system

Abstract

Provide a system. A means for obtaining the latest information from around the world; means for analyzing the acquired latest information and generating commentators and hosts; A means of collecting user opinions and questions in real time; a means for generating a video by combining the acquired information, the generated commentators and presenters, and the user's opinions, and distributing the video in real time; A system including:

Description

[Technical Field]

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

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

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

[0004] In recent years, advances in the Internet and digitalization have made it easier for users to obtain the latest information in real time. However, there are only a limited number of systems that not only provide information but also provide viewer-participation news programs that include commentators and hosts. This has led to a growing need for a system that automatically generates and distributes news programs that allow users to participate and reflect their opinions. The purpose of this invention is to solve these problems by obtaining the latest information, generating commentators and hosts using AI, and providing news programs that reflect viewer opinions in real time. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including means for acquiring the latest information from around the world, means for analyzing the acquired information and generating commentators and presenters, means for collecting opinions and questions from users in real time, and means for generating a video by combining the acquired information, the generated commentators and presenters, and user opinions, and distributing the video in real time, thereby making it possible to provide an interactive news program in which users can participate and have their opinions reflected.

[0006] "Global updates" refers to the latest news and events available via the Internet and without geographical restrictions.

[0007] "Means of acquisition" refers to the technology and methods used to regularly collect the latest information from around the world via the Internet, etc.

[0008] "Analyze" refers to the process of analyzing the latest information obtained from around the world and understanding its contents.

[0009] "Explanator" refers to an artificial intelligence character or program generated to provide explanations and commentary on acquired information from a professional perspective.

[0010] "Host" refers to an artificial intelligence character or program generated to act as the host of a news program.

[0011] "Means for generating" refers to the process of automatically generating commentators and hosts using artificial intelligence technology.

[0012] "Opinions and questions from users" refers to comments, feedback, questions, etc. that viewers input or send regarding a program.

[0013] "Means of collecting information in real time" refers to methods and technologies for receiving and collecting opinions and questions from users in real time.

[0014] "Combining and generating a video" refers to the process of combining the acquired information, the generated commentators and hosts, and the user's opinions to create a single video content.

[0015] "Means for real-time distribution" refers to the technology and methods for distributing the generated video to viewers in real time.

[0016] "System" refers to an organizational setup or configuration that can integrate all of the above means and carry out a series of processes. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention relates to an interactive news program system that acquires the latest information from around the world, generates commentators and hosts, collects opinions from viewers in real time, and generates and distributes videos that combine these information. Specific ways in which the present invention can be implemented are described below.

[0039] 1. News gathering

[0040] The server uses the "Latest News Acquisition Module" as a means of obtaining the latest news from around the world. This module periodically collects the latest news and events via the Internet. The server initializes the "Latest News Acquisition Module," sets up a periodic news collection task, and stores the obtained latest news in a database. For example, the server can collect and store the latest news every 10 minutes.

[0041] 2. Allocation of AI commentators and hosts

[0042] The server uses a "commentator generation module" and a "host generation module" to generate commentators and hosts based on the acquired news data. These modules use artificial intelligence technology to analyze the news data and automatically generate appropriate commentators and hosts. The generated commentators and hosts are stored in a database and used in subsequent processes.

[0043] 3. Gathering opinions from viewers

[0044] Users can use the terminal to send opinions and questions to the system. The terminal is equipped with an input field and a send button, and users can enter their opinions or questions in the input field and send them by clicking the send button. The terminal then sends the submitted opinions and questions to the server, which then stores them in a database.

[0045] 4. Program Creation and Distribution

[0046] The server uses a "program generation module" to generate a news program by combining user opinions and questions, generated commentators and presenters, and collected news data. This module integrates the acquired information with the opinions of commentators, presenters, and users to generate a single video content. The server then distributes the generated video content in real time using a "distribution module." This distribution allows viewers to watch an interactive news program in real time.

[0047] Specific examples

[0048] For example, suppose that on a certain day, the server collects the latest political news. The collected news data is passed to an AI module, which generates a political commentator and a general host. The user then sends a question from their device: "What will happen in the political situation in the future?" The server integrates all the data and generates video content in which the commentator explains future political trends and the host takes up and discusses viewer questions, and distributes it in real time.

[0049] In this way, the present invention makes it possible to provide a news program in which users can participate and have their opinions reflected.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The server initializes the "Latest Information Acquisition Module" to acquire the latest information from around the world. The "Latest Information Acquisition Module" has the function of periodically collecting the latest news and events via the Internet.

[0053] Step 2:

[0054] The server is set up to periodically retrieve the latest information. For example, the server is set up to retrieve the latest news from the Internet every 10 minutes. This ensures that the latest information is always collected.

[0055] Step 3:

[0056] The server uses the latest information acquisition module to collect the latest information from the Internet. The collected information can be in text format, images, or videos.

[0057] Step 4:

[0058] The server stores the collected latest information in a database, which allows the information to be easily retrieved later.

[0059] Step 5:

[0060] The server passes the saved news data to a "commentator generation module" and a "host generation module," which use artificial intelligence to analyze the news data and automatically generate commentators and hosts.

[0061] Step 6:

[0062] The server stores the generated commentators and hosts in a database, making these characters available for subsequent processes.

[0063] Step 7:

[0064] Users use a terminal to input their opinions or questions into the system. The terminal is equipped with an input field and a send button, and users enter their opinions or questions in the input field and click the send button to send their opinions.

[0065] Step 8:

[0066] The device sends the opinions and questions entered by the user to the server using a standard data format such as JSON.

[0067] Step 9:

[0068] The server receives the user's opinions and questions sent from the device and stores them in a database, allowing the user's feedback to be managed for future use.

[0069] Step 10:

[0070] The server launches a "program generation module" to generate a news program by integrating news data, commentators, presenters, and user opinions. This module integrates the data and generates a single video content.

[0071] Step 11:

[0072] The server distributes the generated news programs in real time using a "distribution module," allowing viewers to view the generated content in real time.

[0073] Step 12:

[0074] Users can use their devices to watch news programs delivered in real time and enjoy interactive content that reflects their own opinions and questions.

[0075] Through the above process, the present invention realizes an interactive news program in which users can participate and have their opinions reflected.

[0076] Example 1

[0077] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0078] In conventional news programs, it is difficult for viewers to reflect their opinions and questions in real time, and the creation of commentators and presenters is done manually, making it difficult to provide rapid and diverse content. The goal is to solve these problems and provide interactive and flexible news programs.

[0079] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0080] In this invention, the server includes a module for acquiring the latest information from around the world, an artificial intelligence module for analyzing the acquired latest information and generating commentators and presenters, an interface for collecting opinions and questions from users in real time, and a module for generating and distributing video in real time by combining the acquired information, the generated commentators and presenters, and user opinions, thereby making it possible to provide an interactive news program that reflects viewers' opinions in real time.

[0081] The "module for acquiring the latest information from around the world" is a program for periodically collecting the latest information from news sources around the world via the Internet.

[0082] The "artificial intelligence module" is a program that includes machine learning algorithms for analyzing acquired data and automatically generating commentators and hosts.

[0083] An "interface" is a device or software that provides an input means for users to input opinions and questions and transmit them to a server in real time.

[0084] The "video generation module for real-time distribution" is a program that integrates acquired information, generated commentator and host data, and user opinions to generate video content and distribute it in real time.

[0085] A "generative AI model" is an artificial intelligence algorithm used to automatically generate commentary and host comments.

[0086] A "prompt" is an instruction entered into a generative AI model to prompt it to take action.

[0087] This invention is an interactive news program system that acquires the latest information from around the world, generates commentators and hosts, collects viewers' opinions in real time, and generates and distributes videos that combine these information. This system is composed of the following modules and components:

[0088] News gathering

[0089] The server uses the "latest information acquisition module" to obtain the latest information from around the world. This module periodically collects the latest news and events via the internet through news APIs. For example, the server uses the News API to obtain news data from categories such as politics, economy, and sports, and stores this in a database in JSON format. The server can always maintain the latest information by setting a schedule to run the news collection task every 10 minutes.

[0090] AI commentator and moderator placement

[0091] The server generates AI commentators and hosts using a "commentator generation module" and a "host generation module" based on the acquired news data. These modules use artificial intelligence technology to analyze the news data and automatically generate appropriate commentators and hosts. The server analyzes the news data using Python's NLTK library and generates commentator comments and host scripts using a generative AI model (e.g., GPT-4 (registered trademark)). The generated commentator and host data is stored in a database and used in subsequent processes.

[0092] Gathering opinions from viewers

[0093] Users can use their own devices to send opinions and questions to the system. The devices are equipped with input fields and submit buttons using HTML forms, and users can enter their opinions or questions in the input fields and click the submit button to submit their opinions or questions. The devices use Ajax JavaScript (registered trademark) to asynchronously send the submitted opinions and questions to the server in real time. The server stores this data in a database.

[0094] Program Creation and Distribution

[0095] The server uses a "program generation module" to generate a news program by combining user opinions and questions, generated commentators and hosts, and collected news data. This module integrates the acquired information with the generated commentators, hosts, and user opinions to generate a single video content. The server generates the video content using Python's MoviePy library and sends the generated video to a streaming server (e.g., Wowza). By providing users with a streaming URL, viewers can watch an interactive news program in real time.

[0096] Specific examples

[0097] For example, let's say the server collects the latest political news, analyzes it, and generates a political commentator and a general host. When a user sends a question from their device such as "What will happen in the political situation in the future?", the server can integrate all the data and generate video content in which "the commentator explains the future political trends, and the host takes up the viewer's question and discusses it." As a further example, the prompt text to be input to the generative AI model is as follows:

[0098] "Generate commentator comments in response to the following question: 'What's the political landscape going to be like in the future?'"

[0099] In this way, the present invention is able to provide a news program that allows users to participate and give their opinions.

[0100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0101] Step 1:

[0102] The server initializes the "latest information acquisition module."

[0103] Specifically, the server sends a request to the news API to obtain the latest news data.

[0104] Input: News sources on the internet

[0105] Output: Latest news data (JSON format)

[0106] This news data is stored in a database.

[0107] Step 2:

[0108] The server sets up periodic news gathering tasks.

[0109] Specifically, the server sets a schedule to access the news API every 10 minutes.

[0110] Input: Schedule information

[0111] Output: A newsgathering job with a timer

[0112] This allows the server to automatically collect the latest news on a regular basis.

[0113] Step 3:

[0114] The server analyzes the news data using a "commentator generation module."

[0115] Specifically, the server parses the news data using Python's NLTK library.

[0116] Input: News data (JSON format)

[0117] Output: Analysis results (topics, keywords)

[0118] The analysis results are used in the next step.

[0119] Step 4:

[0120] The server generates commentator profiles and comments based on the analysis results.

[0121] Specifically, the server uses a generative AI model (e.g., GPT-4) to generate the commentator's comments.

[0122] Input: Analysis results (topics, keywords)

[0123] Output: Commentator profile and comments

[0124] The generated data is stored in a database.

[0125] Step 5:

[0126] The server generates a moderator using a "moderator generation module."

[0127] Specifically, the server generates the moderator's script using a generative AI model.

[0128] Input: News data, commentator profiles and comments

[0129] Output: Host profile and script

[0130] The generated data is stored in a database.

[0131] Step 6:

[0132] The user enters their opinion or question into an input field on the terminal.

[0133] Specifically, the user uses an HTML form to input opinions or questions into the terminal.

[0134] Input: User's opinion or question (text format)

[0135] Output: Input data (text format)

[0136] It will be used in the next step.

[0137] Step 7:

[0138] The user clicks the send button.

[0139] Specifically, the terminal uses Ajax in JavaScript to send the transmission data to the server.

[0140] Input: Input data (text format)

[0141] Output: Transmission data (text format)

[0142] The transmitted data is transferred to the server in real time.

[0143] Step 8:

[0144] The server stores the received data in a database.

[0145] As a specific operation, the server stores the transmitted data in a database.

[0146] Input: Send data (text format)

[0147] Output: User comments and questions stored in a database

[0148] Step 9:

[0149] The server initializes the "program generation module."

[0150] Specifically, the program content is planned using a generative AI model.

[0151] Input: News data, commentator and host data, user opinions and questions

[0152] Output: Program content configuration information

[0153] Step 10:

[0154] The server integrates the information and generates it as a single video content.

[0155] Specifically, the server generates video using Python's MoviePy library.

[0156] Input: Program content configuration information

[0157] Output: Generated video data

[0158] Step 11:

[0159] The server uses a "distribution module" to distribute the generated video content in real time.

[0160] As a specific operation, the server transmits the generated video data to the streaming server.

[0161] Input: Generated video data

[0162] Output: Streaming URL, real-time video

[0163] Viewers can watch interactive news programs in real time.

[0164] (Application example 1)

[0165] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0166] Current news distribution systems often only provide information to viewers one-way, and viewer opinions and questions are rarely reflected in real time. There is also room for improvement in the quality and appropriateness of commentators and presenters. Furthermore, while there is a demand for interactive content delivery that can immediately reflect viewer interests, this is difficult to achieve in reality.

[0167] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0168] In this invention, the server includes means for acquiring the latest information from around the world, means for analyzing the acquired latest information and generating commentators and hosts, means for collecting opinions and questions from users in real time, means for combining the acquired information, the generated commentators and hosts, and user opinions to generate video and distribute it in real time, and means for collecting opinions and questions from terminals in real time and generating interactive content based on the opinions and questions, whereby interactive news distribution that instantly reflects viewer opinions and questions is possible.

[0169] "Means for obtaining the latest information" is a function for periodically collecting the latest information from around the world from the Internet.

[0170] The "commentator generation means" is a function for analyzing the collected latest information and generating appropriate commentators using artificial intelligence.

[0171] The "moderator generation means" is a function for analyzing the collected latest information and generating an appropriate moderator using artificial intelligence.

[0172] The "opinion collection means" is a function for collecting opinions and questions from users in real time.

[0173] The "image generation means" is a function for generating an image by combining the acquired information, the generated commentators and presenters, and the user's opinions.

[0174] The "real-time distribution means" is a function for distributing the generated video in real time without delay.

[0175] A "terminal" is a device used by a user to input opinions or questions, and includes smartphones, smart glasses, head-mounted displays, etc.

[0176] The "interactive content generation means" is a function for generating interactive content that is conducted by the generated commentator and moderator based on collected opinions and questions.

[0177] "Artificial intelligence" is an advanced computing technology that analyzes acquired data and generates commentators and presenters based on that data.

[0178] To implement this invention, the following hardware and software are used. Hardware includes a server and viewers' smartphones, smart glasses, head-mounted displays, and other devices. Software includes a news API (such as NewsAPI) and an artificial intelligence generation library (such as GPT-3 (registered trademark) or BERT).

[0179] News gathering

[0180] The server uses a means to obtain the latest news and events from around the world. For example, it periodically obtains news data from the News API and stores it in a database. This process is performed periodically, and the latest news can be collected every 10 minutes, for example.

[0181] AI commentator and presenter generation

[0182] The server uses the commentator generation means and the host generation means to generate AI commentators and hosts based on the acquired news data. The collected news data is analyzed and appropriate commentator and host characters are generated using artificial intelligence technology. Information on the generated AI commentators and hosts is stored in a database.

[0183] Gathering opinions from viewers

[0184] Viewers send their opinions and questions to the system via devices such as smartphones, smart glasses, and head-mounted displays. The devices are equipped with an input field and a send button, and viewers can send their opinions and questions by entering them in the input field and clicking the send button. The submitted opinions and questions are sent to a server and stored in a database.

[0185] Program Creation and Distribution

[0186] The server uses a video generation means to generate video content by combining the collected news data, the generated AI commentators and hosts, and the opinions of viewers. For example, if a political commentator and host are generated based on news data collected on a certain day, and a viewer sends a question such as "What will happen in the political situation in the future?", all the data is integrated to generate video content in which the commentator explains future political trends, and the host takes up the viewer's question and discusses it. The generated video content is then distributed in real time using a distribution means. This allows viewers to watch an interactive news program in real time.

[0187] As a concrete example, an AI-generated commentator will explain the results of the latest sporting event and answer viewers' questions in real time, such as "Who was the MVP player in this game?" In this way, interactive news delivery that instantly reflects viewers' opinions and questions becomes possible.

[0188] Prompt Sentence Examples

[0189] "Provide commentary on the latest sporting events and answer viewer questions such as: 'Who was the MVP player in this game?'"

[0190] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0191] Step 1:

[0192] The server periodically collects the latest news and events from around the world using the latest information retrieval method. In this case, the server retrieves news data using a news API (e.g., NewsAPI) and extracts the necessary information (title, text, date and time, etc.). This data is stored in a database. The input is the response data from the NewsAPI, and the output is the extracted news information.

[0193] Step 2:

[0194] The server uses the commentator generation means and the host generation means to generate AI commentators and hosts based on the acquired news data. The server inputs the news data into an artificial intelligence model (e.g., GPT-3 or BERT) and generates appropriate commentator and host characters based on that. Information about the generated commentators and hosts is stored in a database. The input is news data, and the output is character information for the generated commentators and hosts.

[0195] Step 3:

[0196] A user sends an opinion or question to the system using a device (smartphone, smart glasses, head-mounted display, etc.). The user enters the opinion or question into the input field on the device and clicks the send button. The input is the opinion or question entered by the user in the input field, and the output is the opinion or question sent to the server. The server stores this information in a database.

[0197] Step 4:

[0198] The server uses a video generation means to integrate the collected news data, the generated AI commentators and moderators, and user opinions to generate video content. The server creates a scenario in which the AI ​​commentators provide commentary based on the news data and user opinions, and the moderators take up viewer questions and discuss them. The input is the news data, information on the generated commentators and moderators, and viewer opinions, and the output is the generated video content.

[0199] Step 5:

[0200] The server uses a distribution means to distribute the generated video content to viewers in real time. The server uploads the generated video content to a streaming server or distribution platform, where viewers can watch it in real time from their devices. The input is the generated video content, and the output is the video content distributed to viewers.

[0201] In this way, interactive news distribution that instantly reflects viewer opinions and questions can be realized.

[0202] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0203] The present invention relates to an interactive news program system that acquires the latest information from around the world, generates commentators and presenters, collects viewers' opinions in real time, analyzes them using an emotion engine, and generates and distributes videos that combine these. How the present invention can be put into practice will be described below.

[0204] 1. News gathering

[0205] The server uses the "Latest News Acquisition Module" to acquire the latest news from around the world. This module has the function of periodically collecting the latest news and events via the Internet. The server initializes the "Latest News Acquisition Module", sets up a periodic news collection task, and stores the acquired latest news in a database. For example, the server can collect and store the latest news every 10 minutes.

