system

Abstract

We provide the system. [Solution] A means of saving digital data received from users to cloud storage, A means of classifying and organizing digital data based on the generated tags, A means of automatically generating video content based on conditions specified by the user, A means of setting and managing access rights for digital data to other users, 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, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the digital age, a vast amount of digital data is managed by individuals, and these data are required to be easily organized and inherited by the next generation. However, currently, the management of photo and video data is complicated, and due to the limitations of user skills and tools, they are often left unused. In addition, there is a problem that there is a lack of a simple and secure method for inheriting digital assets, especially among family members.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that stores digital data received from users in cloud storage, and automatically tags, classifies, and organizes it using generation AI. This system automatically generates video content according to conditions specified by the user and also includes a user interface that allows editing and customization of content created by the user. Furthermore, it provides a means to set access rights for digital data to other users, facilitating asset transfer among family members.

[0006] A "user" refers to an individual or organization that uploads digital data to a cloud system and performs operations on it.

[0007] "Digital data" refers to information stored in electronic format, such as photographs and videos.

[0008] "Cloud storage" refers to a storage system on remote servers that allows data to be stored and managed via the internet.

[0009] "Generative AI" refers to a software system that uses artificial intelligence technology to analyze data and automatically generate information.

[0010] A "tag" refers to identification information assigned to data to facilitate classification and searching.

[0011] "Classification and organization" refers to the process of systematically organizing digital data using generated tags to make it easily accessible.

[0012] "Video content" refers to visual multimedia content created by editing and combining images and videos.

[0013] "User interface" refers to the screens and controls that users use to operate a system.

[0014] "Access right" refers to the permission for a specific individual or group to view and operate on data.

[0015] "Asset inheritance" refers to the procedure for passing on digital data to the next generation.

Brief Explanation of Drawings

[0016] [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 multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple 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 the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This system consists of three main components: the user, the terminal, and the server. First, the user uploads digital data to cloud storage using their own terminal. For example, travel videos and family photos taken with a smartphone can be saved to the cloud via a dedicated app.

[0038] Once data is uploaded, the server receives it and uses a generating AI to analyze each piece of data. This analysis includes facial recognition, object detection, and location identification, making it possible to automatically tag images and videos. This process includes information that the user may not have been aware of at the time of shooting. For example, tags such as "Summer 2023," "Beach," and "Family" are generated and managed by the server.

[0039] The server organizes the tagged digital data by category. At this point, users can log in at any time to visually view the organized data. For example, travel photos might be displayed as albums such as "Summer Vacation Trip," allowing users to easily reminisce about past memories.

[0040] Next, the user can use their device to generate video content based on specified conditions. For example, they can create a slideshow on a specific theme using photos and videos tagged with certain elements. The server assembles the slideshow using a template based on the selected data and provides a preview on the device. The user can edit the preview screen and add music and text as needed.

[0041] Finally, users can pass on digital data and generated content to other users, such as family and friends. The server sets access rights to the data for members specified by the user and makes it easy to share via links. This system ensures that precious memories are safely passed on within the family.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user logs into a cloud storage application using their device. The user selects their digital data, such as photos and videos, and begins uploading it to cloud storage. The device sends the selected data to the cloud via the internet.

[0045] Step 2:

[0046] The server saves the received digital data to cloud storage. The server analyzes the metadata associated with the data (such as the date and time of capture and location information) and prepares it to be passed to the data analysis module of the generating AI.

[0047] Step 3:

[0048] The AI ​​on the server analyzes the stored digital data. Using image recognition technology, it identifies elements within the data (people, places, events, etc.) and automatically generates tags based on that. For example, the AI ​​recognizes the sea in a photograph and assigns the tag "beach".

[0049] Step 4:

[0050] The server organizes data into categories based on the generated tags. The server groups related photos and videos and structures them within the database so that users can easily access them.

[0051] Step 5:

[0052] Users can access cloud storage applications through their devices and visually display their organized data. Users can also search and display data based on specific themes or tags as needed.

[0053] Step 6:

[0054] The process of generating video content based on user-specified conditions begins. The user selects a template for creating a slideshow or video on their device, and the server automatically prepares data suitable for those conditions.

[0055] Step 7:

[0056] The server generates video content based on the selected digital data. Images and videos are arranged sequentially according to a template, and music and effects are automatically added. A preview is displayed on the user's device, and editing becomes possible.

[0057] Step 8:

[0058] Users edit the generated content using the user interface provided on their device. Here, users can perform operations such as changing music, adding text, and adjusting the order of images. Once editing is complete, they save the final content.

[0059] Step 9:

[0060] Users utilize the digital data inheritance feature to configure sharing settings with other users. The server grants access rights to the newly designated users and generates a link for sharing data and generated content. Users can share data through this link and inherit it within their family.

[0061] (Example 1)

[0062] 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."

[0063] Storing and managing large amounts of digital information is extremely time-consuming, and organizing it properly is also difficult. Furthermore, to improve information sharing and accessibility, there is a need for a system that automatically classifies information and allows for easy sharing with other users. Conventional systems require manual organization of information, making efficient information management difficult.

[0064] 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.

[0065] In this invention, the server includes means for storing digital information received from an information terminal in a storage device, means for classifying and organizing the digital information based on the generated tags, and means for analyzing the digital information using a generation AI model and automatically generating tags. This makes it possible to efficiently organize information through automatic analysis and tagging, and to easily manage and share diverse information.

[0066] An "information terminal" is an electronic device used by a user to create or manipulate digital information, and includes smartphones and computers.

[0067] "Digital information" refers to all data stored in electronic format, including images, videos, audio, and text.

[0068] A "storage device" is a hardware or software system for storing digital information, and includes cloud storage and local disks.

[0069] A "generative AI model" is an artificial intelligence technology that analyzes digital information and performs pattern recognition and data processing, enabling advanced data analysis.

[0070] A "tag" is an identifier that serves as metadata attached to digital information and is used to help classify and search for information.

[0071] "Classification and organization" refers to the process of organizing digital information based on specific criteria, making it easier to access and manage information through visual organization.

[0072] "Usage rights" refer to access rights to digital information, indicating that a designated user has the right to view, edit, and share the information.

[0073] The "user interface" refers to the interface through which users directly interact with the system, facilitating the manipulation and editing of digital information.

[0074] This invention is implemented through a combination of an information terminal, a server, a storage device, and a generation AI model. Users can generate and manipulate digital information using their own information terminal. For example, they can take photos and videos of their trip using a smartphone or computer and upload this digital information to a storage device in the cloud using a dedicated app.

[0075] The server uses a generative AI model to process digital information received from cloud storage. This AI model has functions such as facial recognition, object detection, and location information analysis, and automatically generates tags for uploaded digital information. This efficiently categorizes the information and organizes it so that users can easily access it later. The server also visually organizes and displays the digital information based on these tags, making it easy for users to browse past data in an album format.

[0076] Users can use this system to generate video content. For example, they can select photos and videos with specific tags and create a slideshow with a theme such as "Family Summer Memories." The server automatically assembles this selected information into a slideshow using a template and provides a preview on the user's device. Users can customize the slideshow by adding music and text while viewing the preview.

[0077] The generated digital information and video content can be shared by users with other users, such as family and friends. The server manages access to this digital information by setting access rights for members specified by the user, and facilitates information sharing through links. This function makes it possible to share important memories with others while keeping information secure.

[0078] For example, users can easily generate video content by using prompts such as, "Upload photos from a family trip, have them automatically organized by theme, and create a slideshow." This feature is particularly useful for users who want to efficiently manage large amounts of digital data.

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

[0080] Step 1:

[0081] Users upload digital information to cloud storage using their own devices. They select photos and video files as input and operate the upload button through a dedicated app. The output is digital information stored in the cloud. This action transfers the user's digital information to the server.

[0082] Step 2:

[0083] The server receives digital information uploaded from cloud storage. It takes data from cloud storage as input and analyzes this digital information using a generative AI model. For data processing, the generative AI model is used to recognize people and objects in images and videos, and outputs the results of face recognition and object detection. Based on the analyzed information, tags are automatically added to the digital information.

[0084] Step 3:

[0085] The server organizes and groups digital information based on tags generated through analysis. The input is the tagged digital information assigned in step 2. The data is categorized as a data calculation, and highly relevant data is organized into albums. The output is the organized and grouped digital information stored in a database. This allows users to easily access the information later.

[0086] Step 4:

[0087] Users access the system using their own devices and view organized and curated digital information. Input consists of login information and the selection of albums to view, while output is a visually organized display of information. Users can also edit the viewed information, such as adding comments or deleting unnecessary information.

[0088] Step 5:

[0089] The user enters prompts on their device to generate video content based on selected photos and videos. The input includes prompt text and tag conditions, and the server selects a template based on the input to automatically generate a slideshow. The output is a preview of the generated slideshow. The user can make additional edits while viewing this preview.

[0090] Step 6:

[0091] Users configure access permissions to share generated video content and digital information with other users. Input includes information about the recipients and the digital information to be shared. Based on this, the server sets access permissions for the specified users and generates a link. Output includes the generation of the sharing link and notification to the shared users. This allows users to easily share information with others.

[0092] (Application Example 1)

[0093] 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."

[0094] In today's digital environment, people are required to take and manage vast amounts of photos and videos. However, manually organizing them is time-consuming and laborious, and there is a need for a way to present them in a visually understandable format. Furthermore, there is a challenge in finding efficient ways to seamlessly share this digital content with other users and make it easy to view and appreciate.

[0095] 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.

[0096] In this invention, the server includes means for storing digital data received from a user in an information storage area, means for classifying and organizing the digital data based on generated tags, and means for distributing automatically generated video content to other users via a viewer communication path. This makes it possible for users to efficiently organize digital data they have captured and easily share related information with others.

[0097] "Digital data received from users" refers to electronic information transmitted by individual users using their devices.

[0098] "Information storage area" refers to storage space on the cloud or server for accumulating and storing digital data.

[0099] "Generated tags" refer to identifiable names or keywords that the AI ​​automatically assigns to received digital data.

[0100] "Means of classification and organization" refers to the process of dividing digital data into related categories based on generated tags, and the arrangement thereof.

[0101] "Viewer communication path" refers to a network channel or method for distributing generated video content to other users.

[0102] "Automatically generated video content" refers to visual displays generated from digital data based on conditions specified by the user.

[0103] "Other users" refers to individuals or organizations other than the sender of the digital data who are capable of receiving this data.

[0104] This invention begins when a user uploads digital data to an information storage area using a device such as a smartphone. Photos and videos taken by the user are stored in the information storage area, which functions as cloud storage. Upon receiving this digital data, the server analyzes the data using a generative AI model to perform functions such as face recognition, object detection, and location identification. As a result, the digital data is automatically tagged with a "generated tag."

[0105] The server classifies and organizes digital data based on the generated tags and stores it by category. It can also distribute automatically generated video content to other users via a communication channel based on user-specified conditions. The distributed data can be easily viewed by recipients on their digital devices.

[0106] This system will utilize cloud storage APIs such as Amazon Web Services (AWS®) and leverage TENSORFLOW® as a generative AI library. The user interface will be designed using a frontend framework such as React Native.

[0107] As a concrete example, photos taken during a family trip are tagged with "Summer 2023," "Beach," and "Children," and an automatically generated slideshow titled "Summer Vacation 2023" is created. This slideshow can be shared with family members living far away via a family communication channel, allowing them to visually enjoy the memories together.

[0108] An example of a prompt message would be, "I want to create a slideshow of my summer vacation photos and share it with my family." This allows the user to easily input a condition, and the system can then process it accordingly.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user uses their device to capture digital data (photos and videos) and uploads it to a cloud-based data storage area. The input is digital data, and the output is data stored in cloud storage. This process involves transferring data to the cloud using an API. During this process, the data is encrypted according to security protocols to prevent unauthorized access.

[0112] Step 2:

[0113] The server receives digital data stored in cloud storage and performs data analysis using a generative AI model. The input is the digital data stored in step 1, and the output is the generated tags as a result of the analysis. In this process, the AI ​​model is used to automatically identify the data and assign appropriate tags by applying face recognition, object detection, and location identification algorithms.

