system

Abstract

We provide the system. [Solution] A method for automatically generating meeting agendas, A method for analyzing participants' schedules and suggesting the optimal meeting date and time, A method for recognizing and analyzing audio during a meeting in real time, A means of providing comments to support the progress of the meeting based on the analysis results, A method for summarizing the content of discussions after a meeting and automatically creating meeting minutes, Methods for distributing meeting minutes to participants, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to smoothly conduct a meeting, there are a number of laborious and time-consuming tasks, such as creating an agenda in advance, adjusting the schedule, activating discussions during the meeting, and creating and distributing meeting minutes after the meeting. As a result, as the frequency of holding meetings increases, the individual burden becomes greater, and there is a problem that productivity is significantly reduced. There is a demand for providing a system for effectively solving this problem.

Means for Solving the Problems

[0005] This invention provides a system that includes means for automatically generating a meeting agenda, analyzing participants' schedules and proposing the optimal meeting date and time, recognizing and analyzing audio during the meeting in real time, providing comments to support the progress of the meeting based on the analysis results, summarizing the content of the discussion after the meeting and automatically creating meeting minutes, and distributing the meeting minutes to participants. This system aims to improve the efficiency of the entire process, from meeting preparation to post-meeting follow-up. As a result, it achieves a significant reduction in the time and improvement in the quality of meeting-related tasks.

[0006] "Automatic agenda generation" is a process that automatically organizes related topics and content based on the meeting theme and creates a schedule.

[0007] "Schedule analysis" is the process of using participants' calendar information to identify the most suitable meeting date and time.

[0008] "Speech recognition" is a technology that converts spoken words during a meeting into text in real time.

[0009] "Real-time analysis" is the process of instantly processing data obtained through speech recognition, understanding its content, and visualizing it.

[0010] "Facilitation support comments" are instructions and advice provided by AI to facilitate the flow of a meeting and encourage active discussion.

[0011] "Automatic meeting minutes creation" is a process that summarizes the content of a meeting, organizes the important points, and puts them into written form.

[0012] "Meeting minutes distribution" refers to the process of sending the created meeting minutes to meeting participants via email or other means. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] 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 the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled 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.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled 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.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention relates to a system using an AI agent configured to streamline the preparation, conduct, and follow-up of meetings. The system consists of a server, user terminals, and a database for storing necessary data.

[0035] The user enters the meeting topic and objectives via their terminal. The server receives this information and collects relevant topics by analyzing past meeting minutes and related documents in its database. The server automatically generates an agenda using natural language processing technology and proposes it to the user's terminal. The user can review, approve, or modify this agenda.

[0036] Next, based on the list of participants specified by the user, the server retrieves each participant's schedule via the calendar API. The server then suggests an optimal meeting date and time and sends it to the user's device. The user can review the suggested date and time and take appropriate action if necessary to reschedule.

[0037] When a meeting begins, the terminal notifies the user of the start of the meeting. The server uses speech recognition technology to analyze the conversation in real time during the meeting and records its content. Based on the analyzed data, the server generates comments and questions to assist in the progress of the meeting and displays them on the user's terminal to stimulate discussion.

[0038] After the meeting ends, the server automatically generates a summary and meeting minutes based on the recorded audio data. These minutes are sent to the user's device, where they can review and edit them. The server then emails the finalized minutes to all participants.

[0039] For example, in a meeting about marketing strategy, this system automatically generates an agenda from topics such as "target market analysis" and "competitor research," and proposes the optimal meeting date. During the meeting, the server provides specific comments to stimulate discussion, and after the meeting, the server efficiently creates meeting minutes and distributes them quickly. This entire process can significantly reduce the time and effort required to manage meetings.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user enters the meeting topic and purpose via their device. The server receives this information and retrieves relevant past information by referring to its database.

[0043] Step 2:

[0044] Based on the information collected by the server, a meeting agenda is automatically generated using natural language processing. The generated agenda is then sent to the user's device in an editable format.

[0045] Step 3:

[0046] The user enters a list of participants into the system. The server retrieves the schedule information of the specified participants via a calendar API, analyzes it, and suggests the optimal meeting date and time. The suggested date and time are displayed on the user's device, allowing for confirmation and adjustment.

[0047] Step 4:

[0048] As the meeting start time approaches, the terminal notifies the user that the meeting has begun. During the meeting, the server uses speech recognition technology to transcribe and analyze the spoken content in real time.

[0049] Step 5:

[0050] The server generates comments and questions from the analysis results to stimulate discussion and provides them to users via their terminals. This facilitates the progress of the meeting.

[0051] Step 6:

[0052] After the meeting ends, the server summarizes the audio data and automatically creates meeting minutes. Users can then review the minutes on their devices and make any necessary corrections.

[0053] Step 7:

[0054] The server will send the meeting minutes, as finalized by the user, to the designated participants via email. Users can check the delivery status through their devices.

[0055] (Example 1)

[0056] 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."

[0057] In modern businesses, the processes of preparing, running, and following up on meetings are labor-intensive and time-consuming, making them difficult to manage efficiently. In particular, there is a growing need to automate a series of tasks, such as agenda creation, participant scheduling, active discussion during meetings, and meeting minute creation and distribution.

[0058] 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.

[0059] In this invention, the server includes means for inputting meeting parameters and analyzing relevant items, means for automatically generating an agenda using a generative artificial intelligence model, and means for obtaining the schedules of meeting participants and calculating the optimal meeting time. This makes it possible to efficiently automate the entire process of meeting management, reduce the burden on stakeholders, and achieve effective communication.

[0060] "Meeting parameters" refer to the basic information necessary to plan and run a meeting, such as the meeting's theme, purpose, time, location, and participant list.

[0061] "Related topics" refer to information and topics related to the theme and objectives of the meeting, extracted from past meeting records and literature data.

[0062] A "generative artificial intelligence model" is an algorithm that learns from vast amounts of data and generates, suggests, or classifies information by mimicking human thought and patterns.

[0063] An "agenda" is a plan that lists the items to be discussed in a meeting, and it is necessary for the smooth running of the meeting.

[0064] "Action schedule" refers to the schedule information of the dates and activities planned by the parties involved, and is used to determine the optimal date and time for a meeting.

[0065] "Audio information" refers to sound data collected in an analyzable digital format from statements and discussions made during a meeting.

[0066] "Textual information" refers to text data that is transcribed from audio information and used for creating meeting minutes.

[0067] "Meeting minutes" are documents that summarize the statements and decisions made during a meeting, and are used for later reference and verification.

[0068] "To transmit via communication" refers to the act of delivering information to a remote location using the internet or network technologies, such as email or messaging.

[0069] The invention is a system for automating and streamlining the preparation, operation, and follow-up of meetings. The system consists of a server, user terminals, and a data storage device.

[0070] Users input basic meeting information, such as the theme and purpose, via their device. The input devices used at this stage are typical computers or mobile devices. Users can input specific meeting themes, such as "marketing strategy."

