system

Abstract

Provide a system. 【Solution means】 Means for inputting user requests, Server means for receiving the requests, Means for analyzing the received requests, Means for generating a video script based on the analysis result, Means for generating a video based on the video script, Means for storing the generated video in storage and generating an access URL, Means for notifying the user of the access URL, A system including the above.

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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 recent years, with the popularization of smartphones and other mobile devices, the desire of users to immediately obtain information corresponding to various needs has been increasing. However, it has been difficult for existing information providing systems to flexibly respond to individual desires of users, and it has been particularly difficult to provide a specific explanation about a specific operation or setting method in the form of a video. Furthermore, there is also a problem that existing video contents cannot be reused, and the cost and time for newly generating videos are high. Against this background, there is a demand for providing a system that can immediately respond to individual desires of users and effectively and efficiently provide an explanation in the form of a video.

Means for Solving the Problems

[0005] The present invention provides a system comprising means for users to input requests, means for analyzing received requests, and means for generating video scripts based on the analysis results. Specifically, it includes a server means for analyzing received requests, means for generating video scripts based on the analysis results, and means for generating videos based on those scripts. It also includes means for saving the generated videos to storage, generating an accessible URL, and notifying the user of that URL, thereby effectively providing video content that immediately responds to the individual requests of the user. In this way, it also includes means for reusing existing video data, realizing a system that can respond to diverse user requests while saving costs and time.

[0006] A "user" refers to an entity that uses this system to input requests and receive video content.

[0007] A "request" refers to a request made by a user through the system for specific information or explanation.

[0008] "Means" refers to functions, devices, or methods established to achieve a specific purpose.

[0009] A "server" refers to a computing system that provides data and services over a network.

[0010] "Analysis" refers to the process of breaking down received data and evaluating and understanding its contents.

[0011] A "video script" refers to a document that describes in detail the specific steps and content elements required for video generation.

[0012] "Generation" refers to creating something new using some means.

[0013] "Storage" refers to memory media or systems used to store data.

[0014] An "access URL" refers to a URL (Uniform Resource Locator) that indicates the location of a specific resource and is provided in a format that users can directly access from a web browser or similar application.

[0015] "Notification" refers to the act or method of sending information to the appropriate recipient and ensuring they receive it.

[0016] "Reuse" refers to using something that has been used once again. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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 Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

[0018] 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.

[0019] First, the language used in the following description will be explained.

[0020] In the following embodiments, a numbered processor (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.

[0021] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention relates to a system that automates a series of processes from when a user inputs a request, to when corresponding video content is generated and delivered to the user. This system mainly consists of three main parts: a user interface, a server, and storage.

[0039] User interface (terminal)

[0040] A user interface provides a means for users to input requests. Examples include applications and web pages that run on devices such as smartphones and personal computers. Users enter specific requests, such as "I would like instructions on initial smartphone setup," into a request input form and then press the submit button.

[0041] Sending and receiving requests (terminal / server)

[0042] The terminal sends the request data entered by the user to the server. The data sent includes the user's request details, request ID, user ID, and timestamp. The server then processes and analyzes this received data.

[0043] Request analysis (server)

[0044] The server analyzes the received request. Specific examples of analysis include using natural language processing techniques to understand the user's request and extract keywords and phrases. Based on the analysis results, it evaluates what kind of video content is suitable.

[0045] Video script generation (server)

[0046] The server generates a video script based on the analysis of the request. The video script describes the specific scenario, text, and visual elements to be used. Based on this script, preparations are made to create a new video using the video generation API or script.

[0047] Video generation and storage (server)

[0048] The server generates videos based on the generated video script. Video generation software or tools are used to create video files according to the script. The generated videos are stored in cloud storage or on the server.

[0049] Generation and notification of access URLs (server / terminal)

[0050] The server generates an access URL for the saved video. It sends notification data containing this access URL to the device. The device displays this received URL to the user. The user can view the video content by clicking the presented URL.

[0051] Specific example

[0052] For example, if a user types and submits "I would like instructions on how to set up my smartphone," the process proceeds as follows: The device sends the request data to the server, which analyzes this data. Based on the analysis, a video script about setting up a smartphone is generated. Next, the server generates the video according to the script and saves the generated video to cloud storage. Then, an access URL for the video is generated and notified to the user. The user accesses that URL and watches the video explaining how to set up their smartphone.

[0053] In this way, the present invention realizes a system that can automatically generate video content in response to user requests and provide it quickly and efficiently.

[0054] The following describes the processing flow.

[0055] Understood. Below, I will explain the program's processing in detail, step by step.

[0056] Step 1:

[0057] The user accesses the system from their smartphone or PC and a request form is displayed. The user enters "I would like instructions on initial smartphone setup" and presses the submit button.

[0058] Step 2:

[0059] The terminal retrieves the request data entered by the user. This retrieved data includes the request details, request ID, user ID, and timestamp. This data is converted to JSON format and sent to the server via an HTTP POST request.

[0060] Step 3:

[0061] The server analyzes the request data received at the API endpoint. The received data is passed to a default parsing module, which performs natural language processing to understand the request and extract keywords and phrases.

[0062] Step 4:

[0063] Based on the analysis results, the server generates a video script to fulfill the request. The video script contains detailed descriptions of the scenario, text, visual elements, and more.

[0064] Step 5:

[0065] The server checks the database and searches for similar existing videos. If a similar video is found, it retrieves the video path and proceeds with the reuse process.

[0066] Step 6:

[0067] If no similar video is found, the server generates a new video based on the video script generated in the previous step. A video generation tool or API is used to create the video file.

[0068] Step 7:

[0069] Upload the generated video file to cloud storage. Once the upload is complete, an access URL for the saved video file will be generated.

[0070] Step 8:

[0071] The server sends the generated access URL back to the terminal in a JSON-formatted response. The response includes the request ID, video URL, metadata, etc.

[0072] Step 9:

[0073] The device analyzes the response received from the server and displays the video URL on the user interface. The user clicks the displayed URL to watch the video.

[0074] The above describes the specific processing flow in the system of the present invention. By executing the relevant operations in detail at each step, it is possible to quickly and efficiently provide video content that meets the user's needs.

[0075] (Example 1)

[0076] 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."

[0077] Conventional video content generation systems have struggled to quickly generate and deliver customized videos tailored to user requests. In particular, the process of automatically generating video scripts based on specific user requests and then creating videos based on those scripts was technically complex and time-consuming. Furthermore, there were insufficient means to quickly and appropriately notify users of the URL to access the generated videos.

[0078] 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.

[0079] In this invention, the server includes server means for receiving requests, natural language processing means for analyzing the received requests, and means using a generative AI model for generating video scripts based on the analysis results. This makes it possible to quickly and efficiently generate and provide customized video content based on requests entered by the user.

[0080] A "user" is someone who uses this system to input requests and request video content.

[0081] A "server" is a central computing system that receives requests, analyzes them, generates video scripts, and produces videos.

[0082] "Natural language processing means" refers to technologies and methods for analyzing text data entered by a user and understanding its meaning, and is used to extract keywords and phrases.

[0083] A "generative AI model" is an artificial intelligence model that can automatically generate text or video scripts based on input data, and specifically includes models such as GPT-3 (registered trademark).

[0084] A "request" refers to the specific demands or requests that a user enters into the system.

[0085] A "video script" is a script that contains details such as the video's scenario, text, and visual elements, and forms the basis of the generated video.

[0086] A "video" is a generated visual content created based on user requests.

[0087] An "access URL" is a unique web address used to access the generated video.

[0088] "Storage" refers to a storage device or cloud storage service used to save generated video files.

[0089] "Notification method" refers to a method of informing the user of the access URL for the generated video, and includes push notifications, email, SMS, etc.

[0090] A "user interface" is an interactive screen that allows users to input requests into a system or view notified access URLs.

[0091] This invention relates to a system that automates a series of processes from when a user inputs a request, to when corresponding video content is generated and delivered to the user. This system consists of three main parts: a user interface, a server, and storage. Specific embodiments of this invention are described below.

[0092] User interface (terminal)

[0093] Users enter their requests using devices such as smartphones or personal computers. They use a dedicated application or webpage running on their device to enter specific requests, such as "I would like instructions on how to set up my smartphone," into a request form and then press the submit button.

[0094] server

[0095] The server is a central computing system that receives and processes request data sent by users. The server generates video content that meets user requests using several methods, including the following:

[0096] 1. Receiving the request

[0097] The server receives request data sent from the terminal via the API endpoint. This data, in JSON format, includes the user's request details, request ID, user ID, and timestamp.

[0098] 2. Analysis of Requirements

[0099] The server analyzes the received request data using natural language processing (NLP) tools. Specifically, it uses NLP libraries (such as spaCy or NLTK) to understand the user's request and extract keywords and phrases.

[0100] 3. Generating the video script

[0101] Based on the analysis results, the server generates video scripts using a generative AI model (e.g., GPT-3). By inputting prompts to the generative AI model, it automatically creates text and scenarios.

[0102] example:

[0103] User A made the following request: "I would like instructions on how to set up my smartphone."

[0104] Based on this request, please generate a step-by-step video script for initial smartphone setup.

[0105] 4. Video generation

[0106] The server generates the video using the API of video creation software (e.g., Adobe After Effects) based on the generated script. It combines visual elements and text according to the script to create a video file.

[0107] 5. Saving the video

[0108] The generated video is stored in a cloud storage service (e.g., Amazon S3). The video file is saved with a unique filename, and an access URL is generated for it.

[0109] 6. Generating the Access URL

[0110] The server generates an access URL for the stored video file. This URL is unique and used by the user to view a specific video.

[0111] 7. Notification to the user

[0112] The server sends notification data containing the generated access URL to the device. Notification methods include push notifications, email, and SMS. The device displays this received URL to the user, who then clicks the URL to view the video content.

[0113] storage

[0114] Cloud storage services are used as storage devices to save the generated video files. Specific examples include Google Cloud Storage and Azure Blob Storage.

[0115] This invention makes it possible to quickly and efficiently generate and provide customized video content that meets user needs. Users can obtain videos based on their requests in a short amount of time, improving convenience.

[0116] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0117] Step 1: User input of requests

[0118] The user enters their request using a device (smartphone or computer). They enter a specific request, such as "I would like instructions on initial smartphone setup," into the request form and press the submit button. This becomes the input data. The output of this step is the user's input data.

[0119] Step 2: Submit your request

[0120] The terminal sends the request data entered by the user to the server. This data includes the request details, request ID, user ID, and timestamp. This data is sent in JSON format. The input is the user's request data, and the output is the request data sent to the server.

[0121] Step 3: Receiving the request

[0122] The server receives request data sent from the terminal via an API endpoint. This received data is stored in temporary storage on the server. The input is the request data sent from the terminal, and the output is the request data stored on the server. Specifically, the data reception process is performed using a web server (for example, Flask or Django).

[0123] Step 4: Analyzing the requirements

[0124] The server analyzes the received request data using natural language processing (NLP) techniques. For example, it might use an NLP library (such as spaCy) to analyze the user's request text and extract keywords like "smartphone" and "initial setup." The input is the request data stored on the server, and the output is the extracted keywords and phrases.

[0125] Step 5: Generate video script

[0126] The server generates a video script using a generative AI model (such as GPT-3) based on the analysis results. During this process, prompt text is entered to automatically generate the script. The input consists of keywords extracted from the analysis results and prompt text, while the output is the generated video script.

[0127] Example of a prompt:

[0128] User A made the following request: "I would like instructions on how to set up my smartphone."

[0129] Based on this request, please generate a step-by-step video script for initial smartphone setup.

[0130] Step 6: Generate the video

[0131] The server generates a video based on the generated script, using the API of video creation software (such as Adobe After Effects). It combines visual elements and text according to the script to create a video file. The input is the generated video script, and the output is the generated video file.

[0132] Step 7: Save the video

[0133] The server saves the generated video file to cloud storage (such as Amazon S3). The file is saved with a unique name, and an access URL is generated. The input is the generated video file, and the output is the access URL of the video stored in cloud storage.

[0134] Step 8: Generate the access URL

[0135] The server generates an access URL for the stored video file. This URL is unique and serves as an access link to a specific video. The input is a video file stored in cloud storage, and the output is the generated access URL.

[0136] Step 9: Notify the user

[0137] The server sends notification data containing the generated access URL to the device. The device then displays this received URL to the user. Notification methods include push notifications, email, and SMS. The input is the access URL, and the output is the notification data displayed on the user's device.

[0138] Step 10: Watch the video

[0139] The user clicks on the URL displayed on their device and watches the video within a web browser or application. The input is the access URL, and the output is the video content the user watches.

[0140] The above is a detailed description of the program processing flow for this system.

[0141] (Application Example 1)

[0142] 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."

[0143] Conventional video content generation systems have struggled to respond quickly and appropriately to users' requests for specific content. Furthermore, no system existed that could accurately understand user needs and automatically generate videos that matched those needs. As a result, users had to spend a considerable amount of time and effort to obtain content that met their requirements, leading to low satisfaction. The problem this invention aims to solve is to automatically generate and provide video content that responds quickly and accurately to user requests.

[0144] 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.

[0145] In this invention, the server includes natural language processing means for analyzing user requests, means for generating video scripts using a generative AI model, and means for automatically saving the generated video to cloud storage and generating an access URL. This makes it possible to quickly generate video content based on user requests and notify the user of a viewable URL.

[0146] "User requests" refer to requests that users enter, such as wanting specific information or content provided in video format.

[0147] "Natural language processing methods" refer to techniques and algorithms used to analyze human language and understand its meaning.

[0148] A "generative AI model" is an artificial intelligence model that uses deep learning or machine learning to generate answers or content for specific problems.

[0149] A "video script" is a set of instructions that specifically describes the content, structure, text, visual elements, and other details of a video.

[0150] "Cloud storage" is a service that allows you to save data to a remote server via the internet.

[0151] An "access URL" is a unique web address that allows a user to access specific content through a web browser or similar application.

[0152] A "user interface" is a display screen that allows users to interact with a system or application through input and output.

[0153] Modes for carrying out the invention

[0154] This invention relates to a system that automatically generates and provides video content based on user requests. This system consists of three main parts: a user interface, a server, and cloud storage.

[0155] 1. User Interface

[0156] A user interface functions as a means for users to input requests. Specifically, this includes applications on smartphones and computers, as well as web pages. When a user inputs a specific request, such as "I want to watch a Python tutorial video," and presses the submit button, that request is sent to the server.

[0157] 2. Server

[0158] The server is responsible for receiving and analyzing user requests. It uses natural language processing techniques to analyze the user's requests and extract keywords and phrases. Then, a generative AI model is used to generate a video script. Specifically, the generative AI model creates a video script explaining the basic usage of Python. Next, video generation software or external APIs are used to create a video file based on the video script. The generated video is stored in cloud storage, and a viewable URL is generated.

[0159] 3. User notifications and displays

[0160] The server notifies the user of the URL to access the generated video and displays it in the user interface. This notification allows the user to access the provided URL and view the desired video content.

[0161] Hardware and software to be used

[0162] Hardware: Web server (PC or cloud server for executing flasks)

[0163] Software: Python, Flask, natural language processing libraries (e.g., NLTK, Spacy), video generation tools or APIs

[0164] Specific example

[0165] If a user types "I want to watch a video tutorial for learning Python," the system will function as follows:

[0166] 1. Users enter and submit their requests using a smartphone or computer interface.

[0167] 2. The server receives the submitted request and parses it using natural language processing.

[0168] 3. The generating AI model generates a video script explaining the basic usage of Python based on the analysis results.

[0169] 4. Generate a video using a video generation tool or API and save it to cloud storage.

[0170] 5. The server generates an access URL and notifies the user.

[0171] 6. The user clicks the URL to watch the video.

[0172] Example of a prompt

[0173] If a user enters "I want to watch a video tutorial for learning Python," analyze the request, extract keywords, and generate a video script explaining the basic usage of Python based on those keywords. Furthermore, generate the video based on that script and create a URL to notify the user.

[0174] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0175] Program processing steps

[0176] Step 1:

[0177] The terminal receives requests from the user. The user requests specific video content and describes the details in the input form. This input data includes the request (e.g., "I want to watch a Python tutorial video"), user ID, timestamp, etc. The entered data is sent to the server when the submit button is pressed.

[0178] Step 2:

[0179] The server receives request data sent from the terminal. The received request data includes the request details, request ID, user ID, and timestamp. Based on this received data, the server prepares for the next analysis.

[0180] Step 3:

[0181] The server analyzes the received request data using natural language processing techniques. Specifically, it uses text analysis libraries (e.g., NLTK, Spacy) to tokenize the request content and extract keywords and related phrases. As a result, a keyword list is generated to accurately understand the user's request.

[0182] Step 4:

[0183] The server generates video scripts using a generative AI model based on keywords obtained through natural language processing. Specifically, keywords are input to the generative AI model to create a script generation prompt (e.g., "Generate a video script explaining the basic usage of Python"). Based on this prompt, the generative AI model outputs a specific video script.

[0184] Step 5:

[0185] The server generates the video based on the generated video script. It automatically generates video files according to the script using video generation tools or external APIs (e.g., video generation APIs). The video files generated at this stage are saved in a specific format (e.g., MP4).

[0186] Step 6:

[0187] The server saves the generated video files to cloud storage. It uses a cloud storage service (e.g., Amazon S3) to upload and save the video files. Once saving is complete, an access URL for the saved video is generated.

[0188] Step 7:

[0189] The server sends a notification to the device containing the generated access URL. This notification includes a link for the user to watch the video. The device displays this notification to the user, allowing the user to watch the video by clicking the URL.

[0190] The above outlines the specific processing steps of the system program that implements the application example.

[0191] 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.