[0114] Step 3:

[0115] The server classifies and organizes the digital data based on the generated tags. The input is the tags and digital data generated in step 2, and the output is a dataset organized by category. This process involves recording the tagged data in a database and grouping related datasets based on the tag information.

[0116] Step 4:

[0117] Based on conditions specified by the user via their device, the server automatically generates video content (a slideshow). The input is data in the specified tags or categories, and the output is the generated video content. This process includes assembling data that matches the conditions using a template engine and then creating a video.

[0118] Step 5:

[0119] The server distributes the generated video content to other users via the viewer communication path. The input is the video content created in step 4, and the output is the distributed content. This process involves distributing content over the network to users with specified access rights.

[0120] 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.

[0121] This invention is a system that stores users' digital data in cloud storage and uses a generative AI and a newly integrated emotion engine to generate richer content and manage the data.

[0122] First, the user uploads digital data such as photos and videos to cloud storage via their device. The server receives this data and begins analyzing it using generative AI and an emotion engine.

[0123] The generative AI identifies key elements from the data and automatically generates tags. During this process, the emotion engine can analyze facial expressions in videos and photos to recognize emotional states. For example, it can detect smiles in family travel photos and add the tag "joy." This allows users to categorize data based on emotion.

[0124] The server categorizes data using tags that include emotions, and organizes it visually for easy user access. The application on the device supports users in creating emotion-based albums and generating slideshows based on emotion themes.

[0125] When a user generates video content, the system uses the emotion engine to analyze emotional information and suggest appropriate music and effects. For example, it suggests cheerful music for videos with many joyful emotions and displays a preview on the device, making it easy for the user to decide whether to accept the suggestion or make a different choice.

[0126] Furthermore, users can receive emotional analysis from real-time facial expressions and voice, and receive suggestions for editing and customizing video content accordingly. For example, if a smile is detected during a video chat, the server will automatically suggest a video composition that connects related happy moments.

[0127] This system also enables the sharing of emotionally charged content with other users through digital data access rights settings. The server sets access permissions for designated users and generates links to share memories associated with emotions. This facilitates digital communication that strengthens emotional bonds with family and friends.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The user logs into a cloud storage service using their device and selects digital data such as photos and videos. The user then uploads the selected data to the cloud.

[0131] Step 2:

[0132] The server receives the uploaded digital data and prepares it for transmission to the AI ​​generation module. Metadata associated with the data (such as date and time of capture, location, etc.) is also extracted at the same time.

[0133] Step 3:

[0134] The generation AI analyzes digital data, identifies key elements such as people, places, and events, and automatically generates tags. The server attaches the tags and stores them in a database.

[0135] Step 4:

[0136] Digital data is analyzed by an emotion engine. The emotion engine identifies facial expressions in images and videos and recognizes emotions such as "joy," "sadness," and "surprise." The server then assigns tags to the data based on those emotions.

[0137] Step 5:

[0138] The server classifies the digital data based on the generated tags and organizes it by category. The data is structured visually and clearly on the cloud for easy user access.

[0139] Step 6:

[0140] Users operate their devices and access organized data. Emotion-based album and category displays are possible, and users can use the search function to view specific photos and videos as needed.

[0141] Step 7:

[0142] When a user specifies the creation of video content, the server selects appropriate music and effects based on information from the emotion engine. A video template is generated based on the user's selection, and a preview is displayed on the device.

[0143] Step 8:

[0144] Users can edit content using the interface on their device. Here, they can accept automatically suggested music and effects, or consider other options. Once the user has finished editing, the final video is generated and saved.

[0145] Step 9:

[0146] The user sets access rights for digital data to other users. The server grants access to the designated family and friends and generates a single link. Through this link, the user can easily share content associated with emotions and pass on digital assets.

[0147] (Example 2)

[0148] 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".

[0149] In today's world, where digital data is increasing, there is a need for users to easily categorize its content, manage it efficiently based on emotions, and generate video content tailored to individual needs. However, conventional systems struggle to effectively manage digital data and associate it with emotions, and they do not adequately support content generation that aligns with the intentions of individual users.

[0150] 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.

[0151] In this invention, the server includes means for storing image information received from the user, means for automatically generating tags using a generative AI model and analyzing the data, and means for identifying a person's emotional state using an emotion analysis engine and adding tags. This makes it possible to effectively classify and organize the user's digital data based on emotions, and to easily generate personalized content.

[0152] "Image information" refers to visual data such as photographs and videos stored in digital format.

[0153] "Means of storage" refers to data storage devices or systems that store digital data and allow access and management as needed.

[0154] A "generative AI model" refers to an algorithm or software that uses artificial intelligence technology to extract information from digital data and automatically generate tags.

[0155] "Methods for automatically generating tags and analyzing data" refers to processes and functions that use AI technology to analyze digital data and automatically assign labels and keywords based on the content.

[0156] An "emotion analysis engine" refers to a technology or tool that analyzes a person's facial expressions and voice in digital data to identify their emotional state at that time.

[0157] "Means of adding tags" refers to methods and mechanisms for adding tags, which are identifying information, to digital data, thereby facilitating subsequent searching and classification.

[0158] This invention relates to a system that efficiently manages digital image information held by users and enables the generation of emotion-based content. The specific method for implementing this system is described below.

[0159] Users upload personal photos and videos to cloud storage using devices such as smartphones and computers via a dedicated app or web interface. The server stores this information in the cloud. Common cloud platforms can be used for data storage.

[0160] The server activates a generative AI model to analyze the collected data. This generative AI model uses machine learning techniques to identify objects and scenes within the image and automatically generate relevant tags. For example, a photo of a beach would generate tags such as "beach" and "waves."

[0161] Furthermore, the server uses an emotion analysis engine to analyze the faces of people in photos and videos. This engine identifies emotions from subtle facial expressions based on an expression recognition algorithm that utilizes face tracking technology. For example, it can tag detected smiles with "joy," enabling users to manage their data based on emotions.

[0162] The device's application visually displays the categorized digital data based on this, helping users easily browse and manipulate data based on emotions and themes. Users can create personalized albums and slideshows according to the generated tags.

[0163] Furthermore, when a user creates new video content, the server suggests music and effects based on the emotion analysis results. A user interface is displayed on the terminal, allowing the user to edit the content according to the suggestions of the generating AI model. For example, if the system is instructed with a prompt such as "Create an album of happy moments from a family trip," music and visual effects that match the theme will be automatically suggested.

[0164] In this way, the present invention provides a concrete method for efficiently and emotionally valuing users' digital data and establishing personalized digital experiences.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] Users upload digital image information, such as photos and videos, to cloud storage using their devices. Specifically, they select the target file using the application or web interface they are using and execute an upload command. The input at this time is the digital file specified by the user, and the data transferred to the server as output is stored in the cloud.

[0168] Step 2:

[0169] The server stores the image information received on the cloud and organizes the data for each user in its storage area. The input is the digital file received in step 1, and the output is the data systematically stored in cloud storage.

[0170] Step 3:

[0171] The server launches a generative AI model and begins processing and analyzing image information stored in the cloud. The input is image information stored in the cloud, and object detection and scene analysis are performed by the generative AI model. The output is a list of automatically generated tags.

[0172] Step 4:

[0173] The server uses an emotion analysis engine to analyze the emotions of people in the image. Specifically, it uses face tracking technology to analyze facial expressions and identify emotional states. The input is the image information analyzed in step 3, and the output includes emotion tags such as "joy" and "sadness."

[0174] Step 5:

[0175] The server categorizes the digital data based on the generated tags and sentiment tags. The input is the tag information, which is the output of steps 3 and 4, and the output is data categorized into visually organized categories.

[0176] Step 6:

[0177] The application on the device displays organized categories when the user accesses it, helping them to manipulate data based on emotions and themes. The input is the category information generated in step 5, and the output is a visual interface displayed on the screen.

[0178] Step 7:

[0179] When a user creates new video content, the server suggests music and effects based on sentiment analysis results. The input is the user's prompt text and selected theme, and the output is the suggested music and effects, which are displayed on the device's screen.

[0180] Step 8:

[0181] The user reviews the suggested content and edits it as needed. The input is the music and effects suggested in step 7, and the output is the final video content created by the user.

[0182] Step 9:

[0183] The server sets access rights to digital data and generates links for sharing with other users. The input is the access rights information set by the user, and the output is the access link and authorized user information.

[0184] (Application Example 2)

[0185] 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".

[0186] In today's world, efficiently managing the vast amount of digital data from user-generated photos and videos, and creating and editing content based on emotion, is challenging. Traditional systems have failed to reflect emotional elements in the generated content, nor have they been able to automatically provide visually and audibly engaging content, thus failing to deliver sufficient value to users.

[0187] 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.

[0188] In this invention, the server includes means for storing digital data received from the user in a remote storage device, means for classifying and organizing the digital data based on the generated symbols, and means for selecting recommended audio and video effects based on the emotional state. This makes it possible for the user to easily perform automatic generation of emotionally responsive content and visually and audibly engaging editing.

[0189] "User" refers to an individual or legal entity that uses the system to manage digital data and create content.

[0190] "Digital data" refers to electronically stored information such as photographs and videos.

[0191] "Remote storage" refers to external storage devices such as cloud storage that can be accessed via the internet.

[0192] "Symbols" refer to tags or labels that represent elements identified by the generating AI from digital data.

[0193] "Emotional state" refers to the type and intensity of emotions analyzed from the user's digital data and real-time interactions.

[0194] "Audio and video effects" refers to background music, filters, effects, etc., used to enhance the visual and audio quality of content.

[0195] "Means of classification and organization" refers to methods for categorizing and visually organizing digital data using generated symbols.

[0196] "Means for selecting recommended audio and video effects" refers to a method for automatically selecting the most suitable music, video filters, etc., based on emotional state.

[0197] To implement this invention, it is necessary to upload digital data captured by the user's device to a remote storage device. The device can be a smartphone or tablet, and an upload application is installed on it. The user selects photos and videos taken by the user using the application and sends them to the cloud server. The cloud server stores the data using cloud storage such as Amazon S3.

[0198] The server temporarily stores the received digital data and analyzes it using a generative AI and an emotion engine. The generative AI uses an open AI model to extract characteristic elements from the digital data and generate them as tags. The emotion engine uses emotion analysis services such as Microsoft® Azure® to identify emotional states in photos and videos. For example, a smile in a photo might be tagged as the emotion "joy."

[0199] After this analysis, the server classifies the data based on tags and organizes and stores it in remote storage. Users can access this data through their devices or head-mounted displays and review emotion-based visual content. Furthermore, appropriate music and visual effects are automatically recommended based on the user's emotional state. This music and effect enhances the atmosphere of the user-generated content, providing a personalized experience.

[0200] For example, if a user on their smartphone responds to a prompt such as, "Analyze photos and videos from a family trip and create a slideshow highlighting happy moments. Add appropriate music and effects," the system will generate a slideshow edited to focus on the joyful moments.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The user operates the device and selects digital data such as photos and videos through an application. The input is the user's digital data, and the output is information about the selected data. The device prepares to send this data to a remote storage device on a cloud server.

[0204] Step 2:

[0205] The terminal uploads the prepared digital data to the cloud server. The input is the selected digital data, and the output is the result of the upload to cloud storage. The server temporarily stores this received data.

[0206] Step 3:

[0207] The server uses a generative AI model to extract key elements from received digital data and automatically generate tags. The input is digital data, and the output is the generated tags. This process identifies keywords and themes related to the user's data.

[0208] Step 4:

[0209] The server uses an emotion engine to analyze emotional states within digital data. The input is digital data, and the output is the analyzed emotion tag. For example, it performs face recognition in an image and classifies a smile as "joy."

[0210] Step 5:

[0211] Based on tags generated by AI and sentiment analysis, the server categorizes and organizes digital data and stores it in cloud storage. The input is tagged digital data, and the output is the organized data storage state. Users can easily access this organized data later.

[0212] Step 6:

[0213] If a user wants to generate content based on emotion tags, they enter a prompt using their terminal. The input might be a prompt such as, "Analyze photos and videos from a family trip and create a slideshow highlighting happy moments. Add appropriate music and effects." The output is an instruction to generate the content. The server then prepares to generate the content accordingly.

