system

Abstract

Provide a system. 【Solution means】 Means for automatically generating an agenda for meeting preparation, Means for obtaining the schedule information of participants and proposing an optimal meeting date and time, Means for supporting the progress of topics during the meeting and summarizing important statements in real time, Means for converting meeting recordings into text and extracting the key points of statements, Means for distributing the generated minutes of the meeting to participants, Means for managing follow-up tasks after the meeting, Means for presenting the meeting content in real time based on speech recognition technology, Means including a meeting content management function applicable also in an online meeting platform, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the frequency of meetings within companies has increased, and the labor involved in preparing for, conducting, creating, and distributing meeting minutes has led to a decline in work efficiency. Therefore, it is required to improve the efficiency of tasks related to meetings and enhance productivity. In addition, adjusting the schedules of participants and generating and distributing accurate meeting minutes also take time and are prone to errors. There is a need for a system that can solve these problems and support meeting operations in a unified manner.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system that includes means for automatically generating an agenda for meeting preparation, means for acquiring participants' schedule information and proposing the optimal meeting date and time, means for supporting agenda progression during the meeting and summarizing important statements in real time, means for converting meeting recordings into text and extracting the main points of the statements, means for distributing the generated meeting minutes to participants, and means for managing follow-up tasks after the meeting. This makes it possible to automate everything from meeting preparation to progress, and the creation and distribution of meeting minutes, thereby streamlining meeting management.

[0006] An "agenda" is a plan that outlines the topics and procedures for conducting a meeting or event.

[0007] "Participants" are individuals who attend meetings or events, and their schedules are taken into consideration when scheduling the meeting.

[0008] "Schedule information" refers to information about participants' schedules and is data used to schedule meetings.

[0009] "Meeting date and time" refers to the date and time the meeting will be held, and should be a date that all participants can attend.

[0010] "Agenda progression" refers to the act of moving through the topics and matters to be discussed in a meeting in the correct order.

[0011] "Statement" refers to the opinion or comment of a speaker in a meeting or discussion.

[0012] "Converting to text" is the process of representing audio or video data as textual information.

[0013] "Meeting minutes" are documents that record the topics discussed, decisions made, and statements made during a meeting or conference.

[0014] "Distribution" refers to the act of sending generated information or data to a specific recipient.

[0015] A "follow-up task" refers to the work to be carried out subsequently to ensure the execution of action items and commitments determined in a meeting.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] To implement this invention, a system is constructed in which a server, a terminal, and a user cooperate to support efficient meeting management. The specific operation of this system is described below.

[0038] First, the server analyzes the user's calendar information. Based on past meeting data, it extracts recurring topics and important discussion points to automatically generate the agenda for the next meeting. Furthermore, the server retrieves the schedule information of all participants and uses an AI algorithm to suggest the optimal meeting date and time.

[0039] Next, once the meeting begins, the device starts recording the meeting. Simultaneously, it uses speech recognition technology to transcribe the conversation in real time. The device also assists with agenda progression and displays important statements and summaries on the screen to ensure the meeting runs smoothly.

[0040] Audio data recorded during meetings is converted to text by a server, and key points for the meeting minutes are automatically extracted. Natural language processing technology is used in this process to summarize the important points from participants' statements, resulting in more efficient minute-taking.

[0041] The server then automatically distributes the generated meeting minutes to the user and other participants. These minutes include the time, speaker, content, and decisions made at the meeting for each statement.

[0042] Finally, the user reviews the follow-up tasks decided upon during the meeting based on the distributed meeting minutes. These tasks are registered in the system and tracked to ensure they are completed before the next meeting. For example, the user might assign the task "Investigate the cause of the communication failure and prepare a report by the next meeting" to a designated person.

[0043] This system automates the entire process from meeting preparation and management to the creation and distribution of meeting minutes, saving users time and effort. It also streamlines the overall meeting flow, enabling faster business decision-making.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server analyzes the user's calendar and collects past meeting data. Using natural language processing technology, it extracts frequently occurring topics and key points, and automatically generates agenda items for the next meeting.

[0047] Step 2:

[0048] The server retrieves the calendar information of all participants. Using an AI algorithm, it calculates the optimal meeting date and time that all participants can attend and proposes it to the user.

[0049] Step 3:

[0050] The device starts recording as soon as the meeting begins. Using speech recognition technology, it transcribes the conversation in real time, displaying an immediate summary and key points. It supports meeting progress and manages time effectively.

[0051] Step 4:

[0052] The server converts the recorded audio data into text and uses natural language processing technology to extract the key points necessary for meeting minutes. It then organizes and summarizes the important points and decisions made by each speaker to create the meeting minutes.

[0053] Step 5:

[0054] The server automatically distributes meeting minutes to users and other participants. Distribution is done via email or a dedicated application.

[0055] Step 6:

[0056] Users review the follow-up tasks confirmed during the meeting based on the distributed meeting minutes. They then assign tasks to responsible individuals and register them in the system to manage progress until the next meeting.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] Traditional meeting management required significant time and effort for scheduling, minute-taking, and managing follow-up tasks. These processes were inefficient and hindered meeting productivity. Therefore, there is a need for a system that automates these processes and manages meetings efficiently and effectively.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for acquiring schedule information and generating a meeting summary, means for analyzing the schedule information and calculating the optimal date and time, and means for converting audio information and extracting key points. This automates a series of processes from meeting preparation and conduct to the creation and distribution of meeting minutes and follow-up, enabling efficient and effective meeting management.

[0062] "Method for acquiring schedule information and generating meeting summaries" refers to technology that collects meeting schedule information and automatically creates an agenda for the next meeting based on that information.

[0063] "Methods for analyzing schedule information and calculating the optimal date and time" refers to technologies that aggregate participants' schedules and use computational technologies such as AI to determine the meeting date and time that is most convenient for the majority of participants.

[0064] "Means of providing summaries of important statements during a meeting using communication methods" refers to technology that analyzes statements made during a meeting in real time and immediately conveys important content to participants.

[0065] "Methods for converting audio information and extracting key points" refers to technology that converts audio data recorded during a meeting into text and automatically extracts the main points of the conversation and important decisions from it.

[0066] "Means of supplying summarized information to stakeholders" refers to technology that automatically sends summarized information about a meeting to meeting participants and relevant individuals.

[0067] "Means for managing post-work procedures" refers to technologies for registering follow-up tasks that arise after a meeting and for tracking and managing their progress.

[0068] The system in this invention streamlines meeting management through the collaboration of a server, a terminal, and a user. The server acquires schedule information and generates a meeting summary. Specifically, it uses a general-purpose calendar API to collect the user's calendar information and a data analysis tool to extract recurring agenda items and key points from past meeting data.

[0069] Furthermore, the server utilizes AI algorithms to analyze participants' schedules and calculate the optimal date and time. This process uses a machine learning framework to suggest the best meeting time, taking participants' availability into account.

[0070] The device records audio as soon as the meeting begins and converts the audio information into text in real time. This conversion utilizes speech recognition technology, and by using a particularly accurate speech conversion service, text is generated instantly. The converted data is then provided to participants on the spot via communication as a summary of important statements.

[0071] Furthermore, the server post-processes the text data of the recorded audio, using natural language processing to extract key points. This data processing extracts the main points of the meeting minutes, and a summary is automatically created. This generated summary is then automatically distributed from the server to the relevant parties.

[0072] Based on the meeting minutes they receive, users manage post-work procedures. Specifically, they access a task management system to register follow-up tasks decided in the meeting and track their progress.

[0073] As a concrete example, a user can input a prompt such as "Prepare the most suitable agenda for the next meeting" into a generative AI model, and the system can then suggest a suitable meeting agenda. This allows users to maximize meeting outcomes while significantly reducing time and effort.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server retrieves meeting information using the user's calendar API. This inputs schedule information and past meeting data. The server analyzes this data, processing it with data analysis tools to extract recurring topics and key points, and generates a summary for the new meeting. The output is a draft of the agenda for the next meeting.

[0077] Step 2:

[0078] The server retrieves each participant's schedule again from the calendar API and uses an AI algorithm to calculate the optimal meeting date and time. The input here is each participant's schedule information, which is analyzed by a machine learning model to calculate the optimal meeting date. The output is the date and time that is easiest for all participants to attend.

[0079] Step 3:

[0080] The terminal activates a device to record audio data as soon as the meeting starts, and uses real-time speech-to-text technology to convert the recording into text. The input is raw audio data. The terminal utilizes speech-to-text software to convert the audio into text, so that the content of the meeting is output as text information in real time. The converted text is summarized as important statements during the meeting and displayed to the participants.

[0081] Step 4:

[0082] The server receives the text data of the meeting converted by the terminal and generates meeting minutes using natural language processing (NLP) technology. The input is the text data from the meeting. The server uses NLP technology to extract important points from the conversation and automatically summarizes the main points, obtaining summarized information as output.

[0083] Step 5:

[0084] The server distributes the generated meeting minutes to the participants. The input is summarized meeting minutes data, which is used as a collection method to send to participants via email or cloud service. The output is a detailed meeting summary that is delivered to the participants.

[0085] Step 6:

[0086] The user reviews the received meeting minutes and registers the follow-up tasks decided at the meeting into the management system. The input is the content of the meeting minutes. Based on this content, the user identifies the tasks, sets deadlines and assignees, and registers them in the management system. The output is the registered follow-up tasks, structured to facilitate progress tracking.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] In both online and in-person meetings, the entire process, from agenda preparation to information management during the meeting and the creation and distribution of meeting minutes, is performed manually, posing a significant challenge due to the time and effort required. In particular, there is a need for a system that allows for efficient scheduling of meeting participants and summarization of important statements. Furthermore, online meeting platforms must maintain similar efficiency while also considering security aspects.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes means for automatically generating an agenda for meeting preparation, means for acquiring participants' schedule information and proposing the optimal meeting date and time, means for supporting agenda progression during the meeting and summarizing important statements in real time, means for converting meeting recordings into text and extracting key points of statements, means for distributing the generated meeting minutes to participants, means for managing post-meeting follow-up tasks, means for presenting meeting content in real time based on speech recognition technology, and means including a meeting content management function applicable to online meeting platforms. This enables efficient meeting management, expands the scope of use in online environments, and enhances collaboration among participants.

[0092] "Methods for automatically generating agendas" refer to functions that analyze past meeting data and schedule information to automatically create the necessary agenda items and important matters for the next meeting.

[0093] The "method for suggesting the optimal meeting date and time" is a function that takes into account the schedules of all participants and uses an AI algorithm to automatically suggest the most convenient meeting date and time.

[0094] "A means to support agenda progression and summarize important statements in real time" refers to a function that uses speech recognition technology to analyze conversations during meetings and instantly summarizes and displays the key points.

[0095] "A means of converting meeting recordings into text and extracting the main points of what was said" refers to a function that converts recorded audio data into text and uses natural language processing to concisely extract the gist of the conversation.

[0096] "Means for distributing generated meeting minutes to participants" refers to a function that automatically sends the generated meeting minutes to participants via email or as a shared document.

[0097] "Means for managing follow-up tasks" refers to a function that records the next actions or tasks decided in a meeting and tracks their progress.

[0098] "A means of presenting meeting content in real time based on speech recognition technology" refers to a function that instantly converts meeting audio into text information and displays that content in real time.

[0099] "Means including meeting content management functions applicable to online meeting platforms" refers to functions that support efficient meeting management, not only for physical meetings but also for online environments.

[0100] The system for realizing this invention primarily involves the collaboration of three parties: a server, a terminal, and a user. First, the server analyzes the user's calendar information and past meeting data. This extracts frequently occurring meeting topics and important discussion points, automatically generating the agenda necessary for the next meeting. At this time, an AI algorithm is used to obtain the schedule information of all participants and suggest the optimal meeting date and time.

[0101] Next, once the meeting begins, the device starts recording the meeting and uses speech recognition technology such as Google Cloud Speech-to-Text API to transcribe the conversation in real time. This allows important statements and summaries to be displayed on the device screen for the user to refer to during the meeting. The recorded data is often stored in Firebase, a cloud database.

[0102] After the meeting concludes, the server converts the audio data into text and automatically generates meeting minutes using natural language processing technology. The generated minutes include the time, speaker, content, and decisions made at the meeting, and are efficiently summarized. These minutes are automatically distributed to the user and other participants via email or other means.

[0103] Users review follow-up tasks based on the distributed meeting minutes. Tasks are managed within the system and tracked to ensure completion before the next meeting. For example, a user could assign themselves or another participant the task of "investigating the cause of a communication failure and preparing a report by the next meeting."

[0104] Examples of prompt statements for a generative AI model are as follows:

[0105] "Prompt to give to the generating AI model: 'Summarize the key discussion points regarding the marketing strategy for a new product in a virtual store meeting.'"

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server retrieves the user's calendar information. The input is the user's calendar information, and the output is an agenda containing the topics and key discussion points needed for the next meeting. An AI algorithm is used to analyze past meeting data and extract recurring meeting themes.

[0109] Step 2:

[0110] The server collects schedule information from all participants. The input is each participant's schedule information, and the output is the optimal meeting date and time suggested by the AI. Based on this information, the server analyzes common free time among participants and sets up the meeting.

[0111] Step 3:

[0112] When the meeting begins, the terminal activates its speech recognition technology and starts recording the meeting. The input is the audio data from the meeting. The output is real-time text data, which is converted into text information by speech recognition. Based on this text, the terminal displays important statements and summaries on the screen.

[0113] Step 4:

[0114] The recorded audio data is stored in a cloud database (e.g., Firebase). The input is the audio data sent from the device. The output is securely stored data that is used for subsequent data processing.

[0115] Step 5:

[0116] After the meeting ends, the server converts the audio data into text. The input is recorded audio data, and the output is a meeting transcript summarized using natural language processing. The server analyzes important statements and discussion points and summarizes them efficiently.

