographs.

[0364] "Incidental information" refers to additional information related to visual data, and consists of text, audio, and other elements.

[0365] "Environment" refers to the surrounding context of objects and elements within visual data.

[0366] "Background" refers to the area of ​​visual data other than the main object, and the part that extends behind it.

[0367] "Means of identifying emotions" refers to methods and techniques for analyzing visual data and associated information to identify the emotions contained in that data.

[0368] "Narrative data" refers to data that includes the content of a story generated based on visual data and emotional information.

[0369] "Motion data" refers to data in animation or video format generated based on narrative data.

[0370] "Encoding" refers to the process of converting data into a specific format.

[0371] This invention is a system that animates memories in an emotionally rich way via a user's device. To implement this system, the user first uses an application on their device to select and upload images of memories. In addition, they can input text related to the images, such as episodes or feelings. The system can utilize the latest smart devices and tablets.

[0372] The device interacts with a software module called the emotion engine. The emotion engine uses natural language processing and image recognition technologies to extract emotions from the user's input text and facial expressions in images. In particular, machine learning models utilizing deep learning technology are used in this process.

[0373] The obtained emotional information and image data are sent to a server. The server uses more advanced computing resources to generate narrative data based on this data. This generation process includes AI-based image analysis and referencing similar past storylines from a database. The generated narrative data is then used to generate video image data that includes emotional expressions.

[0374] As a concrete example, consider a scenario where a user enters "a fun memory from a summer camping trip." The user enters a photo of the campsite and the text, "I had so much fun making a campfire for the first time." Based on this information, the emotion engine senses the enjoyment from the photo and sends it to the server. The server generates narrative data that reflects this enjoyment and creates an animation of a campfire accompanied by cheerful music.

[0375] Users can receive and view the generated animations through the application. This goes beyond simply recording images, allowing them to experience a rich and emotional expression of memories.

[0376] An example of a prompt message might be, "Please create a fun animation based on your camping memories." This prompt message allows the system to understand the user's intent and provide the desired animation.

[0377] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0378] Step 1:

[0379] The user launches the application from their device, selects multiple images of memories, and uploads them. They also enter text describing the episodes and feelings associated with each image. The input data consists of image files and text information. This data forms the basis for the subsequent sentiment analysis.

[0380] Step 2:

[0381] The device sends the uploaded image and entered text to the emotion engine. The emotion engine then performs facial expression analysis in the image and natural language processing on the text to identify the user's emotions. This process utilizes machine learning algorithms to generate emotion tags such as "happy" or "sad" from the image and text information. The output is data with these emotion tags attached.

[0382] Step 3:

[0383] The device transfers the generated emotion-tagged data to the server. Based on the received data, the server uses a generative AI model to perform detailed image analysis, recognizing objects and backgrounds within the image. This analysis extracts elements necessary for the story's context and generates the constituent elements of the narrative data. The output is data with identified objects and backgrounds.

[0384] Step 4:

[0385] The server generates narrative data by combining emotion tags with object and background data. This process develops a storyline that reflects the emotional expression. For example, if a user expresses joy, a positive story matching that emotion is constructed. The output is narrative data.

[0386] Step 5:

[0387] The server creates motion data based on the generated story data. At this stage, computer graphics technology is used to design character motions and scene presentations, generating emotionally expressive animations. The output is animation data that users can view.

[0388] Step 6:

[0389] The server sends the completed animation data to the terminal. The user can then view this animation through an application on their terminal, enjoying a visual experience that richly expresses their memories. The output is the user's emotionally rich animation viewing experience.

[0390] (Application Example 2)

[0391] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0392] There is a growing demand to make users' memories viewable not merely as records, but as dynamic visual expressions that richly convey emotions. However, conventional technologies struggle to systematize narratives that accurately reflect users' emotions. Furthermore, there is a lack of methods to easily generate such visual expressions and provide them as interactive experiences. This necessitates a method for expressing individual experiences in a more moving and shareable way.

[0393] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0394] In this invention, the server includes means for inputting multiple visual data, means for recognizing physical elements and the environment based on the visual data and related input information, and means for analyzing emotional information and generating narrative data based on the recognition results. This enables an interactive narrative experience that responds to the user's emotions.

[0395] "Visual data" refers to all image information, such as photos and videos, that users input.

[0396] "Physical elements" refer to specific objects, people, and their characteristics included within the visual data.

[0397] "Environment" refers to the elements that make up the background and overall situation of a scene within visual data.

[0398] "Emotional information" refers to the results of analyzing the feelings and sensations that users express based on the original data.

[0399] "Narrative data" refers to storylines and narrative structures generated based on visual data and emotional information.

[0400] "Visual representation data" refers to the entirety of animations and visual content generated based on narrative data.

[0401] An "interactive narrative experience" refers to a narrative expression that allows for interaction with the user and provides feedback that responds to the user's emotional state.

[0402] To implement this invention, the user first uses an application on their device. The user selects visual data, uploads images and video data, and inputs associated emotions and episodes as text. The device sends this data to an emotion engine, preparing it for emotion analysis.

[0403] The emotion engine utilizes recognition technologies such as Amazon Rekognition and Google Cloud Vision API to analyze physical elements and the environment within an image and extract the emotional information contained therein. The emotional information thus obtained is further analyzed on the server using machine learning models (e.g., using TensorFlow or PyTorch) to generate narrative data.

[0404] The server uses this narrative data to generate visual representation data using information technologies such as Unity and Blender. The generated visual representation data provides a narrative experience that dynamically and interactively reflects the user's emotions.

[0405] As a concrete example, suppose a user uploads a wedding photo and enters the emotion "tears of emotion." The server uses this data to generate an animation that includes the emotional scene, and mixes it with emotional music, enabling the sharing of the prompted emotion. An example of a prompt message would be: "Upload a wedding photo and enter the text 'emotionally moved.' Our emotion engine will analyze your memory and generate a special animated story."

[0406] As described above, through the collaboration of terminals, servers, and users, the present invention realizes dynamic and interactive visual representations that richly express the user's emotions.

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] The user launches the application on their device and selects and uploads visual data such as images and videos. The input consists of visual data and text information expressing emotions, which the device stores in the application's data storage.

[0410] Step 2:

[0411] The terminal sends stored visual and textual information to the server. The input here is the visual and textual information, and the output is the completion of data transmission via the connection to the server. The terminal uses a network protocol to transmit this data.

[0412] Step 3:

[0413] The server analyzes the received visual data using image recognition software such as Amazon Rekognition or Google Cloud Vision API to recognize physical elements and the environment. The input is visual data, and the output is recognized physical element and environment data. The server uses these analysis results for sentiment information analysis.

[0414] Step 4:

[0415] The server analyzes sentiment information using recognition results and text information. This process applies machine learning models (e.g., using TensorFlow or PyTorch). The input is physical element and environmental data and text information, and the output is sentiment information.

[0416] Step 5:

[0417] Based on emotional information, the server generates narrative data. The input is analyzed emotional information, and the output is narrative data. This process is carried out by a generative AI model, which determines the structure of the story.