[0071] The server uses a generative artificial intelligence model to search for relevant historical information in the database based on the input information and automatically generates an agenda using natural language processing techniques. The software used includes natural language processing libraries and a platform for running the AI ​​model. For example, the server can generate an agenda in response to a prompt such as, "Please suggest relevant agenda topics for the next marketing meeting."

[0072] Next, the server retrieves participants' schedules through their calendar applications and calculates the optimal meeting date and time for all participants. Specifically, it analyzes the schedule information using the Google® Calendar API or an equivalent API.

[0073] During the meeting, the server uses speech recognition technology to analyze the conversation in real time and record important content. This process can utilize tools such as the Google Cloud Speech-to-Text API. The recorded audio information is converted into text, and then comments are generated to stimulate the discussion and displayed on the user's device.

[0074] After the meeting, the server automatically summarizes the data collected and creates a meeting record. This record is then sent directly to all participants via communication from the server. Participants use a standard email client as their means of communication.

[0075] In this way, the system streamlines the entire process from meeting preparation to follow-up, reducing the burden on users.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user enters the meeting theme and purpose via their terminal. This input is sent to the server as text data, such as "Marketing Strategy Meeting." The server receives this input and stores it in a database as basic meeting information. Specifically, the user enters a series of pieces of information in text format on a dedicated input screen.

[0079] Step 2:

[0080] The server searches for relevant information in the database based on the entered theme and objective. It analyzes related items using natural language processing technology and automatically generates an agenda using a generative artificial intelligence model. The input is theme information, and the output is the generated agenda. This agenda is sent to the terminal, where the user can preview it. Specifically, the server generates an agenda that includes topics such as "Target Market Analysis" and "Competitor Research."

[0081] Step 3:

[0082] The server retrieves each participant's schedule through a calendar application based on the participant list specified by the user. It uses an API to collect schedule data and calculate the optimal meeting date and time. Specifically, it uses the Google Calendar API to check each participant's available meeting time slots. The input is the participant list and each participant's schedule, and the output is the optimal meeting date and time.

[0083] Step 4:

[0084] The server suggests the optimal meeting date and time to the user's terminal. The user can review this suggestion and make modifications as needed. The input is the suggested optimal date and time information, and the output is the user's confirmation and modification information. Specifically, the user can drag the suggested date and time to a different day through the terminal's calendar interface.

[0085] Step 5:

[0086] During the meeting, the terminal notifies the user of the start time, and the server analyzes the conversation in real time using speech recognition technology. The input is audio data from the meeting, and the output is the meeting content recorded in text format. Specifically, the server converts the audio via the Google Cloud Speech-to-Text API and automatically records important statements.

[0087] Step 6:

[0088] Once the meeting concludes, the server summarizes the meeting content based on the recorded audio data and automatically creates a meeting transcript. The input is the recorded text data, and the output is a summarized meeting transcript document. Specifically, the server uses a generation AI model to shorten the recording and extract the key points.

[0089] Step 7:

[0090] Finally, the server sends the created meeting minutes to all participants via email. Users can review the meeting minutes in their email clients and make corrections as needed. The input is the summarized meeting minutes, and the output is the mailboxes of the recipient participants. Specifically, the server uses the email protocol to quickly distribute the meeting minutes.

[0091] (Application Example 1)

[0092] 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."

[0093] Improving the efficiency of meeting preparation, management, and follow-up is a critical challenge for many organizations. However, responding quickly to emergencies and grasping and sharing key points of discussions in real time has been difficult with traditional methods.

[0094] 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.

[0095] In this invention, the server includes means for automatically generating a meeting agenda, means for analyzing participants' schedules and proposing an optimal meeting date and time, and means for quickly adjusting participants' schedules and re-proposing an optimal meeting date and time in the event of an emergency. This makes it possible to streamline the entire process from meeting preparation to execution, rapid response in emergency situations, and follow-up.

[0096] "Methods for automatically generating agendas" refer to functions that automatically organize the main topics and agenda items based on the meeting content and plan the order in which the meeting will proceed.

[0097] "A means of analyzing participants' schedules and suggesting the optimal meeting date and time" refers to a function that analyzes participants' schedules and automatically calculates and presents dates and times when everyone is available.

[0098] "A means of recognizing and analyzing speech in real time" refers to a technology that converts audio from a meeting into digital data and performs text analysis on the spot.

[0099] "Means of providing comments to support the progress of meetings" refers to a function that provides instructions and suggestions in real time to stimulate discussion based on the analyzed meeting content.

[0100] "A means of summarizing the content of speeches and automatically creating meeting minutes" refers to a function that summarizes the important points of a meeting and compiles them into an official record.

[0101] "Methods for distributing meeting minutes to participants" refers to a function that quickly distributes the created meeting minutes to relevant parties and promotes information sharing.

[0102] "A means to quickly adjust participants' schedules and propose the optimal meeting date and time in the event of an emergency" refers to a function that immediately re-evaluates participants' schedules and proposes a new meeting date and time in response to unforeseen circumstances.

[0103] "A means of summarizing the key points of a meeting in real time and providing them to participants" refers to a function that summarizes the content of an ongoing meeting and shares its key points with relevant parties in real time.

[0104] The system implementing this invention consists of a server, a user terminal, and a database for storing necessary data. The user uses the terminal to input the meeting topic and purpose. The server receives this information, retrieves past meeting minutes and related documents from the database, and automatically generates an agenda using natural language processing technology. The generated agenda is sent to the user's terminal, where the user can review and modify it.

[0105] Furthermore, the server retrieves each participant's schedule via the calendar API based on the list of participants. The server then suggests an optimal meeting date and time considering all participants' schedules and sends this information to the user's device. The user can review the suggested date and time and readjust if necessary.

[0106] During the meeting, the server uses speech recognition technology to analyze the conversation in real time and automatically generates comments and questions based on the content. These are displayed on the user's device to help stimulate discussion.

[0107] After the meeting ends, the server automatically generates a summary and meeting minutes based on the analyzed audio data and sends them to the user's device. After the user reviews and makes any necessary corrections, the final version of the meeting minutes is distributed to all participants.

[0108] This system uses Google Speech-to-Text for speech recognition and Python's NLTK library for natural language processing. Data analysis is performed on cloud servers and local databases.

[0109] As a concrete example, let's consider an implementation of the system in a security enhancement meeting at a certain facility. For instance, based on the input of the meeting theme, it extracts relevant topics such as "intrusion detection technology" and "employee training programs" and automatically generates an agenda. A possible prompt to the generating AI model would be, "What topics should we automatically generate the agenda for the next security meeting?"

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] Users input the meeting topic and purpose via their terminal. This input data is sent to the server, which searches its database for past meeting minutes and related documents. As a result, relevant information is collected.

[0113] Step 2:

[0114] The server automatically generates an agenda using natural language processing technology based on the collected relevant information. Specifically, it identifies topics through a text analysis engine and creates a structured agenda. The generated agenda is sent to the user's terminal and displayed to the user.