[0117] Step 6:

[0118] The server distributes the generated meeting minutes to users and participants via email, etc. The input is the meeting minutes data, and the output is the distributed emails and shared documents. The server automatically sends the meeting minutes, enabling rapid information sharing among participants.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] To implement this invention, in addition to a basic meeting management support system, an emotion engine that recognizes and analyzes the user's emotional state is incorporated. The specific operation is described below.

[0121] First, the server analyzes participants' calendar information and automatically generates the agenda for the next meeting. This process includes extracting keywords from past meeting data and learning from similar topics. AI also suggests optimal meeting dates and times. Secure data access is crucial in this process, and access to participants' calendar information is managed according to security protocols.

[0122] Next, once the meeting begins, the terminal processes the audio and video data of the meeting participants in real time and performs emotion recognition using an emotion engine. The emotion engine determines the user's emotional state from their tone of voice and facial expressions and collects this data. This information is used to monitor the progress of the meeting and the reactions of the participants.

[0123] Furthermore, based on real-time monitoring during the meeting, the server provides users with advice on how to proceed and adjusts the agenda as needed. For example, if participants' emotions are leaning towards negativity, it may suggest taking more breaks or changing the topic.

[0124] Once the meeting concludes, the server transcribes the recorded data into text and uses natural language processing techniques to create meeting minutes. These minutes also include feedback on the emotional states of participants as perceived during the meeting. Furthermore, follow-up tasks that need to be addressed after the meeting are organized and presented to the user.

[0125] Finally, users adjust their actions based on meeting feedback and prepare for the next meeting. For example, they can take measures such as improving the content of their presentation based on the results of sentiment recognition.

[0126] This invention highly optimizes meeting management and takes into account the emotional state of participants, thereby promoting more effective communication and decision-making. In this way, users can improve the quality of meetings.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server analyzes the user's calendar data and past meeting records. Using machine learning algorithms, it extracts patterns and frequently occurring topics to automatically generate the agenda for the next meeting.

[0130] Step 2:

[0131] The server retrieves calendar information from all participants and uses AI to calculate the date and time when each participant is most likely to attend. It then suggests the calculated meeting date and time to the user.

[0132] Step 3:

[0133] The device begins capturing audio and video data at the start of the meeting. The emotion engine is activated, analyzing participants' facial expressions and tone of voice in real time to obtain their emotional state.

[0134] Step 4:

[0135] The server uses real-time emotional data to adjust the progress of the meeting. For example, if negative emotions increase, it suggests revising the agenda or changing the topic to facilitate smooth meeting management.

[0136] Step 5:

[0137] During the meeting, the device uses speech recognition technology to transcribe the conversation into text. Based on this text data, a summary and key points are extracted and displayed to participants in real time.

[0138] Step 6:

[0139] After the meeting ends, the server completely transcribes the audio data into text and automatically generates meeting minutes using natural language processing technology. The minutes also include feedback on the participants' emotional tendencies.

[0140] Step 7:

[0141] Users review meeting minutes and sentiment feedback delivered from the server. They plan improvements for future meetings and business processes and set up necessary follow-up tasks.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] In meeting management, it is essential to provide an environment that allows participants to proceed efficiently and smoothly. In particular, challenges include ensuring smooth planning, monitoring participants' emotional states during the meeting, and conducting follow-up afterward. Furthermore, a system is needed that can appropriately understand the emotions of meeting participants and make adjustments accordingly.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for analyzing the user's schedule information and automatically forming a meeting plan, means for obtaining knowledge from past meeting data to suggest the optimal date and time, and means for analyzing and predicting the emotional state of participants during the meeting and providing assistance for its progress. This optimizes the entire process from preparation before the meeting to progress management on the day of the meeting and follow-up after the meeting, enabling smooth meeting management that takes into account the emotions of the participants.

[0147] An "electronic computing device" refers to a computer system used for various data processing tasks, and is a device that plays a role in managing each process of conference operations.

[0148] "User schedule information" refers to data showing the schedules and appointments previously set by meeting participants, and is used to plan the meeting optimally.

[0149] "Automatically generating a meeting plan" refers to the process by which a computer automatically assembles the meeting agenda and other necessary information based on the participants' availability.

[0150] "Gaining knowledge and proposing the optimal date and time" means analyzing past data and calculating the date and time when all participants can participate most efficiently.

[0151] "Analyzing and inferring participants' emotional states to assist in meeting progress" refers to the process of analyzing participants' audio and video in real time during a meeting, understanding their emotions, and then providing appropriate advice for conducting the meeting.

[0152] "Progress management" refers to the management activity of appropriately controlling the flow of a meeting and ensuring that it proceeds smoothly according to schedule.

[0153] "Follow-up" refers to the process of identifying tasks and issues that require further review or action after a meeting, and presenting them to the relevant parties.

[0154] This invention is a system that highly optimizes the management of meetings using an electronic computing device. This system consists of a server, terminals, and users, and effectively utilizes various data to achieve smooth meeting management.

[0155] The server first retrieves participants' schedule information. Specifically, it collects schedule data using a calendar API and automatically generates a meeting agenda using an AI model based on past meeting data. During this process, it extracts keywords and important points using natural language processing and proposes the optimal date. The technologies used include a calendar API and natural language processing.

[0156] During the meeting, the terminal collects participants' audio and video in real time and analyzes their emotional state using an emotion engine. Image and audio analysis technologies infer emotions from facial expressions and tone of voice, and send this data to the server. The server then supports the meeting's progress and, in some cases, provides progress advice through a generated AI model. For example, if the server determines that a participant is tired, it may send a notification to the user asking, "Shall we suggest a 5-minute break?"

[0157] After the meeting ends, the server converts the audio data to text and automatically generates meeting minutes. It uses a speech-to-text API to create a meeting summary from the resulting text. The generated minutes also include each participant's emotional feedback and any follow-up tasks.

[0158] The user uses this information to prepare for the next meeting and improve the presentation content as needed. An example of a prompt might be, "Use the meeting audio data collected by the server to generate a summary of the emotional state of the participants during the meeting." This invention provides a means to facilitate more effective communication and appropriate decision-making in order to improve the quality of meetings.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The server retrieves participants' calendar information. It obtains participant schedule data via a calendar API as input and references a database of past meetings. Based on this information, the server inputs data into an AI model to automatically generate a meeting agenda. Specifically, the server sends a prompt to the AI ​​model, asking, "What are the most important topics for the next meeting?" Through this data processing, the server obtains an optimized agenda as output.

[0162] Step 2:

[0163] The server suggests the optimal meeting date and time based on data analyzed by an AI model. Using past meeting attendance history and participants' schedules as input, the AI ​​algorithm calculates multiple candidate dates and times. This allows the server to identify the date and time that is most convenient for everyone. Specifically, a prompt such as "Which time slot is best for everyone?" is sent to the AI ​​model, and the optimal meeting date and time are provided as output.

[0164] Step 3:

[0165] The device collects participants' audio and video during the meeting. It receives real-time feeds from the camera and microphone as input, and the emotion engine processes this data using image and audio analysis technologies. The device sends the emotion data to a server, which is used as information necessary for the progress of the meeting. Specifically, when the voice tone rises, it sends a prompt to the AI ​​model to "perform an emotion analysis on this voice," and the emotional state is analyzed as output.

[0166] Step 4:

[0167] The server provides meeting guidance based on emotional data received from terminals. It receives data from the emotional engine as input, analyzes it, and suggests topic changes or breaks as needed. For example, a notification might be generated stating, "Participants' stress levels are high. Shall we suggest a break?" and the appropriate action to take is determined as output.

[0168] Step 5:

[0169] After the meeting ends, the server converts the recorded audio data into text. Using an audio file as input, it generates text data via a speech-to-text API. Next, this text information is analyzed using natural language processing techniques to create a meeting summary. Specifically, using the prompt "Create a summary from this audio recording," the output is meeting minutes containing the key points of the meeting.

[0170] Step 6:

[0171] The user prepares for the next meeting based on meeting minutes generated by the server and feedback from sentiment analysis. The user receives the meeting minutes as input, reviews their content, and adjusts their actions as needed. Specifically, the user considers "What needs improvement from the last meeting?" and, based on that, creates a concrete action plan for the next meeting as output, improving the presentation content.

[0172] (Application Example 2)

[0173] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0174] Traditional meeting management systems struggled to recognize participants' emotions and respond appropriately according to the meeting's progress. This made it difficult to prevent problems arising from participants' emotional states during meetings, hindering smooth meeting progress. Furthermore, creating meeting minutes and managing follow-up tasks after meetings were time-consuming and inefficient. A system was needed to address these challenges, perform real-time emotion recognition, and quickly detect and address abnormal situations.

[0175] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0176] In this invention, the server includes means for recognizing the audio and video status of participants and detecting abnormal emotional states, and means for issuing an alarm when an abnormal emotional state is detected. This makes it possible to monitor participants' emotional states in real time during a meeting, quickly detect abnormal states, and take appropriate action. It also allows for efficient management of post-meeting follow-up tasks and reduces the burden of creating meeting minutes.

[0177] "Meeting preparation" refers to preparatory activities such as creating an agenda necessary for the smooth running of a meeting and coordinating participants' schedules.

[0178] "Methods for automatically generating agendas" refers to a function where the system automatically creates the agenda and content for the next meeting based on past meeting data and participant schedule information.

[0179] "Participant schedule information" refers to event information registered in the schedules and calendars of meeting participants.

[0180] "A means of suggesting the optimal meeting date and time" refers to a function where the system takes into account the participants' schedules and suggests a date and time that is convenient for everyone to attend.

[0181] "Means of supporting agenda progression" refer to functions that assist in ensuring the agenda progresses smoothly during a meeting.

[0182] "A means of summarizing important statements in real time" refers to a function that quickly compiles the key points from statements made during a meeting.

[0183] "Methods for converting meeting recordings to text" refers to a function that automatically converts audio from a meeting into text data.

[0184] "Methods for extracting the main points of a statement" refers to a function that identifies important information and key points from transcribed meeting recordings.

[0185] "Means of distributing meeting minutes to participants" refers to the function of electronically sending the generated meeting minutes to participants.

[0186] "Means for managing follow-up tasks" refers to functions for tracking and managing actions and tasks that occur after a meeting.

[0187] "Means for recognizing the state of audio and video" refers to a function that analyzes the way participants speak and their facial expressions to determine their emotions and state in real time.

[0188] A "means for detecting abnormal emotional states" is a function that automatically identifies situations where emotional states deviate significantly from normal.

[0189] A "means of issuing an alarm" refers to a function that transmits information to the person in charge when an abnormal emotional state is detected.

[0190] To implement this invention, the server retrieves each participant's schedule information using the international standard open calendar format and proposes the optimal meeting date and time. This process is carried out in accordance with security protocols to prevent the leakage of personal information. In addition, the agenda is automatically generated using machine learning algorithms based on past meeting data.

[0191] During the meeting, the device acquires and analyzes participants' audio and video in real time. Specifically, it collects data using the camera and microphone and performs voice tone analysis and facial expression recognition. This allows it to recognize participants' emotional states and detect abnormal emotional states. Emotion detection uses the Google Cloud Speech-to-Text API to analyze voice tone and facial analysis software to recognize facial expressions.

[0192] Furthermore, if an anomaly is detected, the server will send an alert to the system administrator or designated personnel. SMS notifications using the Twilio API are effective for this purpose, enabling a rapid response.

[0193] After the meeting ends, the server converts the audio recording into text, uses natural language processing technology to highlight key points, and creates meeting minutes. The minutes are distributed to participants, and any follow-up tasks arising from the meeting are organized and managed.

[0194] As a concrete example, by introducing this system into a company's monthly meetings and constantly monitoring the emotional state of all employees, the productivity of the meetings was improved. Furthermore, by quickly identifying follow-up tasks after meetings, work efficiency was enhanced.

[0195] Examples of prompt statements are as follows:

[0196] "Describe a method for detecting unusual emotional states by analyzing video data acquired by security cameras and the voice tone of visitors."

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The server retrieves the meeting participants' schedule information. It obtains participant schedule information from the calendar database using the open calendar format as input and parses it. Based on this parsing, it selects a suitable date and time for the meeting and outputs a suggested date and time.

[0200] Step 2:

[0201] The server collects past meeting data and automatically generates agendas using machine learning algorithms. Using past meeting records as input data, it performs topic modeling with an AI model and provides appropriate agenda items for the next meeting as output.

[0202] Step 3:

[0203] During the meeting, the terminal acquires participants' audio and video data in real time. By using the camera and microphone as input devices and processing this sensor data with voice tone analysis and facial expression recognition software, the emotional state of the participants can be obtained as output.

[0204] Step 4:

[0205] The device detects abnormal emotional states if they differ from the normal state. It sends the output of audio and video analysis to the server, executes logic to determine the abnormality based on the conditions, and outputs the detected abnormality.

[0206] Step 5:

[0207] The server sends an alert to the designated administrator based on the detected abnormal emotional state. It uses the Twilio API to send an SMS or email notification to the system administrator to communicate the alert information.

[0208] Step 6:

[0209] After the meeting ends, the server retrieves the audio recording and uses speech-to-text software to convert the conversation into text. It then takes the audio file as input and outputs a text file.

[0210] Step 7:

[0211] The server summarizes important statements from the converted text data using natural language processing techniques and creates meeting minutes. It analyzes text data as input and generates meeting minutes as output.

[0212] Step 8:

[0213] The server distributes the created meeting minutes to participants and manages follow-up tasks that arise during the meeting. It sends the generated meeting minutes via email, registers the tasks as inputs in the follow-up task management system, and records their completion status as an output.

[0214] Step 9:

[0215] Users receive feedback from meetings and adjust their actions accordingly. They receive information based on meeting minutes and follow-up tasks as input and plan preparations and improvements for the next meeting as output.