[0418] Step 6:

[0419] The server utilizes information technologies such as Unity and Blender to generate visual representation data from narrative data. The input is narrative data, and the output is completed animation data. This animation data is designed to express the user's emotions.

[0420] Step 7:

[0421] The server sends the generated visual representation data to the terminal. The input is the visual representation data, and the output is the completion of the data transfer to the terminal. The server uses stable network communication to transfer this data.

[0422] Step 8:

[0423] The device decodes the received visual data and makes it viewable by the user within the application. The input is visual data, and the output is an interactive animation viewed by the user. The application provides the user with an emotionally rich narrative experience.

[0424] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0425] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0426] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0427] [Third Embodiment]

[0428] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0429] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0430] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0431] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0432] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0433] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0434] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0435] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0436] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0437] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0438] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0439] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0440] This invention is an automated generation system for users to record memories as animations, and is primarily implemented via devices such as smartphones and tablets. The embodiments thereof are described below.

[0441] The user launches the application using their device. This application includes a function that allows the user to select and upload multiple images that represent memories. The user can also input text descriptions of the episodes associated with the images.

[0442] After the terminal inputs this information, it sends the data to the server. The server uses a high-performance machine learning model to analyze the received image data. This analysis identifies objects and backgrounds within the image, and based on this, generates narrative data associated with the episode.

[0443] The generated story data is used on the server as the source for generating animation data. Computer graphics technology is employed to create character motions and scenes based on the story. The generated animation data is then encoded into an appropriate video format to enable user viewing.

[0444] The encoded animation data is sent to the device. Users can then play the animation on their device and view their memories in animated form. In this way, users can save their memories through automatically generated animations without having to learn any special skills.

[0445] As a concrete example, when a user wants to preserve travel memories, they upload a photo of the beach taken during their trip to the app and input an episode describing what happened with their family there. The system then automatically recognizes elements in the photo and generates an animation based on the travel story. The user can watch this animation within the app and save their family memories in vivid visual form.

[0446] Thus, the present invention makes it possible for users to preserve their memories as animations that they can visually enjoy.

[0447] The following describes the processing flow.

[0448] Step 1:

[0449] The user launches the application on their device, selects multiple images that represent memories, and uploads them. They then enter related anecdotes and supplementary information in text format for each image.

[0450] Step 2:

[0451] The device converts the image data and text information entered by the user into JSON or XML format and sends it to the server using an HTTP request. The data is transmitted through a secure connection, and the system ensures that user information is properly protected.

[0452] Step 3:

[0453] The server analyzes the received data and carefully preprocesses it. Image data is normalized and resized to an appropriate size through image processing algorithms. Meanwhile, text data is evaluated by a natural language processing engine, and keywords and contextual information are extracted.

[0454] Step 4:

[0455] The server uses machine learning models to recognize objects, people, and backgrounds from image data. This identifies each element in the image and generates associated tags. This process utilizes techniques such as convolutional neural networks (CNNs).

[0456] Step 5:

[0457] Based on the recognized elements and the user's episode information, the server generates narrative data. Here, a narrative structure algorithm works to determine the story's progression, naturally merging the information obtained from the images with the user's intentions.

[0458] Step 6:

[0459] The server initiates the process of creating animation based on the generated story data. The animation generation engine uses computer graphics technology to design the dynamic representation of characters and scenes, and renders it as a sequence of frames.

[0460] Step 7:

[0461] The server encodes the generated animation data into the specified video format (e.g., MP4, WebM). This makes the data suitable for streaming and local storage, allowing users to easily access it.

[0462] Step 8:

[0463] The server sends the encoded animation data to the device. The device receives the data, makes it playable within the application, and provides it to the user. The user can then view and save the animation to enjoy as a memory.

[0464] (Example 1)

[0465] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0466] Traditionally, users were limited to using photos and videos to visually record their memories and special events, creating a need for new ways to express experiences more richly. Furthermore, there was a lack of easy-to-use methods for visualizing memories without requiring technical knowledge.

[0467] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0468] In this invention, the server includes means for inputting multiple image information, means for recognizing features and the environment based on the image information and the input related information, and means for generating narrative information to be stored based on the recognition results. This makes it possible for users to automatically and effectively generate their memories as animations and enjoy them visually, without requiring any special technical knowledge.

[0469] "Image information" is a general term for image data that users provide as objects to be visually recorded.

[0470] "Features" refer to identifiable elements such as objects, people, and backgrounds contained within image information.

[0471] "Environment" refers to elements that describe the background and surrounding settings in image information.

[0472] "Narrative information" refers to story and episode data constructed based on recognized features and environments.

[0473] "Video information" refers to animation data generated based on narrative information.

[0474] "Digital image technology" is a general term for computer-based image processing techniques used to construct human movements and scenes.

[0475] An "artificial intelligence model" refers to an intelligent processing system that includes machine learning algorithms used to recognize features and environments.

[0476] A description of embodiments for carrying out this invention will be given.

[0477] Users launch the application using devices such as smartphones and tablets. This application includes a function that allows users to select and upload image information that they want to use as a memento. Users can also enter episodes and descriptions related to each image.

[0478] The device sends image and text information entered by the user to the server. To protect data privacy, this transmission is conducted via a secure protocol.

[0479] The server performs analysis using a high-performance artificial intelligence model. Image information is broken down into features and environment by the AI, and each is recognized in detail. Based on the recognition results, narrative information is generated. This narrative information associates the image features with the episode entered by the user.

[0480] Next, the server uses digital image technology to generate video information based on the generated narrative information. During this process, character motion and scene composition are automatically handled and shaped into video data. The generated video information is then encoded so that users can easily view it.

[0481] Ultimately, the device receives encoded video information from the server, allowing the user to play back animated memories on their device. For example, if a user wants to preserve memories of a family trip, they input photos from the trip and anecdotes about it. The system then automatically recognizes elements such as the sea and sky in the photos and generates an animation based on the story of the trip.

[0482] An example of a prompt might be, "I want to animate memories of a family trip. Please create a story based on a photo of the beach and the events that took place there." Based on this prompt, the system will create an animation that incorporates the user's intent.

[0483] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0484] Step 1:

[0485] Users launch the application on their device and select and upload image information to animate their memories. They also enter episodes and descriptions related to each image in text format. The input data consists of multiple image files and text information. This allows the system to concretely convey the user's intentions.

[0486] Step 2:

[0487] The terminal sends image and text information entered by the user to the server. The input here consists of image and text data, which are converted and formatted in a format suitable for transmission to the server. During this process, the data is encrypted and securely transmitted to the server over the network.

[0488] Step 3:

[0489] The server analyzes the received image information using a high-performance artificial intelligence model. This analysis involves a process of recognizing features and the environment within the image. The input image data is broken down into objects and background elements by the AI. As output, metadata for each recognized element is generated.

[0490] Step 4:

[0491] The server generates narrative information associated with text information based on recognized features and environmental data. The input here is the metadata and text information of the recognition results, and the output is narrative data based on these. The AI ​​model determines the relevance and weaves the story.

[0492] Step 5:

[0493] The server generates video information by utilizing narrative information. It uses digital image technology to automate character motion and scene composition. The input is narrative data, and the output is video information, i.e., animation data. In this process, visually appealing images are constructed.

