System

Abstract

A system is provided.SOLUTION: A system comprising: means for recording and recording a meeting; means for converting recorded audio data into text data; means for extracting key points of the meeting from the converted text data; means for summarizing the extracted key points; means for providing the summarized information to a user; means for generating appropriate remarks during the meeting; means for converting the generated remarks into speech; and means for generating a video of the user as needed.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In business environments, long meetings consume a lot of time and effort, often reducing individual work efficiency. It can also be difficult to remember all of the important points and discussions in a meeting, requiring additional time to grasp the key points. Furthermore, in situations where it is difficult to attend a meeting in real time, participants need a way to review the content later. To solve these problems, a system is needed that can efficiently record meeting content and extract and summarize key points. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for recording and filming a conference, a means for converting the recorded audio data into text data, a means for extracting the main points of the conference from the converted text data, a means for summarizing the extracted main points, a means for providing the summarized information to a user, a means for generating appropriate utterances during the conference, a means for converting the generated utterances into audio, and a means for generating a video of the user as needed. This system allows for efficient summarization of long conferences and quick understanding of important points. Furthermore, real-time utterance generation and audio-to-video conversion enable users to check the progress and content of the conference without directly participating.

[0006] A "meeting" is a gathering of multiple participants to discuss and share information based on a specific topic.

[0007] "Recording" is the process of electronically recording audio data.

[0008] "Recording" is the process of electronically recording video data.

[0009] "Audio data" refers to data that electronically stores sound information such as human voices.

[0010] "Text data" refers to data in which character information is recorded and stored electronically.

[0011] "Conversion" is the process of changing data in one format into data in another format.

[0012] "Gist" refers to the most important or core part of the information.

[0013] "Extraction" is the process of extracting specific elements or data from a large amount of information.

[0014] A "summary" is a short summary of a long piece of text, extracting only the main points.

[0015] "Providing information" means conveying specific data or knowledge to a user.

[0016] "Utterance" refers to opinions or information expressed orally by an individual during a conversation or discussion.

[0017] "Generation" is the process of creating new data or information.

[0018] "Convert to speech" is the process of converting text data or other forms of data into audio data.

[0019] "Video generation" is the process of creating visual information in the form of still images or video. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The system according to the present invention is provided to improve the efficiency of meetings. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, detailed embodiments of the present invention will be described.

[0042] 1. Setting up and starting a meeting

[0043] First, the user sets up a meeting using a dedicated interface, inputting the date and time and a list of participants, and then sends this information to the server, which then stores the received information in a database and prepares the meeting.

[0044] 2. Audio and video recording of meetings

[0045] When the meeting starts, the device automatically starts recording audio and video, electronically recording the entire meeting. The device transmits audio and video data to the server in real time.

[0046] 3. Speech Recognition and Text Conversion

[0047] The server converts the received voice data into text data using highly accurate speech recognition technology, which then serves as the basis for understanding the content of the meeting.

[0048] 4. Analysis of meeting content and key points extraction

[0049] The server analyzes the converted text data and extracts key discussion points and conclusions, using natural language processing technology to extract key points based on the context and importance of the conversation.

[0050] 5. Speech generation and speech conversion

[0051] During the conference, the server references the database and generates appropriate utterances as needed. The generated utterances are converted into voice data using synthetic speech technology and sent to the conference terminals as utterances.

[0052] 6. Image Generation

[0053] If necessary, the server uses video generation AI to generate a video that appears to show the user's avatar speaking, which the device adds to the meeting video feed.

[0054] 7. Conference Summary Generation and Distribution

[0055] After the meeting ends, the server re-analyzes the text data from the entire meeting to extract key points. The extracted information is summarized and provided in a format that allows users to quickly understand the content. The summary is then delivered to the user via their device, allowing them to quickly understand the important information from the meeting.

[0056] Specific examples

[0057] For example, in a company's project progress meeting, the user (project manager) may not be able to physically attend. In this case, the device automatically records and records the meeting, and the server converts the meeting content into text in real time. In response to questions about the project's progress, the server retrieves relevant information from a database, generates the necessary utterances, and plays them as synthesized voice. After the meeting, the server summarizes the entire meeting content in a five-minute summary and distributes it to the user via the device. The user can review this summary and immediately grasp the key points of the meeting.

[0058] In this way, the system according to the present invention can improve the efficiency of meetings and the accuracy of information management, thereby significantly improving the work efficiency of users.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user inputs the date and time of the meeting, the participant list, and the agenda using a dedicated interface, and sends the configuration information to the server.

[0062] Step 2:

[0063] The server stores the received configuration information in a database and prepares the meeting, including the meeting date and time, participant list, and agenda.

[0064] Step 3:

[0065] When the meeting starts, the device automatically starts recording audio and video, creating an electronic record of the entire meeting.

[0066] Step 4:

[0067] The device streams the recorded audio data to the server in real time, and the video data is also sent to the server.

[0068] Step 5:

[0069] The server converts the received voice data into text data using highly accurate voice recognition technology, and the converted text data is stored in a database.

[0070] Step 6:

[0071] The server analyzes the converted text data using natural language processing technology and extracts key discussion points and conclusions based on the context and importance of the conversation.

[0072] Step 7:

[0073] During the conference, the server refers to relevant information from the database to generate appropriate utterances as needed, and generates utterances based on the referred information.

[0074] Step 8:

[0075] The server converts the generated speech into voice data using voice synthesis technology and transmits the voice data to the terminal.

[0076] Step 9:

[0077] The device receives synthesized speech data from the server and sends it to the conference in real time, allowing the server to speak on behalf of the user.

[0078] Step 10:

[0079] If necessary, the server uses video generation AI to generate a video that makes it appear as if the user's avatar is speaking, and the generated video is sent to the device.

[0080] Step 11:

[0081] The device integrates the video data it receives from the server into the conference video feed.

[0082] Step 12:

[0083] After the meeting ends, the server reanalyzes the text data from the entire meeting and extracts key points from the meeting, which are then compiled into a summary.

[0084] Step 13:

[0085] The server stores the generated summary in a database and notifies the user through the terminal.

[0086] Step 14:

[0087] Users can use their devices to receive and check summaries from the server, quickly grasping important information about the meeting.

[0088] Example 1

[0089] 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."

[0090] With conventional conference systems, meetings are recorded and filmed separately, which means that reviewing the content later takes a lot of time and effort. Furthermore, even if you cannot physically attend the meeting, it is inevitable that you will miss real-time information sharing and important comments. To efficiently resolve these issues, a system that can quickly and accurately grasp the content of the entire meeting is needed.

[0091] 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.

[0092] In this invention, the server includes: means for a user to set up a conference; means for the server to store received conference information in a database; means for a terminal to start recording and filming the conference; means for the terminal to send recorded audio and video data to the server; means for the server to convert audio data into text data; means for the server to analyze the converted text data and extract key points; means for the server to generate necessary comments and convert them into audio data; means for generating video of the user as needed; and means for generating a summary of the conference content and providing it to the user. This automates the recording and filming of conferences, audio-to-text conversion, extraction of key points, generation of comments, generation of video, and distribution of summaries, thereby enabling improved efficiency of conferences and accuracy of information management.

[0093] "User" refers to a person who uses the system to set up and confirm meetings.

[0094] "Server" refers to a computer device that receives, stores, processes, distributes, etc., conference information.

[0095] A "database" refers to an information management system that organizes and stores data related to meetings and makes it possible to retrieve it as needed.

[0096] "Terminal" refers to a device that records and films conferences and transmits data to a server.

[0097] "Means for converting voice data into text data" refers to technology or devices that convert voices during a meeting into text information.

[0098] "Analysis" refers to the process of examining the converted text data and extracting important information.

[0099] "Key points" refer to the key discussion points and conclusions of a meeting.

[0100] "Means for generating statements" refers to a function that automatically creates statements required during a meeting.

[0101] "Means for converting into voice data" refers to technology that converts the created utterances into voice.

[0102] "Means for generating video" refers to the technology that creates and adds video of users to the conference video feed.

[0103] The "means for generating a summary" refers to a function that provides a user with a concise summary of the contents of the meeting.

[0104] "System" refers to a comprehensive mechanism in which the above technologies and devices work together to improve meeting efficiency and information management.

[0105] The system of this invention is designed for the purpose of improving the efficiency of meetings and information management. In this system, the user sets up a meeting, the server receives the information, stores it in a database, and performs recording and video recording during the meeting, speech recognition, key point extraction, speech generation, video generation, and summary distribution.

[0106] First, the user sets up a conference using a dedicated interface. This interface is provided as a web application, and the user enters the date and time of the conference and the list of participants. The entered information is sent to the server, which receives it and stores it in a database. The stored information is used for subsequent processing.

[0107] When a meeting starts, the device automatically starts recording. The recorded data is sent to the server in real time. The server then converts the received audio data into text using speech recognition technology. This process uses highly accurate speech recognition technology (e.g., Google® Cloud Speech-to-Text).

[0108] The converted text data is then analyzed by the server to extract key points and conclusions. The server uses natural language processing techniques (e.g., NLTK, spaCy) to analyze the text data and extract key points. This extracted information is stored in a database and notified to the user as needed.

[0109] When a necessary statement arises during a meeting, the server references relevant information from the database and generates the appropriate statement. The statement is created using a generative AI model (e.g., GPT-3 (registered trademark)) and converted into voice data using synthetic speech technology (e.g., Amazon Polly). The generated voice is sent to the device and played back during the meeting.

[0110] Additionally, the server uses video generation AI (e.g., Deepfake technology) to generate video of the user as needed, making it appear as if the user's avatar is speaking. This video is then added to the meeting video feed by the device.

[0111] After the meeting ends, the server re-analyzes the text data from the entire meeting to extract key points from the meeting. These key points are summarized and provided in a format that allows users to quickly understand the content. This summary is then delivered to the user via their device, allowing them to quickly understand the important information from the meeting.

[0112] Specific examples

[0113] For example, there may be a case where a company's project manager is unable to physically attend a project progress meeting. In this case, the device automatically records and records the meeting, and the server converts the meeting content into text in real time. Furthermore, in response to questions about the project's progress, the server retrieves relevant information from a database, generates the necessary utterances, and plays them as synthesized speech. After the meeting, the server summarizes the entire meeting content in a five-minute summary and delivers it to the user via the device. The user can review this summary and immediately grasp the key points of the meeting.

[0114] Prompt Sentence Examples

[0115] "Please describe your system for tracking project progress during project meetings. Please provide detailed information, including specific hardware and software examples."

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1:

[0118] The user enters the meeting information using a dedicated interface.

[0119] Input: The user inputs information such as the meeting date and time, participant list, and location.

[0120] Data processing: The web application formats the input data and converts it into a format that can be sent to the server.

[0121] Specific behavior: The user clicks the "Add event" button on the meeting setting screen and enters the meeting details.

[0122] Step 2:

[0123] The server stores the received conference information in a database.

[0124] Input: Conference information received from a dedicated interface.

[0125] Data processing: Converting data into a format suitable for saving in a database.

[0126] Specific operation: The server receives the meeting date and time and the participant list, and stores them in a database using an SQL query.

[0127] Step 3:

[0128] The device starts recording the meeting.

[0129] Input: Instructions from the server based on the meeting start time.

[0130] Data processing: Audio and video data capture.

[0131] Specific operation: The server sends an API request at the specified time, and the device starts recording.

[0132] Step 4:

[0133] The terminal transmits the recorded audio and video data to the server.

[0134] Input: Recorded audio and video data.

[0135] Data processing: Encode for real-time streaming.

[0136] Specific operation: The terminal encodes the video in H.264 format and sends it to the server via the RTMP protocol.

[0137] Step 5:

[0138] The server converts the voice data into text data using voice recognition technology.

[0139] Input: Audio data.

[0140] Data processing: Converting audio data into text format.

[0141] Specific operation: The server sends audio data to the Google Cloud Speech-to-Text API and obtains text data such as "The purpose of today's meeting is as follows."

[0142] Step 6:

[0143] The server analyzes the converted text data and extracts the main points of the meeting.

[0144] Input: The converted text data.

[0145] Data processing: Use natural language processing to identify key points.

[0146] What it does: The server uses NLTK and spaCy to analyze the text data and extract key points such as "Delivery date changed" and "Budget added."

[0147] Step 7:

[0148] The server generates the necessary utterances and converts them into audio data.

[0149] Input: Relevant information retrieved from the database.

[0150] Data processing: Generate text using a generative AI model and convert it into audio data using synthetic speech technology.

[0151] Specific operation: The server uses GPT-3 to generate a statement such as "Progress on this project is going well," and then converts it into voice data using Amazon Polly.

[0152] Step 8:

[0153] Generates an image of the user as needed.

[0154] Input: User avatar information and generated utterance.

[0155] Data processing: Create videos using video generation AI.

[0156] How it works: The server uses Deepfake technology to generate video of a user saying something like, "I'll talk more about this in the next meeting."

[0157] Step 9:

[0158] A summary of the meeting content is generated and provided to the user.

[0159] Input: Parsed meeting text data.

[0160] Data processing: Using summary generation technology, key points are extracted that can be understood in a short amount of time.

[0161] How it works: The server uses GPT-3 to automatically generate a summary of the entire meeting and deliver it to the user via email or a web application.

[0162] (Application example 1)

[0163] 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."

[0164] While advanced autonomous vehicles are becoming more commonplace, there is still a lack of ways for drivers to participate in meetings and obtain and confirm important information in real time while driving. In particular, it is difficult to efficiently understand the content of meetings while driving and respond appropriately in a timely manner. Therefore, there is a need for a system that allows drivers to participate in meetings safely and effectively.

[0165] 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.

[0166] In this invention, the server includes means for recording and videotaping a conference, means for converting recorded audio data into text data, means for extracting key points of the conference from the converted text data, means for summarizing the extracted key points, means for providing the summarized information to a user, means for generating appropriate utterances during the conference, means for converting the generated utterances into audio, means for generating an image of the user as needed, means for capturing audio and video in the vehicle, means for performing real-time speech recognition and displaying the text on an in-vehicle display or a head-mounted display, means for searching a database and responding with synthesized speech in response to audio input, and means for audio notifying the driver of important discussions. This allows the driver to safely and efficiently participate in conferences while driving and obtain and confirm information in real time.

[0167] "Means for recording and filming a meeting" means a device that uses a digital device to record audio and video during a meeting.

[0168] "Means for converting recorded voice data into text data" refers to a system equipped with technology that analyzes voice data and converts it into text information.

[0169] "Means for extracting the main points of a meeting from the converted text data" refers to a technology for analyzing text data and identifying important points of discussion.

[0170] "Means for summarizing extracted key points" refers to a technique for summarizing extracted important arguments in a short and concise manner.

[0171] "Means for providing summarized information to a user" means a system that presents the summarized information in a form accessible to a user.

[0172] "Means for generating appropriate utterances during a meeting" refers to a technology that automatically generates appropriate utterances in accordance with the progress of the meeting.

[0173] "Means for converting generated utterances into speech" refers to a system that converts generated text utterances into speech using synthetic speech.

[0174] "Means for generating video of the user as needed" refers to technology that uses AI technology to generate a user's avatar or video and add it to the video feed.

[0175] "In-vehicle audio and video capture means" means a device that uses a microphone and a camera to record audio and video within a vehicle.

[0176] "Means for real-time speech recognition and displaying text on an in-vehicle display or head-mounted display" refers to technology that converts speech collected inside a vehicle into text in real time and displays it so that the driver can visually confirm it.

[0177] "Means for searching a database in response to a voice input and responding with a synthesized voice" refers to a technology that receives a voice command, references a database based on the content of the command, and provides a response in a synthesized voice.

[0178] "Means for providing audio notification to the driver regarding important discussion points" refers to technology that provides audio notification of important discussion points in a meeting to the driver while driving.

[0179] To implement this invention, it is necessary to link the server with the terminal inside the autonomous vehicle and build a system that incorporates technologies such as voice recognition, natural language processing, and synthetic voice generation.

[0180] When a meeting begins, the vehicle's microphones and cameras automatically capture audio and video. This data is sent to a server in real time. The server receives the audio data and converts it into text using highly accurate speech recognition software (Google Cloud Speech-to-Text). The converted text data is then analyzed using a natural language processing library (spaCy or NTLK) to extract key discussion points from the meeting.

[0181] The extracted discussion points are then concisely summarized using a summary generation algorithm. The summarized information is displayed on an in-car display or a head-mounted display (HMD). Important discussion points are also notified to the driver via audio.

[0182] If the driver asks a question during the meeting, their voice input is sent back to the server, where it is searched against the database. A synthetic voice technology (Amazon Polly) generates a response and provides it to the driver via voice.

[0183] Additionally, if necessary, the server uses video generation AI (TENSORFLOW®-DCGAN) to generate video that appears as if the user's avatar is speaking and adds it to the conference video feed.

[0184] As a concrete example, consider a situation where a driver is participating in a project status meeting while traveling in an autonomous vehicle. In this case, the in-car microphone and camera capture the meeting, and the voice data is converted into text in real time. The driver checks the meeting contents on a display or HMD and receives voice notifications about important points. If a question arises, the driver can input it by voice, and the answer will be instantly provided by synthesized voice.

[0185] Examples of prompts using generative AI models include:

[0186] "Please convert the audio data during the meeting into text in real time. The audio data is recorded in Japanese. Please separate each utterance into paragraphs and convert them into text."

[0187] In this way, the present invention allows drivers to safely and efficiently participate in meetings and quickly obtain the information they need.

[0188] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0189] Step 1:

[0190] At the start of the meeting, the device activates the microphone and camera in the vehicle and starts capturing audio and video. The input is the audio and video from inside the vehicle, which is converted into a digital format and sent to the server in real time. The output is the captured audio and video data.

[0191] Step 2:

[0192] The server converts the received voice data into text data using Google Cloud Speech-to-Text. It receives captured voice data as input, converts it into text using real-time speech recognition processing, and obtains text data as output.

[0193] Step 3:

[0194] The server analyzes the converted text data using a natural language processing library (spaCy or NTLK) to identify key discussion points of the meeting. As input, it receives text data, analyzes it, and extracts key discussion points. As output, it obtains the extracted discussion points.

[0195] Step 4:

[0196] The server then uses a summary generation algorithm to concisely summarize the extracted discussion points. As input, it receives the discussion points, processes them, and converts them into short text. As output, it obtains the summarized discussion points.

[0197] Step 5:

[0198] The terminal displays the summarized information on an in-car display or a head-mounted display. As input, it receives summarized discussion points from the server and displays them on the display. As output, it provides a visual representation for the driver.

[0199] Step 6:

[0200] The server provides voice notifications to the driver about important discussion points during the meeting. As input, it receives important discussion points and converts them into synthesized speech. As output, it obtains the information to be voiced to the driver.

[0201] Step 7:

[0202] When the driver inputs a question by voice, the terminal captures the voice input and sends it to the server. The input is the driver's voice question. This is sent to the server. The output is the voice input data.

[0203] Step 8:

[0204] The server searches the database based on the received voice input data and generates an appropriate answer. The input is the voice question data and a related database, which are then analyzed to generate an answer. The output is the generated text answer.

[0205] Step 9:

[0206] The server converts the generated text response into synthetic speech using Amazon Polly and sends the speech data to the device. The generated text response is used as input for the synthetic speech processing. This is then converted into speech data and sent to the device. The speech data is obtained as output.

[0207] Step 10:

[0208] The terminal receives the voice data from the server and plays it back to the driver. As input, it receives voice data from the server. It plays it back through the speaker. As output, it provides the driver with a voice response.

[0209] Step 11:

[0210] The server uses video generation AI as needed to generate a user avatar. The input is the content of what was said during the meeting and the user's video data. This is processed to generate the user's avatar video. The generated avatar video is obtained as the output.

[0211] Step 12:

[0212] The device adds the generated avatar video to the conference video feed. As input, it receives avatar video data from the server, integrates it into the video feed, and as output, the conference video includes the user avatar.

[0213] 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.

[0214] The system of the present invention is combined with an emotion engine to improve the efficiency of meetings. In addition to the basic functions of recording audio and video, converting the contents of the meeting into text data, and generating a summary, the system also has the function of recognizing the user's emotions and optimizing the content of comments and summaries based on those emotions. Detailed embodiments of the present invention will be described below.