[0216] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0217] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0219] [Second Embodiment]

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

[0221] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0222] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0223] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0224] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0225] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0226] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0227] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0228] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0229] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0230] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0231] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0232] To implement this invention, a system is constructed in which a server, a terminal, and a user cooperate to support efficient meeting management. The specific operation of this system is described below.

[0233] First, the server analyzes the user's calendar information. Based on past meeting data, it extracts recurring topics and important discussion points to automatically generate the agenda for the next meeting. Furthermore, the server retrieves the schedule information of all participants and uses an AI algorithm to suggest the optimal meeting date and time.

[0234] Next, once the meeting begins, the device starts recording the meeting. Simultaneously, it uses speech recognition technology to transcribe the conversation in real time. The device also assists with agenda progression and displays important statements and summaries on the screen to ensure the meeting runs smoothly.

[0235] Audio data recorded during meetings is converted to text by a server, and key points for the meeting minutes are automatically extracted. Natural language processing technology is used in this process to summarize the important points from participants' statements, resulting in more efficient minute-taking.

[0236] The server then automatically distributes the generated meeting minutes to the user and other participants. These minutes include the time, speaker, content, and decisions made at the meeting for each statement.

[0237] Finally, the user reviews the follow-up tasks decided upon during the meeting based on the distributed meeting minutes. These tasks are registered in the system and tracked to ensure they are completed before the next meeting. For example, the user might assign the task "Investigate the cause of the communication failure and prepare a report by the next meeting" to a designated person.

[0238] This system automates the entire process from meeting preparation and management to the creation and distribution of meeting minutes, saving users time and effort. It also streamlines the overall meeting flow, enabling faster business decision-making.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The server analyzes the user's calendar and collects past meeting data. Using natural language processing technology, it extracts frequently occurring topics and key points, and automatically generates agenda items for the next meeting.

[0242] Step 2:

[0243] The server retrieves the calendar information of all participants. Using an AI algorithm, it calculates the optimal meeting date and time that all participants can attend and proposes it to the user.

[0244] Step 3:

[0245] The device starts recording as soon as the meeting begins. Using speech recognition technology, it transcribes the conversation in real time, displaying an immediate summary and key points. It supports meeting progress and manages time effectively.

[0246] Step 4:

[0247] The server converts the recorded audio data into text and uses natural language processing technology to extract the key points necessary for meeting minutes. It then organizes and summarizes the important points and decisions made by each speaker to create the meeting minutes.

[0248] Step 5:

[0249] The server automatically distributes meeting minutes to users and other participants. Distribution is done via email or a dedicated application.

[0250] Step 6:

[0251] Users review the follow-up tasks confirmed during the meeting based on the distributed meeting minutes. They then assign tasks to responsible individuals and register them in the system to manage progress until the next meeting.

[0252] (Example 1)

[0253] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0254] Traditional meeting management required significant time and effort for scheduling, minute-taking, and managing follow-up tasks. These processes were inefficient and hindered meeting productivity. Therefore, there is a need for a system that automates these processes and manages meetings efficiently and effectively.

[0255] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0256] In this invention, the server includes means for acquiring schedule information and generating a meeting summary, means for analyzing the schedule information and calculating the optimal date and time, and means for converting audio information and extracting key points. This automates a series of processes from meeting preparation and conduct to the creation and distribution of meeting minutes and follow-up, enabling efficient and effective meeting management.

[0257] "Method for acquiring schedule information and generating meeting summaries" refers to technology that collects meeting schedule information and automatically creates an agenda for the next meeting based on that information.

[0258] "Methods for analyzing schedule information and calculating the optimal date and time" refers to technologies that aggregate participants' schedules and use computational technologies such as AI to determine the meeting date and time that is most convenient for the majority of participants.

[0259] "Means of providing summaries of important statements during a meeting using communication methods" refers to technology that analyzes statements made during a meeting in real time and immediately conveys important content to participants.

[0260] "Methods for converting audio information and extracting key points" refers to technology that converts audio data recorded during a meeting into text and automatically extracts the main points of the conversation and important decisions from it.

[0261] "Means of supplying summarized information to stakeholders" refers to technology that automatically sends summarized information about a meeting to meeting participants and relevant individuals.

[0262] "Means for managing post-work procedures" refers to technologies for registering follow-up tasks that arise after a meeting and for tracking and managing their progress.

[0263] The system in this invention streamlines meeting management through the collaboration of a server, a terminal, and a user. The server acquires schedule information and generates a meeting summary. Specifically, it uses a general-purpose calendar API to collect the user's calendar information and a data analysis tool to extract recurring agenda items and key points from past meeting data.

[0264] Furthermore, the server utilizes AI algorithms to analyze participants' schedules and calculate the optimal date and time. This process uses a machine learning framework to suggest the best meeting time, taking participants' availability into account.

[0265] The device records audio as soon as the meeting begins and converts the audio information into text in real time. This conversion utilizes speech recognition technology, and by using a particularly accurate speech conversion service, text is generated instantly. The converted data is then provided to participants on the spot via communication as a summary of important statements.

[0266] Furthermore, the server post-processes the text data of the recorded audio, using natural language processing to extract key points. This data processing extracts the main points of the meeting minutes, and a summary is automatically created. This generated summary is then automatically distributed from the server to the relevant parties.

[0267] Based on the meeting minutes they receive, users manage post-work procedures. Specifically, they access a task management system to register follow-up tasks decided in the meeting and track their progress.

[0268] As a concrete example, a user can input a prompt such as "Prepare the most suitable agenda for the next meeting" into a generative AI model, and the system can then suggest a suitable meeting agenda. This allows users to maximize meeting outcomes while significantly reducing time and effort.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The server retrieves meeting information using the user's calendar API. This inputs schedule information and past meeting data. The server analyzes this data, processing it with data analysis tools to extract recurring topics and key points, and generates a summary for the new meeting. The output is a draft of the agenda for the next meeting.

[0272] Step 2:

[0273] The server retrieves each participant's schedule again from the calendar API and uses an AI algorithm to calculate the optimal meeting date and time. The input here is each participant's schedule information, which is analyzed by a machine learning model to calculate the optimal meeting date. The output is the date and time that is easiest for all participants to attend.

[0274] Step 3:

[0275] The terminal activates a device to record audio data as soon as the meeting starts, and uses real-time speech-to-text technology to convert the recording into text. The input is raw audio data. The terminal utilizes speech-to-text software to convert the audio into text, so that the content of the meeting is output as text information in real time. The converted text is summarized as important statements during the meeting and displayed to the participants.

[0276] Step 4:

[0277] The server receives the meeting text data converted by the terminal and generates a meeting minutes using natural language processing technology. The input is the text data during the meeting. The server uses NLP technology to extract important points from the conversation and execute the function of automatically summarizing the main matters, so as to obtain summary information as the output.

[0278] Step 5:

[0279] The server distributes the generated meeting minutes to the participants. The input is the summarized meeting minutes data. This is used as a collection means to send to the participants through the mail service or cloud service. The output is a detailed summary of the meeting delivered to the participants' hands.

[0280] Step 6:

[0281] The user checks the received meeting minutes and registers the follow-up tasks determined in the meeting in the management system. The input is the content of the meeting minutes. Based on this content, the user identifies the tasks, sets the deadlines and responsible persons, and registers them in the management system. The output is the registered follow-up tasks, which have a structure that facilitates the tracking of progress.

[0282] (Application Example 1)

[0283] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0284] In online or physical meetings, a series of processes from agenda preparation, information management during the meeting, to the creation and distribution of meeting minutes are carried out manually, which requires a lot of time and effort. In particular, there is a need for a mechanism that can efficiently adjust the schedules of meeting participants and summarize important remarks. Also, the same efficiency needs to be maintained in online meeting platforms, and considerations in terms of security are also required.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for automatically generating an agenda for meeting preparation, means for obtaining the schedule information of participants and proposing an optimal meeting date and time, means for assisting the progress of the agenda during the meeting and summarizing important speeches in real time, means for converting the meeting recording into text and extracting the key points of the speech, means for distributing the generated minutes of the meeting to the participants, means for managing the follow-up tasks after the meeting, means for presenting the meeting content in real time based on voice recognition technology, and means including a meeting content management function applicable also in an online meeting platform. Thereby, efficient operation of the meeting becomes possible, the scope of utilization in an online environment is expanded, and it becomes possible to enhance the cooperation of the participants.

[0287] The "means for automatically generating an agenda" is a function that analyzes past meeting data and schedule information to automatically create the topics and important matters necessary for the next meeting.

[0288] The "means for proposing an optimal meeting date and time" is a function that grasps the schedules of all participants and automatically presents the most convenient meeting schedule using an AI algorithm.

[0289] The "means for assisting the progress of the agenda and summarizing important speeches in real time" is a function that utilizes voice recognition technology to analyze the conversations during the meeting and immediately summarizes and displays the important points.

[0290] The "means for converting the meeting recording into text and extracting the key points of the speech" is a function that converts the recorded voice data into text and briefly extracts the main idea of the conversation by natural language processing.

[0291] The "means for distributing the generated minutes of the meeting to the participants" is a function that automatically transmits the created minutes of the meeting to the participants as an e-mail or a shared document.

[0292] "Means for managing follow-up tasks" refers to a function that records the next actions or tasks decided in a meeting and tracks their progress.

[0293] "A means of presenting meeting content in real time based on speech recognition technology" refers to a function that instantly converts meeting audio into text information and displays that content in real time.

[0294] "Means including meeting content management functions applicable to online meeting platforms" refers to functions that support efficient meeting management, not only for physical meetings but also for online environments.

[0295] The system for realizing this invention primarily involves the collaboration of three parties: a server, a terminal, and a user. First, the server analyzes the user's calendar information and past meeting data. This extracts frequently occurring meeting topics and important discussion points, automatically generating the agenda necessary for the next meeting. At this time, an AI algorithm is used to obtain the schedule information of all participants and suggest the optimal meeting date and time.

[0296] Next, once the meeting begins, the device starts recording the meeting and uses speech recognition technology such as the Google Cloud Speech-to-Text API to transcribe the conversation in real time. This allows important statements and summaries to be displayed on the device screen for the user to refer to during the meeting. The recorded data is often stored in Firebase, a cloud database.

[0297] After the meeting concludes, the server converts the audio data into text and automatically generates meeting minutes using natural language processing technology. The generated minutes include the time, speaker, content, and decisions made at the meeting, and are efficiently summarized. These minutes are automatically distributed to the user and other participants via email or other means.

[0298] The user checks the follow-up tasks based on the distributed meeting minutes. The tasks are managed within the system and tracked to be completed by the next meeting. As a specific example, the user can assign a task such as "Investigate the cause of the communication failure and create a report by the next time" to themselves or other participants.

[0299] Examples of prompt texts for the generative AI model are as follows.

[0300] "Prompt for the generative AI model: 'Please summarize the important discussion points regarding the marketing strategy of new products at the virtual store meeting.'"

[0301] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0302] Step 1:

[0303] The server acquires the user's calendar information. The input is the user's calendar information, and the output is an agenda including the topics and important discussion points required for the next meeting. Using an AI algorithm, analyze the past meeting data and extract the recurring meeting themes.

[0304] Step 2:

[0305] The server collects the schedule information of all participants. The input is the schedule information of each participant, and the output is the optimal meeting date and time proposed by the AI. Based on this information, the server analyzes the common free time among the participants and sets the meeting.

[0306] Step 3:

[0307] When the meeting starts, the terminal activates the voice recognition technology and starts recording the meeting. The input is the voice data during the meeting. The output is real-time text data, which is converted into character information by voice recognition. Based on this text, the terminal displays important remarks and summaries on the screen.

[0308] Step 4:

[0309] The recorded audio data is stored in a cloud database (e.g., Firebase). The input is the audio data sent from the device. The output is securely stored data that is used for subsequent data processing.

[0310] Step 5:

[0311] After the meeting ends, the server converts the audio data into text. The input is recorded audio data, and the output is a meeting transcript summarized using natural language processing. The server analyzes important statements and discussion points and summarizes them efficiently.

[0312] Step 6:

[0313] The server distributes the generated meeting minutes to users and participants via email, etc. The input is the meeting minutes data, and the output is the distributed emails and shared documents. The server automatically sends the meeting minutes, enabling rapid information sharing among participants.

[0314] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0315] To implement this invention, in addition to a basic meeting management support system, an emotion engine that recognizes and analyzes the user's emotional state is incorporated. The specific operation is described below.

[0316] First, the server analyzes participants' calendar information and automatically generates the agenda for the next meeting. This process includes extracting keywords from past meeting data and learning from similar topics. AI also suggests optimal meeting dates and times. Secure data access is crucial in this process, and access to participants' calendar information is managed according to security protocols.

[0317] Next, once the meeting begins, the terminal processes the audio and video data of the meeting participants in real time and performs emotion recognition using an emotion engine. The emotion engine determines the user's emotional state from their tone of voice and facial expressions and collects this data. This information is used to monitor the progress of the meeting and the reactions of the participants.

[0318] Furthermore, based on real-time monitoring during the meeting, the server provides users with advice on how to proceed and adjusts the agenda as needed. For example, if participants' emotions are leaning towards negativity, it may suggest taking more breaks or changing the topic.

[0319] Once the meeting concludes, the server transcribes the recorded data into text and uses natural language processing techniques to create meeting minutes. These minutes also include feedback on the emotional states of participants as perceived during the meeting. Furthermore, follow-up tasks that need to be addressed after the meeting are organized and presented to the user.

[0320] Finally, users adjust their actions based on meeting feedback and prepare for the next meeting. For example, they can take measures such as improving the content of their presentation based on the results of sentiment recognition.

[0321] This invention highly optimizes meeting management and takes into account the emotional state of participants, thereby promoting more effective communication and decision-making. In this way, users can improve the quality of meetings.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The server analyzes the user's calendar data and past meeting records. Using machine learning algorithms, it extracts patterns and frequently occurring topics to automatically generate the agenda for the next meeting.