[0494] Step 6:

[0495] The server encodes the generated animation data and converts it into a viewable format. This is to ensure that the user can play it smoothly on their device. The input is animation data, and the output is an encoded video file.

[0496] Step 7:

[0497] The device receives an encoded video file sent from the server. The user can play the video using an application on the device and visualize their memories as an animation. The final output is a moving video provided as the user's viewing experience.

[0498] (Application Example 1)

[0499] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0500] In modern times, it has become common to preserve personal memories as digital content and share them with others. However, the process of visually enjoying memories as animation is technically difficult and requires a lot of knowledge and effort. Therefore, there is a need for a system that allows even users without special skills to easily and intuitively animate their memories and share them with others.

[0501] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0502] In this invention, the server includes means for inputting multiple image data and text information; means for recognizing objects and backgrounds based on the image data and text information and automatically generating narrative data; and means for generating animation data based on the narrative data and converting it into a format shareable among users via a platform including visual display and information communication. This makes it possible to easily generate personal memories as animations and share them with others without requiring any special skills or knowledge.

[0503] "Image data" refers to visual information digitally represented as a series of pixel pieces, and includes photographs and image files provided by users.

[0504] "Text information" refers to a collection of sentences and words entered by the user, and is textual information used as a description of image data or as an element of a story.

[0505] "Recognition means" refers to the process of identifying objects and backgrounds from image data and text information using machine learning models, and extracting related information.

[0506] "Narrative data" refers to data that represents the structure and content of a story, generated based on recognition results obtained from image data and text information.

[0507] "Animation data" refers to dynamic visual content generated based on narrative data, and is video data composed of multiple frames.

[0508] A "platform" is the infrastructure for information and communication that users access to view and share animation data, and includes devices and networks.

[0509] "Encoding" is the process of converting data into a specific format, making it easier to store or transmit.

[0510] "A format that can be shared among users" refers to a data format or method that makes the generated animation data easily accessible to other users.

[0511] This invention is designed as a system for users to animate and share their memories. Its implementation primarily involves devices such as smartphones and tablets, cloud servers, and machine learning models.

[0512] 1. Device Usage and User Interface

[0513] Users launch a dedicated application using their smartphone or tablet. This application provides an interface for users to input image data they have taken and text information related to their memories.

[0514] 2. Data transmission and analysis processing

[0515] The device sends the input image data and text information to a server in the cloud. The server analyzes the received data using a pre-trained machine learning model. This model is implemented using a framework such as TensorFlow and identifies objects and backgrounds in the image and analyzes the associated text information.

[0516] 3. Generating the story and animation

[0517] The server generates narrative data based on the analysis results, and then creates animation data based on that. Computer graphics technologies such as Unity are used for animation creation, and specific character motions and scene construction are carried out.

[0518] 4. Encoding and Distribution

[0519] The generated animation data is encoded in an appropriate format so that users can view it. This data is then stored using AWS S3 or similar services and configured for efficient delivery via CloudFront.

[0520] As a concrete example, a user uploads a birthday photo to the app and enters a text description of the events as an episode. Based on this information, the system automatically generates and animates a story that includes scenes such as blowing out candles on a cake. An example of a prompt sentence for the generating AI model would be: "The uploaded photo contains elements of a birthday party. Please check the cake and balloons and generate an animated story based on the following episode: 'Everyone gathers to sing Happy Birthday, and the birthday person blows out the candles on the cake.'"

[0521] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0522] Step 1:

[0523] The user launches an application on their device and inputs image data and text information. They select photos they have taken and use an interface to input stories about those memories as text. The entered data is temporarily stored on the device.

[0524] Step 2:

[0525] The terminal sends the input image data and text information to a server in the cloud. The input consists of image files and text data, while the output is raw data received on the server side. HTTPS is used as the transmission protocol, ensuring secure data transfer.

[0526] Step 3:

[0527] The server analyzes the received data. First, it uses a machine learning model to identify objects and the background on the image data. The input is the image data, and the output is a list of identified objects and their characteristics. TensorFlow is used to perform this calculation.

[0528] Step 4:

[0529] The server generates narrative data by associating text information with identified objects based on the analysis results. This generation uses prompt statements, and the input includes text information and a list of objects. The output is narrative data, structured in JSON format.

[0530] Step 5:

[0531] The server creates animation data based on the generated story data. In this step, character motion and background scenes are created using a graphics engine such as Unity. The input is story data, and the output is a rendered video file.

[0532] Step 6:

[0533] Animation data is encoded and converted into a format suitable for streaming. This conversion is performed using video compression algorithms, with a video file as input and an encoded file as output.

[0534] Step 7:

[0535] The server uploads the encoded animation data to AWS S3 and prepares it for delivery using CloudFront. The input is the encoded file, and the output is a streamable URL. Users can view the animation via this URL and share it with other users.

[0536] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0537] This invention is a technology that animates users' memories along with the emotions they felt at the time, and is implemented using smartphones and tablets. Specific embodiments of the invention are described below.

[0538] Users launch an application on their device, select multiple images that make up a memory, and upload them. They can also enter text describing episodes and feelings associated with each image. The device then works with an emotion engine to recognize emotions from the user's input and facial expressions in the images.

[0539] Based on the information collected by the device, the server receives the data and uses a machine learning model to analyze objects and backgrounds within the image. Furthermore, an emotion engine dynamically detects emotions and incorporates them into the generation of narrative data. In this case, for example, if the user expresses joy, that emotion influences the narrative data, generating a storyline that evokes a sense of happiness.

[0540] The server produces animation data based on the generated story data. Animation generation includes character motion and scene direction that responds to emotional expression, and utilizes computer graphics technology. The created animations are dynamically and interactively expressed, reflecting emotional information.

[0541] For example, if a user wants to preserve memories of a summer camping trip, they would input a photo from the campsite and the text, "It was fun sitting around a campfire for the first time." The emotion engine then detects joyful emotions from the user's text and facial recognition in the photo. Based on these emotions, the server constructs a story and creates a joyful animation for the user. The user can then view and enjoy the generated animation through the application.

[0542] Thus, by generating animations that take emotions into account, the present invention goes beyond mere video recording and makes it possible to express the user's memories in a rich emotional way.

[0543] The following describes the processing flow.

[0544] Step 1:

[0545] The user launches the application on their device, selects multiple images, and uploads them. In addition, the user enters text describing the episode or emotions associated with each image.

[0546] Step 2:

[0547] The device formats the uploaded image data and text information and sends it to the server. The information is sent in a format suitable for processing by the emotion engine.

[0548] Step 3:

[0549] The server analyzes the received data and normalizes the image data using image processing algorithms. Additionally, a natural language processing engine processes the text data, and a sentiment engine identifies the sentiment of the text.

[0550] Step 4:

[0551] The emotion engine extracts emotional information from text data and the facial expressions of people in images. This information is categorized into basic emotions such as happy, anxious, and sad.

[0552] Step 5:

[0553] The server uses a machine learning model to recognize objects and backgrounds within an image. This recognition result, along with data from the emotion engine, is incorporated into narrative data generation to form a storyline based on the user's experience.