[0115] Step 3:

[0116] The user reviews the agenda displayed on their device and makes corrections as needed. The corrected information is then sent back to the server and recorded in the database.

[0117] Step 4:

[0118] The server uses the Calendar API to retrieve each participant's schedule based on the list of participants. It then analyzes the time information obtained from this API call to calculate the optimal meeting date and time. The calculated date and time are sent to the user's device as a suggestion.

[0119] Step 5:

[0120] During the meeting, the server acquires audio data and converts it to text in real time using speech recognition technology. Based on the acquired text data, it performs natural language processing to analyze important agenda items and generate comments and questions. These generated comments are displayed on the user's device.

[0121] Step 6:

[0122] After the meeting ends, the server summarizes the text data and automatically generates meeting minutes. This process includes keyword extraction and summarization of key points. The generated minutes are sent to the user's device, where they can review and edit them. The final reviewed minutes are then distributed to all participants.

[0123] 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.

[0124] This invention relates to a system using an AI agent to streamline meeting management, which in particular includes means for recognizing the emotions of meeting participants and using this to stimulate and facilitate discussion. The main components of the system are a server, user terminals, and an emotion engine.

[0125] Users input the meeting topic and objectives via their device. The server receives this information, analyzes past data, and collects relevant information. Based on the collected data, it automatically generates an agenda using natural language processing and proposes it to the user's device. Users can review the proposed agenda and modify it as needed.

[0126] When a user enters a list of participants, the server analyzes each participant's schedule and suggests the most suitable meeting time. The suggested time is displayed on the user's terminal, and can be approved or rescheduled.

[0127] During the meeting, the server uses speech recognition technology to analyze what is being said in real time, and in addition, an emotion engine analyzes the emotions of the participants. The emotion engine extracts emotional data from the audio and video and provides information to support the progress of the meeting.

[0128] The server generates comments and questions based on spoken content and sentiment data to optimize the flow of the meeting, and displays them through the terminal. This helps to ensure smooth and engaging meetings.

[0129] After the meeting, the server summarizes the conversation content and sentiment data, and automatically generates meeting minutes. Users can review the minutes on their devices, make corrections as needed, and the finalized minutes are distributed to participants by the server.

[0130] For example, in a meeting about marketing strategy, if participants' emotions are leaning towards the negative, the server, based on the analysis results of the emotion engine, displays positive comments on their terminals to boost motivation and correct the flow of the meeting. In this way, comprehensive support can be provided, significantly improving the quality and efficiency of meetings.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user enters the meeting topic and purpose via their device. The server retrieves this information and collects meeting-related data from its database.

[0134] Step 2:

[0135] The server uses collected data to execute a natural language processing algorithm and automatically generates a meeting agenda. The generated agenda is sent to the terminal, where users can review and modify it.

[0136] Step 3:

[0137] The user enters a list of participants on their device. The server retrieves and analyzes each participant's schedule information via a calendar API. The optimal meeting date and time are suggested and displayed on the user's device.

[0138] Step 4:

[0139] Before the meeting begins, the terminal sends a reminder to the user. During the meeting, the server uses speech recognition technology to transcribe what is said in real time, and an emotion engine analyzes the participants' emotions.

[0140] Step 5:

[0141] The server generates comments and questions to optimize the meeting's progress based on analyzed speech content and sentiment data. These comments are provided to users via their terminals to support and stimulate discussion.

[0142] Step 6:

[0143] After the meeting ends, the server analyzes the audio and sentiment data together, summarizes the key points, and automatically creates meeting minutes. The created minutes are then reviewed on the user's device and modified as needed.

[0144] Step 7:

[0145] Finalized meeting minutes are distributed to participants via email by the server. Users can check the delivery status and follow up via their devices.

[0146] (Example 2)

[0147] 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".

[0148] Managing a meeting requires considerable preparation and effort, particularly creating an agenda, coordinating participants' schedules, facilitating the meeting during it, and preparing meeting minutes afterward. Furthermore, effective facilitation through a precise understanding of participants' emotions and conversations is crucial, something difficult for humans to do in real time. There is a need to reduce the burden of meeting management and improve the quality of meetings.

[0149] 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.

[0150] In this invention, the server includes means for automatically creating an agenda based on the meeting plan, means for analyzing participants' schedules to suggest the optimal meeting time, and means for recognizing and immediately analyzing audio acquired during the meeting. This enables more efficient meeting management and optimization of the meeting's progress in accordance with the participants' emotions.

[0151] "Meeting planning" is the process of setting agenda items and clarifying objectives necessary for conducting a meeting effectively.

[0152] An "automatic agenda creation system" is a system that uses machine learning techniques and algorithms to construct an agenda based on the meeting's theme and objectives, minimizing human intervention.

[0153] "Analyzing participants' schedules" means analyzing participants' schedule data to identify the optimal meeting time that everyone can attend.

[0154] A "device that suggests the optimal meeting time" refers to a system that, based on analysis results, proposes the most suitable date and time slot for a meeting.

[0155] "Recognizing audio acquired during a meeting" refers to the process of capturing the audio spoken during a meeting as digital data and converting its content into an analyzable format.

[0156] "Methods for immediate analysis" refers to technologies that process acquired audio data and other information from meetings in real time and generate analysis results instantly.

[0157] A "device for automatically creating record documents" is a system that automatically generates meeting minutes and reports based on audio and text data accumulated during a meeting.

[0158] A "device for transmitting recorded documents to participants" is a system equipped with communication means for electronically distributing the created meeting minutes to participants.

[0159] This invention is a system for automating and streamlining the entire process of meeting planning, implementation, and follow-up. The system primarily consists of a server, user terminals, and an emotion engine.

[0160] First, the user inputs the meeting topic and objectives through their device. The device then sends this information to the server. The server uses this information to search its past database and collect and analyze relevant information. For this purpose, the server utilizes natural language processing technology and a generative AI model to automatically generate the agenda. An example of input for this generation process is a prompt such as, "Please create the agenda for the next marketing meeting."

[0161] The user enters a list of meeting participants and their schedule information into their terminal. The server analyzes this schedule information and suggests the optimal meeting date and time when all participants can attend. A calendar API is often used for this analysis.

[0162] During the meeting, the server's speech recognition technology transcribes spoken content into text in real time. Simultaneously, the server uses an emotion engine to analyze participants' emotions and uses the results to assist in the meeting's progress. Based on this analysis, the server generates appropriate comments and questions to maintain the flow of the meeting and displays them on the participants' terminals.

[0163] After the meeting, the server summarizes the content and sentiment of the participants and automatically generates meeting minutes. Users can review the generated minutes on their terminals and make corrections as needed. The revised minutes are then distributed to each participant from the server.

[0164] For example, in a meeting about marketing strategy, if the server detects a situation where participants' emotions tend to become negative, it can improve the mood of the meeting by offering positive comments. This kind of support dramatically improves the quality and efficiency of the meeting.

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

[0166] Step 1:

[0167] Users input the purpose and theme of the meeting using a terminal. This information serves as the starting data for the system. The data entered by the user is sent to the server by the terminal. The terminal's specific operations include receiving input through the user interface and sending the data to the server via network communication.