[0215] 1. Setting up and starting a meeting

[0216] First, a user sets up a meeting using a dedicated interface, inputting the date and time, participant list, and agenda, and then sends the setup information to the server, which stores the received information in a database and prepares the meeting.

[0217] 2. Audio and video recording of meetings

[0218] When the meeting starts, the device automatically starts recording audio and video, electronically recording the entire meeting. The device transmits audio and video data to the server in real time.

[0219] 3. Speech Recognition and Text Conversion

[0220] The server converts the received voice data into text data using highly accurate voice recognition technology, and the converted text data is stored in a database.

[0221] 4. Analysis of meeting content and key points extraction

[0222] The server analyzes the converted text data using natural language processing technology and extracts key discussion points and conclusions based on the context and importance of the conversation.

[0223] 5. Emotion recognition

[0224] From the recorded voice and video, the server uses an emotion engine to recognize the user's emotions, which includes analyzing voice tone, facial expressions, and gestures.

[0225] 6. Speech production and emotion regulation

[0226] During the meeting, the server references the database and generates appropriate utterances as needed. The generated utterances are adjusted based on the recognized emotional data. For example, if a meeting participant is feeling stressed, the server generates calm and reassuring utterances.

[0227] 7. Voice conversion and speech

[0228] The server converts the generated speech into voice data using synthetic speech technology and sends the voice data to the terminal. The terminal then broadcasts the synthesized voice data received from the server to the conference in real time.

[0229] 8. Image Generation

[0230] If necessary, the server uses video generation AI to generate a video that appears to show the user's avatar speaking, which is then sent to the device, where it is integrated into the meeting video feed.

[0231] 9. Meeting Summary Generation and Emotion Reflection

[0232] After the meeting ends, the server reanalyzes the text data from the entire meeting to extract key points. The extracted information is compiled into a summary that also incorporates the recognized emotion data. For example, points that were the subject of intense discussion are noted in the summary.

[0233] 10. Summary Distribution

[0234] The server stores the generated summary in a database and notifies the user via their device, allowing the user to receive and check the summary and quickly grasp important information about the meeting.

[0235] Specific examples

[0236] For example, consider a situation where a project manager is unable to physically attend a project progress meeting at a company. In this case, the device automatically records and records the meeting, and the server converts the meeting content into text in real time. In response to questions about the project's progress, the server retrieves relevant information from a database, generates the necessary comments, and plays them as synthesized speech. Furthermore, the tone and content of the comments are adjusted based on the emotions of the participants recognized by an emotion engine. After the meeting, the server summarizes the entire meeting content in a five-minute summary and distributes it to the user via the device. This summary also reflects the emotional trends during the meeting, allowing the user to understand the level of enthusiasm and tension in the meeting.

[0237] In this way, the system of the present invention realizes more efficient meetings and more accurate information management, and by taking the user's emotions into consideration, it enables more human-like responses, thereby significantly improving business efficiency.

[0238] The processing flow will be explained below.

[0239] Step 1:

[0240] The user inputs the date and time of the meeting, the participant list, and the agenda using a dedicated interface, and sends the configuration information to the server.

[0241] Step 2:

[0242] The server stores the received configuration information in a database and prepares the meeting, including the meeting date and time, participant list, and agenda.

[0243] Step 3:

[0244] When the meeting starts, the device automatically starts recording audio and video, creating an electronic record of the entire meeting.

[0245] Step 4:

[0246] The device streams the recorded audio data to the server in real time, and the video data is also sent to the server.

[0247] Step 5:

[0248] The server converts the received voice data into text data using highly accurate voice recognition technology, and the converted text data is stored in a database.

[0249] Step 6:

[0250] The server analyzes the converted text data using natural language processing technology and extracts key discussion points and conclusions based on the context and importance of the conversation.

[0251] Step 7:

[0252] The server uses an emotion engine to recognize the user's emotions from the recorded audio and video, which includes analyzing the tone of voice, facial expressions, and gestures.

[0253] Step 8:

[0254] During the conference, the server references relevant information from the database to generate appropriate utterances as needed, which are adjusted based on the recognized emotion data.

[0255] Step 9:

[0256] The server converts the generated speech into voice data using voice synthesis technology and transmits the voice data to the terminal.

[0257] Step 10:

[0258] The device receives synthesized speech data from the server and sends it to the conference in real time, allowing the server to speak on behalf of the user.

[0259] Step 11:

[0260] If necessary, the server uses video generation AI to generate a video that makes it appear as if the user's avatar is speaking, and the generated video is sent to the device.

[0261] Step 12:

[0262] The device integrates the video data it receives from the server into the conference video feed.

[0263] Step 13:

[0264] After the meeting ends, the server reanalyzes the text data from the entire meeting and extracts key points from the meeting, which are then compiled into a summary.

[0265] Step 14:

[0266] The server stores the generated summary in a database and notifies the user through the terminal.

[0267] Step 15:

[0268] Users can use their devices to receive and check summaries from the server, quickly grasp important meeting information, and also refer to recognized emotion data.

[0269] Example 2

[0270] 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."

[0271] Conventional conference systems simply record and record meetings, convert the audio data into text data, and create summaries. Such systems have difficulty identifying changes in emotions and important discussions during meetings, resulting in insufficient efficiency and reduced user stress. Furthermore, providing appropriate feedback to remote participants is difficult, often hindering the smooth progress of meetings.

[0272] The identification process by the identification 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 recording and videotaping the conference, means for converting the recorded voice data into text data, means for analyzing the converted text data and extracting the main points of the conference, means for summarizing the extracted main points, means for providing the summarized information to the user, means for recognizing the user's emotions, means for generating appropriate utterances during the conference, means for adjusting the generated utterances based on the recognized emotion data, means for converting the generated utterances into audio, and means for generating an image of the user as needed. This makes it possible to identify important discussions during the conference and take emotion data into consideration, thereby realizing smoother and more efficient conference management.

[0273] "Audio and video recording" means the electronic recording of audio and video of a meeting.

[0274] "Audio Data" means a digital recording of sounds made during a conference.

[0275] "Text data" is character string information obtained by analyzing voice data.

[0276] "Tasks" are discussion points or conclusions identified as important during a meeting.

[0277] A "summary" is a document that briefly summarizes the contents of the entire meeting.

[0278] A "user" is a person who uses the system to participate in a conference or check the contents of the conference.

[0279] "Emotions" refer to the changes in the user's mind and mood during the meeting.

[0280] "Speech" is the verbal exchange of information that takes place during a meeting.

[0281] "Convert to voice" refers to the process of converting character information such as text data into voice data.

[0282] "Video" refers to visual information during a meeting or digital video showing the actions of generated user avatars.

[0283] "Analysis" is the process of examining collected data to extract useful information.

[0284] "Recognition" is the ability to understand and grasp specific patterns and information from data.

[0285] "Adjustment" means modifying data or information to make it optimal depending on the conditions and circumstances.

[0286] This invention is a system for improving the efficiency of meetings, and in addition to the basic functions of recording audio and video, converting the contents of the meeting into text data and generating a summary, it also has the function of recognizing the user's emotions and optimizing the content of remarks and summaries based on those emotions. This section describes the specific processing procedures required to implement this system, as well as the hardware and software used.

[0287] Setting up and starting a meeting

[0288] First, a user sets up a meeting using a dedicated web interface, where the user inputs the meeting date and time, participant list, and agenda, and sends the configuration information to the server, which stores the received information in a database (e.g., MySQL®) and prepares the meeting.

[0289] Meeting recording and recording

[0290] When the scheduled meeting time arrives, the device (e.g., a conference tablet or PC) automatically starts recording audio and video. The recorded audio and video data is sent to the server in real time, and the server stores this data in storage (e.g., AWS (registered trademark) S3).

[0291] Speech recognition and text conversion

[0292] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API, which is then stored in a database for subsequent analysis.

[0293] Analysis of meeting content and key points extraction

[0294] The server then analyzes the converted text data using natural language processing techniques (e.g., SpaCy, BERT). The analysis results in the extraction of key points of discussion and conclusions based on the context and importance of the conversation. This extracted information is stored in a database and serves as the basis for generating summaries.

[0295] emotion recognition

[0296] The server uses an emotion engine (e.g., Microsoft® Azure® Emotion API) to recognize the user's emotions from the recorded audio and video. Emotion recognition includes analyzing voice tone, facial expressions, and gestures. This allows the user's emotional trends during the meeting to be understood.

[0297] Speech production and emotion regulation

[0298] During the meeting, the server references appropriate utterances from the database as needed and generates new utterances using a generative AI model (e.g., GPT-3). The generated utterances are adjusted based on the recognized emotional data. For example, if a meeting participant is feeling stressed, the server generates calm and reassuring utterances.

[0299] Speech conversion and speech

[0300] The server converts the generated speech into voice data using synthetic speech technology (e.g., Amazon Polly) and sends the voice data to the terminal. The terminal then receives the synthesized voice data from the server and broadcasts it to the conference in real time.

[0301] Image Generation

[0302] If necessary, the server uses video generation AI (e.g., DeepFake technology, FaceRig) to generate a video that appears to show the user's avatar speaking, which is then sent to the device, where it is integrated into the meeting video feed.

[0303] Meeting summary generation and emotional reflection

[0304] After the meeting ends, the server reanalyzes the text data from the entire meeting and extracts key points from the meeting. The extracted information is compiled into a summary that also incorporates the recognized emotion data. For example, points that were the subject of intense discussion may be noted in the summary.

[0305] Summary Delivery

[0306] The server stores the generated summary in a database and notifies the user via their device. The user can receive and check the summary using their device (e.g., a smartphone app) and quickly grasp important information about the meeting.

[0307] Specific examples

[0308] For example, consider a company's project progress meeting where the project manager (PM) cannot physically attend. In this case, the PM can schedule the meeting through a dedicated web interface. At the scheduled time, the device (such as a conference tablet or PC) automatically records audio and video and sends the data to the server.

[0309] The server converts voice data into text data in real time, generates appropriate comments as needed, and plays them as synthesized speech. The server recognizes emotions using an emotion engine and reflects emotional trends in comments and summaries. After the meeting ends, a summary of about five minutes is generated and sent to the PM via the terminal.

[0310] Prompt Sentence Examples

[0311] "For corporate project progress meetings, please automatically record audio and video and convert the meeting content into text in real time. Also, please generate appropriate remarks based on the emotions of the meeting participants and output them as synthesized voices or avatar images as needed. After the meeting, please extract the main points, generate a summary, and notify the user, reflecting the emotional trends."

[0312] In this way, the system of the present invention realizes more efficient meetings and more accurate information management, and by taking the user's emotions into consideration, it enables more human-like responses and significantly improves business efficiency.

[0313] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0314] Step 1:

[0315] A user schedules a meeting. Using a dedicated web interface, the user enters the date and time of the meeting, the list of participants, and the agenda, and then clicks the "Submit" button. The information entered is sent to the server, which stores the received information in a database (e.g., MySQL) and prepares the meeting.

[0316] Step 2:

[0317] The meeting begins. At the set date and time, the device (meeting tablet or PC) automatically starts recording audio and video. The device sends the recorded audio and video data to the server in real time. The server saves this data in storage (e.g., AWS S3).

[0318] Step 3:

[0319] The server converts the voice data into text data. The server converts the received voice data into text data using the Google Cloud Speech-to-Text API. The text data converted from the input voice data is stored in the database.

[0320] Step 4:

[0321] The server analyzes the text data. The server uses natural language processing techniques (e.g., SpaCy, BERT) to analyze the converted text data and extract key discussion points and conclusions based on the context and importance of the conversation. The extracted information is then stored in a database.

[0322] Step 5:

[0323] The server recognizes emotions. From the recorded audio and video, the server uses an emotion engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotions. The analyzed emotion data is stored in a database.

[0324] Step 6:

[0325] The server generates appropriate utterances. Depending on the progress of the meeting, the server retrieves relevant information from the database and generates new utterances using a generative AI model (e.g., GPT-3). These utterances are adjusted based on the recognized emotion data.

[0326] Step 7:

[0327] The server converts the speech into speech. The server then converts the generated speech into voice data using synthetic speech technology (e.g., Amazon Polly). The generated voice data is sent to the device in real time, and the device plays the audio in the conference.

[0328] Step 8:

[0329] The server generates video as needed. The server uses video generation AI (e.g., DeepFake technology, FaceRig) to generate video that appears to be the user's avatar speaking. The generated video is sent to the device, where it is integrated into the conference video feed.

[0330] Step 9:

[0331] The server summarizes the key points of the meeting. After the meeting ends, the server reanalyzes the text data of the entire meeting and extracts the key points. The extracted information is compiled into a summary, which also reflects emotional data. The generated summary is stored in a database.

[0332] Step 10:

[0333] The server delivers the summary. The server then sends the generated summary to the device and notifies the user. The user can receive and check the summary using their device (e.g., a smartphone app) and quickly grasp important information about the meeting.

[0334] Through this procedure, the system according to the present invention can improve the efficiency of meetings and provide a more human-like response that takes into account the user's emotions.

[0335] (Application example 2)

[0336] 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."

[0337] In conventional self-driving vehicles and conference systems, it is difficult to drive or conduct a conference while taking into account the emotions of passengers and participants, and there is a need to improve the efficiency, comfort, and quality of communication in self-driving vehicles and remote conferences.In addition, it is difficult to recognize emotions and provide feedback in real time in self-driving vehicles and remote conferences, so there is a need for a method to improve the efficiency of information transmission while reducing stress for users.

[0338] 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.

[0339] In this invention, the server includes means for recording and filming a meeting or driving situation, means for converting the recorded voice data into text data, means for extracting the main points of the meeting or driving from the converted text data, means for providing summarized information to the user, means for generating appropriate guidance or remarks, means for converting the generated guidance or remarks into voice, means for generating an image of the user as needed, means for recognizing emotions using the voice and video, and means for adjusting the guidance or remarks based on the recognized emotions. This allows the server to recognize the user's emotions in real time and provide appropriate feedback, enabling more efficient and comfortable driving and meeting progress.

[0340] "Means for recording and filming meetings or driving situations" refers to devices or software that capture audio and video during meetings or while driving and record them as data.

[0341] "Means for converting recorded voice data into text data" refers to voice recognition technology or software for converting voice files into text information.

[0342] "Means for extracting meeting or driving key points from converted text data" refers to algorithms or methods that analyze text data and identify and extract important points or key information.

[0343] The "means for providing summarized information to the user" refers to a device or software that summarizes the extracted key points and conveys that information to the user.

[0344] The "means for generating appropriate guidance or statements" refers to a system that references an algorithm or database that automatically creates appropriate responses or guidance according to the situation.

[0345] The "means for converting the generated guidance or speech into speech" refers to technology or software that converts text information into speech using speech synthesis technology and provides it to the user audibly.

[0346] "Means for generating an image of the user on demand" refers to devices or software that create an image or video of the user using a digital avatar or pre-recorded video data.

[0347] "Audio and visual emotion recognition" refers to technologies and algorithms that use voice tone and facial expression analysis to identify a user's emotional state.

[0348] "Means for tailoring prompts and / or statements based on recognized emotions" means a system or algorithm that alters the content or tone of prompts and / or statements created or delivered based on the user's emotional state.

[0349] The system of this invention is primarily used in self-driving vehicles and remote conferences, and includes a series of functions for recording audio and video, converting audio data into text, extracting key points from meetings and driving, recognizing emotions, and generating and adjusting guidance and utterances.

[0350] 1. Setting up and starting a meeting or drive

[0351] First, the user sets up a meeting or a drive using a dedicated interface. They input the date and time, participant list, agenda, and other setting information, and send it to the server. The server stores the received information in a database and prepares for the meeting or drive.

[0352] 2. Audio and video recording of meetings or driving

[0353] When a meeting or drive starts, the device starts recording audio and video, and the recorded data is sent to the server in real time.

[0354] 3. Speech Recognition and Text Conversion

[0355] The server converts the received voice data into text using highly accurate speech recognition technology, using voice recognition software such as Google Cloud Speech-to-Text API.

[0356] 4. Analysis of meeting or driving content and extraction of key points

[0357] The server analyzes the converted text data and uses natural language processing technology to extract important discussion points and driving principles, using an AI model for the analysis.

[0358] 5. Emotion recognition

[0359] The server uses an emotion engine to recognize the user's emotions from the recorded voice and video, using technologies such as DeepFace.

[0360] 6. Guidance or speech production and emotion regulation

[0361] The server then generates appropriate announcements or utterances from a database, adjusting the utterances based on the recognized emotion data. For example, if a passenger is nervous, the server generates calm and reassuring announcements.

[0362] 7. Voice conversion and speech

[0363] The server converts the generated guidance or speech into voice data using synthetic voice technology and transmits the voice data to the terminal, which then provides the synthesized voice data received from the server to the user in real time.

[0364] 8. Image Generation

[0365] When needed, the server uses video generation AI to generate a video that appears to be the user's avatar speaking, which is then sent to the device, where it is integrated into the video feed for meetings or into the display while driving.

[0366] 9. Summary generation and emotional reflection of meetings or driving

[0367] After the meeting or drive, the server reanalyzes the entire text data, extracts key points, and generates a summary that also incorporates the recognized emotion data, allowing the server to grasp the intensity of the discussion or the level of tension while driving.

[0368] 10. Summary Distribution

[0369] The server stores the generated summary in a database and notifies the user via the device, allowing the user to receive and check the summary and quickly grasp important information.

[0370] Specific examples

[0371] For example, consider a user riding in an autonomous vehicle. The device automatically records the situation inside the vehicle, and the server analyzes the data in real time. The server detects the passenger's emotional state and generates calm and reassuring responses to questions and directions while driving, which are then played as synthesized voice. An example of a prompt could be, "Please analyze the data recorded and filmed inside the autonomous vehicle and generate a summary of the passenger's emotions and the journey." In this way, the system of the present invention recognizes the user's emotions in real time and provides appropriate feedback, resulting in a comfortable and efficient driving experience.

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

[0373] Step 1:

[0374] The user sets up a meeting or trip using a dedicated interface. The input is the date and time, participant list, and agenda, and other setting information is sent to the server. The server stores the received information in a database and prepares the meeting or trip. The output is a notification that the preparation is complete.

[0375] Step 2:

[0376] When a meeting or a drive starts, the device automatically starts recording audio and video. The input is audio and video data recorded in real time. The device sends this data to the server in real time. The output is audio and video data.

[0377] Step 3:

[0378] The server converts the received voice data into text data using highly accurate voice recognition technology. The input is voice data, and speech recognition software (e.g., Google Cloud Speech-to-Text API) is used. The output is text data.

[0379] Step 4:

[0380] The server analyzes the text data and extracts key points using natural language processing technology. The input is the converted text data, and a natural language processing algorithm (such as a generative AI model) is used. The output is the extracted key points and the text data.

[0381] Step 5:

[0382] The server recognizes emotions from recorded audio and video. The input is audio and video data, and an emotion recognition engine (e.g., DeepFace) is used. The output is the recognized emotion data.

[0383] Step 6:

[0384] The server generates appropriate guidance or utterances from the database as needed, adjusting the content and tone based on the recognized emotional data. The input is information and emotional data referenced from the database, and generation technology using an AI model is used. The output is the adjusted text data of the utterances.

[0385] Step 7:

[0386] The server converts the generated speech into voice data using synthetic speech technology and sends it to the terminal. The input is the adjusted text data of the speech, and synthetic speech software is used. The output is synthesized voice data. The terminal plays the received synthesized voice data in real time.

[0387] Step 8:

[0388] If necessary, the server uses video generation AI to generate a video that appears to show the user's avatar speaking. The input is the text data of the speech, which the video generation AI uses. The output is the generated video data. The device then integrates this video into the video feed.

[0389] Step 9:

[0390] After the meeting or drive ends, the server re-analyzes the entire text data, extracts key points, and generates a summary. The input is the entire text data of the meeting or drive, and natural language processing technology is used. The output is the summarized data.