[0554] Step 6:

[0555] The server generates animation data based on narrative data that corresponds to emotions. Here, emotional expression is particularly emphasized in character motion and scene direction, and computer graphics technology is utilized.

[0556] Step 7:

[0557] The generated animation data is encoded and converted into a video file that can be saved in a format suitable for the user's device. The server then sends this encoded data to the device.

[0558] Step 8:

[0559] The device receives the transmitted animation data and enables playback within the application. Users can watch this and enjoy their memories as animations that reflect their emotions.

[0560] (Example 2)

[0561] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0562] Many modern visual content generation systems face the challenge of generating animations that take into account the individual emotions and input information of users. This limits the creation of moving images that resonate with users' memories and feelings, resulting in a problem where richer expression cannot be achieved.

[0563] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0564] In this invention, the server includes means for inputting multiple visual data; means for recognizing the environment and background based on the visual data and associated information; means for identifying emotions from the input information and visual data and generating narrative data that reflects the emotional information in the recognition results; means for automatically generating video data including emotional expressions; and means for encoding and presenting the video data. This makes it possible to generate more personalized visual content that takes into account the individual emotions of the user.

[0565] "Visual data" refers to data that includes visual information such as images and photographs.

[0566] "Incidental information" refers to additional information related to visual data, and consists of text, audio, and other elements.

[0567] "Environment" refers to the surrounding context of objects and elements within visual data.

[0568] "Background" refers to the area of ​​visual data other than the main object, and the part that extends behind it.

[0569] "Means of identifying emotions" refers to methods and techniques for analyzing visual data and associated information to identify the emotions contained in that data.

[0570] "Narrative data" refers to data that includes the content of a story generated based on visual data and emotional information.

[0571] "Motion data" refers to data in animation or video format generated based on narrative data.

[0572] "Encoding" refers to the process of converting data into a specific format.

[0573] This invention is a system that animates memories in an emotionally rich way via a user's device. To implement this system, the user first uses an application on their device to select and upload images of memories. In addition, they can input text related to the images, such as episodes or feelings. The system can utilize the latest smart devices and tablets.

[0574] The device interacts with a software module called the emotion engine. The emotion engine uses natural language processing and image recognition technologies to extract emotions from the user's input text and facial expressions in images. In particular, machine learning models utilizing deep learning technology are used in this process.

[0575] The obtained emotional information and image data are sent to a server. The server uses more advanced computing resources to generate narrative data based on this data. This generation process includes AI-based image analysis and referencing similar past storylines from a database. The generated narrative data is then used to generate video image data that includes emotional expressions.

[0576] As a concrete example, consider a scenario where a user enters "a fun memory from a summer camping trip." The user enters a photo of the campsite and the text, "I had so much fun making a campfire for the first time." Based on this information, the emotion engine senses the enjoyment from the photo and sends it to the server. The server generates narrative data that reflects this enjoyment and creates an animation of a campfire accompanied by cheerful music.

[0577] Users can receive and view the generated animations through the application. This goes beyond simply recording images, allowing them to experience a rich and emotional expression of memories.

[0578] An example of a prompt message might be, "Please create a fun animation based on your camping memories." This prompt message allows the system to understand the user's intent and provide the desired animation.

[0579] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0580] Step 1:

[0581] The user launches the application from their device, selects multiple images of memories, and uploads them. They also enter text describing the episodes and feelings associated with each image. The input data consists of image files and text information. This data forms the basis for the subsequent sentiment analysis.

[0582] Step 2:

[0583] The device sends the uploaded image and entered text to the emotion engine. The emotion engine then performs facial expression analysis in the image and natural language processing on the text to identify the user's emotions. This process utilizes machine learning algorithms to generate emotion tags such as "happy" or "sad" from the image and text information. The output is data with these emotion tags attached.

[0584] Step 3:

[0585] The device transfers the generated emotion-tagged data to the server. Based on the received data, the server uses a generative AI model to perform detailed image analysis, recognizing objects and backgrounds within the image. This analysis extracts elements necessary for the story's context and generates the constituent elements of the narrative data. The output is data with identified objects and backgrounds.

[0586] Step 4:

[0587] The server generates narrative data by combining emotion tags with object and background data. This process develops a storyline that reflects the emotional expression. For example, if a user expresses joy, a positive story matching that emotion is constructed. The output is narrative data.

[0588] Step 5:

[0589] The server creates motion data based on the generated story data. At this stage, computer graphics technology is used to design character motions and scene presentations, generating emotionally expressive animations. The output is animation data that users can view.

[0590] Step 6:

[0591] The server sends the completed animation data to the terminal. The user can then view this animation through an application on their terminal, enjoying a visual experience that richly expresses their memories. The output is the user's emotionally rich animation viewing experience.

[0592] (Application Example 2)

[0593] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0594] There is a growing demand to make users' memories viewable not merely as records, but as dynamic visual expressions that richly convey emotions. However, conventional technologies struggle to systematize narratives that accurately reflect users' emotions. Furthermore, there is a lack of methods to easily generate such visual expressions and provide them as interactive experiences. This necessitates a method for expressing individual experiences in a more moving and shareable way.

[0595] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0596] In this invention, the server includes means for inputting multiple visual data, means for recognizing physical elements and the environment based on the visual data and related input information, and means for analyzing emotional information and generating narrative data based on the recognition results. This enables an interactive narrative experience that responds to the user's emotions.

[0597] "Visual data" refers to all image information, such as photos and videos, that users input.

[0598] "Physical elements" refer to specific objects, people, and their characteristics included within the visual data.

[0599] "Environment" refers to the elements that make up the background and overall situation of a scene within visual data.

[0600] "Emotional information" refers to the results of analyzing the feelings and sensations that users express based on the original data.

[0601] "Narrative data" refers to storylines and narrative structures generated based on visual data and emotional information.

[0602] "Visual representation data" refers to the entirety of animations and visual content generated based on narrative data.

[0603] An "interactive narrative experience" refers to a narrative expression that allows for interaction with the user and provides feedback that responds to the user's emotional state.

[0604] To implement this invention, the user first uses an application on their device. The user selects visual data, uploads images and video data, and inputs associated emotions and episodes as text. The device sends this data to an emotion engine, preparing it for emotion analysis.

[0605] The emotion engine utilizes recognition technologies such as Amazon Rekognition and Google Cloud Vision API to analyze physical elements and the environment within an image and extract the emotional information contained therein. The emotional information thus obtained is further analyzed on the server using machine learning models (e.g., using TensorFlow or PyTorch) to generate narrative data.

[0606] The server uses this narrative data to generate visual representation data using information technologies such as Unity and Blender. The generated visual representation data provides a narrative experience that dynamically and interactively reflects the user's emotions.

[0607] As a concrete example, suppose a user uploads a wedding photo and enters the emotion "tears of emotion." The server uses this data to generate an animation that includes the emotional scene, and mixes it with emotional music, enabling the sharing of the prompted emotion. An example of a prompt message would be: "Upload a wedding photo and enter the text 'emotionally moved.' Our emotion engine will analyze your memory and generate a special animated story."

[0608] As described above, through the collaboration of terminals, servers, and users, the present invention realizes dynamic and interactive visual representations that richly express the user's emotions.