[0192] This invention relates to a system that generates and provides appropriate video content based on a user's input of a request and the user's emotional data. This system mainly consists of four main parts: a user interface, a server, storage, and an emotional engine.

[0193] User interface (terminal)

[0194] The user interface provides a means for users to input requests and recognize emotional data. For example, it uses the camera, microphone, or contactless sensor on a smartphone or computer to acquire emotions from the user's facial expressions and voice tone. As soon as the user types "I would like instructions on how to set up my smartphone" and presses the send button, the emotion engine recognizes the user's emotions.

[0195] Sending and receiving request and emotion data (terminal / server)

[0196] The terminal sends the request data entered by the user and the emotion data recognized by the emotion engine to the server. The transmitted data includes the request content, emotion data, request ID, user ID, and timestamp. The server processes and analyzes this received data.

[0197] Analysis of requests and sentiment data (server)

[0198] The server analyzes the received request data and sentiment data. The analysis uses natural language processing and sentiment recognition technologies. By combining and analyzing the request content and sentiment data, the server understands the user's intentions and the emotions behind them, leading to more specific content requests.

[0199] Emotion-based video script generation (server)

[0200] The server generates a video script based on the analysis results. The video script includes content tailored to the user's requests and emotional data. For example, if the user is excited, a script will be generated that provides a quick and concise explanation.

[0201] Reusing similar video data (server)

[0202] The server searches the database and checks if there are any similar existing videos based on the analysis results. If a similar video is found, it reuses that video and makes adjustments based on sentiment data as needed.

[0203] Video generation and storage (server)

[0204] When new video generation is required, the server generates the video based on the generated video script. It generates videos tailored to the user's needs based on sentiment data and saves the generated video files to cloud storage.

[0205] Generation and notification of access URLs (server / terminal)

[0206] The server generates a URL to access the saved video. It sends notification data containing this access URL to the device. The device displays this received access URL to the user. The user clicks the presented URL to watch the video.

[0207] Specific example

[0208] For example, if a user types "I want instructions on how to set up my smartphone" and is also detected as excited, the system will generate a video script that quickly and concisely explains the procedure. If a similar existing video is found, it will be reused with necessary adjustments. If a new video needs to be generated, it will be created and saved based on the script. The user will then be notified of the URL to access the generated video. By accessing this URL and watching a video with content tailored to their emotions, a more satisfying informational experience can be achieved.

[0209] In this way, the present invention provides a system that enables the automatic generation and provision of video content that takes into account the user's requests and emotions.

[0210] The following describes the processing flow.

[0211] Step 1:

[0212] The user accesses the system from their smartphone or PC, and a request input form is displayed. The user types "I would like instructions on initial smartphone setup" and presses the submit button. At the same time, the device uses its camera and microphone to collect the user's facial expressions and voice, which are then analyzed by an emotion engine.

[0213] Step 2:

[0214] The terminal retrieves the request data entered by the user and the emotion data recognized by the emotion engine. The retrieved data includes the request content, emotion data, request ID, user ID, and timestamp. This is converted to JSON format and sent to the server via an HTTP POST request.

[0215] Step 3:

[0216] The server analyzes the request and sentiment data received at the API endpoint. It executes an analysis module, using natural language processing to understand the request and sentiment recognition to evaluate the user's emotions. The analysis results include details of the request and the user's emotional state.

[0217] Step 4:

[0218] Based on the analysis results, the server generates a video script to meet the request. The video script includes a scenario, text, and visual elements, as well as content adapted to the user's emotions. For example, if the user is frustrated, a concise and easy-to-understand explanation will be prioritized.

[0219] Step 5:

[0220] The server consults the database and searches for similar existing videos based on the analysis results. If a similar video is found, it reuses that video and makes adjustments based on the video script as needed.

[0221] Step 6:

[0222] If no similar video is found, the server generates a new video based on the video script generated in the previous step. A video generation tool or API is used to create the specific video file. For example, the video generation engine integrates narration and visual effects according to the script.

[0223] Step 7:

[0224] The generated video file is uploaded to cloud storage. Once the upload is complete, an access URL for the saved video file is generated. The server records this URL, along with metadata that manages it, in a database.

[0225] Step 8:

[0226] The server sends the generated access URL back to the device in a JSON-formatted response. This response includes the request ID, video URL, and metadata about the user's emotional state.

[0227] Step 9:

[0228] The device analyzes the response received from the server and displays the video URL on the user interface. The user clicks the displayed URL and watches the generated video. For example, the URL displayed might be "https: / / storage.service.com / video / 12345".

[0229] Specific example

[0230] For example, if a user enters "I want instructions on how to set up my smartphone" and simultaneously, sentiment analysis identifies frustration, the system generates a video script containing a concise and quick explanation tailored to the user's request. If a similar existing video is found, it is reused, with necessary adjustments. If a new video is needed, it is generated based on the script and saved to cloud storage. An access URL is then generated and sent to the device. The user accesses this URL and watches the requested video, receiving an explanation that aligns with their emotions.

[0231] As described above, the present invention provides a specific system and processing procedure for automatically generating and providing video content, taking into account user requests and emotional data.

[0232] (Example 2)

[0233] 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".

[0234] Conventional video content generation systems generate videos based solely on user requests, resulting in a failure to provide appropriate content that reflects the user's emotions and circumstances. This also leads to a decline in the user experience and a lack of improvement in the quality of information provided. Furthermore, there is inefficiency in that existing similar videos cannot be utilized, and new content must be generated, incurring costs and time. Therefore, there is a need for a system that enables the automatic generation and delivery of video content that takes into account user requests and emotions.

[0235] 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.

[0236] In this invention, the server includes means for analyzing request and emotion data, means for generating video scripts based on the analysis results, and means for generating videos based on the video scripts. This enables the automatic generation and provision of more effective and efficient video content based on user requests and emotions.

[0237] A "user" refers to an individual or entity that uses the system to input requests and emotional data.

[0238] "Requests" refer to specific requests or requirements that users have for the system.

[0239] "Emotional data" refers to digital information that indicates a user's emotional state, and is data acquired from emotion recognition devices such as cameras and microphones.

[0240] "Device" refers to hardware or software components used by users to input requests or receive notifications from the system.

[0241] An "information processing device" refers to a computer system that performs a series of processes such as receiving and analyzing data, generating scripts, creating videos, and sending notifications.

[0242] "Analysis" refers to the data processing process used to understand the user's intentions and emotions based on received request and emotion data.

[0243] A "video script" is text information that defines the content and structure of the generated video, and refers to a script that describes the specific content of the video that the system aims to create.

[0244] A "storage device" refers to a digital storage device used to store generated video data.

[0245] "Access instructions" refer to information such as URLs or links that guide users to access the generated video.

[0246] "Video" refers to video content that has been generated or adjusted based on the analysis results.

[0247] "Notification" refers to the act of a system sending access instructions or other information to a user.

[0248] "Similar video data" refers to video data from past creations that are relevant to current user requests and can be reused.

[0249] Modes for carrying out the invention

[0250] This invention relates to a system in which a user inputs a request, and appropriate video content is generated and provided based on that request and the user's emotional data. This system consists of four main parts: a user interface, a server, storage, and an emotional engine.

[0251] User interface (terminal)

[0252] The user interface provides a means for users to input requests and recognize emotional data. For example, it uses the camera, microphone, or contactless sensor on a smartphone or computer to acquire emotions from the user's facial expressions and voice tone. As soon as the user types "I would like instructions on how to set up my smartphone" and presses the send button, the emotion engine recognizes the user's emotions.

[0253] Sending and receiving request and emotion data (terminal / server)

[0254] The terminal sends the request data entered by the user and the emotion data recognized by the emotion engine to the server. The transmitted data includes the request content, emotion data, request ID, user ID, and timestamp. The server processes and analyzes this received data.

[0255] Analysis of requests and sentiment data (server)

[0256] The server analyzes the received request data and sentiment data. The analysis methods utilize natural language processing and sentiment recognition technologies. Specifically, general natural language processing APIs can be used for natural language processing, and general sentiment recognition APIs can be used for sentiment recognition. By combining and analyzing the request content and sentiment data, the server understands the user's intentions and the emotions behind them, leading to more specific content requests.

[0257] Emotion-based video script generation (server)

[0258] The server generates a video script based on the analysis results. The video script includes content tailored to the user's requests and emotional data. For example, if the user is excited, a script will be generated that provides a quick and concise explanation.

[0259] Reusing similar video data (server)

[0260] The server searches the database and checks if there are any similar existing videos based on the analysis results. If a similar video is found, it reuses that video and makes adjustments based on sentiment data as needed.

[0261] Video generation and storage (server)

[0262] When new video generation is required, the server generates the video based on the generated video script. It generates videos tailored to the user's needs based on sentiment data and saves the generated video files to cloud storage.

[0263] Generation and notification of access URLs (server / terminal)

[0264] The server generates a URL to access the saved video. It sends notification data containing this access URL to the device. The device displays this received access URL to the user. The user clicks the presented URL to watch the video.

[0265] Specific example

[0266] For example, if a user types "I want instructions on how to set up my smartphone" and is also detected as excited, the system will generate a video script that quickly and concisely explains the procedure. If a similar existing video is found, it will be reused with necessary adjustments. If a new video needs to be generated, it will be created and saved based on the script. The user will then be notified of the URL to access the generated video. By accessing this URL and watching a video with content tailored to their emotions, a more satisfying informational experience can be achieved.

[0267] Example of a prompt:

[0268] Please generate a video script for when a user is excited and says, "I want instructions on how to set up my smartphone."

[0269] Please generate a video script for when a user calmly enters the question, "I would like to learn how to keep a household budget."

[0270] In this way, the present invention provides a system that enables the automatic generation and provision of video content that takes into account the user's requests and emotions.

[0271] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0272] Step 1:

[0273] The user enters their request.

[0274] Users enter their requests using devices such as smartphones or computers. These requests are in text or audio format and include specific requests. For example, a request might be, "I would like instructions on how to set up my smartphone."

[0275] Input: Request via voice or text

[0276] Output: Input desired data

[0277] Step 2:

[0278] The terminal acquires emotion data

[0279] The terminal acquires emotion data from the user's facial expressions and voice tones using a camera and a microphone. For this, emotion recognition technology is used, for example, to recognize whether the user is excited or calm.

[0280] Input: User's facial expression data and voice data

[0281] Output: Emotion data (excited, calm, etc.)

[0282] Step 3:

[0283] The terminal sends the desired data and emotion data to the server

[0284] The terminal sends the user's desired data and the acquired emotion data to the server together with the request ID, user ID, and timestamp.

[0285] Input: Desired data, emotion data, request ID, user ID, timestamp

[0286] Output: Data packet sent to the server

[0287] Step 4:

[0288] The server receives and analyzes the data

[0289] The server analyzes the received request data and emotion data. This analysis utilizes natural language processing technology (e.g., Google Cloud Natural Language API) and emotion recognition technology (e.g., Microsoft® Azure Emotion API). This allows the server to understand the user's requests and emotions and extract specific requirements.

[0290] Input: Request data, sentiment data

[0291] Output: Analysis results (specific content requirements)

[0292] Step 5:

[0293] The server generates the video script.

[0294] The server generates a video script based on the analysis results. The video script includes content tailored to the user's requests and emotional data. For example, if the user is excited, a script will be generated that provides a quick and concise explanation.

[0295] Input: Analysis results

[0296] Output: Video script

[0297] Step 6:

[0298] The server searches the database and checks for similar videos.

[0299] The server searches the database and checks if there are any similar existing videos based on the analysis results. If a similar video is found, it retrieves that video.

[0300] Input: Analysis results

[0301] Output: Similar video data or new video generation flag

[0302] Step 7:

[0303] The server generates or adjusts videos as needed.

[0304] If similar videos are found, the server performs fine-tuning of the videos based on the emotion data. If not found, new videos are generated. For this, a video generation tool (such as FFmpeg) is used based on the video script.

[0305] Input: Video script, similar video data

[0306] Output: Adjusted video data or newly generated video data

[0307] Step 8:

[0308] The server saves the video to storage and generates an access URL.

[0309] The server saves the generated or adjusted video to cloud storage. Then, it generates an access URL and provides this URL to the user.

[0310] Input: Adjusted video data or newly generated video data

[0311] Output: Access URL

[0312] Step 9:

[0313] The terminal notifies and displays the access URL to the user.

[0314] The terminal notifies the user of the access URL received from the server and displays it. The user can access the video by clicking on this URL.

[0315] Input: Access URL <000​​​​​Through the processing steps described above, this system enables the automatic generation and delivery of video content that takes into account the user's requests and emotions.

[0318] (Application Example 2)

[0319] 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".

[0320] Conventional video content generation systems could only provide uniform content in response to user requests, making it difficult to provide personalized information tailored to individual user emotions and circumstances. This resulted in decreased user satisfaction and problems with users being unable to obtain necessary information quickly and appropriately.

[0321] 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. In this invention, the server includes means for receiving user requests and sentiment data, means for analyzing the data, and means for generating a video script. This makes it possible to automatically generate and provide personalized video content based on user requests and sentiment data.

[0322] "User requests" refer to the information and services that users want the system to provide.

[0323] "Emotional data" refers to information indicating the emotional state obtained by analyzing the user's facial expressions, voice tone, and other sensor data.

[0324] A "server" refers to a central computing device that receives and analyzes user requests and sentiment data, and generates video scripts.

[0325] A "video script" refers to a scenario that specifically describes the content of video material, generated based on user requests and emotional data.

[0326] "Storage" refers to memory devices and cloud services used to save generated video data.

[0327] An "access URL" refers to a unique web address used to access saved video data.

[0328] A "user interface" refers to the devices and software that allow users to input requests and emotional data and receive access URLs.

[0329] "Existing video data" refers to a collection of video content already stored within the system.

[0330] This invention is a system that generates and provides appropriate video content based on user requests and emotional data. This system mainly consists of four main parts: a user interface (terminal), a server, storage, and an emotional engine.

[0331] User interface (terminal)

[0332] The user interface provides a means for users to input and recognize their requests and emotional data. Using devices such as smartphones and head-mounted displays, cameras, microphones, and contactless sensors are used to capture the user's facial expressions and voice tone.

[0333] Sending requests and emotion data (terminal / server)

[0334] The terminal sends the user-entered request and sentiment data to the server. The transmitted data includes the request details, sentiment data, request ID, user ID, and timestamp. The server analyzes the received data.

[0335] Analysis of requests and sentiment data (server)

[0336] The server analyzes data using natural language processing technologies (e.g., NLTK, GPT-4®) and emotion recognition technologies (e.g., OpenFace, Affectiva). It combines the request content with emotion data to understand the user's intentions and the emotions behind them. This allows for the concretization of the video script needs.

[0337] Emotion-based video script generation (server)

[0338] The server generates a video script based on the analysis results. For example, if the user is feeling stressed, a script recommending relaxing movies will be generated. This video script contains content that provides the user with the information they are looking for in a concise and quick manner.

[0339] Reusing similar video data (server)

[0340] The server searches the existing video database and checks for similar content based on the analysis results. If similar videos are found, it is possible to reuse those videos and make adjustments based on sentiment data.

[0341] Video generation and storage (server)

[0342] When a new video needs to be generated, the server creates the video based on the generated video script. This video will include personalized content based on the user's sentiment data. The generated video file is stored in cloud storage (e.g., AWS® S3).

[0343] Generation and notification of access URLs (server / terminal)

[0344] The server generates a URL to access the stored video. It sends notification data containing the access URL to the device, which then displays this URL to the user. The user accesses the URL and watches the video. This mechanism improves user satisfaction.

[0345] Specific example

[0346] For example, if a user types "Recommend some relaxing movies" and the emotion engine simultaneously recognizes the user's emotion as "stressful," the server will recommend movies suitable for stress relief. The server adds emotion-based comments to the movie script, selects appropriate content from the video database, and makes adjustments as needed. Finally, it notifies the user of an access URL, and the user can access that URL and watch a personalized video, resulting in a high level of satisfaction.

[0347] Example of a prompt

[0348] "The user typed 'Recommend some relaxing movies.' According to the emotional data, the user is stressed. Please recommend movies that are effective for stress relief. Generate a video script that includes specific recommendations."

[0349] To implement this invention, the server performs the above-described processing to provide personalized content that meets the individual needs of each user.

[0350] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0351] Step 1:

[0352] Users open the application using a smartphone or head-mounted display and input requests and emotional data. Cameras, microphones, and contactless sensors capture the user's facial expressions and voice tone. The input requests are in text format, such as "Recommend some relaxing movies." Emotional data is analyzed in real time and may be recognized as, for example, "stressful."

[0353] Step 2:

[0354] The terminal sends the acquired user request data and sentiment data to the server. The transmitted data includes the request details, sentiment data, request ID, user ID, and timestamp. This data is received by the server.

[0355] Step 3:

[0356] The server analyzes the received request data and sentiment data. Specifically, it analyzes the request content using natural language processing technology (e.g., GPT-4) and the sentiment data using sentiment recognition technology (e.g., OpenFace). This allows the server to understand the user's intent and emotional state. In this step, the input is the user's request and sentiment data, and the output is the specific content request resulting from the analysis.

[0357] Step 4:

[0358] The server generates video scripts based on the analysis results. Using a generation AI model, it creates a script, for example, "Recommends relaxing movies." This script includes comments and explanations based on the user's emotional data. The input is the analysis results, and the output is a specific video script.

[0359] Step 5:

[0360] The server searches the video database and checks for similar content based on the analysis results. It selects and outputs appropriate content from existing video data. If it does not exist, new video generation is required.

[0361] Step 6:

[0362] The server generates new videos based on video scripts as needed. It uses a video generation engine to create video content that conforms to the specified requirements. The input is a video script, and the output is the generated video file.