[0206] 2. Allocation of AI commentators and hosts

[0207] The server uses a "commentator generation module" and a "host generation module" to generate commentators and hosts based on the acquired news data. These modules use artificial intelligence technology to analyze the news data and automatically generate appropriate commentators and hosts. The generated commentators and hosts are stored in a database and used in subsequent processes.

[0208] 3. Gathering opinions from viewers

[0209] Users can use the terminal to send opinions and questions to the system. The terminal is equipped with an input field and a send button, and users can enter their opinions or questions in the input field and send them by clicking the send button. The terminal then sends the submitted opinions and questions to the server, which then stores them in a database.

[0210] 4. User sentiment analysis

[0211] The server uses an "emotion analysis engine" to analyze opinions and questions sent by users and recognize their emotions. The emotion analysis engine extracts emotions such as joy, sadness, and anger from the user's text data. This emotional data is reflected in the content and progress of the news program.

[0212] 5. Program Creation and Adjustment

[0213] The server launches a "program generation module" to generate a news program by integrating news data, commentators, presenters, and user opinions and emotion data. This module integrates the data and generates a single video content. It also adjusts the program content based on the emotion data to improve user satisfaction.

[0214] 6. Program Creation and Distribution

[0215] The server distributes the generated news program in real time using a "distribution module," allowing viewers to view the generated interactive content in real time.

[0216] Specific examples

[0217] For example, suppose that on a certain day, the server collects the latest sports news. The collected news data is passed to an AI module, which generates a sports commentator and a general host. After that, a user submits their opinion from their device, saying, "I'm very satisfied with the outcome of the game." The server passes the submitted opinion to an emotion analysis engine for analysis, and recognizes that the user's emotion is "joy." The server integrates all the data and generates video content in which the commentator explains the outcome of the game in a positive tone, and the host addresses the satisfied opinions of the viewers, and distributes it in real time.

[0218] In this way, the present invention makes it possible to provide a news program that reflects the opinions and feelings of the user.

[0219] The processing flow will be explained below.

[0220] Step 1:

[0221] The server initializes the "latest information acquisition module" to acquire the latest information from around the world. It is set up to collect the latest news and events via the Internet.

[0222] Step 2:

[0223] The server sets up a schedule to periodically retrieve updates, for example, scheduling a task to gather news every 10 minutes.

[0224] Step 3:

[0225] The server periodically collects the latest news from the Internet, uses the "latest news acquisition module" to collect news data, and stores it in a database.

[0226] Step 4:

[0227] The server passes the collected news data to a "commentator generation module" and a "host generation module," which analyze the news data and generate appropriate commentators and hosts.

[0228] Step 5:

[0229] The server stores the generated commentator and host data in a database, which is used in subsequent processes.

[0230] Step 6:

[0231] The user inputs their opinions and questions using a terminal, which is equipped with an input field for inputting opinions and questions and a submit button.

[0232] Step 7:

[0233] The user enters their opinion or question into the input field and clicks the send button, which causes the device to send the entered data to the server.

[0234] Step 8:

[0235] The device sends user opinions and questions in JSON format to the server, which receives this data and stores it in a database.

[0236] Step 9:

[0237] The server uses an "emotion analysis engine" to analyze opinions and questions sent by users. The emotion analysis engine extracts emotions from the user's text data and stores them in a database.

[0238] Step 10:

[0239] The server launches a "program generation module" for integrating the acquired news data, the generated commentators and hosts, and the user's opinion and emotion data to generate a news program.

[0240] Step 11:

[0241] The server then adjusts the content of the program based on the emotion data. The "program generation module" changes the tone of commentary and commentary or focuses on specific topics depending on the user's emotions.

[0242] Step 12:

[0243] The server distributes the generated news program in real time using the "distribution module," allowing viewers to watch the interactive news program in real time.

[0244] Step 13:

[0245] Users can use their devices to watch the news broadcast in real time and confirm that their opinions and feelings are reflected.

[0246] In this way, the present invention can provide an interactive news program that reflects users' opinions and feelings in real time.

[0247] Example 2

[0248] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0249] Conventional news systems have difficulty collecting and analyzing user opinions and emotions in real time and reflecting them in program content. Furthermore, generating commentators and presenters requires manual labor, which is inefficient. This tends to result in low viewer satisfaction and interactivity.

[0250] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring the latest information from all over the world, means for analyzing the acquired latest information and generating comments, means for collecting opinions and questions from users in real time, means for analyzing the opinions and questions from users and recognizing emotions, and means for generating video by combining the acquired information, the generated comments, and the opinions and emotions of users, and distributing the video in real time. This makes it possible to efficiently provide interactive news programs that reflect the opinions and emotions of viewers.

[0251] "Global Updates" refers to the latest data on ongoing events and news obtained from the internet and other sources.

[0252] "Comments" are text data that provide commentary or explanations about news or events, and are primarily generated by commentators or presenters.

[0253] "Opinions and questions from users" refers to text data such as impressions, feedback, inquiries, etc. sent by viewers through the system.

[0254] "Means for recognizing emotions" refers to technology that analyzes text data sent by users and extracts emotions such as joy, sadness, and anger from the text.

[0255] "Means for generating video and distributing it in real time" refers to a technology that integrates acquired information, generated comments, and user opinions and emotional data, and instantly streams the video to viewers.

[0256] A "generative AI model" is an artificial intelligence model that has been trained to perform natural language processing and generation tasks, and has the ability to generate text in a human-like manner.

[0257] This invention relates to an interactive news program system that acquires the latest information from around the world, generates commentators and presenters, collects user opinions in real time, analyzes them using an emotion engine, and combines these to generate and distribute video.

[0258] News gathering

[0259] The server uses a "latest information acquisition module" to obtain the latest information from around the world. This module is implemented using Python and scrapes data from news sites and APIs. The server periodically obtains the latest information from the news sites and API endpoints via the Internet and stores it in a MySQL (registered trademark) database.

[0260] Commentator and host generation

[0261] The server uses a "comment generation module" to generate comments based on the acquired news data. This module uses a generative AI model (e.g., GPT-3) to analyze the news data and automatically generate appropriate commentators and host comments. As a concrete example, the server generates the following prompt sentence and passes it to the AI ​​model:

[0262] News data: "The soccer World Cup was held, and Japan won the final."

[0263] Instructions for the Generative AI Model: Based on the news data above, generate comments from a sports commentator and a host. The commentator should analyze the game, and the host should introduce opinions from viewers.

[0264] Gathering opinions from viewers

[0265] Users can use a device (smartphone or PC) to send opinions and questions to the system. An HTML form is installed on the device, and users can enter their opinions or questions in the input field and send them by clicking the send button. The device sends the submitted opinions and questions to the server via an HTTP POST request, which the server stores in a database.

[0266] User sentiment analysis

[0267] The server uses an "emotion analysis engine" (e.g., IBM Watson (registered trademark)) to analyze opinions and questions sent by users and recognize their emotions. This engine extracts emotions such as joy, sadness, and anger from the user's text data. The analyzed emotional data is reflected in the content and progress of the news program.

[0268] Program Creation and Distribution

[0269] The server launches a "program generation module" that integrates news data, generated comments, user opinions, and emotion data to generate a news program. This module integrates all data and generates a single video content. It also adjusts the program content based on the emotion data to improve viewer satisfaction. The generated news program is distributed in real time via a "distribution module," allowing viewers to watch it in real time.

[0270] As a concrete example, a server collects the latest sports news and uses a generative AI model to generate comments from sports commentators and hosts. A user then transmits their opinion from their device, saying, "I'm very satisfied with the outcome of the game," which is recognized as "joy" by the emotion analysis engine. Finally, all the data is integrated to generate video content in which the commentator explains the outcome of the game in a positive tone and the host addresses the satisfied opinions of viewers, and this content is distributed in real time.

[0271] In this way, the present invention makes it possible to provide an interactive news program that reflects the user's opinions and feelings.

[0272] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0273] Step 1:

[0274] Regular news gathering

[0275] The server initializes the "latest information acquisition module" with a Python script and sets the schedule for news gathering.

[0276] Input: News site or API endpoint URL

[0277] Specific operation: Set up a server scheduler (e.g., a cron job) to automatically collect news every 10 minutes. The server will call the API endpoint of the specified news site based on the set schedule.

[0278] Output: Retrieved news data (JSON format)

[0279] Step 2:

[0280] Acquiring and storing news data

[0281] The server analyzes the data obtained from the news API and stores it in a database.

[0282] Input: JSON-formatted news data obtained from the news gathering task

[0283] Specific operation: The server parses the received JSON data, extracts the necessary information (title, content, posting date, etc.), and saves it in the MySQL database using the INSERT statement.

[0284] Output: News records stored in a database

[0285] Step 3:

[0286] Generate AI prompts

[0287] The server retrieves the latest news data from the database and generates prompt sentences to pass to the generative AI model.

[0288] Input: News data stored in a database

[0289] Specific operation: The server applies the news data to a template and generates a prompt sentence. For example, the prompt sentence is generated in the following format: "News data: {Title: 'The FIFA World Cup is being held,' Content: 'Japan won the final.'} Instructions to the generation AI model: Based on the above news data, generate comments from a sports commentator and a host. The commentator should analyze the match, and the host should introduce opinions from viewers."

[0290] Output: Generated prompt statement

[0291] Step 4:

[0292] Running an AI model

[0293] The server passes the prompt to a generative AI model (e.g., GPT-3) to generate comments for the commentator and host.

[0294] Input: Generated prompt text

[0295] Specific operation: The server sends a prompt to the generative AI model via an API request and receives the generated text data.

[0296] Output: Commentary by commentators and hosts generated from the AI ​​model

[0297] Step 5:

[0298] Saving comment data

[0299] The server stores the comments generated by the AI ​​model in a database.

[0300] Input: Commentators and host comments generated from an AI model

[0301] Specific operation: The server saves the received comment data in the database using the INSERT statement.

[0302] Output: Comment records stored in the database

[0303] Step 6:

[0304] Submit your user feedback

[0305] The user enters their opinion or question in the input field on the terminal and clicks the send button.

[0306] Input: User's opinion or question (text)

[0307] Specific operation: The terminal obtains the user's opinion from the form and sends it to the server via an HTTP POST request.

[0308] Output: Submitted opinions and questions (text data)

[0309] Step 7:

[0310] Storing opinion data

[0311] The server stores the submitted comments and questions in a database.

[0312] Input: User comments and questions sent from the device

[0313] Specific operation: The server saves the received opinion data in the database using the INSERT statement.

[0314] Output: Opinion records stored in the database

[0315] Step 8:

[0316] Performing sentiment analysis

[0317] The server uses a sentiment analysis engine to analyze the user's opinions and questions and recognize their emotions.

[0318] Input: opinion data stored in a database

[0319] Specific operation: The server sends opinion data to the sentiment analysis engine and receives analyzed sentiment data.

[0320] Output: Analysis results from the emotion analysis engine (emotion data)

[0321] Step 9:

[0322] Storing Emotional Data

[0323] The server stores the analysis results in a database.

[0324] Input: Analysis results from the sentiment analysis engine

[0325] Specific operation: The server saves the received emotion data to the corresponding opinion record using an UPDATE statement.

[0326] Output: Emotion data stored in a database

[0327] Step 10:

[0328] Launching program generation

[0329] The server activates a "program generation module" to generate a news program by integrating the news data, the generated comments, and the user's opinion and emotion data.

[0330] Input: News data, commentators' and moderators' comments, user opinions and sentiment data

[0331] Specific operation: The server integrates these data and executes a program to generate video content.

[0332] Output: Generated video content

[0333] Step 11:

[0334] Adjusting program content

[0335] The server adjusts the content of the program based on the emotional data to improve viewer satisfaction.

[0336] Input: Emotion data

[0337] Specific operation: The server analyzes the emotional data, generates a script based on positive emotions, and reflects it in the video content.

[0338] Output: Adjusted video content

[0339] Step 12:

[0340] Program distribution

[0341] The server uses a "distribution module" to distribute the generated news programs in real time.

[0342] Input: Generated video content

[0343] How it works: The server uses the RTMP protocol to send video content to the streaming service, which viewers can then watch in real time.

[0344] Output: Video content delivered to viewers

[0345] (Application example 2)

[0346] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0347] Conventional news distribution systems lacked the means to collect real-time audience opinions and emotions and reflect them in news programs, resulting in little interaction with viewers. Furthermore, commentators and presenters were fixed, making it difficult to flexibly respond to the diverse needs and emotions of viewers. This led to problems such as a decline in the appeal of news programs and viewer satisfaction.

[0348] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0349] In this invention, the server includes means for acquiring the latest information from around the world, means for analyzing the acquired latest information and generating commentators and presenters, means for collecting opinions and questions from users in real time, means for analyzing the emotions of the collected user opinions, means for generating video by combining the acquired information, the generated commentators and presenters, and the opinions and emotions of users, and means for distributing the generated video in real time, thereby making it possible to provide an interactive news program that reflects the opinions and emotions of viewers in real time.

[0350] The "means for obtaining the latest information" is a system that has the function of periodically collecting the latest information from around the world via the Internet.

[0351] The "analysis means" is a system that uses artificial intelligence technology to generate appropriate commentators and hosts based on the latest information obtained.

[0352] The "means for generating commentators and hosts" is a system that uses artificial intelligence to analyze news data and generate commentators who will provide appropriate explanations to viewers and hosts who will conduct the program.

[0353] The "opinion collection means" is a system that collects opinions and questions from users in real time and stores them in a database.

[0354] The "emotion analysis means" is a system that analyzes opinions and questions sent by users as text data and extracts emotions such as joy, sadness, and anger.

[0355] The "video generation means" is a system that combines acquired news data, generated commentators and presenters, and the user's opinions and feelings to generate a single video content.

[0356] The "video distribution means" is a system that distributes the generated video content to viewers in real time.

[0357] The present invention relates to a system that obtains the latest information from around the world, generates AI commentators and presenters, collects and analyzes viewer opinions in real time, and delivers interactive news programs. Specific ways in which the present invention can be implemented are described below.

[0358] Gathering the latest information

[0359] The server uses a "latest information retrieval method" that retrieves the latest information from around the world. This method has the function of periodically collecting the latest news and events via the Internet. For example, using Python and the News API, the server can collect and store the latest news every 10 minutes. This makes it possible to always maintain the latest information.

[0360] AI commentator and host generation

[0361] The server uses a "commentator and host generation means" to generate commentators and hosts based on the acquired news data. This means uses a generative AI model such as GPT-3 to analyze the news data and automatically generate appropriate commentators and hosts. For example, OpenAI (registered trademark) technology is applied to generate commentator and host characters using prompt sentences for the latest news.

[0362] Gathering opinions from viewers

[0363] Users can use the terminal to send their opinions and questions to the system. The terminal is equipped with an input field and a send button, and users can enter their opinions or questions in the input field and click the send button to send their opinions. These opinions are sent to the server and stored in the database.

[0364] User sentiment analysis

[0365] The server uses an "emotion analysis means" to analyze the opinions and questions sent by the user and recognize the user's emotions. This means uses an emotion analysis tool such as TextBlob to extract emotions such as joy, sadness, and anger from the user's text data.

[0366] Program Creation and Adjustment

[0367] The server activates a "video generation means" that generates a news program by integrating the acquired news data, the generated commentators and presenters, and the user's opinions and emotional data. This means integrates the data and generates a single video content. It also adjusts the program content based on the emotional data to improve user satisfaction.

[0368] Specific examples

[0369] For example, if the server collects the latest sports news, it passes the collected news data to an AI module to generate a sports commentator and a general host. The user then transmits their opinion from their device, saying, "I'm very satisfied with the outcome of the game." The server then passes the transmitted opinion to an emotion analysis module for analysis, and recognizes that the user's emotion is "joy." The server integrates all the data, generates video content in which the commentator explains the outcome of the game in a positive tone and the host addresses the viewers' satisfied opinions, and distributes it in real time.

[0370] Prompt Sentence Examples

[0371] "Breaking News: Candidates give final speeches ahead of tomorrow's presidential election. Generate commentators."

[0372] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0373] Step 1:

[0374] News gathering

[0375] The server starts the "latest information acquisition means" and periodically collects the latest news from around the world. The server uses the News API via the Internet to obtain the latest news data. This acquisition process is automated by a Python script, and is scheduled to run every 10 minutes, for example. The collected news data is stored in the server's database.

[0376] Input: Internet connection, News API key

[0377] Output: Latest news data

[0378] Specific operation: The server runs a Python script, accesses the News API to retrieve the latest news, and stores it in a database.

[0379] Step 2:

[0380] AI commentator and host generation

[0381] The server uses a "commentator and host generation means" to analyze the acquired news data and generate commentators and hosts. A generative AI model such as OpenAI's GPT-3 is used to automatically generate appropriate commentators and hosts based on the prompt text. The generated information is then stored in a database.

[0382] Input: News data, prompt text

[0383] Output: AI-generated commentary and host descriptions

[0384] Specific operation: The server launches the generative AI model, inputs the news data and prompt text, generates explanatory text for the commentator and host, and stores it in the database.

[0385] Step 3:

[0386] Gathering opinions from viewers

[0387] The user uses the terminal to input their opinion or question and clicks the submit button to send it to the server. The user enters their opinion or question in the input field and sends it to the server, which then stores this information in a database. This operation is achieved using a web framework such as Flask.

[0388] Input: User opinions and questions

[0389] Output: User opinions and questions stored on the server

[0390] Specific operation: The user enters an opinion or question into the input field on the terminal and clicks the send button. The server receives it through Flask and saves it in the database.

[0391] Step 4:

[0392] User sentiment analysis

[0393] The server uses "sentiment analysis means" to analyze opinions and questions sent by users. It uses sentiment analysis tools such as TextBlob to extract emotional information from text data. The analysis results are stored in a database.

[0394] Input: User opinions and questions

[0395] Output: Sentiment analysis data

[0396] Specific operation: The server uses TextBlob to analyze the sentiment from the user's comments and questions and saves the results in the database.

[0397] Step 5:

[0398] Program Generation

[0399] The server uses a "video generation means" to generate video content based on the collected news data, the generated commentators and presenters, and the user's opinions and emotion data. This means integrates the data to generate video content as a program.

[0400] Input: News data, commentator and host descriptions, user opinions and sentiment data

[0401] Output: Generated program video

[0402] Specific operation: News data, AI-generated commentators and hosts, user opinions, and emotional data are integrated on the server, and then generated as a single video content using a program content generation module.