[0168] Step 2:

[0169] The server accesses internal databases or external sources to collect relevant information based on the received meeting theme and objectives. The collected data includes past meeting data and relevant literature. Using this information, the server creates prompts for a generative AI model and generates an appropriate agenda. The input is the meeting theme, and the output is the generated agenda. Specifically, the server executes database queries via an API and sends the collected results to the AI ​​model.

[0170] Step 3:

[0171] The user enters a list of meeting participants via a terminal. The terminal then sends this list to the server. The input is a list of participant names, and the output is participant information in digital format. The specific actions include creating the list and sending the data to the server.

[0172] Step 4:

[0173] The server retrieves and analyzes each participant's schedule data from an external calendar service based on the received participant list. Based on the analysis, it proposes a meeting time that allows the most participants to attend. The input is the participants' schedules, and the output is the optimal meeting date and time. Specifically, the process involves fetching calendar data via an API and applying an analysis algorithm.

[0174] Step 5:

[0175] During the meeting, the server uses speech recognition technology to convert spoken content into text in real time. The server also works in conjunction with an emotion engine to analyze participants' speech and facial expressions to generate emotion data. The input is the audio and video from the meeting, and the output is the transcribed conversation and emotion data. Specifically, the server performs tasks such as capturing and transcribing audio data and analyzing video data.

[0176] Step 6:

[0177] The server generates comments and questions to assist in meeting progress based on the acquired speech content and sentiment data. The generated comments are sent to the terminal and displayed to the user. Input is real-time conversation text and sentiment data, while output is comments to assist in progress. Specifically, reply generation is performed using natural language generation technology.

[0178] Step 7:

[0179] After the meeting ends, the server summarizes the conversation content and sentiment data to automatically generate meeting minutes. Users can review and edit the generated minutes on their terminals. Finally, the minutes are distributed from the server to the participants. The input is the conversation content and sentiment data, and the output is the finalized meeting minutes. The specific operation involves the use of a text summarization algorithm and a distribution system.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0182] In modern cities, meetings involving diverse stakeholders are held frequently, requiring efficient management and smooth collection of participants' opinions. However, optimizing the progress of a meeting in real time, accurately understanding participants' emotions, and facilitating active discussion are not easy. As a result, the quality and efficiency of meetings decline, and important discussions may end without taking place. This invention aims to solve these problems.

[0183] 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.

[0184] In this invention, the server includes means for automatically generating a meeting agenda, means for analyzing participants' schedules and suggesting the optimal meeting date and time, and means for recognizing and analyzing voice and emotions during the meeting in real time. This makes it possible to significantly improve the quality and efficiency of meetings.

[0185] "Methods for automatically generating meeting agendas" refer to functions that automatically create necessary topics and schedules based on the content and purpose of the meeting.

[0186] "A method for analyzing participants' schedules and suggesting the optimal meeting date and time" refers to a function that analyzes each participant's schedule and automatically selects the most suitable date and time for everyone to attend.

[0187] "Means for recognizing and analyzing audio and emotions during meetings in real time" refers to a function that uses speech recognition technology to transcribe the content of speech during meetings into text, and simultaneously detects and analyzes the emotions of participants in real time using emotion analysis technology.

[0188] "Means for providing comments to support the progress of a meeting based on analysis results and sentiment data" refers to a function that automatically generates appropriate comments and suggestions to ensure that a meeting proceeds effectively, based on the results of voice and sentiment analysis.

[0189] "A means of summarizing the content of discussions and sentiment data after a meeting and automatically creating meeting minutes" refers to a function that extracts important points based on the content of discussions and sentiment data from the meeting and automatically creates meeting minutes.

[0190] "Method for distributing meeting minutes to participants" refers to a function that electronically distributes automatically generated meeting minutes to meeting participants.

[0191] "A means of analyzing emotions and supporting the operation of city council meetings" refers to a function that analyzes the emotions of participants to help city council meetings run more actively and smoothly.

[0192] This system consists of a server and user terminals. The server receives and analyzes information about the meeting's theme, purpose, and participants, which is entered by the user using their terminal.

[0193] The server automatically generates the meeting agenda using natural language processing technology. The software used could be Google Cloud's natural language processing API. The server also has the functionality to analyze participant schedules received from users' devices and calculate the optimal meeting date and time. This requires computing resources to process schedule information in real time.

[0194] During the meeting, the server uses speech recognition technology to convert participants' speech into text in real time. Text conversion of audio data is possible using the Google Cloud Speech-to-Text API. Simultaneously, an emotion analysis engine analyzes audio and video data to determine participants' emotional states and obtain analytical information.

[0195] To support the progress of the meeting, the server generates appropriate comments and questions based on speech and sentiment analysis results and displays them on the user's terminal. This application can utilize IBM Watson®'s Natural Language Understanding API.

[0196] After the meeting ends, the server summarizes the collected statements and sentiment data, automatically creates meeting minutes, and distributes them to participants after they have been reviewed on their user terminals.

[0197] For example, if a city council meeting is held to discuss the construction of a new public facility, the server can generate questions such as, "What kind of community contributions can be expected from the construction of this facility?" when the discussion stalls, thereby helping to stimulate the discussion. An example of a prompt from the generating AI model would be, "Please tell me how to analyze the statements and sentiments of meeting participants in real time and generate comments and questions to stimulate the discussion."

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The user uses a terminal to input the meeting's theme, purpose, and participant information. The entered data is sent to the server. The server records the received data and uses natural language processing technology to organize it into data necessary for automatic agenda generation. This organized data then becomes the input for the next step.

[0201] Step 2:

[0202] The server uses the organized data to call Google Cloud's natural language processing API to automatically generate the meeting agenda. Using the API, the agenda and schedule, tailored to the meeting's theme, are output as text. The generated agenda is sent to the user's device for approval or modification.

[0203] Step 3:

[0204] The user's device sends participant schedule information to the server. The server analyzes this information and calculates the optimal meeting date and time. The analysis uses an algorithm that takes into account available time slots and avoids overlaps in the schedule. The calculated optimal date and time is proposed to the user's device, and the user can choose to accept or reschedule.

[0205] Step 4:

[0206] During the meeting, the server converts audio acquired from the user's device into text in real time using the Google Cloud Speech-to-Text API. The input is audio data, and the output is the transcribed text of that audio. Furthermore, an emotion analysis engine analyzes emotion data from the audio and video to classify the emotional state. The analysis results are stored on the server.

[0207] Step 5:

[0208] The server uses IBM Watson's Natural Language Understanding API to generate comments and questions to support the meeting, based on real-time text and sentiment data. This involves generating prompts using a generative AI model. The generated comments and questions are displayed on the user's device.

[0209] Step 6:

[0210] After the meeting ends, the server summarizes the recorded statements and sentiment data, extracts key points, and automatically creates meeting minutes. This is done by the server's internal algorithm and output as text. After users review the created minutes on their devices, they are electronically distributed to participants.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] This invention relates to a system using an AI agent configured to streamline the preparation, conduct, and follow-up of meetings. The system consists of a server, user terminals, and a database for storing necessary data.