[0609] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0610] Step 1:

[0611] The user launches the application on their device and selects and uploads visual data such as images and videos. The input consists of visual data and text information expressing emotions, which the device stores in the application's data storage.

[0612] Step 2:

[0613] The terminal sends stored visual and textual information to the server. The input here is the visual and textual information, and the output is the completion of data transmission via the connection to the server. The terminal uses a network protocol to transmit this data.

[0614] Step 3:

[0615] The server analyzes the received visual data using image recognition software such as Amazon Rekognition or Google Cloud Vision API to recognize physical elements and the environment. The input is visual data, and the output is recognized physical element and environment data. The server uses these analysis results for sentiment information analysis.

[0616] Step 4:

[0617] The server analyzes sentiment information using recognition results and text information. This process applies machine learning models (e.g., using TensorFlow or PyTorch). The input is physical element and environmental data and text information, and the output is sentiment information.

[0618] Step 5:

[0619] Based on emotional information, the server generates narrative data. The input is analyzed emotional information, and the output is narrative data. This process is carried out by a generative AI model, which determines the structure of the story.

[0620] Step 6:

[0621] The server utilizes information technologies such as Unity and Blender to generate visual representation data from narrative data. The input is narrative data, and the output is completed animation data. This animation data is designed to express the user's emotions.

[0622] Step 7:

[0623] The server sends the generated visual representation data to the terminal. The input is the visual representation data, and the output is the completion of the data transfer to the terminal. The server uses stable network communication to transfer this data.

[0624] Step 8:

[0625] The device decodes the received visual data and makes it viewable by the user within the application. The input is visual data, and the output is an interactive animation viewed by the user. The application provides the user with an emotionally rich narrative experience.

[0626] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0627] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0628] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0629] [Fourth Embodiment]

[0630] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0631] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0632] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0633] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0634] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0635] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0636] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0637] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0638] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0639] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0640] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0641] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0642] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] This invention is an automated generation system for users to record memories as animations, and is primarily implemented via devices such as smartphones and tablets. The embodiments thereof are described below.

[0644] The user launches the application using their device. This application includes a function that allows the user to select and upload multiple images that represent memories. The user can also input text descriptions of the episodes associated with the images.

[0645] After the terminal inputs this information, it sends the data to the server. The server uses a high-performance machine learning model to analyze the received image data. This analysis identifies objects and backgrounds within the image, and based on this, generates narrative data associated with the episode.

[0646] The generated story data is used on the server as the source for generating animation data. Computer graphics technology is employed to create character motions and scenes based on the story. The generated animation data is then encoded into an appropriate video format to enable user viewing.

[0647] The encoded animation data is sent to the device. Users can then play the animation on their device and view their memories in animated form. In this way, users can save their memories through automatically generated animations without having to learn any special skills.

[0648] As a concrete example, when a user wants to preserve travel memories, they upload a photo of the beach taken during their trip to the app and input an episode describing what happened with their family there. The system then automatically recognizes elements in the photo and generates an animation based on the travel story. The user can watch this animation within the app and save their family memories in vivid visual form.

[0649] Thus, the present invention makes it possible for users to preserve their memories as animations that they can visually enjoy.

[0650] The following describes the processing flow.

[0651] Step 1:

[0652] The user launches the application on their device, selects multiple images that represent memories, and uploads them. They then enter related anecdotes and supplementary information in text format for each image.

[0653] Step 2:

[0654] The device converts the image data and text information entered by the user into JSON or XML format and sends it to the server using an HTTP request. The data is transmitted through a secure connection, and the system ensures that user information is properly protected.

[0655] Step 3:

[0656] The server analyzes the received data and carefully preprocesses it. Image data is normalized and resized to an appropriate size through image processing algorithms. Meanwhile, text data is evaluated by a natural language processing engine, and keywords and contextual information are extracted.

[0657] Step 4:

[0658] The server uses machine learning models to recognize objects, people, and backgrounds from image data. This identifies each element in the image and generates associated tags. This process utilizes techniques such as convolutional neural networks (CNNs).

[0659] Step 5:

[0660] Based on the recognized elements and the user's episode information, the server generates narrative data. Here, a narrative structure algorithm works to determine the story's progression, naturally merging the information obtained from the images with the user's intentions.

[0661] Step 6:

[0662] The server initiates the process of creating animation based on the generated story data. The animation generation engine uses computer graphics technology to design the dynamic representation of characters and scenes, and renders it as a sequence of frames.

[0663] Step 7:

[0664] The server encodes the generated animation data into the specified video format (e.g., MP4, WebM). This makes the data suitable for streaming and local storage, allowing users to easily access it.

[0665] Step 8:

[0666] The server sends the encoded animation data to the device. The device receives the data, makes it playable within the application, and provides it to the user. The user can then view and save the animation to enjoy as a memory.

[0667] (Example 1)

[0668] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0669] Traditionally, users were limited to using photos and videos to visually record their memories and special events, creating a need for new ways to express experiences more richly. Furthermore, there was a lack of easy-to-use methods for visualizing memories without requiring technical knowledge.

[0670] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0671] In this invention, the server includes means for inputting multiple image information, means for recognizing features and the environment based on the image information and the input related information, and means for generating narrative information to be stored based on the recognition results. This makes it possible for users to automatically and effectively generate their memories as animations and enjoy them visually, without requiring any special technical knowledge.

[0672] "Image information" is a general term for image data that users provide as objects to be visually recorded.

[0673] "Features" refer to identifiable elements such as objects, people, and backgrounds contained within image information.

[0674] "Environment" refers to elements that describe the background and surrounding settings in image information.

[0675] "Narrative information" refers to story and episode data constructed based on recognized features and environments.

[0676] "Video information" refers to animation data generated based on narrative information.

[0677] "Digital image technology" is a general term for computer-based image processing techniques used to construct human movements and scenes.

[0678] An "artificial intelligence model" refers to an intelligent processing system that includes machine learning algorithms used to recognize features and environments.

[0679] A description of embodiments for carrying out this invention will be given.

[0680] Users launch the application using devices such as smartphones and tablets. This application includes a function that allows users to select and upload image information that they want to use as a memento. Users can also enter episodes and descriptions related to each image.

[0681] The device sends image and text information entered by the user to the server. To protect data privacy, this transmission is conducted via a secure protocol.

[0682] The server performs analysis using a high-performance artificial intelligence model. Image information is broken down into features and environment by the AI, and each is recognized in detail. Based on the recognition results, narrative information is generated. This narrative information associates the image features with the episode entered by the user.

[0683] Next, the server uses digital image technology to generate video information based on the generated narrative information. During this process, character motion and scene composition are automatically handled and shaped into video data. The generated video information is then encoded so that users can easily view it.

[0684] Ultimately, the device receives encoded video information from the server, allowing the user to play back animated memories on their device. For example, if a user wants to preserve memories of a family trip, they input photos from the trip and anecdotes about it. The system then automatically recognizes elements such as the sea and sky in the photos and generates an animation based on the story of the trip.

[0685] An example of a prompt might be, "I want to animate memories of a family trip. Please create a story based on a photo of the beach and the events that took place there." Based on this prompt, the system will create an animation that incorporates the user's intent.