[0403] Step 6:

[0404] Program distribution

[0405] The server distributes the generated program video to viewers in real time using the "video distribution means." The generated interactive news program is distributed to viewers using a live streaming service.

[0406] Input: Generated program video

[0407] Output: Streamed real-time newscast

[0408] Specific operation: The server distributes the generated video content in real time through a live streaming service, making it available to viewers.

[0409] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0410] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0411] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0412] [Second embodiment]

[0413] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0414] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0415] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0416] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0417] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0418] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0419] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0420] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0421] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0422] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0423] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0424] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0425] The present invention relates to an interactive news program system that acquires the latest information from around the world, generates commentators and hosts, collects opinions from viewers in real time, and generates and distributes videos that combine these information. Specific ways in which the present invention can be implemented are described below.

[0426] 1. News gathering

[0427] The server uses the "Latest News Acquisition Module" as a means of obtaining the latest news from around the world. This module periodically collects the latest news and events via the Internet. The server initializes the "Latest News Acquisition Module," sets up a periodic news collection task, and stores the obtained latest news in a database. For example, the server can collect and store the latest news every 10 minutes.

[0428] 2. Allocation of AI commentators and hosts

[0429] The server uses a "commentator generation module" and a "host generation module" to generate commentators and hosts based on the acquired news data. These modules use artificial intelligence technology to analyze the news data and automatically generate appropriate commentators and hosts. The generated commentators and hosts are stored in a database and used in subsequent processes.

[0430] 3. Gathering opinions from viewers

[0431] Users can use the terminal to send opinions and questions to the system. The terminal is equipped with an input field and a send button, and users can enter their opinions or questions in the input field and send them by clicking the send button. The terminal then sends the submitted opinions and questions to the server, which then stores them in a database.

[0432] 4. Program Creation and Distribution

[0433] The server uses a "program generation module" to generate a news program by combining user opinions and questions, generated commentators and presenters, and collected news data. This module integrates the acquired information with the opinions of commentators, presenters, and users to generate a single video content. The server then distributes the generated video content in real time using a "distribution module." This distribution allows viewers to watch an interactive news program in real time.

[0434] Specific examples

[0435] For example, suppose that on a certain day, the server collects the latest political news. The collected news data is passed to an AI module, which generates a political commentator and a general host. The user then sends a question from their device: "What will happen in the political situation in the future?" The server integrates all the data and generates video content in which the commentator explains future political trends and the host takes up and discusses viewer questions, and distributes it in real time.

[0436] In this way, the present invention makes it possible to provide a news program in which users can participate and have their opinions reflected.

[0437] The processing flow will be explained below.

[0438] Step 1:

[0439] The server initializes the "Latest Information Acquisition Module" to acquire the latest information from around the world. The "Latest Information Acquisition Module" has the function of periodically collecting the latest news and events via the Internet.

[0440] Step 2:

[0441] The server is set up to periodically retrieve the latest information. For example, the server is set up to retrieve the latest news from the Internet every 10 minutes. This ensures that the latest information is always collected.

[0442] Step 3:

[0443] The server uses the latest information acquisition module to collect the latest information from the Internet. The collected information can be in text format, images, or videos.

[0444] Step 4:

[0445] The server stores the collected latest information in a database, which allows the information to be easily retrieved later.

[0446] Step 5:

[0447] The server passes the saved news data to a "commentator generation module" and a "host generation module," which use artificial intelligence to analyze the news data and automatically generate commentators and hosts.

[0448] Step 6:

[0449] The server stores the generated commentators and hosts in a database, making these characters available for subsequent processes.

[0450] Step 7:

[0451] Users use a terminal to input their opinions or questions into the system. The terminal is equipped with an input field and a send button, and users enter their opinions or questions in the input field and click the send button to send their opinions.

[0452] Step 8:

[0453] The device sends the opinions and questions entered by the user to the server using a standard data format such as JSON.

[0454] Step 9:

[0455] The server receives the user's opinions and questions sent from the device and stores them in a database, allowing the user's feedback to be managed for future use.

[0456] Step 10:

[0457] The server launches a "program generation module" to generate a news program by integrating news data, commentators, presenters, and user opinions. This module integrates the data and generates a single video content.

[0458] Step 11:

[0459] The server distributes the generated news programs in real time using a "distribution module," allowing viewers to view the generated content in real time.

[0460] Step 12:

[0461] Users can use their devices to watch news programs delivered in real time and enjoy interactive content that reflects their own opinions and questions.

[0462] Through the above process, the present invention realizes an interactive news program in which users can participate and have their opinions reflected.

[0463] Example 1

[0464] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0465] In conventional news programs, it is difficult for viewers to reflect their opinions and questions in real time, and the creation of commentators and presenters is done manually, making it difficult to provide rapid and diverse content. The goal is to solve these problems and provide interactive and flexible news programs.

[0466] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0467] In this invention, the server includes a module for acquiring the latest information from around the world, an artificial intelligence module for analyzing the acquired latest information and generating commentators and presenters, an interface for collecting opinions and questions from users in real time, and a module for generating and distributing video in real time by combining the acquired information, the generated commentators and presenters, and user opinions, thereby making it possible to provide an interactive news program that reflects viewers' opinions in real time.

[0468] The "module for acquiring the latest information from around the world" is a program for periodically collecting the latest information from news sources around the world via the Internet.

[0469] The "artificial intelligence module" is a program that includes machine learning algorithms for analyzing acquired data and automatically generating commentators and hosts.

[0470] An "interface" is a device or software that provides an input means for users to input opinions and questions and transmit them to a server in real time.

[0471] The "video generation module for real-time distribution" is a program that integrates acquired information, generated commentator and host data, and user opinions to generate video content and distribute it in real time.

[0472] A "generative AI model" is an artificial intelligence algorithm used to automatically generate commentary and host comments.

[0473] A "prompt" is an instruction entered into a generative AI model to prompt it to take action.

[0474] This invention is an interactive news program system that acquires the latest information from around the world, generates commentators and hosts, collects viewers' opinions in real time, and generates and distributes videos that combine these information. This system is composed of the following modules and components:

[0475] News gathering

[0476] The server uses the "latest information acquisition module" to obtain the latest information from around the world. This module periodically collects the latest news and events via the internet through news APIs. For example, the server uses the News API to obtain news data from categories such as politics, economy, and sports, and stores this in a database in JSON format. The server can always maintain the latest information by setting a schedule to run the news collection task every 10 minutes.

[0477] AI commentator and moderator placement

[0478] The server generates AI commentators and hosts using a "commentator generation module" and a "host generation module" based on the acquired news data. These modules use artificial intelligence technology to analyze the news data and automatically generate appropriate commentators and hosts. The server analyzes the news data using Python's NLTK library and generates commentator comments and host scripts using a generative AI model (e.g., GPT-4). The generated commentator and host data is stored in a database and used in subsequent processes.

[0479] Gathering opinions from viewers

[0480] Users can use their own devices to send opinions and questions to the system. The devices are equipped with input fields and submit buttons using HTML forms, and users can enter their opinions or questions in the input fields and click the submit button to submit their opinions or questions. The devices use JavaScript Ajax to asynchronously send the submitted opinions and questions to the server in real time. The server stores this data in a database.

[0481] Program Creation and Distribution

[0482] The server uses a "program generation module" to generate a news program by combining user opinions and questions, generated commentators and hosts, and collected news data. This module integrates the acquired information with the generated commentators, hosts, and user opinions to generate a single video content. The server generates the video content using Python's MoviePy library and sends the generated video to a streaming server (e.g., Wowza). By providing users with a streaming URL, viewers can watch an interactive news program in real time.

[0483] Specific examples

[0484] For example, let's say the server collects the latest political news, analyzes it, and generates a political commentator and a general host. When a user sends a question from their device such as "What will happen in the political situation in the future?", the server can integrate all the data and generate video content in which "the commentator explains the future political trends, and the host takes up the viewer's question and discusses it." As a further example, the prompt text to be input to the generative AI model is as follows:

[0485] "Generate commentator comments in response to the following question: 'What's the political landscape going to be like in the future?'"

[0486] In this way, the present invention is able to provide a news program that allows users to participate and give their opinions.

[0487] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0488] Step 1:

[0489] The server initializes the "latest information acquisition module."

[0490] Specifically, the server sends a request to the news API to obtain the latest news data.

[0491] Input: News sources on the internet

[0492] Output: Latest news data (JSON format)

[0493] This news data is stored in a database.

[0494] Step 2:

[0495] The server sets up periodic news gathering tasks.

[0496] Specifically, the server sets a schedule to access the news API every 10 minutes.

[0497] Input: Schedule information

[0498] Output: A newsgathering job with a timer

[0499] This allows the server to automatically collect the latest news on a regular basis.

[0500] Step 3:

[0501] The server analyzes the news data using a "commentator generation module."

[0502] Specifically, the server parses the news data using Python's NLTK library.

[0503] Input: News data (JSON format)

[0504] Output: Analysis results (topics, keywords)

[0505] The analysis results are used in the next step.

[0506] Step 4:

[0507] The server generates commentator profiles and comments based on the analysis results.

[0508] Specifically, the server uses a generative AI model (e.g., GPT-4) to generate the commentator's comments.

[0509] Input: Analysis results (topics, keywords)

[0510] Output: Commentator profile and comments

[0511] The generated data is stored in a database.

[0512] Step 5:

[0513] The server generates a moderator using a "moderator generation module."

[0514] Specifically, the server generates the moderator's script using a generative AI model.

[0515] Input: News data, commentator profiles and comments

[0516] Output: Host profile and script

[0517] The generated data is stored in a database.

[0518] Step 6:

[0519] The user enters their opinion or question into an input field on the terminal.

[0520] Specifically, the user uses an HTML form to input opinions or questions into the terminal.

[0521] Input: User's opinion or question (text format)

[0522] Output: Input data (text format)

[0523] It will be used in the next step.

[0524] Step 7:

[0525] The user clicks the send button.

[0526] Specifically, the terminal uses Ajax in JavaScript to send the transmission data to the server.

[0527] Input: Input data (text format)

[0528] Output: Transmission data (text format)

[0529] The transmitted data is transferred to the server in real time.

[0530] Step 8:

[0531] The server stores the received data in a database.

[0532] As a specific operation, the server stores the transmitted data in a database.

[0533] Input: Send data (text format)

[0534] Output: User comments and questions stored in a database

[0535] Step 9:

[0536] The server initializes the "program generation module."

[0537] Specifically, the program content is planned using a generative AI model.

[0538] Input: News data, commentator and host data, user opinions and questions

[0539] Output: Program content configuration information

[0540] Step 10:

[0541] The server integrates the information and generates it as a single video content.

[0542] Specifically, the server generates video using Python's MoviePy library.

[0543] Input: Program content configuration information

[0544] Output: Generated video data

[0545] Step 11:

[0546] The server uses a "distribution module" to distribute the generated video content in real time.

[0547] As a specific operation, the server transmits the generated video data to the streaming server.

[0548] Input: Generated video data

[0549] Output: Streaming URL, real-time video

[0550] Viewers can watch interactive news programs in real time.

[0551] (Application example 1)

[0552] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0553] Current news distribution systems often only provide information to viewers one-way, and viewer opinions and questions are rarely reflected in real time. There is also room for improvement in the quality and appropriateness of commentators and presenters. Furthermore, while there is a demand for interactive content delivery that can immediately reflect viewer interests, this is difficult to achieve in reality.

[0554] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0555] In this invention, the server includes means for acquiring the latest information from around the world, means for analyzing the acquired latest information and generating commentators and hosts, means for collecting opinions and questions from users in real time, means for combining the acquired information, the generated commentators and hosts, and user opinions to generate video and distribute it in real time, and means for collecting opinions and questions from terminals in real time and generating interactive content based on the opinions and questions, whereby interactive news distribution that instantly reflects viewer opinions and questions is possible.

[0556] "Means for obtaining the latest information" is a function for periodically collecting the latest information from around the world from the Internet.

[0557] The "commentator generation means" is a function for analyzing the collected latest information and generating appropriate commentators using artificial intelligence.

[0558] The "moderator generation means" is a function for analyzing the collected latest information and generating an appropriate moderator using artificial intelligence.

[0559] The "opinion collection means" is a function for collecting opinions and questions from users in real time.

[0560] The "image generation means" is a function for generating an image by combining the acquired information, the generated commentators and presenters, and the user's opinions.

[0561] The "real-time distribution means" is a function for distributing the generated video in real time without delay.

[0562] A "terminal" is a device used by a user to input opinions or questions, and includes smartphones, smart glasses, head-mounted displays, etc.

[0563] The "interactive content generation means" is a function for generating interactive content that is conducted by the generated commentator and moderator based on collected opinions and questions.

[0564] "Artificial intelligence" is an advanced computing technology that analyzes acquired data and generates commentators and presenters based on that data.

[0565] To implement this invention, the following hardware and software are used. The hardware requires a server, a viewer's smartphone, smart glasses, a head-mounted display, or other device. The software requires a news API (such as NewsAPI) and an artificial intelligence generation library (such as GPT-3 or BERT).

[0566] News gathering

[0567] The server uses a means to obtain the latest news and events from around the world. For example, it periodically obtains news data from the News API and stores it in a database. This process is performed periodically, and the latest news can be collected every 10 minutes, for example.

[0568] AI commentator and presenter generation

[0569] The server uses the commentator generation means and the host generation means to generate AI commentators and hosts based on the acquired news data. The collected news data is analyzed and appropriate commentator and host characters are generated using artificial intelligence technology. Information on the generated AI commentators and hosts is stored in a database.

[0570] Gathering opinions from viewers

[0571] Viewers send their opinions and questions to the system via devices such as smartphones, smart glasses, and head-mounted displays. The devices are equipped with an input field and a send button, and viewers can send their opinions and questions by entering them in the input field and clicking the send button. The submitted opinions and questions are sent to a server and stored in a database.

[0572] Program Creation and Distribution

[0573] The server uses a video generation means to generate video content by combining the collected news data, the generated AI commentators and hosts, and the opinions of viewers. For example, if a political commentator and host are generated based on news data collected on a certain day, and a viewer sends a question such as "What will happen in the political situation in the future?", all the data is integrated to generate video content in which the commentator explains future political trends, and the host takes up the viewer's question and discusses it. The generated video content is then distributed in real time using a distribution means. This allows viewers to watch an interactive news program in real time.

[0574] As a concrete example, an AI-generated commentator will explain the results of the latest sporting event and answer viewers' questions in real time, such as "Who was the MVP player in this game?" In this way, interactive news delivery that instantly reflects viewers' opinions and questions becomes possible.

[0575] Prompt Sentence Examples

[0576] "Provide commentary on the latest sporting events and answer viewer questions such as: 'Who was the MVP player in this game?'"

[0577] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0578] Step 1:

[0579] The server periodically collects the latest news and events from around the world using the latest information retrieval method. In this case, the server retrieves news data using a news API (e.g., NewsAPI) and extracts the necessary information (title, text, date and time, etc.). This data is stored in a database. The input is the response data from the NewsAPI, and the output is the extracted news information.

[0580] Step 2:

[0581] The server uses the commentator generation means and the host generation means to generate AI commentators and hosts based on the acquired news data. The server inputs the news data into an artificial intelligence model (e.g., GPT-3 or BERT) and generates appropriate commentator and host characters based on that. Information about the generated commentators and hosts is stored in a database. The input is news data, and the output is character information for the generated commentators and hosts.

[0582] Step 3:

[0583] A user sends an opinion or question to the system using a device (smartphone, smart glasses, head-mounted display, etc.). The user enters the opinion or question into the input field on the device and clicks the send button. The input is the opinion or question entered by the user in the input field, and the output is the opinion or question sent to the server. The server stores this information in a database.

[0584] Step 4:

[0585] The server uses a video generation means to integrate the collected news data, the generated AI commentators and moderators, and user opinions to generate video content. The server creates a scenario in which the AI ​​commentators provide commentary based on the news data and user opinions, and the moderators take up viewer questions and discuss them. The input is the news data, information on the generated commentators and moderators, and viewer opinions, and the output is the generated video content.

[0586] Step 5:

[0587] The server uses a distribution means to distribute the generated video content to viewers in real time. The server uploads the generated video content to a streaming server or distribution platform, where viewers can watch it in real time from their devices. The input is the generated video content, and the output is the video content distributed to viewers.

[0588] In this way, interactive news distribution that instantly reflects viewer opinions and questions can be realized.

[0589] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0590] The present invention relates to an interactive news program system that acquires the latest information from around the world, generates commentators and presenters, collects viewers' opinions in real time, analyzes them using an emotion engine, and generates and distributes videos that combine these. How the present invention can be put into practice will be described below.

[0591] 1. News gathering

[0592] The server uses the "Latest News Acquisition Module" to acquire the latest news from around the world. This module has the function of periodically collecting the latest news and events via the Internet. The server initializes the "Latest News Acquisition Module", sets up a periodic news collection task, and stores the acquired latest news in a database. For example, the server can collect and store the latest news every 10 minutes.

[0593] 2. Allocation of AI commentators and hosts

[0594] The server uses a "commentator generation module" and a "host generation module" to generate commentators and hosts based on the acquired news data. These modules use artificial intelligence technology to analyze the news data and automatically generate appropriate commentators and hosts. The generated commentators and hosts are stored in a database and used in subsequent processes.

[0595] 3. Gathering opinions from viewers

[0596] Users can use the terminal to send opinions and questions to the system. The terminal is equipped with an input field and a send button, and users can enter their opinions or questions in the input field and send them by clicking the send button. The terminal then sends the submitted opinions and questions to the server, which then stores them in a database.

[0597] 4. User sentiment analysis

[0598] The server uses an "emotion analysis engine" to analyze opinions and questions sent by users and recognize their emotions. The emotion analysis engine extracts emotions such as joy, sadness, and anger from the user's text data. This emotional data is reflected in the content and progress of the news program.

[0599] 5. Program Creation and Adjustment

[0600] The server launches a "program generation module" to generate a news program by integrating news data, commentators, presenters, and user opinions and emotion data. This module integrates the data and generates a single video content. It also adjusts the program content based on the emotion data to improve user satisfaction.

[0601] 6. Program Creation and Distribution

[0602] The server distributes the generated news program in real time using a "distribution module," allowing viewers to view the generated interactive content in real time.