[0391] Step 10:

[0392] The server stores the generated summary in a database and notifies the user via the terminal. The user can receive and check the summary using the terminal, and quickly grasp important information. The input is the generated summary data, and the output is a notification to the user.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Second embodiment]

[0397] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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."

[0409] The system according to the present invention is provided to improve the efficiency of meetings. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, detailed embodiments of the present invention will be described.

[0410] 1. Setting up and starting a meeting

[0411] First, the user sets up a meeting using a dedicated interface, inputting the date and time and a list of participants, and then sends this information to the server, which then stores the received information in a database and prepares the meeting.

[0412] 2. Audio and video recording of meetings

[0413] When the meeting starts, the device automatically starts recording audio and video, electronically recording the entire meeting. The device transmits audio and video data to the server in real time.

[0414] 3. Speech Recognition and Text Conversion

[0415] The server converts the received voice data into text data using highly accurate speech recognition technology, which then serves as the basis for understanding the content of the meeting.

[0416] 4. Analysis of meeting content and key points extraction

[0417] The server analyzes the converted text data and extracts key discussion points and conclusions, using natural language processing technology to extract key points based on the context and importance of the conversation.

[0418] 5. Speech generation and speech conversion

[0419] During the conference, the server references the database and generates appropriate utterances as needed. The generated utterances are converted into voice data using synthetic speech technology and sent to the conference terminals as utterances.

[0420] 6. Image Generation

[0421] If necessary, the server uses video generation AI to generate a video that appears to show the user's avatar speaking, which the device adds to the meeting video feed.

[0422] 7. Conference Summary Generation and Distribution

[0423] After the meeting ends, the server re-analyzes the text data from the entire meeting to extract key points. The extracted information is summarized and provided in a format that allows users to quickly understand the content. The summary is then delivered to the user via their device, allowing them to quickly understand the important information from the meeting.

[0424] Specific examples

[0425] For example, in a company's project progress meeting, the user (project manager) may not be able to physically attend. In this case, the device automatically records and records the meeting, and the server converts the meeting content into text in real time. In response to questions about the project's progress, the server retrieves relevant information from a database, generates the necessary utterances, and plays them as synthesized voice. After the meeting, the server summarizes the entire meeting content in a five-minute summary and distributes it to the user via the device. The user can review this summary and immediately grasp the key points of the meeting.

[0426] In this way, the system according to the present invention can improve the efficiency of meetings and the accuracy of information management, thereby significantly improving the work efficiency of users.

[0427] The processing flow will be explained below.

[0428] Step 1:

[0429] The user inputs the date and time of the meeting, the participant list, and the agenda using a dedicated interface, and sends the configuration information to the server.

[0430] Step 2:

[0431] The server stores the received configuration information in a database and prepares the meeting, including the meeting date and time, participant list, and agenda.

[0432] Step 3:

[0433] When the meeting starts, the device automatically starts recording audio and video, creating an electronic record of the entire meeting.

[0434] Step 4:

[0435] The device streams the recorded audio data to the server in real time, and the video data is also sent to the server.

[0436] Step 5:

[0437] The server converts the received voice data into text data using highly accurate voice recognition technology, and the converted text data is stored in a database.

[0438] Step 6:

[0439] The server analyzes the converted text data using natural language processing technology and extracts key discussion points and conclusions based on the context and importance of the conversation.

[0440] Step 7:

[0441] During the conference, the server refers to relevant information from the database to generate appropriate utterances as needed, and generates utterances based on the referred information.

[0442] Step 8:

[0443] The server converts the generated speech into voice data using voice synthesis technology and transmits the voice data to the terminal.

[0444] Step 9:

[0445] The device receives synthesized speech data from the server and sends it to the conference in real time, allowing the server to speak on behalf of the user.

[0446] Step 10:

[0447] If necessary, the server uses video generation AI to generate a video that makes it appear as if the user's avatar is speaking, and the generated video is sent to the device.

[0448] Step 11:

[0449] The device integrates the video data it receives from the server into the conference video feed.

[0450] Step 12:

[0451] After the meeting ends, the server reanalyzes the text data from the entire meeting and extracts key points from the meeting, which are then compiled into a summary.

[0452] Step 13:

[0453] The server stores the generated summary in a database and notifies the user through the terminal.

[0454] Step 14:

[0455] Users can use their devices to receive and check summaries from the server, quickly grasping important information about the meeting.

[0456] Example 1

[0457] 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."

[0458] With conventional conference systems, meetings are recorded and filmed separately, which means that reviewing the content later takes a lot of time and effort. Furthermore, even if you cannot physically attend the meeting, it is inevitable that you will miss real-time information sharing and important comments. To efficiently resolve these issues, a system that can quickly and accurately grasp the content of the entire meeting is needed.

[0459] 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.

[0460] In this invention, the server includes: means for a user to set up a conference; means for the server to store received conference information in a database; means for a terminal to start recording and filming the conference; means for the terminal to send recorded audio and video data to the server; means for the server to convert audio data into text data; means for the server to analyze the converted text data and extract key points; means for the server to generate necessary comments and convert them into audio data; means for generating video of the user as needed; and means for generating a summary of the conference content and providing it to the user. This automates the recording and filming of conferences, audio-to-text conversion, extraction of key points, generation of comments, generation of video, and distribution of summaries, thereby enabling improved efficiency of conferences and accuracy of information management.

[0461] "User" refers to a person who uses the system to set up and confirm meetings.

[0462] "Server" refers to a computer device that receives, stores, processes, distributes, etc., conference information.

[0463] A "database" refers to an information management system that organizes and stores data related to meetings and makes it possible to retrieve it as needed.

[0464] "Terminal" refers to a device that records and films conferences and transmits data to a server.

[0465] "Means for converting voice data into text data" refers to technology or devices that convert voices during a meeting into text information.

[0466] "Analysis" refers to the process of examining the converted text data and extracting important information.

[0467] "Key points" refer to the key discussion points and conclusions of a meeting.

[0468] "Means for generating statements" refers to a function that automatically creates statements required during a meeting.

[0469] "Means for converting into voice data" refers to technology that converts the created utterances into voice.

[0470] "Means for generating video" refers to the technology that creates and adds video of users to the conference video feed.

[0471] The "means for generating a summary" refers to a function that provides a user with a concise summary of the contents of the meeting.

[0472] "System" refers to a comprehensive mechanism in which the above technologies and devices work together to improve meeting efficiency and information management.

[0473] The system of this invention is designed for the purpose of improving the efficiency of meetings and information management. In this system, the user sets up a meeting, the server receives the information, stores it in a database, and performs recording and video recording during the meeting, speech recognition, key point extraction, speech generation, video generation, and summary distribution.

[0474] First, the user sets up a conference using a dedicated interface. This interface is provided as a web application, and the user enters the date and time of the conference and the list of participants. The entered information is sent to the server, which receives it and stores it in a database. The stored information is used for subsequent processing.

[0475] When a meeting starts, the device automatically starts recording. The recorded data is sent to the server in real time. The server then converts the received audio data into text using speech recognition technology. This process uses highly accurate speech recognition technology (e.g., Google Cloud Speech-to-Text).

[0476] The converted text data is then analyzed by the server to extract key points and conclusions. The server uses natural language processing techniques (e.g., NLTK, spaCy) to analyze the text data and extract key points. This extracted information is stored in a database and notified to the user as needed.

[0477] When a necessary statement arises during a meeting, the server references relevant information from the database and generates the appropriate statement. The statement is created using a generative AI model (e.g., GPT-3) and converted into voice data using synthetic speech technology (e.g., Amazon Polly). The generated voice is sent to the device and played back during the meeting.

[0478] Additionally, the server uses video generation AI (e.g., Deepfake technology) to generate video of the user as needed, making it appear as if the user's avatar is speaking. This video is then added to the meeting video feed by the device.

[0479] After the meeting ends, the server re-analyzes the text data from the entire meeting to extract key points from the meeting. These key points are summarized and provided in a format that allows users to quickly understand the content. This summary is then delivered to the user via their device, allowing them to quickly understand the important information from the meeting.

[0480] Specific examples

[0481] For example, there may be a case where a company's project manager is unable to physically attend a project progress meeting. In this case, the device automatically records and records the meeting, and the server converts the meeting content into text in real time. Furthermore, in response to questions about the project's progress, the server retrieves relevant information from a database, generates the necessary utterances, and plays them as synthesized speech. After the meeting, the server summarizes the entire meeting content in a five-minute summary and delivers it to the user via the device. The user can review this summary and immediately grasp the key points of the meeting.

[0482] Prompt Sentence Examples

[0483] "Please describe your system for tracking project progress during project meetings. Please provide detailed information, including specific hardware and software examples."

[0484] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0485] Step 1:

[0486] The user enters the meeting information using a dedicated interface.

[0487] Input: The user inputs information such as the meeting date and time, participant list, and location.

[0488] Data processing: The web application formats the input data and converts it into a format that can be sent to the server.

[0489] Specific behavior: The user clicks the "Add event" button on the meeting setting screen and enters the meeting details.

[0490] Step 2:

[0491] The server stores the received conference information in a database.

[0492] Input: Conference information received from a dedicated interface.

[0493] Data processing: Converting data into a format suitable for saving in a database.

[0494] Specific operation: The server receives the meeting date and time and the participant list, and stores them in a database using an SQL query.

[0495] Step 3:

[0496] The device starts recording the meeting.

[0497] Input: Instructions from the server based on the meeting start time.

[0498] Data processing: Audio and video data capture.

[0499] Specific operation: The server sends an API request at the specified time, and the device starts recording.

[0500] Step 4:

[0501] The terminal transmits the recorded audio and video data to the server.

[0502] Input: Recorded audio and video data.

[0503] Data processing: Encode for real-time streaming.

[0504] Specific operation: The terminal encodes the video in H.264 format and sends it to the server via the RTMP protocol.

[0505] Step 5:

[0506] The server converts the voice data into text data using voice recognition technology.

[0507] Input: Audio data.

[0508] Data processing: Converting audio data into text format.

[0509] Specific operation: The server sends audio data to the Google Cloud Speech-to-Text API and obtains text data such as "The purpose of today's meeting is as follows."

[0510] Step 6:

[0511] The server analyzes the converted text data and extracts the main points of the meeting.

[0512] Input: The converted text data.

[0513] Data processing: Use natural language processing to identify key points.

[0514] What it does: The server uses NLTK and spaCy to analyze the text data and extract key points such as "Delivery date changed" and "Budget added."

[0515] Step 7:

[0516] The server generates the necessary utterances and converts them into audio data.

[0517] Input: Relevant information retrieved from the database.

[0518] Data processing: Generate text using a generative AI model and convert it into audio data using synthetic speech technology.

[0519] Specific operation: The server uses GPT-3 to generate a statement such as "Progress on this project is going well," and then converts it into voice data using Amazon Polly.

[0520] Step 8:

[0521] Generates an image of the user as needed.

[0522] Input: User avatar information and generated utterance.

[0523] Data processing: Create videos using video generation AI.

[0524] How it works: The server uses Deepfake technology to generate video of a user saying something like, "I'll talk more about this in the next meeting."

[0525] Step 9:

[0526] A summary of the meeting content is generated and provided to the user.

[0527] Input: Parsed meeting text data.

[0528] Data processing: Using summary generation technology, key points are extracted that can be understood in a short amount of time.

[0529] How it works: The server uses GPT-3 to automatically generate a summary of the entire meeting and deliver it to the user via email or a web application.

[0530] (Application example 1)

[0531] 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."

[0532] While advanced autonomous vehicles are becoming more commonplace, there is still a lack of ways for drivers to participate in meetings and obtain and confirm important information in real time while driving. In particular, it is difficult to efficiently understand the content of meetings while driving and respond appropriately in a timely manner. Therefore, there is a need for a system that allows drivers to participate in meetings safely and effectively.

[0533] 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.

[0534] In this invention, the server includes means for recording and videotaping a conference, means for converting recorded audio data into text data, means for extracting key points of the conference from the converted text data, means for summarizing the extracted key points, means for providing the summarized information to a user, means for generating appropriate utterances during the conference, means for converting the generated utterances into audio, means for generating an image of the user as needed, means for capturing audio and video in the vehicle, means for performing real-time speech recognition and displaying the text on an in-vehicle display or a head-mounted display, means for searching a database and responding with synthesized speech in response to audio input, and means for audio notifying the driver of important discussions. This allows the driver to safely and efficiently participate in conferences while driving and obtain and confirm information in real time.

[0535] "Means for recording and filming a meeting" means a device that uses a digital device to record audio and video during a meeting.

[0536] "Means for converting recorded voice data into text data" refers to a system equipped with technology that analyzes voice data and converts it into text information.

[0537] "Means for extracting the main points of a meeting from the converted text data" refers to a technology for analyzing text data and identifying important points of discussion.

[0538] "Means for summarizing extracted key points" refers to a technique for summarizing extracted important arguments in a short and concise manner.

[0539] "Means for providing summarized information to a user" means a system that presents the summarized information in a form accessible to a user.

[0540] "Means for generating appropriate utterances during a meeting" refers to a technology that automatically generates appropriate utterances in accordance with the progress of the meeting.

[0541] "Means for converting generated utterances into speech" refers to a system that converts generated text utterances into speech using synthetic speech.

[0542] "Means for generating video of the user as needed" refers to technology that uses AI technology to generate a user's avatar or video and add it to the video feed.

[0543] "In-vehicle audio and video capture means" means a device that uses a microphone and a camera to record audio and video within a vehicle.

[0544] "Means for real-time speech recognition and displaying text on an in-vehicle display or head-mounted display" refers to technology that converts speech collected inside a vehicle into text in real time and displays it so that the driver can visually confirm it.

[0545] "Means for searching a database in response to a voice input and responding with a synthesized voice" refers to a technology that receives a voice command, references a database based on the content of the command, and provides a response in a synthesized voice.

[0546] "Means for providing audio notification to the driver regarding important discussion points" refers to technology that provides audio notification of important discussion points in a meeting to the driver while driving.

[0547] To implement this invention, it is necessary to link the server with the terminal inside the autonomous vehicle and build a system that incorporates technologies such as voice recognition, natural language processing, and synthetic voice generation.

[0548] When a meeting begins, the vehicle's microphones and cameras automatically capture audio and video. This data is sent to a server in real time. The server receives the audio data and converts it into text using highly accurate speech recognition software (Google Cloud Speech-to-Text). The converted text data is then analyzed using a natural language processing library (spaCy or NTLK) to extract key discussion points from the meeting.

[0549] The extracted discussion points are then concisely summarized using a summary generation algorithm. The summarized information is displayed on an in-car display or a head-mounted display (HMD). Important discussion points are also notified to the driver via audio.

[0550] If the driver asks a question during the meeting, their voice input is sent back to the server, where it is searched against the database. A synthetic voice technology (Amazon Polly) generates a response and provides it to the driver via voice.

[0551] Additionally, if necessary, the server uses video generation AI (TensorFlow-DCGAN) to generate video that appears as if the user's avatar is speaking and adds it to the meeting video feed.

[0552] As a concrete example, consider a situation where a driver is participating in a project status meeting while traveling in an autonomous vehicle. In this case, the in-car microphone and camera capture the meeting, and the voice data is converted into text in real time. The driver checks the meeting contents on a display or HMD and receives voice notifications about important points. If a question arises, the driver can input it by voice, and the answer will be instantly provided by synthesized voice.

[0553] Examples of prompts using generative AI models include:

[0554] "Please convert the audio data during the meeting into text in real time. The audio data is recorded in Japanese. Please separate each utterance into paragraphs and convert them into text."

[0555] In this way, the present invention allows drivers to safely and efficiently participate in meetings and quickly obtain the information they need.

[0556] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0557] Step 1:

[0558] At the start of the meeting, the device activates the microphone and camera in the vehicle and starts capturing audio and video. The input is the audio and video from inside the vehicle, which is converted into a digital format and sent to the server in real time. The output is the captured audio and video data.

[0559] Step 2:

[0560] The server converts the received voice data into text data using Google Cloud Speech-to-Text. It receives captured voice data as input, converts it into text using real-time speech recognition processing, and obtains text data as output.

[0561] Step 3:

[0562] The server analyzes the converted text data using a natural language processing library (spaCy or NTLK) to identify key discussion points of the meeting. As input, it receives text data, analyzes it, and extracts key discussion points. As output, it obtains the extracted discussion points.

[0563] Step 4:

[0564] The server then uses a summary generation algorithm to concisely summarize the extracted discussion points. As input, it receives the discussion points, processes them, and converts them into short text. As output, it obtains the summarized discussion points.

[0565] Step 5:

[0566] The terminal displays the summarized information on an in-car display or a head-mounted display. As input, it receives summarized discussion points from the server and displays them on the display. As output, it provides a visual representation for the driver.

[0567] Step 6:

[0568] The server provides voice notifications to the driver about important discussion points during the meeting. As input, it receives important discussion points and converts them into synthesized speech. As output, it obtains the information to be voiced to the driver.

[0569] Step 7:

[0570] When the driver inputs a question by voice, the terminal captures the voice input and sends it to the server. The input is the driver's voice question. This is sent to the server. The output is the voice input data.

[0571] Step 8:

[0572] The server searches the database based on the received voice input data and generates an appropriate answer. The input is the voice question data and a related database, which are then analyzed to generate an answer. The output is the generated text answer.

[0573] Step 9:

[0574] The server converts the generated text response into synthetic speech using Amazon Polly and sends the speech data to the device. The generated text response is used as input for the synthetic speech processing. This is then converted into speech data and sent to the device. The speech data is obtained as output.

[0575] Step 10:

[0576] The terminal receives the voice data from the server and plays it back to the driver. As input, it receives voice data from the server. It plays it back through the speaker. As output, it provides the driver with a voice response.

[0577] Step 11:

[0578] The server uses video generation AI as needed to generate a user avatar. The input is the content of what was said during the meeting and the user's video data. This is processed to generate the user's avatar video. The generated avatar video is obtained as the output.

[0579] Step 12:

[0580] The device adds the generated avatar video to the conference video feed. As input, it receives avatar video data from the server, integrates it into the video feed, and as output, the conference video includes the user avatar.

[0581] 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.

[0582] The system of the present invention is combined with an emotion engine to improve the efficiency of meetings. In addition to the basic functions of recording audio and video, converting the contents of the meeting into text data, and generating a summary, the system also has the function of recognizing the user's emotions and optimizing the content of comments and summaries based on those emotions. Detailed embodiments of the present invention will be described below.

[0583] 1. Setting up and starting a meeting

[0584] First, a user sets up a meeting using a dedicated interface, inputting the date and time, participant list, and agenda, and then sends the setup information to the server, which stores the received information in a database and prepares the meeting.

[0585] 2. Audio and video recording of meetings

[0586] When the meeting starts, the device automatically starts recording audio and video, electronically recording the entire meeting. The device transmits audio and video data to the server in real time.

[0587] 3. Speech Recognition and Text Conversion

[0588] The server converts the received voice data into text data using highly accurate voice recognition technology, and the converted text data is stored in a database.

[0589] 4. Analysis of meeting content and key points extraction

[0590] The server analyzes the converted text data using natural language processing technology and extracts key discussion points and conclusions based on the context and importance of the conversation.

[0591] 5. Emotion recognition

[0592] From the recorded voice and video, the server uses an emotion engine to recognize the user's emotions, which includes analyzing voice tone, facial expressions, and gestures.

[0593] 6. Speech production and emotion regulation

[0594] During the meeting, the server references the database and generates appropriate utterances as needed. The generated utterances are adjusted based on the recognized emotional data. For example, if a meeting participant is feeling stressed, the server generates calm and reassuring utterances.

[0595] 7. Voice conversion and speech

[0596] The server converts the generated speech into voice data using synthetic speech technology and sends the voice data to the terminal. The terminal then broadcasts the synthesized voice data received from the server to the conference in real time.

[0597] 8. Image Generation

[0598] If necessary, the server uses video generation AI to generate a video that appears to show the user's avatar speaking, which is then sent to the device, where it is integrated into the meeting video feed.

[0599] 9. Meeting Summary Generation and Emotion Reflection

[0600] After the meeting ends, the server reanalyzes the text data from the entire meeting to extract key points. The extracted information is compiled into a summary that also incorporates the recognized emotion data. For example, points that were the subject of intense discussion are noted in the summary.