[0228] The user enters the meeting topic and objectives via their terminal. The server receives this information and collects relevant topics by analyzing past meeting minutes and related documents in its database. The server automatically generates an agenda using natural language processing technology and proposes it to the user's terminal. The user can review, approve, or modify this agenda.

[0229] Next, based on the list of participants specified by the user, the server retrieves each participant's schedule via the calendar API. The server then suggests an optimal meeting date and time and sends it to the user's device. The user can review the suggested date and time and take appropriate action if necessary to reschedule.

[0230] When a meeting begins, the terminal notifies the user of the start of the meeting. The server uses speech recognition technology to analyze the conversation in real time during the meeting and records its content. Based on the analyzed data, the server generates comments and questions to assist in the progress of the meeting and displays them on the user's terminal to stimulate discussion.

[0231] After the meeting ends, the server automatically generates a summary and meeting minutes based on the recorded audio data. These minutes are sent to the user's device, where they can review and edit them. The server then emails the finalized minutes to all participants.

[0232] For example, in a meeting about marketing strategy, this system automatically generates an agenda from topics such as "target market analysis" and "competitor research," and proposes the optimal meeting date. During the meeting, the server provides specific comments to stimulate discussion, and after the meeting, the server efficiently creates meeting minutes and distributes them quickly. This entire process can significantly reduce the time and effort required to manage meetings.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The user enters the meeting topic and purpose via their device. The server receives this information and retrieves relevant past information by referring to its database.

[0236] Step 2:

[0237] Based on the information collected by the server, a meeting agenda is automatically generated using natural language processing. The generated agenda is then sent to the user's device in an editable format.

[0238] Step 3:

[0239] The user enters a list of participants into the system. The server retrieves the schedule information of the specified participants via a calendar API, analyzes it, and suggests the optimal meeting date and time. The suggested date and time are displayed on the user's device, allowing for confirmation and adjustment.

[0240] Step 4:

[0241] As the meeting start time approaches, the terminal notifies the user that the meeting has begun. During the meeting, the server uses speech recognition technology to transcribe and analyze the spoken content in real time.

[0242] Step 5:

[0243] The server generates comments and questions from the analysis results to stimulate discussion and provides them to users via their terminals. This facilitates the progress of the meeting.

[0244] Step 6:

[0245] After the meeting ends, the server summarizes the audio data and automatically creates meeting minutes. Users can then review the minutes on their devices and make any necessary corrections.

[0246] Step 7:

[0247] The server will send the meeting minutes, as finalized by the user, to the designated participants via email. Users can check the delivery status through their devices.

[0248] (Example 1)

[0249] 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."

[0250] In modern businesses, the processes of preparing, running, and following up on meetings are labor-intensive and time-consuming, making them difficult to manage efficiently. In particular, there is a growing need to automate a series of tasks, such as agenda creation, participant scheduling, active discussion during meetings, and meeting minute creation and distribution.

[0251] 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.

[0252] In this invention, the server includes means for inputting meeting parameters and analyzing relevant items, means for automatically generating an agenda using a generative artificial intelligence model, and means for obtaining the schedules of meeting participants and calculating the optimal meeting time. This makes it possible to efficiently automate the entire process of meeting management, reduce the burden on stakeholders, and achieve effective communication.

[0253] "Meeting parameters" refer to the basic information necessary to plan and run a meeting, such as the meeting's theme, purpose, time, location, and participant list.

[0254] "Related topics" refer to information and topics related to the theme and objectives of the meeting, extracted from past meeting records and literature data.

[0255] A "generative artificial intelligence model" is an algorithm that learns from vast amounts of data and generates, suggests, or classifies information by mimicking human thought and patterns.

[0256] An "agenda" is a plan that lists the items to be discussed in a meeting, and it is necessary for the smooth running of the meeting.

[0257] "Action schedule" refers to the schedule information of the dates and activities planned by the parties involved, and is used to determine the optimal date and time for a meeting.

[0258] "Audio information" refers to sound data collected in an analyzable digital format from statements and discussions made during a meeting.

[0259] "Textual information" refers to text data that is transcribed from audio information and used for creating meeting minutes.

[0260] "Meeting minutes" are documents that summarize the statements and decisions made during a meeting, and are used for later reference and verification.

[0261] "To transmit via communication" refers to the act of delivering information to a remote location using the internet or network technologies, such as email or messaging.

[0262] The invention is a system for automating and streamlining the preparation, operation, and follow-up of meetings. The system consists of a server, user terminals, and a data storage device.

[0263] Users input basic meeting information, such as the theme and purpose, via their device. The input devices used at this stage are typical computers or mobile devices. Users can input specific meeting themes, such as "marketing strategy."

[0264] The server uses a generative artificial intelligence model to search for relevant historical information in the database based on the input information and automatically generates an agenda using natural language processing techniques. The software used includes natural language processing libraries and a platform for running the AI ​​model. For example, the server can generate an agenda in response to a prompt such as, "Please suggest relevant agenda topics for the next marketing meeting."

[0265] Next, the server retrieves participants' schedules through their calendar applications and calculates the optimal meeting date and time for all participants. Specifically, it analyzes the schedule information using the Google Calendar API or an equivalent API.

[0266] During the meeting, the server uses speech recognition technology to analyze the conversation in real time and record important content. This process can utilize tools such as the Google Cloud Speech-to-Text API. The recorded audio information is converted into text, and then comments are generated to stimulate the discussion and displayed on the user's device.

[0267] After the meeting, the server automatically summarizes the data collected and creates a meeting record. This record is then sent directly to all participants via communication from the server. Participants use a standard email client as their means of communication.

[0268] In this way, the system streamlines the entire process from meeting preparation to follow-up, reducing the burden on users.

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

[0270] Step 1:

[0271] The user enters the meeting theme and purpose via their terminal. This input is sent to the server as text data, such as "Marketing Strategy Meeting." The server receives this input and stores it in a database as basic meeting information. Specifically, the user enters a series of pieces of information in text format on a dedicated input screen.

[0272] Step 2:

[0273] The server searches for relevant information in the database based on the entered theme and objective. It analyzes related items using natural language processing technology and automatically generates an agenda using a generative artificial intelligence model. The input is theme information, and the output is the generated agenda. This agenda is sent to the terminal, where the user can preview it. Specifically, the server generates an agenda that includes topics such as "Target Market Analysis" and "Competitor Research."

[0274] Step 3:

[0275] The server retrieves each participant's schedule through a calendar application based on the participant list specified by the user. It uses an API to collect schedule data and calculate the optimal meeting date and time. Specifically, it uses the Google Calendar API to check each participant's available meeting time slots. The input is the participant list and each participant's schedule, and the output is the optimal meeting date and time.