[0214] Step 7:

[0215] The server selects music and visual effects recommended based on the user's emotional state and automatically generates visual content such as slideshows. Input consists of prompt text and emotional tags, while output is automatically generated content. Users can then download and view the final slideshow or video.

[0216] 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.

[0217] 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 the following. 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 indicated 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] This system consists of three main components: the user, the terminal, and the server. First, the user uploads digital data to cloud storage using their own terminal. For example, travel videos and family photos taken with a smartphone can be saved to the cloud via a dedicated app.

[0233] Once data is uploaded, the server receives it and uses a generating AI to analyze each piece of data. This analysis includes facial recognition, object detection, and location identification, making it possible to automatically tag images and videos. This process includes information that the user may not have been aware of at the time of shooting. For example, tags such as "Summer 2023," "Beach," and "Family" are generated and managed by the server.

[0234] The server organizes the tagged digital data by category. At this point, users can log in at any time to visually view the organized data. For example, travel photos might be displayed as albums such as "Summer Vacation Trip," allowing users to easily reminisce about past memories.

[0235] Next, the user can use their device to generate video content based on specified conditions. For example, they can create a slideshow on a specific theme using photos and videos tagged with certain elements. The server assembles the slideshow using a template based on the selected data and provides a preview on the device. The user can edit the preview screen and add music and text as needed.

[0236] Finally, users can pass on digital data and generated content to other users, such as family and friends. The server sets access rights to the data for members specified by the user and makes it easy to share via links. This system ensures that precious memories are safely passed on within the family.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The user logs into a cloud storage application using their device. The user selects their digital data, such as photos and videos, and begins uploading it to cloud storage. The device sends the selected data to the cloud via the internet.

[0240] Step 2:

[0241] The server saves the received digital data to cloud storage. The server analyzes the metadata associated with the data (such as the date and time of capture and location information) and prepares it to be passed to the data analysis module of the generating AI.

[0242] Step 3:

[0243] The AI ​​on the server analyzes the stored digital data. Using image recognition technology, it identifies elements within the data (people, places, events, etc.) and automatically generates tags based on that. For example, the AI ​​recognizes the sea in a photograph and assigns the tag "beach".

[0244] Step 4:

[0245] The server organizes data into categories based on the generated tags. The server groups related photos and videos and structures them within the database so that users can easily access them.

[0246] Step 5:

[0247] Users can access cloud storage applications through their devices and visually display their organized data. Users can also search and display data based on specific themes or tags as needed.

[0248] Step 6:

[0249] The process of generating video content based on user-specified conditions begins. The user selects a template for creating a slideshow or video on their device, and the server automatically prepares data suitable for those conditions.

[0250] Step 7:

[0251] The server generates video content based on the selected digital data. Images and videos are arranged in order according to a template, and music and effects are automatically added. A preview is displayed on the user's device, and editing becomes possible.

[0252] Step 8:

[0253] Users edit the generated content using the user interface provided on their device. Here, users can perform operations such as changing music, adding text, and adjusting the order of images. Once editing is complete, they save the final content.

[0254] Step 9:

[0255] Users utilize the digital data inheritance feature to configure sharing settings with other users. The server grants access rights to the newly designated users and generates a link for sharing data and generated content. Users can share data through this link and inherit it within their family.

[0256] (Example 1)

[0257] 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."

[0258] Storing and managing large amounts of digital information is extremely time-consuming, and organizing it properly is also difficult. Furthermore, to improve information sharing and accessibility, there is a need for a system that automatically classifies information and allows for easy sharing with other users. Conventional systems require manual organization of information, making efficient information management difficult.

[0259] 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.

[0260] In this invention, the server includes means for storing digital information received from an information terminal in a storage device, means for classifying and organizing the digital information based on the generated tags, and means for analyzing the digital information using a generation AI model and automatically generating tags. This makes it possible to efficiently organize information through automatic analysis and tagging, and to easily manage and share diverse information.

[0261] An "information terminal" is an electronic device used by a user to create or manipulate digital information, and includes smartphones and computers.

[0262] "Digital information" refers to all data stored in electronic format, including images, videos, audio, and text.

[0263] A "storage device" is a hardware or software system for storing digital information, and includes cloud storage and local disks.

[0264] A "generative AI model" is an artificial intelligence technology that analyzes digital information and performs pattern recognition and data processing, enabling advanced data analysis.

[0265] A "tag" is an identifier that serves as metadata attached to digital information and is used to help classify and search for information.

[0266] "Classification and organization" refers to the process of organizing digital information based on specific criteria, making it easier to access and manage information through visual organization.

[0267] "Usage rights" refer to access rights to digital information, indicating that a designated user has the right to view, edit, and share the information.

[0268] The "user interface" refers to the interface through which users directly interact with the system, facilitating the manipulation and editing of digital information.

[0269] This invention is implemented through a combination of an information terminal, a server, a storage device, and a generation AI model. Users can generate and manipulate digital information using their own information terminal. For example, they can take photos and videos of their trip using a smartphone or computer and upload this digital information to a storage device in the cloud using a dedicated app.

[0270] The server uses a generative AI model to process digital information received from cloud storage. This AI model has functions such as facial recognition, object detection, and location information analysis, and automatically generates tags for uploaded digital information. This efficiently categorizes the information and organizes it so that users can easily access it later. The server also visually organizes and displays the digital information based on these tags, making it easy for users to browse past data in an album format.

[0271] Users can use this system to generate video content. For example, they can select photos and videos with specific tags and create a slideshow with a theme such as "Family Summer Memories." The server automatically assembles this selected information into a slideshow using a template and provides a preview on the user's device. Users can customize the slideshow by adding music and text while viewing the preview.

[0272] The generated digital information and video content can be shared by users with other users, such as family and friends. The server manages access to this digital information by setting access rights for members specified by the user, and facilitates information sharing through links. This function makes it possible to share important memories with others while keeping information secure.

[0273] For example, users can easily generate video content by using prompts such as, "Upload photos from a family trip, have them automatically organized by theme, and create a slideshow." This feature is particularly useful for users who want to efficiently manage large amounts of digital data.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] Users upload digital information to cloud storage using their own devices. They select photos and video files as input and operate the upload button through a dedicated app. The output is digital information stored in the cloud. This action transfers the user's digital information to the server.

[0277] Step 2:

[0278] The server receives digital information uploaded from cloud storage. It takes data from cloud storage as input and analyzes this digital information using a generative AI model. For data processing, the generative AI model is used to recognize people and objects in images and videos, and outputs the results of face recognition and object detection. Based on the analyzed information, tags are automatically added to the digital information.

[0279] Step 3:

[0280] Based on the tags generated by the analysis, the server sorts and compiles the digital information. The input is the tagged digital information given in Step 2, which classifies the information by category as a data operation and compiles the highly relevant data in the form of an album. As output, the sorted and compiled digital information is saved in a database. This enables the user to easily view the information later.

[0281] Step 4:

[0282] The user accesses the system using their own terminal and views the sorted and compiled digital information. The input includes login information and the selection of the album to be viewed, and the output is the display of the visually compiled information. The user can also edit the viewed information, such as adding comments or deleting unnecessary information.

[0283] Step 5:

[0284] The user inputs, via the terminal, a prompt for generating video content based on the selected photos and videos. The input includes a prompt sentence and tag conditions, and the server selects a template based on the input and automatically generates a slide show as output. The output is a preview of the generated slide show. The user can make additional edits while viewing this preview.

[0285] Step 6:

[0286] The user sets access rights to share the generated video content and digital information with other users. The input includes information about the sharing destination and the selection of the digital information to be shared. Based on this, the server sets access rights for the specified users and generates a link. The output is the generation of a sharing link and a notification to the users who shared it. This enables the user to easily share information with others.

[0287] (Application Example 1)

[0288] 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."

[0289] In today's digital environment, people are required to take and manage vast amounts of photos and videos. However, manually organizing them is time-consuming and laborious, and there is a need for a way to present them in a visually understandable format. Furthermore, there is a challenge in finding efficient ways to seamlessly share this digital content with other users and make it easy to view and appreciate.

[0290] 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.

[0291] In this invention, the server includes means for storing digital data received from a user in an information storage area, means for classifying and organizing the digital data based on generated tags, and means for distributing automatically generated video content to other users via a viewer communication path. This makes it possible for users to efficiently organize digital data they have captured and easily share related information with others.

[0292] "Digital data received from users" refers to electronic information transmitted by individual users using their devices.

[0293] "Information storage area" refers to storage space on the cloud or server for accumulating and storing digital data.

[0294] "Generated tags" refer to identifiable names or keywords that the AI ​​automatically assigns to received digital data.

[0295] "Means of classification and organization" refers to the process of dividing digital data into related categories based on generated tags, and the arrangement of such data.

[0296] "Viewer communication path" refers to a network channel or method for distributing generated video content to other users.

[0297] "Automatically generated video content" refers to visual displays generated from digital data based on conditions specified by the user.

[0298] "Other users" refers to individuals or organizations other than the sender of the digital data who are capable of receiving this data.

[0299] This invention begins when a user uploads digital data to an information storage area using a device such as a smartphone. Photos and videos taken by the user are stored in the information storage area, which functions as cloud storage. Upon receiving this digital data, the server analyzes the data using a generative AI model to perform functions such as face recognition, object detection, and location identification. As a result, the digital data is automatically tagged with a "generated tag."

[0300] The server classifies and organizes digital data based on the generated tags and stores it by category. It can also distribute automatically generated video content to other users via a communication channel based on user-specified conditions. The distributed data can be easily viewed by recipients on their digital devices.

[0301] This system will utilize cloud storage APIs such as Amazon Web Services (AWS) and leverage TensorFlow as the AI ​​generation library. The user interface will be designed using a frontend framework such as React Native.

[0302] As a specific example, tags such as "Summer 2023", "Beach", and "Children" are added to the photos taken during a family trip, and an automatically generated slide show titled "Summer Vacation 2023" is created. This slide show is also shared with family members living in distant locations through a family communication channel, allowing them to visually enjoy the memories together.

[0303] An example of a prompt sentence is "I want to create a slide show from the travel photos taken during the summer vacation and share it with my family." This enables the user to easily input the conditions, and the system can perform processing according to the content.

[0304] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0305] Step 1:

[0306] [[ID=1�]] The user uses the terminal to upload the captured digital data (photos and videos) to the information storage area on the cloud. The input is the digital data, and the output is the state of being saved in cloud storage. This process includes the operation of transferring data to the cloud using an API. At this time, the data is encrypted according to the security protocol to prevent unauthorized access during the process.

[0307] Step 2:

[0308] The server receives the digital data saved in the cloud storage and performs data analysis using the generated AI model. The input is the digital data saved in Step 1, and the output is the generated tags as the analysis result. In this process, the AI model is utilized, and face recognition, object detection, and location identification algorithms are applied to automatically identify the data and assign appropriate tags.

[0309] Step 3:

[0310] The server classifies and organizes the digital data based on the generated tags. The input is the tags and digital data generated in step 2, and the output is a dataset organized by category. This process involves recording the tagged data in a database and grouping related datasets based on the tag information.

[0311] Step 4:

[0312] Based on conditions specified by the user via their device, the server automatically generates video content (a slideshow). The input is data in the specified tags or categories, and the output is the generated video content. This process includes assembling data that matches the conditions using a template engine and then creating a video.

[0313] Step 5:

[0314] The server distributes the generated video content to other users via the viewer communication path. The input is the video content created in step 4, and the output is the distributed content. This process involves distributing content over the network to users with specified access rights.

[0315] 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.

[0316] This invention is a system that stores users' digital data in cloud storage and uses a generative AI and a newly integrated emotion engine to generate richer content and manage the data.

[0317] First, the user uploads digital data such as photos and videos to cloud storage via their device. The server receives this data and begins analyzing it using generative AI and an emotion engine.

[0318] The generative AI identifies key elements from the data and automatically generates tags. During this process, the emotion engine can analyze facial expressions in videos and photos to recognize emotional states. For example, it can detect smiles in family travel photos and add the tag "joy." This allows users to categorize data based on emotion.

[0319] The server categorizes data using tags that include emotions, and organizes it visually for easy user access. The application on the device supports users in creating emotion-based albums and generating slideshows based on emotion themes.