[0601] 10. Summary Distribution

[0602] The server stores the generated summary in a database and notifies the user via their device, allowing the user to receive and check the summary and quickly grasp important information about the meeting.

[0603] Specific examples

[0604] For example, consider a situation where a project manager is unable to physically attend a project progress meeting at a company. In this case, the device automatically records and records the meeting, and the server converts the meeting content into text in real time. In response to questions about the project's progress, the server retrieves relevant information from a database, generates the necessary comments, and plays them as synthesized speech. Furthermore, the tone and content of the comments are adjusted based on the emotions of the participants recognized by an emotion engine. After the meeting, the server summarizes the entire meeting content in a five-minute summary and distributes it to the user via the device. This summary also reflects the emotional trends during the meeting, allowing the user to understand the level of enthusiasm and tension in the meeting.

[0605] In this way, the system of the present invention realizes more efficient meetings and more accurate information management, and by taking the user's emotions into consideration, it enables more human-like responses, thereby significantly improving business efficiency.

[0606] The processing flow will be explained below.

[0607] Step 1:

[0608] The user inputs the date and time of the meeting, the participant list, and the agenda using a dedicated interface, and sends the configuration information to the server.

[0609] Step 2:

[0610] The server stores the received configuration information in a database and prepares the meeting, including the meeting date and time, participant list, and agenda.

[0611] Step 3:

[0612] When the meeting starts, the device automatically starts recording audio and video, creating an electronic record of the entire meeting.

[0613] Step 4:

[0614] The device streams the recorded audio data to the server in real time, and the video data is also sent to the server.

[0615] Step 5:

[0616] The server converts the received voice data into text data using highly accurate voice recognition technology, and the converted text data is stored in a database.

[0617] Step 6:

[0618] The server analyzes the converted text data using natural language processing technology and extracts key discussion points and conclusions based on the context and importance of the conversation.

[0619] Step 7:

[0620] The server uses an emotion engine to recognize the user's emotions from the recorded audio and video, which includes analyzing the tone of voice, facial expressions, and gestures.

[0621] Step 8:

[0622] During the conference, the server references relevant information from the database to generate appropriate utterances as needed, which are adjusted based on the recognized emotion data.

[0623] Step 9:

[0624] The server converts the generated speech into voice data using voice synthesis technology and transmits the voice data to the terminal.

[0625] Step 10:

[0626] The device receives synthesized speech data from the server and sends it to the conference in real time, allowing the server to speak on behalf of the user.

[0627] Step 11:

[0628] If necessary, the server uses video generation AI to generate a video that makes it appear as if the user's avatar is speaking, and the generated video is sent to the device.

[0629] Step 12:

[0630] The device integrates the video data it receives from the server into the conference video feed.

[0631] Step 13:

[0632] After the meeting ends, the server reanalyzes the text data from the entire meeting and extracts key points from the meeting, which are then compiled into a summary.

[0633] Step 14:

[0634] The server stores the generated summary in a database and notifies the user through the terminal.

[0635] Step 15:

[0636] Users can use their devices to receive and check summaries from the server, quickly grasp important meeting information, and also refer to recognized emotion data.

[0637] Example 2

[0638] 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."

[0639] Conventional conference systems simply record and record meetings, convert the audio data into text data, and create summaries. Such systems have difficulty identifying changes in emotions and important discussions during meetings, resulting in insufficient efficiency and reduced user stress. Furthermore, providing appropriate feedback to remote participants is difficult, often hindering the smooth progress of meetings.

[0640] The identification process by the identification 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 recording and videotaping the conference, means for converting the recorded voice data into text data, means for analyzing the converted text data and extracting the main points of the conference, means for summarizing the extracted main points, means for providing the summarized information to the user, means for recognizing the user's emotions, means for generating appropriate utterances during the conference, means for adjusting the generated utterances based on the recognized emotion data, means for converting the generated utterances into audio, and means for generating an image of the user as needed. This makes it possible to identify important discussions during the conference and take emotion data into consideration, thereby realizing smoother and more efficient conference management.

[0641] "Audio and video recording" means the electronic recording of audio and video of a meeting.

[0642] "Audio Data" means a digital recording of sounds made during a conference.

[0643] "Text data" is character string information obtained by analyzing voice data.

[0644] "Tasks" are discussion points or conclusions identified as important during a meeting.

[0645] A "summary" is a document that briefly summarizes the contents of the entire meeting.

[0646] A "user" is a person who uses the system to participate in a conference or check the contents of the conference.

[0647] "Emotions" refer to the changes in the user's mind and mood during the meeting.

[0648] "Speech" is the verbal exchange of information that takes place during a meeting.

[0649] "Convert to voice" refers to the process of converting character information such as text data into voice data.

[0650] "Video" refers to visual information during a meeting or digital video showing the actions of generated user avatars.

[0651] "Analysis" is the process of examining collected data to extract useful information.

[0652] "Recognition" is the ability to understand and grasp specific patterns and information from data.

[0653] "Adjustment" means modifying data or information to make it optimal depending on the conditions and circumstances.

[0654] This invention is a system for improving the efficiency of meetings, and in addition to the basic functions of recording audio and video, converting the contents of the meeting into text data and generating a summary, it also has the function of recognizing the user's emotions and optimizing the content of remarks and summaries based on those emotions. This section describes the specific processing procedures required to implement this system, as well as the hardware and software used.

[0655] Setting up and starting a meeting

[0656] First, a user sets up a meeting using a dedicated web interface, where the user inputs the meeting date and time, participant list, and agenda, and sends the configuration information to the server, which stores the information in a database (e.g., MySQL) and prepares the meeting.

[0657] Meeting recording and recording

[0658] When the scheduled meeting time arrives, the device (e.g., the conference tablet or PC) automatically starts recording audio and video. The recorded audio and video data is sent to the server in real time, and the server stores this data in storage (e.g., AWS S3).

[0659] Speech recognition and text conversion

[0660] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API, which is then stored in a database for subsequent analysis.

[0661] Analysis of meeting content and key points extraction

[0662] The server then analyzes the converted text data using natural language processing techniques (e.g., SpaCy, BERT). The analysis results in the extraction of key points of discussion and conclusions based on the context and importance of the conversation. This extracted information is stored in a database and serves as the basis for generating summaries.

[0663] emotion recognition

[0664] The server uses an emotion engine (e.g., Microsoft Azure Emotion API) to recognize the user's emotions from the recorded audio and video. Emotion recognition includes analyzing voice tone, facial expressions, and gestures. This allows the user's emotional trends during the meeting to be understood.

[0665] Speech production and emotion regulation

[0666] During the meeting, the server references appropriate utterances from the database as needed and generates new utterances using a generative AI model (e.g., GPT-3). The generated utterances are adjusted based on the recognized emotional data. For example, if a meeting participant is feeling stressed, the server generates calm and reassuring utterances.

[0667] Speech conversion and speech

[0668] The server converts the generated speech into voice data using synthetic speech technology (e.g., Amazon Polly) and sends the voice data to the terminal. The terminal then receives the synthesized voice data from the server and broadcasts it to the conference in real time.

[0669] Image Generation

[0670] If necessary, the server uses video generation AI (e.g., DeepFake technology, FaceRig) to generate a video that appears to show the user's avatar speaking, which is then sent to the device, where it is integrated into the meeting video feed.

[0671] Meeting summary generation and emotional reflection

[0672] After the meeting ends, the server reanalyzes the text data from the entire meeting and extracts key points from the meeting. The extracted information is compiled into a summary that also incorporates the recognized emotion data. For example, points that were the subject of intense discussion may be noted in the summary.

[0673] Summary Delivery

[0674] The server stores the generated summary in a database and notifies the user via their device. The user can receive and check the summary using their device (e.g., a smartphone app) and quickly grasp important information about the meeting.

[0675] Specific examples

[0676] For example, consider a company's project progress meeting where the project manager (PM) cannot physically attend. In this case, the PM can schedule the meeting through a dedicated web interface. At the scheduled time, the device (such as a conference tablet or PC) automatically records audio and video and sends the data to the server.

[0677] The server converts voice data into text data in real time, generates appropriate comments as needed, and plays them as synthesized speech. The server recognizes emotions using an emotion engine and reflects emotional trends in comments and summaries. After the meeting ends, a summary of about five minutes is generated and sent to the PM via the terminal.

[0678] Prompt Sentence Examples

[0679] "For corporate project progress meetings, please automatically record audio and video and convert the meeting content into text in real time. Also, please generate appropriate remarks based on the emotions of the meeting participants and output them as synthesized voices or avatar images as needed. After the meeting, please extract the main points, generate a summary, and notify the user, reflecting the emotional trends."

[0680] In this way, the system of the present invention realizes more efficient meetings and more accurate information management, and by taking the user's emotions into consideration, it enables more human-like responses and significantly improves business efficiency.

[0681] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0682] Step 1:

[0683] A user schedules a meeting. Using a dedicated web interface, the user enters the date and time of the meeting, the list of participants, and the agenda, and then clicks the "Submit" button. The information entered is sent to the server, which stores the received information in a database (e.g., MySQL) and prepares the meeting.

[0684] Step 2:

[0685] The meeting begins. At the set date and time, the device (meeting tablet or PC) automatically starts recording audio and video. The device sends the recorded audio and video data to the server in real time. The server saves this data in storage (e.g., AWS S3).

[0686] Step 3:

[0687] The server converts the voice data into text data. The server converts the received voice data into text data using the Google Cloud Speech-to-Text API. The text data converted from the input voice data is stored in the database.

[0688] Step 4:

[0689] The server analyzes the text data. The server uses natural language processing techniques (e.g., SpaCy, BERT) to analyze the converted text data and extract key discussion points and conclusions based on the context and importance of the conversation. The extracted information is then stored in a database.

[0690] Step 5:

[0691] The server recognizes emotions. From the recorded audio and video, the server uses an emotion engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotions. The analyzed emotion data is stored in a database.

[0692] Step 6:

[0693] The server generates appropriate utterances. Depending on the progress of the meeting, the server retrieves relevant information from the database and generates new utterances using a generative AI model (e.g., GPT-3). These utterances are adjusted based on the recognized emotion data.

[0694] Step 7:

[0695] The server converts the speech into speech. The server then converts the generated speech into voice data using synthetic speech technology (e.g., Amazon Polly). The generated voice data is sent to the device in real time, and the device plays the audio in the conference.

[0696] Step 8:

[0697] The server generates video as needed. The server uses video generation AI (e.g., DeepFake technology, FaceRig) to generate video that appears to be the user's avatar speaking. The generated video is sent to the device, where it is integrated into the conference video feed.

[0698] Step 9:

[0699] The server summarizes the key points of the meeting. After the meeting ends, the server reanalyzes the text data of the entire meeting and extracts the key points. The extracted information is compiled into a summary, which also reflects emotional data. The generated summary is stored in a database.

[0700] Step 10:

[0701] The server delivers the summary. The server then sends the generated summary to the device and notifies the user. The user can receive and check the summary using their device (e.g., a smartphone app) and quickly grasp important information about the meeting.

[0702] Through this procedure, the system according to the present invention can improve the efficiency of meetings and provide a more human-like response that takes into account the user's emotions.

[0703] (Application example 2)

[0704] 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."

[0705] In conventional self-driving vehicles and conference systems, it is difficult to drive or conduct a conference while taking into account the emotions of passengers and participants, and there is a need to improve the efficiency, comfort, and quality of communication in self-driving vehicles and remote conferences.In addition, it is difficult to recognize emotions and provide feedback in real time in self-driving vehicles and remote conferences, so there is a need for a method to improve the efficiency of information transmission while reducing stress for users.

[0706] 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.

[0707] In this invention, the server includes means for recording and filming a meeting or driving situation, means for converting the recorded voice data into text data, means for extracting the main points of the meeting or driving from the converted text data, means for providing summarized information to the user, means for generating appropriate guidance or remarks, means for converting the generated guidance or remarks into voice, means for generating an image of the user as needed, means for recognizing emotions using the voice and video, and means for adjusting the guidance or remarks based on the recognized emotions. This allows the server to recognize the user's emotions in real time and provide appropriate feedback, enabling more efficient and comfortable driving and meeting progress.

[0708] "Means for recording and filming meetings or driving situations" refers to devices or software that capture audio and video during meetings or while driving and record them as data.

[0709] "Means for converting recorded voice data into text data" refers to voice recognition technology or software for converting voice files into text information.

[0710] "Means for extracting meeting or driving key points from converted text data" refers to algorithms or methods that analyze text data and identify and extract important points or key information.

[0711] The "means for providing summarized information to the user" refers to a device or software that summarizes the extracted key points and conveys that information to the user.

[0712] The "means for generating appropriate guidance or statements" refers to a system that references an algorithm or database that automatically creates appropriate responses or guidance according to the situation.

[0713] The "means for converting the generated guidance or speech into speech" refers to technology or software that converts text information into speech using speech synthesis technology and provides it to the user audibly.

[0714] "Means for generating an image of the user on demand" refers to devices or software that create an image or video of the user using a digital avatar or pre-recorded video data.

[0715] "Audio and visual emotion recognition" refers to technologies and algorithms that use voice tone and facial expression analysis to identify a user's emotional state.

[0716] "Means for tailoring prompts and / or statements based on recognized emotions" means a system or algorithm that alters the content or tone of prompts and / or statements created or delivered based on the user's emotional state.

[0717] The system of this invention is primarily used in self-driving vehicles and remote conferences, and includes a series of functions for recording audio and video, converting audio data into text, extracting key points from meetings and driving, recognizing emotions, and generating and adjusting guidance and utterances.

[0718] 1. Setting up and starting a meeting or drive

[0719] First, the user sets up a meeting or a drive using a dedicated interface. They input the date and time, participant list, agenda, and other setting information, and send it to the server. The server stores the received information in a database and prepares for the meeting or drive.

[0720] 2. Audio and video recording of meetings or driving

[0721] When a meeting or drive starts, the device starts recording audio and video, and the recorded data is sent to the server in real time.

[0722] 3. Speech Recognition and Text Conversion

[0723] The server converts the received voice data into text using highly accurate speech recognition technology, using voice recognition software such as Google Cloud Speech-to-Text API.

[0724] 4. Analysis of meeting or driving content and extraction of key points

[0725] The server analyzes the converted text data and uses natural language processing technology to extract important discussion points and driving principles, using an AI model for the analysis.

[0726] 5. Emotion recognition

[0727] The server uses an emotion engine to recognize the user's emotions from the recorded voice and video, using technologies such as DeepFace.

[0728] 6. Guidance or speech production and emotion regulation

[0729] The server then generates appropriate announcements or utterances from a database, adjusting the utterances based on the recognized emotion data. For example, if a passenger is nervous, the server generates calm and reassuring announcements.

[0730] 7. Voice conversion and speech

[0731] The server converts the generated guidance or speech into voice data using synthetic voice technology and transmits the voice data to the terminal, which then provides the synthesized voice data received from the server to the user in real time.

[0732] 8. Image Generation

[0733] When needed, the server uses video generation AI to generate a video that appears to be the user's avatar speaking, which is then sent to the device, where it is integrated into the video feed for meetings or into the display while driving.

[0734] 9. Summary generation and emotional reflection of meetings or driving

[0735] After the meeting or drive, the server reanalyzes the entire text data, extracts key points, and generates a summary that also incorporates the recognized emotion data, allowing the server to grasp the intensity of the discussion or the level of tension while driving.

[0736] 10. Summary Distribution

[0737] The server stores the generated summary in a database and notifies the user via the device, allowing the user to receive and check the summary and quickly grasp important information.

[0738] Specific examples

[0739] For example, consider a user riding in an autonomous vehicle. The device automatically records the situation inside the vehicle, and the server analyzes the data in real time. The server detects the passenger's emotional state and generates calm and reassuring responses to questions and directions while driving, which are then played as synthesized voice. An example of a prompt could be, "Please analyze the data recorded and filmed inside the autonomous vehicle and generate a summary of the passenger's emotions and the journey." In this way, the system of the present invention recognizes the user's emotions in real time and provides appropriate feedback, resulting in a comfortable and efficient driving experience.

[0740] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0741] Step 1:

[0742] The user sets up a meeting or trip using a dedicated interface. The input is the date and time, participant list, and agenda, and other setting information is sent to the server. The server stores the received information in a database and prepares the meeting or trip. The output is a notification that the preparation is complete.

[0743] Step 2:

[0744] When a meeting or a drive starts, the device automatically starts recording audio and video. The input is audio and video data recorded in real time. The device sends this data to the server in real time. The output is audio and video data.

[0745] Step 3:

[0746] The server converts the received voice data into text data using highly accurate voice recognition technology. The input is voice data, and speech recognition software (e.g., Google Cloud Speech-to-Text API) is used. The output is text data.

[0747] Step 4:

[0748] The server analyzes the text data and extracts key points using natural language processing technology. The input is the converted text data, and a natural language processing algorithm (such as a generative AI model) is used. The output is the extracted key points and the text data.

[0749] Step 5:

[0750] The server recognizes emotions from recorded audio and video. The input is audio and video data, and an emotion recognition engine (e.g., DeepFace) is used. The output is the recognized emotion data.

[0751] Step 6:

[0752] The server generates appropriate guidance or utterances from the database as needed, adjusting the content and tone based on the recognized emotional data. The input is information and emotional data referenced from the database, and generation technology using an AI model is used. The output is the adjusted text data of the utterances.

[0753] Step 7:

[0754] The server converts the generated speech into voice data using synthetic speech technology and sends it to the terminal. The input is the adjusted text data of the speech, and synthetic speech software is used. The output is synthesized voice data. The terminal plays the received synthesized voice data in real time.

[0755] Step 8:

[0756] If necessary, the server uses video generation AI to generate a video that appears to show the user's avatar speaking. The input is the text data of the speech, which the video generation AI uses. The output is the generated video data. The device then integrates this video into the video feed.

[0757] Step 9:

[0758] After the meeting or drive ends, the server re-analyzes the entire text data, extracts key points, and generates a summary. The input is the entire text data of the meeting or drive, and natural language processing technology is used. The output is the summarized data.

[0759] Step 10:

[0760] The server stores the generated summary in a database and notifies the user via the terminal. The user can receive and check the summary using the terminal, and quickly grasp important information. The input is the generated summary data, and the output is a notification to the user.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] [Third embodiment]

[0765] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0766] 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.

[0767] 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).

[0768] 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.

[0769] 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.

[0770] 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).

[0771] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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."

[0777] The system according to the present invention is provided to improve the efficiency of meetings. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, detailed embodiments of the present invention will be described.

[0778] 1. Setting up and starting a meeting

[0779] First, the user sets up a meeting using a dedicated interface, inputting the date and time and a list of participants, and then sends this information to the server, which then stores the received information in a database and prepares the meeting.

[0780] 2. Audio and video recording of meetings

[0781] When the meeting starts, the device automatically starts recording audio and video, electronically recording the entire meeting. The device transmits audio and video data to the server in real time.

[0782] 3. Speech Recognition and Text Conversion

[0783] The server converts the received voice data into text data using highly accurate speech recognition technology, which then serves as the basis for understanding the content of the meeting.

[0784] 4. Analysis of meeting content and key points extraction

[0785] The server analyzes the converted text data and extracts key discussion points and conclusions, using natural language processing technology to extract key points based on the context and importance of the conversation.

[0786] 5. Speech generation and speech conversion

[0787] During the conference, the server references the database and generates appropriate utterances as needed. The generated utterances are converted into voice data using synthetic speech technology and sent to the conference terminals as utterances.

[0788] 6. Image Generation

[0789] If necessary, the server uses video generation AI to generate a video that appears to show the user's avatar speaking, which the device adds to the meeting video feed.

[0790] 7. Conference Summary Generation and Distribution

[0791] After the meeting ends, the server re-analyzes the text data from the entire meeting to extract key points. The extracted information is summarized and provided in a format that allows users to quickly understand the content. The summary is then delivered to the user via their device, allowing them to quickly understand the important information from the meeting.