[0603] Specific examples

[0604] For example, suppose that on a certain day, the server collects the latest sports news. The collected news data is passed to an AI module, which generates a sports commentator and a general host. After that, a user submits their opinion from their device, saying, "I'm very satisfied with the outcome of the game." The server passes the submitted opinion to an emotion analysis engine for analysis, and recognizes that the user's emotion is "joy." The server integrates all the data and generates video content in which the commentator explains the outcome of the game in a positive tone, and the host addresses the satisfied opinions of the viewers, and distributes it in real time.

[0605] In this way, the present invention makes it possible to provide a news program that reflects the opinions and feelings of the user.

[0606] The processing flow will be explained below.

[0607] Step 1:

[0608] The server initializes the "latest information acquisition module" to acquire the latest information from around the world. It is set up to collect the latest news and events via the Internet.

[0609] Step 2:

[0610] The server sets up a schedule to periodically retrieve updates, for example, scheduling a task to gather news every 10 minutes.

[0611] Step 3:

[0612] The server periodically collects the latest news from the Internet, uses the "latest news acquisition module" to collect news data, and stores it in a database.

[0613] Step 4:

[0614] The server passes the collected news data to a "commentator generation module" and a "host generation module," which analyze the news data and generate appropriate commentators and hosts.

[0615] Step 5:

[0616] The server stores the generated commentator and host data in a database, which is used in subsequent processes.

[0617] Step 6:

[0618] The user inputs their opinions and questions using a terminal, which is equipped with an input field for inputting opinions and questions and a submit button.

[0619] Step 7:

[0620] The user enters their opinion or question into the input field and clicks the send button, which causes the device to send the entered data to the server.

[0621] Step 8:

[0622] The device sends user opinions and questions in JSON format to the server, which receives this data and stores it in a database.

[0623] Step 9:

[0624] The server uses an "emotion analysis engine" to analyze opinions and questions sent by users. The emotion analysis engine extracts emotions from the user's text data and stores them in a database.

[0625] Step 10:

[0626] The server launches a "program generation module" for integrating the acquired news data, the generated commentators and hosts, and the user's opinion and emotion data to generate a news program.

[0627] Step 11:

[0628] The server then adjusts the content of the program based on the emotion data. The "program generation module" changes the tone of commentary and commentary or focuses on specific topics depending on the user's emotions.

[0629] Step 12:

[0630] The server distributes the generated news program in real time using the "distribution module," allowing viewers to watch the interactive news program in real time.

[0631] Step 13:

[0632] Users can use their devices to watch the news broadcast in real time and confirm that their opinions and feelings are reflected.

[0633] In this way, the present invention can provide an interactive news program that reflects users' opinions and feelings in real time.

[0634] Example 2

[0635] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0636] Conventional news systems have difficulty collecting and analyzing user opinions and emotions in real time and reflecting them in program content. Furthermore, generating commentators and presenters requires manual labor, which is inefficient. This tends to result in low viewer satisfaction and interactivity.

[0637] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring the latest information from all over the world, means for analyzing the acquired latest information and generating comments, means for collecting opinions and questions from users in real time, means for analyzing the opinions and questions from users and recognizing emotions, and means for generating video by combining the acquired information, the generated comments, and the opinions and emotions of users, and distributing the video in real time. This makes it possible to efficiently provide interactive news programs that reflect the opinions and emotions of viewers.

[0638] "Global Updates" refers to the latest data on ongoing events and news obtained from the internet and other sources.

[0639] "Comments" are text data that provide commentary or explanations about news or events, and are primarily generated by commentators or presenters.

[0640] "Opinions and questions from users" refers to text data such as impressions, feedback, inquiries, etc. sent by viewers through the system.

[0641] "Means for recognizing emotions" refers to technology that analyzes text data sent by users and extracts emotions such as joy, sadness, and anger from the text.

[0642] "Means for generating video and distributing it in real time" refers to a technology that integrates acquired information, generated comments, and user opinions and emotional data, and instantly streams the video to viewers.

[0643] A "generative AI model" is an artificial intelligence model that has been trained to perform natural language processing and generation tasks, and has the ability to generate text in a human-like manner.

[0644] This invention relates to an interactive news program system that acquires the latest information from around the world, generates commentators and presenters, collects user opinions in real time, analyzes them using an emotion engine, and combines these to generate and distribute video.

[0645] News gathering

[0646] The server uses a "latest information acquisition module" to obtain the latest information from around the world. This module is implemented using Python and scrapes data from news sites and APIs. The server periodically obtains the latest information from the news sites and API endpoints via the Internet and stores it in a MySQL database.

[0647] Commentator and host generation

[0648] The server uses a "comment generation module" to generate comments based on the acquired news data. This module uses a generative AI model (e.g., GPT-3) to analyze the news data and automatically generate appropriate commentators and host comments. As a concrete example, the server generates the following prompt sentence and passes it to the AI ​​model:

[0649] News data: "The soccer World Cup was held, and Japan won the final."

[0650] Instructions for the Generative AI Model: Based on the news data above, generate comments from a sports commentator and a host. The commentator should analyze the game, and the host should introduce opinions from viewers.

[0651] Gathering opinions from viewers

[0652] Users can use a device (smartphone or PC) to send opinions and questions to the system. An HTML form is installed on the device, and users can enter their opinions or questions in the input field and send them by clicking the send button. The device sends the submitted opinions and questions to the server via an HTTP POST request, which the server stores in a database.

[0653] User sentiment analysis

[0654] The server uses an "emotion analysis engine" (e.g., IBM Watson) to analyze opinions and questions sent by users and recognize their emotions. This engine extracts emotions such as joy, sadness, and anger from the user's text data. The analyzed emotional data is reflected in the content and progress of the news program.

[0655] Program Creation and Distribution

[0656] The server launches a "program generation module" that integrates news data, generated comments, user opinions, and emotion data to generate a news program. This module integrates all data and generates a single video content. It also adjusts the program content based on the emotion data to improve viewer satisfaction. The generated news program is distributed in real time via a "distribution module," allowing viewers to watch it in real time.

[0657] As a concrete example, a server collects the latest sports news and uses a generative AI model to generate comments from sports commentators and hosts. A user then transmits their opinion from their device, saying, "I'm very satisfied with the outcome of the game," which is recognized as "joy" by the emotion analysis engine. Finally, all the data is integrated to generate video content in which the commentator explains the outcome of the game in a positive tone and the host addresses the satisfied opinions of viewers, and this content is distributed in real time.

[0658] In this way, the present invention makes it possible to provide an interactive news program that reflects the user's opinions and feelings.

[0659] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0660] Step 1:

[0661] Regular news gathering

[0662] The server initializes the "latest information acquisition module" with a Python script and sets the schedule for news gathering.

[0663] Input: News site or API endpoint URL

[0664] Specific operation: Set up a server scheduler (e.g., a cron job) to automatically collect news every 10 minutes. The server will call the API endpoint of the specified news site based on the set schedule.

[0665] Output: Retrieved news data (JSON format)

[0666] Step 2:

[0667] Acquiring and storing news data

[0668] The server analyzes the data obtained from the news API and stores it in a database.

[0669] Input: JSON-formatted news data obtained from the news gathering task

[0670] Specific operation: The server parses the received JSON data, extracts the necessary information (title, content, posting date, etc.), and saves it in the MySQL database using the INSERT statement.

[0671] Output: News records stored in a database

[0672] Step 3:

[0673] Generate AI prompts

[0674] The server retrieves the latest news data from the database and generates prompt sentences to pass to the generative AI model.

[0675] Input: News data stored in a database

[0676] Specific operation: The server applies the news data to a template and generates a prompt sentence. For example, the prompt sentence is generated in the following format: "News data: {Title: 'The FIFA World Cup is being held,' Content: 'Japan won the final.'} Instructions to the generation AI model: Based on the above news data, generate comments from a sports commentator and a host. The commentator should analyze the match, and the host should introduce opinions from viewers."

[0677] Output: Generated prompt statement

[0678] Step 4:

[0679] Running an AI model

[0680] The server passes the prompt to a generative AI model (e.g., GPT-3) to generate comments for the commentator and host.

[0681] Input: Generated prompt text

[0682] Specific operation: The server sends a prompt to the generative AI model via an API request and receives the generated text data.

[0683] Output: Commentary by commentators and hosts generated from the AI ​​model

[0684] Step 5:

[0685] Saving comment data

[0686] The server stores the comments generated by the AI ​​model in a database.

[0687] Input: Commentators and host comments generated from an AI model

[0688] Specific operation: The server saves the received comment data in the database using the INSERT statement.

[0689] Output: Comment records stored in the database

[0690] Step 6:

[0691] Submit your user feedback

[0692] The user enters their opinion or question in the input field on the terminal and clicks the send button.

[0693] Input: User's opinion or question (text)

[0694] Specific operation: The terminal obtains the user's opinion from the form and sends it to the server via an HTTP POST request.

[0695] Output: Submitted opinions and questions (text data)

[0696] Step 7:

[0697] Storing opinion data

[0698] The server stores the submitted comments and questions in a database.

[0699] Input: User comments and questions sent from the device

[0700] Specific operation: The server saves the received opinion data in the database using the INSERT statement.

[0701] Output: Opinion records stored in the database

[0702] Step 8:

[0703] Performing sentiment analysis

[0704] The server uses a sentiment analysis engine to analyze the user's opinions and questions and recognize their emotions.

[0705] Input: opinion data stored in a database

[0706] Specific operation: The server sends opinion data to the sentiment analysis engine and receives analyzed sentiment data.

[0707] Output: Analysis results from the emotion analysis engine (emotion data)

[0708] Step 9:

[0709] Storing Emotional Data

[0710] The server stores the analysis results in a database.

[0711] Input: Analysis results from the sentiment analysis engine

[0712] Specific operation: The server saves the received emotion data to the corresponding opinion record using an UPDATE statement.

[0713] Output: Emotion data stored in a database

[0714] Step 10:

[0715] Launching program generation

[0716] The server activates a "program generation module" to generate a news program by integrating the news data, the generated comments, and the user's opinion and emotion data.

[0717] Input: News data, commentators' and moderators' comments, user opinions and sentiment data

[0718] Specific operation: The server integrates these data and executes a program to generate video content.

[0719] Output: Generated video content

[0720] Step 11:

[0721] Adjusting program content

[0722] The server adjusts the content of the program based on the emotional data to improve viewer satisfaction.

[0723] Input: Emotion data

[0724] Specific operation: The server analyzes the emotional data, generates a script based on positive emotions, and reflects it in the video content.

[0725] Output: Adjusted video content

[0726] Step 12:

[0727] Program distribution

[0728] The server uses a "distribution module" to distribute the generated news programs in real time.

[0729] Input: Generated video content

[0730] How it works: The server uses the RTMP protocol to send video content to the streaming service, which viewers can then watch in real time.

[0731] Output: Video content delivered to viewers

[0732] (Application example 2)

[0733] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0734] Conventional news distribution systems lacked the means to collect real-time audience opinions and emotions and reflect them in news programs, resulting in little interaction with viewers. Furthermore, commentators and presenters were fixed, making it difficult to flexibly respond to the diverse needs and emotions of viewers. This led to problems such as a decline in the appeal of news programs and viewer satisfaction.

[0735] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0736] In this invention, the server includes means for acquiring the latest information from around the world, means for analyzing the acquired latest information and generating commentators and presenters, means for collecting opinions and questions from users in real time, means for analyzing the emotions of the collected user opinions, means for generating video by combining the acquired information, the generated commentators and presenters, and the opinions and emotions of users, and means for distributing the generated video in real time, thereby making it possible to provide an interactive news program that reflects the opinions and emotions of viewers in real time.

[0737] The "means for obtaining the latest information" is a system that has the function of periodically collecting the latest information from around the world via the Internet.

[0738] The "analysis means" is a system that uses artificial intelligence technology to generate appropriate commentators and hosts based on the latest information obtained.

[0739] The "means for generating commentators and hosts" is a system that uses artificial intelligence to analyze news data and generate commentators who will provide appropriate explanations to viewers and hosts who will conduct the program.

[0740] The "opinion collection means" is a system that collects opinions and questions from users in real time and stores them in a database.

[0741] The "emotion analysis means" is a system that analyzes opinions and questions sent by users as text data and extracts emotions such as joy, sadness, and anger.

[0742] The "video generation means" is a system that combines acquired news data, generated commentators and presenters, and the user's opinions and feelings to generate a single video content.

[0743] The "video distribution means" is a system that distributes the generated video content to viewers in real time.

[0744] The present invention relates to a system that obtains the latest information from around the world, generates AI commentators and presenters, collects and analyzes viewer opinions in real time, and delivers interactive news programs. Specific ways in which the present invention can be implemented are described below.

[0745] Gathering the latest information

[0746] The server uses a "latest information retrieval method" that retrieves the latest information from around the world. This method has the function of periodically collecting the latest news and events via the Internet. For example, using Python and the News API, the server can collect and store the latest news every 10 minutes. This makes it possible to always maintain the latest information.

[0747] AI commentator and host generation

[0748] The server uses a "commentator and host generation means" to generate commentators and hosts based on the acquired news data. This means uses a generative AI model such as GPT-3 to analyze the news data and automatically generate appropriate commentators and hosts. For example, OpenAI technology can be applied to generate commentator and host characters using prompts for the latest news.

[0749] Gathering opinions from viewers

[0750] Users can use the terminal to send their opinions and questions to the system. The terminal is equipped with an input field and a send button, and users can enter their opinions or questions in the input field and click the send button to send their opinions. These opinions are sent to the server and stored in the database.

[0751] User sentiment analysis

[0752] The server uses an "emotion analysis means" to analyze the opinions and questions sent by the user and recognize the user's emotions. This means uses an emotion analysis tool such as TextBlob to extract emotions such as joy, sadness, and anger from the user's text data.

[0753] Program Creation and Adjustment

[0754] The server activates a "video generation means" that generates a news program by integrating the acquired news data, the generated commentators and presenters, and the user's opinions and emotional data. This means integrates the data and generates a single video content. It also adjusts the program content based on the emotional data to improve user satisfaction.

[0755] Specific examples

[0756] For example, if the server collects the latest sports news, it passes the collected news data to an AI module to generate a sports commentator and a general host. The user then transmits their opinion from their device, saying, "I'm very satisfied with the outcome of the game." The server then passes the transmitted opinion to an emotion analysis module for analysis, and recognizes that the user's emotion is "joy." The server integrates all the data, generates video content in which the commentator explains the outcome of the game in a positive tone and the host addresses the viewers' satisfied opinions, and distributes it in real time.

[0757] Prompt Sentence Examples

[0758] "Breaking News: Candidates give final speeches ahead of tomorrow's presidential election. Generate commentators."

[0759] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0760] Step 1:

[0761] News gathering

[0762] The server starts the "latest information acquisition means" and periodically collects the latest news from around the world. The server uses the News API via the Internet to obtain the latest news data. This acquisition process is automated by a Python script, and is scheduled to run every 10 minutes, for example. The collected news data is stored in the server's database.

[0763] Input: Internet connection, News API key

[0764] Output: Latest news data

[0765] Specific operation: The server runs a Python script, accesses the News API to retrieve the latest news, and stores it in a database.

[0766] Step 2:

[0767] AI commentator and host generation

[0768] The server uses a "commentator and host generation means" to analyze the acquired news data and generate commentators and hosts. A generative AI model such as OpenAI's GPT-3 is used to automatically generate appropriate commentators and hosts based on the prompt text. The generated information is then stored in a database.

[0769] Input: News data, prompt text

[0770] Output: AI-generated commentary and host descriptions

[0771] Specific operation: The server launches the generative AI model, inputs the news data and prompt text, generates explanatory text for the commentator and host, and stores it in the database.

[0772] Step 3:

[0773] Gathering opinions from viewers

[0774] The user uses the terminal to input their opinion or question and clicks the submit button to send it to the server. The user enters their opinion or question in the input field and sends it to the server, which then stores this information in a database. This operation is achieved using a web framework such as Flask.

[0775] Input: User opinions and questions

[0776] Output: User opinions and questions stored on the server

[0777] Specific operation: The user enters an opinion or question into the input field on the terminal and clicks the send button. The server receives it through Flask and saves it in the database.

[0778] Step 4:

[0779] User sentiment analysis

[0780] The server uses "sentiment analysis means" to analyze opinions and questions sent by users. It uses sentiment analysis tools such as TextBlob to extract emotional information from text data. The analysis results are stored in a database.

[0781] Input: User opinions and questions

[0782] Output: Sentiment analysis data

[0783] Specific operation: The server uses TextBlob to analyze the sentiment from the user's comments and questions and saves the results in the database.

[0784] Step 5:

[0785] Program Generation

[0786] The server uses a "video generation means" to generate video content based on the collected news data, the generated commentators and presenters, and the user's opinions and emotion data. This means integrates the data to generate video content as a program.

[0787] Input: News data, commentator and host descriptions, user opinions and sentiment data

[0788] Output: Generated program video

[0789] Specific operation: News data, AI-generated commentators and hosts, user opinions, and emotional data are integrated on the server, and then generated as a single video content using a program content generation module.

[0790] Step 6:

[0791] Program distribution

[0792] The server distributes the generated program video to viewers in real time using the "video distribution means." The generated interactive news program is distributed to viewers using a live streaming service.

[0793] Input: Generated program video

[0794] Output: Streamed real-time newscast

[0795] Specific operation: The server distributes the generated video content in real time through a live streaming service, making it available to viewers.

[0796] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0797] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0798] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0799] [Third embodiment]

[0800] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0801] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0802] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0803] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0804] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0805] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0806] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0807] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0808] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0809] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0810] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0811] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0812] The present invention relates to an interactive news program system that acquires the latest information from around the world, generates commentators and hosts, collects opinions from viewers in real time, and generates and distributes videos that combine these information. Specific ways in which the present invention can be implemented are described below.

[0813] 1. News gathering

[0814] The server uses the "Latest News Acquisition Module" as a means of obtaining the latest news from around the world. This module periodically collects the latest news and events via the Internet. The server initializes the "Latest News Acquisition Module," sets up a periodic news collection task, and stores the obtained latest news in a database. For example, the server can collect and store the latest news every 10 minutes.

[0815] 2. Allocation of AI commentators and hosts

[0816] The server uses a "commentator generation module" and a "host generation module" to generate commentators and hosts based on the acquired news data. These modules use artificial intelligence technology to analyze the news data and automatically generate appropriate commentators and hosts. The generated commentators and hosts are stored in a database and used in subsequent processes.