[0320] When a user generates video content, the system uses the emotion engine to analyze emotional information and suggest appropriate music and effects. For example, it suggests cheerful music for videos with many joyful emotions and displays a preview on the device, making it easy for the user to decide whether to accept the suggestion or make a different choice.

[0321] Furthermore, users can receive emotional analysis from real-time facial expressions and voice, and receive suggestions for editing and customizing video content accordingly. For example, if a smile is detected during a video chat, the server will automatically suggest a video composition that connects related happy moments.

[0322] This system also enables the sharing of emotionally charged content with other users through digital data access rights settings. The server sets access permissions for designated users and generates links to share memories associated with emotions. This facilitates digital communication that strengthens emotional bonds with family and friends.

[0323] The following describes the processing flow.

[0324] Step 1:

[0325] The user logs into a cloud storage service using their device and selects digital data such as photos and videos. The user then uploads the selected data to the cloud.

[0326] Step 2:

[0327] The server receives the uploaded digital data and prepares it for transmission to the AI ​​generation module. Metadata associated with the data (such as date and time of capture, location, etc.) is also extracted at the same time.

[0328] Step 3:

[0329] The generation AI analyzes digital data, identifies key elements such as people, places, and events, and automatically generates tags. The server attaches the tags and stores them in a database.

[0330] Step 4:

[0331] Digital data is analyzed by an emotion engine. The emotion engine identifies facial expressions in images and videos and recognizes emotions such as "joy," "sadness," and "surprise." The server then assigns tags to the data based on those emotions.

[0332] Step 5:

[0333] The server classifies the digital data based on the generated tags and organizes it by category. The data is structured visually and clearly on the cloud for easy user access.

[0334] Step 6:

[0335] Users operate their devices and access organized data. Emotion-based album and category displays are possible, and users can use the search function to view specific photos and videos as needed.

[0336] Step 7:

[0337] When a user specifies the creation of video content, the server selects appropriate music and effects based on information from the emotion engine. A video template is generated based on the user's selection, and a preview is displayed on the device.

[0338] Step 8:

[0339] Users can edit content using the interface on their device. Here, they can accept automatically suggested music and effects, or consider other options. Once the user has finished editing, the final video is generated and saved.

[0340] Step 9:

[0341] The user sets access rights for digital data to other users. The server grants access to the designated family and friends and generates a single link. Through this link, the user can easily share content associated with emotions and pass on digital assets.

[0342] (Example 2)

[0343] 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".

[0344] In today's world, where digital data is increasing, there is a need for users to easily categorize its content, manage it efficiently based on emotions, and generate video content tailored to individual needs. However, conventional systems struggle to effectively manage digital data and associate it with emotions, and they do not adequately support content generation that aligns with the intentions of individual users.

[0345] 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.

[0346] In this invention, the server includes means for storing image information received from the user, means for automatically generating tags using a generative AI model and analyzing the data, and means for identifying a person's emotional state using an emotion analysis engine and adding tags. This makes it possible to effectively classify and organize the user's digital data based on emotions, and to easily generate personalized content.

[0347] "Image information" refers to visual data such as photographs and videos stored in digital format.

[0348] "Means of storage" refers to data storage devices or systems that store digital data and allow access and management as needed.

[0349] A "generative AI model" refers to an algorithm or software that uses artificial intelligence technology to extract information from digital data and automatically generate tags.

[0350] "Methods for automatically generating tags and analyzing data" refers to processes and functions that use AI technology to analyze digital data and automatically assign labels and keywords based on the content.

[0351] An "emotion analysis engine" refers to a technology or tool that analyzes a person's facial expressions and voice in digital data to identify their emotional state at that time.

[0352] "Means of adding tags" refers to methods and mechanisms for adding tags, which are identifying information, to digital data, thereby facilitating subsequent searching and classification.

[0353] This invention relates to a system that efficiently manages digital image information held by users and enables the generation of emotion-based content. The specific method for implementing this system is described below.

[0354] Users upload personal photos and videos to cloud storage using devices such as smartphones and computers via a dedicated app or web interface. The server stores this information in the cloud. Common cloud platforms can be used for data storage.

[0355] The server activates a generative AI model to analyze the collected data. This generative AI model uses machine learning techniques to identify objects and scenes within the image and automatically generate relevant tags. For example, a photo of a beach would generate tags such as "beach" and "waves."

[0356] Furthermore, the server uses an emotion analysis engine to analyze the faces of people in photos and videos. This engine identifies emotions from subtle facial expressions based on an expression recognition algorithm that utilizes face tracking technology. For example, it can tag detected smiles with "joy," enabling users to manage their data based on emotions.

[0357] The device's application visually displays the categorized digital data based on this, helping users easily browse and manipulate data based on emotions and themes. Users can create personalized albums and slideshows according to the generated tags.

[0358] Furthermore, when a user creates new video content, the server suggests music and effects based on the emotion analysis results. A user interface is displayed on the terminal, allowing the user to edit the content according to the suggestions of the generating AI model. For example, if the system is instructed with a prompt such as "Create an album of happy moments from a family trip," music and visual effects that match the theme will be automatically suggested.

[0359] In this way, the present invention provides a concrete method for efficiently and emotionally valuing users' digital data and establishing personalized digital experiences.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] Users upload digital image information, such as photos and videos, to cloud storage using their devices. Specifically, they select the target file using the application or web interface they are using and execute an upload command. The input at this time is the digital file specified by the user, and the data transferred to the server as output is stored in the cloud.

[0363] Step 2:

[0364] The server stores the image information received on the cloud and organizes the data for each user in its storage area. The input is the digital file received in step 1, and the output is the data systematically stored in cloud storage.

[0365] Step 3:

[0366] The server launches a generative AI model and begins processing and analyzing image information stored in the cloud. The input is image information stored in the cloud, and object detection and scene analysis are performed by the generative AI model. The output is a list of automatically generated tags.

[0367] Step 4:

[0368] The server uses an emotion analysis engine to analyze the emotions of people in the image. Specifically, it uses face tracking technology to analyze facial expressions and identify emotional states. The input is the image information analyzed in step 3, and the output includes emotion tags such as "joy" and "sadness."

[0369] Step 5:

[0370] The server categorizes the digital data based on the generated tags and sentiment tags. The input is the tag information, which is the output of steps 3 and 4, and the output is data categorized into visually organized categories.

[0371] Step 6:

[0372] The application on the device displays organized categories when the user accesses it, helping them to manipulate data based on emotions and themes. The input is the category information generated in step 5, and the output is a visual interface displayed on the screen.

[0373] Step 7:

[0374] When a user creates new video content, the server suggests music and effects based on sentiment analysis results. The input is the user's prompt text and selected theme, and the output is the suggested music and effects, which are displayed on the device's screen.

[0375] Step 8:

[0376] The user reviews the suggested content and edits it as needed. The input is the music and effects suggested in step 7, and the output is the final video content created by the user.

[0377] Step 9:

[0378] The server sets access rights to digital data and generates links for sharing with other users. The input is the access rights information set by the user, and the output is the access link and authorized user information.

[0379] (Application Example 2)

[0380] 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."

[0381] In today's world, efficiently managing the vast amount of digital data from user-generated photos and videos, and creating and editing content based on emotion, is challenging. Traditional systems have failed to reflect emotional elements in the generated content, nor have they been able to automatically provide visually and audibly engaging content, thus failing to deliver sufficient value to users.

[0382] 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.

[0383] In this invention, the server includes means for storing digital data received from the user in a remote storage device, means for classifying and organizing the digital data based on the generated symbols, and means for selecting recommended audio and video effects based on the emotional state. This makes it possible for the user to easily perform automatic generation of emotionally responsive content and visually and audibly engaging editing.

[0384] "User" refers to an individual or legal entity that uses the system to manage digital data and create content.

[0385] "Digital data" refers to electronically stored information such as photographs and videos.

[0386] "Remote storage" refers to external storage devices such as cloud storage that can be accessed via the internet.

[0387] "Symbols" refer to tags or labels that represent elements identified by the generating AI from digital data.

[0388] "Emotional state" refers to the type and intensity of emotions analyzed from the user's digital data and real-time interactions.

[0389] "Audio and video effects" refers to background music, filters, effects, etc., used to enhance the visual and audio quality of content.

[0390] "Means of classification and organization" refers to methods for categorizing and visually organizing digital data using generated symbols.

[0391] "Means for selecting recommended audio and video effects" refers to a method for automatically selecting the most suitable music, video filters, etc., based on emotional state.

[0392] To implement this invention, it is necessary to upload digital data captured by the user's device to a remote storage device. The device can be a smartphone or tablet, and an upload application is installed on it. The user selects photos and videos taken by the user using the application and sends them to the cloud server. The cloud server stores the data using cloud storage such as Amazon S3.

[0393] The server temporarily stores the received digital data and analyzes it using generative AI and an emotion engine. The generative AI uses open AI models to extract characteristic elements from the digital data and generate them as tags. The emotion engine uses emotion analysis services such as Microsoft Azure to identify emotional states in photos and videos. For example, a smile in a photo might be tagged as the emotion "joy."

[0394] After this analysis, the server classifies the data based on tags and organizes and stores it in remote storage. Users can access this data through their devices or head-mounted displays and review emotion-based visual content. Furthermore, appropriate music and visual effects are automatically recommended based on the user's emotional state. This music and effect enhances the atmosphere of the user-generated content, providing a personalized experience.

[0395] For example, if a user on their smartphone responds to a prompt such as, "Analyze photos and videos from a family trip and create a slideshow highlighting happy moments. Add appropriate music and effects," the system will generate a slideshow edited to focus on the joyful moments.

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The user operates the device and selects digital data such as photos and videos through an application. The input is the user's digital data, and the output is information about the selected data. The device prepares to send this data to a remote storage device on a cloud server.

[0399] Step 2:

[0400] The terminal uploads the prepared digital data to the cloud server. The input is the selected digital data, and the output is the result of the upload to cloud storage. The server temporarily stores this received data.

[0401] Step 3:

[0402] The server uses a generative AI model to extract key elements from received digital data and automatically generate tags. The input is digital data, and the output is the generated tags. This process identifies keywords and themes related to the user's data.

[0403] Step 4:

[0404] The server uses an emotion engine to analyze emotional states within digital data. The input is digital data, and the output is the analyzed emotion tag. For example, it performs face recognition in an image and classifies a smile as "joy."

[0405] Step 5:

[0406] Based on tags generated by AI and sentiment analysis, the server categorizes and organizes digital data and stores it in cloud storage. The input is tagged digital data, and the output is the organized data storage state. Users can easily access this organized data later.

[0407] Step 6:

[0408] If a user wants to generate content based on emotion tags, they enter a prompt using their terminal. The input might be a prompt such as, "Analyze photos and videos from a family trip and create a slideshow highlighting happy moments. Add appropriate music and effects." The output is an instruction to generate the content. The server then prepares to generate the content accordingly.

[0409] Step 7:

[0410] The server selects music and visual effects recommended based on the user's emotional state and automatically generates visual content such as slideshows. Input consists of prompt text and emotional tags, while output is automatically generated content. Users can then download and view the final slideshow or video.

[0411] 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.

[0412] 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 the following. 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 indicated 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.

[0413] 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.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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".

[0427] This system consists of three main components: the user, the terminal, and the server. First, the user uploads digital data to cloud storage using their own terminal. For example, travel videos and family photos taken with a smartphone can be saved to the cloud via a dedicated app.

[0428] Once data is uploaded, the server receives it and uses a generating AI to analyze each piece of data. This analysis includes facial recognition, object detection, and location identification, making it possible to automatically tag images and videos. This process includes information that the user may not have been aware of at the time of shooting. For example, tags such as "Summer 2023," "Beach," and "Family" are generated and managed by the server.

[0429] The server organizes the tagged digital data by category. At this point, users can log in at any time to visually view the organized data. For example, travel photos might be displayed as albums such as "Summer Vacation Trip," allowing users to easily reminisce about past memories.

[0430] Next, the user can use their device to generate video content based on specified conditions. For example, they can create a slideshow on a specific theme using photos and videos tagged with certain elements. The server assembles the slideshow using a template based on the selected data and provides a preview on the device. The user can edit the preview screen and add music and text as needed.