[0792] Specific examples

[0793] For example, in a company's project progress meeting, the user (project manager) may not be able to physically attend. In this case, the device automatically records and records the meeting, and the server converts the meeting content into text in real time. In response to questions about the project's progress, the server retrieves relevant information from a database, generates the necessary utterances, and plays them as synthesized voice. After the meeting, the server summarizes the entire meeting content in a five-minute summary and distributes it to the user via the device. The user can review this summary and immediately grasp the key points of the meeting.

[0794] In this way, the system according to the present invention can improve the efficiency of meetings and the accuracy of information management, thereby significantly improving the work efficiency of users.

[0795] The processing flow will be explained below.

[0796] Step 1:

[0797] The user inputs the date and time of the meeting, the participant list, and the agenda using a dedicated interface, and sends the configuration information to the server.

[0798] Step 2:

[0799] The server stores the received configuration information in a database and prepares the meeting, including the meeting date and time, participant list, and agenda.

[0800] Step 3:

[0801] When the meeting starts, the device automatically starts recording audio and video, creating an electronic record of the entire meeting.

[0802] Step 4:

[0803] The device streams the recorded audio data to the server in real time, and the video data is also sent to the server.

[0804] Step 5:

[0805] The server converts the received voice data into text data using highly accurate voice recognition technology, and the converted text data is stored in a database.

[0806] Step 6:

[0807] The server analyzes the converted text data using natural language processing technology and extracts key discussion points and conclusions based on the context and importance of the conversation.

[0808] Step 7:

[0809] During the conference, the server refers to relevant information from the database to generate appropriate utterances as needed, and generates utterances based on the referred information.

[0810] Step 8:

[0811] The server converts the generated speech into voice data using voice synthesis technology and transmits the voice data to the terminal.

[0812] Step 9:

[0813] The device receives synthesized speech data from the server and sends it to the conference in real time, allowing the server to speak on behalf of the user.

[0814] Step 10:

[0815] If necessary, the server uses video generation AI to generate a video that makes it appear as if the user's avatar is speaking, and the generated video is sent to the device.

[0816] Step 11:

[0817] The device integrates the video data it receives from the server into the conference video feed.

[0818] Step 12:

[0819] After the meeting ends, the server reanalyzes the text data from the entire meeting and extracts key points from the meeting, which are then compiled into a summary.

[0820] Step 13:

[0821] The server stores the generated summary in a database and notifies the user through the terminal.

[0822] Step 14:

[0823] Users can use their devices to receive and check summaries from the server, quickly grasping important information about the meeting.

[0824] Example 1

[0825] 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."

[0826] With conventional conference systems, meetings are recorded and filmed separately, which means that reviewing the content later takes a lot of time and effort. Furthermore, even if you cannot physically attend the meeting, it is inevitable that you will miss real-time information sharing and important comments. To efficiently resolve these issues, a system that can quickly and accurately grasp the content of the entire meeting is needed.

[0827] 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.

[0828] In this invention, the server includes: means for a user to set up a conference; means for the server to store received conference information in a database; means for a terminal to start recording and filming the conference; means for the terminal to send recorded audio and video data to the server; means for the server to convert audio data into text data; means for the server to analyze the converted text data and extract key points; means for the server to generate necessary comments and convert them into audio data; means for generating video of the user as needed; and means for generating a summary of the conference content and providing it to the user. This automates the recording and filming of conferences, audio-to-text conversion, extraction of key points, generation of comments, generation of video, and distribution of summaries, thereby enabling improved efficiency of conferences and accuracy of information management.

[0829] "User" refers to a person who uses the system to set up and confirm meetings.

[0830] "Server" refers to a computer device that receives, stores, processes, distributes, etc., conference information.

[0831] A "database" refers to an information management system that organizes and stores data related to meetings and makes it possible to retrieve it as needed.

[0832] "Terminal" refers to a device that records and films conferences and transmits data to a server.

[0833] "Means for converting voice data into text data" refers to technology or devices that convert voices during a meeting into text information.

[0834] "Analysis" refers to the process of examining the converted text data and extracting important information.

[0835] "Key points" refer to the key discussion points and conclusions of a meeting.

[0836] "Means for generating statements" refers to a function that automatically creates statements required during a meeting.

[0837] "Means for converting into voice data" refers to technology that converts the created utterances into voice.

[0838] "Means for generating video" refers to the technology that creates and adds video of users to the conference video feed.

[0839] The "means for generating a summary" refers to a function that provides a user with a concise summary of the contents of the meeting.

[0840] "System" refers to a comprehensive mechanism in which the above technologies and devices work together to improve meeting efficiency and information management.

[0841] The system of this invention is designed for the purpose of improving the efficiency of meetings and information management. In this system, the user sets up a meeting, the server receives the information, stores it in a database, and performs recording and video recording during the meeting, speech recognition, key point extraction, speech generation, video generation, and summary distribution.

[0842] First, the user sets up a conference using a dedicated interface. This interface is provided as a web application, and the user enters the date and time of the conference and the list of participants. The entered information is sent to the server, which receives it and stores it in a database. The stored information is used for subsequent processing.

[0843] When a meeting starts, the device automatically starts recording. The recorded data is sent to the server in real time. The server then converts the received audio data into text using speech recognition technology. This process uses highly accurate speech recognition technology (e.g., Google Cloud Speech-to-Text).

[0844] The converted text data is then analyzed by the server to extract key points and conclusions. The server uses natural language processing techniques (e.g., NLTK, spaCy) to analyze the text data and extract key points. This extracted information is stored in a database and notified to the user as needed.

[0845] When a necessary statement arises during a meeting, the server references relevant information from the database and generates the appropriate statement. The statement is created using a generative AI model (e.g., GPT-3) and converted into voice data using synthetic speech technology (e.g., Amazon Polly). The generated voice is sent to the device and played back during the meeting.

[0846] Additionally, the server uses video generation AI (e.g., Deepfake technology) to generate video of the user as needed, making it appear as if the user's avatar is speaking. This video is then added to the meeting video feed by the device.

[0847] After the meeting ends, the server re-analyzes the text data from the entire meeting to extract key points from the meeting. These key points are summarized and provided in a format that allows users to quickly understand the content. This summary is then delivered to the user via their device, allowing them to quickly understand the important information from the meeting.

[0848] Specific examples

[0849] For example, there may be a case where a company's project manager is unable to physically attend a project progress meeting. In this case, the device automatically records and records the meeting, and the server converts the meeting content into text in real time. Furthermore, in response to questions about the project's progress, the server retrieves relevant information from a database, generates the necessary utterances, and plays them as synthesized speech. After the meeting, the server summarizes the entire meeting content in a five-minute summary and delivers it to the user via the device. The user can review this summary and immediately grasp the key points of the meeting.

[0850] Prompt Sentence Examples

[0851] "Please describe your system for tracking project progress during project meetings. Please provide detailed information, including specific hardware and software examples."

[0852] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0853] Step 1:

[0854] The user enters the meeting information using a dedicated interface.

[0855] Input: The user inputs information such as the meeting date and time, participant list, and location.

[0856] Data processing: The web application formats the input data and converts it into a format that can be sent to the server.

[0857] Specific behavior: The user clicks the "Add event" button on the meeting setting screen and enters the meeting details.

[0858] Step 2:

[0859] The server stores the received conference information in a database.

[0860] Input: Conference information received from a dedicated interface.

[0861] Data processing: Converting data into a format suitable for saving in a database.

[0862] Specific operation: The server receives the meeting date and time and the participant list, and stores them in a database using an SQL query.

[0863] Step 3:

[0864] The device starts recording the meeting.

[0865] Input: Instructions from the server based on the meeting start time.

[0866] Data processing: Audio and video data capture.

[0867] Specific operation: The server sends an API request at the specified time, and the device starts recording.

[0868] Step 4:

[0869] The terminal transmits the recorded audio and video data to the server.

[0870] Input: Recorded audio and video data.

[0871] Data processing: Encode for real-time streaming.

[0872] Specific operation: The terminal encodes the video in H.264 format and sends it to the server via the RTMP protocol.

[0873] Step 5:

[0874] The server converts the voice data into text data using voice recognition technology.

[0875] Input: Audio data.

[0876] Data processing: Converting audio data into text format.

[0877] Specific operation: The server sends audio data to the Google Cloud Speech-to-Text API and obtains text data such as "The purpose of today's meeting is as follows."

[0878] Step 6:

[0879] The server analyzes the converted text data and extracts the main points of the meeting.

[0880] Input: The converted text data.

[0881] Data processing: Use natural language processing to identify key points.

[0882] What it does: The server uses NLTK and spaCy to analyze the text data and extract key points such as "Delivery date changed" and "Budget added."

[0883] Step 7:

[0884] The server generates the necessary utterances and converts them into audio data.

[0885] Input: Relevant information retrieved from the database.

[0886] Data processing: Generate text using a generative AI model and convert it into audio data using synthetic speech technology.

[0887] Specific operation: The server uses GPT-3 to generate a statement such as "Progress on this project is going well," and then converts it into voice data using Amazon Polly.

[0888] Step 8:

[0889] Generates an image of the user as needed.

[0890] Input: User avatar information and generated utterance.

[0891] Data processing: Create videos using video generation AI.

[0892] How it works: The server uses Deepfake technology to generate video of a user saying something like, "I'll talk more about this in the next meeting."

[0893] Step 9:

[0894] A summary of the meeting content is generated and provided to the user.

[0895] Input: Parsed meeting text data.

[0896] Data processing: Using summary generation technology, key points are extracted that can be understood in a short amount of time.

[0897] How it works: The server uses GPT-3 to automatically generate a summary of the entire meeting and deliver it to the user via email or a web application.

[0898] (Application example 1)

[0899] 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."

[0900] While advanced autonomous vehicles are becoming more commonplace, there is still a lack of ways for drivers to participate in meetings and obtain and confirm important information in real time while driving. In particular, it is difficult to efficiently understand the content of meetings while driving and respond appropriately in a timely manner. Therefore, there is a need for a system that allows drivers to participate in meetings safely and effectively.

[0901] 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.

[0902] In this invention, the server includes means for recording and videotaping a conference, means for converting recorded audio data into text data, means for extracting key points of the conference from the converted text data, means for summarizing the extracted key points, means for providing the summarized information to a user, means for generating appropriate utterances during the conference, means for converting the generated utterances into audio, means for generating an image of the user as needed, means for capturing audio and video in the vehicle, means for performing real-time speech recognition and displaying the text on an in-vehicle display or a head-mounted display, means for searching a database and responding with synthesized speech in response to audio input, and means for audio notifying the driver of important discussions. This allows the driver to safely and efficiently participate in conferences while driving and obtain and confirm information in real time.

[0903] "Means for recording and filming a meeting" means a device that uses a digital device to record audio and video during a meeting.

[0904] "Means for converting recorded voice data into text data" refers to a system equipped with technology that analyzes voice data and converts it into text information.

[0905] "Means for extracting the main points of a meeting from the converted text data" refers to a technology for analyzing text data and identifying important points of discussion.

[0906] "Means for summarizing extracted key points" refers to a technique for summarizing extracted important arguments in a short and concise manner.

[0907] "Means for providing summarized information to a user" means a system that presents the summarized information in a form accessible to a user.

[0908] "Means for generating appropriate utterances during a meeting" refers to a technology that automatically generates appropriate utterances in accordance with the progress of the meeting.

[0909] "Means for converting generated utterances into speech" refers to a system that converts generated text utterances into speech using synthetic speech.

[0910] "Means for generating video of the user as needed" refers to technology that uses AI technology to generate a user's avatar or video and add it to the video feed.

[0911] "In-vehicle audio and video capture means" means a device that uses a microphone and a camera to record audio and video within a vehicle.

[0912] "Means for real-time speech recognition and displaying text on an in-vehicle display or head-mounted display" refers to technology that converts speech collected inside a vehicle into text in real time and displays it so that the driver can visually confirm it.

[0913] "Means for searching a database in response to a voice input and responding with a synthesized voice" refers to a technology that receives a voice command, references a database based on the content of the command, and provides a response in a synthesized voice.

[0914] "Means for providing audio notification to the driver regarding important discussion points" refers to technology that provides audio notification of important discussion points in a meeting to the driver while driving.

[0915] To implement this invention, it is necessary to link the server with the terminal inside the autonomous vehicle and build a system that incorporates technologies such as voice recognition, natural language processing, and synthetic voice generation.

[0916] When a meeting begins, the vehicle's microphones and cameras automatically capture audio and video. This data is sent to a server in real time. The server receives the audio data and converts it into text using highly accurate speech recognition software (Google Cloud Speech-to-Text). The converted text data is then analyzed using a natural language processing library (spaCy or NTLK) to extract key discussion points from the meeting.

[0917] The extracted discussion points are then concisely summarized using a summary generation algorithm. The summarized information is displayed on an in-car display or a head-mounted display (HMD). Important discussion points are also notified to the driver via audio.

[0918] If the driver asks a question during the meeting, their voice input is sent back to the server, where it is searched against the database. A synthetic voice technology (Amazon Polly) generates a response and provides it to the driver via voice.

[0919] Additionally, if necessary, the server uses video generation AI (TensorFlow-DCGAN) to generate video that appears as if the user's avatar is speaking and adds it to the meeting video feed.

[0920] As a concrete example, consider a situation where a driver is participating in a project status meeting while traveling in an autonomous vehicle. In this case, the in-car microphone and camera capture the meeting, and the voice data is converted into text in real time. The driver checks the meeting contents on a display or HMD and receives voice notifications about important points. If a question arises, the driver can input it by voice, and the answer will be instantly provided by synthesized voice.

[0921] Examples of prompts using generative AI models include:

[0922] "Please convert the audio data during the meeting into text in real time. The audio data is recorded in Japanese. Please separate each utterance into paragraphs and convert them into text."

[0923] In this way, the present invention allows drivers to safely and efficiently participate in meetings and quickly obtain the information they need.

[0924] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0925] Step 1:

[0926] At the start of the meeting, the device activates the microphone and camera in the vehicle and starts capturing audio and video. The input is the audio and video from inside the vehicle, which is converted into a digital format and sent to the server in real time. The output is the captured audio and video data.

[0927] Step 2:

[0928] The server converts the received voice data into text data using Google Cloud Speech-to-Text. It receives captured voice data as input, converts it into text using real-time speech recognition processing, and obtains text data as output.

[0929] Step 3:

[0930] The server analyzes the converted text data using a natural language processing library (spaCy or NTLK) to identify key discussion points of the meeting. As input, it receives text data, analyzes it, and extracts key discussion points. As output, it obtains the extracted discussion points.

[0931] Step 4:

[0932] The server then uses a summary generation algorithm to concisely summarize the extracted discussion points. As input, it receives the discussion points, processes them, and converts them into short text. As output, it obtains the summarized discussion points.

[0933] Step 5:

[0934] The terminal displays the summarized information on an in-car display or a head-mounted display. As input, it receives summarized discussion points from the server and displays them on the display. As output, it provides a visual representation for the driver.

[0935] Step 6:

[0936] The server provides voice notifications to the driver about important discussion points during the meeting. As input, it receives important discussion points and converts them into synthesized speech. As output, it obtains the information to be voiced to the driver.

[0937] Step 7:

[0938] When the driver inputs a question by voice, the terminal captures the voice input and sends it to the server. The input is the driver's voice question. This is sent to the server. The output is the voice input data.

[0939] Step 8:

[0940] The server searches the database based on the received voice input data and generates an appropriate answer. The input is the voice question data and a related database, which are then analyzed to generate an answer. The output is the generated text answer.

[0941] Step 9:

[0942] The server converts the generated text response into synthetic speech using Amazon Polly and sends the speech data to the device. The generated text response is used as input for the synthetic speech processing. This is then converted into speech data and sent to the device. The speech data is obtained as output.

[0943] Step 10:

[0944] The terminal receives the voice data from the server and plays it back to the driver. As input, it receives voice data from the server. It plays it back through the speaker. As output, it provides the driver with a voice response.

[0945] Step 11:

[0946] The server uses video generation AI as needed to generate a user avatar. The input is the content of what was said during the meeting and the user's video data. This is processed to generate the user's avatar video. The generated avatar video is obtained as the output.

[0947] Step 12:

[0948] The device adds the generated avatar video to the conference video feed. As input, it receives avatar video data from the server, integrates it into the video feed, and as output, the conference video includes the user avatar.

[0949] 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.

[0950] The system of the present invention is combined with an emotion engine to improve the efficiency of meetings. In addition to the basic functions of recording audio and video, converting the contents of the meeting into text data, and generating a summary, the system also has the function of recognizing the user's emotions and optimizing the content of comments and summaries based on those emotions. Detailed embodiments of the present invention will be described below.

[0951] 1. Setting up and starting a meeting

[0952] First, a user sets up a meeting using a dedicated interface, inputting the date and time, participant list, and agenda, and then sends the setup information to the server, which stores the received information in a database and prepares the meeting.

[0953] 2. Audio and video recording of meetings

[0954] When the meeting starts, the device automatically starts recording audio and video, electronically recording the entire meeting. The device transmits audio and video data to the server in real time.

[0955] 3. Speech Recognition and Text Conversion

[0956] The server converts the received voice data into text data using highly accurate voice recognition technology, and the converted text data is stored in a database.

[0957] 4. Analysis of meeting content and key points extraction

[0958] The server analyzes the converted text data using natural language processing technology and extracts key discussion points and conclusions based on the context and importance of the conversation.

[0959] 5. Emotion recognition

[0960] From the recorded voice and video, the server uses an emotion engine to recognize the user's emotions, which includes analyzing voice tone, facial expressions, and gestures.

[0961] 6. Speech production and emotion regulation

[0962] During the meeting, the server references the database and generates appropriate utterances as needed. The generated utterances are adjusted based on the recognized emotional data. For example, if a meeting participant is feeling stressed, the server generates calm and reassuring utterances.

[0963] 7. Voice conversion and speech

[0964] The server converts the generated speech into voice data using synthetic speech technology and sends the voice data to the terminal. The terminal then broadcasts the synthesized voice data received from the server to the conference in real time.

[0965] 8. Image Generation

[0966] If necessary, the server uses video generation AI to generate a video that appears to show the user's avatar speaking, which is then sent to the device, where it is integrated into the meeting video feed.

[0967] 9. Meeting Summary Generation and Emotion Reflection

[0968] After the meeting ends, the server reanalyzes the text data from the entire meeting to extract key points. The extracted information is compiled into a summary that also incorporates the recognized emotion data. For example, points that were the subject of intense discussion are noted in the summary.

[0969] 10. Summary Distribution

[0970] The server stores the generated summary in a database and notifies the user via their device, allowing the user to receive and check the summary and quickly grasp important information about the meeting.

[0971] Specific examples

[0972] For example, consider a situation where a project manager is unable to physically attend a project progress meeting at a company. In this case, the device automatically records and records the meeting, and the server converts the meeting content into text in real time. In response to questions about the project's progress, the server retrieves relevant information from a database, generates the necessary comments, and plays them as synthesized speech. Furthermore, the tone and content of the comments are adjusted based on the emotions of the participants recognized by an emotion engine. After the meeting, the server summarizes the entire meeting content in a five-minute summary and distributes it to the user via the device. This summary also reflects the emotional trends during the meeting, allowing the user to understand the level of enthusiasm and tension in the meeting.

[0973] In this way, the system of the present invention realizes more efficient meetings and more accurate information management, and by taking the user's emotions into consideration, it enables more human-like responses, thereby significantly improving business efficiency.

[0974] The processing flow will be explained below.

[0975] Step 1:

[0976] The user inputs the date and time of the meeting, the participant list, and the agenda using a dedicated interface, and sends the configuration information to the server.

[0977] Step 2:

[0978] The server stores the received configuration information in a database and prepares the meeting, including the meeting date and time, participant list, and agenda.

[0979] Step 3:

[0980] When the meeting starts, the device automatically starts recording audio and video, creating an electronic record of the entire meeting.

[0981] Step 4:

[0982] The device streams the recorded audio data to the server in real time, and the video data is also sent to the server.

[0983] Step 5:

[0984] The server converts the received voice data into text data using highly accurate voice recognition technology, and the converted text data is stored in a database.

[0985] Step 6:

[0986] The server analyzes the converted text data using natural language processing technology and extracts key discussion points and conclusions based on the context and importance of the conversation.