[0325] Step 2:

[0326] The server retrieves calendar information from all participants and uses AI to calculate the date and time when each participant is most likely to attend. It then suggests the calculated meeting date and time to the user.

[0327] Step 3:

[0328] The device begins capturing audio and video data at the start of the meeting. The emotion engine is activated, analyzing participants' facial expressions and tone of voice in real time to obtain their emotional state.

[0329] Step 4:

[0330] The server uses real-time emotional data to adjust the progress of the meeting. For example, if negative emotions increase, it suggests revising the agenda or changing the topic to facilitate smooth meeting management.

[0331] Step 5:

[0332] During the meeting, the device uses speech recognition technology to transcribe the conversation into text. Based on this text data, a summary and key points are extracted and displayed to participants in real time.

[0333] Step 6:

[0334] After the meeting ends, the server completely transcribes the audio data into text and automatically generates meeting minutes using natural language processing technology. The minutes also include feedback on the participants' emotional tendencies.

[0335] Step 7:

[0336] Users review meeting minutes and sentiment feedback delivered from the server. They plan improvements for future meetings and business processes and set up necessary follow-up tasks.

[0337] (Example 2)

[0338] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0339] In meeting management, it is essential to provide an environment that allows participants to proceed efficiently and smoothly. In particular, challenges include ensuring smooth planning, monitoring participants' emotional states during the meeting, and conducting follow-up afterward. Furthermore, a system is needed that can appropriately understand the emotions of meeting participants and make adjustments accordingly.

[0340] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0341] In this invention, the server includes means for analyzing the user's schedule information and automatically forming a meeting plan, means for obtaining knowledge from past meeting data to suggest the optimal date and time, and means for analyzing and predicting the emotional state of participants during the meeting and providing assistance for its progress. This optimizes the entire process from preparation before the meeting to progress management on the day of the meeting and follow-up after the meeting, enabling smooth meeting management that takes into account the emotions of the participants.

[0342] An "electronic computing device" refers to a computer system used for various data processing tasks, and is a device that plays a role in managing each process of conference operations.

[0343] "User schedule information" refers to data showing the schedules and appointments previously set by meeting participants, and is used to plan the meeting optimally.

[0344] "Automatically generating a meeting plan" refers to the process by which a computer automatically assembles the meeting agenda and other necessary information based on the participants' availability.

[0345] "Gaining knowledge and proposing the optimal date and time" means analyzing past data and calculating the date and time when all participants can participate most efficiently.

[0346] "Analyzing and inferring participants' emotional states to assist in meeting progress" refers to the process of analyzing participants' audio and video in real time during a meeting, understanding their emotions, and then providing appropriate advice for conducting the meeting.

[0347] "Progress management" refers to the management activity of appropriately controlling the flow of a meeting and ensuring that it proceeds smoothly according to schedule.

[0348] "Follow-up" refers to the process of identifying tasks and issues that require further review or action after a meeting, and presenting them to the relevant parties.

[0349] This invention is a system that highly optimizes the management of meetings using an electronic computing device. This system consists of a server, terminals, and users, and effectively utilizes various data to achieve smooth meeting management.

[0350] The server first retrieves participants' schedule information. Specifically, it collects schedule data using a calendar API and automatically generates a meeting agenda using an AI model based on past meeting data. During this process, it extracts keywords and important points using natural language processing and proposes the optimal date. The technologies used include a calendar API and natural language processing.

[0351] During the meeting, the terminal collects participants' audio and video in real time and analyzes their emotional state using an emotion engine. Image and audio analysis technologies infer emotions from facial expressions and tone of voice, and send this data to the server. The server then supports the meeting's progress and, in some cases, provides progress advice through a generated AI model. For example, if the server determines that a participant is tired, it may send a notification to the user asking, "Shall we suggest a 5-minute break?"

[0352] After the meeting ends, the server converts the audio data to text and automatically generates meeting minutes. It uses a speech-to-text API to create a meeting summary from the resulting text. The generated minutes also include each participant's emotional feedback and any follow-up tasks.

[0353] The user uses this information to prepare for the next meeting and improve the presentation content as needed. An example of a prompt might be, "Use the meeting audio data collected by the server to generate a summary of the emotional state of the participants during the meeting." This invention provides a means to facilitate more effective communication and appropriate decision-making in order to improve the quality of meetings.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The server retrieves participants' calendar information. It obtains participant schedule data via a calendar API as input and references a database of past meetings. Based on this information, the server inputs data into an AI model to automatically generate a meeting agenda. Specifically, the server sends a prompt to the AI ​​model, asking, "What are the most important topics for the next meeting?" Through this data processing, the server obtains an optimized agenda as output.

[0357] Step 2:

[0358] The server suggests the optimal meeting date and time based on data analyzed by an AI model. Using past meeting attendance history and participants' schedules as input, the AI ​​algorithm calculates multiple candidate dates and times. This allows the server to identify the date and time that is most convenient for everyone. Specifically, a prompt such as "Which time slot is best for everyone?" is sent to the AI ​​model, and the optimal meeting date and time are provided as output.

[0359] Step 3:

[0360] The device collects participants' audio and video during the meeting. It receives real-time feeds from the camera and microphone as input, and the emotion engine processes this data using image and audio analysis technologies. The device sends the emotion data to a server, which is used as information necessary for the progress of the meeting. Specifically, when the voice tone rises, it sends a prompt to the AI ​​model to "perform an emotion analysis on this voice," and the emotional state is analyzed as output.

[0361] Step 4:

[0362] The server provides meeting guidance based on emotional data received from terminals. It receives data from the emotional engine as input, analyzes it, and suggests topic changes or breaks as needed. For example, a notification might be generated stating, "Participants' stress levels are high. Shall we suggest a break?" and the appropriate action to take is determined as output.

[0363] Step 5:

[0364] After the meeting ends, the server converts the recorded audio data into text. Using an audio file as input, it generates text data via a speech-to-text API. Next, this text information is analyzed using natural language processing techniques to create a meeting summary. Specifically, using the prompt "Create a summary from this audio recording," the output is meeting minutes containing the key points of the meeting.

[0365] Step 6:

[0366] The user prepares for the next meeting based on meeting minutes generated by the server and feedback from sentiment analysis. The user receives the meeting minutes as input, reviews their content, and adjusts their actions as needed. Specifically, the user considers "What needs improvement from the last meeting?" and, based on that, creates a concrete action plan for the next meeting as output, improving the presentation content.

[0367] (Application Example 2)

[0368] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0369] Traditional meeting management systems struggled to recognize participants' emotions and respond appropriately according to the meeting's progress. This made it difficult to prevent problems arising from participants' emotional states during meetings, hindering smooth meeting progress. Furthermore, creating meeting minutes and managing follow-up tasks after meetings were time-consuming and inefficient. A system was needed to address these challenges, perform real-time emotion recognition, and quickly detect and address abnormal situations.

[0370] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0371] In this invention, the server includes means for recognizing the audio and video status of participants and detecting abnormal emotional states, and means for issuing an alarm when an abnormal emotional state is detected. This makes it possible to monitor participants' emotional states in real time during a meeting, quickly detect abnormal states, and take appropriate action. It also allows for efficient management of post-meeting follow-up tasks and reduces the burden of creating meeting minutes.

[0372] "Meeting preparation" refers to preparatory activities such as creating an agenda necessary for the smooth running of a meeting and coordinating participants' schedules.

[0373] "Methods for automatically generating agendas" refers to a function where the system automatically creates the agenda and content for the next meeting based on past meeting data and participant schedule information.

[0374] "Participant schedule information" refers to event information registered in the schedules and calendars of meeting participants.

[0375] "A means of suggesting the optimal meeting date and time" refers to a function where the system takes into account the participants' schedules and suggests a date and time that is convenient for everyone to attend.

[0376] "Means of supporting agenda progression" refer to functions that assist in ensuring the agenda progresses smoothly during a meeting.

[0377] "A means of summarizing important statements in real time" refers to a function that quickly compiles the key points from statements made during a meeting.

[0378] "Methods for converting meeting recordings to text" refers to a function that automatically converts audio from a meeting into text data.

[0379] "Methods for extracting the main points of a statement" refers to a function that identifies important information and key points from transcribed meeting recordings.

[0380] "Means of distributing meeting minutes to participants" refers to the function of electronically sending the generated meeting minutes to participants.

[0381] "Means for managing follow-up tasks" refers to functions for tracking and managing actions and tasks that occur after a meeting.

[0382] "Means for recognizing the state of audio and video" refers to a function that analyzes the way participants speak and their facial expressions to determine their emotions and state in real time.

[0383] A "means for detecting abnormal emotional states" is a function that automatically identifies situations where emotional states deviate significantly from normal.

[0384] A "means of issuing an alarm" refers to a function that transmits information to the person in charge when an abnormal emotional state is detected.

[0385] To implement this invention, the server retrieves each participant's schedule information using the international standard open calendar format and proposes the optimal meeting date and time. This process is carried out in accordance with security protocols to prevent the leakage of personal information. In addition, the agenda is automatically generated using machine learning algorithms based on past meeting data.

[0386] During the meeting, the device acquires and analyzes participants' audio and video in real time. Specifically, it collects data using the camera and microphone and performs voice tone analysis and facial expression recognition. This allows it to recognize participants' emotional states and detect abnormal emotional states. Emotion detection uses the Google Cloud Speech-to-Text API to analyze voice tone and facial analysis software to recognize facial expressions.

[0387] Furthermore, if an anomaly is detected, the server will send an alert to the system administrator or designated personnel. SMS notifications using the Twilio API are effective for this purpose, enabling a rapid response.

[0388] After the meeting ends, the server converts the audio recording into text, uses natural language processing technology to highlight key points, and creates meeting minutes. The minutes are distributed to participants, and any follow-up tasks arising from the meeting are organized and managed.

[0389] As a concrete example, by introducing this system into a company's monthly meetings and constantly monitoring the emotional state of all employees, the productivity of the meetings was improved. Furthermore, by quickly identifying follow-up tasks after meetings, work efficiency was enhanced.

[0390] Examples of prompt statements are as follows:

[0391] "Describe a method for detecting unusual emotional states by analyzing video data acquired by security cameras and the voice tone of visitors."

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The server retrieves the meeting participants' schedule information. It obtains participant schedule information from the calendar database using the open calendar format as input and parses it. Based on this parsing, it selects a suitable date and time for the meeting and outputs a suggested date and time.

[0395] Step 2:

[0396] The server collects past meeting data and automatically generates agendas using machine learning algorithms. Using past meeting records as input data, it performs topic modeling with an AI model and provides appropriate agenda items for the next meeting as output.

[0397] Step 3:

[0398] During the meeting, the terminal acquires participants' audio and video data in real time. By using the camera and microphone as input devices and processing this sensor data with voice tone analysis and facial expression recognition software, the emotional state of the participants can be obtained as output.

[0399] Step 4:

[0400] The device detects abnormal emotional states if they differ from the normal state. It sends the output of audio and video analysis to the server, executes logic to determine the abnormality based on the conditions, and outputs the detected abnormality.

[0401] Step 5:

[0402] The server sends an alert to the designated administrator based on the detected abnormal emotional state. It uses the Twilio API to send an SMS or email notification to the system administrator to communicate the alert information.

[0403] Step 6:

[0404] After the meeting ends, the server retrieves the audio recording and uses speech-to-text software to convert the conversation into text. It then takes the audio file as input and outputs a text file.

[0405] Step 7:

[0406] The server summarizes important statements from the converted text data using natural language processing techniques and creates meeting minutes. It analyzes text data as input and generates meeting minutes as output.

[0407] Step 8:

[0408] The server distributes the created meeting minutes to participants and manages follow-up tasks that arise during the meeting. It sends the generated meeting minutes via email, registers the tasks as inputs in the follow-up task management system, and records their completion status as an output.

[0409] Step 9:

[0410] Users receive feedback from meetings and adjust their actions accordingly. They receive information based on meeting minutes and follow-up tasks as input and plan preparations and improvements for the next meeting as output.

[0411] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0412] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0413] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0414] [Third Embodiment]

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

[0416] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0417] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0418] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0419] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0420] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0421] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0422] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0423] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0424] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0425] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0426] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0427] To implement this invention, a system is constructed in which a server, a terminal, and a user cooperate to support efficient meeting management. The specific operation of this system is described below.

[0428] First, the server analyzes the user's calendar information. Based on past meeting data, it extracts recurring topics and important discussion points to automatically generate the agenda for the next meeting. Furthermore, the server retrieves the schedule information of all participants and uses an AI algorithm to suggest the optimal meeting date and time.

[0429] Next, once the meeting begins, the device starts recording the meeting. Simultaneously, it uses speech recognition technology to transcribe the conversation in real time. The device also assists with agenda progression and displays important statements and summaries on the screen to ensure the meeting runs smoothly.

[0430] Audio data recorded during meetings is converted to text by a server, and key points for the meeting minutes are automatically extracted. Natural language processing technology is used in this process to summarize the important points from participants' statements, resulting in more efficient minute-taking.

[0431] The server then automatically distributes the generated meeting minutes to the user and other participants. These minutes include the time, speaker, content, and decisions made at the meeting for each statement.

[0432] Finally, the user reviews the follow-up tasks decided upon during the meeting based on the distributed meeting minutes. These tasks are registered in the system and tracked to ensure they are completed before the next meeting. For example, the user might assign the task "Investigate the cause of the communication failure and prepare a report by the next meeting" to a designated person.

[0433] This system automates the entire process from meeting preparation and management to the creation and distribution of meeting minutes, saving users time and effort. It also streamlines the overall meeting flow, enabling faster business decision-making.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] The server analyzes the user's calendar and collects past meeting data. Using natural language processing technology, it extracts frequently occurring topics and key points, and automatically generates agenda items for the next meeting.