[0363] Step 7:

[0364] The server saves the generated video file to cloud storage (e.g., AWS S3). A URL for accessing the saved video is generated. The input is the video file, and the output is the access URL.

[0365] Step 8:

[0366] The server notifies the user's device of the generated access URL. The device receives this URL and displays it to the user. The user clicks this URL to view personalized video content. The input is the access URL, and the output is the notification to the user.

[0367] This series of steps allows users to efficiently obtain video content that matches their emotions and desires.

[0368] 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.

[0369] 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.

[0370] 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.

[0371] [Second Embodiment]

[0372] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0373] 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.

[0374] 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).

[0375] 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.

[0376] 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.

[0377] 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).

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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".

[0384] This invention relates to a system that automates a series of processes from when a user inputs a request, to when corresponding video content is generated and delivered to the user. This system mainly consists of three main parts: a user interface, a server, and storage.

[0385] User interface (terminal)

[0386] A user interface provides a means for users to input requests. Examples include applications and web pages that run on devices such as smartphones and personal computers. Users enter specific requests, such as "I would like instructions on initial smartphone setup," into a request input form and then press the submit button.

[0387] Sending and receiving requests (terminal / server)

[0388] The terminal sends the request data entered by the user to the server. The data sent includes the user's request details, request ID, user ID, and timestamp. The server then processes and analyzes this received data.

[0389] Request analysis (server)

[0390] The server analyzes the received request. Specific examples of analysis include using natural language processing techniques to understand the user's request and extract keywords and phrases. Based on the analysis results, it evaluates what kind of video content is suitable.

[0391] Video script generation (server)

[0392] The server generates a video script based on the analysis of the request. The video script describes the specific scenario, text, and visual elements to be used. Based on this script, preparations are made to create a new video using the video generation API or script.

[0393] Video generation and storage (server)

[0394] The server generates videos based on the generated video script. Video generation software or tools are used to create video files according to the script. The generated videos are stored in cloud storage or on the server.

[0395] Generation and notification of access URLs (server / terminal)

[0396] The server generates an access URL for the saved video. It sends notification data containing this access URL to the device. The device displays this received URL to the user. The user can view the video content by clicking the presented URL.

[0397] Specific example

[0398] For example, if a user types and submits "I would like instructions on how to set up my smartphone," the process proceeds as follows: The device sends the request data to the server, which analyzes this data. Based on the analysis, a video script about setting up a smartphone is generated. Next, the server generates the video according to the script and saves the generated video to cloud storage. Then, an access URL for the video is generated and notified to the user. The user accesses that URL and watches the video explaining how to set up their smartphone.

[0399] In this way, the present invention realizes a system that can automatically generate video content in response to user requests and provide it quickly and efficiently.

[0400] The following describes the processing flow.

[0401] Understood. Below, I will explain the program's processing in detail, step by step.

[0402] Step 1:

[0403] The user accesses the system from their smartphone or PC and a request form is displayed. The user enters "I would like instructions on initial smartphone setup" and presses the submit button.

[0404] Step 2:

[0405] The terminal retrieves the request data entered by the user. This retrieved data includes the request details, request ID, user ID, and timestamp. This data is converted to JSON format and sent to the server via an HTTP POST request.

[0406] Step 3:

[0407] The server analyzes the request data received at the API endpoint. The received data is passed to a default parsing module, which performs natural language processing to understand the request and extract keywords and phrases.

[0408] Step 4:

[0409] Based on the analysis results, the server generates a video script to fulfill the request. The video script contains detailed descriptions of the scenario, text, visual elements, and more.

[0410] Step 5:

[0411] The server checks the database and searches for similar existing videos. If a similar video is found, it retrieves the video path and proceeds with the reuse process.

[0412] Step 6:

[0413] If no similar video is found, the server generates a new video based on the video script generated in the previous step. A video generation tool or API is used to create the video file.

[0414] Step 7:

[0415] Upload the generated video file to cloud storage. Once the upload is complete, an access URL for the saved video file will be generated.

[0416] Step 8:

[0417] The server sends the generated access URL back to the terminal in a JSON-formatted response. The response includes the request ID, video URL, metadata, etc.

[0418] Step 9:

[0419] The device analyzes the response received from the server and displays the video URL on the user interface. The user clicks the displayed URL to watch the video.

[0420] The above describes the specific processing flow in the system of the present invention. By executing the relevant operations in detail at each step, it is possible to quickly and efficiently provide video content that meets the user's needs.

[0421] (Example 1)

[0422] 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."

[0423] Conventional video content generation systems have struggled to quickly generate and deliver customized videos tailored to user requests. In particular, the process of automatically generating video scripts based on specific user requests and then creating videos based on those scripts was technically complex and time-consuming. Furthermore, there were insufficient means to quickly and appropriately notify users of the URL to access the generated videos.

[0424] 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.

[0425] In this invention, the server includes server means for receiving requests, natural language processing means for analyzing the received requests, and means using a generative AI model for generating video scripts based on the analysis results. This makes it possible to quickly and efficiently generate and provide customized video content based on requests entered by the user.

[0426] A "user" is someone who uses this system to input requests and request video content.

[0427] A "server" is a central computing system that receives requests, analyzes them, generates video scripts, and produces videos.

[0428] "Natural language processing means" refers to technologies and methods for analyzing text data entered by a user and understanding its meaning, and is used to extract keywords and phrases.

[0429] A "generative AI model" is an artificial intelligence model that can automatically generate text or video scripts based on input data, and specifically includes models such as GPT-3.

[0430] A "request" refers to the specific demands or requests that a user enters into the system.

[0431] A "video script" is a script that contains details such as the video's scenario, text, and visual elements, and forms the basis of the generated video.

[0432] A "video" is a generated visual content created based on user requests.

[0433] An "access URL" is a unique web address used to access the generated video.

[0434] "Storage" refers to a storage device or cloud storage service used to save generated video files.

[0435] "Notification method" refers to a method of informing the user of the access URL for the generated video, and includes push notifications, email, SMS, etc.

[0436] A "user interface" is an interactive screen that allows users to input requests into a system or view notified access URLs.

[0437] This invention relates to a system that automates a series of processes from when a user inputs a request, to when corresponding video content is generated and delivered to the user. This system consists of three main parts: a user interface, a server, and storage. Specific embodiments of this invention are described below.

[0438] User interface (terminal)

[0439] Users enter their requests using devices such as smartphones or personal computers. They use a dedicated application or webpage running on their device to enter specific requests, such as "I would like instructions on how to set up my smartphone," into a request form and then press the submit button.

[0440] server

[0441] The server is a central computing system that receives and processes request data sent by users. The server generates video content that meets user requests using several methods, including the following:

[0442] 1. Receiving the request

[0443] The server receives request data sent from the terminal via the API endpoint. This data, in JSON format, includes the user's request details, request ID, user ID, and timestamp.

[0444] 2. Analysis of Requirements

[0445] The server analyzes the received request data using natural language processing (NLP) tools. Specifically, it uses NLP libraries (such as spaCy or NLTK) to understand the user's request and extract keywords and phrases.

[0446] 3. Generating the video script

[0447] Based on the analysis results, the server generates video scripts using a generative AI model (e.g., GPT-3). By inputting prompts to the generative AI model, it automatically creates text and scenarios.

[0448] example:

[0449] User A made the following request: "I would like instructions on how to set up my smartphone."

[0450] Based on this request, please generate a step-by-step video script for initial smartphone setup.

[0451] 4. Video generation

[0452] The server generates the video using the API of video creation software (e.g., Adobe After Effects) based on the generated script. It combines visual elements and text according to the script to create a video file.

[0453] 5. Saving the video

[0454] The generated video is stored in a cloud storage service (e.g., Amazon S3). The video file is saved with a unique filename, and an access URL is generated for it.

[0455] 6. Generating the Access URL

[0456] The server generates an access URL for the stored video file. This URL is unique and used by the user to view a specific video.

[0457] 7. Notification to the user

[0458] The server sends notification data containing the generated access URL to the device. Notification methods include push notifications, email, and SMS. The device displays this received URL to the user, who then clicks the URL to view the video content.

[0459] storage

[0460] Cloud storage services are used as storage devices to save the generated video files. Specific examples include Google Cloud Storage and Azure Blob Storage.

[0461] This invention makes it possible to quickly and efficiently generate and provide customized video content that meets user needs. Users can obtain videos based on their requests in a short amount of time, improving convenience.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1: User input of requests

[0464] The user enters their request using a device (smartphone or computer). They enter a specific request, such as "I would like instructions on initial smartphone setup," into the request form and press the submit button. This becomes the input data. The output of this step is the user's input data.

[0465] Step 2: Submit your request

[0466] The terminal sends the request data entered by the user to the server. This data includes the request details, request ID, user ID, and timestamp. This data is sent in JSON format. The input is the user's request data, and the output is the request data sent to the server.

[0467] Step 3: Receiving the request

[0468] The server receives request data sent from the terminal via an API endpoint. This received data is stored in temporary storage on the server. The input is the request data sent from the terminal, and the output is the request data stored on the server. Specifically, the data reception process is performed using a web server (for example, Flask or Django).

[0469] Step 4: Analyzing the requirements

[0470] The server analyzes the received request data using natural language processing (NLP) techniques. For example, it might use an NLP library (such as spaCy) to analyze the user's request text and extract keywords like "smartphone" and "initial setup." The input is the request data stored on the server, and the output is the extracted keywords and phrases.

[0471] Step 5: Generate video script

[0472] The server generates a video script using a generative AI model (such as GPT-3) based on the analysis results. During this process, prompt text is entered to automatically generate the script. The input consists of keywords extracted from the analysis results and prompt text, while the output is the generated video script.

[0473] Example of a prompt:

[0474] User A made the following request: "I would like instructions on how to set up my smartphone."

[0475] Based on this request, please generate a step-by-step video script for initial smartphone setup.

[0476] Step 6: Generate the video

[0477] The server generates a video based on the generated script, using the API of video creation software (such as Adobe After Effects). It combines visual elements and text according to the script to create a video file. The input is the generated video script, and the output is the generated video file.

[0478] Step 7: Save the video

[0479] The server saves the generated video file to cloud storage (such as Amazon S3). The file is saved with a unique name, and an access URL is generated. The input is the generated video file, and the output is the access URL of the video stored in cloud storage.

[0480] Step 8: Generate the access URL

[0481] The server generates an access URL for the stored video file. This URL is unique and serves as an access link to a specific video. The input is a video file stored in cloud storage, and the output is the generated access URL.

[0482] Step 9: Notify the user

[0483] The server sends notification data containing the generated access URL to the device. The device then displays this received URL to the user. Notification methods include push notifications, email, and SMS. The input is the access URL, and the output is the notification data displayed on the user's device.

[0484] Step 10: Watch the video

[0485] The user clicks on the URL displayed on their device and watches the video within a web browser or application. The input is the access URL, and the output is the video content the user watches.

[0486] The above is a detailed description of the program processing flow for this system.

[0487] (Application Example 1)

[0488] 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."

[0489] Conventional video content generation systems have struggled to respond quickly and appropriately to users' requests for specific content. Furthermore, no system existed that could accurately understand user needs and automatically generate videos that matched those needs. As a result, users had to spend a considerable amount of time and effort to obtain content that met their requirements, leading to low satisfaction. The problem this invention aims to solve is to automatically generate and provide video content that responds quickly and accurately to user requests.

[0490] 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.

[0491] In this invention, the server includes natural language processing means for analyzing user requests, means for generating video scripts using a generative AI model, and means for automatically saving the generated video to cloud storage and generating an access URL. This makes it possible to quickly generate video content based on user requests and notify the user of a viewable URL.

[0492] "User requests" refer to requests that users enter, such as wanting specific information or content provided in video format.

[0493] "Natural language processing methods" refer to techniques and algorithms used to analyze human language and understand its meaning.

[0494] A "generative AI model" is an artificial intelligence model that uses deep learning or machine learning to generate answers or content for specific problems.

[0495] A "video script" is a set of instructions that specifically describes the content, structure, text, visual elements, and other details of a video.

[0496] "Cloud storage" is a service that allows you to save data to a remote server via the internet.

[0497] An "access URL" is a unique web address that allows a user to access specific content through a web browser or similar application.

[0498] A "user interface" is a display screen that allows users to interact with a system or application through input and output.

[0499] Modes for carrying out the invention

[0500] This invention relates to a system that automatically generates and provides video content based on user requests. This system consists of three main parts: a user interface, a server, and cloud storage.

[0501] 1. User Interface

[0502] A user interface functions as a means for users to input requests. Specifically, this includes applications on smartphones and computers, as well as web pages. When a user inputs a specific request, such as "I want to watch a Python tutorial video," and presses the submit button, that request is sent to the server.

[0503] 2. Server

[0504] The server is responsible for receiving and analyzing user requests. It uses natural language processing techniques to analyze the user's requests and extract keywords and phrases. Then, a generative AI model is used to generate a video script. Specifically, the generative AI model creates a video script explaining the basic usage of Python. Next, video generation software or external APIs are used to create a video file based on the video script. The generated video is stored in cloud storage, and a viewable URL is generated.

[0505] 3. User notifications and displays

[0506] The server notifies the user of the URL to access the generated video and displays it in the user interface. This notification allows the user to access the provided URL and view the desired video content.

[0507] Hardware and software to be used

[0508] Hardware: Web server (PC or cloud server for executing flasks)

[0509] Software: Python, Flask, natural language processing libraries (e.g., NLTK, Spacy), video generation tools or APIs

[0510] Specific example

[0511] If a user types "I want to watch a video tutorial for learning Python," the system will function as follows:

[0512] 1. Users enter and submit their requests using a smartphone or computer interface.

[0513] 2. The server receives the submitted request and parses it using natural language processing.

[0514] 3. The generating AI model generates a video script explaining the basic usage of Python based on the analysis results.

[0515] 4. Generate a video using a video generation tool or API and save it to cloud storage.

[0516] 5. The server generates an access URL and notifies the user.

[0517] 6. The user clicks the URL to watch the video.

[0518] Example of a prompt

[0519] If a user enters "I want to watch a video tutorial for learning Python," analyze the request, extract keywords, and generate a video script explaining the basic usage of Python based on those keywords. Furthermore, generate the video based on that script and create a URL to notify the user.

[0520] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0521] Program processing steps

[0522] Step 1:

[0523] The terminal receives requests from the user. The user requests specific video content and describes the details in the input form. This input data includes the request (e.g., "I want to watch a Python tutorial video"), user ID, timestamp, etc. The entered data is sent to the server when the submit button is pressed.

[0524] Step 2:

[0525] The server receives request data sent from the terminal. The received request data includes the request details, request ID, user ID, and timestamp. Based on this received data, the server prepares for the next analysis.

[0526] Step 3:

[0527] The server analyzes the received request data using natural language processing techniques. Specifically, it uses text analysis libraries (e.g., NLTK, Spacy) to tokenize the request content and extract keywords and related phrases. As a result, a keyword list is generated to accurately understand the user's request.

[0528] Step 4:

[0529] The server generates video scripts using a generative AI model based on keywords obtained through natural language processing. Specifically, keywords are input to the generative AI model to create a script generation prompt (e.g., "Generate a video script explaining the basic usage of Python"). Based on this prompt, the generative AI model outputs a specific video script.

[0530] Step 5:

[0531] The server generates the video based on the generated video script. It automatically generates video files according to the script using video generation tools or external APIs (e.g., video generation APIs). The video files generated at this stage are saved in a specific format (e.g., MP4).

[0532] Step 6:

[0533] The server saves the generated video files to cloud storage. It uses a cloud storage service (e.g., Amazon S3) to upload and save the video files. Once saving is complete, an access URL for the saved video is generated.

[0534] Step 7:

[0535] The server sends a notification to the device containing the generated access URL. This notification includes a link for the user to watch the video. The device displays this notification to the user, allowing the user to watch the video by clicking the URL.

[0536] The above outlines the specific processing steps of the system program that implements the application example.

[0537] 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.

[0538] This invention relates to a system that generates and provides appropriate video content based on a user's input of a request and the user's emotional data. This system mainly consists of four main parts: a user interface, a server, storage, and an emotional engine.

[0539] User interface (terminal)

[0540] The user interface provides a means for users to input requests and recognize emotional data. For example, it uses the camera, microphone, or contactless sensor on a smartphone or computer to acquire emotions from the user's facial expressions and voice tone. As soon as the user types "I would like instructions on how to set up my smartphone" and presses the send button, the emotion engine recognizes the user's emotions.

[0541] Sending and receiving request and emotion data (terminal / server)

[0542] The terminal sends the request data entered by the user and the emotion data recognized by the emotion engine to the server. The transmitted data includes the request content, emotion data, request ID, user ID, and timestamp. The server processes and analyzes this received data.

[0543] Analysis of requests and sentiment data (server)

[0544] The server analyzes the received request data and sentiment data. The analysis uses natural language processing and sentiment recognition technologies. By combining and analyzing the request content and sentiment data, the server understands the user's intentions and the emotions behind them, leading to more specific content requests.

[0545] Emotion-based video script generation (server)

[0546] The server generates a video script based on the analysis results. The video script includes content tailored to the user's requests and emotional data. For example, if the user is excited, a script will be generated that provides a quick and concise explanation.

[0547] Reusing similar video data (server)

[0548] The server searches the database and checks if there are any similar existing videos based on the analysis results. If a similar video is found, it reuses that video and makes adjustments based on sentiment data as needed.

[0549] Video generation and storage (server)

[0550] When new video generation is required, the server generates the video based on the generated video script. It generates videos tailored to the user's needs based on sentiment data and saves the generated video files to cloud storage.

[0551] Generation and notification of access URLs (server / terminal)

[0552] The server generates a URL to access the saved video. It sends notification data containing this access URL to the device. The device displays this received access URL to the user. The user clicks the presented URL to watch the video.