[0431] Finally, users can pass on digital data and generated content to other users, such as family and friends. The server sets access rights to the data for members specified by the user and makes it easy to share via links. This system ensures that precious memories are safely passed on within the family.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] The user logs into a cloud storage application using their device. The user selects their digital data, such as photos and videos, and begins uploading it to cloud storage. The device sends the selected data to the cloud via the internet.

[0435] Step 2:

[0436] The server saves the received digital data to cloud storage. The server analyzes the metadata associated with the data (such as the date and time of capture and location information) and prepares it to be passed to the data analysis module of the generating AI.

[0437] Step 3:

[0438] The AI ​​on the server analyzes the stored digital data. Using image recognition technology, it identifies elements within the data (people, places, events, etc.) and automatically generates tags based on that. For example, the AI ​​recognizes the sea in a photograph and assigns the tag "beach".

[0439] Step 4:

[0440] The server organizes data into categories based on the generated tags. The server groups related photos and videos and structures them within the database so that users can easily access them.

[0441] Step 5:

[0442] Users can access cloud storage applications through their devices and visually display their organized data. Users can also search and display data based on specific themes or tags as needed.

[0443] Step 6:

[0444] The process of generating video content based on user-specified conditions begins. The user selects a template for creating a slideshow or video on their device, and the server automatically prepares data suitable for those conditions.

[0445] Step 7:

[0446] The server generates video content based on the selected digital data. Images and videos are arranged in order according to a template, and music and effects are automatically added. A preview is displayed on the user's device, and editing becomes possible.

[0447] Step 8:

[0448] Users edit the generated content using the user interface provided on their device. Here, users can perform operations such as changing music, adding text, and adjusting the order of images. Once editing is complete, they save the final content.

[0449] Step 9:

[0450] Users utilize the digital data inheritance feature to configure sharing settings with other users. The server grants access rights to the newly designated users and generates a link for sharing data and generated content. Users can share data through this link and inherit it within their family.

[0451] (Example 1)

[0452] 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."

[0453] Storing and managing large amounts of digital information is extremely time-consuming, and organizing it properly is also difficult. Furthermore, to improve information sharing and accessibility, there is a need for a system that automatically classifies information and allows for easy sharing with other users. Conventional systems require manual organization of information, making efficient information management difficult.

[0454] 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.

[0455] In this invention, the server includes means for storing digital information received from an information terminal in a storage device, means for classifying and organizing the digital information based on the generated tags, and means for analyzing the digital information using a generation AI model and automatically generating tags. This makes it possible to efficiently organize information through automatic analysis and tagging, and to easily manage and share diverse information.

[0456] An "information terminal" is an electronic device used by a user to create or manipulate digital information, and includes smartphones and computers.

[0457] "Digital information" refers to all data stored in electronic format, including images, videos, audio, and text.

[0458] A "storage device" is a hardware or software system for storing digital information, and includes cloud storage and local disks.

[0459] A "generative AI model" is an artificial intelligence technology that analyzes digital information and performs pattern recognition and data processing, enabling advanced data analysis.

[0460] A "tag" is an identifier that serves as metadata attached to digital information and is used to help classify and search for information.

[0461] "Classification and organization" refers to the process of organizing digital information based on specific criteria, making it easier to access and manage information through visual organization.

[0462] "Usage rights" refer to access rights to digital information, indicating that a designated user has the right to view, edit, and share the information.

[0463] The "user interface" refers to the interface through which users directly interact with the system, facilitating the manipulation and editing of digital information.

[0464] This invention is implemented through a combination of an information terminal, a server, a storage device, and a generation AI model. Users can generate and manipulate digital information using their own information terminal. For example, they can take photos and videos of their trip using a smartphone or computer and upload this digital information to a storage device in the cloud using a dedicated app.

[0465] The server uses a generative AI model to process digital information received from cloud storage. This AI model has functions such as facial recognition, object detection, and location information analysis, and automatically generates tags for uploaded digital information. This efficiently categorizes the information and organizes it so that users can easily access it later. The server also visually organizes and displays the digital information based on these tags, making it easy for users to browse past data in an album format.

[0466] Users can use this system to generate video content. For example, they can select photos and videos with specific tags and create a slideshow with a theme such as "Family Summer Memories." The server automatically assembles this selected information into a slideshow using a template and provides a preview on the user's device. Users can customize the slideshow by adding music and text while viewing the preview.

[0467] The generated digital information and video content can be shared by users with other users, such as family and friends. The server manages access to this digital information by setting access rights for members specified by the user, and facilitates information sharing through links. This function makes it possible to share important memories with others while keeping information secure.

[0468] For example, users can easily generate video content by using prompts such as, "Upload photos from a family trip, have them automatically organized by theme, and create a slideshow." This feature is particularly useful for users who want to efficiently manage large amounts of digital data.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] Users upload digital information to cloud storage using their own devices. They select photos and video files as input and operate the upload button through a dedicated app. The output is digital information stored in the cloud. This action transfers the user's digital information to the server.

[0472] Step 2:

[0473] The server receives digital information uploaded from cloud storage. It takes data from cloud storage as input and analyzes this digital information using a generative AI model. For data processing, the generative AI model is used to recognize people and objects in images and videos, and outputs the results of face recognition and object detection. Based on the analyzed information, tags are automatically added to the digital information.

[0474] Step 3:

[0475] The server organizes and groups digital information based on tags generated through analysis. The input is the tagged digital information assigned in step 2. The data is categorized as a data calculation, and highly relevant data is organized into albums. The output is the organized and grouped digital information stored in a database. This allows users to easily access the information later.

[0476] Step 4:

[0477] Users access the system using their own devices and view organized and curated digital information. Input consists of login information and the selection of albums to view, while output is a visually organized display of information. Users can also edit the viewed information, such as adding comments or deleting unnecessary information.

[0478] Step 5:

[0479] The user enters prompts on their device to generate video content based on selected photos and videos. The input includes prompt text and tag conditions, and the server selects a template based on the input to automatically generate a slideshow. The output is a preview of the generated slideshow. The user can make additional edits while viewing this preview.

[0480] Step 6:

[0481] Users configure access permissions to share generated video content and digital information with other users. Input includes information about the recipients and the digital information to be shared. Based on this, the server sets access permissions for the specified users and generates a link. Output includes the generation of the sharing link and notification to the shared users. This allows users to easily share information with others.

[0482] (Application Example 1)

[0483] 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."

[0484] In today's digital environment, people are required to take and manage vast amounts of photos and videos. However, manually organizing them is time-consuming and laborious, and there is a need for a way to present them in a visually understandable format. Furthermore, there is a challenge in finding efficient ways to seamlessly share this digital content with other users and make it easy to view and appreciate.

[0485] 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.

[0486] In this invention, the server includes means for storing digital data received from a user in an information storage area, means for classifying and organizing the digital data based on generated tags, and means for distributing automatically generated video content to other users via a viewer communication path. This makes it possible for users to efficiently organize digital data they have captured and easily share related information with others.

[0487] "Digital data received from users" refers to electronic information transmitted by individual users using their devices.

[0488] "Information storage area" refers to storage space on the cloud or server for accumulating and storing digital data.

[0489] "Generated tags" refer to identifiable names or keywords that the AI ​​automatically assigns to received digital data.

[0490] "Means of classification and organization" refers to the process of dividing digital data into related categories based on generated tags, and the arrangement of such data.

[0491] "Viewer communication path" refers to a network channel or method for distributing generated video content to other users.

[0492] "Automatically generated video content" refers to visual displays generated from digital data based on conditions specified by the user.

[0493] "Other users" refers to individuals or organizations other than the sender of the digital data who are capable of receiving this data.

[0494] This invention begins when a user uploads digital data to an information storage area using a device such as a smartphone. Photos and videos taken by the user are stored in the information storage area, which functions as cloud storage. Upon receiving this digital data, the server analyzes the data using a generative AI model to perform functions such as face recognition, object detection, and location identification. As a result, the digital data is automatically tagged with a "generated tag."

[0495] The server classifies and organizes digital data based on the generated tags and stores it by category. It can also distribute automatically generated video content to other users via a communication channel based on user-specified conditions. The distributed data can be easily viewed by recipients on their digital devices.

[0496] This system will utilize cloud storage APIs such as Amazon Web Services (AWS) and leverage TensorFlow as the AI ​​generation library. The user interface will be designed using a frontend framework such as React Native.

[0497] As a concrete example, photos taken during a family trip are tagged with "Summer 2023," "Beach," and "Children," and an automatically generated slideshow titled "Summer Vacation 2023" is created. This slideshow can be shared with family members living far away via a family communication channel, allowing them to visually enjoy the memories together.

[0498] An example of a prompt message would be, "I want to create a slideshow of my summer vacation photos and share it with my family." This allows the user to easily input a condition, and the system can then process it accordingly.

[0499] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0500] Step 1:

[0501] The user uses their device to capture digital data (photos and videos) and uploads it to a cloud-based data storage area. The input is digital data, and the output is data stored in cloud storage. This process involves transferring data to the cloud using an API. During this process, the data is encrypted according to security protocols to prevent unauthorized access.

[0502] Step 2:

[0503] The server receives digital data stored in cloud storage and performs data analysis using a generative AI model. The input is the digital data stored in step 1, and the output is the generated tags as a result of the analysis. In this process, the AI ​​model is used to automatically identify the data and assign appropriate tags by applying face recognition, object detection, and location identification algorithms.

[0504] Step 3:

[0505] The server classifies and organizes the digital data based on the generated tags. The input is the tags and digital data generated in step 2, and the output is a dataset organized by category. This process involves recording the tagged data in a database and grouping related datasets based on the tag information.

[0506] Step 4:

[0507] Based on conditions specified by the user via their device, the server automatically generates video content (a slideshow). The input is data in the specified tags or categories, and the output is the generated video content. This process includes assembling data that matches the conditions using a template engine and then creating a video.

[0508] Step 5:

[0509] The server distributes the generated video content to other users via the viewer communication path. The input is the video content created in step 4, and the output is the distributed content. This process involves distributing content over the network to users with specified access rights.

[0510] 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.

[0511] This invention is a system that stores users' digital data in cloud storage and uses a generative AI and a newly integrated emotion engine to generate richer content and manage the data.

[0512] First, the user uploads digital data such as photos and videos to cloud storage via their device. The server receives this data and begins analyzing it using a generative AI and emotion engine.

[0513] The generative AI identifies key elements from the data and automatically generates tags. During this process, the emotion engine can analyze facial expressions in videos and photos to recognize emotional states. For example, it can detect smiles in family travel photos and add the tag "joy." This allows users to categorize data based on emotion.

[0514] The server categorizes data using tags that include emotions, and organizes it visually for easy user access. The application on the device supports users in creating emotion-based albums and generating slideshows based on emotion themes.

[0515] When a user generates video content, the system uses the emotion engine to analyze emotional information and suggest appropriate music and effects. For example, it suggests cheerful music for videos with many joyful emotions and displays a preview on the device, making it easy for the user to decide whether to accept the suggestion or make a different choice.

[0516] Furthermore, users can receive emotional analysis from real-time facial expressions and voice, and receive suggestions for editing and customizing video content accordingly. For example, if a smile is detected during a video chat, the server will automatically suggest a video composition that connects related happy moments.

[0517] This system also enables the sharing of emotionally charged content with other users through digital data access rights settings. The server sets access permissions for designated users and generates links to share memories associated with emotions. This facilitates digital communication that strengthens emotional bonds with family and friends.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The user logs into a cloud storage service using their device and selects digital data such as photos and videos. The user then uploads the selected data to the cloud.

[0521] Step 2:

[0522] The server receives the uploaded digital data and prepares it for transmission to the AI ​​generation module. Metadata associated with the data (such as date and time of capture, location, etc.) is also extracted at the same time.

[0523] Step 3:

[0524] The generation AI analyzes digital data, identifies key elements such as people, places, and events, and automatically generates tags. The server attaches the tags and stores them in a database.

[0525] Step 4:

[0526] Digital data is analyzed by an emotion engine. The emotion engine identifies facial expressions in images and videos and recognizes emotions such as "joy," "sadness," and "surprise." The server then assigns tags to the data based on those emotions.