[0437] Step 2:

[0438] The server retrieves the calendar information of all participants. Using an AI algorithm, it calculates the optimal meeting date and time that all participants can attend and proposes it to the user.

[0439] Step 3:

[0440] The device starts recording as soon as the meeting begins. Using speech recognition technology, it transcribes the conversation in real time, displaying an immediate summary and key points. It supports meeting progress and manages time effectively.

[0441] Step 4:

[0442] The server converts the recorded audio data into text and uses natural language processing technology to extract the key points necessary for meeting minutes. It then organizes and summarizes the important points and decisions made by each speaker to create the meeting minutes.

[0443] Step 5:

[0444] The server automatically distributes meeting minutes to users and other participants. Distribution is done via email or a dedicated application.

[0445] Step 6:

[0446] Users review the follow-up tasks confirmed during the meeting based on the distributed meeting minutes. They then assign tasks to responsible individuals and register them in the system to manage progress until the next meeting.

[0447] (Example 1)

[0448] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0449] Traditional meeting management required significant time and effort for scheduling, minute-taking, and managing follow-up tasks. These processes were inefficient and hindered meeting productivity. Therefore, there is a need for a system that automates these processes and manages meetings efficiently and effectively.

[0450] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0451] In this invention, the server includes means for acquiring schedule information and generating a meeting summary, means for analyzing the schedule information and calculating the optimal date and time, and means for converting audio information and extracting key points. This automates a series of processes from meeting preparation and conduct to the creation and distribution of meeting minutes and follow-up, enabling efficient and effective meeting management.

[0452] "Method for acquiring schedule information and generating meeting summaries" refers to technology that collects meeting schedule information and automatically creates an agenda for the next meeting based on that information.

[0453] "Methods for analyzing schedule information and calculating the optimal date and time" refers to technologies that aggregate participants' schedules and use computational technologies such as AI to determine the meeting date and time that is most convenient for the majority of participants.

[0454] "Means of providing summaries of important statements during a meeting using communication methods" refers to technology that analyzes statements made during a meeting in real time and immediately conveys important content to participants.

[0455] "Methods for converting audio information and extracting key points" refers to technology that converts audio data recorded during a meeting into text and automatically extracts the main points of the conversation and important decisions from it.

[0456] "Means of supplying summarized information to stakeholders" refers to technology that automatically sends summarized information about a meeting to meeting participants and relevant individuals.

[0457] "Means for managing post-work procedures" refers to technologies for registering follow-up tasks that arise after a meeting and for tracking and managing their progress.

[0458] The system in this invention streamlines meeting management through the collaboration of a server, a terminal, and a user. The server acquires schedule information and generates a meeting summary. Specifically, it uses a general-purpose calendar API to collect the user's calendar information and a data analysis tool to extract recurring agenda items and key points from past meeting data.

[0459] Furthermore, the server utilizes AI algorithms to analyze participants' schedules and calculate the optimal date and time. This process uses a machine learning framework to suggest the best meeting time, taking participants' availability into account.

[0460] The device records audio as soon as the meeting begins and converts the audio information into text in real time. This conversion utilizes speech recognition technology, and by using a particularly accurate speech conversion service, text is generated instantly. The converted data is then provided to participants on the spot via communication as a summary of important statements.

[0461] Furthermore, the server post-processes the text data of the recorded audio, using natural language processing to extract key points. This data processing extracts the main points of the meeting minutes, and a summary is automatically created. This generated summary is then automatically distributed from the server to the relevant parties.

[0462] Based on the meeting minutes they receive, users manage post-work procedures. Specifically, they access a task management system to register follow-up tasks decided in the meeting and track their progress.

[0463] As a concrete example, a user can input a prompt such as "Prepare the most suitable agenda for the next meeting" into a generative AI model, and the system can then suggest a suitable meeting agenda. This allows users to maximize meeting outcomes while significantly reducing time and effort.

[0464] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0465] Step 1:

[0466] The server retrieves meeting information using the user's calendar API. This inputs schedule information and past meeting data. The server analyzes this data, processing it with data analysis tools to extract recurring topics and key points, and generates a summary for the new meeting. The output is a draft of the agenda for the next meeting.

[0467] Step 2:

[0468] The server retrieves each participant's schedule again from the calendar API and uses an AI algorithm to calculate the optimal meeting date and time. The input here is each participant's schedule information, which is analyzed by a machine learning model to calculate the optimal meeting date. The output is the date and time that is easiest for all participants to attend.

[0469] Step 3:

[0470] The terminal activates a device to record audio data as soon as the meeting starts, and uses real-time speech-to-text technology to convert the recording into text. The input is raw audio data. The terminal utilizes speech-to-text software to convert the audio into text, so that the content of the meeting is output as text information in real time. The converted text is summarized as important statements during the meeting and displayed to the participants.

[0471] Step 4:

[0472] The server receives the text data of the meeting converted by the terminal and generates meeting minutes using natural language processing (NLP) technology. The input is the text data from the meeting. The server uses NLP technology to extract important points from the conversation and automatically summarizes the main points, obtaining summarized information as output.

[0473] Step 5:

[0474] The server distributes the generated meeting minutes to the participants. The input is summarized meeting minutes data, which is used as a collection method to send to participants via email or cloud service. The output is a detailed meeting summary that is delivered to the participants.

[0475] Step 6:

[0476] The user reviews the received meeting minutes and registers the follow-up tasks decided at the meeting into the management system. The input is the content of the meeting minutes. Based on this content, the user identifies the tasks, sets deadlines and assignees, and registers them in the management system. The output is the registered follow-up tasks, structured to facilitate progress tracking.

[0477] (Application Example 1)

[0478] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0479] In both online and in-person meetings, the entire process, from agenda preparation to information management during the meeting and the creation and distribution of meeting minutes, is performed manually, posing a significant challenge due to the time and effort required. In particular, there is a need for a system that allows for efficient scheduling of meeting participants and summarization of important statements. Furthermore, online meeting platforms must maintain similar efficiency while also considering security aspects.

[0480] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0481] In this invention, the server includes means for automatically generating an agenda for meeting preparation, means for acquiring participants' schedule information and proposing the optimal meeting date and time, means for supporting agenda progression during the meeting and summarizing important statements in real time, means for converting meeting recordings into text and extracting key points of statements, means for distributing the generated meeting minutes to participants, means for managing post-meeting follow-up tasks, means for presenting meeting content in real time based on speech recognition technology, and means including a meeting content management function applicable to online meeting platforms. This enables efficient meeting management, expands the scope of use in online environments, and enhances collaboration among participants.

[0482] "Methods for automatically generating agendas" refer to functions that analyze past meeting data and schedule information to automatically create the necessary agenda items and important matters for the next meeting.

[0483] The "method for suggesting the optimal meeting date and time" is a function that takes into account the schedules of all participants and uses an AI algorithm to automatically suggest the most convenient meeting date and time.

[0484] "A means to support agenda progression and summarize important statements in real time" refers to a function that uses speech recognition technology to analyze conversations during meetings and instantly summarizes and displays the key points.

[0485] "A means of converting meeting recordings into text and extracting the main points of what was said" refers to a function that converts recorded audio data into text and uses natural language processing to concisely extract the gist of the conversation.

[0486] "Means for distributing generated meeting minutes to participants" refers to a function that automatically sends the generated meeting minutes to participants via email or as a shared document.

[0487] "Means for managing follow-up tasks" refers to a function that records the next actions or tasks decided in a meeting and tracks their progress.

[0488] "A means of presenting meeting content in real time based on speech recognition technology" refers to a function that instantly converts meeting audio into text information and displays that content in real time.

[0489] "Means including meeting content management functions applicable to online meeting platforms" refers to functions that support efficient meeting management, not only for physical meetings but also for online environments.

[0490] The system for realizing this invention primarily involves the collaboration of three parties: a server, a terminal, and a user. First, the server analyzes the user's calendar information and past meeting data. This extracts frequently occurring meeting topics and important discussion points, automatically generating the agenda necessary for the next meeting. At this time, an AI algorithm is used to obtain the schedule information of all participants and suggest the optimal meeting date and time.

[0491] Next, once the meeting begins, the device starts recording the meeting and uses speech recognition technology such as the Google Cloud Speech-to-Text API to transcribe the conversation in real time. This allows important statements and summaries to be displayed on the device screen for the user to refer to during the meeting. The recorded data is often stored in Firebase, a cloud database.

[0492] After the meeting concludes, the server converts the audio data into text and automatically generates meeting minutes using natural language processing technology. The generated minutes include the time, speaker, content, and decisions made at the meeting, and are efficiently summarized. These minutes are automatically distributed to the user and other participants via email or other means.

[0493] Users review follow-up tasks based on the distributed meeting minutes. Tasks are managed within the system and tracked to ensure completion before the next meeting. For example, a user could assign themselves or another participant the task of "investigating the cause of a communication failure and preparing a report by the next meeting."

[0494] Examples of prompt statements for a generative AI model are as follows:

[0495] "Prompt to give to the generating AI model: 'Summarize the key discussion points regarding the marketing strategy for a new product in a virtual store meeting.'"

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The server retrieves the user's calendar information. The input is the user's calendar information, and the output is an agenda containing the topics and key discussion points needed for the next meeting. An AI algorithm is used to analyze past meeting data and extract recurring meeting themes.

[0499] Step 2:

[0500] The server collects schedule information from all participants. The input is each participant's schedule information, and the output is the optimal meeting date and time suggested by the AI. Based on this information, the server analyzes common free time among participants and sets up the meeting.

[0501] Step 3:

[0502] When the meeting begins, the terminal activates its speech recognition technology and starts recording the meeting. The input is the audio data from the meeting. The output is real-time text data, which is converted into text information by speech recognition. Based on this text, the terminal displays important statements and summaries on the screen.

[0503] Step 4:

[0504] The recorded audio data is stored in a cloud database (e.g., Firebase). The input is the audio data sent from the device. The output is securely stored data that is used for subsequent data processing.

[0505] Step 5:

[0506] After the meeting ends, the server converts the audio data into text. The input is recorded audio data, and the output is a meeting transcript summarized using natural language processing. The server analyzes important statements and discussion points and summarizes them efficiently.

[0507] Step 6:

[0508] The server distributes the generated meeting minutes to users and participants via email, etc. The input is the meeting minutes data, and the output is the distributed emails and shared documents. The server automatically sends the meeting minutes, enabling rapid information sharing among participants.

[0509] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0510] To implement this invention, in addition to a basic meeting management support system, an emotion engine that recognizes and analyzes the user's emotional state is incorporated. The specific operation is described below.

[0511] First, the server analyzes participants' calendar information and automatically generates the agenda for the next meeting. This process includes extracting keywords from past meeting data and learning from similar topics. AI also suggests optimal meeting dates and times. Secure data access is crucial in this process, and access to participants' calendar information is managed according to security protocols.

[0512] Next, once the meeting begins, the terminal processes the audio and video data of the meeting participants in real time and performs emotion recognition using an emotion engine. The emotion engine determines the user's emotional state from their tone of voice and facial expressions and collects this data. This information is used to monitor the progress of the meeting and the reactions of the participants.

[0513] Furthermore, based on real-time monitoring during the meeting, the server provides users with advice on how to proceed and adjusts the agenda as needed. For example, if participants' emotions are leaning towards negativity, it may suggest taking more breaks or changing the topic.

[0514] Once the meeting concludes, the server transcribes the recorded data into text and uses natural language processing techniques to create meeting minutes. These minutes also include feedback on the emotional states of participants as perceived during the meeting. Furthermore, follow-up tasks that need to be addressed after the meeting are organized and presented to the user.

[0515] Finally, users adjust their actions based on meeting feedback and prepare for the next meeting. For example, they can take measures such as improving the content of their presentation based on the results of sentiment recognition.

[0516] This invention highly optimizes meeting management and takes into account the emotional state of participants, thereby promoting more effective communication and decision-making. In this way, users can improve the quality of meetings.

[0517] The following describes the processing flow.

[0518] Step 1:

[0519] The server analyzes the user's calendar data and past meeting records. Using machine learning algorithms, it extracts patterns and frequently occurring topics to automatically generate the agenda for the next meeting.

[0520] Step 2:

[0521] The server retrieves calendar information from all participants and uses AI to calculate the date and time when each participant is most likely to attend. It then suggests the calculated meeting date and time to the user.

[0522] Step 3:

[0523] The device begins capturing audio and video data at the start of the meeting. The emotion engine is activated, analyzing participants' facial expressions and tone of voice in real time to obtain their emotional state.

[0524] Step 4:

[0525] The server uses real-time emotional data to adjust the progress of the meeting. For example, if negative emotions increase, it suggests revising the agenda or changing the topic to facilitate smooth meeting management.

[0526] Step 5:

[0527] During the meeting, the device uses speech recognition technology to transcribe the conversation into text. Based on this text data, a summary and key points are extracted and displayed to participants in real time.

[0528] Step 6:

[0529] After the meeting ends, the server completely transcribes the audio data into text and automatically generates meeting minutes using natural language processing technology. The minutes also include feedback on the participants' emotional tendencies.

[0530] Step 7:

[0531] Users review meeting minutes and sentiment feedback delivered from the server. They plan improvements for future meetings and business processes and set up necessary follow-up tasks.

[0532] (Example 2)

[0533] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0534] In meeting management, it is essential to provide an environment that allows participants to proceed efficiently and smoothly. In particular, challenges include ensuring smooth planning, monitoring participants' emotional states during the meeting, and conducting follow-up afterward. Furthermore, a system is needed that can appropriately understand the emotions of meeting participants and make adjustments accordingly.