[0553] Specific example

[0554] For example, if a user types "I want instructions on how to set up my smartphone" and is also detected as excited, the system will generate a video script that quickly and concisely explains the procedure. If a similar existing video is found, it will be reused with necessary adjustments. If a new video needs to be generated, it will be created and saved based on the script. The user will then be notified of the URL to access the generated video. By accessing this URL and watching a video with content tailored to their emotions, a more satisfying informational experience can be achieved.

[0555] In this way, the present invention provides a system that enables the automatic generation and provision of video content that takes into account the user's requests and emotions.

[0556] The following describes the processing flow.

[0557] Step 1:

[0558] The user accesses the system from their smartphone or PC, and a request input form is displayed. The user types "I would like instructions on initial smartphone setup" and presses the submit button. At the same time, the device uses its camera and microphone to collect the user's facial expressions and voice, which are then analyzed by an emotion engine.

[0559] Step 2:

[0560] The terminal retrieves the request data entered by the user and the emotion data recognized by the emotion engine. The retrieved data includes the request content, emotion data, request ID, user ID, and timestamp. This is converted to JSON format and sent to the server via an HTTP POST request.

[0561] Step 3:

[0562] The server analyzes the request and sentiment data received at the API endpoint. It executes an analysis module, using natural language processing to understand the request and sentiment recognition to evaluate the user's emotions. The analysis results include details of the request and the user's emotional state.

[0563] Step 4:

[0564] Based on the analysis results, the server generates a video script to meet the request. The video script includes a scenario, text, and visual elements, as well as content adapted to the user's emotions. For example, if the user is frustrated, a concise and easy-to-understand explanation will be prioritized.

[0565] Step 5:

[0566] The server consults the database and searches for similar existing videos based on the analysis results. If a similar video is found, it reuses that video and makes adjustments based on the video script as needed.

[0567] Step 6:

[0568] If no similar video is found, the server generates a new video based on the video script generated in the previous step. A video generation tool or API is used to create the specific video file. For example, the video generation engine integrates narration and visual effects according to the script.

[0569] Step 7:

[0570] The generated video file is uploaded to cloud storage. Once the upload is complete, an access URL for the saved video file is generated. The server records this URL, along with metadata that manages it, in a database.

[0571] Step 8:

[0572] The server sends the generated access URL back to the device in a JSON-formatted response. This response includes the request ID, video URL, and metadata about the user's emotional state.

[0573] Step 9:

[0574] The device analyzes the response received from the server and displays the video URL on the user interface. The user clicks the displayed URL and watches the generated video. For example, the URL displayed might be "https: / / storage.service.com / video / 12345".

[0575] Specific example

[0576] For example, if a user enters "I want instructions on how to set up my smartphone" and simultaneously, sentiment analysis identifies frustration, the system generates a video script containing a concise and quick explanation tailored to the user's request. If a similar existing video is found, it is reused, with necessary adjustments. If a new video is needed, it is generated based on the script and saved to cloud storage. An access URL is then generated and sent to the device. The user accesses this URL and watches the requested video, receiving an explanation that aligns with their emotions.

[0577] As described above, the present invention provides a specific system and processing procedure for automatically generating and providing video content, taking into account user requests and emotional data.

[0578] (Example 2)

[0579] 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".

[0580] Conventional video content generation systems generate videos based solely on user requests, resulting in a failure to provide appropriate content that reflects the user's emotions and circumstances. This also leads to a decline in the user experience and a lack of improvement in the quality of information provided. Furthermore, there is inefficiency in that existing similar videos cannot be utilized, and new content must be generated, incurring costs and time. Therefore, there is a need for a system that enables the automatic generation and delivery of video content that takes into account user requests and emotions.

[0581] 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.

[0582] In this invention, the server includes means for analyzing request and emotion data, means for generating video scripts based on the analysis results, and means for generating videos based on the video scripts. This enables the automatic generation and provision of more effective and efficient video content based on user requests and emotions.

[0583] A "user" refers to an individual or entity that uses the system to input requests and emotional data.

[0584] "Requests" refer to specific requests or requirements that users have for the system.

[0585] "Emotional data" refers to digital information that indicates a user's emotional state, and is data acquired from emotion recognition devices such as cameras and microphones.

[0586] "Device" refers to hardware or software components used by users to input requests or receive notifications from the system.

[0587] An "information processing device" refers to a computer system that performs a series of processes such as receiving and analyzing data, generating scripts, creating videos, and sending notifications.

[0588] "Analysis" refers to the data processing process used to understand the user's intentions and emotions based on received request and emotion data.

[0589] A "video script" is text information that defines the content and structure of the generated video, and refers to a script that describes the specific content of the video that the system aims to create.

[0590] A "storage device" refers to a digital storage device used to store generated video data.

[0591] "Access instructions" refer to information such as URLs or links that guide users to access the generated video.

[0592] "Video" refers to video content that has been generated or adjusted based on the analysis results.

[0593] "Notification" refers to the act of a system sending access instructions or other information to a user.

[0594] "Similar video data" refers to video data from past creations that are relevant to current user requests and can be reused.

[0595] Modes for carrying out the invention

[0596] This invention relates to a system in which a user inputs a request, and appropriate video content is generated and provided based on that request and the user's emotional data. This system consists of four main parts: a user interface, a server, storage, and an emotional engine.

[0597] User interface (terminal)

[0598] The user interface provides a means for users to input requests and recognize emotional data. For example, it uses the camera, microphone, or contactless sensor on a smartphone or computer to acquire emotions from the user's facial expressions and voice tone. As soon as the user types "I would like instructions on how to set up my smartphone" and presses the send button, the emotion engine recognizes the user's emotions.

[0599] Sending and receiving request and emotion data (terminal / server)

[0600] The terminal sends the request data entered by the user and the emotion data recognized by the emotion engine to the server. The transmitted data includes the request content, emotion data, request ID, user ID, and timestamp. The server processes and analyzes this received data.

[0601] Analysis of requests and sentiment data (server)

[0602] The server analyzes the received request data and sentiment data. The analysis methods utilize natural language processing and sentiment recognition technologies. Specifically, general natural language processing APIs can be used for natural language processing, and general sentiment recognition APIs can be used for sentiment recognition. By combining and analyzing the request content and sentiment data, the server understands the user's intentions and the emotions behind them, leading to more specific content requests.

[0603] Emotion-based video script generation (server)

[0604] The server generates a video script based on the analysis results. The video script includes content tailored to the user's requests and emotional data. For example, if the user is excited, a script will be generated that provides a quick and concise explanation.

[0605] Reusing similar video data (server)

[0606] The server searches the database and checks if there are any similar existing videos based on the analysis results. If a similar video is found, it reuses that video and makes adjustments based on sentiment data as needed.

[0607] Video generation and storage (server)

[0608] When new video generation is required, the server generates the video based on the generated video script. It generates videos tailored to the user's needs based on sentiment data and saves the generated video files to cloud storage.

[0609] Generation and notification of access URLs (server / terminal)

[0610] The server generates a URL to access the saved video. It sends notification data containing this access URL to the device. The device displays this received access URL to the user. The user clicks the presented URL to watch the video.

[0611] Specific example

[0612] For example, if a user types "I want instructions on how to set up my smartphone" and is also detected as excited, the system will generate a video script that quickly and concisely explains the procedure. If a similar existing video is found, it will be reused with necessary adjustments. If a new video needs to be generated, it will be created and saved based on the script. The user will then be notified of the URL to access the generated video. By accessing this URL and watching a video with content tailored to their emotions, a more satisfying informational experience can be achieved.

[0613] Example of a prompt:

[0614] Please generate a video script for when a user is excited and says, "I want instructions on how to set up my smartphone."

[0615] Please generate a video script for when a user calmly enters the question, "I would like to learn how to keep a household budget."

[0616] In this way, the present invention provides a system that enables the automatic generation and provision of video content that takes into account the user's requests and emotions.

[0617] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0618] Step 1:

[0619] The user enters their request.

[0620] Users enter their requests using devices such as smartphones or computers. These requests are in text or audio format and include specific requests. For example, a request might be, "I would like instructions on how to set up my smartphone."

[0621] Input: Request via voice or text

[0622] Output: Input request data

[0623] Step 2:

[0624] The device acquires emotional data.

[0625] The device uses its camera and microphone to acquire emotional data from the user's facial expressions and voice tone. This uses emotion recognition technology to recognize, for example, whether the user is excited or calm.

[0626] Input: User facial expression data and voice data

[0627] Output: Emotional data (e.g., excited, calm)

[0628] Step 3:

[0629] The device sends request data and emotion data to the server.

[0630] The terminal sends the user's request data and acquired sentiment data to the server along with the request ID, user ID, and timestamp.

[0631] Input: Request data, sentiment data, request ID, user ID, timestamp

[0632] Output: Data packets sent to the server

[0633] Step 4:

[0634] The server receives and analyzes the data.

[0635] The server analyzes the received request and emotion data. This analysis utilizes natural language processing technology (e.g., Google Cloud Natural Language API) and emotion recognition technology (e.g., Microsoft Azure Emotion API). This allows the server to understand the user's requests and emotions and extract specific needs.

[0636] Input: Request data, sentiment data

[0637] Output: Analysis results (specific content requirements)

[0638] Step 5:

[0639] The server generates the video script.

[0640] The server generates a video script based on the analysis results. The video script includes content tailored to the user's requests and emotional data. For example, if the user is excited, a script will be generated that provides a quick and concise explanation.

[0641] Input: Analysis results

[0642] Output: Video script

[0643] Step 6:

[0644] The server searches the database and checks for similar videos.

[0645] The server searches the database and checks if there are any similar existing videos based on the analysis results. If a similar video is found, it retrieves that video.

[0646] Input: Analysis results

[0647] Output: Similar video data or new video generation flag

[0648] Step 7:

[0649] The server generates or adjusts the video as needed.

[0650] If a similar video is found, the server fine-tunes the video based on sentiment data. If no similar video is found, a new video is generated. This uses a video generation tool (such as FFmpeg) based on the video script.

[0651] Input: Video script, similar video data

[0652] Output: Adjusted video data or newly generated video data

[0653] Step 8:

[0654] The server saves the video to storage and generates an access URL.

[0655] The server saves the generated or edited video to cloud storage. It then generates an access URL and provides this URL to the user.

[0656] Input: Adjusted video data or newly generated video data

[0657] Output: Access URL

[0658] Step 9:

[0659] The device notifies and displays the access URL to the user.

[0660] The device notifies and displays the user of the access URL received from the server. The user can access the video by clicking this URL.

[0661] Input: Access URL

[0662] Output: Access URL displayed to the user

[0663] Through the processing steps described above, this system enables the automatic generation and delivery of video content that takes into account the user's requests and emotions.

[0664] (Application Example 2)

[0665] 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."

[0666] Conventional video content generation systems could only provide uniform content in response to user requests, making it difficult to provide personalized information tailored to individual user emotions and circumstances. This resulted in decreased user satisfaction and problems with users being unable to obtain necessary information quickly and appropriately.

[0667] 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. In this invention, the server includes means for receiving user requests and sentiment data, means for analyzing the data, and means for generating a video script. This makes it possible to automatically generate and provide personalized video content based on user requests and sentiment data.

[0668] "User requests" refer to the information and services that users want the system to provide.

[0669] "Emotional data" refers to information indicating the emotional state obtained by analyzing the user's facial expressions, voice tone, and other sensor data.

[0670] A "server" refers to a central computing device that receives and analyzes user requests and sentiment data, and generates video scripts.

[0671] A "video script" refers to a scenario that specifically describes the content of video material, generated based on user requests and emotional data.

[0672] "Storage" refers to memory devices and cloud services used to save generated video data.

[0673] An "access URL" refers to a unique web address used to access saved video data.

[0674] A "user interface" refers to the devices and software that allow users to input requests and emotional data and receive access URLs.

[0675] "Existing video data" refers to a collection of video content already stored within the system.

[0676] This invention is a system that generates and provides appropriate video content based on user requests and emotional data. This system mainly consists of four main parts: a user interface (terminal), a server, storage, and an emotional engine.

[0677] User interface (terminal)

[0678] The user interface provides a means for users to input and recognize their requests and emotional data. Using devices such as smartphones and head-mounted displays, cameras, microphones, and contactless sensors are used to capture the user's facial expressions and voice tone.

[0679] Sending requests and emotion data (terminal / server)

[0680] The terminal sends the user-entered request and sentiment data to the server. The transmitted data includes the request details, sentiment data, request ID, user ID, and timestamp. The server analyzes the received data.

[0681] Analysis of requests and sentiment data (server)

[0682] The server analyzes data using natural language processing technologies (e.g., NLTK, GPT-4) and emotion recognition technologies (e.g., OpenFace, Affectiva). It combines the request content with emotion data to understand the user's intentions and the emotions behind them. This then materializes the needs for the video script.

[0683] Emotion-based video script generation (server)

[0684] The server generates a video script based on the analysis results. For example, if the user is feeling stressed, a script recommending relaxing movies will be generated. This video script contains content that provides the user with the information they are looking for in a concise and quick manner.

[0685] Reusing similar video data (server)

[0686] The server searches the existing video database and checks for similar content based on the analysis results. If similar videos are found, it is possible to reuse those videos and make adjustments based on sentiment data.

[0687] Video generation and storage (server)

[0688] When a new video needs to be generated, the server creates the video based on the generated video script. This video will include personalized content based on the user's sentiment data. The generated video file is stored in cloud storage (e.g., AWS S3).

[0689] Generation and notification of access URLs (server / terminal)

[0690] The server generates a URL to access the stored video. It sends notification data containing the access URL to the device, which then displays this URL to the user. The user accesses the URL and watches the video. This mechanism improves user satisfaction.

[0691] Specific example

[0692] For example, if a user types "Recommend some relaxing movies" and the emotion engine simultaneously recognizes the user's emotion as "stressful," the server will recommend movies suitable for stress relief. The server adds emotion-based comments to the movie script, selects appropriate content from the video database, and makes adjustments as needed. Finally, it notifies the user of an access URL, and the user can access that URL and watch a personalized video, resulting in a high level of satisfaction.

[0693] Example of a prompt

[0694] "The user typed 'Recommend some relaxing movies.' According to the emotional data, the user is stressed. Please recommend movies that are effective for stress relief. Generate a video script that includes specific recommendations."

[0695] To implement this invention, the server performs the above-described processing to provide personalized content that meets the individual needs of each user.

[0696] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0697] Step 1:

[0698] Users open the application using a smartphone or head-mounted display and input requests and emotional data. Cameras, microphones, and contactless sensors capture the user's facial expressions and voice tone. The input requests are in text format, such as "Recommend some relaxing movies." Emotional data is analyzed in real time and may be recognized as, for example, "stressful."

[0699] Step 2:

[0700] The terminal sends the acquired user request data and sentiment data to the server. The transmitted data includes the request details, sentiment data, request ID, user ID, and timestamp. This data is received by the server.

[0701] Step 3:

[0702] The server analyzes the received request data and sentiment data. Specifically, it analyzes the request content using natural language processing technology (e.g., GPT-4) and the sentiment data using sentiment recognition technology (e.g., OpenFace). This allows the server to understand the user's intent and emotional state. In this step, the input is the user's request and sentiment data, and the output is the specific content request resulting from the analysis.

[0703] Step 4:

[0704] The server generates video scripts based on the analysis results. Using a generation AI model, it creates a script, for example, "Recommends relaxing movies." This script includes comments and explanations based on the user's emotional data. The input is the analysis results, and the output is a specific video script.

[0705] Step 5:

[0706] The server searches the video database and checks for similar content based on the analysis results. It selects and outputs appropriate content from existing video data. If it does not exist, new video generation is required.

[0707] Step 6:

[0708] The server generates new videos based on video scripts as needed. It uses a video generation engine to create video content that conforms to the specified requirements. The input is a video script, and the output is the generated video file.

[0709] Step 7:

[0710] The server saves the generated video file to cloud storage (e.g., AWS S3). A URL for accessing the saved video is generated. The input is the video file, and the output is the access URL.

[0711] Step 8:

[0712] The server notifies the user's device of the generated access URL. The device receives this URL and displays it to the user. The user clicks this URL to view personalized video content. The input is the access URL, and the output is the notification to the user.

[0713] This series of steps allows users to efficiently obtain video content that matches their emotions and desires.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] [Third Embodiment]

[0718] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0719] 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.

[0720] 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).

[0721] 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.

[0722] 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.

[0723] 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).

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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".

[0730] This invention relates to a system that automates a series of processes from when a user inputs a request, to when corresponding video content is generated and delivered to the user. This system mainly consists of three main parts: a user interface, a server, and storage.

[0731] User interface (terminal)

[0732] A user interface provides a means for users to input requests. Examples include applications and web pages that run on devices such as smartphones and personal computers. Users enter specific requests, such as "I would like instructions on initial smartphone setup," into a request input form and then press the submit button.

[0733] Sending and receiving requests (terminal / server)

[0734] The terminal sends the request data entered by the user to the server. The data sent includes the user's request details, request ID, user ID, and timestamp. The server then processes and analyzes this received data.

[0735] Request analysis (server)

[0736] The server analyzes the received request. Specific examples of analysis include using natural language processing techniques to understand the user's request and extract keywords and phrases. Based on the analysis results, it evaluates what kind of video content is suitable.

[0737] Video script generation (server)

[0738] The server generates a video script based on the analysis of the request. The video script describes the specific scenario, text, and visual elements to be used. Based on this script, preparations are made to create a new video using the video generation API or script.

[0739] Video generation and storage (server)

[0740] The server generates videos based on the generated video script. Video generation software or tools are used to create video files according to the script. The generated videos are stored in cloud storage or on the server.