[0686] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0687] Step 1:

[0688] Users launch the application on their device and select and upload image information to animate their memories. They also enter episodes and descriptions related to each image in text format. The input data consists of multiple image files and text information. This allows the system to concretely convey the user's intentions.

[0689] Step 2:

[0690] The terminal sends image and text information entered by the user to the server. The input here consists of image and text data, which are converted and formatted in a format suitable for transmission to the server. During this process, the data is encrypted and securely transmitted to the server over the network.

[0691] Step 3:

[0692] The server analyzes the received image information using a high-performance artificial intelligence model. This analysis involves a process of recognizing features and the environment within the image. The input image data is broken down into objects and background elements by the AI. As output, metadata for each recognized element is generated.

[0693] Step 4:

[0694] The server generates narrative information associated with text information based on recognized features and environmental data. The input here is the metadata and text information of the recognition results, and the output is narrative data based on these. The AI ​​model determines the relevance and weaves the story.

[0695] Step 5:

[0696] The server generates video information by utilizing narrative information. It uses digital image technology to automate character motion and scene composition. The input is narrative data, and the output is video information, i.e., animation data. In this process, visually appealing images are constructed.

[0697] Step 6:

[0698] The server encodes the generated animation data and converts it into a viewable format. This is to ensure that the user can play it smoothly on their device. The input is animation data, and the output is an encoded video file.

[0699] Step 7:

[0700] The device receives an encoded video file sent from the server. The user can play the video using an application on the device and visualize their memories as an animation. The final output is a moving video provided as the user's viewing experience.

[0701] (Application Example 1)

[0702] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0703] In modern times, it has become common to preserve personal memories as digital content and share them with others. However, the process of visually enjoying memories as animation is technically difficult and requires a lot of knowledge and effort. Therefore, there is a need for a system that allows even users without special skills to easily and intuitively animate their memories and share them with others.

[0704] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0705] In this invention, the server includes means for inputting multiple image data and text information; means for recognizing objects and backgrounds based on the image data and text information and automatically generating narrative data; and means for generating animation data based on the narrative data and converting it into a format shareable among users via a platform including visual display and information communication. This makes it possible to easily generate personal memories as animations and share them with others without requiring any special skills or knowledge.

[0706] "Image data" refers to visual information digitally represented as a series of pixel pieces, and includes photographs and image files provided by users.

[0707] "Text information" refers to a collection of sentences and words entered by the user, and is textual information used as a description of image data or as an element of a story.

[0708] "Recognition means" refers to the process of identifying objects and backgrounds from image data and text information using machine learning models, and extracting related information.

[0709] "Narrative data" refers to data that represents the structure and content of a story, generated based on recognition results obtained from image data and text information.

[0710] "Animation data" refers to dynamic visual content generated based on narrative data, and is video data composed of multiple frames.

[0711] A "platform" is the infrastructure for information and communication that users access to view and share animation data, and includes devices and networks.

[0712] "Encoding" is the process of converting data into a specific format, making it easier to store or transmit.

[0713] "A format that can be shared among users" refers to a data format or method that makes the generated animation data easily accessible to other users.

[0714] This invention is designed as a system for users to animate and share their memories. Its implementation primarily involves devices such as smartphones and tablets, cloud servers, and machine learning models.

[0715] 1. Device Usage and User Interface

[0716] Users launch a dedicated application using their smartphone or tablet. This application provides an interface for users to input image data they have taken and text information related to their memories.

[0717] 2. Data transmission and analysis processing

[0718] The device sends the input image data and text information to a server in the cloud. The server analyzes the received data using a pre-trained machine learning model. This model is implemented using a framework such as TensorFlow and identifies objects and backgrounds in the image and analyzes the associated text information.

[0719] 3. Generating the story and animation

[0720] The server generates narrative data based on the analysis results, and then creates animation data based on that. Computer graphics technologies such as Unity are used for animation creation, and specific character motions and scene construction are carried out.

[0721] 4. Encoding and Distribution

[0722] The generated animation data is encoded in an appropriate format so that users can view it. This data is then stored using AWS S3 or similar services and configured for efficient delivery via CloudFront.

[0723] As a concrete example, a user uploads a birthday photo to the app and enters a text description of the events as an episode. Based on this information, the system automatically generates and animates a story that includes scenes such as blowing out candles on a cake. An example of a prompt sentence for the generating AI model would be: "The uploaded photo contains elements of a birthday party. Please check the cake and balloons and generate an animated story based on the following episode: 'Everyone gathers to sing Happy Birthday, and the birthday person blows out the candles on the cake.'"

[0724] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0725] Step 1:

[0726] The user launches an application on their device and inputs image data and text information. They select photos they have taken and use an interface to input stories about those memories as text. The entered data is temporarily stored on the device.

[0727] Step 2:

[0728] The terminal sends the input image data and text information to a server in the cloud. The input consists of image files and text data, while the output is raw data received on the server side. HTTPS is used as the transmission protocol, ensuring secure data transfer.

[0729] Step 3:

[0730] The server analyzes the received data. First, it uses a machine learning model to identify objects and the background on the image data. The input is the image data, and the output is a list of identified objects and their characteristics. TensorFlow is used to perform this calculation.

[0731] Step 4:

[0732] The server generates narrative data by associating text information with identified objects based on the analysis results. This generation uses prompt statements, and the input includes text information and a list of objects. The output is narrative data, structured in JSON format.

[0733] Step 5:

[0734] The server creates animation data based on the generated story data. In this step, character motion and background scenes are created using a graphics engine such as Unity. The input is story data, and the output is a rendered video file.

[0735] Step 6:

[0736] Animation data is encoded and converted into a format suitable for streaming. This conversion is performed using video compression algorithms, with a video file as input and an encoded file as output.

[0737] Step 7:

[0738] The server uploads the encoded animation data to AWS S3 and prepares it for delivery using CloudFront. The input is the encoded file, and the output is a streamable URL. Users can view the animation via this URL and share it with other users.

[0739] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0740] This invention is a technology that animates users' memories along with the emotions they felt at the time, and is implemented using smartphones and tablets. Specific embodiments of the invention are described below.

[0741] Users launch an application on their device, select multiple images that make up a memory, and upload them. They can also enter text describing episodes and feelings associated with each image. The device then works with an emotion engine to recognize emotions from the user's input and facial expressions in the images.

[0742] Based on the information collected by the device, the server receives the data and uses a machine learning model to analyze objects and backgrounds within the image. Furthermore, an emotion engine dynamically detects emotions and incorporates them into the generation of narrative data. In this case, for example, if the user expresses joy, that emotion influences the narrative data, generating a storyline that evokes a sense of happiness.

[0743] The server produces animation data based on the generated story data. Animation generation includes character motion and scene direction that responds to emotional expression, and utilizes computer graphics technology. The created animations are dynamically and interactively expressed, reflecting emotional information.

[0744] For example, if a user wants to preserve memories of a summer camping trip, they would input a photo from the campsite and the text, "It was fun sitting around a campfire for the first time." The emotion engine then detects joyful emotions from the user's text and facial recognition in the photo. Based on these emotions, the server constructs a story and creates a joyful animation for the user. The user can then view and enjoy the generated animation through the application.