[0817] 3. Gathering opinions from viewers

[0818] Users can use the terminal to send opinions and questions to the system. The terminal is equipped with an input field and a send button, and users can enter their opinions or questions in the input field and send them by clicking the send button. The terminal then sends the submitted opinions and questions to the server, which then stores them in a database.

[0819] 4. Program Creation and Distribution

[0820] The server uses a "program generation module" to generate a news program by combining user opinions and questions, generated commentators and presenters, and collected news data. This module integrates the acquired information with the opinions of commentators, presenters, and users to generate a single video content. The server then distributes the generated video content in real time using a "distribution module." This distribution allows viewers to watch an interactive news program in real time.

[0821] Specific examples

[0822] For example, suppose that on a certain day, the server collects the latest political news. The collected news data is passed to an AI module, which generates a political commentator and a general host. The user then sends a question from their device: "What will happen in the political situation in the future?" The server integrates all the data and generates video content in which the commentator explains future political trends and the host takes up and discusses viewer questions, and distributes it in real time.

[0823] In this way, the present invention makes it possible to provide a news program in which users can participate and have their opinions reflected.

[0824] The processing flow will be explained below.

[0825] Step 1:

[0826] The server initializes the "Latest Information Acquisition Module" to acquire the latest information from around the world. The "Latest Information Acquisition Module" has the function of periodically collecting the latest news and events via the Internet.

[0827] Step 2:

[0828] The server is set up to periodically retrieve the latest information. For example, the server is set up to retrieve the latest news from the Internet every 10 minutes. This ensures that the latest information is always collected.

[0829] Step 3:

[0830] The server uses the latest information acquisition module to collect the latest information from the Internet. The collected information can be in text format, images, or videos.

[0831] Step 4:

[0832] The server stores the collected latest information in a database, which allows the information to be easily retrieved later.

[0833] Step 5:

[0834] The server passes the saved news data to a "commentator generation module" and a "host generation module," which use artificial intelligence to analyze the news data and automatically generate commentators and hosts.

[0835] Step 6:

[0836] The server stores the generated commentators and hosts in a database, making these characters available for subsequent processes.

[0837] Step 7:

[0838] Users use a terminal to input their opinions or questions into the system. The terminal is equipped with an input field and a send button, and users enter their opinions or questions in the input field and click the send button to send their opinions.

[0839] Step 8:

[0840] The device sends the opinions and questions entered by the user to the server using a standard data format such as JSON.

[0841] Step 9:

[0842] The server receives the user's opinions and questions sent from the device and stores them in a database, allowing the user's feedback to be managed for future use.

[0843] Step 10:

[0844] The server launches a "program generation module" to generate a news program by integrating news data, commentators, presenters, and user opinions. This module integrates the data and generates a single video content.

[0845] Step 11:

[0846] The server distributes the generated news programs in real time using a "distribution module," allowing viewers to view the generated content in real time.

[0847] Step 12:

[0848] Users can use their devices to watch news programs delivered in real time and enjoy interactive content that reflects their own opinions and questions.

[0849] Through the above process, the present invention realizes an interactive news program in which users can participate and have their opinions reflected.

[0850] Example 1

[0851] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0852] In conventional news programs, it is difficult for viewers to reflect their opinions and questions in real time, and the creation of commentators and presenters is done manually, making it difficult to provide rapid and diverse content. The goal is to solve these problems and provide interactive and flexible news programs.

[0853] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0854] In this invention, the server includes a module for acquiring the latest information from around the world, an artificial intelligence module for analyzing the acquired latest information and generating commentators and presenters, an interface for collecting opinions and questions from users in real time, and a module for generating and distributing video in real time by combining the acquired information, the generated commentators and presenters, and user opinions, thereby making it possible to provide an interactive news program that reflects viewers' opinions in real time.

[0855] The "module for acquiring the latest information from around the world" is a program for periodically collecting the latest information from news sources around the world via the Internet.

[0856] The "artificial intelligence module" is a program that includes machine learning algorithms for analyzing acquired data and automatically generating commentators and hosts.

[0857] An "interface" is a device or software that provides an input means for users to input opinions and questions and transmit them to a server in real time.

[0858] The "video generation module for real-time distribution" is a program that integrates acquired information, generated commentator and host data, and user opinions to generate video content and distribute it in real time.

[0859] A "generative AI model" is an artificial intelligence algorithm used to automatically generate commentary and host comments.

[0860] A "prompt" is an instruction entered into a generative AI model to prompt it to take action.

[0861] This invention is an interactive news program system that acquires the latest information from around the world, generates commentators and hosts, collects viewers' opinions in real time, and generates and distributes videos that combine these information. This system is composed of the following modules and components:

[0862] News gathering

[0863] The server uses the "latest information acquisition module" to obtain the latest information from around the world. This module periodically collects the latest news and events via the internet through news APIs. For example, the server uses the News API to obtain news data from categories such as politics, economy, and sports, and stores this in a database in JSON format. The server can always maintain the latest information by setting a schedule to run the news collection task every 10 minutes.

[0864] AI commentator and moderator placement

[0865] The server generates AI commentators and hosts using a "commentator generation module" and a "host generation module" based on the acquired news data. These modules use artificial intelligence technology to analyze the news data and automatically generate appropriate commentators and hosts. The server analyzes the news data using Python's NLTK library and generates commentator comments and host scripts using a generative AI model (e.g., GPT-4). The generated commentator and host data is stored in a database and used in subsequent processes.

[0866] Gathering opinions from viewers

[0867] Users can use their own devices to send opinions and questions to the system. The devices are equipped with input fields and submit buttons using HTML forms, and users can enter their opinions or questions in the input fields and click the submit button to submit their opinions or questions. The devices use JavaScript Ajax to asynchronously send the submitted opinions and questions to the server in real time. The server stores this data in a database.

[0868] Program Creation and Distribution

[0869] The server uses a "program generation module" to generate a news program by combining user opinions and questions, generated commentators and hosts, and collected news data. This module integrates the acquired information with the generated commentators, hosts, and user opinions to generate a single video content. The server generates the video content using Python's MoviePy library and sends the generated video to a streaming server (e.g., Wowza). By providing users with a streaming URL, viewers can watch an interactive news program in real time.

[0870] Specific examples

[0871] For example, let's say the server collects the latest political news, analyzes it, and generates a political commentator and a general host. When a user sends a question from their device such as "What will happen in the political situation in the future?", the server can integrate all the data and generate video content in which "the commentator explains the future political trends, and the host takes up the viewer's question and discusses it." As a further example, the prompt text to be input to the generative AI model is as follows:

[0872] "Generate commentator comments in response to the following question: 'What's the political landscape going to be like in the future?'"

[0873] In this way, the present invention is able to provide a news program that allows users to participate and give their opinions.

[0874] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0875] Step 1:

[0876] The server initializes the "latest information acquisition module."

[0877] Specifically, the server sends a request to the news API to obtain the latest news data.

[0878] Input: News sources on the internet

[0879] Output: Latest news data (JSON format)

[0880] This news data is stored in a database.

[0881] Step 2:

[0882] The server sets up periodic news gathering tasks.

[0883] Specifically, the server sets a schedule to access the news API every 10 minutes.

[0884] Input: Schedule information

[0885] Output: A newsgathering job with a timer

[0886] This allows the server to automatically collect the latest news on a regular basis.

[0887] Step 3:

[0888] The server analyzes the news data using a "commentator generation module."

[0889] Specifically, the server parses the news data using Python's NLTK library.

[0890] Input: News data (JSON format)

[0891] Output: Analysis results (topics, keywords)

[0892] The analysis results are used in the next step.

[0893] Step 4:

[0894] The server generates commentator profiles and comments based on the analysis results.

[0895] Specifically, the server uses a generative AI model (e.g., GPT-4) to generate the commentator's comments.

[0896] Input: Analysis results (topics, keywords)

[0897] Output: Commentator profile and comments

[0898] The generated data is stored in a database.

[0899] Step 5:

[0900] The server generates a moderator using a "moderator generation module."

[0901] Specifically, the server generates the moderator's script using a generative AI model.

[0902] Input: News data, commentator profiles and comments

[0903] Output: Host profile and script

[0904] The generated data is stored in a database.

[0905] Step 6:

[0906] The user enters their opinion or question into an input field on the terminal.

[0907] Specifically, the user uses an HTML form to input opinions or questions into the terminal.

[0908] Input: User's opinion or question (text format)

[0909] Output: Input data (text format)

[0910] It will be used in the next step.

[0911] Step 7:

[0912] The user clicks the send button.

[0913] Specifically, the terminal uses Ajax in JavaScript to send the transmission data to the server.

[0914] Input: Input data (text format)

[0915] Output: Transmission data (text format)

[0916] The transmitted data is transferred to the server in real time.

[0917] Step 8:

[0918] The server stores the received data in a database.

[0919] As a specific operation, the server stores the transmitted data in a database.

[0920] Input: Send data (text format)

[0921] Output: User comments and questions stored in a database

[0922] Step 9:

[0923] The server initializes the "program generation module."

[0924] Specifically, the program content is planned using a generative AI model.

[0925] Input: News data, commentator and host data, user opinions and questions

[0926] Output: Program content configuration information

[0927] Step 10:

[0928] The server integrates the information and generates it as a single video content.

[0929] Specifically, the server generates video using Python's MoviePy library.

[0930] Input: Program content configuration information

[0931] Output: Generated video data

[0932] Step 11:

[0933] The server uses a "distribution module" to distribute the generated video content in real time.

[0934] As a specific operation, the server transmits the generated video data to the streaming server.

[0935] Input: Generated video data

[0936] Output: Streaming URL, real-time video

[0937] Viewers can watch interactive news programs in real time.

[0938] (Application example 1)

[0939] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0940] Current news distribution systems often only provide information to viewers one-way, and viewer opinions and questions are rarely reflected in real time. There is also room for improvement in the quality and appropriateness of commentators and presenters. Furthermore, while there is a demand for interactive content delivery that can immediately reflect viewer interests, this is difficult to achieve in reality.

[0941] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0942] In this invention, the server includes means for acquiring the latest information from around the world, means for analyzing the acquired latest information and generating commentators and hosts, means for collecting opinions and questions from users in real time, means for combining the acquired information, the generated commentators and hosts, and user opinions to generate video and distribute it in real time, and means for collecting opinions and questions from terminals in real time and generating interactive content based on the opinions and questions, whereby interactive news distribution that instantly reflects viewer opinions and questions is possible.

[0943] "Means for obtaining the latest information" is a function for periodically collecting the latest information from around the world from the Internet.

[0944] The "commentator generation means" is a function for analyzing the collected latest information and generating appropriate commentators using artificial intelligence.

[0945] The "moderator generation means" is a function for analyzing the collected latest information and generating an appropriate moderator using artificial intelligence.

[0946] The "opinion collection means" is a function for collecting opinions and questions from users in real time.

[0947] The "image generation means" is a function for generating an image by combining the acquired information, the generated commentators and presenters, and the user's opinions.

[0948] The "real-time distribution means" is a function for distributing the generated video in real time without delay.

[0949] A "terminal" is a device used by a user to input opinions or questions, and includes smartphones, smart glasses, head-mounted displays, etc.

[0950] The "interactive content generation means" is a function for generating interactive content that is conducted by the generated commentator and moderator based on collected opinions and questions.

[0951] "Artificial intelligence" is an advanced computing technology that analyzes acquired data and generates commentators and presenters based on that data.

[0952] To implement this invention, the following hardware and software are used. The hardware requires a server, a viewer's smartphone, smart glasses, a head-mounted display, or other device. The software requires a news API (such as NewsAPI) and an artificial intelligence generation library (such as GPT-3 or BERT).

[0953] News gathering

[0954] The server uses a means to obtain the latest news and events from around the world. For example, it periodically obtains news data from the News API and stores it in a database. This process is performed periodically, and the latest news can be collected every 10 minutes, for example.

[0955] AI commentator and presenter generation

[0956] The server uses the commentator generation means and the host generation means to generate AI commentators and hosts based on the acquired news data. The collected news data is analyzed and appropriate commentator and host characters are generated using artificial intelligence technology. Information on the generated AI commentators and hosts is stored in a database.

[0957] Gathering opinions from viewers

[0958] Viewers send their opinions and questions to the system via devices such as smartphones, smart glasses, and head-mounted displays. The devices are equipped with an input field and a send button, and viewers can send their opinions and questions by entering them in the input field and clicking the send button. The submitted opinions and questions are sent to a server and stored in a database.

[0959] Program Creation and Distribution

[0960] The server uses a video generation means to generate video content by combining the collected news data, the generated AI commentators and hosts, and the opinions of viewers. For example, if a political commentator and host are generated based on news data collected on a certain day, and a viewer sends a question such as "What will happen in the political situation in the future?", all the data is integrated to generate video content in which the commentator explains future political trends, and the host takes up the viewer's question and discusses it. The generated video content is then distributed in real time using a distribution means. This allows viewers to watch an interactive news program in real time.

[0961] As a concrete example, an AI-generated commentator will explain the results of the latest sporting event and answer viewers' questions in real time, such as "Who was the MVP player in this game?" In this way, interactive news delivery that instantly reflects viewers' opinions and questions becomes possible.

[0962] Prompt Sentence Examples

[0963] "Provide commentary on the latest sporting events and answer viewer questions such as: 'Who was the MVP player in this game?'"

[0964] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0965] Step 1:

[0966] The server periodically collects the latest news and events from around the world using the latest information retrieval method. In this case, the server retrieves news data using a news API (e.g., NewsAPI) and extracts the necessary information (title, text, date and time, etc.). This data is stored in a database. The input is the response data from the NewsAPI, and the output is the extracted news information.

[0967] Step 2:

[0968] The server uses the commentator generation means and the host generation means to generate AI commentators and hosts based on the acquired news data. The server inputs the news data into an artificial intelligence model (e.g., GPT-3 or BERT) and generates appropriate commentator and host characters based on that. Information about the generated commentators and hosts is stored in a database. The input is news data, and the output is character information for the generated commentators and hosts.

[0969] Step 3:

[0970] A user sends an opinion or question to the system using a device (smartphone, smart glasses, head-mounted display, etc.). The user enters the opinion or question into the input field on the device and clicks the send button. The input is the opinion or question entered by the user in the input field, and the output is the opinion or question sent to the server. The server stores this information in a database.

[0971] Step 4:

[0972] The server uses a video generation means to integrate the collected news data, the generated AI commentators and moderators, and user opinions to generate video content. The server creates a scenario in which the AI ​​commentators provide commentary based on the news data and user opinions, and the moderators take up viewer questions and discuss them. The input is the news data, information on the generated commentators and moderators, and viewer opinions, and the output is the generated video content.

[0973] Step 5:

[0974] The server uses a distribution means to distribute the generated video content to viewers in real time. The server uploads the generated video content to a streaming server or distribution platform, where viewers can watch it in real time from their devices. The input is the generated video content, and the output is the video content distributed to viewers.

[0975] In this way, interactive news distribution that instantly reflects viewer opinions and questions can be realized.

[0976] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0977] The present invention relates to an interactive news program system that acquires the latest information from around the world, generates commentators and presenters, collects viewers' opinions in real time, analyzes them using an emotion engine, and generates and distributes videos that combine these. How the present invention can be put into practice will be described below.

[0978] 1. News gathering

[0979] The server uses the "Latest News Acquisition Module" to acquire the latest news from around the world. This module has the function of periodically collecting the latest news and events via the Internet. The server initializes the "Latest News Acquisition Module", sets up a periodic news collection task, and stores the acquired latest news in a database. For example, the server can collect and store the latest news every 10 minutes.

[0980] 2. Allocation of AI commentators and hosts

[0981] The server uses a "commentator generation module" and a "host generation module" to generate commentators and hosts based on the acquired news data. These modules use artificial intelligence technology to analyze the news data and automatically generate appropriate commentators and hosts. The generated commentators and hosts are stored in a database and used in subsequent processes.

[0982] 3. Gathering opinions from viewers

[0983] Users can use the terminal to send opinions and questions to the system. The terminal is equipped with an input field and a send button, and users can enter their opinions or questions in the input field and send them by clicking the send button. The terminal then sends the submitted opinions and questions to the server, which then stores them in a database.

[0984] 4. User sentiment analysis

[0985] The server uses an "emotion analysis engine" to analyze opinions and questions sent by users and recognize their emotions. The emotion analysis engine extracts emotions such as joy, sadness, and anger from the user's text data. This emotional data is reflected in the content and progress of the news program.

[0986] 5. Program Creation and Adjustment

[0987] The server launches a "program generation module" to generate a news program by integrating news data, commentators, presenters, and user opinions and emotion data. This module integrates the data and generates a single video content. It also adjusts the program content based on the emotion data to improve user satisfaction.

[0988] 6. Program Creation and Distribution

[0989] The server distributes the generated news program in real time using a "distribution module," allowing viewers to view the generated interactive content in real time.

[0990] Specific examples

[0991] For example, suppose that on a certain day, the server collects the latest sports news. The collected news data is passed to an AI module, which generates a sports commentator and a general host. After that, a user submits their opinion from their device, saying, "I'm very satisfied with the outcome of the game." The server passes the submitted opinion to an emotion analysis engine for analysis, and recognizes that the user's emotion is "joy." The server integrates all the data and generates video content in which the commentator explains the outcome of the game in a positive tone, and the host addresses the satisfied opinions of the viewers, and distributes it in real time.

[0992] In this way, the present invention makes it possible to provide a news program that reflects the opinions and feelings of the user.

[0993] The processing flow will be explained below.

[0994] Step 1:

[0995] The server initializes the "latest information acquisition module" to acquire the latest information from around the world. It is set up to collect the latest news and events via the Internet.

[0996] Step 2:

[0997] The server sets up a schedule to periodically retrieve updates, for example, scheduling a task to gather news every 10 minutes.

[0998] Step 3:

[0999] The server periodically collects the latest news from the Internet, uses the "latest news acquisition module" to collect news data, and stores it in a database.

[1000] Step 4:

[1001] The server passes the collected news data to a "commentator generation module" and a "host generation module," which analyze the news data and generate appropriate commentators and hosts.

[1002] Step 5:

[1003] The server stores the generated commentator and host data in a database, which is used in subsequent processes.

[1004] Step 6:

[1005] The user inputs their opinions and questions using a terminal, which is equipped with an input field for inputting opinions and questions and a submit button.

[1006] Step 7:

[1007] The user enters their opinion or question into the input field and clicks the send button, which causes the device to send the entered data to the server.

[1008] Step 8:

[1009] The device sends user opinions and questions in JSON format to the server, which receives this data and stores it in a database.