[0276] Step 4:

[0277] The server suggests the optimal meeting date and time to the user's terminal. The user can review this suggestion and make modifications as needed. The input is the suggested optimal date and time information, and the output is the user's confirmation and modification information. Specifically, the user can drag the suggested date and time to a different day through the terminal's calendar interface.

[0278] Step 5:

[0279] During the meeting, the terminal notifies the user of the start time, and the server analyzes the conversation in real time using speech recognition technology. The input is audio data from the meeting, and the output is the meeting content recorded in text format. Specifically, the server converts the audio via the Google Cloud Speech-to-Text API and automatically records important statements.

[0280] Step 6:

[0281] When the meeting ends, the server summarizes the meeting content based on the recorded voice data and automatically creates a meeting record. The input is the recorded text data, and the output is the summarized meeting record document. As a specific operation, the server uses a generative AI model to shorten the record and extract key points.

[0282] Step 7:

[0283] Finally, the server sends the created meeting record to all participants via communication. The user can check the meeting record in the mail client and make corrections if necessary. The input is the summarized meeting record, and the output is the mailbox of the recipient participants. As a specific operation, the server uses the email protocol to quickly distribute the meeting record.

[0284] (Application Example 1)

[0285] 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".

[0286] Improving the efficiency of meeting preparation, progress, and follow-up is an important issue for many organizations. However, it has been difficult with conventional methods to respond quickly in case of an emergency and to grasp and share the key points of the discussion during the meeting in real time.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0288] In this invention, the server includes means for automatically generating a meeting agenda, means for analyzing the schedules of participants and proposing an optimal meeting date and time, and means for quickly adjusting the schedules of participants and re-proposing an optimal meeting date and time when an emergency occurs. Thereby, it becomes possible to streamline a series of processes from the meeting preparation stage through the progress and rapid response in an emergency situation to the follow-up.

[0289] "Methods for automatically generating agendas" refer to functions that automatically organize the main topics and agenda items based on the meeting content and plan the order in which the meeting will proceed.

[0290] "A means of analyzing participants' schedules and suggesting the optimal meeting date and time" refers to a function that analyzes participants' schedules and automatically calculates and presents dates and times when everyone is available.

[0291] "A means of recognizing and analyzing speech in real time" refers to a technology that converts audio from a meeting into digital data and performs text analysis on the spot.

[0292] "Means of providing comments to support the progress of meetings" refers to a function that provides instructions and suggestions in real time to stimulate discussion based on the analyzed meeting content.

[0293] "A means of summarizing the content of speeches and automatically creating meeting minutes" refers to a function that summarizes the important points of a meeting and compiles them into an official record.

[0294] "Methods for distributing meeting minutes to participants" refers to a function that quickly distributes the created meeting minutes to relevant parties and promotes information sharing.

[0295] "A means to quickly adjust participants' schedules and propose the optimal meeting date and time in the event of an emergency" refers to a function that immediately re-evaluates participants' schedules and proposes a new meeting date and time in response to unforeseen circumstances.

[0296] "A means of summarizing the key points of a meeting in real time and providing them to participants" refers to a function that summarizes the content of an ongoing meeting and shares its key points with relevant parties in real time.

[0297] The system implementing this invention consists of a server, a user terminal, and a database for storing necessary data. The user uses the terminal to input the meeting topic and purpose. The server receives this information, retrieves past meeting minutes and related documents from the database, and automatically generates an agenda using natural language processing technology. The generated agenda is sent to the user's terminal, where the user can review and modify it.

[0298] Furthermore, the server retrieves each participant's schedule via the calendar API based on the list of participants. The server then suggests an optimal meeting date and time considering all participants' schedules and sends this information to the user's device. The user can review the suggested date and time and readjust if necessary.

[0299] During the meeting, the server uses speech recognition technology to analyze the conversation in real time and automatically generates comments and questions based on the content. These are displayed on the user's device to help stimulate discussion.

[0300] After the meeting ends, the server automatically generates a summary and meeting minutes based on the analyzed audio data and sends them to the user's device. After the user reviews and makes any necessary corrections, the final version of the meeting minutes is distributed to all participants.

[0301] This system uses Google Speech-to-Text for speech recognition and Python's NLTK library for natural language processing. Data analysis is performed on cloud servers and local databases.

[0302] As a concrete example, let's consider an implementation of the system in a security enhancement meeting at a certain facility. For instance, based on the input of the meeting theme, it extracts relevant topics such as "intrusion detection technology" and "employee training programs" and automatically generates an agenda. A possible prompt to the generating AI model would be, "What topics should we automatically generate the agenda for the next security meeting?"

[0303] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0304] Step 1:

[0305] The user inputs the theme and purpose of the meeting via the terminal. This input data is sent to the server, and the server searches the database for past meeting minutes and related documents. As a result, relevant information is collected.

[0306] Step 2:

[0307] Based on the collected relevant information, the server automatically generates an agenda using natural language processing technology. Specifically, topics are identified through a text analysis engine, and a structured agenda is created. The generated agenda is sent to the user's terminal and displayed to the user.

[0308] Step 3:

[0309] The user checks the agenda displayed on the terminal and makes corrections if necessary. The corrected information is sent to the server again and recorded in the database.

[0310] Step 4:

[0311] Based on the list of participants, the server uses the Calendar API to obtain the schedule of each participant. The time information obtained as a result of this API call is analyzed, and the optimal meeting date and time are calculated. The calculated date and time are sent as a proposal to the user's terminal.

[0312] Step 5:

[0313] During the meeting, the server obtains voice data and uses speech recognition technology to convert it into text in real time. Based on the obtained text data, important topics are analyzed by natural language processing, and comments and questions are generated. These generated comments are displayed on the user's terminal.

[0314] Step 6:

[0315] After the meeting ends, the server summarizes the text data and automatically generates meeting minutes. This process includes keyword extraction and summarization of key points. The generated minutes are sent to the user's device, where they can review and edit them. The final reviewed minutes are then distributed to all participants.

[0316] 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.

[0317] This invention relates to a system using an AI agent to streamline meeting management, which in particular includes means for recognizing the emotions of meeting participants and using this to stimulate and facilitate discussion. The main components of the system are a server, user terminals, and an emotion engine.

[0318] Users input the meeting topic and objectives via their device. The server receives this information, analyzes past data, and collects relevant information. Based on the collected data, it automatically generates an agenda using natural language processing and proposes it to the user's device. Users can review the proposed agenda and modify it as needed.

[0319] When a user enters a list of participants, the server analyzes each participant's schedule and suggests the most suitable meeting time. The suggested time is displayed on the user's terminal, and can be approved or rescheduled.

[0320] During the meeting, the server uses speech recognition technology to analyze what is being said in real time, and in addition, an emotion engine analyzes the emotions of the participants. The emotion engine extracts emotional data from the audio and video and provides information to support the progress of the meeting.

[0321] The server generates comments and questions based on spoken content and sentiment data to optimize the flow of the meeting, and displays them through the terminal. This helps to ensure smooth and engaging meetings.