[0741] Generation and notification of access URLs (server / terminal)

[0742] The server generates an access URL for the saved video. It sends notification data containing this access URL to the device. The device displays this received URL to the user. The user can view the video content by clicking the presented URL.

[0743] Specific example

[0744] For example, if a user types and submits "I would like instructions on how to set up my smartphone," the process proceeds as follows: The device sends the request data to the server, which analyzes this data. Based on the analysis, a video script about setting up a smartphone is generated. Next, the server generates the video according to the script and saves the generated video to cloud storage. Then, an access URL for the video is generated and notified to the user. The user accesses that URL and watches the video explaining how to set up their smartphone.

[0745] In this way, the present invention realizes a system that can automatically generate video content in response to user requests and provide it quickly and efficiently.

[0746] The following describes the processing flow.

[0747] Understood. Below, I will explain the program's processing in detail, step by step.

[0748] Step 1:

[0749] The user accesses the system from their smartphone or PC and a request form is displayed. The user enters "I would like instructions on initial smartphone setup" and presses the submit button.

[0750] Step 2:

[0751] The terminal retrieves the request data entered by the user. This retrieved data includes the request details, request ID, user ID, and timestamp. This data is converted to JSON format and sent to the server via an HTTP POST request.

[0752] Step 3:

[0753] The server analyzes the request data received at the API endpoint. The received data is passed to a default parsing module, which performs natural language processing to understand the request and extract keywords and phrases.

[0754] Step 4:

[0755] Based on the analysis results, the server generates a video script to fulfill the request. The video script contains detailed descriptions of the scenario, text, visual elements, and more.

[0756] Step 5:

[0757] The server checks the database and searches for similar existing videos. If a similar video is found, it retrieves the video path and proceeds with the reuse process.

[0758] Step 6:

[0759] If no similar video is found, the server generates a new video based on the video script generated in the previous step. A video generation tool or API is used to create the video file.

[0760] Step 7:

[0761] Upload the generated video file to cloud storage. Once the upload is complete, an access URL for the saved video file will be generated.

[0762] Step 8:

[0763] The server sends the generated access URL back to the terminal in a JSON-formatted response. The response includes the request ID, video URL, metadata, etc.

[0764] Step 9:

[0765] The device analyzes the response received from the server and displays the video URL on the user interface. The user clicks the displayed URL to watch the video.

[0766] The above describes the specific processing flow in the system of the present invention. By executing the relevant operations in detail at each step, it is possible to quickly and efficiently provide video content that meets the user's needs.

[0767] (Example 1)

[0768] 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."

[0769] Conventional video content generation systems have struggled to quickly generate and deliver customized videos tailored to user requests. In particular, the process of automatically generating video scripts based on specific user requests and then creating videos based on those scripts was technically complex and time-consuming. Furthermore, there were insufficient means to quickly and appropriately notify users of the URL to access the generated videos.

[0770] 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.

[0771] In this invention, the server includes server means for receiving requests, natural language processing means for analyzing the received requests, and means using a generative AI model for generating video scripts based on the analysis results. This makes it possible to quickly and efficiently generate and provide customized video content based on requests entered by the user.

[0772] A "user" is someone who uses this system to input requests and request video content.

[0773] A "server" is a central computing system that receives requests, analyzes them, generates video scripts, and produces videos.

[0774] "Natural language processing means" refers to technologies and methods for analyzing text data entered by a user and understanding its meaning, and is used to extract keywords and phrases.

[0775] A "generative AI model" is an artificial intelligence model that can automatically generate text or video scripts based on input data, and specifically includes models such as GPT-3.

[0776] A "request" refers to the specific demands or requests that a user enters into the system.

[0777] A "video script" is a script that contains details such as the video's scenario, text, and visual elements, and forms the basis of the generated video.

[0778] A "video" is a generated visual content created based on user requests.

[0779] An "access URL" is a unique web address used to access the generated video.

[0780] "Storage" refers to a storage device or cloud storage service used to save generated video files.

[0781] "Notification method" refers to a method of informing the user of the access URL for the generated video, and includes push notifications, email, SMS, etc.

[0782] A "user interface" is an interactive screen that allows users to input requests into a system or view notified access URLs.

[0783] This invention relates to a system that automates a series of processes from when a user inputs a request, to when corresponding video content is generated and delivered to the user. This system consists of three main parts: a user interface, a server, and storage. Specific embodiments of this invention are described below.

[0784] User interface (terminal)

[0785] Users enter their requests using devices such as smartphones or personal computers. They use a dedicated application or webpage running on their device to enter specific requests, such as "I would like instructions on how to set up my smartphone," into a request form and then press the submit button.

[0786] server

[0787] The server is a central computing system that receives and processes request data sent by users. The server generates video content that meets user requests using several methods, including the following:

[0788] 1. Receiving the request

[0789] The server receives request data sent from the terminal via the API endpoint. This data, in JSON format, includes the user's request details, request ID, user ID, and timestamp.

[0790] 2. Analysis of Requirements

[0791] The server analyzes the received request data using natural language processing (NLP) tools. Specifically, it uses NLP libraries (such as spaCy or NLTK) to understand the user's request and extract keywords and phrases.

[0792] 3. Generating the video script

[0793] Based on the analysis results, the server generates video scripts using a generative AI model (e.g., GPT-3). By inputting prompts to the generative AI model, it automatically creates text and scenarios.

[0794] example:

[0795] User A made the following request: "I would like instructions on how to set up my smartphone."

[0796] Based on this request, please generate a step-by-step video script for initial smartphone setup.

[0797] 4. Video generation

[0798] The server generates the video using the API of video creation software (e.g., Adobe After Effects) based on the generated script. It combines visual elements and text according to the script to create a video file.

[0799] 5. Saving the video

[0800] The generated video is stored in a cloud storage service (e.g., Amazon S3). The video file is saved with a unique filename, and an access URL is generated for it.

[0801] 6. Generating the Access URL

[0802] The server generates an access URL for the stored video file. This URL is unique and used by the user to view a specific video.

[0803] 7. Notification to the user

[0804] The server sends notification data containing the generated access URL to the device. Notification methods include push notifications, email, and SMS. The device displays this received URL to the user, who then clicks the URL to view the video content.

[0805] storage

[0806] Cloud storage services are used as storage devices to save the generated video files. Specific examples include Google Cloud Storage and Azure Blob Storage.

[0807] This invention makes it possible to quickly and efficiently generate and provide customized video content that meets user needs. Users can obtain videos based on their requests in a short amount of time, improving convenience.

[0808] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0809] Step 1: User input of requests

[0810] The user enters their request using a device (smartphone or computer). They enter a specific request, such as "I would like instructions on initial smartphone setup," into the request form and press the submit button. This becomes the input data. The output of this step is the user's input data.

[0811] Step 2: Submit your request

[0812] The terminal sends the request data entered by the user to the server. This data includes the request details, request ID, user ID, and timestamp. This data is sent in JSON format. The input is the user's request data, and the output is the request data sent to the server.

[0813] Step 3: Receiving the request

[0814] The server receives request data sent from the terminal via an API endpoint. This received data is stored in temporary storage on the server. The input is the request data sent from the terminal, and the output is the request data stored on the server. Specifically, the data reception process is performed using a web server (for example, Flask or Django).

[0815] Step 4: Analyzing the requirements

[0816] The server analyzes the received request data using natural language processing (NLP) techniques. For example, it might use an NLP library (such as spaCy) to analyze the user's request text and extract keywords like "smartphone" and "initial setup." The input is the request data stored on the server, and the output is the extracted keywords and phrases.

[0817] Step 5: Generate video script

[0818] The server generates a video script using a generative AI model (such as GPT-3) based on the analysis results. During this process, prompt text is entered to automatically generate the script. The input consists of keywords extracted from the analysis results and prompt text, while the output is the generated video script.

[0819] Example of a prompt:

[0820] User A made the following request: "I would like instructions on how to set up my smartphone."

[0821] Based on this request, please generate a step-by-step video script for initial smartphone setup.

[0822] Step 6: Generate the video

[0823] The server generates a video based on the generated script, using the API of video creation software (such as Adobe After Effects). It combines visual elements and text according to the script to create a video file. The input is the generated video script, and the output is the generated video file.

[0824] Step 7: Save the video

[0825] The server saves the generated video file to cloud storage (such as Amazon S3). The file is saved with a unique name, and an access URL is generated. The input is the generated video file, and the output is the access URL of the video stored in cloud storage.

[0826] Step 8: Generate the access URL

[0827] The server generates an access URL for the stored video file. This URL is unique and serves as an access link to a specific video. The input is a video file stored in cloud storage, and the output is the generated access URL.

[0828] Step 9: Notify the user

[0829] The server sends notification data containing the generated access URL to the device. The device then displays this received URL to the user. Notification methods include push notifications, email, and SMS. The input is the access URL, and the output is the notification data displayed on the user's device.

[0830] Step 10: Watch the video

[0831] The user clicks on the URL displayed on their device and watches the video within a web browser or application. The input is the access URL, and the output is the video content the user watches.

[0832] The above is a detailed description of the program processing flow for this system.

[0833] (Application Example 1)

[0834] 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."

[0835] Conventional video content generation systems have struggled to respond quickly and appropriately to users' requests for specific content. Furthermore, no system existed that could accurately understand user needs and automatically generate videos that matched those needs. As a result, users had to spend a considerable amount of time and effort to obtain content that met their requirements, leading to low satisfaction. The problem this invention aims to solve is to automatically generate and provide video content that responds quickly and accurately to user requests.

[0836] 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.

[0837] In this invention, the server includes natural language processing means for analyzing user requests, means for generating video scripts using a generative AI model, and means for automatically saving the generated video to cloud storage and generating an access URL. This makes it possible to quickly generate video content based on user requests and notify the user of a viewable URL.

[0838] "User requests" refer to requests that users enter, such as wanting specific information or content provided in video format.

[0839] "Natural language processing methods" refer to techniques and algorithms used to analyze human language and understand its meaning.

[0840] A "generative AI model" is an artificial intelligence model that uses deep learning or machine learning to generate answers or content for specific problems.

[0841] A "video script" is a set of instructions that specifically describes the content, structure, text, visual elements, and other details of a video.

[0842] "Cloud storage" is a service that allows you to save data to a remote server via the internet.

[0843] An "access URL" is a unique web address that allows a user to access specific content through a web browser or similar application.

[0844] A "user interface" is a display screen that allows users to interact with a system or application through input and output.

[0845] Modes for carrying out the invention

[0846] This invention relates to a system that automatically generates and provides video content based on user requests. This system consists of three main parts: a user interface, a server, and cloud storage.

[0847] 1. User Interface

[0848] A user interface functions as a means for users to input requests. Specifically, this includes applications on smartphones and computers, as well as web pages. When a user inputs a specific request, such as "I want to watch a Python tutorial video," and presses the submit button, that request is sent to the server.

[0849] 2. Server

[0850] The server is responsible for receiving and analyzing user requests. It uses natural language processing techniques to analyze the user's requests and extract keywords and phrases. Then, a generative AI model is used to generate a video script. Specifically, the generative AI model creates a video script explaining the basic usage of Python. Next, video generation software or external APIs are used to create a video file based on the video script. The generated video is stored in cloud storage, and a viewable URL is generated.

[0851] 3. User notifications and displays

[0852] The server notifies the user of the URL to access the generated video and displays it in the user interface. This notification allows the user to access the provided URL and view the desired video content.

[0853] Hardware and software to be used

[0854] Hardware: Web server (PC or cloud server for executing flasks)

[0855] Software: Python, Flask, natural language processing libraries (e.g., NLTK, Spacy), video generation tools or APIs

[0856] Specific example

[0857] If a user types "I want to watch a video tutorial for learning Python," the system will function as follows:

[0858] 1. Users enter and submit their requests using a smartphone or computer interface.

[0859] 2. The server receives the submitted request and parses it using natural language processing.

[0860] 3. The generating AI model generates a video script explaining the basic usage of Python based on the analysis results.

[0861] 4. Generate a video using a video generation tool or API and save it to cloud storage.

[0862] 5. The server generates an access URL and notifies the user.

[0863] 6. The user clicks the URL to watch the video.

[0864] Example of a prompt

[0865] If a user enters "I want to watch a video tutorial for learning Python," analyze the request, extract keywords, and generate a video script explaining the basic usage of Python based on those keywords. Furthermore, generate the video based on that script and create a URL to notify the user.

[0866] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0867] Program processing steps

[0868] Step 1:

[0869] The terminal receives requests from the user. The user requests specific video content and describes the details in the input form. This input data includes the request (e.g., "I want to watch a Python tutorial video"), user ID, timestamp, etc. The entered data is sent to the server when the submit button is pressed.

[0870] Step 2:

[0871] The server receives request data sent from the terminal. The received request data includes the request details, request ID, user ID, and timestamp. Based on this received data, the server prepares for the next analysis.

[0872] Step 3:

[0873] The server analyzes the received request data using natural language processing techniques. Specifically, it uses text analysis libraries (e.g., NLTK, Spacy) to tokenize the request content and extract keywords and related phrases. As a result, a keyword list is generated to accurately understand the user's request.

[0874] Step 4:

[0875] The server generates video scripts using a generative AI model based on keywords obtained through natural language processing. Specifically, keywords are input to the generative AI model to create a script generation prompt (e.g., "Generate a video script explaining the basic usage of Python"). Based on this prompt, the generative AI model outputs a specific video script.

[0876] Step 5:

[0877] The server generates the video based on the generated video script. It automatically generates video files according to the script using video generation tools or external APIs (e.g., video generation APIs). The video files generated at this stage are saved in a specific format (e.g., MP4).

[0878] Step 6:

[0879] The server saves the generated video files to cloud storage. It uses a cloud storage service (e.g., Amazon S3) to upload and save the video files. Once saving is complete, an access URL for the saved video is generated.

[0880] Step 7:

[0881] The server sends a notification to the device containing the generated access URL. This notification includes a link for the user to watch the video. The device displays this notification to the user, allowing the user to watch the video by clicking the URL.

[0882] The above outlines the specific processing steps of the system program that implements the application example.

[0883] 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.

[0884] This invention relates to a system that generates and provides appropriate video content based on a user's input of a request and the user's emotional data. This system mainly consists of four main parts: a user interface, a server, storage, and an emotional engine.

[0885] User interface (terminal)

[0886] The user interface provides a means for users to input requests and recognize emotional data. For example, it uses the camera, microphone, or contactless sensor on a smartphone or computer to acquire emotions from the user's facial expressions and voice tone. As soon as the user types "I would like instructions on how to set up my smartphone" and presses the send button, the emotion engine recognizes the user's emotions.

[0887] Sending and receiving request and emotion data (terminal / server)

[0888] The terminal sends the request data entered by the user and the emotion data recognized by the emotion engine to the server. The transmitted data includes the request content, emotion data, request ID, user ID, and timestamp. The server processes and analyzes this received data.

[0889] Analysis of requests and sentiment data (server)

[0890] The server analyzes the received request data and sentiment data. The analysis uses natural language processing and sentiment recognition technologies. By combining and analyzing the request content and sentiment data, the server understands the user's intentions and the emotions behind them, leading to more specific content requests.

[0891] Emotion-based video script generation (server)

[0892] The server generates a video script based on the analysis results. The video script includes content tailored to the user's requests and emotional data. For example, if the user is excited, a script will be generated that provides a quick and concise explanation.

[0893] Reusing similar video data (server)

[0894] The server searches the database and checks if there are any similar existing videos based on the analysis results. If a similar video is found, it reuses that video and makes adjustments based on sentiment data as needed.

[0895] Video generation and storage (server)

[0896] When new video generation is required, the server generates the video based on the generated video script. It generates videos tailored to the user's needs based on sentiment data and saves the generated video files to cloud storage.

[0897] Generation and notification of access URLs (server / terminal)

[0898] The server generates a URL to access the saved video. It sends notification data containing this access URL to the device. The device displays this received access URL to the user. The user clicks the presented URL to watch the video.

[0899] Specific example

[0900] For example, if a user types "I want instructions on how to set up my smartphone" and is also detected as excited, the system will generate a video script that quickly and concisely explains the procedure. If a similar existing video is found, it will be reused with necessary adjustments. If a new video needs to be generated, it will be created and saved based on the script. The user will then be notified of the URL to access the generated video. By accessing this URL and watching a video with content tailored to their emotions, a more satisfying informational experience can be achieved.

[0901] In this way, the present invention provides a system that enables the automatic generation and provision of video content that takes into account the user's requests and emotions.

[0902] The following describes the processing flow.

[0903] Step 1:

[0904] The user accesses the system from their smartphone or PC, and a request input form is displayed. The user types "I would like instructions on initial smartphone setup" and presses the submit button. At the same time, the device uses its camera and microphone to collect the user's facial expressions and voice, which are then analyzed by an emotion engine.

[0905] Step 2:

[0906] The terminal retrieves the request data entered by the user and the emotion data recognized by the emotion engine. The retrieved data includes the request content, emotion data, request ID, user ID, and timestamp. This is converted to JSON format and sent to the server via an HTTP POST request.

[0907] Step 3:

[0908] The server analyzes the request and sentiment data received at the API endpoint. It executes an analysis module, using natural language processing to understand the request and sentiment recognition to evaluate the user's emotions. The analysis results include details of the request and the user's emotional state.

[0909] Step 4:

[0910] Based on the analysis results, the server generates a video script to meet the request. The video script includes a scenario, text, and visual elements, as well as content adapted to the user's emotions. For example, if the user is frustrated, a concise and easy-to-understand explanation will be prioritized.

[0911] Step 5:

[0912] The server consults the database and searches for similar existing videos based on the analysis results. If a similar video is found, it reuses that video and makes adjustments based on the video script as needed.

[0913] Step 6:

[0914] If no similar video is found, the server generates a new video based on the video script generated in the previous step. A video generation tool or API is used to create the specific video file. For example, the video generation engine integrates narration and visual effects according to the script.