[0745] Thus, by generating animations that take emotions into account, the present invention goes beyond mere video recording and makes it possible to express the user's memories in a rich emotional way.

[0746] The following describes the processing flow.

[0747] Step 1:

[0748] The user launches the application on their device, selects multiple images, and uploads them. In addition, the user enters text describing the episode or emotions associated with each image.

[0749] Step 2:

[0750] The device formats the uploaded image data and text information and sends it to the server. The information is sent in a format suitable for processing by the emotion engine.

[0751] Step 3:

[0752] The server analyzes the received data and normalizes the image data using image processing algorithms. Additionally, a natural language processing engine processes the text data, and a sentiment engine identifies the sentiment of the text.

[0753] Step 4:

[0754] The emotion engine extracts emotional information from text data and the facial expressions of people in images. This information is categorized into basic emotions such as happy, anxious, and sad.

[0755] Step 5:

[0756] The server uses a machine learning model to recognize objects and backgrounds within an image. This recognition result, along with data from the emotion engine, is incorporated into narrative data generation to form a storyline based on the user's experience.

[0757] Step 6:

[0758] The server generates animation data based on narrative data that corresponds to emotions. Here, emotional expression is particularly emphasized in character motion and scene direction, and computer graphics technology is utilized.

[0759] Step 7:

[0760] The generated animation data is encoded and converted into a video file that can be saved in a format suitable for the user's device. The server then sends this encoded data to the device.

[0761] Step 8:

[0762] The device receives the transmitted animation data and enables playback within the application. Users can watch this and enjoy their memories as animations that reflect their emotions.

[0763] (Example 2)

[0764] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0765] Many modern visual content generation systems face the challenge of generating animations that take into account the individual emotions and input information of users. This limits the creation of moving images that resonate with users' memories and feelings, resulting in a problem where richer expression cannot be achieved.

[0766] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0767] In this invention, the server includes means for inputting multiple visual data; means for recognizing the environment and background based on the visual data and associated information; means for identifying emotions from the input information and visual data and generating narrative data that reflects the emotional information in the recognition results; means for automatically generating video data including emotional expressions; and means for encoding and presenting the video data. This makes it possible to generate more personalized visual content that takes into account the individual emotions of the user.

[0768] "Visual data" refers to data that includes visual information such as images and photographs.

[0769] "Incidental information" refers to additional information related to visual data, and consists of text, audio, and other elements.

[0770] "Environment" refers to the surrounding context of objects and elements within visual data.

[0771] "Background" refers to the area of ​​visual data other than the main object, and the part that extends behind it.

[0772] "Means of identifying emotions" refers to methods and techniques for analyzing visual data and associated information to identify the emotions contained in that data.

[0773] "Narrative data" refers to data that includes the content of a story generated based on visual data and emotional information.

[0774] "Motion data" refers to data in animation or video format generated based on narrative data.

[0775] "Encoding" refers to the process of converting data into a specific format.

[0776] This invention is a system that animates memories in an emotionally rich way via a user's device. To implement this system, the user first uses an application on their device to select and upload images of memories. In addition, they can input text related to the images, such as episodes or feelings. The system can utilize the latest smart devices and tablets.

[0777] The device interacts with a software module called the emotion engine. The emotion engine uses natural language processing and image recognition technologies to extract emotions from the user's input text and facial expressions in images. In particular, machine learning models utilizing deep learning technology are used in this process.

[0778] The obtained emotional information and image data are sent to a server. The server uses more advanced computing resources to generate narrative data based on this data. This generation process includes AI-based image analysis and referencing similar past storylines from a database. The generated narrative data is then used to generate video image data that includes emotional expressions.

[0779] As a concrete example, consider a scenario where a user enters "a fun memory from a summer camping trip." The user enters a photo of the campsite and the text, "I had so much fun making a campfire for the first time." Based on this information, the emotion engine senses the enjoyment from the photo and sends it to the server. The server generates narrative data that reflects this enjoyment and creates an animation of a campfire accompanied by cheerful music.

[0780] Users can receive and view the generated animations through the application. This goes beyond simply recording images, allowing them to experience a rich and emotional expression of memories.

[0781] An example of a prompt message might be, "Please create a fun animation based on your camping memories." This prompt message allows the system to understand the user's intent and provide the desired animation.

[0782] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0783] Step 1:

[0784] The user launches the application from their device, selects multiple images of memories, and uploads them. They also enter text describing the episodes and feelings associated with each image. The input data consists of image files and text information. This data forms the basis for the subsequent sentiment analysis.

[0785] Step 2:

[0786] The device sends the uploaded image and entered text to the emotion engine. The emotion engine then performs facial expression analysis in the image and natural language processing on the text to identify the user's emotions. This process utilizes machine learning algorithms to generate emotion tags such as "happy" or "sad" from the image and text information. The output is data with these emotion tags attached.

[0787] Step 3:

[0788] The device transfers the generated emotion-tagged data to the server. Based on the received data, the server uses a generative AI model to perform detailed image analysis, recognizing objects and backgrounds within the image. This analysis extracts elements necessary for the story's context and generates the constituent elements of the narrative data. The output is data with identified objects and backgrounds.

[0789] Step 4:

[0790] The server generates narrative data by combining emotion tags with object and background data. This process develops a storyline that reflects the emotional expression. For example, if a user expresses joy, a positive story matching that emotion is constructed. The output is narrative data.

[0791] Step 5:

[0792] The server creates motion data based on the generated story data. At this stage, computer graphics technology is used to design character motions and scene presentations, generating emotionally expressive animations. The output is animation data that users can view.

[0793] Step 6:

[0794] The server sends the completed animation data to the terminal. The user can then view this animation through an application on their terminal, enjoying a visual experience that richly expresses their memories. The output is the user's emotionally rich animation viewing experience.

[0795] (Application Example 2)

[0796] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0797] There is a growing demand to make users' memories viewable not merely as records, but as dynamic visual expressions that richly convey emotions. However, conventional technologies struggle to systematize narratives that accurately reflect users' emotions. Furthermore, there is a lack of methods to easily generate such visual expressions and provide them as interactive experiences. This necessitates a method for expressing individual experiences in a more moving and shareable way.

[0798] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0799] In this invention, the server includes means for inputting multiple visual data, means for recognizing physical elements and the environment based on the visual data and related input information, and means for analyzing emotional information and generating narrative data based on the recognition results. This enables an interactive narrative experience that responds to the user's emotions.

[0800] "Visual data" refers to all image information, such as photos and videos, that users input.

[0801] "Physical elements" refer to specific objects, people, and their characteristics included within the visual data.

[0802] "Environment" refers to the elements that make up the background and overall situation of a scene within visual data.

[0803] "Emotional information" refers to the results of analyzing the feelings and sensations that users express based on the original data.

[0804] "Narrative data" refers to storylines and narrative structures generated based on visual data and emotional information.

[0805] "Visual representation data" refers to the entirety of animations and visual content generated based on narrative data.

[0806] An "interactive narrative experience" refers to a narrative expression that allows for interaction with the user and provides feedback that responds to the user's emotional state.