[0987] Step 7:

[0988] The server uses an emotion engine to recognize the user's emotions from the recorded audio and video, which includes analyzing the tone of voice, facial expressions, and gestures.

[0989] Step 8:

[0990] During the conference, the server references relevant information from the database to generate appropriate utterances as needed, which are adjusted based on the recognized emotion data.

[0991] Step 9:

[0992] The server converts the generated speech into voice data using voice synthesis technology and transmits the voice data to the terminal.

[0993] Step 10:

[0994] The device receives synthesized speech data from the server and sends it to the conference in real time, allowing the server to speak on behalf of the user.

[0995] Step 11:

[0996] If necessary, the server uses video generation AI to generate a video that makes it appear as if the user's avatar is speaking, and the generated video is sent to the device.

[0997] Step 12:

[0998] The device integrates the video data it receives from the server into the conference video feed.

[0999] Step 13:

[1000] After the meeting ends, the server reanalyzes the text data from the entire meeting and extracts key points from the meeting, which are then compiled into a summary.

[1001] Step 14:

[1002] The server stores the generated summary in a database and notifies the user through the terminal.

[1003] Step 15:

[1004] Users can use their devices to receive and check summaries from the server, quickly grasp important meeting information, and also refer to recognized emotion data.

[1005] Example 2

[1006] 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."

[1007] Conventional conference systems simply record and record meetings, convert the audio data into text data, and create summaries. Such systems have difficulty identifying changes in emotions and important discussions during meetings, resulting in insufficient efficiency and reduced user stress. Furthermore, providing appropriate feedback to remote participants is difficult, often hindering the smooth progress of meetings.

[1008] The identification process by the identification 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 recording and videotaping the conference, means for converting the recorded voice data into text data, means for analyzing the converted text data and extracting the main points of the conference, means for summarizing the extracted main points, means for providing the summarized information to the user, means for recognizing the user's emotions, means for generating appropriate utterances during the conference, means for adjusting the generated utterances based on the recognized emotion data, means for converting the generated utterances into audio, and means for generating an image of the user as needed. This makes it possible to identify important discussions during the conference and take emotion data into consideration, thereby realizing smoother and more efficient conference management.

[1009] "Audio and video recording" means the electronic recording of audio and video of a meeting.

[1010] "Audio Data" means a digital recording of sounds made during a conference.

[1011] "Text data" is character string information obtained by analyzing voice data.

[1012] "Tasks" are discussion points or conclusions identified as important during a meeting.

[1013] A "summary" is a document that briefly summarizes the contents of the entire meeting.

[1014] A "user" is a person who uses the system to participate in a conference or check the contents of the conference.

[1015] "Emotions" refer to the changes in the user's mind and mood during the meeting.

[1016] "Speech" is the verbal exchange of information that takes place during a meeting.

[1017] "Convert to voice" refers to the process of converting character information such as text data into voice data.

[1018] "Video" refers to visual information during a meeting or digital video showing the actions of generated user avatars.

[1019] "Analysis" is the process of examining collected data to extract useful information.

[1020] "Recognition" is the ability to understand and grasp specific patterns and information from data.

[1021] "Adjustment" means modifying data or information to make it optimal depending on the conditions and circumstances.

[1022] This invention is a system for improving the efficiency of meetings, and in addition to the basic functions of recording audio and video, converting the contents of the meeting into text data and generating a summary, it also has the function of recognizing the user's emotions and optimizing the content of remarks and summaries based on those emotions. This section describes the specific processing procedures required to implement this system, as well as the hardware and software used.

[1023] Setting up and starting a meeting

[1024] First, a user sets up a meeting using a dedicated web interface, where the user inputs the meeting date and time, participant list, and agenda, and sends the configuration information to the server, which stores the information in a database (e.g., MySQL) and prepares the meeting.

[1025] Meeting recording and recording

[1026] When the scheduled meeting time arrives, the device (e.g., the conference tablet or PC) automatically starts recording audio and video. The recorded audio and video data is sent to the server in real time, and the server stores this data in storage (e.g., AWS S3).

[1027] Speech recognition and text conversion

[1028] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API, which is then stored in a database for subsequent analysis.

[1029] Analysis of meeting content and key points extraction

[1030] The server then analyzes the converted text data using natural language processing techniques (e.g., SpaCy, BERT). The analysis results in the extraction of key points of discussion and conclusions based on the context and importance of the conversation. This extracted information is stored in a database and serves as the basis for generating summaries.

[1031] emotion recognition

[1032] The server uses an emotion engine (e.g., Microsoft Azure Emotion API) to recognize the user's emotions from the recorded audio and video. Emotion recognition includes analyzing voice tone, facial expressions, and gestures. This allows the user's emotional trends during the meeting to be understood.

[1033] Speech production and emotion regulation

[1034] During the meeting, the server references appropriate utterances from the database as needed and generates new utterances using a generative AI model (e.g., GPT-3). The generated utterances are adjusted based on the recognized emotional data. For example, if a meeting participant is feeling stressed, the server generates calm and reassuring utterances.

[1035] Speech conversion and speech

[1036] The server converts the generated speech into voice data using synthetic speech technology (e.g., Amazon Polly) and sends the voice data to the terminal. The terminal then receives the synthesized voice data from the server and broadcasts it to the conference in real time.

[1037] Image Generation

[1038] If necessary, the server uses video generation AI (e.g., DeepFake technology, FaceRig) to generate a video that appears to show the user's avatar speaking, which is then sent to the device, where it is integrated into the meeting video feed.

[1039] Meeting summary generation and emotional reflection

[1040] After the meeting ends, the server reanalyzes the text data from the entire meeting and extracts key points from the meeting. The extracted information is compiled into a summary that also incorporates the recognized emotion data. For example, points that were the subject of intense discussion may be noted in the summary.

[1041] Summary Delivery

[1042] The server stores the generated summary in a database and notifies the user via their device. The user can receive and check the summary using their device (e.g., a smartphone app) and quickly grasp important information about the meeting.

[1043] Specific examples

[1044] For example, consider a company's project progress meeting where the project manager (PM) cannot physically attend. In this case, the PM can schedule the meeting through a dedicated web interface. At the scheduled time, the device (such as a conference tablet or PC) automatically records audio and video and sends the data to the server.

[1045] The server converts voice data into text data in real time, generates appropriate comments as needed, and plays them as synthesized speech. The server recognizes emotions using an emotion engine and reflects emotional trends in comments and summaries. After the meeting ends, a summary of about five minutes is generated and sent to the PM via the terminal.

[1046] Prompt Sentence Examples

[1047] "For corporate project progress meetings, please automatically record audio and video and convert the meeting content into text in real time. Also, please generate appropriate remarks based on the emotions of the meeting participants and output them as synthesized voices or avatar images as needed. After the meeting, please extract the main points, generate a summary, and notify the user, reflecting the emotional trends."

[1048] In this way, the system of the present invention realizes more efficient meetings and more accurate information management, and by taking the user's emotions into consideration, it enables more human-like responses and significantly improves business efficiency.

[1049] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1050] Step 1:

[1051] A user schedules a meeting. Using a dedicated web interface, the user enters the date and time of the meeting, the list of participants, and the agenda, and then clicks the "Submit" button. The information entered is sent to the server, which stores the received information in a database (e.g., MySQL) and prepares the meeting.

[1052] Step 2:

[1053] The meeting begins. At the set date and time, the device (meeting tablet or PC) automatically starts recording audio and video. The device sends the recorded audio and video data to the server in real time. The server saves this data in storage (e.g., AWS S3).

[1054] Step 3:

[1055] The server converts the voice data into text data. The server converts the received voice data into text data using the Google Cloud Speech-to-Text API. The text data converted from the input voice data is stored in the database.

[1056] Step 4:

[1057] The server analyzes the text data. The server uses natural language processing techniques (e.g., SpaCy, BERT) to analyze the converted text data and extract key discussion points and conclusions based on the context and importance of the conversation. The extracted information is then stored in a database.

[1058] Step 5:

[1059] The server recognizes emotions. From the recorded audio and video, the server uses an emotion engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotions. The analyzed emotion data is stored in a database.

[1060] Step 6:

[1061] The server generates appropriate utterances. Depending on the progress of the meeting, the server retrieves relevant information from the database and generates new utterances using a generative AI model (e.g., GPT-3). These utterances are adjusted based on the recognized emotion data.

[1062] Step 7:

[1063] The server converts the speech into speech. The server then converts the generated speech into voice data using synthetic speech technology (e.g., Amazon Polly). The generated voice data is sent to the device in real time, and the device plays the audio in the conference.

[1064] Step 8:

[1065] The server generates video as needed. The server uses video generation AI (e.g., DeepFake technology, FaceRig) to generate video that appears to be the user's avatar speaking. The generated video is sent to the device, where it is integrated into the conference video feed.

[1066] Step 9:

[1067] The server summarizes the key points of the meeting. After the meeting ends, the server reanalyzes the text data of the entire meeting and extracts the key points. The extracted information is compiled into a summary, which also reflects emotional data. The generated summary is stored in a database.

[1068] Step 10:

[1069] The server delivers the summary. The server then sends the generated summary to the device and notifies the user. The user can receive and check the summary using their device (e.g., a smartphone app) and quickly grasp important information about the meeting.

[1070] Through this procedure, the system according to the present invention can improve the efficiency of meetings and provide a more human-like response that takes into account the user's emotions.

[1071] (Application example 2)

[1072] 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."

[1073] In conventional self-driving vehicles and conference systems, it is difficult to drive or conduct a conference while taking into account the emotions of passengers and participants, and there is a need to improve the efficiency, comfort, and quality of communication in self-driving vehicles and remote conferences.In addition, it is difficult to recognize emotions and provide feedback in real time in self-driving vehicles and remote conferences, so there is a need for a method to improve the efficiency of information transmission while reducing stress for users.

[1074] 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.

[1075] In this invention, the server includes means for recording and filming a meeting or driving situation, means for converting the recorded voice data into text data, means for extracting the main points of the meeting or driving from the converted text data, means for providing summarized information to the user, means for generating appropriate guidance or remarks, means for converting the generated guidance or remarks into voice, means for generating an image of the user as needed, means for recognizing emotions using the voice and video, and means for adjusting the guidance or remarks based on the recognized emotions. This allows the server to recognize the user's emotions in real time and provide appropriate feedback, enabling more efficient and comfortable driving and meeting progress.

[1076] "Means for recording and filming meetings or driving situations" refers to devices or software that capture audio and video during meetings or while driving and record them as data.

[1077] "Means for converting recorded voice data into text data" refers to voice recognition technology or software for converting voice files into text information.

[1078] "Means for extracting meeting or driving key points from converted text data" refers to algorithms or methods that analyze text data and identify and extract important points or key information.

[1079] The "means for providing summarized information to the user" refers to a device or software that summarizes the extracted key points and conveys that information to the user.

[1080] The "means for generating appropriate guidance or statements" refers to a system that references an algorithm or database that automatically creates appropriate responses or guidance according to the situation.

[1081] The "means for converting the generated guidance or speech into speech" refers to technology or software that converts text information into speech using speech synthesis technology and provides it to the user audibly.

[1082] "Means for generating an image of the user on demand" refers to devices or software that create an image or video of the user using a digital avatar or pre-recorded video data.

[1083] "Audio and visual emotion recognition" refers to technologies and algorithms that use voice tone and facial expression analysis to identify a user's emotional state.

[1084] "Means for tailoring prompts and / or statements based on recognized emotions" means a system or algorithm that alters the content or tone of prompts and / or statements created or delivered based on the user's emotional state.

[1085] The system of this invention is primarily used in self-driving vehicles and remote conferences, and includes a series of functions for recording audio and video, converting audio data into text, extracting key points from meetings and driving, recognizing emotions, and generating and adjusting guidance and utterances.

[1086] 1. Setting up and starting a meeting or drive

[1087] First, the user sets up a meeting or a drive using a dedicated interface. They input the date and time, participant list, agenda, and other setting information, and send it to the server. The server stores the received information in a database and prepares for the meeting or drive.

[1088] 2. Audio and video recording of meetings or driving

[1089] When a meeting or drive starts, the device starts recording audio and video, and the recorded data is sent to the server in real time.

[1090] 3. Speech Recognition and Text Conversion

[1091] The server converts the received voice data into text using highly accurate speech recognition technology, using voice recognition software such as Google Cloud Speech-to-Text API.

[1092] 4. Analysis of meeting or driving content and extraction of key points

[1093] The server analyzes the converted text data and uses natural language processing technology to extract important discussion points and driving principles, using an AI model for the analysis.

[1094] 5. Emotion recognition

[1095] The server uses an emotion engine to recognize the user's emotions from the recorded voice and video, using technologies such as DeepFace.

[1096] 6. Guidance or speech production and emotion regulation

[1097] The server then generates appropriate announcements or utterances from a database, adjusting the utterances based on the recognized emotion data. For example, if a passenger is nervous, the server generates calm and reassuring announcements.

[1098] 7. Voice conversion and speech

[1099] The server converts the generated guidance or speech into voice data using synthetic voice technology and transmits the voice data to the terminal, which then provides the synthesized voice data received from the server to the user in real time.

[1100] 8. Image Generation

[1101] When needed, the server uses video generation AI to generate a video that appears to be the user's avatar speaking, which is then sent to the device, where it is integrated into the video feed for meetings or into the display while driving.

[1102] 9. Summary generation and emotional reflection of meetings or driving

[1103] After the meeting or drive, the server reanalyzes the entire text data, extracts key points, and generates a summary that also incorporates the recognized emotion data, allowing the server to grasp the intensity of the discussion or the level of tension while driving.

[1104] 10. Summary Distribution

[1105] The server stores the generated summary in a database and notifies the user via the device, allowing the user to receive and check the summary and quickly grasp important information.

[1106] Specific examples

[1107] For example, consider a user riding in an autonomous vehicle. The device automatically records the situation inside the vehicle, and the server analyzes the data in real time. The server detects the passenger's emotional state and generates calm and reassuring responses to questions and directions while driving, which are then played as synthesized voice. An example of a prompt could be, "Please analyze the data recorded and filmed inside the autonomous vehicle and generate a summary of the passenger's emotions and the journey." In this way, the system of the present invention recognizes the user's emotions in real time and provides appropriate feedback, resulting in a comfortable and efficient driving experience.

[1108] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1109] Step 1:

[1110] The user sets up a meeting or trip using a dedicated interface. The input is the date and time, participant list, and agenda, and other setting information is sent to the server. The server stores the received information in a database and prepares the meeting or trip. The output is a notification that the preparation is complete.

[1111] Step 2:

[1112] When a meeting or a drive starts, the device automatically starts recording audio and video. The input is audio and video data recorded in real time. The device sends this data to the server in real time. The output is audio and video data.

[1113] Step 3:

[1114] The server converts the received voice data into text data using highly accurate voice recognition technology. The input is voice data, and speech recognition software (e.g., Google Cloud Speech-to-Text API) is used. The output is text data.

[1115] Step 4:

[1116] The server analyzes the text data and extracts key points using natural language processing technology. The input is the converted text data, and a natural language processing algorithm (such as a generative AI model) is used. The output is the extracted key points and the text data.

[1117] Step 5:

[1118] The server recognizes emotions from recorded audio and video. The input is audio and video data, and an emotion recognition engine (e.g., DeepFace) is used. The output is the recognized emotion data.

[1119] Step 6:

[1120] The server generates appropriate guidance or utterances from the database as needed, adjusting the content and tone based on the recognized emotional data. The input is information and emotional data referenced from the database, and generation technology using an AI model is used. The output is the adjusted text data of the utterances.

[1121] Step 7:

[1122] The server converts the generated speech into voice data using synthetic speech technology and sends it to the terminal. The input is the adjusted text data of the speech, and synthetic speech software is used. The output is synthesized voice data. The terminal plays the received synthesized voice data in real time.

[1123] Step 8:

[1124] If necessary, the server uses video generation AI to generate a video that appears to show the user's avatar speaking. The input is the text data of the speech, which the video generation AI uses. The output is the generated video data. The device then integrates this video into the video feed.

[1125] Step 9:

[1126] After the meeting or drive ends, the server re-analyzes the entire text data, extracts key points, and generates a summary. The input is the entire text data of the meeting or drive, and natural language processing technology is used. The output is the summarized data.

[1127] Step 10:

[1128] The server stores the generated summary in a database and notifies the user via the terminal. The user can receive and check the summary using the terminal, and quickly grasp important information. The input is the generated summary data, and the output is a notification to the user.

[1129] 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.

[1130] 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.

[1131] 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.

[1132] [Fourth embodiment]

[1133] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1134] 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.

[1135] 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).

[1136] 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.

[1137] 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.

[1138] 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).

[1139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1140] 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.

[1141] 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.

[1142] 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.

[1143] 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.

[1144] 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.

[1145] 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."

[1146] The system according to the present invention is provided to improve the efficiency of meetings. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, detailed embodiments of the present invention will be described.

[1147] 1. Setting up and starting a meeting

[1148] First, the user sets up a meeting using a dedicated interface, inputting the date and time and a list of participants, and then sends this information to the server, which then stores the received information in a database and prepares the meeting.

[1149] 2. Audio and video recording of meetings

[1150] When the meeting starts, the device automatically starts recording audio and video, electronically recording the entire meeting. The device transmits audio and video data to the server in real time.

[1151] 3. Speech Recognition and Text Conversion

[1152] The server converts the received voice data into text data using highly accurate speech recognition technology, which then serves as the basis for understanding the content of the meeting.

[1153] 4. Analysis of meeting content and key points extraction

[1154] The server analyzes the converted text data and extracts key discussion points and conclusions, using natural language processing technology to extract key points based on the context and importance of the conversation.

[1155] 5. Speech generation and speech conversion

[1156] During the conference, the server references the database and generates appropriate utterances as needed. The generated utterances are converted into voice data using synthetic speech technology and sent to the conference terminals as utterances.

[1157] 6. Image Generation

[1158] If necessary, the server uses video generation AI to generate a video that appears to show the user's avatar speaking, which the device adds to the meeting video feed.

[1159] 7. Conference Summary Generation and Distribution

[1160] After the meeting ends, the server re-analyzes the text data from the entire meeting to extract key points. The extracted information is summarized and provided in a format that allows users to quickly understand the content. The summary is then delivered to the user via their device, allowing them to quickly understand the important information from the meeting.

[1161] Specific examples

[1162] For example, in a company's project progress meeting, the user (project manager) may not be able to physically attend. In this case, the device automatically records and records the meeting, and the server converts the meeting content into text in real time. In response to questions about the project's progress, the server retrieves relevant information from a database, generates the necessary utterances, and plays them as synthesized voice. After the meeting, the server summarizes the entire meeting content in a five-minute summary and distributes it to the user via the device. The user can review this summary and immediately grasp the key points of the meeting.

[1163] In this way, the system according to the present invention can improve the efficiency of meetings and the accuracy of information management, thereby significantly improving the work efficiency of users.

[1164] The processing flow will be explained below.

[1165] Step 1:

[1166] The user inputs the date and time of the meeting, the participant list, and the agenda using a dedicated interface, and sends the configuration information to the server.

[1167] Step 2:

[1168] The server stores the received configuration information in a database and prepares the meeting, including the meeting date and time, participant list, and agenda.

[1169] Step 3:

[1170] When the meeting starts, the device automatically starts recording audio and video, creating an electronic record of the entire meeting.

[1171] Step 4:

[1172] The device streams the recorded audio data to the server in real time, and the video data is also sent to the server.

[1173] Step 5:

[1174] The server converts the received voice data into text data using highly accurate voice recognition technology, and the converted text data is stored in a database.

[1175] Step 6:

[1176] The server analyzes the converted text data using natural language processing technology and extracts key discussion points and conclusions based on the context and importance of the conversation.

[1177] Step 7:

[1178] During the conference, the server refers to relevant information from the database to generate appropriate utterances as needed, and generates utterances based on the referred information.

[1179] Step 8:

[1180] The server converts the generated speech into voice data using voice synthesis technology and transmits the voice data to the terminal.

[1181] Step 9:

[1182] The device receives synthesized speech data from the server and sends it to the conference in real time, allowing the server to speak on behalf of the user.