[0535] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0536] In this invention, the server includes means for analyzing the user's schedule information and automatically forming a meeting plan, means for obtaining knowledge from past meeting data to suggest the optimal date and time, and means for analyzing and predicting the emotional state of participants during the meeting and providing assistance for its progress. This optimizes the entire process from preparation before the meeting to progress management on the day of the meeting and follow-up after the meeting, enabling smooth meeting management that takes into account the emotions of the participants.

[0537] An "electronic computing device" refers to a computer system used for various data processing tasks, and is a device that plays a role in managing each process of conference operations.

[0538] "User schedule information" refers to data showing the schedules and appointments previously set by meeting participants, and is used to plan the meeting optimally.

[0539] "Automatically generating a meeting plan" refers to the process by which a computer automatically assembles the meeting agenda and other necessary information based on the participants' availability.

[0540] "Gaining knowledge and proposing the optimal date and time" means analyzing past data and calculating the date and time when all participants can participate most efficiently.

[0541] "Analyzing and inferring participants' emotional states to assist in meeting progress" refers to the process of analyzing participants' audio and video in real time during a meeting, understanding their emotions, and then providing appropriate advice for conducting the meeting.

[0542] "Progress management" refers to the management activity of appropriately controlling the flow of a meeting and ensuring that it proceeds smoothly according to schedule.

[0543] "Follow-up" refers to the process of identifying tasks and issues that require further review or action after a meeting, and presenting them to the relevant parties.

[0544] This invention is a system that highly optimizes the management of meetings using an electronic computing device. This system consists of a server, terminals, and users, and effectively utilizes various data to achieve smooth meeting management.

[0545] The server first retrieves participants' schedule information. Specifically, it collects schedule data using a calendar API and automatically generates a meeting agenda using an AI model based on past meeting data. During this process, it extracts keywords and important points using natural language processing and proposes the optimal date. The technologies used include a calendar API and natural language processing.

[0546] During the meeting, the terminal collects participants' audio and video in real time and analyzes their emotional state using an emotion engine. Image and audio analysis technologies infer emotions from facial expressions and tone of voice, and send this data to the server. The server then supports the meeting's progress and, in some cases, provides progress advice through a generated AI model. For example, if the server determines that a participant is tired, it may send a notification to the user asking, "Shall we suggest a 5-minute break?"

[0547] After the meeting ends, the server converts the audio data to text and automatically generates meeting minutes. It uses a speech-to-text API to create a meeting summary from the resulting text. The generated minutes also include each participant's emotional feedback and any follow-up tasks.

[0548] The user uses this information to prepare for the next meeting and improve the presentation content as needed. An example of a prompt might be, "Use the meeting audio data collected by the server to generate a summary of the emotional state of the participants during the meeting." This invention provides a means to facilitate more effective communication and appropriate decision-making in order to improve the quality of meetings.

[0549] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0550] Step 1:

[0551] The server retrieves participants' calendar information. It obtains participant schedule data via a calendar API as input and references a database of past meetings. Based on this information, the server inputs data into an AI model to automatically generate a meeting agenda. Specifically, the server sends a prompt to the AI ​​model, asking, "What are the most important topics for the next meeting?" Through this data processing, the server obtains an optimized agenda as output.

[0552] Step 2:

[0553] The server suggests the optimal meeting date and time based on data analyzed by an AI model. Using past meeting attendance history and participants' schedules as input, the AI ​​algorithm calculates multiple candidate dates and times. This allows the server to identify the date and time that is most convenient for everyone. Specifically, a prompt such as "Which time slot is best for everyone?" is sent to the AI ​​model, and the optimal meeting date and time are provided as output.

[0554] Step 3:

[0555] The device collects participants' audio and video during the meeting. It receives real-time feeds from the camera and microphone as input, and the emotion engine processes this data using image and audio analysis technologies. The device sends the emotion data to a server, which is used as information necessary for the progress of the meeting. Specifically, when the voice tone rises, it sends a prompt to the AI ​​model to "perform an emotion analysis on this voice," and the emotional state is analyzed as output.

[0556] Step 4:

[0557] The server provides meeting guidance based on emotional data received from terminals. It receives data from the emotional engine as input, analyzes it, and suggests topic changes or breaks as needed. For example, a notification might be generated stating, "Participants' stress levels are high. Shall we suggest a break?" and the appropriate action to take is determined as output.

[0558] Step 5:

[0559] After the meeting ends, the server converts the recorded audio data into text. Using an audio file as input, it generates text data via a speech-to-text API. Next, this text information is analyzed using natural language processing techniques to create a meeting summary. Specifically, using the prompt "Create a summary from this audio recording," the output is meeting minutes containing the key points of the meeting.

[0560] Step 6:

[0561] The user prepares for the next meeting based on meeting minutes generated by the server and feedback from sentiment analysis. The user receives the meeting minutes as input, reviews their content, and adjusts their actions as needed. Specifically, the user considers "What needs improvement from the last meeting?" and, based on that, creates a concrete action plan for the next meeting as output, improving the presentation content.

[0562] (Application Example 2)

[0563] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0564] Traditional meeting management systems struggled to recognize participants' emotions and respond appropriately according to the meeting's progress. This made it difficult to prevent problems arising from participants' emotional states during meetings, hindering smooth meeting progress. Furthermore, creating meeting minutes and managing follow-up tasks after meetings were time-consuming and inefficient. A system was needed to address these challenges, perform real-time emotion recognition, and quickly detect and address abnormal situations.

[0565] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0566] In this invention, the server includes means for recognizing the audio and video status of participants and detecting abnormal emotional states, and means for issuing an alarm when an abnormal emotional state is detected. This makes it possible to monitor participants' emotional states in real time during a meeting, quickly detect abnormal states, and take appropriate action. It also allows for efficient management of post-meeting follow-up tasks and reduces the burden of creating meeting minutes.

[0567] "Meeting preparation" refers to preparatory activities such as creating an agenda necessary for the smooth running of a meeting and coordinating participants' schedules.

[0568] "Methods for automatically generating agendas" refers to a function where the system automatically creates the agenda and content for the next meeting based on past meeting data and participant schedule information.

[0569] "Participant schedule information" refers to event information registered in the schedules and calendars of meeting participants.

[0570] "A means of suggesting the optimal meeting date and time" refers to a function where the system takes into account the participants' schedules and suggests a date and time that is convenient for everyone to attend.

[0571] "Means of supporting agenda progression" refer to functions that assist in ensuring the agenda progresses smoothly during a meeting.

[0572] "A means of summarizing important statements in real time" refers to a function that quickly compiles the key points from statements made during a meeting.

[0573] "Methods for converting meeting recordings to text" refers to a function that automatically converts audio from a meeting into text data.

[0574] "Methods for extracting the main points of a statement" refers to a function that identifies important information and key points from transcribed meeting recordings.

[0575] "Means of distributing meeting minutes to participants" refers to the function of electronically sending the generated meeting minutes to participants.

[0576] "Means for managing follow-up tasks" refers to functions for tracking and managing actions and tasks that occur after a meeting.

[0577] "Means for recognizing the state of audio and video" refers to a function that analyzes the way participants speak and their facial expressions to determine their emotions and state in real time.

[0578] A "means for detecting abnormal emotional states" is a function that automatically identifies situations where emotional states deviate significantly from normal.

[0579] A "means of issuing an alarm" refers to a function that transmits information to the person in charge when an abnormal emotional state is detected.

[0580] To implement this invention, the server retrieves each participant's schedule information using the international standard open calendar format and proposes the optimal meeting date and time. This process is carried out in accordance with security protocols to prevent the leakage of personal information. In addition, the agenda is automatically generated using machine learning algorithms based on past meeting data.

[0581] During the meeting, the device acquires and analyzes participants' audio and video in real time. Specifically, it collects data using the camera and microphone and performs voice tone analysis and facial expression recognition. This allows it to recognize participants' emotional states and detect abnormal emotional states. Emotion detection uses the Google Cloud Speech-to-Text API to analyze voice tone and facial analysis software to recognize facial expressions.

[0582] Furthermore, if an anomaly is detected, the server will send an alert to the system administrator or designated personnel. SMS notifications using the Twilio API are effective for this purpose, enabling a rapid response.

[0583] After the meeting ends, the server converts the audio recording into text, uses natural language processing technology to highlight key points, and creates meeting minutes. The minutes are distributed to participants, and any follow-up tasks arising from the meeting are organized and managed.

[0584] As a concrete example, by introducing this system into a company's monthly meetings and constantly monitoring the emotional state of all employees, the productivity of the meetings was improved. Furthermore, by quickly identifying follow-up tasks after meetings, work efficiency was enhanced.

[0585] Examples of prompt statements are as follows:

[0586] "Describe a method for detecting unusual emotional states by analyzing video data acquired by security cameras and the voice tone of visitors."

[0587] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0588] Step 1:

[0589] The server retrieves the meeting participants' schedule information. It obtains participant schedule information from the calendar database using the open calendar format as input and parses it. Based on this parsing, it selects a suitable date and time for the meeting and outputs a suggested date and time.

[0590] Step 2:

[0591] The server collects past meeting data and automatically generates agendas using machine learning algorithms. Using past meeting records as input data, it performs topic modeling with an AI model and provides appropriate agenda items for the next meeting as output.

[0592] Step 3:

[0593] During the meeting, the terminal acquires participants' audio and video data in real time. By using the camera and microphone as input devices and processing this sensor data with voice tone analysis and facial expression recognition software, the emotional state of the participants can be obtained as output.

[0594] Step 4:

[0595] The device detects abnormal emotional states if they differ from the normal state. It sends the output of audio and video analysis to the server, executes logic to determine the abnormality based on the conditions, and outputs the detected abnormality.

[0596] Step 5:

[0597] The server sends an alert to the designated administrator based on the detected abnormal emotional state. It uses the Twilio API to send an SMS or email notification to the system administrator to communicate the alert information.

[0598] Step 6:

[0599] After the meeting ends, the server retrieves the audio recording and uses speech-to-text software to convert the conversation into text. It then takes the audio file as input and outputs a text file.

[0600] Step 7:

[0601] The server summarizes important statements from the converted text data using natural language processing techniques and creates meeting minutes. It analyzes text data as input and generates meeting minutes as output.

[0602] Step 8:

[0603] The server distributes the created meeting minutes to participants and manages follow-up tasks that arise during the meeting. It sends the generated meeting minutes via email, registers the tasks as inputs in the follow-up task management system, and records their completion status as an output.

[0604] Step 9:

[0605] Users receive feedback from meetings and adjust their actions accordingly. They receive information based on meeting minutes and follow-up tasks as input and plan preparations and improvements for the next meeting as output.

[0606] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0607] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0608] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0609] [Fourth Embodiment]

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

[0611] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0612] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0613] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0614] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0615] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0616] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0617] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0618] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0619] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0620] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0621] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0622] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0623] To implement this invention, a system is constructed in which a server, a terminal, and a user cooperate to support efficient meeting management. The specific operation of this system is described below.

[0624] First, the server analyzes the user's calendar information. Based on past meeting data, it extracts recurring topics and important discussion points to automatically generate the agenda for the next meeting. Furthermore, the server retrieves the schedule information of all participants and uses an AI algorithm to suggest the optimal meeting date and time.

[0625] Next, once the meeting begins, the device starts recording the meeting. Simultaneously, it uses speech recognition technology to transcribe the conversation in real time. The device also assists with agenda progression and displays important statements and summaries on the screen to ensure the meeting runs smoothly.

[0626] Audio data recorded during meetings is converted to text by a server, and key points for the meeting minutes are automatically extracted. Natural language processing technology is used in this process to summarize the important points from participants' statements, resulting in more efficient minute-taking.

[0627] The server then automatically distributes the generated meeting minutes to the user and other participants. These minutes include the time, speaker, content, and decisions made at the meeting for each statement.

[0628] Finally, the user reviews the follow-up tasks decided upon during the meeting based on the distributed meeting minutes. These tasks are registered in the system and tracked to ensure they are completed before the next meeting. For example, the user might assign the task "Investigate the cause of the communication failure and prepare a report by the next meeting" to a designated person.

[0629] This system automates the entire process from meeting preparation and management to the creation and distribution of meeting minutes, saving users time and effort. It also streamlines the overall meeting flow, enabling faster business decision-making.

[0630] The following describes the processing flow.

[0631] Step 1:

[0632] The server analyzes the user's calendar and collects past meeting data. Using natural language processing technology, it extracts frequently occurring topics and key points, and automatically generates agenda items for the next meeting.

[0633] Step 2:

[0634] The server retrieves the calendar information of all participants. Using an AI algorithm, it calculates the optimal meeting date and time that all participants can attend and proposes it to the user.

[0635] Step 3:

[0636] The device starts recording as soon as the meeting begins. Using speech recognition technology, it transcribes the conversation in real time, displaying an immediate summary and key points. It supports meeting progress and manages time effectively.

[0637] Step 4:

[0638] The server converts the recorded audio data into text and uses natural language processing technology to extract the key points necessary for meeting minutes. It then organizes and summarizes the important points and decisions made by each speaker to create the meeting minutes.

[0639] Step 5:

[0640] The server automatically distributes meeting minutes to users and other participants. Distribution is done via email or a dedicated application.

[0641] Step 6:

[0642] Users review the follow-up tasks confirmed during the meeting based on the distributed meeting minutes. They then assign tasks to responsible individuals and register them in the system to manage progress until the next meeting.

[0643] (Example 1)

[0644] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0645] Traditional meeting management required significant time and effort for scheduling, minute-taking, and managing follow-up tasks. These processes were inefficient and hindered meeting productivity. Therefore, there is a need for a system that automates these processes and manages meetings efficiently and effectively.

[0646] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0647] In this invention, the server includes means for acquiring schedule information and generating a meeting summary, means for analyzing the schedule information and calculating the optimal date and time, and means for converting audio information and extracting key points. This automates a series of processes from meeting preparation and conduct to the creation and distribution of meeting minutes and follow-up, enabling efficient and effective meeting management.