[0915] Step 7:

[0916] The generated video file is uploaded to cloud storage. Once the upload is complete, an access URL for the saved video file is generated. The server records this URL, along with metadata that manages it, in a database.

[0917] Step 8:

[0918] The server sends the generated access URL back to the device in a JSON-formatted response. This response includes the request ID, video URL, and metadata about the user's emotional state.

[0919] Step 9:

[0920] The device analyzes the response received from the server and displays the video URL on the user interface. The user clicks the displayed URL and watches the generated video. For example, the URL displayed might be "https: / / storage.service.com / video / 12345".

[0921] Specific example

[0922] For example, if a user enters "I want instructions on how to set up my smartphone" and simultaneously, sentiment analysis identifies frustration, the system generates a video script containing a concise and quick explanation tailored to the user's request. If a similar existing video is found, it is reused, with necessary adjustments. If a new video is needed, it is generated based on the script and saved to cloud storage. An access URL is then generated and sent to the device. The user accesses this URL and watches the requested video, receiving an explanation that aligns with their emotions.

[0923] As described above, the present invention provides a specific system and processing procedure for automatically generating and providing video content, taking into account user requests and emotional data.

[0924] (Example 2)

[0925] 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."

[0926] Conventional video content generation systems generate videos based solely on user requests, resulting in a failure to provide appropriate content that reflects the user's emotions and circumstances. This also leads to a decline in the user experience and a lack of improvement in the quality of information provided. Furthermore, there is inefficiency in that existing similar videos cannot be utilized, and new content must be generated, incurring costs and time. Therefore, there is a need for a system that enables the automatic generation and delivery of video content that takes into account user requests and emotions.

[0927] 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.

[0928] In this invention, the server includes means for analyzing request and emotion data, means for generating video scripts based on the analysis results, and means for generating videos based on the video scripts. This enables the automatic generation and provision of more effective and efficient video content based on user requests and emotions.

[0929] A "user" refers to an individual or entity that uses the system to input requests and emotional data.

[0930] "Requests" refer to specific requests or requirements that users have for the system.

[0931] "Emotional data" refers to digital information that indicates a user's emotional state, and is data acquired from emotion recognition devices such as cameras and microphones.

[0932] "Device" refers to hardware or software components used by users to input requests or receive notifications from the system.

[0933] An "information processing device" refers to a computer system that performs a series of processes such as receiving and analyzing data, generating scripts, creating videos, and sending notifications.

[0934] "Analysis" refers to the data processing process used to understand the user's intentions and emotions based on received request and emotion data.

[0935] A "video script" is text information that defines the content and structure of the generated video, and refers to a script that describes the specific content of the video that the system aims to create.

[0936] A "storage device" refers to a digital storage device used to store generated video data.

[0937] "Access instructions" refer to information such as URLs or links that guide users to access the generated video.

[0938] "Video" refers to video content that has been generated or adjusted based on the analysis results.

[0939] "Notification" refers to the act of a system sending access instructions or other information to a user.

[0940] "Similar video data" refers to video data from past creations that are relevant to current user requests and can be reused.

[0941] Modes for carrying out the invention

[0942] This invention relates to a system in which a user inputs a request, and appropriate video content is generated and provided based on that request and the user's emotional data. This system consists of four main parts: a user interface, a server, storage, and an emotional engine.

[0943] User interface (terminal)

[0944] The user interface provides a means for users to input requests and recognize emotional data. For example, it uses the camera, microphone, or contactless sensor on a smartphone or computer to acquire emotions from the user's facial expressions and voice tone. As soon as the user types "I would like instructions on how to set up my smartphone" and presses the send button, the emotion engine recognizes the user's emotions.

[0945] Sending and receiving request and emotion data (terminal / server)

[0946] The terminal sends the request data entered by the user and the emotion data recognized by the emotion engine to the server. The transmitted data includes the request content, emotion data, request ID, user ID, and timestamp. The server processes and analyzes this received data.

[0947] Analysis of requests and sentiment data (server)

[0948] The server analyzes the received request data and sentiment data. The analysis methods utilize natural language processing and sentiment recognition technologies. Specifically, general natural language processing APIs can be used for natural language processing, and general sentiment recognition APIs can be used for sentiment recognition. By combining and analyzing the request content and sentiment data, the server understands the user's intentions and the emotions behind them, leading to more specific content requests.

[0949] Emotion-based video script generation (server)

[0950] The server generates a video script based on the analysis results. The video script includes content tailored to the user's requests and emotional data. For example, if the user is excited, a script will be generated that provides a quick and concise explanation.

[0951] Reusing similar video data (server)

[0952] The server searches the database and checks if there are any similar existing videos based on the analysis results. If a similar video is found, it reuses that video and makes adjustments based on sentiment data as needed.

[0953] Video generation and storage (server)

[0954] When new video generation is required, the server generates the video based on the generated video script. It generates videos tailored to the user's needs based on sentiment data and saves the generated video files to cloud storage.

[0955] Generation and notification of access URLs (server / terminal)

[0956] The server generates a URL to access the saved video. It sends notification data containing this access URL to the device. The device displays this received access URL to the user. The user clicks the presented URL to watch the video.

[0957] Specific example

[0958] For example, if a user types "I want instructions on how to set up my smartphone" and is also detected as excited, the system will generate a video script that quickly and concisely explains the procedure. If a similar existing video is found, it will be reused with necessary adjustments. If a new video needs to be generated, it will be created and saved based on the script. The user will then be notified of the URL to access the generated video. By accessing this URL and watching a video with content tailored to their emotions, a more satisfying informational experience can be achieved.

[0959] Example of a prompt:

[0960] Please generate a video script for when a user is excited and says, "I want instructions on how to set up my smartphone."

[0961] Please generate a video script for when a user calmly enters the question, "I would like to learn how to keep a household budget."

[0962] In this way, the present invention provides a system that enables the automatic generation and provision of video content that takes into account the user's requests and emotions.

[0963] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0964] Step 1:

[0965] The user enters their request.

[0966] Users enter their requests using devices such as smartphones or computers. These requests are in text or audio format and include specific requests. For example, a request might be, "I would like instructions on how to set up my smartphone."

[0967] Input: Request via voice or text

[0968] Output: Input request data

[0969] Step 2:

[0970] The device acquires emotional data.

[0971] The device uses its camera and microphone to acquire emotional data from the user's facial expressions and voice tone. This uses emotion recognition technology to recognize, for example, whether the user is excited or calm.

[0972] Input: User facial expression data and voice data

[0973] Output: Emotional data (e.g., excited, calm)

[0974] Step 3:

[0975] The device sends request data and emotion data to the server.

[0976] The terminal sends the user's request data and acquired sentiment data to the server along with the request ID, user ID, and timestamp.

[0977] Input: Request data, sentiment data, request ID, user ID, timestamp

[0978] Output: Data packets sent to the server

[0979] Step 4:

[0980] The server receives and analyzes the data.

[0981] The server analyzes the received request and emotion data. This analysis utilizes natural language processing technology (e.g., Google Cloud Natural Language API) and emotion recognition technology (e.g., Microsoft Azure Emotion API). This allows the server to understand the user's requests and emotions and extract specific needs.

[0982] Input: Request data, sentiment data

[0983] Output: Analysis results (specific content requirements)

[0984] Step 5:

[0985] The server generates the video script.

[0986] The server generates a video script based on the analysis results. The video script includes content tailored to the user's requests and emotional data. For example, if the user is excited, a script will be generated that provides a quick and concise explanation.

[0987] Input: Analysis results

[0988] Output: Video script

[0989] Step 6:

[0990] The server searches the database and checks for similar videos.

[0991] The server searches the database and checks if there are any similar existing videos based on the analysis results. If a similar video is found, it retrieves that video.

[0992] Input: Analysis results

[0993] Output: Similar video data or new video generation flag

[0994] Step 7:

[0995] The server generates or adjusts the video as needed.

[0996] If a similar video is found, the server fine-tunes the video based on sentiment data. If no similar video is found, a new video is generated. This uses a video generation tool (such as FFmpeg) based on the video script.

[0997] Input: Video script, similar video data

[0998] Output: Adjusted video data or newly generated video data

[0999] Step 8:

[1000] The server saves the video to storage and generates an access URL.

[1001] The server saves the generated or edited video to cloud storage. It then generates an access URL and provides this URL to the user.

[1002] Input: Adjusted video data or newly generated video data

[1003] Output: Access URL

[1004] Step 9:

[1005] The device notifies and displays the access URL to the user.

[1006] The device notifies and displays the user of the access URL received from the server. The user can access the video by clicking this URL.

[1007] Input: Access URL

[1008] Output: Access URL displayed to the user

[1009] Through the processing steps described above, this system enables the automatic generation and delivery of video content that takes into account the user's requests and emotions.

[1010] (Application Example 2)

[1011] 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."

[1012] Conventional video content generation systems could only provide uniform content in response to user requests, making it difficult to provide personalized information tailored to individual user emotions and circumstances. This resulted in decreased user satisfaction and problems with users being unable to obtain necessary information quickly and appropriately.

[1013] 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. In this invention, the server includes means for receiving user requests and sentiment data, means for analyzing the data, and means for generating a video script. This makes it possible to automatically generate and provide personalized video content based on user requests and sentiment data.

[1014] "User requests" refer to the information and services that users want the system to provide.

[1015] "Emotional data" refers to information indicating the emotional state obtained by analyzing the user's facial expressions, voice tone, and other sensor data.

[1016] A "server" refers to a central computing device that receives and analyzes user requests and sentiment data, and generates video scripts.

[1017] A "video script" refers to a scenario that specifically describes the content of video material, generated based on user requests and emotional data.

[1018] "Storage" refers to memory devices and cloud services used to save generated video data.

[1019] An "access URL" refers to a unique web address used to access saved video data.

[1020] A "user interface" refers to the devices and software that allow users to input requests and emotional data and receive access URLs.

[1021] "Existing video data" refers to a collection of video content already stored within the system.

[1022] This invention is a system that generates and provides appropriate video content based on user requests and emotional data. This system mainly consists of four main parts: a user interface (terminal), a server, storage, and an emotional engine.

[1023] User interface (terminal)

[1024] The user interface provides a means for users to input and recognize their requests and emotional data. Using devices such as smartphones and head-mounted displays, cameras, microphones, and contactless sensors are used to capture the user's facial expressions and voice tone.

[1025] Sending requests and emotion data (terminal / server)

[1026] The terminal sends the user-entered request and sentiment data to the server. The transmitted data includes the request details, sentiment data, request ID, user ID, and timestamp. The server analyzes the received data.

[1027] Analysis of requests and sentiment data (server)

[1028] The server analyzes data using natural language processing technologies (e.g., NLTK, GPT-4) and emotion recognition technologies (e.g., OpenFace, Affectiva). It combines the request content with emotion data to understand the user's intentions and the emotions behind them. This then materializes the needs for the video script.

[1029] Emotion-based video script generation (server)

[1030] The server generates a video script based on the analysis results. For example, if the user is feeling stressed, a script recommending relaxing movies will be generated. This video script contains content that provides the user with the information they are looking for in a concise and quick manner.

[1031] Reusing similar video data (server)

[1032] The server searches the existing video database and checks for similar content based on the analysis results. If similar videos are found, it is possible to reuse those videos and make adjustments based on sentiment data.

[1033] Video generation and storage (server)

[1034] When a new video needs to be generated, the server creates the video based on the generated video script. This video will include personalized content based on the user's sentiment data. The generated video file is stored in cloud storage (e.g., AWS S3).

[1035] Generation and notification of access URLs (server / terminal)

[1036] The server generates a URL to access the stored video. It sends notification data containing the access URL to the device, which then displays this URL to the user. The user accesses the URL and watches the video. This mechanism improves user satisfaction.

[1037] Specific example

[1038] For example, if a user types "Recommend some relaxing movies" and the emotion engine simultaneously recognizes the user's emotion as "stressful," the server will recommend movies suitable for stress relief. The server adds emotion-based comments to the movie script, selects appropriate content from the video database, and makes adjustments as needed. Finally, it notifies the user of an access URL, and the user can access that URL and watch a personalized video, resulting in a high level of satisfaction.

[1039] Example of a prompt

[1040] "The user typed 'Recommend some relaxing movies.' According to the emotional data, the user is stressed. Please recommend movies that are effective for stress relief. Generate a video script that includes specific recommendations."

[1041] To implement this invention, the server performs the above-described processing to provide personalized content that meets the individual needs of each user.

[1042] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1043] Step 1:

[1044] Users open the application using a smartphone or head-mounted display and input requests and emotional data. Cameras, microphones, and contactless sensors capture the user's facial expressions and voice tone. The input requests are in text format, such as "Recommend some relaxing movies." Emotional data is analyzed in real time and may be recognized as, for example, "stressful."

[1045] Step 2:

[1046] The terminal sends the acquired user request data and sentiment data to the server. The transmitted data includes the request details, sentiment data, request ID, user ID, and timestamp. This data is received by the server.

[1047] Step 3:

[1048] The server analyzes the received request data and sentiment data. Specifically, it analyzes the request content using natural language processing technology (e.g., GPT-4) and the sentiment data using sentiment recognition technology (e.g., OpenFace). This allows the server to understand the user's intent and emotional state. In this step, the input is the user's request and sentiment data, and the output is the specific content request resulting from the analysis.

[1049] Step 4:

[1050] The server generates video scripts based on the analysis results. Using a generation AI model, it creates a script, for example, "Recommends relaxing movies." This script includes comments and explanations based on the user's emotional data. The input is the analysis results, and the output is a specific video script.

[1051] Step 5:

[1052] The server searches the video database and checks for similar content based on the analysis results. It selects and outputs appropriate content from existing video data. If it does not exist, new video generation is required.

[1053] Step 6:

[1054] The server generates new videos based on video scripts as needed. It uses a video generation engine to create video content that conforms to the specified requirements. The input is a video script, and the output is the generated video file.

[1055] Step 7:

[1056] The server saves the generated video file to cloud storage (e.g., AWS S3). A URL for accessing the saved video is generated. The input is the video file, and the output is the access URL.

[1057] Step 8:

[1058] The server notifies the user's device of the generated access URL. The device receives this URL and displays it to the user. The user clicks this URL to view personalized video content. The input is the access URL, and the output is the notification to the user.

[1059] This series of steps allows users to efficiently obtain video content that matches their emotions and desires.

[1060] 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.

[1061] 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.

[1062] 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.

[1063] [Fourth Embodiment]

[1064] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1065] 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.

[1066] 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).

[1067] 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.

[1068] 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.

[1069] 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).

[1070] 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.

[1071] 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.

[1072] 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.

[1073] 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.

[1074] 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.

[1075] 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.

[1076] 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".

[1077] This invention relates to a system that automates a series of processes from when a user inputs a request, to when corresponding video content is generated and delivered to the user. This system mainly consists of three main parts: a user interface, a server, and storage.

[1078] User interface (terminal)

[1079] A user interface provides a means for users to input requests. Examples include applications and web pages that run on devices such as smartphones and personal computers. Users enter specific requests, such as "I would like instructions on initial smartphone setup," into a request input form and then press the submit button.

[1080] Sending and receiving requests (terminal / server)

[1081] The terminal sends the request data entered by the user to the server. The data sent includes the user's request details, request ID, user ID, and timestamp. The server then processes and analyzes this received data.

[1082] Request analysis (server)

[1083] The server analyzes the received request. Specific examples of analysis include using natural language processing techniques to understand the user's request and extract keywords and phrases. Based on the analysis results, it evaluates what kind of video content is suitable.

[1084] Video script generation (server)

[1085] The server generates a video script based on the analysis of the request. The video script describes the specific scenario, text, and visual elements to be used. Based on this script, preparations are made to create a new video using the video generation API or script.

[1086] Video generation and storage (server)

[1087] The server generates videos based on the generated video script. Video generation software or tools are used to create video files according to the script. The generated videos are stored in cloud storage or on the server.

[1088] Generation and notification of access URLs (server / terminal)

[1089] The server generates an access URL for the saved video. It sends notification data containing this access URL to the device. The device displays this received URL to the user. The user can view the video content by clicking the presented URL.

[1090] Specific example

[1091] For example, if a user types and submits "I would like instructions on how to set up my smartphone," the process proceeds as follows: The device sends the request data to the server, which analyzes this data. Based on the analysis, a video script about setting up a smartphone is generated. Next, the server generates the video according to the script and saves the generated video to cloud storage. Then, an access URL for the video is generated and notified to the user. The user accesses that URL and watches the video explaining how to set up their smartphone.

[1092] In this way, the present invention realizes a system that can automatically generate video content in response to user requests and provide it quickly and efficiently.

[1093] The following describes the processing flow.

[1094] Understood. Below, I will explain the program's processing in detail, step by step.

[1095] Step 1:

[1096] The user accesses the system from their smartphone or PC and a request form is displayed. The user enters "I would like instructions on initial smartphone setup" and presses the submit button.

[1097] Step 2:

[1098] The terminal retrieves the request data entered by the user. This retrieved data includes the request details, request ID, user ID, and timestamp. This data is converted to JSON format and sent to the server via an HTTP POST request.

[1099] Step 3:

[1100] The server analyzes the request data received at the API endpoint. The received data is passed to a default parsing module, which performs natural language processing to understand the request and extract keywords and phrases.

[1101] Step 4:

[1102] Based on the analysis results, the server generates a video script to fulfill the request. The video script contains detailed descriptions of the scenario, text, visual elements, and more.

[1103] Step 5:

[1104] The server checks the database and searches for similar existing videos. If a similar video is found, it retrieves the video path and proceeds with the reuse process.