[0527] Step 5:

[0528] The server classifies the digital data based on the generated tags and organizes it by category. The data is structured visually and clearly on the cloud for easy user access.

[0529] Step 6:

[0530] Users operate the device and access organized data. Emotion-based album and category displays are possible, and users can use the search function to view specific photos and videos as needed.

[0531] Step 7:

[0532] When a user specifies the creation of video content, the server selects appropriate music and effects based on information from the emotion engine. A video template is generated based on the user's selection, and a preview is displayed on the device.

[0533] Step 8:

[0534] Users can edit content using the interface on their device. Here, they can accept automatically suggested music and effects, or consider other options. Once the user has finished editing, the final video is generated and saved.

[0535] Step 9:

[0536] The user sets access rights for digital data to other users. The server grants access to the specified family and friends and generates a single link. Through this link, the user can easily share content associated with emotions and pass on digital assets.

[0537] (Example 2)

[0538] 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."

[0539] In today's world, where digital data is increasing, there is a need for users to easily categorize its content, manage it efficiently based on emotions, and generate video content tailored to individual needs. However, conventional systems struggle to effectively manage digital data and associate it with emotions, and they do not adequately support content generation that aligns with the intentions of individual users.

[0540] 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.

[0541] In this invention, the server includes means for storing image information received from the user, means for automatically generating tags using a generative AI model and analyzing the data, and means for identifying a person's emotional state using an emotion analysis engine and adding tags. This makes it possible to effectively classify and organize the user's digital data based on emotions, and to easily generate personalized content.

[0542] "Image information" refers to visual data such as photographs and videos stored in digital format.

[0543] "Means of storage" refers to data storage devices or systems that store digital data and allow access and management as needed.

[0544] A "generative AI model" refers to an algorithm or software that uses artificial intelligence technology to extract information from digital data and automatically generate tags.

[0545] "Methods for automatically generating tags and analyzing data" refers to processes and functions that use AI technology to analyze digital data and automatically assign labels and keywords based on the content.

[0546] An "emotion analysis engine" refers to a technology or tool that analyzes a person's facial expressions and voice in digital data to identify their emotional state at that time.

[0547] "Means of adding tags" refers to methods and mechanisms for adding tags, which are identifying information, to digital data, thereby facilitating subsequent searching and classification.

[0548] This invention relates to a system that efficiently manages digital image information held by users and enables the generation of emotion-based content. The specific method for implementing this system is described below.

[0549] Users upload personal photos and videos to cloud storage using devices such as smartphones and computers via a dedicated app or web interface. The server stores this information in the cloud. Common cloud platforms can be used for data storage.

[0550] The server activates a generative AI model to analyze the collected data. This generative AI model uses machine learning techniques to identify objects and scenes within the image and automatically generate relevant tags. For example, a photo of a beach would generate tags such as "beach" and "waves."

[0551] Furthermore, the server uses an emotion analysis engine to analyze the faces of people in photos and videos. This engine identifies emotions from subtle facial expressions based on an expression recognition algorithm that utilizes face tracking technology. For example, it can tag detected smiles with "joy," enabling users to manage their data based on emotions.

[0552] The device's application visually displays the categorized digital data based on this, helping users easily browse and manipulate data based on emotions and themes. Users can create personalized albums and slideshows according to the generated tags.

[0553] Furthermore, when a user creates new video content, the server suggests music and effects based on the emotion analysis results. A user interface is displayed on the terminal, allowing the user to edit the content according to the suggestions of the generating AI model. For example, if the system is instructed with a prompt such as "Create an album of happy moments from a family trip," music and visual effects that match the theme will be automatically suggested.

[0554] In this way, the present invention provides a concrete method for efficiently and emotionally valuing users' digital data and establishing personalized digital experiences.

[0555] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0556] Step 1:

[0557] Users upload digital image information, such as photos and videos, to cloud storage using their devices. Specifically, they select the target file using the application or web interface they are using and execute an upload command. The input at this time is the digital file specified by the user, and the data transferred to the server as output is stored in the cloud.

[0558] Step 2:

[0559] The server stores the image information received on the cloud and organizes the data for each user in its storage area. The input is the digital file received in step 1, and the output is the data systematically stored in cloud storage.

[0560] Step 3:

[0561] The server launches a generative AI model and begins processing and analyzing image information stored in the cloud. The input is image information stored in the cloud, and object detection and scene analysis are performed by the generative AI model. The output is a list of automatically generated tags.

[0562] Step 4:

[0563] The server uses an emotion analysis engine to analyze the emotions of people in the image. Specifically, it uses face tracking technology to analyze facial expressions and identify emotional states. The input is the image information analyzed in step 3, and the output includes emotion tags such as "joy" and "sadness."

[0564] Step 5:

[0565] The server categorizes the digital data based on the generated tags and sentiment tags. The input is the tag information, which is the output of steps 3 and 4, and the output is data categorized into visually organized categories.

[0566] Step 6:

[0567] The application on the device displays organized categories when the user accesses it, helping them to manipulate data based on emotions and themes. The input is the category information generated in step 5, and the output is a visual interface displayed on the screen.

[0568] Step 7:

[0569] When a user creates new video content, the server suggests music and effects based on sentiment analysis results. The input is the user's prompt text and selected theme, and the output is the suggested music and effects, which are displayed on the device's screen.

[0570] Step 8:

[0571] The user reviews the suggested content and edits it as needed. The input is the music and effects suggested in step 7, and the output is the final video content created by the user.

[0572] Step 9:

[0573] The server sets access rights to digital data and generates links for sharing with other users. The input is the access rights information set by the user, and the output is the access link and authorized user information.

[0574] (Application Example 2)

[0575] 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."

[0576] In today's world, efficiently managing the vast amount of digital data from user-generated photos and videos, and creating and editing content based on emotion, is challenging. Traditional systems have failed to reflect emotional elements in the generated content, nor have they been able to automatically provide visually and audibly engaging content, thus failing to deliver sufficient value to users.

[0577] 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.

[0578] In this invention, the server includes means for storing digital data received from the user in a remote storage device, means for classifying and organizing the digital data based on the generated symbols, and means for selecting recommended audio and video effects based on the emotional state. This makes it possible for the user to easily perform automatic generation of emotionally responsive content and visually and audibly engaging editing.

[0579] "User" refers to an individual or legal entity that uses the system to manage digital data and create content.

[0580] "Digital data" refers to electronically stored information such as photographs and videos.

[0581] "Remote storage" refers to external storage devices such as cloud storage that can be accessed via the internet.

[0582] "Symbols" refer to tags or labels that represent elements identified by the generating AI from digital data.

[0583] "Emotional state" refers to the type and intensity of emotions analyzed from the user's digital data and real-time interactions.

[0584] "Audio and video effects" refers to background music, filters, effects, etc., used to enhance the visual and audio quality of content.

[0585] "Means of classification and organization" refers to methods for categorizing and visually organizing digital data using generated symbols.

[0586] "Means for selecting recommended audio and video effects" refers to a method for automatically selecting the most suitable music, video filters, etc., based on emotional state.

[0587] To implement this invention, it is necessary to upload digital data captured by the user's device to a remote storage device. The device can be a smartphone or tablet, and an upload application is installed on it. The user selects photos and videos taken by the user using the application and sends them to the cloud server. The cloud server stores the data using cloud storage such as Amazon S3.

[0588] The server temporarily stores the received digital data and analyzes it using generative AI and an emotion engine. The generative AI uses open AI models to extract characteristic elements from the digital data and generate them as tags. The emotion engine uses emotion analysis services such as Microsoft Azure to identify emotional states in photos and videos. For example, a smile in a photo might be tagged as the emotion "joy."

[0589] After this analysis, the server classifies the data based on tags and organizes and stores it in remote storage. Users can access this data through their devices or head-mounted displays and review emotion-based visual content. Furthermore, appropriate music and visual effects are automatically recommended based on the user's emotional state. This music and effect enhances the atmosphere of the user-generated content, providing a personalized experience.

[0590] For example, if a user on their smartphone responds to a prompt such as, "Analyze photos and videos from a family trip and create a slideshow highlighting happy moments. Add appropriate music and effects," the system will generate a slideshow edited to focus on the joyful moments.

[0591] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0592] Step 1:

[0593] The user operates the device and selects digital data such as photos and videos through an application. The input is the user's digital data, and the output is information about the selected data. The device prepares to send this data to a remote storage device on a cloud server.

[0594] Step 2:

[0595] The terminal uploads the prepared digital data to the cloud server. The input is the selected digital data, and the output is the result of the upload to cloud storage. The server temporarily stores this received data.

[0596] Step 3:

[0597] The server uses a generative AI model to extract key elements from received digital data and automatically generate tags. The input is digital data, and the output is the generated tags. This process identifies keywords and themes related to the user's data.

[0598] Step 4:

[0599] The server uses an emotion engine to analyze emotional states within digital data. The input is digital data, and the output is the analyzed emotion tag. For example, it performs face recognition in an image and classifies a smile as "joy."

[0600] Step 5:

[0601] Based on tags generated by AI and sentiment analysis, the server categorizes and organizes digital data and stores it in cloud storage. The input is tagged digital data, and the output is the organized data storage state. Users can easily access this organized data later.

[0602] Step 6:

[0603] If a user wants to generate content based on emotion tags, they enter a prompt using their terminal. The input might be a prompt such as, "Analyze photos and videos from a family trip and create a slideshow highlighting happy moments. Add appropriate music and effects." The output is an instruction to generate the content. The server then prepares to generate the content accordingly.

[0604] Step 7:

[0605] The server selects music and visual effects recommended based on the user's emotional state and automatically generates visual content such as slideshows. Input consists of prompt text and emotional tags, while output is automatically generated content. Users can then download and view the final slideshow or video.

[0606] 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.

[0607] 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 the following. 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 indicated 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.

[0608] 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.

[0609] [Fourth Embodiment]

[0610] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0611] 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.

[0612] 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).

[0613] 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.

[0614] 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.

[0615] 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).

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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".

[0623] This system consists of three main components: the user, the terminal, and the server. First, the user uploads digital data to cloud storage using their own terminal. For example, travel videos and family photos taken with a smartphone can be saved to the cloud via a dedicated app.

[0624] Once data is uploaded, the server receives it and uses a generating AI to analyze each piece of data. This analysis includes facial recognition, object detection, and location identification, making it possible to automatically tag images and videos. This process includes information that the user may not have been aware of at the time of shooting. For example, tags such as "Summer 2023," "Beach," and "Family" are generated and managed by the server.

[0625] The server organizes the tagged digital data by category. At this point, users can log in at any time to visually view the organized data. For example, travel photos might be displayed as albums such as "Summer Vacation Trip," allowing users to easily reminisce about past memories.

[0626] Next, the user can use their device to generate video content based on specified conditions. For example, they can create a slideshow on a specific theme using photos and videos tagged with certain elements. The server assembles the slideshow using a template based on the selected data and provides a preview on the device. The user can edit the preview screen and add music and text as needed.

[0627] Finally, users can pass on digital data and generated content to other users, such as family and friends. The server sets access rights to the data for members specified by the user and makes it easy to share via links. This system ensures that precious memories are safely passed on within the family.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The user logs into a cloud storage application using their device. The user selects their digital data, such as photos and videos, and begins uploading it to cloud storage. The device sends the selected data to the cloud via the internet.

[0631] Step 2:

[0632] The server saves the received digital data to cloud storage. The server analyzes the metadata associated with the data (such as the date and time of capture and location information) and prepares it to be passed to the data analysis module of the generating AI.

[0633] Step 3:

[0634] The AI ​​on the server analyzes the stored digital data. Using image recognition technology, it identifies elements within the data (people, places, events, etc.) and automatically generates tags based on that. For example, the AI ​​recognizes the sea in a photograph and assigns the tag "beach".

[0635] Step 4:

[0636] The server organizes data into categories based on the generated tags. The server groups related photos and videos and structures them within the database so that users can easily access them.

[0637] Step 5:

[0638] Users can access cloud storage applications through their devices and visually display their organized data. Users can also search and display data based on specific themes or tags as needed.