[0648] "Method for acquiring schedule information and generating meeting summaries" refers to technology that collects meeting schedule information and automatically creates an agenda for the next meeting based on that information.

[0649] "Methods for analyzing schedule information and calculating the optimal date and time" refers to technologies that aggregate participants' schedules and use computational technologies such as AI to determine the meeting date and time that is most convenient for the majority of participants.

[0650] "Means of providing summaries of important statements during a meeting using communication methods" refers to technology that analyzes statements made during a meeting in real time and immediately conveys important content to participants.

[0651] "Methods for converting audio information and extracting key points" refers to technology that converts audio data recorded during a meeting into text and automatically extracts the main points of the conversation and important decisions from it.

[0652] "Means of supplying summarized information to stakeholders" refers to technology that automatically sends summarized information about a meeting to meeting participants and relevant individuals.

[0653] "Means for managing post-work procedures" refers to technologies for registering follow-up tasks that arise after a meeting and for tracking and managing their progress.

[0654] The system in this invention streamlines meeting management through the collaboration of a server, a terminal, and a user. The server acquires schedule information and generates a meeting summary. Specifically, it uses a general-purpose calendar API to collect the user's calendar information and a data analysis tool to extract recurring agenda items and key points from past meeting data.

[0655] Furthermore, the server utilizes AI algorithms to analyze participants' schedules and calculate the optimal date and time. This process uses a machine learning framework to suggest the best meeting time, taking participants' availability into account.

[0656] The device records audio as soon as the meeting begins and converts the audio information into text in real time. This conversion utilizes speech recognition technology, and by using a particularly accurate speech conversion service, text is generated instantly. The converted data is then provided to participants on the spot via communication as a summary of important statements.

[0657] Furthermore, the server post-processes the text data of the recorded audio, using natural language processing to extract key points. This data processing extracts the main points of the meeting minutes, and a summary is automatically created. This generated summary is then automatically distributed from the server to the relevant parties.

[0658] Based on the meeting minutes they receive, users manage post-work procedures. Specifically, they access a task management system to register follow-up tasks decided in the meeting and track their progress.

[0659] As a concrete example, a user can input a prompt such as "Prepare the most suitable agenda for the next meeting" into a generative AI model, and the system can then suggest a suitable meeting agenda. This allows users to maximize meeting outcomes while significantly reducing time and effort.

[0660] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0661] Step 1:

[0662] The server retrieves meeting information using the user's calendar API. This inputs schedule information and past meeting data. The server analyzes this data, processing it with data analysis tools to extract recurring topics and key points, and generates a summary for the new meeting. The output is a draft of the agenda for the next meeting.

[0663] Step 2:

[0664] The server retrieves each participant's schedule again from the calendar API and uses an AI algorithm to calculate the optimal meeting date and time. The input here is each participant's schedule information, which is analyzed by a machine learning model to calculate the optimal meeting date. The output is the date and time that is easiest for all participants to attend.

[0665] Step 3:

[0666] The terminal activates a device to record audio data as soon as the meeting starts, and uses real-time speech-to-text technology to convert the recording into text. The input is raw audio data. The terminal utilizes speech-to-text software to convert the audio into text, so that the content of the meeting is output as text information in real time. The converted text is summarized as important statements during the meeting and displayed to the participants.

[0667] Step 4:

[0668] The server receives the text data of the meeting converted by the terminal and generates meeting minutes using natural language processing (NLP) technology. The input is the text data from the meeting. The server uses NLP technology to extract important points from the conversation and automatically summarizes the main points, obtaining summarized information as output.

[0669] Step 5:

[0670] The server distributes the generated meeting minutes to the participants. The input is summarized meeting minutes data, which is used as a collection method to send to participants via email or cloud service. The output is a detailed meeting summary that is delivered to the participants.

[0671] Step 6:

[0672] The user reviews the received meeting minutes and registers the follow-up tasks decided at the meeting into the management system. The input is the content of the meeting minutes. Based on this content, the user identifies the tasks, sets deadlines and assignees, and registers them in the management system. The output is the registered follow-up tasks, structured to facilitate progress tracking.

[0673] (Application Example 1)

[0674] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0675] In both online and in-person meetings, the entire process, from agenda preparation to information management during the meeting and the creation and distribution of meeting minutes, is performed manually, posing a significant challenge due to the time and effort required. In particular, there is a need for a system that allows for efficient scheduling of meeting participants and summarization of important statements. Furthermore, online meeting platforms must maintain similar efficiency while also considering security aspects.

[0676] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0677] In this invention, the server includes means for automatically generating an agenda for meeting preparation, means for acquiring participants' schedule information and proposing the optimal meeting date and time, means for supporting agenda progression during the meeting and summarizing important statements in real time, means for converting meeting recordings into text and extracting key points of statements, means for distributing the generated meeting minutes to participants, means for managing post-meeting follow-up tasks, means for presenting meeting content in real time based on speech recognition technology, and means including a meeting content management function applicable to online meeting platforms. This enables efficient meeting management, expands the scope of use in online environments, and enhances collaboration among participants.

[0678] "Methods for automatically generating agendas" refer to functions that analyze past meeting data and schedule information to automatically create the necessary agenda items and important matters for the next meeting.

[0679] The "method for suggesting the optimal meeting date and time" is a function that takes into account the schedules of all participants and uses an AI algorithm to automatically suggest the most convenient meeting date and time.

[0680] "A means to support agenda progression and summarize important statements in real time" refers to a function that uses speech recognition technology to analyze conversations during meetings and instantly summarizes and displays the key points.

[0681] "A means of converting meeting recordings into text and extracting the main points of what was said" refers to a function that converts recorded audio data into text and uses natural language processing to concisely extract the gist of the conversation.

[0682] "Means for distributing generated meeting minutes to participants" refers to a function that automatically sends the generated meeting minutes to participants via email or as a shared document.

[0683] "Means for managing follow-up tasks" refers to a function that records the next actions or tasks decided in a meeting and tracks their progress.

[0684] "A means of presenting meeting content in real time based on speech recognition technology" refers to a function that instantly converts meeting audio into text information and displays that content in real time.

[0685] "Means including meeting content management functions applicable to online meeting platforms" refers to functions that support efficient meeting management, not only for physical meetings but also for online environments.

[0686] The system for realizing this invention primarily involves the collaboration of three parties: a server, a terminal, and a user. First, the server analyzes the user's calendar information and past meeting data. This extracts frequently occurring meeting topics and important discussion points, automatically generating the agenda necessary for the next meeting. At this time, an AI algorithm is used to obtain the schedule information of all participants and suggest the optimal meeting date and time.

[0687] Next, once the meeting begins, the device starts recording the meeting and uses speech recognition technology such as the Google Cloud Speech-to-Text API to transcribe the conversation in real time. This allows important statements and summaries to be displayed on the device screen for the user to refer to during the meeting. The recorded data is often stored in Firebase, a cloud database.

[0688] After the meeting concludes, the server converts the audio data into text and automatically generates meeting minutes using natural language processing technology. The generated minutes include the time, speaker, content, and decisions made at the meeting, and are efficiently summarized. These minutes are automatically distributed to the user and other participants via email or other means.

[0689] Users review follow-up tasks based on the distributed meeting minutes. Tasks are managed within the system and tracked to ensure completion before the next meeting. For example, a user could assign themselves or another participant the task of "investigating the cause of a communication failure and preparing a report by the next meeting."

[0690] Examples of prompt statements for a generative AI model are as follows:

[0691] "Prompt to give to the generating AI model: 'Summarize the key discussion points regarding the marketing strategy for a new product in a virtual store meeting.'"

[0692] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0693] Step 1:

[0694] The server retrieves the user's calendar information. The input is the user's calendar information, and the output is an agenda containing the topics and key discussion points needed for the next meeting. An AI algorithm is used to analyze past meeting data and extract recurring meeting themes.

[0695] Step 2:

[0696] The server collects schedule information from all participants. The input is each participant's schedule information, and the output is the optimal meeting date and time suggested by the AI. Based on this information, the server analyzes common free time among participants and sets up the meeting.

[0697] Step 3:

[0698] When the meeting begins, the terminal activates its speech recognition technology and starts recording the meeting. The input is the audio data from the meeting. The output is real-time text data, which is converted into text information by speech recognition. Based on this text, the terminal displays important statements and summaries on the screen.

[0699] Step 4:

[0700] The recorded audio data is stored in a cloud database (e.g., Firebase). The input is the audio data sent from the device. The output is securely stored data that is used for subsequent data processing.

[0701] Step 5:

[0702] After the meeting ends, the server converts the audio data into text. The input is recorded audio data, and the output is a meeting transcript summarized using natural language processing. The server analyzes important statements and discussion points and summarizes them efficiently.

[0703] Step 6:

[0704] The server distributes the generated meeting minutes to users and participants via email, etc. The input is the meeting minutes data, and the output is the distributed emails and shared documents. The server automatically sends the meeting minutes, enabling rapid information sharing among participants.

[0705] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0706] To implement this invention, in addition to a basic meeting management support system, an emotion engine that recognizes and analyzes the user's emotional state is incorporated. The specific operation is described below.

[0707] First, the server analyzes participants' calendar information and automatically generates the agenda for the next meeting. This process includes extracting keywords from past meeting data and learning from similar topics. AI also suggests optimal meeting dates and times. Secure data access is crucial in this process, and access to participants' calendar information is managed according to security protocols.

[0708] Next, once the meeting begins, the terminal processes the audio and video data of the meeting participants in real time and performs emotion recognition using an emotion engine. The emotion engine determines the user's emotional state from their tone of voice and facial expressions and collects this data. This information is used to monitor the progress of the meeting and the reactions of the participants.

[0709] Furthermore, based on real-time monitoring during the meeting, the server provides users with advice on how to proceed and adjusts the agenda as needed. For example, if participants' emotions are leaning towards negativity, it may suggest taking more breaks or changing the topic.

[0710] Once the meeting concludes, the server transcribes the recorded data into text and uses natural language processing techniques to create meeting minutes. These minutes also include feedback on the emotional states of participants as perceived during the meeting. Furthermore, follow-up tasks that need to be addressed after the meeting are organized and presented to the user.

[0711] Finally, users adjust their actions based on meeting feedback and prepare for the next meeting. For example, they can take measures such as improving the content of their presentation based on the results of sentiment recognition.

[0712] This invention highly optimizes meeting management and takes into account the emotional state of participants, thereby promoting more effective communication and decision-making. In this way, users can improve the quality of meetings.

[0713] The following describes the processing flow.

[0714] Step 1:

[0715] The server analyzes the user's calendar data and past meeting records. Using machine learning algorithms, it extracts patterns and frequently occurring topics to automatically generate the agenda for the next meeting.

[0716] Step 2:

[0717] The server retrieves calendar information from all participants and uses AI to calculate the date and time when each participant is most likely to attend. It then suggests the calculated meeting date and time to the user.

[0718] Step 3:

[0719] The device begins capturing audio and video data at the start of the meeting. The emotion engine is activated, analyzing participants' facial expressions and tone of voice in real time to obtain their emotional state.

[0720] Step 4:

[0721] The server uses real-time emotional data to adjust the progress of the meeting. For example, if negative emotions increase, it suggests revising the agenda or changing the topic to facilitate smooth meeting management.

[0722] Step 5:

[0723] During the meeting, the device uses speech recognition technology to transcribe the conversation into text. Based on this text data, a summary and key points are extracted and displayed to participants in real time.

[0724] Step 6:

[0725] After the meeting ends, the server completely transcribes the audio data into text and automatically generates meeting minutes using natural language processing technology. The minutes also include feedback on the participants' emotional tendencies.

[0726] Step 7:

[0727] Users review meeting minutes and sentiment feedback delivered from the server. They plan improvements for future meetings and business processes and set up necessary follow-up tasks.

[0728] (Example 2)

[0729] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0730] In meeting management, it is essential to provide an environment that allows participants to proceed efficiently and smoothly. In particular, challenges include ensuring smooth planning, monitoring participants' emotional states during the meeting, and conducting follow-up afterward. Furthermore, a system is needed that can appropriately understand the emotions of meeting participants and make adjustments accordingly.

[0731] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0732] In this invention, the server includes means for analyzing the user's schedule information and automatically forming a meeting plan, means for obtaining knowledge from past meeting data to suggest the optimal date and time, and means for analyzing and predicting the emotional state of participants during the meeting and providing assistance for its progress. This optimizes the entire process from preparation before the meeting to progress management on the day of the meeting and follow-up after the meeting, enabling smooth meeting management that takes into account the emotions of the participants.

[0733] An "electronic computing device" refers to a computer system used for various data processing tasks, and is a device that plays a role in managing each process of conference operations.

[0734] "User schedule information" refers to data showing the schedules and appointments previously set by meeting participants, and is used to plan the meeting optimally.

[0735] "Automatically generating a meeting plan" refers to the process by which a computer automatically assembles the meeting agenda and other necessary information based on the participants' availability.

[0736] "Gaining knowledge and proposing the optimal date and time" means analyzing past data and calculating the date and time when all participants can participate most efficiently.

[0737] "Analyzing and inferring participants' emotional states to assist in meeting progress" refers to the process of analyzing participants' audio and video in real time during a meeting, understanding their emotions, and then providing appropriate advice for conducting the meeting.

[0738] "Progress management" refers to the management activity of appropriately controlling the flow of a meeting and ensuring that it proceeds smoothly according to schedule.

[0739] "Follow-up" refers to the process of identifying tasks and issues that require further review or action after a meeting, and presenting them to the relevant parties.

[0740] This invention is a system that highly optimizes the management of meetings using an electronic computing device. This system consists of a server, terminals, and users, and effectively utilizes various data to achieve smooth meeting management.