[0322] After the meeting, the server summarizes the conversation content and sentiment data, and automatically generates meeting minutes. Users can review the minutes on their devices, make corrections as needed, and the finalized minutes are distributed to participants by the server.

[0323] For example, in a meeting about marketing strategy, if participants' emotions are leaning towards the negative, the server, based on the analysis results of the emotion engine, displays positive comments on their terminals to boost motivation and correct the flow of the meeting. In this way, comprehensive support can be provided, significantly improving the quality and efficiency of meetings.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The user enters the meeting topic and purpose via their device. The server retrieves this information and collects meeting-related data from its database.

[0327] Step 2:

[0328] The server uses collected data to execute a natural language processing algorithm and automatically generates a meeting agenda. The generated agenda is sent to the terminal, where users can review and modify it.

[0329] Step 3:

[0330] The user enters a list of participants on their device. The server retrieves and analyzes each participant's schedule information via a calendar API. The optimal meeting date and time are suggested and displayed on the user's device.

[0331] Step 4:

[0332] Before the meeting begins, the terminal sends a reminder to the user. During the meeting, the server uses speech recognition technology to transcribe what is said in real time, and an emotion engine analyzes the participants' emotions.

[0333] Step 5:

[0334] The server generates comments and questions to optimize the meeting's progress based on analyzed speech content and sentiment data. These comments are provided to users via their terminals to support and stimulate discussion.

[0335] Step 6:

[0336] After the meeting ends, the server analyzes the audio and sentiment data together, summarizes the key points, and automatically creates meeting minutes. The created minutes are then reviewed on the user's device and modified as needed.

[0337] Step 7:

[0338] Finalized meeting minutes are distributed to participants via email by the server. Users can check the delivery status and follow up via their devices.

[0339] (Example 2)

[0340] 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".

[0341] Managing a meeting requires considerable preparation and effort, particularly creating an agenda, coordinating participants' schedules, facilitating the meeting during it, and preparing meeting minutes afterward. Furthermore, effective facilitation through a precise understanding of participants' emotions and conversations is crucial, something difficult for humans to do in real time. There is a need to reduce the burden of meeting management and improve the quality of meetings.

[0342] 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.

[0343] In this invention, the server includes means for automatically creating an agenda based on the meeting plan, means for analyzing participants' schedules to suggest the optimal meeting time, and means for recognizing and immediately analyzing audio acquired during the meeting. This enables more efficient meeting management and optimization of the meeting's progress in accordance with the participants' emotions.

[0344] "Meeting planning" is the process of setting agenda items and clarifying objectives necessary for conducting a meeting effectively.

[0345] An "automatic agenda creation system" is a system that uses machine learning techniques and algorithms to construct an agenda based on the meeting's theme and objectives, minimizing human intervention.

[0346] "Analyzing participants' schedules" means analyzing participants' schedule data to identify the optimal meeting time that everyone can attend.

[0347] A "device that suggests the optimal meeting time" refers to a system that, based on analysis results, proposes the most suitable date and time slot for a meeting.

[0348] "Recognizing audio acquired during a meeting" refers to the process of capturing the audio spoken during a meeting as digital data and converting its content into an analyzable format.

[0349] "Methods for immediate analysis" refers to technologies that process acquired audio data and other information from meetings in real time and generate analysis results instantly.

[0350] A "device for automatically creating record documents" is a system that automatically generates meeting minutes and reports based on audio and text data accumulated during a meeting.

[0351] A "device for transmitting recorded documents to participants" is a system equipped with communication means for electronically distributing the created meeting minutes to participants.

[0352] This invention is a system for automating and streamlining the entire process of meeting planning, implementation, and follow-up. The system primarily consists of a server, user terminals, and an emotion engine.

[0353] First, the user inputs the meeting topic and objectives through their device. The device then sends this information to the server. The server uses this information to search its past database and collect and analyze relevant information. For this purpose, the server utilizes natural language processing technology and a generative AI model to automatically generate the agenda. An example of input for this generation process is a prompt such as, "Please create the agenda for the next marketing meeting."

[0354] The user enters a list of meeting participants and their schedule information into their terminal. The server analyzes this schedule information and suggests the optimal meeting date and time when all participants can attend. A calendar API is often used for this analysis.

[0355] During the meeting, the server's speech recognition technology transcribes spoken content into text in real time. Simultaneously, the server uses an emotion engine to analyze participants' emotions and uses the results to assist in the meeting's progress. Based on this analysis, the server generates appropriate comments and questions to maintain the flow of the meeting and displays them on the participants' terminals.

[0356] After the meeting, the server summarizes the content and sentiment of the participants and automatically generates meeting minutes. Users can review the generated minutes on their terminals and make corrections as needed. The revised minutes are then distributed to each participant from the server.

[0357] For example, in a meeting about marketing strategy, if the server detects a situation where participants' emotions tend to become negative, it can improve the mood of the meeting by offering positive comments. This kind of support dramatically improves the quality and efficiency of the meeting.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] Users input the purpose and theme of the meeting using a terminal. This information serves as the starting data for the system. The data entered by the user is sent to the server by the terminal. The terminal's specific operations include receiving input through the user interface and sending the data to the server via network communication.

[0361] Step 2:

[0362] The server accesses internal databases or external sources to collect relevant information based on the received meeting theme and objectives. The collected data includes past meeting data and relevant literature. Using this information, the server creates prompts for a generative AI model and generates an appropriate agenda. The input is the meeting theme, and the output is the generated agenda. Specifically, the server executes database queries via an API and sends the collected results to the AI ​​model.

[0363] Step 3:

[0364] The user enters a list of meeting participants via a terminal. The terminal then sends this list to the server. The input is a list of participant names, and the output is participant information in digital format. The specific actions include creating the list and sending the data to the server.

[0365] Step 4:

[0366] The server retrieves and analyzes each participant's schedule data from an external calendar service based on the received participant list. Based on the analysis, it proposes a meeting time that allows the most participants to attend. The input is the participants' schedules, and the output is the optimal meeting date and time. Specifically, the process involves fetching calendar data via an API and applying an analysis algorithm.

[0367] Step 5:

[0368] During the meeting, the server uses speech recognition technology to convert spoken content into text in real time. The server also works in conjunction with an emotion engine to analyze participants' speech and facial expressions to generate emotion data. The input is the audio and video from the meeting, and the output is the transcribed conversation and emotion data. Specifically, the server performs tasks such as capturing and transcribing audio data and analyzing video data.

[0369] Step 6:

[0370] The server generates comments and questions to assist in meeting progress based on the acquired speech content and sentiment data. The generated comments are sent to the terminal and displayed to the user. Input is real-time conversation text and sentiment data, while output is comments to assist in progress. Specifically, reply generation is performed using natural language generation technology.

[0371] Step 7:

[0372] After the meeting ends, the server summarizes the conversation content and sentiment data to automatically generate meeting minutes. Users can review and edit the generated minutes on their terminals. Finally, the minutes are distributed from the server to the participants. The input is the conversation content and sentiment data, and the output is the finalized meeting minutes. The specific operation involves the use of a text summarization algorithm and a distribution system.