[0639] Step 6:

[0640] The process of generating video content based on user-specified conditions begins. The user selects a template for creating a slideshow or video on their device, and the server automatically prepares data suitable for those conditions.

[0641] Step 7:

[0642] The server generates video content based on the selected digital data. Images and videos are arranged sequentially according to a template, and music and effects are automatically added. A preview is displayed on the user's device, and editing becomes possible.

[0643] Step 8:

[0644] Users edit the generated content using the user interface provided on their device. Here, users can perform operations such as changing music, adding text, and adjusting the order of images. Once editing is complete, they save the final content.

[0645] Step 9:

[0646] Users utilize the digital data inheritance feature to configure sharing settings with other users. The server grants access rights to the newly designated users and generates a link for sharing data and generated content. Users can share data through this link and inherit it within their family.

[0647] (Example 1)

[0648] 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".

[0649] Storing and managing large amounts of digital information is extremely time-consuming, and organizing it properly is also difficult. Furthermore, to improve information sharing and accessibility, there is a need for a system that automatically classifies information and allows for easy sharing with other users. Conventional systems require manual organization of information, making efficient information management difficult.

[0650] 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.

[0651] In this invention, the server includes means for storing digital information received from an information terminal in a storage device, means for classifying and organizing the digital information based on the generated tags, and means for analyzing the digital information using a generation AI model and automatically generating tags. This makes it possible to efficiently organize information through automatic analysis and tagging, and to easily manage and share diverse information.

[0652] An "information terminal" is an electronic device used by a user to create or manipulate digital information, and includes smartphones and computers.

[0653] "Digital information" refers to all data stored in electronic format, including images, videos, audio, and text.

[0654] A "storage device" is a hardware or software system for storing digital information, and includes cloud storage and local disks.

[0655] A "generative AI model" is an artificial intelligence technology that analyzes digital information and performs pattern recognition and data processing, enabling advanced data analysis.

[0656] A "tag" is an identifier that serves as metadata attached to digital information and is used to help classify and search for information.

[0657] "Classification and organization" refers to the process of organizing digital information based on specific criteria, making it easier to access and manage information through visual organization.

[0658] "Usage rights" refer to access rights to digital information, indicating that a designated user has the right to view, edit, and share the information.

[0659] The "user interface" refers to the interface through which users directly interact with the system, facilitating the manipulation and editing of digital information.

[0660] This invention is implemented through a combination of an information terminal, a server, a storage device, and a generation AI model. Users can generate and manipulate digital information using their own information terminal. For example, they can take photos and videos of their trip using a smartphone or computer and upload this digital information to a storage device in the cloud using a dedicated app.

[0661] The server uses a generative AI model to process digital information received from cloud storage. This AI model has functions such as facial recognition, object detection, and location information analysis, and automatically generates tags for uploaded digital information. This efficiently categorizes the information and organizes it so that users can easily access it later. The server also visually organizes and displays the digital information based on these tags, making it easy for users to browse past data in an album format.

[0662] Users can use this system to generate video content. For example, they can select photos and videos with specific tags and create a slideshow with a theme such as "Family Summer Memories." The server automatically assembles this selected information into a slideshow using a template and provides a preview on the user's device. Users can customize the slideshow by adding music and text while viewing the preview.

[0663] The generated digital information and video content can be shared by users with other users, such as family and friends. The server manages access to this digital information by setting access rights for members specified by the user, and facilitates information sharing through links. This function makes it possible to share important memories with others while keeping information secure.

[0664] For example, users can easily generate video content by using prompts such as, "Upload photos from a family trip, have them automatically organized by theme, and create a slideshow." This feature is particularly useful for users who want to efficiently manage large amounts of digital data.

[0665] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0666] Step 1:

[0667] Users upload digital information to cloud storage using their own devices. They select photos and video files as input and operate the upload button through a dedicated app. The output is digital information stored in the cloud. This action transfers the user's digital information to the server.

[0668] Step 2:

[0669] The server receives digital information uploaded from cloud storage. It takes data from cloud storage as input and analyzes this digital information using a generative AI model. For data processing, the generative AI model is used to recognize people and objects in images and videos, and outputs the results of face recognition and object detection. Based on the analyzed information, tags are automatically added to the digital information.

[0670] Step 3:

[0671] The server organizes and groups digital information based on tags generated through analysis. The input is the tagged digital information assigned in step 2. The data is categorized as a data calculation, and highly relevant data is organized into albums. The output is the organized and grouped digital information stored in a database. This allows users to easily access the information later.

[0672] Step 4:

[0673] Users access the system using their own devices and view organized and curated digital information. Input consists of login information and the selection of albums to view, while output is a visually organized display of information. Users can also edit the viewed information, such as adding comments or deleting unnecessary information.

[0674] Step 5:

[0675] The user enters prompts on their device to generate video content based on selected photos and videos. The input includes prompt text and tag conditions, and the server selects a template based on the input to automatically generate a slideshow. The output is a preview of the generated slideshow. The user can make additional edits while viewing this preview.

[0676] Step 6:

[0677] Users configure access permissions to share generated video content and digital information with other users. Input includes information about the recipients and the digital information to be shared. Based on this, the server sets access permissions for the specified users and generates a link. Output includes the generation of the sharing link and notification to the shared users. This allows users to easily share information with others.

[0678] (Application Example 1)

[0679] 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".

[0680] In today's digital environment, people are required to take and manage vast amounts of photos and videos. However, manually organizing them is time-consuming and laborious, and there is a need for a way to present them in a visually understandable format. Furthermore, there is a challenge in finding efficient ways to seamlessly share this digital content with other users and make it easy to view and appreciate.

[0681] 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.

[0682] In this invention, the server includes means for storing digital data received from a user in an information storage area, means for classifying and organizing the digital data based on generated tags, and means for distributing automatically generated video content to other users via a viewer communication path. This makes it possible for users to efficiently organize digital data they have captured and easily share related information with others.

[0683] "Digital data received from users" refers to electronic information transmitted by individual users using their devices.

[0684] "Information storage area" refers to storage space on the cloud or server for accumulating and storing digital data.

[0685] "Generated tags" refer to identifiable names or keywords that the AI ​​automatically assigns to received digital data.

[0686] "Means of classification and organization" refers to the process of dividing digital data into related categories based on generated tags, and the arrangement thereof.

[0687] "Viewer communication path" refers to a network channel or method for distributing generated video content to other users.

[0688] "Automatically generated video content" refers to visual displays generated from digital data based on conditions specified by the user.

[0689] "Other users" refers to individuals or organizations other than the sender of the digital data who are capable of receiving this data.

[0690] This invention begins when a user uploads digital data to an information storage area using a device such as a smartphone. Photos and videos taken by the user are stored in the information storage area, which functions as cloud storage. Upon receiving this digital data, the server analyzes the data using a generative AI model to perform functions such as face recognition, object detection, and location identification. As a result, the digital data is automatically tagged with a "generated tag."

[0691] The server classifies and organizes digital data based on the generated tags and stores it by category. It can also distribute automatically generated video content to other users via a communication channel based on user-specified conditions. The distributed data can be easily viewed by recipients on their digital devices.

[0692] This system will utilize cloud storage APIs such as Amazon Web Services (AWS) and leverage TensorFlow as the AI ​​generation library. The user interface will be designed using a frontend framework such as React Native.

[0693] As a concrete example, photos taken during a family trip are tagged with "Summer 2023," "Beach," and "Children," and an automatically generated slideshow titled "Summer Vacation 2023" is created. This slideshow can be shared with family members living far away via a family communication channel, allowing them to visually enjoy the memories together.

[0694] An example of a prompt message would be, "I want to create a slideshow of my summer vacation photos and share it with my family." This allows the user to easily input a condition, and the system can then process it accordingly.

[0695] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0696] Step 1:

[0697] The user uses their device to capture digital data (photos and videos) and uploads it to a cloud-based data storage area. The input is digital data, and the output is data stored in cloud storage. This process involves transferring data to the cloud using an API. During this process, the data is encrypted according to security protocols to prevent unauthorized access.

[0698] Step 2:

[0699] The server receives digital data stored in cloud storage and performs data analysis using a generative AI model. The input is the digital data stored in step 1, and the output is the generated tags as a result of the analysis. In this process, the AI ​​model is used to automatically identify the data and assign appropriate tags by applying face recognition, object detection, and location identification algorithms.

[0700] Step 3:

[0701] The server classifies and organizes the digital data based on the generated tags. The input is the tags and digital data generated in step 2, and the output is a dataset organized by category. This process involves recording the tagged data in a database and grouping related datasets based on the tag information.

[0702] Step 4:

[0703] Based on conditions specified by the user via their device, the server automatically generates video content (a slideshow). The input is data in the specified tags or categories, and the output is the generated video content. This process includes assembling data that matches the conditions using a template engine and then creating a video.

[0704] Step 5:

[0705] The server distributes the generated video content to other users via the viewer communication path. The input is the video content created in step 4, and the output is the distributed content. This process involves distributing content over the network to users with specified access rights.

[0706] 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.

[0707] This invention is a system that stores users' digital data in cloud storage and uses a generative AI and a newly integrated emotion engine to generate richer content and manage the data.

[0708] First, the user uploads digital data such as photos and videos to cloud storage via their device. The server receives this data and begins analyzing it using generative AI and an emotion engine.

[0709] The generative AI identifies key elements from the data and automatically generates tags. During this process, the emotion engine can analyze facial expressions in videos and photos to recognize emotional states. For example, it can detect smiles in family travel photos and add the tag "joy." This allows users to categorize data based on emotion.

[0710] The server categorizes data using tags that include emotions, and organizes it visually for easy user access. The application on the device supports users in creating emotion-based albums and generating slideshows based on emotion themes.

[0711] When a user generates video content, the system uses the emotion engine to analyze emotional information and suggest appropriate music and effects. For example, it suggests cheerful music for videos with many joyful emotions and displays a preview on the device, making it easy for the user to decide whether to accept the suggestion or make a different choice.

[0712] Furthermore, users can receive emotional analysis from real-time facial expressions and voice, and receive suggestions for editing and customizing video content accordingly. For example, if a smile is detected during a video chat, the server will automatically suggest a video composition that connects related happy moments.

[0713] This system also enables the sharing of emotionally charged content with other users through digital data access rights settings. The server sets access permissions for designated users and generates links to share memories associated with emotions. This facilitates digital communication that strengthens emotional bonds with family and friends.

[0714] The following describes the processing flow.

[0715] Step 1:

[0716] The user logs into a cloud storage service using their device and selects digital data such as photos and videos. The user then uploads the selected data to the cloud.

[0717] Step 2:

[0718] The server receives the uploaded digital data and prepares it for transmission to the AI ​​generation module. Metadata associated with the data (such as date and time of capture, location, etc.) is also extracted at the same time.

[0719] Step 3:

[0720] The generation AI analyzes digital data, identifies key elements such as people, places, and events, and automatically generates tags. The server attaches the tags and stores them in a database.

[0721] Step 4:

[0722] Digital data is analyzed by an emotion engine. The emotion engine identifies facial expressions in images and videos and recognizes emotions such as "joy," "sadness," and "surprise." The server then assigns tags to the data based on those emotions.

[0723] Step 5:

[0724] The server classifies the digital data based on the generated tags and organizes it by category. The data is structured visually and clearly on the cloud for easy user access.

[0725] Step 6:

[0726] Users operate their devices and access organized data. Emotion-based album and category displays are possible, and users can use the search function to view specific photos and videos as needed.

[0727] Step 7:

[0728] When a user specifies the creation of video content, the server selects appropriate music and effects based on information from the emotion engine. A video template is generated based on the user's selection, and a preview is displayed on the device.

[0729] Step 8:

[0730] Users can edit content using the interface on their device. Here, they can accept automatically suggested music and effects, or consider other options. Once the user has finished editing, the final video is generated and saved.

[0731] Step 9:

[0732] The user sets access rights for digital data to other users. The server grants access to the designated family and friends and generates a single link. Through this link, the user can easily share content associated with emotions and pass on digital assets.

[0733] (Example 2)

[0734] 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".