[1183] Step 10:

[1184] If necessary, the server uses video generation AI to generate a video that makes it appear as if the user's avatar is speaking, and the generated video is sent to the device.

[1185] Step 11:

[1186] The device integrates the video data it receives from the server into the conference video feed.

[1187] Step 12:

[1188] After the meeting ends, the server reanalyzes the text data from the entire meeting and extracts key points from the meeting, which are then compiled into a summary.

[1189] Step 13:

[1190] The server stores the generated summary in a database and notifies the user through the terminal.

[1191] Step 14:

[1192] Users can use their devices to receive and check summaries from the server, quickly grasping important information about the meeting.

[1193] Example 1

[1194] 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."

[1195] With conventional conference systems, meetings are recorded and filmed separately, which means that reviewing the content later takes a lot of time and effort. Furthermore, even if you cannot physically attend the meeting, it is inevitable that you will miss real-time information sharing and important comments. To efficiently resolve these issues, a system that can quickly and accurately grasp the content of the entire meeting is needed.

[1196] 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.

[1197] In this invention, the server includes: means for a user to set up a conference; means for the server to store received conference information in a database; means for a terminal to start recording and filming the conference; means for the terminal to send recorded audio and video data to the server; means for the server to convert audio data into text data; means for the server to analyze the converted text data and extract key points; means for the server to generate necessary comments and convert them into audio data; means for generating video of the user as needed; and means for generating a summary of the conference content and providing it to the user. This automates the recording and filming of conferences, audio-to-text conversion, extraction of key points, generation of comments, generation of video, and distribution of summaries, thereby enabling improved efficiency of conferences and accuracy of information management.

[1198] "User" refers to a person who uses the system to set up and confirm meetings.

[1199] "Server" refers to a computer device that receives, stores, processes, distributes, etc., conference information.

[1200] A "database" refers to an information management system that organizes and stores data related to meetings and makes it possible to retrieve it as needed.

[1201] "Terminal" refers to a device that records and films conferences and transmits data to a server.

[1202] "Means for converting voice data into text data" refers to technology or devices that convert voices during a meeting into text information.

[1203] "Analysis" refers to the process of examining the converted text data and extracting important information.

[1204] "Key points" refer to the key discussion points and conclusions of a meeting.

[1205] "Means for generating statements" refers to a function that automatically creates statements required during a meeting.

[1206] "Means for converting into voice data" refers to technology that converts the created utterances into voice.

[1207] "Means for generating video" refers to the technology that creates and adds video of users to the conference video feed.

[1208] The "means for generating a summary" refers to a function that provides a user with a concise summary of the contents of the meeting.

[1209] "System" refers to a comprehensive mechanism in which the above technologies and devices work together to improve meeting efficiency and information management.

[1210] The system of this invention is designed for the purpose of improving the efficiency of meetings and information management. In this system, the user sets up a meeting, the server receives the information, stores it in a database, and performs recording and video recording during the meeting, speech recognition, key point extraction, speech generation, video generation, and summary distribution.

[1211] First, the user sets up a conference using a dedicated interface. This interface is provided as a web application, and the user enters the date and time of the conference and the list of participants. The entered information is sent to the server, which receives it and stores it in a database. The stored information is used for subsequent processing.

[1212] When a meeting starts, the device automatically starts recording. The recorded data is sent to the server in real time. The server then converts the received audio data into text using speech recognition technology. This process uses highly accurate speech recognition technology (e.g., Google Cloud Speech-to-Text).

[1213] The converted text data is then analyzed by the server to extract key points and conclusions. The server uses natural language processing techniques (e.g., NLTK, spaCy) to analyze the text data and extract key points. This extracted information is stored in a database and notified to the user as needed.

[1214] When a necessary statement arises during a meeting, the server references relevant information from the database and generates the appropriate statement. The statement is created using a generative AI model (e.g., GPT-3) and converted into voice data using synthetic speech technology (e.g., Amazon Polly). The generated voice is sent to the device and played back during the meeting.

[1215] Additionally, the server uses video generation AI (e.g., Deepfake technology) to generate video of the user as needed, making it appear as if the user's avatar is speaking. This video is then added to the meeting video feed by the device.

[1216] After the meeting ends, the server re-analyzes the text data from the entire meeting to extract key points from the meeting. These key points are summarized and provided in a format that allows users to quickly understand the content. This summary is then delivered to the user via their device, allowing them to quickly understand the important information from the meeting.

[1217] Specific examples

[1218] For example, there may be a case where a company's project manager is unable to physically attend a project progress meeting. In this case, the device automatically records and records the meeting, and the server converts the meeting content into text in real time. Furthermore, in response to questions about the project's progress, the server retrieves relevant information from a database, generates the necessary utterances, and plays them as synthesized speech. After the meeting, the server summarizes the entire meeting content in a five-minute summary and delivers it to the user via the device. The user can review this summary and immediately grasp the key points of the meeting.

[1219] Prompt Sentence Examples

[1220] "Please describe your system for tracking project progress during project meetings. Please provide detailed information, including specific hardware and software examples."

[1221] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1222] Step 1:

[1223] The user enters the meeting information using a dedicated interface.

[1224] Input: The user inputs information such as the meeting date and time, participant list, and location.

[1225] Data processing: The web application formats the input data and converts it into a format that can be sent to the server.

[1226] Specific behavior: The user clicks the "Add event" button on the meeting setting screen and enters the meeting details.

[1227] Step 2:

[1228] The server stores the received conference information in a database.

[1229] Input: Conference information received from a dedicated interface.

[1230] Data processing: Converting data into a format suitable for saving in a database.

[1231] Specific operation: The server receives the meeting date and time and the participant list, and stores them in a database using an SQL query.

[1232] Step 3:

[1233] The device starts recording the meeting.

[1234] Input: Instructions from the server based on the meeting start time.

[1235] Data processing: Audio and video data capture.

[1236] Specific operation: The server sends an API request at the specified time, and the device starts recording.

[1237] Step 4:

[1238] The terminal transmits the recorded audio and video data to the server.

[1239] Input: Recorded audio and video data.

[1240] Data processing: Encode for real-time streaming.

[1241] Specific operation: The terminal encodes the video in H.264 format and sends it to the server via the RTMP protocol.

[1242] Step 5:

[1243] The server converts the voice data into text data using voice recognition technology.

[1244] Input: Audio data.

[1245] Data processing: Converting audio data into text format.

[1246] Specific operation: The server sends audio data to the Google Cloud Speech-to-Text API and obtains text data such as "The purpose of today's meeting is as follows."

[1247] Step 6:

[1248] The server analyzes the converted text data and extracts the main points of the meeting.

[1249] Input: The converted text data.

[1250] Data processing: Use natural language processing to identify key points.

[1251] What it does: The server uses NLTK and spaCy to analyze the text data and extract key points such as "Delivery date changed" and "Budget added."

[1252] Step 7:

[1253] The server generates the necessary utterances and converts them into audio data.

[1254] Input: Relevant information retrieved from the database.

[1255] Data processing: Generate text using a generative AI model and convert it into audio data using synthetic speech technology.

[1256] Specific operation: The server uses GPT-3 to generate a statement such as "Progress on this project is going well," and then converts it into voice data using Amazon Polly.

[1257] Step 8:

[1258] Generates an image of the user as needed.

[1259] Input: User avatar information and generated utterance.

[1260] Data processing: Create videos using video generation AI.

[1261] How it works: The server uses Deepfake technology to generate video of a user saying something like, "I'll talk more about this in the next meeting."

[1262] Step 9:

[1263] A summary of the meeting content is generated and provided to the user.

[1264] Input: Parsed meeting text data.

[1265] Data processing: Using summary generation technology, key points are extracted that can be understood in a short amount of time.

[1266] How it works: The server uses GPT-3 to automatically generate a summary of the entire meeting and deliver it to the user via email or a web application.

[1267] (Application example 1)

[1268] 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."

[1269] While advanced autonomous vehicles are becoming more commonplace, there is still a lack of ways for drivers to participate in meetings and obtain and confirm important information in real time while driving. In particular, it is difficult to efficiently understand the content of meetings while driving and respond appropriately in a timely manner. Therefore, there is a need for a system that allows drivers to participate in meetings safely and effectively.

[1270] 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.

[1271] In this invention, the server includes means for recording and videotaping a conference, means for converting recorded audio data into text data, means for extracting key points of the conference from the converted text data, means for summarizing the extracted key points, means for providing the summarized information to a user, means for generating appropriate utterances during the conference, means for converting the generated utterances into audio, means for generating an image of the user as needed, means for capturing audio and video in the vehicle, means for performing real-time speech recognition and displaying the text on an in-vehicle display or a head-mounted display, means for searching a database and responding with synthesized speech in response to audio input, and means for audio notifying the driver of important discussions. This allows the driver to safely and efficiently participate in conferences while driving and obtain and confirm information in real time.

[1272] "Means for recording and filming a meeting" means a device that uses a digital device to record audio and video during a meeting.

[1273] "Means for converting recorded voice data into text data" refers to a system equipped with technology that analyzes voice data and converts it into text information.

[1274] "Means for extracting the main points of a meeting from the converted text data" refers to a technology for analyzing text data and identifying important points of discussion.

[1275] "Means for summarizing extracted key points" refers to a technique for summarizing extracted important arguments in a short and concise manner.

[1276] "Means for providing summarized information to a user" means a system that presents the summarized information in a form accessible to a user.

[1277] "Means for generating appropriate utterances during a meeting" refers to a technology that automatically generates appropriate utterances in accordance with the progress of the meeting.

[1278] "Means for converting generated utterances into speech" refers to a system that converts generated text utterances into speech using synthetic speech.

[1279] "Means for generating video of the user as needed" refers to technology that uses AI technology to generate a user's avatar or video and add it to the video feed.

[1280] "In-vehicle audio and video capture means" means a device that uses a microphone and a camera to record audio and video within a vehicle.

[1281] "Means for real-time speech recognition and displaying text on an in-vehicle display or head-mounted display" refers to technology that converts speech collected inside a vehicle into text in real time and displays it so that the driver can visually confirm it.

[1282] "Means for searching a database in response to a voice input and responding with a synthesized voice" refers to a technology that receives a voice command, references a database based on the content of the command, and provides a response in a synthesized voice.

[1283] "Means for providing audio notification to the driver regarding important discussion points" refers to technology that provides audio notification of important discussion points in a meeting to the driver while driving.

[1284] To implement this invention, it is necessary to link the server with the terminal inside the autonomous vehicle and build a system that incorporates technologies such as voice recognition, natural language processing, and synthetic voice generation.

[1285] When a meeting begins, the vehicle's microphones and cameras automatically capture audio and video. This data is sent to a server in real time. The server receives the audio data and converts it into text using highly accurate speech recognition software (Google Cloud Speech-to-Text). The converted text data is then analyzed using a natural language processing library (spaCy or NTLK) to extract key discussion points from the meeting.

[1286] The extracted discussion points are then concisely summarized using a summary generation algorithm. The summarized information is displayed on an in-car display or a head-mounted display (HMD). Important discussion points are also notified to the driver via audio.

[1287] If the driver asks a question during the meeting, their voice input is sent back to the server, where it is searched against the database. A synthetic voice technology (Amazon Polly) generates a response and provides it to the driver via voice.

[1288] Additionally, if necessary, the server uses video generation AI (TensorFlow-DCGAN) to generate video that appears as if the user's avatar is speaking and adds it to the meeting video feed.

[1289] As a concrete example, consider a situation where a driver is participating in a project status meeting while traveling in an autonomous vehicle. In this case, the in-car microphone and camera capture the meeting, and the voice data is converted into text in real time. The driver checks the meeting contents on a display or HMD and receives voice notifications about important points. If a question arises, the driver can input it by voice, and the answer will be instantly provided by synthesized voice.

[1290] Examples of prompts using generative AI models include:

[1291] "Please convert the audio data during the meeting into text in real time. The audio data is recorded in Japanese. Please separate each utterance into paragraphs and convert them into text."

[1292] In this way, the present invention allows drivers to safely and efficiently participate in meetings and quickly obtain the information they need.

[1293] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1294] Step 1:

[1295] At the start of the meeting, the device activates the microphone and camera in the vehicle and starts capturing audio and video. The input is the audio and video from inside the vehicle, which is converted into a digital format and sent to the server in real time. The output is the captured audio and video data.

[1296] Step 2:

[1297] The server converts the received voice data into text data using Google Cloud Speech-to-Text. It receives captured voice data as input, converts it into text using real-time speech recognition processing, and obtains text data as output.

[1298] Step 3:

[1299] The server analyzes the converted text data using a natural language processing library (spaCy or NTLK) to identify key discussion points of the meeting. As input, it receives text data, analyzes it, and extracts key discussion points. As output, it obtains the extracted discussion points.

[1300] Step 4:

[1301] The server then uses a summary generation algorithm to concisely summarize the extracted discussion points. As input, it receives the discussion points, processes them, and converts them into short text. As output, it obtains the summarized discussion points.

[1302] Step 5:

[1303] The terminal displays the summarized information on an in-car display or a head-mounted display. As input, it receives summarized discussion points from the server and displays them on the display. As output, it provides a visual representation for the driver.

[1304] Step 6:

[1305] The server provides voice notifications to the driver about important discussion points during the meeting. As input, it receives important discussion points and converts them into synthesized speech. As output, it obtains the information to be voiced to the driver.

[1306] Step 7:

[1307] When the driver inputs a question by voice, the terminal captures the voice input and sends it to the server. The input is the driver's voice question. This is sent to the server. The output is the voice input data.

[1308] Step 8:

[1309] The server searches the database based on the received voice input data and generates an appropriate answer. The input is the voice question data and a related database, which are then analyzed to generate an answer. The output is the generated text answer.

[1310] Step 9:

[1311] The server converts the generated text response into synthetic speech using Amazon Polly and sends the speech data to the device. The generated text response is used as input for the synthetic speech processing. This is then converted into speech data and sent to the device. The speech data is obtained as output.

[1312] Step 10:

[1313] The terminal receives the voice data from the server and plays it back to the driver. As input, it receives voice data from the server. It plays it back through the speaker. As output, it provides the driver with a voice response.

[1314] Step 11:

[1315] The server uses video generation AI as needed to generate a user avatar. The input is the content of what was said during the meeting and the user's video data. This is processed to generate the user's avatar video. The generated avatar video is obtained as the output.

[1316] Step 12:

[1317] The device adds the generated avatar video to the conference video feed. As input, it receives avatar video data from the server, integrates it into the video feed, and as output, the conference video includes the user avatar.

[1318] 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.

[1319] The system of the present invention is combined with an emotion engine to improve the efficiency of meetings. In addition to the basic functions of recording audio and video, converting the contents of the meeting into text data, and generating a summary, the system also has the function of recognizing the user's emotions and optimizing the content of comments and summaries based on those emotions. Detailed embodiments of the present invention will be described below.

[1320] 1. Setting up and starting a meeting

[1321] First, a user sets up a meeting using a dedicated interface, inputting the date and time, participant list, and agenda, and then sends the setup information to the server, which stores the received information in a database and prepares the meeting.

[1322] 2. Audio and video recording of meetings

[1323] When the meeting starts, the device automatically starts recording audio and video, electronically recording the entire meeting. The device transmits audio and video data to the server in real time.

[1324] 3. Speech Recognition and Text Conversion

[1325] The server converts the received voice data into text data using highly accurate voice recognition technology, and the converted text data is stored in a database.

[1326] 4. Analysis of meeting content and key points extraction

[1327] The server analyzes the converted text data using natural language processing technology and extracts key discussion points and conclusions based on the context and importance of the conversation.

[1328] 5. Emotion recognition

[1329] From the recorded voice and video, the server uses an emotion engine to recognize the user's emotions, which includes analyzing voice tone, facial expressions, and gestures.

[1330] 6. Speech production and emotion regulation

[1331] During the meeting, the server references the database and generates appropriate utterances as needed. The generated utterances are adjusted based on the recognized emotional data. For example, if a meeting participant is feeling stressed, the server generates calm and reassuring utterances.

[1332] 7. Voice conversion and speech

[1333] The server converts the generated speech into voice data using synthetic speech technology and sends the voice data to the terminal. The terminal then broadcasts the synthesized voice data received from the server to the conference in real time.

[1334] 8. Image Generation

[1335] If necessary, the server uses video generation AI to generate a video that appears to show the user's avatar speaking, which is then sent to the device, where it is integrated into the meeting video feed.

[1336] 9. Meeting Summary Generation and Emotion Reflection

[1337] After the meeting ends, the server reanalyzes the text data from the entire meeting to extract key points. The extracted information is compiled into a summary that also incorporates the recognized emotion data. For example, points that were the subject of intense discussion are noted in the summary.

[1338] 10. Summary Distribution

[1339] The server stores the generated summary in a database and notifies the user via their device, allowing the user to receive and check the summary and quickly grasp important information about the meeting.

[1340] Specific examples

[1341] For example, consider a situation where a project manager is unable to physically attend a project progress meeting at a company. In this case, the device automatically records and records the meeting, and the server converts the meeting content into text in real time. In response to questions about the project's progress, the server retrieves relevant information from a database, generates the necessary comments, and plays them as synthesized speech. Furthermore, the tone and content of the comments are adjusted based on the emotions of the participants recognized by an emotion engine. After the meeting, the server summarizes the entire meeting content in a five-minute summary and distributes it to the user via the device. This summary also reflects the emotional trends during the meeting, allowing the user to understand the level of enthusiasm and tension in the meeting.

[1342] In this way, the system of the present invention realizes more efficient meetings and more accurate information management, and by taking the user's emotions into consideration, it enables more human-like responses, thereby significantly improving business efficiency.

[1343] The processing flow will be explained below.

[1344] Step 1:

[1345] The user inputs the date and time of the meeting, the participant list, and the agenda using a dedicated interface, and sends the configuration information to the server.

[1346] Step 2:

[1347] The server stores the received configuration information in a database and prepares the meeting, including the meeting date and time, participant list, and agenda.

[1348] Step 3:

[1349] When the meeting starts, the device automatically starts recording audio and video, creating an electronic record of the entire meeting.

[1350] Step 4:

[1351] The device streams the recorded audio data to the server in real time, and the video data is also sent to the server.

[1352] Step 5:

[1353] The server converts the received voice data into text data using highly accurate voice recognition technology, and the converted text data is stored in a database.

[1354] Step 6:

[1355] The server analyzes the converted text data using natural language processing technology and extracts key discussion points and conclusions based on the context and importance of the conversation.

[1356] Step 7:

[1357] The server uses an emotion engine to recognize the user's emotions from the recorded audio and video, which includes analyzing the tone of voice, facial expressions, and gestures.

[1358] Step 8:

[1359] During the conference, the server references relevant information from the database to generate appropriate utterances as needed, which are adjusted based on the recognized emotion data.

[1360] Step 9:

[1361] The server converts the generated speech into voice data using voice synthesis technology and transmits the voice data to the terminal.

[1362] Step 10:

[1363] The device receives synthesized speech data from the server and sends it to the conference in real time, allowing the server to speak on behalf of the user.

[1364] Step 11:

[1365] If necessary, the server uses video generation AI to generate a video that makes it appear as if the user's avatar is speaking, and the generated video is sent to the device.

[1366] Step 12:

[1367] The device integrates the video data it receives from the server into the conference video feed.

[1368] Step 13:

[1369] After the meeting ends, the server reanalyzes the text data from the entire meeting and extracts key points from the meeting, which are then compiled into a summary.

[1370] Step 14:

[1371] The server stores the generated summary in a database and notifies the user through the terminal.

[1372] Step 15:

[1373] Users can use their devices to receive and check summaries from the server, quickly grasp important meeting information, and also refer to recognized emotion data.

[1374] Example 2

[1375] 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."

[1376] Conventional conference systems simply record and record meetings, convert the audio data into text data, and create summaries. Such systems have difficulty identifying changes in emotions and important discussions during meetings, resulting in insufficient efficiency and reduced user stress. Furthermore, providing appropriate feedback to remote participants is difficult, often hindering the smooth progress of meetings.