[1105] Step 6:

[1106] If no similar video is found, the server generates a new video based on the video script generated in the previous step. A video generation tool or API is used to create the video file.

[1107] Step 7:

[1108] Upload the generated video file to cloud storage. Once the upload is complete, an access URL for the saved video file will be generated.

[1109] Step 8:

[1110] The server sends the generated access URL back to the terminal in a JSON-formatted response. The response includes the request ID, video URL, metadata, etc.

[1111] Step 9:

[1112] The device analyzes the response received from the server and displays the video URL on the user interface. The user clicks the displayed URL to watch the video.

[1113] The above describes the specific processing flow in the system of the present invention. By executing the relevant operations in detail at each step, it is possible to quickly and efficiently provide video content that meets the user's needs.

[1114] (Example 1)

[1115] 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".

[1116] Conventional video content generation systems have struggled to quickly generate and deliver customized videos tailored to user requests. In particular, the process of automatically generating video scripts based on specific user requests and then creating videos based on those scripts was technically complex and time-consuming. Furthermore, there were insufficient means to quickly and appropriately notify users of the URL to access the generated videos.

[1117] 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.

[1118] In this invention, the server includes server means for receiving requests, natural language processing means for analyzing the received requests, and means using a generative AI model for generating video scripts based on the analysis results. This makes it possible to quickly and efficiently generate and provide customized video content based on requests entered by the user.

[1119] A "user" is someone who uses this system to input requests and request video content.

[1120] A "server" is a central computing system that receives requests, analyzes them, generates video scripts, and produces videos.

[1121] "Natural language processing means" refers to technologies and methods for analyzing text data entered by a user and understanding its meaning, and is used to extract keywords and phrases.

[1122] A "generative AI model" is an artificial intelligence model that can automatically generate text or video scripts based on input data, and specifically includes models such as GPT-3.

[1123] A "request" refers to the specific demands or requests that a user enters into the system.

[1124] A "video script" is a script that contains details such as the video's scenario, text, and visual elements, and forms the basis of the generated video.

[1125] A "video" is a generated visual content created based on user requests.

[1126] An "access URL" is a unique web address used to access the generated video.

[1127] "Storage" refers to a storage device or cloud storage service used to save generated video files.

[1128] "Notification method" refers to a method of informing the user of the access URL for the generated video, and includes push notifications, email, SMS, etc.

[1129] A "user interface" is an interactive screen that allows users to input requests into a system or view notified access URLs.

[1130] This invention relates to a system that automates a series of processes from when a user inputs a request, to when corresponding video content is generated and delivered to the user. This system consists of three main parts: a user interface, a server, and storage. Specific embodiments of this invention are described below.

[1131] User interface (terminal)

[1132] Users enter their requests using devices such as smartphones or personal computers. They use a dedicated application or webpage running on their device to enter specific requests, such as "I would like instructions on how to set up my smartphone," into a request form and then press the submit button.

[1133] server

[1134] The server is a central computing system that receives and processes request data sent by users. The server generates video content that meets user requests using several methods, including the following:

[1135] 1. Receiving the request

[1136] The server receives request data sent from the terminal via the API endpoint. This data, in JSON format, includes the user's request details, request ID, user ID, and timestamp.

[1137] 2. Analysis of Requirements

[1138] The server analyzes the received request data using natural language processing (NLP) tools. Specifically, it uses NLP libraries (such as spaCy or NLTK) to understand the user's request and extract keywords and phrases.

[1139] 3. Generating the video script

[1140] Based on the analysis results, the server generates video scripts using a generative AI model (e.g., GPT-3). By inputting prompts to the generative AI model, it automatically creates text and scenarios.

[1141] example:

[1142] User A made the following request: "I would like instructions on how to set up my smartphone."

[1143] Based on this request, please generate a step-by-step video script for initial smartphone setup.

[1144] 4. Video generation

[1145] The server generates the video using the API of video creation software (e.g., Adobe After Effects) based on the generated script. It combines visual elements and text according to the script to create a video file.

[1146] 5. Saving the video

[1147] The generated video is stored in a cloud storage service (e.g., Amazon S3). The video file is saved with a unique filename, and an access URL is generated for it.

[1148] 6. Generating the Access URL

[1149] The server generates an access URL for the stored video file. This URL is unique and used by the user to view a specific video.

[1150] 7. Notification to the user

[1151] The server sends notification data containing the generated access URL to the device. Notification methods include push notifications, email, and SMS. The device displays this received URL to the user, who then clicks the URL to view the video content.

[1152] storage

[1153] Cloud storage services are used as storage devices to save the generated video files. Specific examples include Google Cloud Storage and Azure Blob Storage.

[1154] This invention makes it possible to quickly and efficiently generate and provide customized video content that meets user needs. Users can obtain videos based on their requests in a short amount of time, improving convenience.

[1155] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1156] Step 1: User input of requests

[1157] The user enters their request using a device (smartphone or computer). They enter a specific request, such as "I would like instructions on initial smartphone setup," into the request form and press the submit button. This becomes the input data. The output of this step is the user's input data.

[1158] Step 2: Submit your request

[1159] The terminal sends the request data entered by the user to the server. This data includes the request details, request ID, user ID, and timestamp. This data is sent in JSON format. The input is the user's request data, and the output is the request data sent to the server.

[1160] Step 3: Receiving the request

[1161] The server receives request data sent from the terminal via an API endpoint. This received data is stored in temporary storage on the server. The input is the request data sent from the terminal, and the output is the request data stored on the server. Specifically, the data reception process is performed using a web server (for example, Flask or Django).

[1162] Step 4: Analyzing the requirements

[1163] The server analyzes the received request data using natural language processing (NLP) techniques. For example, it might use an NLP library (such as spaCy) to analyze the user's request text and extract keywords like "smartphone" and "initial setup." The input is the request data stored on the server, and the output is the extracted keywords and phrases.

[1164] Step 5: Generate video script

[1165] The server generates a video script using a generative AI model (such as GPT-3) based on the analysis results. During this process, prompt text is entered to automatically generate the script. The input consists of keywords extracted from the analysis results and prompt text, while the output is the generated video script.

[1166] Example of a prompt:

[1167] User A made the following request: "I would like instructions on how to set up my smartphone."

[1168] Based on this request, please generate a step-by-step video script for initial smartphone setup.

[1169] Step 6: Generate the video

[1170] The server generates a video based on the generated script, using the API of video creation software (such as Adobe After Effects). It combines visual elements and text according to the script to create a video file. The input is the generated video script, and the output is the generated video file.

[1171] Step 7: Save the video

[1172] The server saves the generated video file to cloud storage (such as Amazon S3). The file is saved with a unique name, and an access URL is generated. The input is the generated video file, and the output is the access URL of the video stored in cloud storage.

[1173] Step 8: Generate the access URL

[1174] The server generates an access URL for the stored video file. This URL is unique and serves as an access link to a specific video. The input is a video file stored in cloud storage, and the output is the generated access URL.

[1175] Step 9: Notify the user

[1176] The server sends notification data containing the generated access URL to the device. The device then displays this received URL to the user. Notification methods include push notifications, email, and SMS. The input is the access URL, and the output is the notification data displayed on the user's device.

[1177] Step 10: Watch the video

[1178] The user clicks on the URL displayed on their device and watches the video within a web browser or application. The input is the access URL, and the output is the video content the user watches.

[1179] The above is a detailed description of the program processing flow for this system.

[1180] (Application Example 1)

[1181] 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".

[1182] Conventional video content generation systems have struggled to respond quickly and appropriately to users' requests for specific content. Furthermore, no system existed that could accurately understand user needs and automatically generate videos that matched those needs. As a result, users had to spend a considerable amount of time and effort to obtain content that met their requirements, leading to low satisfaction. The problem this invention aims to solve is to automatically generate and provide video content that responds quickly and accurately to user requests.

[1183] 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.

[1184] In this invention, the server includes natural language processing means for analyzing user requests, means for generating video scripts using a generative AI model, and means for automatically saving the generated video to cloud storage and generating an access URL. This makes it possible to quickly generate video content based on user requests and notify the user of a viewable URL.

[1185] "User requests" refer to requests that users enter, such as wanting specific information or content provided in video format.

[1186] "Natural language processing methods" refer to techniques and algorithms used to analyze human language and understand its meaning.

[1187] A "generative AI model" is an artificial intelligence model that uses deep learning or machine learning to generate answers or content for specific problems.

[1188] A "video script" is a set of instructions that specifically describes the content, structure, text, visual elements, and other details of a video.

[1189] "Cloud storage" is a service that allows you to save data to a remote server via the internet.

[1190] An "access URL" is a unique web address that allows a user to access specific content through a web browser or similar application.

[1191] A "user interface" is a display screen that allows users to interact with a system or application through input and output.

[1192] Modes for carrying out the invention

[1193] This invention relates to a system that automatically generates and provides video content based on user requests. This system consists of three main parts: a user interface, a server, and cloud storage.

[1194] 1. User Interface

[1195] A user interface functions as a means for users to input requests. Specifically, this includes applications on smartphones and computers, as well as web pages. When a user inputs a specific request, such as "I want to watch a Python tutorial video," and presses the submit button, that request is sent to the server.

[1196] 2. Server

[1197] The server is responsible for receiving and analyzing user requests. It uses natural language processing techniques to analyze the user's requests and extract keywords and phrases. Then, a generative AI model is used to generate a video script. Specifically, the generative AI model creates a video script explaining the basic usage of Python. Next, video generation software or external APIs are used to create a video file based on the video script. The generated video is stored in cloud storage, and a viewable URL is generated.

[1198] 3. User notifications and displays

[1199] The server notifies the user of the URL to access the generated video and displays it in the user interface. This notification allows the user to access the provided URL and view the desired video content.

[1200] Hardware and software to be used

[1201] Hardware: Web server (PC or cloud server for executing flasks)

[1202] Software: Python, Flask, natural language processing libraries (e.g., NLTK, Spacy), video generation tools or APIs

[1203] Specific example

[1204] If a user types "I want to watch a video tutorial for learning Python," the system will function as follows:

[1205] 1. Users enter and submit their requests using a smartphone or computer interface.

[1206] 2. The server receives the submitted request and parses it using natural language processing.

[1207] 3. The generating AI model generates a video script explaining the basic usage of Python based on the analysis results.

[1208] 4. Generate a video using a video generation tool or API and save it to cloud storage.

[1209] 5. The server generates an access URL and notifies the user.

[1210] 6. The user clicks the URL to watch the video.

[1211] Example of a prompt

[1212] If a user enters "I want to watch a video tutorial for learning Python," analyze the request, extract keywords, and generate a video script explaining the basic usage of Python based on those keywords. Furthermore, generate the video based on that script and create a URL to notify the user.

[1213] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1214] Program processing steps

[1215] Step 1:

[1216] The terminal receives requests from the user. The user requests specific video content and describes the details in the input form. This input data includes the request (e.g., "I want to watch a Python tutorial video"), user ID, timestamp, etc. The entered data is sent to the server when the submit button is pressed.

[1217] Step 2:

[1218] The server receives request data sent from the terminal. The received request data includes the request details, request ID, user ID, and timestamp. Based on this received data, the server prepares for the next analysis.

[1219] Step 3:

[1220] The server analyzes the received request data using natural language processing techniques. Specifically, it uses text analysis libraries (e.g., NLTK, Spacy) to tokenize the request content and extract keywords and related phrases. As a result, a keyword list is generated to accurately understand the user's request.

[1221] Step 4:

[1222] The server generates video scripts using a generative AI model based on keywords obtained through natural language processing. Specifically, keywords are input to the generative AI model to create a script generation prompt (e.g., "Generate a video script explaining the basic usage of Python"). Based on this prompt, the generative AI model outputs a specific video script.

[1223] Step 5:

[1224] The server generates the video based on the generated video script. It automatically generates video files according to the script using video generation tools or external APIs (e.g., video generation APIs). The video files generated at this stage are saved in a specific format (e.g., MP4).

[1225] Step 6:

[1226] The server saves the generated video files to cloud storage. It uses a cloud storage service (e.g., Amazon S3) to upload and save the video files. Once saving is complete, an access URL for the saved video is generated.

[1227] Step 7:

[1228] The server sends a notification to the device containing the generated access URL. This notification includes a link for the user to watch the video. The device displays this notification to the user, allowing the user to watch the video by clicking the URL.

[1229] The above outlines the specific processing steps of the system program that implements the application example.

[1230] 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.

[1231] This invention relates to a system that generates and provides appropriate video content based on a user's input of a request and the user's emotional data. This system mainly consists of four main parts: a user interface, a server, storage, and an emotional engine.

[1232] User interface (terminal)

[1233] The user interface provides a means for users to input requests and recognize emotional data. For example, it uses the camera, microphone, or contactless sensor on a smartphone or computer to acquire emotions from the user's facial expressions and voice tone. As soon as the user types "I would like instructions on how to set up my smartphone" and presses the send button, the emotion engine recognizes the user's emotions.

[1234] Sending and receiving request and emotion data (terminal / server)

[1235] The terminal sends the request data entered by the user and the emotion data recognized by the emotion engine to the server. The transmitted data includes the request content, emotion data, request ID, user ID, and timestamp. The server processes and analyzes this received data.

[1236] Analysis of requests and sentiment data (server)

[1237] The server analyzes the received request data and sentiment data. The analysis uses natural language processing and sentiment recognition technologies. By combining and analyzing the request content and sentiment data, the server understands the user's intentions and the emotions behind them, leading to more specific content requests.

[1238] Emotion-based video script generation (server)

[1239] The server generates a video script based on the analysis results. The video script includes content tailored to the user's requests and emotional data. For example, if the user is excited, a script will be generated that provides a quick and concise explanation.

[1240] Reusing similar video data (server)

[1241] The server searches the database and checks if there are any similar existing videos based on the analysis results. If a similar video is found, it reuses that video and makes adjustments based on sentiment data as needed.

[1242] Video generation and storage (server)

[1243] When new video generation is required, the server generates the video based on the generated video script. It generates videos tailored to the user's needs based on sentiment data and saves the generated video files to cloud storage.

[1244] Generation and notification of access URLs (server / terminal)

[1245] The server generates a URL to access the saved video. It sends notification data containing this access URL to the device. The device displays this received access URL to the user. The user clicks the presented URL to watch the video.

[1246] Specific example

[1247] For example, if a user types "I want instructions on how to set up my smartphone" and is also detected as excited, the system will generate a video script that quickly and concisely explains the procedure. If a similar existing video is found, it will be reused with necessary adjustments. If a new video needs to be generated, it will be created and saved based on the script. The user will then be notified of the URL to access the generated video. By accessing this URL and watching a video with content tailored to their emotions, a more satisfying informational experience can be achieved.

[1248] In this way, the present invention provides a system that enables the automatic generation and provision of video content that takes into account the user's requests and emotions.

[1249] The following describes the processing flow.

[1250] Step 1:

[1251] The user accesses the system from their smartphone or PC, and a request input form is displayed. The user types "I would like instructions on initial smartphone setup" and presses the submit button. At the same time, the device uses its camera and microphone to collect the user's facial expressions and voice, which are then analyzed by an emotion engine.

[1252] Step 2:

[1253] The terminal retrieves the request data entered by the user and the emotion data recognized by the emotion engine. The retrieved data includes the request content, emotion data, request ID, user ID, and timestamp. This is converted to JSON format and sent to the server via an HTTP POST request.

[1254] Step 3:

[1255] The server analyzes the request and sentiment data received at the API endpoint. It executes an analysis module, using natural language processing to understand the request and sentiment recognition to evaluate the user's emotions. The analysis results include details of the request and the user's emotional state.

[1256] Step 4:

[1257] Based on the analysis results, the server generates a video script to meet the request. The video script includes a scenario, text, and visual elements, as well as content adapted to the user's emotions. For example, if the user is frustrated, a concise and easy-to-understand explanation will be prioritized.

[1258] Step 5:

[1259] The server consults the database and searches for similar existing videos based on the analysis results. If a similar video is found, it reuses that video and makes adjustments based on the video script as needed.

[1260] Step 6:

[1261] If no similar video is found, the server generates a new video based on the video script generated in the previous step. A video generation tool or API is used to create the specific video file. For example, the video generation engine integrates narration and visual effects according to the script.

[1262] Step 7:

[1263] The generated video file is uploaded to cloud storage. Once the upload is complete, an access URL for the saved video file is generated. The server records this URL, along with metadata that manages it, in a database.

[1264] Step 8:

[1265] The server sends the generated access URL back to the device in a JSON-formatted response. This response includes the request ID, video URL, and metadata about the user's emotional state.

[1266] Step 9:

[1267] The device analyzes the response received from the server and displays the video URL on the user interface. The user clicks the displayed URL and watches the generated video. For example, the URL displayed might be "https: / / storage.service.com / video / 12345".

[1268] Specific example

[1269] For example, if a user enters "I want instructions on how to set up my smartphone" and simultaneously, sentiment analysis identifies frustration, the system generates a video script containing a concise and quick explanation tailored to the user's request. If a similar existing video is found, it is reused, with necessary adjustments. If a new video is needed, it is generated based on the script and saved to cloud storage. An access URL is then generated and sent to the device. The user accesses this URL and watches the requested video, receiving an explanation that aligns with their emotions.

[1270] As described above, the present invention provides a specific system and processing procedure for automatically generating and providing video content, taking into account user requests and emotional data.

[1271] (Example 2)

[1272] 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".

[1273] Conventional video content generation systems generate videos based solely on user requests, resulting in a failure to provide appropriate content that reflects the user's emotions and circumstances. This also leads to a decline in the user experience and a lack of improvement in the quality of information provided. Furthermore, there is inefficiency in that existing similar videos cannot be utilized, and new content must be generated, incurring costs and time. Therefore, there is a need for a system that enables the automatic generation and delivery of video content that takes into account user requests and emotions.