[1010] Step 9:

[1011] The server uses an "emotion analysis engine" to analyze opinions and questions sent by users. The emotion analysis engine extracts emotions from the user's text data and stores them in a database.

[1012] Step 10:

[1013] The server launches a "program generation module" for integrating the acquired news data, the generated commentators and hosts, and the user's opinion and emotion data to generate a news program.

[1014] Step 11:

[1015] The server then adjusts the content of the program based on the emotion data. The "program generation module" changes the tone of commentary and commentary or focuses on specific topics depending on the user's emotions.

[1016] Step 12:

[1017] The server distributes the generated news program in real time using the "distribution module," allowing viewers to watch the interactive news program in real time.

[1018] Step 13:

[1019] Users can use their devices to watch the news broadcast in real time and confirm that their opinions and feelings are reflected.

[1020] In this way, the present invention can provide an interactive news program that reflects users' opinions and feelings in real time.

[1021] Example 2

[1022] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1023] Conventional news systems have difficulty collecting and analyzing user opinions and emotions in real time and reflecting them in program content. Furthermore, generating commentators and presenters requires manual labor, which is inefficient. This tends to result in low viewer satisfaction and interactivity.

[1024] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring the latest information from all over the world, means for analyzing the acquired latest information and generating comments, means for collecting opinions and questions from users in real time, means for analyzing the opinions and questions from users and recognizing emotions, and means for generating video by combining the acquired information, the generated comments, and the opinions and emotions of users, and distributing the video in real time. This makes it possible to efficiently provide interactive news programs that reflect the opinions and emotions of viewers.

[1025] "Global Updates" refers to the latest data on ongoing events and news obtained from the internet and other sources.

[1026] "Comments" are text data that provide commentary or explanations about news or events, and are primarily generated by commentators or presenters.

[1027] "Opinions and questions from users" refers to text data such as impressions, feedback, inquiries, etc. sent by viewers through the system.

[1028] "Means for recognizing emotions" refers to technology that analyzes text data sent by users and extracts emotions such as joy, sadness, and anger from the text.

[1029] "Means for generating video and distributing it in real time" refers to a technology that integrates acquired information, generated comments, and user opinions and emotional data, and instantly streams the video to viewers.

[1030] A "generative AI model" is an artificial intelligence model that has been trained to perform natural language processing and generation tasks, and has the ability to generate text in a human-like manner.

[1031] This invention relates to an interactive news program system that acquires the latest information from around the world, generates commentators and presenters, collects user opinions in real time, analyzes them using an emotion engine, and combines these to generate and distribute video.

[1032] News gathering

[1033] The server uses a "latest information acquisition module" to obtain the latest information from around the world. This module is implemented using Python and scrapes data from news sites and APIs. The server periodically obtains the latest information from the news sites and API endpoints via the Internet and stores it in a MySQL database.

[1034] Commentator and host generation

[1035] The server uses a "comment generation module" to generate comments based on the acquired news data. This module uses a generative AI model (e.g., GPT-3) to analyze the news data and automatically generate appropriate commentators and host comments. As a concrete example, the server generates the following prompt sentence and passes it to the AI ​​model:

[1036] News data: "The soccer World Cup was held, and Japan won the final."

[1037] Instructions for the Generative AI Model: Based on the news data above, generate comments from a sports commentator and a host. The commentator should analyze the game, and the host should introduce opinions from viewers.

[1038] Gathering opinions from viewers

[1039] Users can use a device (smartphone or PC) to send opinions and questions to the system. An HTML form is installed on the device, and users can enter their opinions or questions in the input field and send them by clicking the send button. The device sends the submitted opinions and questions to the server via an HTTP POST request, which the server stores in a database.

[1040] User sentiment analysis

[1041] The server uses an "emotion analysis engine" (e.g., IBM Watson) to analyze opinions and questions sent by users and recognize their emotions. This engine extracts emotions such as joy, sadness, and anger from the user's text data. The analyzed emotional data is reflected in the content and progress of the news program.

[1042] Program Creation and Distribution

[1043] The server launches a "program generation module" that integrates news data, generated comments, user opinions, and emotion data to generate a news program. This module integrates all data and generates a single video content. It also adjusts the program content based on the emotion data to improve viewer satisfaction. The generated news program is distributed in real time via a "distribution module," allowing viewers to watch it in real time.

[1044] As a concrete example, a server collects the latest sports news and uses a generative AI model to generate comments from sports commentators and hosts. A user then transmits their opinion from their device, saying, "I'm very satisfied with the outcome of the game," which is recognized as "joy" by the emotion analysis engine. Finally, all the data is integrated to generate video content in which the commentator explains the outcome of the game in a positive tone and the host addresses the satisfied opinions of viewers, and this content is distributed in real time.

[1045] In this way, the present invention makes it possible to provide an interactive news program that reflects the user's opinions and feelings.

[1046] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1047] Step 1:

[1048] Regular news gathering

[1049] The server initializes the "latest information acquisition module" with a Python script and sets the schedule for news gathering.

[1050] Input: News site or API endpoint URL

[1051] Specific operation: Set up a server scheduler (e.g., a cron job) to automatically collect news every 10 minutes. The server will call the API endpoint of the specified news site based on the set schedule.

[1052] Output: Retrieved news data (JSON format)

[1053] Step 2:

[1054] Acquiring and storing news data

[1055] The server analyzes the data obtained from the news API and stores it in a database.

[1056] Input: JSON-formatted news data obtained from the news gathering task

[1057] Specific operation: The server parses the received JSON data, extracts the necessary information (title, content, posting date, etc.), and saves it in the MySQL database using the INSERT statement.

[1058] Output: News records stored in a database

[1059] Step 3:

[1060] Generate AI prompts

[1061] The server retrieves the latest news data from the database and generates prompt sentences to pass to the generative AI model.

[1062] Input: News data stored in a database

[1063] Specific operation: The server applies the news data to a template and generates a prompt sentence. For example, the prompt sentence is generated in the following format: "News data: {Title: 'The FIFA World Cup is being held,' Content: 'Japan won the final.'} Instructions to the generation AI model: Based on the above news data, generate comments from a sports commentator and a host. The commentator should analyze the match, and the host should introduce opinions from viewers."

[1064] Output: Generated prompt statement

[1065] Step 4:

[1066] Running an AI model

[1067] The server passes the prompt to a generative AI model (e.g., GPT-3) to generate comments for the commentator and host.

[1068] Input: Generated prompt text

[1069] Specific operation: The server sends a prompt to the generative AI model via an API request and receives the generated text data.

[1070] Output: Commentary by commentators and hosts generated from the AI ​​model

[1071] Step 5:

[1072] Saving comment data

[1073] The server stores the comments generated by the AI ​​model in a database.

[1074] Input: Commentators and host comments generated from an AI model

[1075] Specific operation: The server saves the received comment data in the database using the INSERT statement.

[1076] Output: Comment records stored in the database

[1077] Step 6:

[1078] Submit your user feedback

[1079] The user enters their opinion or question in the input field on the terminal and clicks the send button.

[1080] Input: User's opinion or question (text)

[1081] Specific operation: The terminal obtains the user's opinion from the form and sends it to the server via an HTTP POST request.

[1082] Output: Submitted opinions and questions (text data)

[1083] Step 7:

[1084] Storing opinion data

[1085] The server stores the submitted comments and questions in a database.

[1086] Input: User comments and questions sent from the device

[1087] Specific operation: The server saves the received opinion data in the database using the INSERT statement.

[1088] Output: Opinion records stored in the database

[1089] Step 8:

[1090] Performing sentiment analysis

[1091] The server uses a sentiment analysis engine to analyze the user's opinions and questions and recognize their emotions.

[1092] Input: opinion data stored in a database

[1093] Specific operation: The server sends opinion data to the sentiment analysis engine and receives analyzed sentiment data.

[1094] Output: Analysis results from the emotion analysis engine (emotion data)

[1095] Step 9:

[1096] Storing Emotional Data

[1097] The server stores the analysis results in a database.

[1098] Input: Analysis results from the sentiment analysis engine

[1099] Specific operation: The server saves the received emotion data to the corresponding opinion record using an UPDATE statement.

[1100] Output: Emotion data stored in a database

[1101] Step 10:

[1102] Launching program generation

[1103] The server activates a "program generation module" to generate a news program by integrating the news data, the generated comments, and the user's opinion and emotion data.

[1104] Input: News data, commentators' and moderators' comments, user opinions and sentiment data

[1105] Specific operation: The server integrates these data and executes a program to generate video content.

[1106] Output: Generated video content

[1107] Step 11:

[1108] Adjusting program content

[1109] The server adjusts the content of the program based on the emotional data to improve viewer satisfaction.

[1110] Input: Emotion data

[1111] Specific operation: The server analyzes the emotional data, generates a script based on positive emotions, and reflects it in the video content.

[1112] Output: Adjusted video content

[1113] Step 12:

[1114] Program distribution

[1115] The server uses a "distribution module" to distribute the generated news programs in real time.

[1116] Input: Generated video content

[1117] How it works: The server uses the RTMP protocol to send video content to the streaming service, which viewers can then watch in real time.

[1118] Output: Video content delivered to viewers

[1119] (Application example 2)

[1120] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1121] Conventional news distribution systems lacked the means to collect real-time audience opinions and emotions and reflect them in news programs, resulting in little interaction with viewers. Furthermore, commentators and presenters were fixed, making it difficult to flexibly respond to the diverse needs and emotions of viewers. This led to problems such as a decline in the appeal of news programs and viewer satisfaction.

[1122] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1123] In this invention, the server includes means for acquiring the latest information from around the world, means for analyzing the acquired latest information and generating commentators and presenters, means for collecting opinions and questions from users in real time, means for analyzing the emotions of the collected user opinions, means for generating video by combining the acquired information, the generated commentators and presenters, and the opinions and emotions of users, and means for distributing the generated video in real time, thereby making it possible to provide an interactive news program that reflects the opinions and emotions of viewers in real time.

[1124] The "means for obtaining the latest information" is a system that has the function of periodically collecting the latest information from around the world via the Internet.

[1125] The "analysis means" is a system that uses artificial intelligence technology to generate appropriate commentators and hosts based on the latest information obtained.

[1126] The "means for generating commentators and hosts" is a system that uses artificial intelligence to analyze news data and generate commentators who will provide appropriate explanations to viewers and hosts who will conduct the program.

[1127] The "opinion collection means" is a system that collects opinions and questions from users in real time and stores them in a database.

[1128] The "emotion analysis means" is a system that analyzes opinions and questions sent by users as text data and extracts emotions such as joy, sadness, and anger.

[1129] The "video generation means" is a system that combines acquired news data, generated commentators and presenters, and the user's opinions and feelings to generate a single video content.

[1130] The "video distribution means" is a system that distributes the generated video content to viewers in real time.

[1131] The present invention relates to a system that obtains the latest information from around the world, generates AI commentators and presenters, collects and analyzes viewer opinions in real time, and delivers interactive news programs. Specific ways in which the present invention can be implemented are described below.

[1132] Gathering the latest information

[1133] The server uses a "latest information retrieval method" that retrieves the latest information from around the world. This method has the function of periodically collecting the latest news and events via the Internet. For example, using Python and the News API, the server can collect and store the latest news every 10 minutes. This makes it possible to always maintain the latest information.

[1134] AI commentator and host generation

[1135] The server uses a "commentator and host generation means" to generate commentators and hosts based on the acquired news data. This means uses a generative AI model such as GPT-3 to analyze the news data and automatically generate appropriate commentators and hosts. For example, OpenAI technology can be applied to generate commentator and host characters using prompts for the latest news.

[1136] Gathering opinions from viewers

[1137] Users can use the terminal to send their opinions and questions to the system. The terminal is equipped with an input field and a send button, and users can enter their opinions or questions in the input field and click the send button to send their opinions. These opinions are sent to the server and stored in the database.

[1138] User sentiment analysis

[1139] The server uses an "emotion analysis means" to analyze the opinions and questions sent by the user and recognize the user's emotions. This means uses an emotion analysis tool such as TextBlob to extract emotions such as joy, sadness, and anger from the user's text data.

[1140] Program Creation and Adjustment

[1141] The server activates a "video generation means" that generates a news program by integrating the acquired news data, the generated commentators and presenters, and the user's opinions and emotional data. This means integrates the data and generates a single video content. It also adjusts the program content based on the emotional data to improve user satisfaction.

[1142] Specific examples

[1143] For example, if the server collects the latest sports news, it passes the collected news data to an AI module to generate a sports commentator and a general host. The user then transmits their opinion from their device, saying, "I'm very satisfied with the outcome of the game." The server then passes the transmitted opinion to an emotion analysis module for analysis, and recognizes that the user's emotion is "joy." The server integrates all the data, generates video content in which the commentator explains the outcome of the game in a positive tone and the host addresses the viewers' satisfied opinions, and distributes it in real time.

[1144] Prompt Sentence Examples

[1145] "Breaking News: Candidates give final speeches ahead of tomorrow's presidential election. Generate commentators."

[1146] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1147] Step 1:

[1148] News gathering

[1149] The server starts the "latest information acquisition means" and periodically collects the latest news from around the world. The server uses the News API via the Internet to obtain the latest news data. This acquisition process is automated by a Python script, and is scheduled to run every 10 minutes, for example. The collected news data is stored in the server's database.

[1150] Input: Internet connection, News API key

[1151] Output: Latest news data

[1152] Specific operation: The server runs a Python script, accesses the News API to retrieve the latest news, and stores it in a database.

[1153] Step 2:

[1154] AI commentator and host generation

[1155] The server uses a "commentator and host generation means" to analyze the acquired news data and generate commentators and hosts. A generative AI model such as OpenAI's GPT-3 is used to automatically generate appropriate commentators and hosts based on the prompt text. The generated information is then stored in a database.

[1156] Input: News data, prompt text

[1157] Output: AI-generated commentary and host descriptions

[1158] Specific operation: The server launches the generative AI model, inputs the news data and prompt text, generates explanatory text for the commentator and host, and stores it in the database.

[1159] Step 3:

[1160] Gathering opinions from viewers

[1161] The user uses the terminal to input their opinion or question and clicks the submit button to send it to the server. The user enters their opinion or question in the input field and sends it to the server, which then stores this information in a database. This operation is achieved using a web framework such as Flask.

[1162] Input: User opinions and questions

[1163] Output: User opinions and questions stored on the server

[1164] Specific operation: The user enters an opinion or question into the input field on the terminal and clicks the send button. The server receives it through Flask and saves it in the database.

[1165] Step 4:

[1166] User sentiment analysis

[1167] The server uses "sentiment analysis means" to analyze opinions and questions sent by users. It uses sentiment analysis tools such as TextBlob to extract emotional information from text data. The analysis results are stored in a database.

[1168] Input: User opinions and questions

[1169] Output: Sentiment analysis data

[1170] Specific operation: The server uses TextBlob to analyze the sentiment from the user's comments and questions and saves the results in the database.

[1171] Step 5:

[1172] Program Generation

[1173] The server uses a "video generation means" to generate video content based on the collected news data, the generated commentators and presenters, and the user's opinions and emotion data. This means integrates the data to generate video content as a program.

[1174] Input: News data, commentator and host descriptions, user opinions and sentiment data

[1175] Output: Generated program video

[1176] Specific operation: News data, AI-generated commentators and hosts, user opinions, and emotional data are integrated on the server, and then generated as a single video content using a program content generation module.

[1177] Step 6:

[1178] Program distribution

[1179] The server distributes the generated program video to viewers in real time using the "video distribution means." The generated interactive news program is distributed to viewers using a live streaming service.

[1180] Input: Generated program video

[1181] Output: Streamed real-time newscast

[1182] Specific operation: The server distributes the generated video content in real time through a live streaming service, making it available to viewers.

[1183] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1184] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1185] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1186] [Fourth embodiment]

[1187] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1188] 7, a 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.

[1189] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1190] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1191] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1192] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1193] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1194] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1195] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1196] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1197] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1198] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1199] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1200] The present invention relates to an interactive news program system that acquires the latest information from around the world, generates commentators and hosts, collects opinions from viewers in real time, and generates and distributes videos that combine these information. Specific ways in which the present invention can be implemented are described below.

[1201] 1. News gathering

[1202] The server uses the "Latest News Acquisition Module" as a means of obtaining the latest news from around the world. This module periodically collects the latest news and events via the Internet. The server initializes the "Latest News Acquisition Module," sets up a periodic news collection task, and stores the obtained latest news in a database. For example, the server can collect and store the latest news every 10 minutes.

[1203] 2. Allocation of AI commentators and hosts

[1204] The server uses a "commentator generation module" and a "host generation module" to generate commentators and hosts based on the acquired news data. These modules use artificial intelligence technology to analyze the news data and automatically generate appropriate commentators and hosts. The generated commentators and hosts are stored in a database and used in subsequent processes.

[1205] 3. Gathering opinions from viewers

[1206] Users can use the terminal to send opinions and questions to the system. The terminal is equipped with an input field and a send button, and users can enter their opinions or questions in the input field and send them by clicking the send button. The terminal then sends the submitted opinions and questions to the server, which then stores them in a database.

[1207] 4. Program Creation and Distribution

[1208] The server uses a "program generation module" to generate a news program by combining user opinions and questions, generated commentators and presenters, and collected news data. This module integrates the acquired information with the opinions of commentators, presenters, and users to generate a single video content. The server then distributes the generated video content in real time using a "distribution module." This distribution allows viewers to watch an interactive news program in real time.

[1209] Specific examples

[1210] For example, suppose that on a certain day, the server collects the latest political news. The collected news data is passed to an AI module, which generates a political commentator and a general host. The user then sends a question from their device: "What will happen in the political situation in the future?" The server integrates all the data and generates video content in which the commentator explains future political trends and the host takes up and discusses viewer questions, and distributes it in real time.

[1211] In this way, the present invention makes it possible to provide a news program in which users can participate and have their opinions reflected.

[1212] The processing flow will be explained below.

[1213] Step 1:

[1214] The server initializes the "Latest Information Acquisition Module" to acquire the latest information from around the world. The "Latest Information Acquisition Module" has the function of periodically collecting the latest news and events via the Internet.

[1215] Step 2:

[1216] The server is set up to periodically retrieve the latest information. For example, the server is set up to retrieve the latest news from the Internet every 10 minutes. This ensures that the latest information is always collected.

[1217] Step 3:

[1218] The server uses the latest information acquisition module to collect the latest information from the Internet. The collected information can be in text format, images, or videos.

[1219] Step 4:

[1220] The server stores the collected latest information in a database, which allows the information to be easily retrieved later.