[0741] The server first retrieves participants' schedule information. Specifically, it collects schedule data using a calendar API and automatically generates a meeting agenda using an AI model based on past meeting data. During this process, it extracts keywords and important points using natural language processing and proposes the optimal date. The technologies used include a calendar API and natural language processing.

[0742] During the meeting, the terminal collects participants' audio and video in real time and analyzes their emotional state using an emotion engine. Image and audio analysis technologies infer emotions from facial expressions and tone of voice, and send this data to the server. The server then supports the meeting's progress and, in some cases, provides progress advice through a generated AI model. For example, if the server determines that a participant is tired, it may send a notification to the user asking, "Shall we suggest a 5-minute break?"

[0743] After the meeting ends, the server converts the audio data to text and automatically generates meeting minutes. It uses a speech-to-text API to create a meeting summary from the resulting text. The generated minutes also include each participant's emotional feedback and any follow-up tasks.

[0744] The user uses this information to prepare for the next meeting and improve the presentation content as needed. An example of a prompt might be, "Use the meeting audio data collected by the server to generate a summary of the emotional state of the participants during the meeting." This invention provides a means to facilitate more effective communication and appropriate decision-making in order to improve the quality of meetings.

[0745] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0746] Step 1:

[0747] The server retrieves participants' calendar information. It obtains participant schedule data via a calendar API as input and references a database of past meetings. Based on this information, the server inputs data into an AI model to automatically generate a meeting agenda. Specifically, the server sends a prompt to the AI ​​model, asking, "What are the most important topics for the next meeting?" Through this data processing, the server obtains an optimized agenda as output.

[0748] Step 2:

[0749] The server suggests the optimal meeting date and time based on data analyzed by an AI model. Using past meeting attendance history and participants' schedules as input, the AI ​​algorithm calculates multiple candidate dates and times. This allows the server to identify the date and time that is most convenient for everyone. Specifically, a prompt such as "Which time slot is best for everyone?" is sent to the AI ​​model, and the optimal meeting date and time are provided as output.

[0750] Step 3:

[0751] The device collects participants' audio and video during the meeting. It receives real-time feeds from the camera and microphone as input, and the emotion engine processes this data using image and audio analysis technologies. The device sends the emotion data to a server, which is used as information necessary for the progress of the meeting. Specifically, when the voice tone rises, it sends a prompt to the AI ​​model to "perform an emotion analysis on this voice," and the emotional state is analyzed as output.

[0752] Step 4:

[0753] The server provides meeting guidance based on emotional data received from terminals. It receives data from the emotional engine as input, analyzes it, and suggests topic changes or breaks as needed. For example, a notification might be generated stating, "Participants' stress levels are high. Shall we suggest a break?" and the appropriate action to take is determined as output.

[0754] Step 5:

[0755] After the meeting ends, the server converts the recorded audio data into text. Using an audio file as input, it generates text data via a speech-to-text API. Next, this text information is analyzed using natural language processing techniques to create a meeting summary. Specifically, using the prompt "Create a summary from this audio recording," the output is meeting minutes containing the key points of the meeting.

[0756] Step 6:

[0757] The user prepares for the next meeting based on meeting minutes generated by the server and feedback from sentiment analysis. The user receives the meeting minutes as input, reviews their content, and adjusts their actions as needed. Specifically, the user considers "What needs improvement from the last meeting?" and, based on that, creates a concrete action plan for the next meeting as output, improving the presentation content.

[0758] (Application Example 2)

[0759] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0760] Traditional meeting management systems struggled to recognize participants' emotions and respond appropriately according to the meeting's progress. This made it difficult to prevent problems arising from participants' emotional states during meetings, hindering smooth meeting progress. Furthermore, creating meeting minutes and managing follow-up tasks after meetings were time-consuming and inefficient. A system was needed to address these challenges, perform real-time emotion recognition, and quickly detect and address abnormal situations.

[0761] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0762] In this invention, the server includes means for recognizing the audio and video status of participants and detecting abnormal emotional states, and means for issuing an alarm when an abnormal emotional state is detected. This makes it possible to monitor participants' emotional states in real time during a meeting, quickly detect abnormal states, and take appropriate action. It also allows for efficient management of post-meeting follow-up tasks and reduces the burden of creating meeting minutes.

[0763] "Meeting preparation" refers to preparatory activities such as creating an agenda necessary for the smooth running of a meeting and coordinating participants' schedules.

[0764] "Methods for automatically generating agendas" refers to a function where the system automatically creates the agenda and content for the next meeting based on past meeting data and participant schedule information.

[0765] "Participant schedule information" refers to event information registered in the schedules and calendars of meeting participants.

[0766] "A means of suggesting the optimal meeting date and time" refers to a function where the system takes into account the participants' schedules and suggests a date and time that is convenient for everyone to attend.

[0767] "Means of supporting agenda progression" refer to functions that assist in ensuring the agenda progresses smoothly during a meeting.

[0768] "A means of summarizing important statements in real time" refers to a function that quickly compiles the key points from statements made during a meeting.

[0769] "Methods for converting meeting recordings to text" refers to a function that automatically converts audio from a meeting into text data.

[0770] "Methods for extracting the main points of a statement" refers to a function that identifies important information and key points from transcribed meeting recordings.

[0771] "Means of distributing meeting minutes to participants" refers to the function of electronically sending the generated meeting minutes to participants.

[0772] "Means for managing follow-up tasks" refers to functions for tracking and managing actions and tasks that occur after a meeting.

[0773] "Means for recognizing the state of audio and video" refers to a function that analyzes the way participants speak and their facial expressions to determine their emotions and state in real time.

[0774] A "means for detecting abnormal emotional states" is a function that automatically identifies situations where emotional states deviate significantly from normal.

[0775] A "means of issuing an alarm" refers to a function that transmits information to the person in charge when an abnormal emotional state is detected.

[0776] To implement this invention, the server retrieves each participant's schedule information using the international standard open calendar format and proposes the optimal meeting date and time. This process is carried out in accordance with security protocols to prevent the leakage of personal information. In addition, the agenda is automatically generated using machine learning algorithms based on past meeting data.

[0777] During the meeting, the device acquires and analyzes participants' audio and video in real time. Specifically, it collects data using the camera and microphone and performs voice tone analysis and facial expression recognition. This allows it to recognize participants' emotional states and detect abnormal emotional states. Emotion detection uses the Google Cloud Speech-to-Text API to analyze voice tone and facial analysis software to recognize facial expressions.

[0778] Furthermore, if an anomaly is detected, the server will send an alert to the system administrator or designated personnel. SMS notifications using the Twilio API are effective for this purpose, enabling a rapid response.

[0779] After the meeting ends, the server converts the audio recording into text, uses natural language processing technology to highlight key points, and creates meeting minutes. The minutes are distributed to participants, and any follow-up tasks arising from the meeting are organized and managed.

[0780] As a concrete example, by introducing this system into a company's monthly meetings and constantly monitoring the emotional state of all employees, the productivity of the meetings was improved. Furthermore, by quickly identifying follow-up tasks after meetings, work efficiency was enhanced.

[0781] Examples of prompt statements are as follows:

[0782] "Describe a method for detecting unusual emotional states by analyzing video data acquired by security cameras and the voice tone of visitors."

[0783] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0784] Step 1:

[0785] The server retrieves the meeting participants' schedule information. It obtains participant schedule information from the calendar database using the open calendar format as input and parses it. Based on this parsing, it selects a suitable date and time for the meeting and outputs a suggested date and time.

[0786] Step 2:

[0787] The server collects past meeting data and automatically generates agendas using machine learning algorithms. Using past meeting records as input data, it performs topic modeling with an AI model and provides appropriate agenda items for the next meeting as output.

[0788] Step 3:

[0789] During the meeting, the terminal acquires participants' audio and video data in real time. By using the camera and microphone as input devices and processing this sensor data with voice tone analysis and facial expression recognition software, the emotional state of the participants can be obtained as output.

[0790] Step 4:

[0791] The device detects abnormal emotional states if they differ from the normal state. It sends the output of audio and video analysis to the server, executes logic to determine the abnormality based on the conditions, and outputs the detected abnormality.

[0792] Step 5:

[0793] The server sends an alert to the designated administrator based on the detected abnormal emotional state. It uses the Twilio API to send an SMS or email notification to the system administrator to communicate the alert information.

[0794] Step 6:

[0795] After the meeting ends, the server retrieves the audio recording and uses speech-to-text software to convert the conversation into text. It then takes the audio file as input and outputs a text file.

[0796] Step 7:

[0797] The server summarizes important statements from the converted text data using natural language processing techniques and creates meeting minutes. It analyzes text data as input and generates meeting minutes as output.

[0798] Step 8:

[0799] The server distributes the created meeting minutes to participants and manages follow-up tasks that arise during the meeting. It sends the generated meeting minutes via email, registers the tasks as inputs in the follow-up task management system, and records their completion status as an output.

[0800] Step 9:

[0801] Users receive feedback from meetings and adjust their actions accordingly. They receive information based on meeting minutes and follow-up tasks as input and plan preparations and improvements for the next meeting as output.

[0802] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0803] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0804] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0805] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0806] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0807] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0808] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0809] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0810] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0811] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0812] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0813] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0814] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0815] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0816] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0817] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0818] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0819] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0820] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0821] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0822] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

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

[0824] (Claim 1)

[0825] A means of automatically generating an agenda for meeting preparation,

[0826] A method for obtaining participants' schedule information and suggesting the optimal meeting date and time,

[0827] A means to support the progress of the agenda during a meeting and summarize important statements in real time,

[0828] A method for converting meeting recordings into text and extracting the main points of what was said,

[0829] A means of distributing the generated meeting minutes to participants,

[0830] A means of managing follow-up tasks after a meeting,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, characterized in that it includes means for managing access rights to information based on a security protocol when acquiring participant schedule information.

[0834] (Claim 3)

[0835] The system according to claim 1, further comprising means for notifying participants of a summary in real time in order to facilitate important discussions during a meeting.

[0836] "Example 1"

[0837] (Claim 1)

[0838] A means of obtaining schedule information and generating a meeting summary,

[0839] A means of analyzing schedule information and calculating the optimal date and time,

[0840] A means of providing summaries of important statements during a meeting using communication methods,

[0841] A means of converting audio information and extracting key points,

[0842] Means of supplying summary information to stakeholders,

[0843] A means of managing post-work procedures,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, which has means for controlling eligibility to acquire information based on information protection technology.

[0847] (Claim 3)

[0848] The system according to claim 1, further comprising means for communicating summary information in chronological order to support the progress of a meeting.

[0849] "Application Example 1"

[0850] (Claim 1)

[0851] A means of automatically generating an agenda for meeting preparation,

[0852] A method for obtaining participants' schedule information and suggesting the optimal meeting date and time,

[0853] A means to support the progress of the agenda during a meeting and summarize important statements in real time,

[0854] A method for converting meeting recordings into text and extracting the main points of what was said,

[0855] A means of distributing the generated meeting minutes to participants,

[0856] A means of managing follow-up tasks after a meeting,

[0857] A means of presenting meeting content in real time based on speech recognition technology,

[0858] A means including a meeting content management function that can also be applied to online meeting platforms,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, characterized in that it includes means for managing access rights to information based on a security protocol when acquiring participant schedule information.

[0862] (Claim 3)

[0863] The system according to claim 1, further comprising means for notifying participants of a summary in real time in order to facilitate important discussions during a meeting.

[0864] "Example 2 of combining an emotion engine"

[0865] (Claim 1)

[0866] A means by which an electronic computer analyzes the user's schedule information and automatically forms a meeting plan,

[0867] A method to suggest the optimal date and time by gaining knowledge from past meeting data,

[0868] A means of analyzing and inferring the emotional state of participants during a meeting and assisting in its progress,

[0869] A means of processing audio and video information in real time to decipher emotions,

[0870] After the meeting concludes, a means to convert the audio recording into a document format and summarize its contents,

[0871] A means of generating meeting minutes including sentiment analysis results and distributing them to relevant individuals,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, characterized in that it includes means for restricting the handling of information based on secure communication standards when acquiring scheduled information.

[0875] (Claim 3)

[0876] The system according to claim 1, further comprising means for providing guidance on the progress of a meeting and adjusting topics as needed during the meeting.

[0877] "Application example 2 when combining with an emotional engine"

[0878] (Claim 1)

[0879] A means of automatically generating an agenda for meeting preparation,

[0880] A method for obtaining participants' schedule information and suggesting the optimal meeting date and time,

[0881] A means to support the progress of the agenda during a meeting and summarize important statements in real time,

[0882] A method for converting meeting recordings into text and extracting the main points of what was said,

[0883] A means of distributing the generated meeting minutes to participants,

[0884] A means of managing follow-up tasks after a meeting,

[0885] A means for recognizing the audio and video state of participants and detecting abnormal emotional states,

[0886] A means of issuing an alarm when an abnormal emotional state is detected,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, characterized in that it includes means for managing access rights to information based on a security protocol when acquiring participant schedule information.

[0890] (Claim 3)

[0891] The system according to claim 1, further comprising means for notifying participants of a summary in real time in order to facilitate important discussions during a meeting. [Explanation of Symbols]

[0892] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of automatically generating an agenda for meeting preparation, A method for obtaining participants' schedule information and suggesting the optimal meeting date and time, A means to support the progress of the agenda during a meeting and summarize important statements in real time, A method for converting meeting recordings into text and extracting the main points of what was said, A means of distributing the generated meeting minutes to participants, A means of managing follow-up tasks after a meeting, A means of presenting meeting content in real time based on speech recognition technology, A means including a meeting content management function that can also be applied to online meeting platforms, A system that includes this.

2. The system according to claim 1, characterized in that it includes means for managing access rights to information based on a security protocol when acquiring participant schedule information.

3. The system according to claim 1, further comprising means for notifying participants of a summary in real time in order to facilitate important discussions during a meeting.

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