[0807] To implement this invention, the user first uses an application on their device. The user selects visual data, uploads images and video data, and inputs associated emotions and episodes as text. The device sends this data to an emotion engine, preparing it for emotion analysis.

[0808] The emotion engine utilizes recognition technologies such as Amazon Rekognition and Google Cloud Vision API to analyze physical elements and the environment within an image and extract the emotional information contained therein. The emotional information thus obtained is further analyzed on the server using machine learning models (e.g., using TensorFlow or PyTorch) to generate narrative data.

[0809] The server uses this narrative data to generate visual representation data using information technologies such as Unity and Blender. The generated visual representation data provides a narrative experience that dynamically and interactively reflects the user's emotions.

[0810] As a concrete example, suppose a user uploads a wedding photo and enters the emotion "tears of emotion." The server uses this data to generate an animation that includes the emotional scene, and mixes it with emotional music, enabling the sharing of the prompted emotion. An example of a prompt message would be: "Upload a wedding photo and enter the text 'emotionally moved.' Our emotion engine will analyze your memory and generate a special animated story."

[0811] As described above, through the collaboration of terminals, servers, and users, the present invention realizes dynamic and interactive visual representations that richly express the user's emotions.

[0812] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0813] Step 1:

[0814] The user launches the application on their device and selects and uploads visual data such as images and videos. The input consists of visual data and text information expressing emotions, which the device stores in the application's data storage.

[0815] Step 2:

[0816] The terminal sends stored visual and textual information to the server. The input here is the visual and textual information, and the output is the completion of data transmission via the connection to the server. The terminal uses a network protocol to transmit this data.

[0817] Step 3:

[0818] The server analyzes the received visual data using image recognition software such as Amazon Rekognition or Google Cloud Vision API to recognize physical elements and the environment. The input is visual data, and the output is recognized physical element and environment data. The server uses these analysis results for sentiment information analysis.

[0819] Step 4:

[0820] The server analyzes sentiment information using recognition results and text information. This process applies machine learning models (e.g., using TensorFlow or PyTorch). The input is physical element and environmental data and text information, and the output is sentiment information.

[0821] Step 5:

[0822] Based on emotional information, the server generates narrative data. The input is analyzed emotional information, and the output is narrative data. This process is carried out by a generative AI model, which determines the structure of the story.

[0823] Step 6:

[0824] The server utilizes information technologies such as Unity and Blender to generate visual representation data from narrative data. The input is narrative data, and the output is completed animation data. This animation data is designed to express the user's emotions.

[0825] Step 7:

[0826] The server sends the generated visual representation data to the terminal. The input is the visual representation data, and the output is the completion of the data transfer to the terminal. The server uses stable network communication to transfer this data.

[0827] Step 8:

[0828] The device decodes the received visual data and makes it viewable by the user within the application. The input is visual data, and the output is an interactive animation viewed by the user. The application provides the user with an emotionally rich narrative experience.

[0829] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0830] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0831] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0832] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0833] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0834] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0835] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0836] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0837] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0838] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0839] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0840] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0841] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0842] 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.

[0843] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0844] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0845] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0846] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0847] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0848] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0849] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0850] The following is further disclosed regarding the embodiments described above.

[0851] (Claim 1)

[0852] A means of inputting multiple image data,

[0853] Means for recognizing objects and backgrounds based on the aforementioned image data and input related information,

[0854] A means for generating narrative data based on the aforementioned recognition results,

[0855] A means for automatically generating animation data based on the aforementioned story data,

[0856] Means for encoding and outputting the aforementioned animation data,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, wherein the animation data generation means uses computer graphics technology for character motion and scene construction.

[0860] (Claim 3)

[0861] The system according to claim 1, wherein the image recognition means recognizes an object and a background using a machine learning model.

[0862] "Example 1"

[0863] (Claim 1)

[0864] A means of inputting multiple image information,

[0865] Means for recognizing features and the environment based on the aforementioned image information and input related information,

[0866] Means for generating narrative information to be stored based on the aforementioned recognition results,

[0867] A means for automatically generating video information based on the aforementioned narrative information,

[0868] A means for encoding and outputting the aforementioned video information,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, wherein the video information generation means uses digital image technology for performing human movements and composing scenes.

[0872] (Claim 3)

[0873] The system according to claim 1, wherein the feature recognition means recognizes features and the environment using an artificial intelligence model.

[0874] "Application Example 1"

[0875] (Claim 1)

[0876] A means for inputting multiple image data and text information,

[0877] A means for recognizing objects and backgrounds based on the aforementioned image data and text information, and for automatically generating narrative data,

[0878] A means for generating animation data based on the aforementioned narrative data and converting it into a format shareable among users via a platform including visual display and information communication,

[0879] A means for encoding and outputting the aforementioned animation data and distributing the content,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, wherein the animation data generation means uses computer graphics technology and data communication technology for character motion and scene construction, enabling playback on various terminals.

[0883] (Claim 3)

[0884] The system according to claim 1, wherein the image and text information recognition means recognizes objects, backgrounds and related information using a learning model, and generates narrative data based on prompt sentences generated based on the results.

[0885] "Example 2 of combining an emotion engine"

[0886] (Claim 1)

[0887] A means of inputting multiple visual data,

[0888] Means for recognizing the environment and background based on the aforementioned visual data and input supplementary information,

[0889] A means for identifying emotions from input information and visual data, and for generating narrative data that reflects the emotional information in the recognition result,

[0890] A means for automatically generating motion image data including emotional expressions based on the aforementioned narrative data,

[0891] Means for encoding and presenting the aforementioned video data,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, wherein the motion image data generation means uses drawing techniques for character movement and scene staging.

[0895] (Claim 3)

[0896] The system according to claim 1, wherein the environmental recognition means recognizes the environment and background using a computational model.

[0897] "Application example 2 when combining with an emotional engine"

[0898] (Claim 1)

[0899] A means of inputting multiple visual data,

[0900] Means for recognizing physical elements and the environment based on the aforementioned visual data and input related information,

[0901] A means for analyzing emotional information and generating narrative data based on the aforementioned recognition results,

[0902] A means for automatically generating dynamic visual representation data based on the aforementioned narrative data,

[0903] means for outputting the aforementioned visual representation data to a recording device,

[0904] A means of providing an interactive narrative experience based on the user's emotional state,

[0905] A system that includes this.

[0906] (Claim 2)

[0907] The system according to claim 1, wherein the visual representation data generation means uses information technology for expressing actions and constructing scenes.

[0908] (Claim 3)

[0909] The system according to claim 1, wherein the physical element recognition means recognizes physical elements and the environment using a machine learning algorithm. [Explanation of Symbols]

[0910] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting multiple image data, Means for recognizing objects and backgrounds based on the aforementioned image data and input related information, A means for generating narrative data based on the aforementioned recognition results, A means for automatically generating animation data based on the aforementioned story data, Means for encoding and outputting the aforementioned animation data, A system that includes this.

2. The system according to claim 1, wherein the animation data generation means uses computer graphics technology for character motion and scene construction.

3. The system according to claim 1, wherein the image recognition means recognizes an object and a background using a machine learning model.

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