[1274] 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.

[1275] In this invention, the server includes means for analyzing request and emotion data, means for generating video scripts based on the analysis results, and means for generating videos based on the video scripts. This enables the automatic generation and provision of more effective and efficient video content based on user requests and emotions.

[1276] A "user" refers to an individual or entity that uses the system to input requests and emotional data.

[1277] "Requests" refer to specific requests or requirements that users have for the system.

[1278] "Emotional data" refers to digital information that indicates a user's emotional state, and is data acquired from emotion recognition devices such as cameras and microphones.

[1279] "Device" refers to hardware or software components used by users to input requests or receive notifications from the system.

[1280] An "information processing device" refers to a computer system that performs a series of processes such as receiving and analyzing data, generating scripts, creating videos, and sending notifications.

[1281] "Analysis" refers to the data processing process used to understand the user's intentions and emotions based on received request and emotion data.

[1282] A "video script" is text information that defines the content and structure of the generated video, and refers to a script that describes the specific content of the video that the system aims to create.

[1283] A "storage device" refers to a digital storage device used to store generated video data.

[1284] "Access instructions" refer to information such as URLs or links that guide users to access the generated video.

[1285] "Video" refers to video content that has been generated or adjusted based on the analysis results.

[1286] "Notification" refers to the act of a system sending access instructions or other information to a user.

[1287] "Similar video data" refers to video data from past creations that are relevant to current user requests and can be reused.

[1288] Modes for carrying out the invention

[1289] This invention relates to a system in which a user inputs a request, and appropriate video content is generated and provided based on that request and the user's emotional data. This system consists of four main parts: a user interface, a server, storage, and an emotional engine.

[1290] User interface (terminal)

[1291] The user interface provides a means for users to input requests and recognize emotional data. For example, it uses the camera, microphone, or contactless sensor on a smartphone or computer to acquire emotions from the user's facial expressions and voice tone. As soon as the user types "I would like instructions on how to set up my smartphone" and presses the send button, the emotion engine recognizes the user's emotions.

[1292] Sending and receiving request and emotion data (terminal / server)

[1293] The terminal sends the request data entered by the user and the emotion data recognized by the emotion engine to the server. The transmitted data includes the request content, emotion data, request ID, user ID, and timestamp. The server processes and analyzes this received data.

[1294] Analysis of requests and sentiment data (server)

[1295] The server analyzes the received request data and sentiment data. The analysis methods utilize natural language processing and sentiment recognition technologies. Specifically, general natural language processing APIs can be used for natural language processing, and general sentiment recognition APIs can be used for sentiment recognition. By combining and analyzing the request content and sentiment data, the server understands the user's intentions and the emotions behind them, leading to more specific content requests.

[1296] Emotion-based video script generation (server)

[1297] The server generates a video script based on the analysis results. The video script includes content tailored to the user's requests and emotional data. For example, if the user is excited, a script will be generated that provides a quick and concise explanation.

[1298] Reusing similar video data (server)

[1299] The server searches the database and checks if there are any similar existing videos based on the analysis results. If a similar video is found, it reuses that video and makes adjustments based on sentiment data as needed.

[1300] Video generation and storage (server)

[1301] When new video generation is required, the server generates the video based on the generated video script. It generates videos tailored to the user's needs based on sentiment data and saves the generated video files to cloud storage.

[1302] Generation and notification of access URLs (server / terminal)

[1303] The server generates a URL to access the saved video. It sends notification data containing this access URL to the device. The device displays this received access URL to the user. The user clicks the presented URL to watch the video.

[1304] Specific example

[1305] For example, if a user types "I want instructions on how to set up my smartphone" and is also detected as excited, the system will generate a video script that quickly and concisely explains the procedure. If a similar existing video is found, it will be reused with necessary adjustments. If a new video needs to be generated, it will be created and saved based on the script. The user will then be notified of the URL to access the generated video. By accessing this URL and watching a video with content tailored to their emotions, a more satisfying informational experience can be achieved.

[1306] Example of a prompt:

[1307] Please generate a video script for when a user is excited and says, "I want instructions on how to set up my smartphone."

[1308] Please generate a video script for when a user calmly enters the question, "I would like to learn how to keep a household budget."

[1309] In this way, the present invention provides a system that enables the automatic generation and provision of video content that takes into account the user's requests and emotions.

[1310] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1311] Step 1:

[1312] The user enters their request.

[1313] Users enter their requests using devices such as smartphones or computers. These requests are in text or audio format and include specific requests. For example, a request might be, "I would like instructions on how to set up my smartphone."

[1314] Input: Request via voice or text

[1315] Output: Input request data

[1316] Step 2:

[1317] The device acquires emotional data.

[1318] The device uses its camera and microphone to acquire emotional data from the user's facial expressions and voice tone. This uses emotion recognition technology to recognize, for example, whether the user is excited or calm.

[1319] Input: User facial expression data and voice data

[1320] Output: Emotional data (e.g., excited, calm)

[1321] Step 3:

[1322] The device sends request data and emotion data to the server.

[1323] The terminal sends the user's request data and acquired sentiment data to the server along with the request ID, user ID, and timestamp.

[1324] Input: Request data, sentiment data, request ID, user ID, timestamp

[1325] Output: Data packets sent to the server

[1326] Step 4:

[1327] The server receives and analyzes the data.

[1328] The server analyzes the received request and emotion data. This analysis utilizes natural language processing technology (e.g., Google Cloud Natural Language API) and emotion recognition technology (e.g., Microsoft Azure Emotion API). This allows the server to understand the user's requests and emotions and extract specific needs.

[1329] Input: Request data, sentiment data

[1330] Output: Analysis results (specific content requirements)

[1331] Step 5:

[1332] The server generates the video script.

[1333] The server generates a video script based on the analysis results. The video script includes content tailored to the user's requests and emotional data. For example, if the user is excited, a script will be generated that provides a quick and concise explanation.

[1334] Input: Analysis results

[1335] Output: Video script

[1336] Step 6:

[1337] The server searches the database and checks for similar videos.

[1338] The server searches the database and checks if there are any similar existing videos based on the analysis results. If a similar video is found, it retrieves that video.

[1339] Input: Analysis results

[1340] Output: Similar video data or new video generation flag

[1341] Step 7:

[1342] The server generates or adjusts the video as needed.

[1343] If a similar video is found, the server fine-tunes the video based on sentiment data. If no similar video is found, a new video is generated. This uses a video generation tool (such as FFmpeg) based on the video script.

[1344] Input: Video script, similar video data

[1345] Output: Adjusted video data or newly generated video data

[1346] Step 8:

[1347] The server saves the video to storage and generates an access URL.

[1348] The server saves the generated or edited video to cloud storage. It then generates an access URL and provides this URL to the user.

[1349] Input: Adjusted video data or newly generated video data

[1350] Output: Access URL

[1351] Step 9:

[1352] The device notifies and displays the access URL to the user.

[1353] The device notifies and displays the user of the access URL received from the server. The user can access the video by clicking this URL.

[1354] Input: Access URL

[1355] Output: Access URL displayed to the user

[1356] Through the processing steps described above, this system enables the automatic generation and delivery of video content that takes into account the user's requests and emotions.

[1357] (Application Example 2)

[1358] 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".

[1359] Conventional video content generation systems could only provide uniform content in response to user requests, making it difficult to provide personalized information tailored to individual user emotions and circumstances. This resulted in decreased user satisfaction and problems with users being unable to obtain necessary information quickly and appropriately.

[1360] 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. In this invention, the server includes means for receiving user requests and sentiment data, means for analyzing the data, and means for generating a video script. This makes it possible to automatically generate and provide personalized video content based on user requests and sentiment data.

[1361] "User requests" refer to the information and services that users want the system to provide.

[1362] "Emotional data" refers to information indicating the emotional state obtained by analyzing the user's facial expressions, voice tone, and other sensor data.

[1363] A "server" refers to a central computing device that receives and analyzes user requests and sentiment data, and generates video scripts.

[1364] A "video script" refers to a scenario that specifically describes the content of video material, generated based on user requests and emotional data.

[1365] "Storage" refers to memory devices and cloud services used to save generated video data.

[1366] An "access URL" refers to a unique web address used to access saved video data.

[1367] A "user interface" refers to the devices and software that allow users to input requests and emotional data and receive access URLs.

[1368] "Existing video data" refers to a collection of video content already stored within the system.

[1369] This invention is a system that generates and provides appropriate video content based on user requests and emotional data. This system mainly consists of four main parts: a user interface (terminal), a server, storage, and an emotional engine.

[1370] User interface (terminal)

[1371] The user interface provides a means for users to input and recognize their requests and emotional data. Using devices such as smartphones and head-mounted displays, cameras, microphones, and contactless sensors are used to capture the user's facial expressions and voice tone.

[1372] Sending requests and emotion data (terminal / server)

[1373] The terminal sends the user-entered request and sentiment data to the server. The transmitted data includes the request details, sentiment data, request ID, user ID, and timestamp. The server analyzes the received data.

[1374] Analysis of requests and sentiment data (server)

[1375] The server analyzes data using natural language processing technologies (e.g., NLTK, GPT-4) and emotion recognition technologies (e.g., OpenFace, Affectiva). It combines the request content with emotion data to understand the user's intentions and the emotions behind them. This then materializes the needs for the video script.

[1376] Emotion-based video script generation (server)

[1377] The server generates a video script based on the analysis results. For example, if the user is feeling stressed, a script recommending relaxing movies will be generated. This video script contains content that provides the user with the information they are looking for in a concise and quick manner.

[1378] Reusing similar video data (server)

[1379] The server searches the existing video database and checks for similar content based on the analysis results. If similar videos are found, it is possible to reuse those videos and make adjustments based on sentiment data.

[1380] Video generation and storage (server)

[1381] When a new video needs to be generated, the server creates the video based on the generated video script. This video will include personalized content based on the user's sentiment data. The generated video file is stored in cloud storage (e.g., AWS S3).

[1382] Generation and notification of access URLs (server / terminal)

[1383] The server generates a URL to access the stored video. It sends notification data containing the access URL to the device, which then displays this URL to the user. The user accesses the URL and watches the video. This mechanism improves user satisfaction.

[1384] Specific example

[1385] For example, if a user types "Recommend some relaxing movies" and the emotion engine simultaneously recognizes the user's emotion as "stressful," the server will recommend movies suitable for stress relief. The server adds emotion-based comments to the movie script, selects appropriate content from the video database, and makes adjustments as needed. Finally, it notifies the user of an access URL, and the user can access that URL and watch a personalized video, resulting in a high level of satisfaction.

[1386] Example of a prompt

[1387] "The user typed 'Recommend some relaxing movies.' According to the emotional data, the user is stressed. Please recommend movies that are effective for stress relief. Generate a video script that includes specific recommendations."

[1388] To implement this invention, the server performs the above-described processing to provide personalized content that meets the individual needs of each user.

[1389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1390] Step 1:

[1391] Users open the application using a smartphone or head-mounted display and input requests and emotional data. Cameras, microphones, and contactless sensors capture the user's facial expressions and voice tone. The input requests are in text format, such as "Recommend some relaxing movies." Emotional data is analyzed in real time and may be recognized as, for example, "stressful."

[1392] Step 2:

[1393] The terminal sends the acquired user request data and sentiment data to the server. The transmitted data includes the request details, sentiment data, request ID, user ID, and timestamp. This data is received by the server.

[1394] Step 3:

[1395] The server analyzes the received request data and sentiment data. Specifically, it analyzes the request content using natural language processing technology (e.g., GPT-4) and the sentiment data using sentiment recognition technology (e.g., OpenFace). This allows the server to understand the user's intent and emotional state. In this step, the input is the user's request and sentiment data, and the output is the specific content request resulting from the analysis.

[1396] Step 4:

[1397] The server generates video scripts based on the analysis results. Using a generation AI model, it creates a script, for example, "Recommends relaxing movies." This script includes comments and explanations based on the user's emotional data. The input is the analysis results, and the output is a specific video script.

[1398] Step 5:

[1399] The server searches the video database and checks for similar content based on the analysis results. It selects and outputs appropriate content from existing video data. If it does not exist, new video generation is required.

[1400] Step 6:

[1401] The server generates new videos based on video scripts as needed. It uses a video generation engine to create video content that conforms to the specified requirements. The input is a video script, and the output is the generated video file.

[1402] Step 7:

[1403] The server saves the generated video file to cloud storage (e.g., AWS S3). A URL for accessing the saved video is generated. The input is the video file, and the output is the access URL.

[1404] Step 8:

[1405] The server notifies the user's device of the generated access URL. The device receives this URL and displays it to the user. The user clicks this URL to view personalized video content. The input is the access URL, and the output is the notification to the user.

[1406] This series of steps allows users to efficiently obtain video content that matches their emotions and desires.

[1407] 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.

[1408] 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.

[1409] 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 robot 414.

[1410] 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.

[1411] 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.

[1412] 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.

[1413] 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.

[1414] 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.

[1415] 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."

[1416] 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.

[1417] 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.

[1418] 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.

[1419] 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.

[1420] 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.

[1421] 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.

[1422] 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.

[1423] 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.

[1424] 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.

[1425] 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.

[1426] 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.

[1427] 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.

[1428] The following is further disclosed regarding the embodiments described above.

[1429] (Claim 1)

[1430] A means for users to input their requests,

[1431] A server means for receiving the aforementioned request,

[1432] Means for analyzing the received request,

[1433] A means for generating a video script based on the aforementioned analysis results,

[1434] Means for generating a video based on the aforementioned video script,

[1435] A means for saving the generated video to storage and generating an access URL,

[1436] A means for notifying the user of the aforementioned access URL,

[1437] A system that includes this.

[1438] (Claim 2)

[1439] The system according to claim 1, which includes means for a server receiving the aforementioned request to search for and reuse similar existing video data.

[1440] (Claim 3)

[1441] The system according to claim 1, further comprising means for displaying the access URL of the generated video on the user interface.

[1442] "Example 1"

[1443] (Claim 1)

[1444] A means for users to input their requests,

[1445] A server means for receiving the aforementioned request,

[1446] A natural language processing means for analyzing the received request,

[1447] A means using a generative AI model to generate a video script based on the aforementioned analysis results,

[1448] Means for generating a video based on the aforementioned video script,

[1449] A means for saving the generated video to storage and generating an access URL,

[1450] A means for notifying the user of the aforementioned access URL,

[1451] A system that includes this.

[1452] (Claim 2)

[1453] The system according to claim 1, comprising means for inputting prompt text into the generating AI model and automatically generating a video script.

[1454] (Claim 3)

[1455] A means for displaying the access URL of the generated video on the user interface,

[1456] The system according to claim 1, which includes means for inputting the request using the user interface.

[1457] "Application Example 1"

[1458] (Claim 1)

[1459] A means for users to input their requests,

[1460] A server means for receiving the aforementioned request,

[1461] A natural language processing means for analyzing the received request,

[1462] A generation AI model means for generating a video script based on the aforementioned analysis results,

[1463] A request means for generating a video based on the aforementioned video script,

[1464] A means for saving the generated video to storage and generating an access URL,

[1465] A means for notifying the user of the aforementioned access URL,

[1466] Means for displaying the aforementioned URL on the user interface,

[1467] A system that includes this.

[1468] (Claim 2)

[1469] The system according to claim 1, which includes means for a server receiving the aforementioned request to search for and reuse similar existing video data.

[1470] (Claim 3)

[1471] The system according to claim 1, further comprising means for inputting an access URL to a generating AI model as a prompt message to enable a user to view the generated video.

[1472] "Example 2 of combining an emotion engine"

[1473] (Claim 1)

[1474] A device for inputting user requests,

[1475] An information processing device for receiving the aforementioned request,

[1476] An information processing device for analyzing the aforementioned requests and emotional data,

[1477] An information processing device for generating a video script based on the aforementioned analysis results,

[1478] An information processing device for generating a video based on the aforementioned video script,

[1479] An information processing device for storing the generated video in a storage device and generating an access instruction,

[1480] A device for notifying the user of the aforementioned access instruction,

[1481] A system that includes this.

[1482] (Claim 2)

[1483] The system according to claim 1, wherein the information processing device has means for searching for and reusing similar existing video data.

[1484] (Claim 3)

[1485] The system according to claim 1, further comprising means for displaying access instructions for the generated video on a user interface.

[1486] "Application example 2 when combining with an emotional engine"

[1487] (Claim 1)

[1488] A means for inputting user requests and sentiment data,

[1489] A server means for receiving the aforementioned requests and sentiment data,

[1490] Means for analyzing the received request and emotion data,

[1491] A means for generating a video script based on the aforementioned analysis results,

[1492] Means for generating a video based on the aforementioned video script,

[1493] A means for saving the generated video to storage and generating an access URL,

[1494] A means for notifying the user of the aforementioned access URL,

[1495] A system that includes this.

[1496] (Claim 2)

[1497] The system according to claim 1, comprising means for a server receiving the aforementioned request and emotion data to search for and reuse similar existing video data.

[1498] (Claim 3)

[1499] The system according to claim 1, further comprising means for displaying the access URL of the generated video on the user interface.

[1500] (Claim 4)

[1501] The system according to claim 1, comprising means for adding comments or explanations based on the aforementioned sentiment data. [Explanation of Symbols]

[1502] 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 for users to input their requests, A server means for receiving the aforementioned request, Means for analyzing the received request, A means for generating a video script based on the aforementioned analysis results, Means for generating a video based on the aforementioned video script, A means for saving the generated video to storage and generating an access URL, A means for notifying the user of the aforementioned access URL, A system that includes this.

2. The system according to claim 1, which includes means for a server receiving the aforementioned request to search for and reuse similar existing video data.

3. The system according to claim 1, further comprising means for displaying the access URL of the generated video on the user interface.

Images