[1221] Step 5:

[1222] The server passes the saved news data to a "commentator generation module" and a "host generation module," which use artificial intelligence to analyze the news data and automatically generate commentators and hosts.

[1223] Step 6:

[1224] The server stores the generated commentators and hosts in a database, making these characters available for subsequent processes.

[1225] Step 7:

[1226] Users use a terminal to input their opinions or questions into the system. The terminal is equipped with an input field and a send button, and users enter their opinions or questions in the input field and click the send button to send their opinions.

[1227] Step 8:

[1228] The device sends the opinions and questions entered by the user to the server using a standard data format such as JSON.

[1229] Step 9:

[1230] The server receives the user's opinions and questions sent from the device and stores them in a database, allowing the user's feedback to be managed for future use.

[1231] Step 10:

[1232] The server launches a "program generation module" to generate a news program by integrating news data, commentators, presenters, and user opinions. This module integrates the data and generates a single video content.

[1233] Step 11:

[1234] The server distributes the generated news programs in real time using a "distribution module," allowing viewers to view the generated content in real time.

[1235] Step 12:

[1236] Users can use their devices to watch news programs delivered in real time and enjoy interactive content that reflects their own opinions and questions.

[1237] Through the above process, the present invention realizes an interactive news program in which users can participate and have their opinions reflected.

[1238] Example 1

[1239] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1240] In conventional news programs, it is difficult for viewers to reflect their opinions and questions in real time, and the creation of commentators and presenters is done manually, making it difficult to provide rapid and diverse content. The goal is to solve these problems and provide interactive and flexible news programs.

[1241] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1242] In this invention, the server includes a module for acquiring the latest information from around the world, an artificial intelligence module for analyzing the acquired latest information and generating commentators and presenters, an interface for collecting opinions and questions from users in real time, and a module for generating and distributing video in real time by combining the acquired information, the generated commentators and presenters, and user opinions, thereby making it possible to provide an interactive news program that reflects viewers' opinions in real time.

[1243] The "module for acquiring the latest information from around the world" is a program for periodically collecting the latest information from news sources around the world via the Internet.

[1244] The "artificial intelligence module" is a program that includes machine learning algorithms for analyzing acquired data and automatically generating commentators and hosts.

[1245] An "interface" is a device or software that provides an input means for users to input opinions and questions and transmit them to a server in real time.

[1246] The "video generation module for real-time distribution" is a program that integrates acquired information, generated commentator and host data, and user opinions to generate video content and distribute it in real time.

[1247] A "generative AI model" is an artificial intelligence algorithm used to automatically generate commentary and host comments.

[1248] A "prompt" is an instruction entered into a generative AI model to prompt it to take action.

[1249] This invention is an interactive news program system that acquires the latest information from around the world, generates commentators and hosts, collects viewers' opinions in real time, and generates and distributes videos that combine these information. This system is composed of the following modules and components:

[1250] News gathering

[1251] The server uses the "latest information acquisition module" to obtain the latest information from around the world. This module periodically collects the latest news and events via the internet through news APIs. For example, the server uses the News API to obtain news data from categories such as politics, economy, and sports, and stores this in a database in JSON format. The server can always maintain the latest information by setting a schedule to run the news collection task every 10 minutes.

[1252] AI commentator and moderator placement

[1253] The server generates AI commentators and hosts using a "commentator generation module" and a "host generation module" based on the acquired news data. These modules use artificial intelligence technology to analyze the news data and automatically generate appropriate commentators and hosts. The server analyzes the news data using Python's NLTK library and generates commentator comments and host scripts using a generative AI model (e.g., GPT-4). The generated commentator and host data is stored in a database and used in subsequent processes.

[1254] Gathering opinions from viewers

[1255] Users can use their own devices to send opinions and questions to the system. The devices are equipped with input fields and submit buttons using HTML forms, and users can enter their opinions or questions in the input fields and click the submit button to submit their opinions or questions. The devices use JavaScript Ajax to asynchronously send the submitted opinions and questions to the server in real time. The server stores this data in a database.

[1256] Program Creation and Distribution

[1257] The server uses a "program generation module" to generate a news program by combining user opinions and questions, generated commentators and hosts, and collected news data. This module integrates the acquired information with the generated commentators, hosts, and user opinions to generate a single video content. The server generates the video content using Python's MoviePy library and sends the generated video to a streaming server (e.g., Wowza). By providing users with a streaming URL, viewers can watch an interactive news program in real time.

[1258] Specific examples

[1259] For example, let's say the server collects the latest political news, analyzes it, and generates a political commentator and a general host. When a user sends a question from their device such as "What will happen in the political situation in the future?", the server can integrate all the data and generate video content in which "the commentator explains the future political trends, and the host takes up the viewer's question and discusses it." As a further example, the prompt text to be input to the generative AI model is as follows:

[1260] "Generate commentator comments in response to the following question: 'What's the political landscape going to be like in the future?'"

[1261] In this way, the present invention is able to provide a news program that allows users to participate and give their opinions.

[1262] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1263] Step 1:

[1264] The server initializes the "latest information acquisition module."

[1265] Specifically, the server sends a request to the news API to obtain the latest news data.

[1266] Input: News sources on the internet

[1267] Output: Latest news data (JSON format)

[1268] This news data is stored in a database.

[1269] Step 2:

[1270] The server sets up periodic news gathering tasks.

[1271] Specifically, the server sets a schedule to access the news API every 10 minutes.

[1272] Input: Schedule information

[1273] Output: A newsgathering job with a timer

[1274] This allows the server to automatically collect the latest news on a regular basis.

[1275] Step 3:

[1276] The server analyzes the news data using a "commentator generation module."

[1277] Specifically, the server parses the news data using Python's NLTK library.

[1278] Input: News data (JSON format)

[1279] Output: Analysis results (topics, keywords)

[1280] The analysis results are used in the next step.

[1281] Step 4:

[1282] The server generates commentator profiles and comments based on the analysis results.

[1283] Specifically, the server uses a generative AI model (e.g., GPT-4) to generate the commentator's comments.

[1284] Input: Analysis results (topics, keywords)

[1285] Output: Commentator profile and comments

[1286] The generated data is stored in a database.

[1287] Step 5:

[1288] The server generates a moderator using a "moderator generation module."

[1289] Specifically, the server generates the moderator's script using a generative AI model.

[1290] Input: News data, commentator profiles and comments

[1291] Output: Host profile and script

[1292] The generated data is stored in a database.

[1293] Step 6:

[1294] The user enters their opinion or question into an input field on the terminal.

[1295] Specifically, the user uses an HTML form to input opinions or questions into the terminal.

[1296] Input: User's opinion or question (text format)

[1297] Output: Input data (text format)

[1298] It will be used in the next step.

[1299] Step 7:

[1300] The user clicks the send button.

[1301] Specifically, the terminal uses Ajax in JavaScript to send the transmission data to the server.

[1302] Input: Input data (text format)

[1303] Output: Transmission data (text format)

[1304] The transmitted data is transferred to the server in real time.

[1305] Step 8:

[1306] The server stores the received data in a database.

[1307] As a specific operation, the server stores the transmitted data in a database.

[1308] Input: Send data (text format)

[1309] Output: User comments and questions stored in a database

[1310] Step 9:

[1311] The server initializes the "program generation module."

[1312] Specifically, the program content is planned using a generative AI model.

[1313] Input: News data, commentator and host data, user opinions and questions

[1314] Output: Program content configuration information

[1315] Step 10:

[1316] The server integrates the information and generates it as a single video content.

[1317] Specifically, the server generates video using Python's MoviePy library.

[1318] Input: Program content configuration information

[1319] Output: Generated video data

[1320] Step 11:

[1321] The server uses a "distribution module" to distribute the generated video content in real time.

[1322] As a specific operation, the server transmits the generated video data to the streaming server.

[1323] Input: Generated video data

[1324] Output: Streaming URL, real-time video

[1325] Viewers can watch interactive news programs in real time.

[1326] (Application example 1)

[1327] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1328] Current news distribution systems often only provide information to viewers one-way, and viewer opinions and questions are rarely reflected in real time. There is also room for improvement in the quality and appropriateness of commentators and presenters. Furthermore, while there is a demand for interactive content delivery that can immediately reflect viewer interests, this is difficult to achieve in reality.

[1329] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1330] In this invention, the server includes means for acquiring the latest information from around the world, means for analyzing the acquired latest information and generating commentators and hosts, means for collecting opinions and questions from users in real time, means for combining the acquired information, the generated commentators and hosts, and user opinions to generate video and distribute it in real time, and means for collecting opinions and questions from terminals in real time and generating interactive content based on the opinions and questions, whereby interactive news distribution that instantly reflects viewer opinions and questions is possible.

[1331] "Means for obtaining the latest information" is a function for periodically collecting the latest information from around the world from the Internet.

[1332] The "commentator generation means" is a function for analyzing the collected latest information and generating appropriate commentators using artificial intelligence.

[1333] The "moderator generation means" is a function for analyzing the collected latest information and generating an appropriate moderator using artificial intelligence.

[1334] The "opinion collection means" is a function for collecting opinions and questions from users in real time.

[1335] The "image generation means" is a function for generating an image by combining the acquired information, the generated commentators and presenters, and the user's opinions.

[1336] The "real-time distribution means" is a function for distributing the generated video in real time without delay.

[1337] A "terminal" is a device used by a user to input opinions or questions, and includes smartphones, smart glasses, head-mounted displays, etc.

[1338] The "interactive content generation means" is a function for generating interactive content that is conducted by the generated commentator and moderator based on collected opinions and questions.

[1339] "Artificial intelligence" is an advanced computing technology that analyzes acquired data and generates commentators and presenters based on that data.

[1340] To implement this invention, the following hardware and software are used. The hardware requires a server, a viewer's smartphone, smart glasses, a head-mounted display, or other device. The software requires a news API (such as NewsAPI) and an artificial intelligence generation library (such as GPT-3 or BERT).

[1341] News gathering

[1342] The server uses a means to obtain the latest news and events from around the world. For example, it periodically obtains news data from the News API and stores it in a database. This process is performed periodically, and the latest news can be collected every 10 minutes, for example.

[1343] AI commentator and presenter generation

[1344] The server uses the commentator generation means and the host generation means to generate AI commentators and hosts based on the acquired news data. The collected news data is analyzed and appropriate commentator and host characters are generated using artificial intelligence technology. Information on the generated AI commentators and hosts is stored in a database.

[1345] Gathering opinions from viewers

[1346] Viewers send their opinions and questions to the system via devices such as smartphones, smart glasses, and head-mounted displays. The devices are equipped with an input field and a send button, and viewers can send their opinions and questions by entering them in the input field and clicking the send button. The submitted opinions and questions are sent to a server and stored in a database.

[1347] Program Creation and Distribution

[1348] The server uses a video generation means to generate video content by combining the collected news data, the generated AI commentators and hosts, and the opinions of viewers. For example, if a political commentator and host are generated based on news data collected on a certain day, and a viewer sends a question such as "What will happen in the political situation in the future?", all the data is integrated to generate video content in which the commentator explains future political trends, and the host takes up the viewer's question and discusses it. The generated video content is then distributed in real time using a distribution means. This allows viewers to watch an interactive news program in real time.

[1349] As a concrete example, an AI-generated commentator will explain the results of the latest sporting event and answer viewers' questions in real time, such as "Who was the MVP player in this game?" In this way, interactive news delivery that instantly reflects viewers' opinions and questions becomes possible.

[1350] Prompt Sentence Examples

[1351] "Provide commentary on the latest sporting events and answer viewer questions such as: 'Who was the MVP player in this game?'"

[1352] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1353] Step 1:

[1354] The server periodically collects the latest news and events from around the world using the latest information retrieval method. In this case, the server retrieves news data using a news API (e.g., NewsAPI) and extracts the necessary information (title, text, date and time, etc.). This data is stored in a database. The input is the response data from the NewsAPI, and the output is the extracted news information.

[1355] Step 2:

[1356] The server uses the commentator generation means and the host generation means to generate AI commentators and hosts based on the acquired news data. The server inputs the news data into an artificial intelligence model (e.g., GPT-3 or BERT) and generates appropriate commentator and host characters based on that. Information about the generated commentators and hosts is stored in a database. The input is news data, and the output is character information for the generated commentators and hosts.

[1357] Step 3:

[1358] A user sends an opinion or question to the system using a device (smartphone, smart glasses, head-mounted display, etc.). The user enters the opinion or question into the input field on the device and clicks the send button. The input is the opinion or question entered by the user in the input field, and the output is the opinion or question sent to the server. The server stores this information in a database.

[1359] Step 4:

[1360] The server uses a video generation means to integrate the collected news data, the generated AI commentators and moderators, and user opinions to generate video content. The server creates a scenario in which the AI ​​commentators provide commentary based on the news data and user opinions, and the moderators take up viewer questions and discuss them. The input is the news data, information on the generated commentators and moderators, and viewer opinions, and the output is the generated video content.

[1361] Step 5:

[1362] The server uses a distribution means to distribute the generated video content to viewers in real time. The server uploads the generated video content to a streaming server or distribution platform, where viewers can watch it in real time from their devices. The input is the generated video content, and the output is the video content distributed to viewers.

[1363] In this way, interactive news distribution that instantly reflects viewer opinions and questions can be realized.

[1364] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1365] The present invention relates to an interactive news program system that acquires the latest information from around the world, generates commentators and presenters, collects viewers' opinions in real time, analyzes them using an emotion engine, and generates and distributes videos that combine these. How the present invention can be put into practice will be described below.

[1366] 1. News gathering

[1367] The server uses the "Latest News Acquisition Module" to acquire the latest news from around the world. This module has the function of periodically collecting the latest news and events via the Internet. The server initializes the "Latest News Acquisition Module", sets up a periodic news collection task, and stores the acquired latest news in a database. For example, the server can collect and store the latest news every 10 minutes.

[1368] 2. Allocation of AI commentators and hosts

[1369] The server uses a "commentator generation module" and a "host generation module" to generate commentators and hosts based on the acquired news data. These modules use artificial intelligence technology to analyze the news data and automatically generate appropriate commentators and hosts. The generated commentators and hosts are stored in a database and used in subsequent processes.

[1370] 3. Gathering opinions from viewers

[1371] Users can use the terminal to send opinions and questions to the system. The terminal is equipped with an input field and a send button, and users can enter their opinions or questions in the input field and send them by clicking the send button. The terminal then sends the submitted opinions and questions to the server, which then stores them in a database.

[1372] 4. User sentiment analysis

[1373] The server uses an "emotion analysis engine" to analyze opinions and questions sent by users and recognize their emotions. The emotion analysis engine extracts emotions such as joy, sadness, and anger from the user's text data. This emotional data is reflected in the content and progress of the news program.

[1374] 5. Program Creation and Adjustment

[1375] The server launches a "program generation module" to generate a news program by integrating news data, commentators, presenters, and user opinions and emotion data. This module integrates the data and generates a single video content. It also adjusts the program content based on the emotion data to improve user satisfaction.

[1376] 6. Program Creation and Distribution

[1377] The server distributes the generated news program in real time using a "distribution module," allowing viewers to view the generated interactive content in real time.

[1378] Specific examples

[1379] For example, suppose that on a certain day, the server collects the latest sports news. The collected news data is passed to an AI module, which generates a sports commentator and a general host. After that, a user submits their opinion from their device, saying, "I'm very satisfied with the outcome of the game." The server passes the submitted opinion to an emotion analysis engine for analysis, and recognizes that the user's emotion is "joy." The server integrates all the data and generates video content in which the commentator explains the outcome of the game in a positive tone, and the host addresses the satisfied opinions of the viewers, and distributes it in real time.

[1380] In this way, the present invention makes it possible to provide a news program that reflects the opinions and feelings of the user.

[1381] The processing flow will be explained below.

[1382] Step 1:

[1383] The server initializes the "latest information acquisition module" to acquire the latest information from around the world. It is set up to collect the latest news and events via the Internet.

[1384] Step 2:

[1385] The server sets up a schedule to periodically retrieve updates, for example, scheduling a task to gather news every 10 minutes.

[1386] Step 3:

[1387] The server periodically collects the latest news from the Internet, uses the "latest news acquisition module" to collect news data, and stores it in a database.

[1388] Step 4:

[1389] The server passes the collected news data to a "commentator generation module" and a "host generation module," which analyze the news data and generate appropriate commentators and hosts.

[1390] Step 5:

[1391] The server stores the generated commentator and host data in a database, which is used in subsequent processes.

[1392] Step 6:

[1393] The user inputs their opinions and questions using a terminal, which is equipped with an input field for inputting opinions and questions and a submit button.

[1394] Step 7:

[1395] The user enters their opinion or question into the input field and clicks the send button, which causes the device to send the entered data to the server.

[1396] Step 8:

[1397] The device sends user opinions and questions in JSON format to the server, which receives this data and stores it in a database.

[1398] Step 9:

[1399] The server uses an "emotion analysis engine" to analyze opinions and questions sent by users. The emotion analysis engine extracts emotions from the user's text data and stores them in a database.

[1400] Step 10:

[1401] The server launches a "program generation module" for integrating the acquired news data, the generated commentators and hosts, and the user's opinion and emotion data to generate a news program.

[1402] Step 11:

[1403] The server then adjusts the content of the program based on the emotion data. The "program generation module" changes the tone of commentary and commentary or focuses on specific topics depending on the user's emotions.

[1404] Step 12:

[1405] The server distributes the generated news program in real time using the "distribution module," allowing viewers to watch the interactive news program in real time.

[1406] Step 13:

[1407] Users can use their devices to watch the news broadcast in real time and confirm that their opinions and feelings are reflected.

[1408] In this way, the present invention can provide an interactive news program that reflects users' opinions and feelings in real time.

[1409] Example 2

[1410] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1411] Conventional news systems have difficulty collecting and analyzing user opinions and emotions in real time and reflecting them in program content. Furthermore, generating commentators and presenters requires manual labor, which is inefficient. This tends to result in low viewer satisfaction and interactivity.

[1412] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring the latest information from all over the world, means for analyzing the acquired latest information and generating comments, means for collecting opinions and questions from users in real time, means for analyzing the opinions and questions from users and recognizing emotions, and means for generating video by combining the acquired information, the generated comments, and the opinions and emotions of users, and distributing the video in real time. This makes it possible to efficiently provide interactive news programs that reflect the opinions and emotions of viewers.

[1413] "Global Updates" refers to the latest data on ongoing events and news obtained from the internet and other sources.