[1377] The identification process by the identification 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 recording and videotaping the conference, means for converting the recorded voice data into text data, means for analyzing the converted text data and extracting the main points of the conference, means for summarizing the extracted main points, means for providing the summarized information to the user, means for recognizing the user's emotions, means for generating appropriate utterances during the conference, means for adjusting the generated utterances based on the recognized emotion data, means for converting the generated utterances into audio, and means for generating an image of the user as needed. This makes it possible to identify important discussions during the conference and take emotion data into consideration, thereby realizing smoother and more efficient conference management.

[1378] "Audio and video recording" means the electronic recording of audio and video of a meeting.

[1379] "Audio Data" means a digital recording of sounds made during a conference.

[1380] "Text data" is character string information obtained by analyzing voice data.

[1381] "Tasks" are discussion points or conclusions identified as important during a meeting.

[1382] A "summary" is a document that briefly summarizes the contents of the entire meeting.

[1383] A "user" is a person who uses the system to participate in a conference or check the contents of the conference.

[1384] "Emotions" refer to the changes in the user's mind and mood during the meeting.

[1385] "Speech" is the verbal exchange of information that takes place during a meeting.

[1386] "Convert to voice" refers to the process of converting character information such as text data into voice data.

[1387] "Video" refers to visual information during a meeting or digital video showing the actions of generated user avatars.

[1388] "Analysis" is the process of examining collected data to extract useful information.

[1389] "Recognition" is the ability to understand and grasp specific patterns and information from data.

[1390] "Adjustment" means modifying data or information to make it optimal depending on the conditions and circumstances.

[1391] This invention is a system for improving the efficiency of meetings, and in addition to the basic functions of recording audio and video, converting the contents of the meeting into text data and generating a summary, it also has the function of recognizing the user's emotions and optimizing the content of remarks and summaries based on those emotions. This section describes the specific processing procedures required to implement this system, as well as the hardware and software used.

[1392] Setting up and starting a meeting

[1393] First, a user sets up a meeting using a dedicated web interface, where the user inputs the meeting date and time, participant list, and agenda, and sends the configuration information to the server, which stores the information in a database (e.g., MySQL) and prepares the meeting.

[1394] Meeting recording and recording

[1395] When the scheduled meeting time arrives, the device (e.g., the conference tablet or PC) automatically starts recording audio and video. The recorded audio and video data is sent to the server in real time, and the server stores this data in storage (e.g., AWS S3).

[1396] Speech recognition and text conversion

[1397] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API, which is then stored in a database for subsequent analysis.

[1398] Analysis of meeting content and key points extraction

[1399] The server then analyzes the converted text data using natural language processing techniques (e.g., SpaCy, BERT). The analysis results in the extraction of key points of discussion and conclusions based on the context and importance of the conversation. This extracted information is stored in a database and serves as the basis for generating summaries.

[1400] emotion recognition

[1401] The server uses an emotion engine (e.g., Microsoft Azure Emotion API) to recognize the user's emotions from the recorded audio and video. Emotion recognition includes analyzing voice tone, facial expressions, and gestures. This allows the user's emotional trends during the meeting to be understood.

[1402] Speech production and emotion regulation

[1403] During the meeting, the server references appropriate utterances from the database as needed and generates new utterances using a generative AI model (e.g., GPT-3). The generated utterances are adjusted based on the recognized emotional data. For example, if a meeting participant is feeling stressed, the server generates calm and reassuring utterances.

[1404] Speech conversion and speech

[1405] The server converts the generated speech into voice data using synthetic speech technology (e.g., Amazon Polly) and sends the voice data to the terminal. The terminal then receives the synthesized voice data from the server and broadcasts it to the conference in real time.

[1406] Image Generation

[1407] If necessary, the server uses video generation AI (e.g., DeepFake technology, FaceRig) to generate a video that appears to show the user's avatar speaking, which is then sent to the device, where it is integrated into the meeting video feed.

[1408] Meeting summary generation and emotional reflection

[1409] After the meeting ends, the server reanalyzes the text data from the entire meeting and extracts key points from the meeting. The extracted information is compiled into a summary that also incorporates the recognized emotion data. For example, points that were the subject of intense discussion may be noted in the summary.

[1410] Summary Delivery

[1411] The server stores the generated summary in a database and notifies the user via their device. The user can receive and check the summary using their device (e.g., a smartphone app) and quickly grasp important information about the meeting.

[1412] Specific examples

[1413] For example, consider a company's project progress meeting where the project manager (PM) cannot physically attend. In this case, the PM can schedule the meeting through a dedicated web interface. At the scheduled time, the device (such as a conference tablet or PC) automatically records audio and video and sends the data to the server.

[1414] The server converts voice data into text data in real time, generates appropriate comments as needed, and plays them as synthesized speech. The server recognizes emotions using an emotion engine and reflects emotional trends in comments and summaries. After the meeting ends, a summary of about five minutes is generated and sent to the PM via the terminal.

[1415] Prompt Sentence Examples

[1416] "For corporate project progress meetings, please automatically record audio and video and convert the meeting content into text in real time. Also, please generate appropriate remarks based on the emotions of the meeting participants and output them as synthesized voices or avatar images as needed. After the meeting, please extract the main points, generate a summary, and notify the user, reflecting the emotional trends."

[1417] In this way, the system of the present invention realizes more efficient meetings and more accurate information management, and by taking the user's emotions into consideration, it enables more human-like responses and significantly improves business efficiency.

[1418] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1419] Step 1:

[1420] A user schedules a meeting. Using a dedicated web interface, the user enters the date and time of the meeting, the list of participants, and the agenda, and then clicks the "Submit" button. The information entered is sent to the server, which stores the received information in a database (e.g., MySQL) and prepares the meeting.

[1421] Step 2:

[1422] The meeting begins. At the set date and time, the device (meeting tablet or PC) automatically starts recording audio and video. The device sends the recorded audio and video data to the server in real time. The server saves this data in storage (e.g., AWS S3).

[1423] Step 3:

[1424] The server converts the voice data into text data. The server converts the received voice data into text data using the Google Cloud Speech-to-Text API. The text data converted from the input voice data is stored in the database.

[1425] Step 4:

[1426] The server analyzes the text data. The server uses natural language processing techniques (e.g., SpaCy, BERT) to analyze the converted text data and extract key discussion points and conclusions based on the context and importance of the conversation. The extracted information is then stored in a database.

[1427] Step 5:

[1428] The server recognizes emotions. From the recorded audio and video, the server uses an emotion engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotions. The analyzed emotion data is stored in a database.

[1429] Step 6:

[1430] The server generates appropriate utterances. Depending on the progress of the meeting, the server retrieves relevant information from the database and generates new utterances using a generative AI model (e.g., GPT-3). These utterances are adjusted based on the recognized emotion data.

[1431] Step 7:

[1432] The server converts the speech into speech. The server then converts the generated speech into voice data using synthetic speech technology (e.g., Amazon Polly). The generated voice data is sent to the device in real time, and the device plays the audio in the conference.

[1433] Step 8:

[1434] The server generates video as needed. The server uses video generation AI (e.g., DeepFake technology, FaceRig) to generate video that appears to be the user's avatar speaking. The generated video is sent to the device, where it is integrated into the conference video feed.

[1435] Step 9:

[1436] The server summarizes the key points of the meeting. After the meeting ends, the server reanalyzes the text data of the entire meeting and extracts the key points. The extracted information is compiled into a summary, which also reflects emotional data. The generated summary is stored in a database.

[1437] Step 10:

[1438] The server delivers the summary. The server then sends the generated summary to the device and notifies the user. The user can receive and check the summary using their device (e.g., a smartphone app) and quickly grasp important information about the meeting.

[1439] Through this procedure, the system according to the present invention can improve the efficiency of meetings and provide a more human-like response that takes into account the user's emotions.

[1440] (Application example 2)

[1441] 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."

[1442] In conventional self-driving vehicles and conference systems, it is difficult to drive or conduct a conference while taking into account the emotions of passengers and participants, and there is a need to improve the efficiency, comfort, and quality of communication in self-driving vehicles and remote conferences.In addition, it is difficult to recognize emotions and provide feedback in real time in self-driving vehicles and remote conferences, so there is a need for a method to improve the efficiency of information transmission while reducing stress for users.

[1443] 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.

[1444] In this invention, the server includes means for recording and filming a meeting or driving situation, means for converting the recorded voice data into text data, means for extracting the main points of the meeting or driving from the converted text data, means for providing summarized information to the user, means for generating appropriate guidance or remarks, means for converting the generated guidance or remarks into voice, means for generating an image of the user as needed, means for recognizing emotions using the voice and video, and means for adjusting the guidance or remarks based on the recognized emotions. This allows the server to recognize the user's emotions in real time and provide appropriate feedback, enabling more efficient and comfortable driving and meeting progress.

[1445] "Means for recording and filming meetings or driving situations" refers to devices or software that capture audio and video during meetings or while driving and record them as data.

[1446] "Means for converting recorded voice data into text data" refers to voice recognition technology or software for converting voice files into text information.

[1447] "Means for extracting meeting or driving key points from converted text data" refers to algorithms or methods that analyze text data and identify and extract important points or key information.

[1448] The "means for providing summarized information to the user" refers to a device or software that summarizes the extracted key points and conveys that information to the user.

[1449] The "means for generating appropriate guidance or statements" refers to a system that references an algorithm or database that automatically creates appropriate responses or guidance according to the situation.

[1450] The "means for converting the generated guidance or speech into speech" refers to technology or software that converts text information into speech using speech synthesis technology and provides it to the user audibly.

[1451] "Means for generating an image of the user on demand" refers to devices or software that create an image or video of the user using a digital avatar or pre-recorded video data.

[1452] "Audio and visual emotion recognition" refers to technologies and algorithms that use voice tone and facial expression analysis to identify a user's emotional state.

[1453] "Means for tailoring prompts and / or statements based on recognized emotions" means a system or algorithm that alters the content or tone of prompts and / or statements created or delivered based on the user's emotional state.

[1454] The system of this invention is primarily used in self-driving vehicles and remote conferences, and includes a series of functions for recording audio and video, converting audio data into text, extracting key points from meetings and driving, recognizing emotions, and generating and adjusting guidance and utterances.

[1455] 1. Setting up and starting a meeting or drive

[1456] First, the user sets up a meeting or a drive using a dedicated interface. They input the date and time, participant list, agenda, and other setting information, and send it to the server. The server stores the received information in a database and prepares for the meeting or drive.

[1457] 2. Audio and video recording of meetings or driving

[1458] When a meeting or drive starts, the device starts recording audio and video, and the recorded data is sent to the server in real time.

[1459] 3. Speech Recognition and Text Conversion

[1460] The server converts the received voice data into text using highly accurate speech recognition technology, using voice recognition software such as Google Cloud Speech-to-Text API.

[1461] 4. Analysis of meeting or driving content and extraction of key points

[1462] The server analyzes the converted text data and uses natural language processing technology to extract important discussion points and driving principles, using an AI model for the analysis.

[1463] 5. Emotion recognition

[1464] The server uses an emotion engine to recognize the user's emotions from the recorded voice and video, using technologies such as DeepFace.

[1465] 6. Guidance or speech production and emotion regulation

[1466] The server then generates appropriate announcements or utterances from a database, adjusting the utterances based on the recognized emotion data. For example, if a passenger is nervous, the server generates calm and reassuring announcements.

[1467] 7. Voice conversion and speech

[1468] The server converts the generated guidance or speech into voice data using synthetic voice technology and transmits the voice data to the terminal, which then provides the synthesized voice data received from the server to the user in real time.

[1469] 8. Image Generation

[1470] When needed, the server uses video generation AI to generate a video that appears to be the user's avatar speaking, which is then sent to the device, where it is integrated into the video feed for meetings or into the display while driving.

[1471] 9. Summary generation and emotional reflection of meetings or driving

[1472] After the meeting or drive, the server reanalyzes the entire text data, extracts key points, and generates a summary that also incorporates the recognized emotion data, allowing the server to grasp the intensity of the discussion or the level of tension while driving.

[1473] 10. Summary Distribution

[1474] The server stores the generated summary in a database and notifies the user via the device, allowing the user to receive and check the summary and quickly grasp important information.

[1475] Specific examples

[1476] For example, consider a user riding in an autonomous vehicle. The device automatically records the situation inside the vehicle, and the server analyzes the data in real time. The server detects the passenger's emotional state and generates calm and reassuring responses to questions and directions while driving, which are then played as synthesized voice. An example of a prompt could be, "Please analyze the data recorded and filmed inside the autonomous vehicle and generate a summary of the passenger's emotions and the journey." In this way, the system of the present invention recognizes the user's emotions in real time and provides appropriate feedback, resulting in a comfortable and efficient driving experience.

[1477] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1478] Step 1:

[1479] The user sets up a meeting or trip using a dedicated interface. The input is the date and time, participant list, and agenda, and other setting information is sent to the server. The server stores the received information in a database and prepares the meeting or trip. The output is a notification that the preparation is complete.

[1480] Step 2:

[1481] When a meeting or a drive starts, the device automatically starts recording audio and video. The input is audio and video data recorded in real time. The device sends this data to the server in real time. The output is audio and video data.

[1482] Step 3:

[1483] The server converts the received voice data into text data using highly accurate voice recognition technology. The input is voice data, and speech recognition software (e.g., Google Cloud Speech-to-Text API) is used. The output is text data.

[1484] Step 4:

[1485] The server analyzes the text data and extracts key points using natural language processing technology. The input is the converted text data, and a natural language processing algorithm (such as a generative AI model) is used. The output is the extracted key points and the text data.

[1486] Step 5:

[1487] The server recognizes emotions from recorded audio and video. The input is audio and video data, and an emotion recognition engine (e.g., DeepFace) is used. The output is the recognized emotion data.

[1488] Step 6:

[1489] The server generates appropriate guidance or utterances from the database as needed, adjusting the content and tone based on the recognized emotional data. The input is information and emotional data referenced from the database, and generation technology using an AI model is used. The output is the adjusted text data of the utterances.

[1490] Step 7:

[1491] The server converts the generated speech into voice data using synthetic speech technology and sends it to the terminal. The input is the adjusted text data of the speech, and synthetic speech software is used. The output is synthesized voice data. The terminal plays the received synthesized voice data in real time.

[1492] Step 8:

[1493] If necessary, the server uses video generation AI to generate a video that appears to show the user's avatar speaking. The input is the text data of the speech, which the video generation AI uses. The output is the generated video data. The device then integrates this video into the video feed.

[1494] Step 9:

[1495] After the meeting or drive ends, the server re-analyzes the entire text data, extracts key points, and generates a summary. The input is the entire text data of the meeting or drive, and natural language processing technology is used. The output is the summarized data.

[1496] Step 10:

[1497] The server stores the generated summary in a database and notifies the user via the terminal. The user can receive and check the summary using the terminal, and quickly grasp important information. The input is the generated summary data, and the output is a notification to the user.

[1498] 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.

[1499] 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.

[1500] 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.

[1501] 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.

[1502] 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.

[1503] 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.

[1504] 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).

[1505] 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.

[1506] 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."

[1507] 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.

[1508] 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).

[1509] 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.

[1510] 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.

[1511] 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.

[1512] 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.

[1513] 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.

[1514] 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.

[1515] 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.

[1516] 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.

[1517] 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.

[1518] 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.

[1519] The following is further disclosed regarding the above embodiment.

[1520] (Claim 1)

[1521] a means of recording and recording meetings;

[1522] A means for converting the recorded voice data into text data;

[1523] A means for extracting the main points of the meeting from the converted text data;

[1524] a means of summarizing the extracted key points;

[1525] means for providing summarized information to a user;

[1526] a means for generating appropriate utterances during a meeting;

[1527] means for converting the generated utterances into speech;

[1528] A means to generate an image of the user on demand

[1529] A system including:

[1530] (Claim 2)

[1531] 10. The system of claim 1, further comprising means for analyzing the converted text data and identifying important discussions of the meeting.

[1532] (Claim 3)

[1533] 10. The system of claim 1, further comprising means for generating utterances based on information referenced from a database.

[1534] "Example 1"

[1535] (Claim 1)

[1536] a means for a user to set up a conference;

[1537] means for storing the received conference information in a database;

[1538] a means for the terminal to start recording and recording the meeting;

[1539] a means for transmitting the recorded audio data and video data from the terminal to a server;

[1540] A means for the server to convert the voice data into text data;

[1541] A means for the server to analyze the converted text data and extract key points;

[1542] A means for the server to generate the necessary utterances and convert them into voice data;

[1543] means for generating an image of the user on demand;

[1544] A means to generate a summary of the meeting contents and provide it to the user

[1545] A system including:

[1546] (Claim 2)

[1547] 10. The system of claim 1, further comprising means for analyzing the converted text data and identifying important discussions of the meeting.

[1548] (Claim 3)

[1549] 10. The system of claim 1, further comprising means for generating utterances based on information referenced from a database.

[1550] "Application Example 1"

[1551] (Claim 1)

[1552] a means of recording and recording meetings;

[1553] A means for converting the recorded voice data into text data;

[1554] A means for extracting the main points of the meeting from the converted text data;

[1555] a means of summarizing the extracted key points;

[1556] means for providing summarized information to a user;

[1557] a means for generating appropriate utterances during a meeting;

[1558] means for converting the generated utterances into speech;

[1559] means for generating an image of the user on demand;

[1560] means for capturing audio and video within the vehicle;

[1561] means for real-time speech recognition and displaying text on an in-vehicle display or a head-mounted display;

[1562] means for searching a database in response to a voice input and responding with a synthesized voice;

[1563] A means of providing audio notifications to drivers about important discussions

[1564] A system including:

[1565] (Claim 2)

[1566] 10. The system of claim 1, further comprising means for analyzing the converted text data and identifying important discussions of the meeting.

[1567] (Claim 3)

[1568] 10. The system of claim 1, further comprising means for generating utterances based on information referenced from a database.

[1569] "Example 2: Combining Emotion Engines"

[1570] (Claim 1)

[1571] a means of recording and recording meetings;

[1572] A means for converting the recorded voice data into text data;

[1573] A means for analyzing the converted text data and extracting key points of the meeting;

[1574] a means of summarizing the extracted key points;

[1575] means for providing summarized information to a user;

[1576] means for recognizing a user's emotion;

[1577] a means for generating appropriate utterances during a meeting;

[1578] means for adjusting the generated utterances based on the recognized emotion data;

[1579] means for converting the generated utterances into speech;

[1580] A means to generate an image of the user on demand

[1581] A system including:

[1582] (Claim 2)

[1583] 10. The system of claim 1, further comprising means for analyzing the converted text data and identifying important discussions of the meeting.

[1584] (Claim 3)

[1585] 10. The system of claim 1, further comprising means for generating utterances based on information referenced from a database.

[1586] "Application example 2 when combining emotion engines"

[1587] (Claim 1)

[1588] A means for recording and recording a meeting or driving situation;

[1589] A means for converting the recorded voice data into text data;

[1590] A means for extracting key points of a meeting or driving from the converted text data;

[1591] a means of summarizing the extracted key points;

[1592] means for providing summarized information to a user;

[1593] means for generating appropriate prompts or utterances;

[1594] means for converting the generated guidance or utterance into speech;

[1595] means for generating an image of the user on demand;

[1596] means for recognizing emotions using audio and video;

[1597] a means for adjusting guidance and speech based on the perceived emotion;

[1598] A system including:

[1599] (Claim 2)

[1600] 10. The system of claim 1, further comprising means for analyzing the converted text data to identify important discussions or events of the meeting or driving.

[1601] (Claim 3)

[1602] 10. The system of claim 1, further comprising means for generating the guidance or remarks based on information referenced from a database. [Explanation of symbols]

[1603] 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 recording and recording meetings; A means for converting the recorded voice data into text data; A means for extracting the main points of the meeting from the converted text data; a means of summarizing the extracted key points; means for providing summarized information to a user; a means for generating appropriate utterances during a meeting; means for converting the generated utterances into speech; A means to generate an image of the user on demand A system including:

2. The system of claim 1 , further comprising means for analyzing the converted text data and identifying important discussions of the meeting.

3. 2. The system of claim 1, further comprising means for generating comments based on information referenced from a database.

Images