[0735] In today's world, where digital data is increasing, there is a need for users to easily categorize its content, manage it efficiently based on emotions, and generate video content tailored to individual needs. However, conventional systems struggle to effectively manage digital data and associate it with emotions, and they do not adequately support content generation that aligns with the intentions of individual users.

[0736] 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.

[0737] In this invention, the server includes means for storing image information received from the user, means for automatically generating tags using a generative AI model and analyzing the data, and means for identifying a person's emotional state using an emotion analysis engine and adding tags. This makes it possible to effectively classify and organize the user's digital data based on emotions, and to easily generate personalized content.

[0738] "Image information" refers to visual data such as photographs and videos stored in digital format.

[0739] "Means of storage" refers to data storage devices or systems that store digital data and allow access and management as needed.

[0740] A "generative AI model" refers to an algorithm or software that uses artificial intelligence technology to extract information from digital data and automatically generate tags.

[0741] "Methods for automatically generating tags and analyzing data" refers to processes and functions that use AI technology to analyze digital data and automatically assign labels and keywords based on the content.

[0742] An "emotion analysis engine" refers to a technology or tool that analyzes a person's facial expressions and voice in digital data to identify their emotional state at that time.

[0743] "Means of adding tags" refers to methods and mechanisms for adding tags, which are identifying information, to digital data, thereby facilitating subsequent searching and classification.

[0744] This invention relates to a system that efficiently manages digital image information held by users and enables the generation of emotion-based content. The specific method for implementing this system is described below.

[0745] Users upload personal photos and videos to cloud storage using devices such as smartphones and computers via a dedicated app or web interface. The server stores this information in the cloud. Common cloud platforms can be used for data storage.

[0746] The server activates a generative AI model to analyze the collected data. This generative AI model uses machine learning techniques to identify objects and scenes within the image and automatically generate relevant tags. For example, a photo of a beach would generate tags such as "beach" and "waves."

[0747] Furthermore, the server uses an emotion analysis engine to analyze the faces of people in photos and videos. This engine identifies emotions from subtle facial expressions based on an expression recognition algorithm that utilizes face tracking technology. For example, it can tag detected smiles with "joy," enabling users to manage their data based on emotions.

[0748] The device's application visually displays the categorized digital data based on this, helping users easily browse and manipulate data based on emotions and themes. Users can create personalized albums and slideshows according to the generated tags.

[0749] Furthermore, when a user creates new video content, the server suggests music and effects based on the emotion analysis results. A user interface is displayed on the terminal, allowing the user to edit the content according to the suggestions of the generating AI model. For example, if the system is instructed with a prompt such as "Create an album of happy moments from a family trip," music and visual effects that match the theme will be automatically suggested.

[0750] In this way, the present invention provides a concrete method for efficiently and emotionally valuing users' digital data and establishing personalized digital experiences.

[0751] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0752] Step 1:

[0753] Users upload digital image information, such as photos and videos, to cloud storage using their devices. Specifically, they select the target file using the application or web interface they are using and execute an upload command. The input at this time is the digital file specified by the user, and the data transferred to the server as output is stored in the cloud.

[0754] Step 2:

[0755] The server stores the image information received on the cloud and organizes the data for each user in its storage area. The input is the digital file received in step 1, and the output is the data systematically stored in cloud storage.

[0756] Step 3:

[0757] The server launches a generative AI model and begins processing and analyzing image information stored in the cloud. The input is image information stored in the cloud, and object detection and scene analysis are performed by the generative AI model. The output is a list of automatically generated tags.

[0758] Step 4:

[0759] The server uses an emotion analysis engine to analyze the emotions of people in the image. Specifically, it uses face tracking technology to analyze facial expressions and identify emotional states. The input is the image information analyzed in step 3, and the output includes emotion tags such as "joy" and "sadness."

[0760] Step 5:

[0761] The server categorizes the digital data based on the generated tags and sentiment tags. The input is the tag information, which is the output of steps 3 and 4, and the output is data categorized into visually organized categories.

[0762] Step 6:

[0763] The application on the device displays organized categories when the user accesses it, helping them to manipulate data based on emotions and themes. The input is the category information generated in step 5, and the output is a visual interface displayed on the screen.

[0764] Step 7:

[0765] When a user creates new video content, the server suggests music and effects based on sentiment analysis results. The input is the user's prompt text and selected theme, and the output is the suggested music and effects, which are displayed on the device's screen.

[0766] Step 8:

[0767] The user reviews the suggested content and edits it as needed. The input is the music and effects suggested in step 7, and the output is the final video content created by the user.

[0768] Step 9:

[0769] The server sets access rights to digital data and generates links for sharing with other users. The input is the access rights information set by the user, and the output is the access link and authorized user information.

[0770] (Application Example 2)

[0771] 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".

[0772] In today's world, efficiently managing the vast amount of digital data from user-generated photos and videos, and creating and editing content based on emotion, is challenging. Traditional systems have failed to reflect emotional elements in the generated content, nor have they been able to automatically provide visually and audibly engaging content, thus failing to deliver sufficient value to users.

[0773] 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.

[0774] In this invention, the server includes means for storing digital data received from the user in a remote storage device, means for classifying and organizing the digital data based on the generated symbols, and means for selecting recommended audio and video effects based on the emotional state. This makes it possible for the user to easily perform automatic generation of emotionally responsive content and visually and audibly engaging editing.

[0775] "User" refers to an individual or legal entity that uses the system to manage digital data and create content.

[0776] "Digital data" refers to electronically stored information such as photographs and videos.

[0777] "Remote storage" refers to external storage devices such as cloud storage that can be accessed via the internet.

[0778] "Symbols" refer to tags or labels that represent elements identified by the generating AI from digital data.

[0779] "Emotional state" refers to the type and intensity of emotions analyzed from the user's digital data and real-time interactions.

[0780] "Audio and video effects" refers to background music, filters, effects, etc., used to enhance the visual and audio quality of content.

[0781] "Means of classification and organization" refers to methods for categorizing and visually organizing digital data using generated symbols.

[0782] "Means for selecting recommended audio and video effects" refers to a method for automatically selecting the most suitable music, video filters, etc., based on emotional state.

[0783] To implement this invention, it is necessary to upload digital data captured by the user's device to a remote storage device. The device can be a smartphone or tablet, and an upload application is installed on it. The user selects photos and videos taken by the user using the application and sends them to the cloud server. The cloud server stores the data using cloud storage such as Amazon S3.

[0784] The server temporarily stores the received digital data and analyzes it using generative AI and an emotion engine. The generative AI uses open AI models to extract characteristic elements from the digital data and generate them as tags. The emotion engine uses emotion analysis services such as Microsoft Azure to identify emotional states in photos and videos. For example, a smile in a photo might be tagged as the emotion "joy."

[0785] After this analysis, the server classifies the data based on tags and organizes and stores it in remote storage. Users can access this data through their devices or head-mounted displays and review emotion-based visual content. Furthermore, appropriate music and visual effects are automatically recommended based on the user's emotional state. This music and effect enhances the atmosphere of the user-generated content, providing a personalized experience.

[0786] For example, if a user on their smartphone responds to a prompt such as, "Analyze photos and videos from a family trip and create a slideshow highlighting happy moments. Add appropriate music and effects," the system will generate a slideshow edited to focus on the joyful moments.

[0787] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0788] Step 1:

[0789] The user operates the device and selects digital data such as photos and videos through an application. The input is the user's digital data, and the output is information about the selected data. The device prepares to send this data to a remote storage device on a cloud server.

[0790] Step 2:

[0791] The terminal uploads the prepared digital data to the cloud server. The input is the selected digital data, and the output is the result of the upload to cloud storage. The server temporarily stores this received data.

[0792] Step 3:

[0793] The server uses a generative AI model to extract key elements from received digital data and automatically generate tags. The input is digital data, and the output is the generated tags. This process identifies keywords and themes related to the user's data.

[0794] Step 4:

[0795] The server uses an emotion engine to analyze emotional states within digital data. The input is digital data, and the output is the analyzed emotion tag. For example, it performs face recognition in an image and classifies a smile as "joy."

[0796] Step 5:

[0797] Based on tags generated by AI and sentiment analysis, the server categorizes and organizes digital data and stores it in cloud storage. The input is tagged digital data, and the output is the organized data storage state. Users can easily access this organized data later.

[0798] Step 6:

[0799] If a user wants to generate content based on emotion tags, they enter a prompt using their terminal. The input might be a prompt such as, "Analyze photos and videos from a family trip and create a slideshow highlighting happy moments. Add appropriate music and effects." The output is an instruction to generate the content. The server then prepares to generate the content accordingly.

[0800] Step 7:

[0801] The server selects music and visual effects recommended based on the user's emotional state and automatically generates visual content such as slideshows. Input consists of prompt text and emotional tags, while output is automatically generated content. Users can then download and view the final slideshow or video.

[0802] 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.

[0803] 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 the following. 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 indicated 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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."

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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 to be incorporated by reference.

[0823] The following is further disclosed regarding the embodiments described above.

[0824] (Claim 1)

[0825] A means of saving digital data received from users to cloud storage,

[0826] A means of classifying and organizing digital data based on the generated tags,

[0827] A means of automatically generating video content based on conditions specified by the user,

[0828] A means of setting and managing access rights for digital data to other users,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, further comprising means for visually displaying the classification and organization based on the generated tags as individual albums.

[0832] (Claim 3)

[0833] The system according to claim 1, comprising a user interface that provides editing and customization for video content created by the user.

[0834] "Example 1"

[0835] (Claim 1)

[0836] A means for storing digital information received from an information terminal in a storage device,

[0837] A means of classifying and organizing digital information based on generated tags,

[0838] A method for analyzing digital information using a generative AI model and automatically generating tags,

[0839] A means of automatically generating video information according to criteria specified by the user,

[0840] Means for setting and managing the right to use digital information for other users,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, further comprising means for visually displaying the classification and organization based on the generated tags as individual sets.

[0844] (Claim 3)

[0845] The system according to claim 1, further comprising a user connection interface that provides modifications and adjustments to video information created by the user.

[0846] "Application Example 1"

[0847] (Claim 1)

[0848] A means for storing digital data received from a user in an information storage area,

[0849] A means of classifying and organizing digital data based on the generated tags,

[0850] A means of distributing automatically generated video content to other users via a communication path for viewers,

[0851] Means for setting and managing access rights to digital data for other users,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, further comprising means for visually displaying the classification and organization based on the generated tags as individual sets of information.

[0855] (Claim 3)

[0856] The system according to claim 1, comprising an operation screen that provides editing and customization for video content created by the user.

[0857] "Example 2 of combining an emotion engine"

[0858] (Claim 1)

[0859] A means for storing image information received from the user,

[0860] A method for automatically generating tags using a generative AI model and analyzing the data,

[0861] A means of identifying and tagging a person's emotional state using an emotion analysis engine,

[0862] A means of classifying and organizing data based on the generated tags,

[0863] A means for automatically generating video content based on conditions specified by the user,

[0864] Means for setting and managing data access rights for other users,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, comprising means for visually displaying data classified and organized based on generated tags as individual sets.

[0868] (Claim 3)

[0869] The system according to claim 1, comprising an operation screen that facilitates editing and customization of video content created by the user.

[0870] "Application example 2 when combining with an emotional engine"

[0871] (Claim 1)

[0872] A means of storing digital data received from a user in a remote storage device,

[0873] A means of classifying and organizing digital data based on generated symbols,

[0874] A means of automatically generating visual content based on user-specified conditions,

[0875] Means for setting and managing usage rights for digital data to other users,

[0876] A means of selecting recommended audio and visual effects based on emotional state,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, further comprising means for visually displaying the classification and organization based on the generated symbols as individual aggregate records.

[0880] (Claim 3)

[0881] The system according to claim 1, comprising a user interface that provides editing and personalization for user-created visual content. [Explanation of Symbols]

[0882] 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 saving digital data received from users to cloud storage, A means of classifying and organizing digital data based on the generated tags, A means of automatically generating video content based on conditions specified by the user, A means of setting and managing access rights for digital data to other users, A system that includes this.

2. The system according to claim 1, further comprising means for visually displaying the classification and organization based on the generated tags as individual albums.

3. The system according to claim 1, comprising a user interface that provides editing and customization for video content created by the user.

Images