[1414] "Comments" are text data that provide commentary or explanations about news or events, and are primarily generated by commentators or presenters.

[1415] "Opinions and questions from users" refers to text data such as impressions, feedback, inquiries, etc. sent by viewers through the system.

[1416] "Means for recognizing emotions" refers to technology that analyzes text data sent by users and extracts emotions such as joy, sadness, and anger from the text.

[1417] "Means for generating video and distributing it in real time" refers to a technology that integrates acquired information, generated comments, and user opinions and emotional data, and instantly streams the video to viewers.

[1418] A "generative AI model" is an artificial intelligence model that has been trained to perform natural language processing and generation tasks, and has the ability to generate text in a human-like manner.

[1419] This invention relates to an interactive news program system that acquires the latest information from around the world, generates commentators and presenters, collects user opinions in real time, analyzes them using an emotion engine, and combines these to generate and distribute video.

[1420] News gathering

[1421] The server uses a "latest information acquisition module" to obtain the latest information from around the world. This module is implemented using Python and scrapes data from news sites and APIs. The server periodically obtains the latest information from the news sites and API endpoints via the Internet and stores it in a MySQL database.

[1422] Commentator and host generation

[1423] The server uses a "comment generation module" to generate comments based on the acquired news data. This module uses a generative AI model (e.g., GPT-3) to analyze the news data and automatically generate appropriate commentators and host comments. As a concrete example, the server generates the following prompt sentence and passes it to the AI ​​model:

[1424] News data: "The soccer World Cup was held, and Japan won the final."

[1425] Instructions for the Generative AI Model: Based on the news data above, generate comments from a sports commentator and a host. The commentator should analyze the game, and the host should introduce opinions from viewers.

[1426] Gathering opinions from viewers

[1427] Users can use a device (smartphone or PC) to send opinions and questions to the system. An HTML form is installed on the device, and users can enter their opinions or questions in the input field and send them by clicking the send button. The device sends the submitted opinions and questions to the server via an HTTP POST request, which the server stores in a database.

[1428] User sentiment analysis

[1429] The server uses an "emotion analysis engine" (e.g., IBM Watson) to analyze opinions and questions sent by users and recognize their emotions. This engine extracts emotions such as joy, sadness, and anger from the user's text data. The analyzed emotional data is reflected in the content and progress of the news program.

[1430] Program Creation and Distribution

[1431] The server launches a "program generation module" that integrates news data, generated comments, user opinions, and emotion data to generate a news program. This module integrates all data and generates a single video content. It also adjusts the program content based on the emotion data to improve viewer satisfaction. The generated news program is distributed in real time via a "distribution module," allowing viewers to watch it in real time.

[1432] As a concrete example, a server collects the latest sports news and uses a generative AI model to generate comments from sports commentators and hosts. A user then transmits their opinion from their device, saying, "I'm very satisfied with the outcome of the game," which is recognized as "joy" by the emotion analysis engine. Finally, all the data is integrated to generate video content in which the commentator explains the outcome of the game in a positive tone and the host addresses the satisfied opinions of viewers, and this content is distributed in real time.

[1433] In this way, the present invention makes it possible to provide an interactive news program that reflects the user's opinions and feelings.

[1434] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1435] Step 1:

[1436] Regular news gathering

[1437] The server initializes the "latest information acquisition module" with a Python script and sets the schedule for news gathering.

[1438] Input: News site or API endpoint URL

[1439] Specific operation: Set up a server scheduler (e.g., a cron job) to automatically collect news every 10 minutes. The server will call the API endpoint of the specified news site based on the set schedule.

[1440] Output: Retrieved news data (JSON format)

[1441] Step 2:

[1442] Acquiring and storing news data

[1443] The server analyzes the data obtained from the news API and stores it in a database.

[1444] Input: JSON-formatted news data obtained from the news gathering task

[1445] Specific operation: The server parses the received JSON data, extracts the necessary information (title, content, posting date, etc.), and saves it in the MySQL database using the INSERT statement.

[1446] Output: News records stored in a database

[1447] Step 3:

[1448] Generate AI prompts

[1449] The server retrieves the latest news data from the database and generates prompt sentences to pass to the generative AI model.

[1450] Input: News data stored in a database

[1451] Specific operation: The server applies the news data to a template and generates a prompt sentence. For example, the prompt sentence is generated in the following format: "News data: {Title: 'The FIFA World Cup is being held,' Content: 'Japan won the final.'} Instructions to the generation AI model: Based on the above news data, generate comments from a sports commentator and a host. The commentator should analyze the match, and the host should introduce opinions from viewers."

[1452] Output: Generated prompt statement

[1453] Step 4:

[1454] Running an AI model

[1455] The server passes the prompt to a generative AI model (e.g., GPT-3) to generate comments for the commentator and host.

[1456] Input: Generated prompt text

[1457] Specific operation: The server sends a prompt to the generative AI model via an API request and receives the generated text data.

[1458] Output: Commentary by commentators and hosts generated from the AI ​​model

[1459] Step 5:

[1460] Saving comment data

[1461] The server stores the comments generated by the AI ​​model in a database.

[1462] Input: Commentators and host comments generated from an AI model

[1463] Specific operation: The server saves the received comment data in the database using the INSERT statement.

[1464] Output: Comment records stored in the database

[1465] Step 6:

[1466] Submit your user feedback

[1467] The user enters their opinion or question in the input field on the terminal and clicks the send button.

[1468] Input: User's opinion or question (text)

[1469] Specific operation: The terminal obtains the user's opinion from the form and sends it to the server via an HTTP POST request.

[1470] Output: Submitted opinions and questions (text data)

[1471] Step 7:

[1472] Storing opinion data

[1473] The server stores the submitted comments and questions in a database.

[1474] Input: User comments and questions sent from the device

[1475] Specific operation: The server saves the received opinion data in the database using the INSERT statement.

[1476] Output: Opinion records stored in the database

[1477] Step 8:

[1478] Performing sentiment analysis

[1479] The server uses a sentiment analysis engine to analyze the user's opinions and questions and recognize their emotions.

[1480] Input: opinion data stored in a database

[1481] Specific operation: The server sends opinion data to the sentiment analysis engine and receives analyzed sentiment data.

[1482] Output: Analysis results from the emotion analysis engine (emotion data)

[1483] Step 9:

[1484] Storing Emotional Data

[1485] The server stores the analysis results in a database.

[1486] Input: Analysis results from the sentiment analysis engine

[1487] Specific operation: The server saves the received emotion data to the corresponding opinion record using an UPDATE statement.

[1488] Output: Emotion data stored in a database

[1489] Step 10:

[1490] Launching program generation

[1491] The server activates a "program generation module" to generate a news program by integrating the news data, the generated comments, and the user's opinion and emotion data.

[1492] Input: News data, commentators' and moderators' comments, user opinions and sentiment data

[1493] Specific operation: The server integrates these data and executes a program to generate video content.

[1494] Output: Generated video content

[1495] Step 11:

[1496] Adjusting program content

[1497] The server adjusts the content of the program based on the emotional data to improve viewer satisfaction.

[1498] Input: Emotion data

[1499] Specific operation: The server analyzes the emotional data, generates a script based on positive emotions, and reflects it in the video content.

[1500] Output: Adjusted video content

[1501] Step 12:

[1502] Program distribution

[1503] The server uses a "distribution module" to distribute the generated news programs in real time.

[1504] Input: Generated video content

[1505] How it works: The server uses the RTMP protocol to send video content to the streaming service, which viewers can then watch in real time.

[1506] Output: Video content delivered to viewers

[1507] (Application example 2)

[1508] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1509] Conventional news distribution systems lacked the means to collect real-time audience opinions and emotions and reflect them in news programs, resulting in little interaction with viewers. Furthermore, commentators and presenters were fixed, making it difficult to flexibly respond to the diverse needs and emotions of viewers. This led to problems such as a decline in the appeal of news programs and viewer satisfaction.

[1510] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1511] In this invention, the server includes means for acquiring the latest information from around the world, means for analyzing the acquired latest information and generating commentators and presenters, means for collecting opinions and questions from users in real time, means for analyzing the emotions of the collected user opinions, means for generating video by combining the acquired information, the generated commentators and presenters, and the opinions and emotions of users, and means for distributing the generated video in real time, thereby making it possible to provide an interactive news program that reflects the opinions and emotions of viewers in real time.

[1512] The "means for obtaining the latest information" is a system that has the function of periodically collecting the latest information from around the world via the Internet.

[1513] The "analysis means" is a system that uses artificial intelligence technology to generate appropriate commentators and hosts based on the latest information obtained.

[1514] The "means for generating commentators and hosts" is a system that uses artificial intelligence to analyze news data and generate commentators who will provide appropriate explanations to viewers and hosts who will conduct the program.

[1515] The "opinion collection means" is a system that collects opinions and questions from users in real time and stores them in a database.

[1516] The "emotion analysis means" is a system that analyzes opinions and questions sent by users as text data and extracts emotions such as joy, sadness, and anger.

[1517] The "video generation means" is a system that combines acquired news data, generated commentators and presenters, and the user's opinions and feelings to generate a single video content.

[1518] The "video distribution means" is a system that distributes the generated video content to viewers in real time.

[1519] The present invention relates to a system that obtains the latest information from around the world, generates AI commentators and presenters, collects and analyzes viewer opinions in real time, and delivers interactive news programs. Specific ways in which the present invention can be implemented are described below.

[1520] Gathering the latest information

[1521] The server uses a "latest information retrieval method" that retrieves the latest information from around the world. This method has the function of periodically collecting the latest news and events via the Internet. For example, using Python and the News API, the server can collect and store the latest news every 10 minutes. This makes it possible to always maintain the latest information.

[1522] AI commentator and host generation

[1523] The server uses a "commentator and host generation means" to generate commentators and hosts based on the acquired news data. This means uses a generative AI model such as GPT-3 to analyze the news data and automatically generate appropriate commentators and hosts. For example, OpenAI technology can be applied to generate commentator and host characters using prompts for the latest news.

[1524] Gathering opinions from viewers

[1525] Users can use the terminal to send their opinions and questions to the system. The terminal is equipped with an input field and a send button, and users can enter their opinions or questions in the input field and click the send button to send their opinions. These opinions are sent to the server and stored in the database.

[1526] User sentiment analysis

[1527] The server uses an "emotion analysis means" to analyze the opinions and questions sent by the user and recognize the user's emotions. This means uses an emotion analysis tool such as TextBlob to extract emotions such as joy, sadness, and anger from the user's text data.

[1528] Program Creation and Adjustment

[1529] The server activates a "video generation means" that generates a news program by integrating the acquired news data, the generated commentators and presenters, and the user's opinions and emotional data. This means integrates the data and generates a single video content. It also adjusts the program content based on the emotional data to improve user satisfaction.

[1530] Specific examples

[1531] For example, if the server collects the latest sports news, it passes the collected news data to an AI module to generate a sports commentator and a general host. The user then transmits their opinion from their device, saying, "I'm very satisfied with the outcome of the game." The server then passes the transmitted opinion to an emotion analysis module for analysis, and recognizes that the user's emotion is "joy." The server integrates all the data, generates video content in which the commentator explains the outcome of the game in a positive tone and the host addresses the viewers' satisfied opinions, and distributes it in real time.

[1532] Prompt Sentence Examples

[1533] "Breaking News: Candidates give final speeches ahead of tomorrow's presidential election. Generate commentators."

[1534] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1535] Step 1:

[1536] News gathering

[1537] The server starts the "latest information acquisition means" and periodically collects the latest news from around the world. The server uses the News API via the Internet to obtain the latest news data. This acquisition process is automated by a Python script, and is scheduled to run every 10 minutes, for example. The collected news data is stored in the server's database.

[1538] Input: Internet connection, News API key

[1539] Output: Latest news data

[1540] Specific operation: The server runs a Python script, accesses the News API to retrieve the latest news, and stores it in a database.

[1541] Step 2:

[1542] AI commentator and host generation

[1543] The server uses a "commentator and host generation means" to analyze the acquired news data and generate commentators and hosts. A generative AI model such as OpenAI's GPT-3 is used to automatically generate appropriate commentators and hosts based on the prompt text. The generated information is then stored in a database.

[1544] Input: News data, prompt text

[1545] Output: AI-generated commentary and host descriptions

[1546] Specific operation: The server launches the generative AI model, inputs the news data and prompt text, generates explanatory text for the commentator and host, and stores it in the database.

[1547] Step 3:

[1548] Gathering opinions from viewers

[1549] The user uses the terminal to input their opinion or question and clicks the submit button to send it to the server. The user enters their opinion or question in the input field and sends it to the server, which then stores this information in a database. This operation is achieved using a web framework such as Flask.

[1550] Input: User opinions and questions

[1551] Output: User opinions and questions stored on the server

[1552] Specific operation: The user enters an opinion or question into the input field on the terminal and clicks the send button. The server receives it through Flask and saves it in the database.

[1553] Step 4:

[1554] User sentiment analysis

[1555] The server uses "sentiment analysis means" to analyze opinions and questions sent by users. It uses sentiment analysis tools such as TextBlob to extract emotional information from text data. The analysis results are stored in a database.

[1556] Input: User opinions and questions

[1557] Output: Sentiment analysis data

[1558] Specific operation: The server uses TextBlob to analyze the sentiment from the user's comments and questions and saves the results in the database.

[1559] Step 5:

[1560] Program Generation

[1561] The server uses a "video generation means" to generate video content based on the collected news data, the generated commentators and presenters, and the user's opinions and emotion data. This means integrates the data to generate video content as a program.

[1562] Input: News data, commentator and host descriptions, user opinions and sentiment data

[1563] Output: Generated program video

[1564] Specific operation: News data, AI-generated commentators and hosts, user opinions, and emotional data are integrated on the server, and then generated as a single video content using a program content generation module.

[1565] Step 6:

[1566] Program distribution

[1567] The server distributes the generated program video to viewers in real time using the "video distribution means." The generated interactive news program is distributed to viewers using a live streaming service.

[1568] Input: Generated program video

[1569] Output: Streamed real-time newscast

[1570] Specific operation: The server distributes the generated video content in real time through a live streaming service, making it available to viewers.

[1571] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1572] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1573] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1574] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1575] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1576] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1577] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1578] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1579] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1580] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1581] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1582] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1583] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1584] 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.

[1585] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1586] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1587] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1588] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1589] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1590] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1591] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1592] The following is further disclosed regarding the above embodiment.

[1593] (Claim 1)

[1594] A means of obtaining the latest information from around the world,

[1595] means for analyzing the acquired latest information and generating commentators and hosts;

[1596] A means of collecting user opinions and questions in real time;

[1597] a means for generating a video by combining the acquired information, the generated commentators and presenters, and the user's opinions, and distributing the video in real time;

[1598] A system including:

[1599] (Claim 2)

[1600] 2. The system according to claim 1, wherein the latest information acquisition means sets a schedule for periodically acquiring the latest information.

[1601] (Claim 3)

[1602] 2. The system according to claim 1, wherein the commentator and host generating means generates commentators and hosts using artificial intelligence.

[1603] "Example 1"

[1604] (Claim 1)

[1605] A module that acquires the latest information from around the world,

[1606] an artificial intelligence module that analyzes the acquired latest information and generates commentators and hosts;

[1607] An interface that collects user opinions and questions in real time,

[1608] a module that combines the acquired information, the generated commentators and presenters, and the user's opinions to generate a video and distribute it in real time;

[1609] A system including:

[1610] (Claim 2)

[1611] 2. The system according to claim 1, wherein the module for acquiring the latest information from around the world sets a schedule for periodically acquiring the latest information.

[1612] (Claim 3)

[1613] 2. The system of claim 1, wherein the commentator and host generation artificial intelligence module generates commentators and hosts using a generative AI model.

[1614] "Application Example 1"

[1615] (Claim 1)

[1616] A means of obtaining the latest information from around the world,

[1617] means for analyzing the acquired latest information and generating commentators and hosts;

[1618] A means of collecting user opinions and questions in real time;

[1619] a means for generating a video by combining the acquired information, the generated commentators and presenters, and the user's opinions, and distributing the video in real time;

[1620] A means of collecting opinions and questions from devices in real time and generating interactive content based on them, with commentators and hosts.

[1621] A system including:

[1622] (Claim 2)

[1623] 2. The system according to claim 1, wherein the latest information acquisition means sets a schedule for periodically acquiring the latest information.

[1624] (Claim 3)

[1625] 2. The system according to claim 1, wherein the commentator and host generating means generates commentators and hosts using artificial intelligence.

[1626] "Example 2: Combining Emotion Engines"

[1627] (Claim 1)

[1628] A means of obtaining the latest information from around the world,

[1629] means for analyzing the acquired latest information and generating comments;

[1630] A means of collecting user opinions and questions in real time;

[1631] means for analyzing opinions and questions from the user and recognizing emotions;

[1632] a means for generating a video by combining the acquired information, the generated comments, and the user's opinions and emotions, and distributing the video in real time;

[1633] A system including:

[1634] (Claim 2)

[1635] 2. The system according to claim 1, wherein the latest information acquisition means sets a schedule for periodically acquiring the latest information.

[1636] (Claim 3)

[1637] 2. The system according to claim 1, wherein the comment generating means generates comments using a generative AI model.

[1638] "Application example 2 when combining emotion engines"

[1639] (Claim 1)

[1640] A means of obtaining the latest information from around the world,

[1641] means for analyzing the acquired latest information and generating commentators and hosts;

[1642] A means of collecting user opinions and questions in real time;

[1643] means for performing sentiment analysis on the collected user opinions;

[1644] a means for generating a video by combining the acquired information, the generated commentators and presenters, and the opinions and emotions of the user;

[1645] A means for distributing the generated video in real time;

[1646] A system including:

[1647] (Claim 2)

[1648] 2. The system according to claim 1, wherein the latest information acquisition means sets a schedule for periodically acquiring the latest information.

[1649] (Claim 3)

[1650] 2. The system according to claim 1, wherein the commentator and host generating means generates commentators and hosts using artificial intelligence. [Explanation of symbols]

[1651] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining the latest information from around the world, means for analyzing the acquired latest information and generating commentators and hosts; A means of collecting user opinions and questions in real time; a means for generating a video by combining the acquired information, the generated commentators and presenters, and the user's opinions, and distributing the video in real time; A system including:

2. 2. The system according to claim 1, wherein the latest information acquisition means sets a schedule for periodically acquiring the latest information.

3. 2. The system of claim 1, wherein the commentator and host generating means generates the commentators and hosts using artificial intelligence.

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