[0373] (Application Example 2)

[0374] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0375] In modern cities, meetings involving diverse stakeholders are held frequently, requiring efficient management and smooth collection of participants' opinions. However, optimizing the progress of a meeting in real time, accurately understanding participants' emotions, and facilitating active discussion are not easy. As a result, the quality and efficiency of meetings decline, and important discussions may end without taking place. This invention aims to solve these problems.

[0376] 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.

[0377] In this invention, the server includes means for automatically generating a meeting agenda, means for analyzing participants' schedules and suggesting the optimal meeting date and time, and means for recognizing and analyzing voice and emotions during the meeting in real time. This makes it possible to significantly improve the quality and efficiency of meetings.

[0378] "Methods for automatically generating meeting agendas" refer to functions that automatically create necessary topics and schedules based on the content and purpose of the meeting.

[0379] "A method for analyzing participants' schedules and suggesting the optimal meeting date and time" refers to a function that analyzes each participant's schedule and automatically selects the most suitable date and time for everyone to attend.

[0380] "Means for recognizing and analyzing audio and emotions during meetings in real time" refers to a function that uses speech recognition technology to transcribe the content of speech during meetings into text, and simultaneously detects and analyzes the emotions of participants in real time using emotion analysis technology.

[0381] "Means for providing comments to support the progress of a meeting based on analysis results and sentiment data" refers to a function that automatically generates appropriate comments and suggestions to ensure that a meeting proceeds effectively, based on the results of voice and sentiment analysis.

[0382] "A means of summarizing the content of discussions and sentiment data after a meeting and automatically creating meeting minutes" refers to a function that extracts important points based on the content of discussions and sentiment data from the meeting and automatically creates meeting minutes.

[0383] "Method for distributing meeting minutes to participants" refers to a function that electronically distributes automatically generated meeting minutes to meeting participants.

[0384] "A means of analyzing emotions and supporting the operation of city council meetings" refers to a function that analyzes the emotions of participants to help city council meetings run more actively and smoothly.

[0385] This system consists of a server and user terminals. The server receives and analyzes information about the meeting's theme, purpose, and participants, which is entered by the user using their terminal.

[0386] The server automatically generates the meeting agenda using natural language processing technology. The software used could be Google Cloud's natural language processing API. The server also has the functionality to analyze participant schedules received from users' devices and calculate the optimal meeting date and time. This requires computing resources to process schedule information in real time.

[0387] During the meeting, the server uses speech recognition technology to convert participants' speech into text in real time. Text conversion of audio data is possible using the Google Cloud Speech-to-Text API. Simultaneously, an emotion analysis engine analyzes audio and video data to determine participants' emotional states and obtain analytical information.

[0388] To support the progress of the meeting, the server generates appropriate comments and questions based on speech and sentiment analysis results and displays them on the user's terminal. IBM Watson's Natural Language Understanding API can be used for this application.

[0389] After the meeting ends, the server summarizes the collected statements and sentiment data, automatically creates meeting minutes, and distributes them to participants after they have been reviewed on their user terminals.

[0390] For example, if a city council meeting is held to discuss the construction of a new public facility, the server can generate questions such as, "What kind of community contributions can be expected from the construction of this facility?" when the discussion stalls, thereby helping to stimulate the discussion. An example of a prompt from the generating AI model would be, "Please tell me how to analyze the statements and sentiments of meeting participants in real time and generate comments and questions to stimulate the discussion."

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] The user uses a terminal to input the meeting's theme, purpose, and participant information. The entered data is sent to the server. The server records the received data and uses natural language processing technology to organize it into data necessary for automatic agenda generation. This organized data then becomes the input for the next step.

[0394] Step 2:

[0395] The server uses the organized data to call Google Cloud's natural language processing API to automatically generate the meeting agenda. Using the API, the agenda and schedule, tailored to the meeting's theme, are output as text. The generated agenda is sent to the user's device for approval or modification.

[0396] Step 3:

[0397] The user's device sends participant schedule information to the server. The server analyzes this information and calculates the optimal meeting date and time. The analysis uses an algorithm that takes into account available time slots and avoids overlaps in the schedule. The calculated optimal date and time is proposed to the user's device, and the user can choose to accept or reschedule.

[0398] Step 4:

[0399] During the meeting, the server converts audio acquired from the user's device into text in real time using the Google Cloud Speech-to-Text API. The input is audio data, and the output is the transcribed text of that audio. Furthermore, an emotion analysis engine analyzes emotion data from the audio and video to classify the emotional state. The analysis results are stored on the server.

[0400] Step 5:

[0401] The server uses IBM Watson's Natural Language Understanding API to generate comments and questions to support the meeting, based on real-time text and sentiment data. This involves generating prompts using a generative AI model. The generated comments and questions are displayed on the user's device.

[0402] Step 6:

[0403] After the meeting ends, the server summarizes the recorded statements and sentiment data, extracts key points, and automatically creates meeting minutes. This is done by the server's internal algorithm and output as text. After users review the created minutes on their devices, they are electronically distributed to participants.

[0404] 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.

[0405] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0406] 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.

[0407] [Third Embodiment]

[0408] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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".

[0420] This invention relates to a system using an AI agent configured to streamline the preparation, conduct, and follow-up of meetings. The system consists of a server, user terminals, and a database for storing necessary data.

[0421] The user enters the meeting topic and objectives via their terminal. The server receives this information and collects relevant topics by analyzing past meeting minutes and related documents in its database. The server automatically generates an agenda using natural language processing technology and proposes it to the user's terminal. The user can review, approve, or modify this agenda.

[0422] Next, based on the list of participants specified by the user, the server retrieves each participant's schedule via the calendar API. The server then suggests an optimal meeting date and time and sends it to the user's device. The user can review the suggested date and time and take appropriate action if necessary to reschedule.

[0423] When a meeting begins, the terminal notifies the user of the start of the meeting. The server uses speech recognition technology to analyze the conversation in real time during the meeting and records its content. Based on the analyzed data, the server generates comments and questions to assist in the progress of the meeting and displays them on the user's terminal to stimulate discussion.

[0424] After the meeting ends, the server automatically generates a summary and meeting minutes based on the recorded audio data. These minutes are sent to the user's device, where they can review and edit them. The server then emails the finalized minutes to all participants.

[0425] For example, in a meeting about marketing strategy, this system automatically generates an agenda from topics such as "target market analysis" and "competitor research," and proposes the optimal meeting date. During the meeting, the server provides specific comments to stimulate discussion, and after the meeting, the server efficiently creates meeting minutes and distributes them quickly. This entire process can significantly reduce the time and effort required to manage meetings.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] The user enters the meeting topic and purpose via their device. The server receives this information and retrieves relevant past information by referring to its database.

[0429] Step 2:

[0430] Based on the information collected by the server, a meeting agenda is automatically generated using natural language processing. The generated agenda is then sent to the user's device in an editable format.

[0431] Step 3:

[0432] The user enters a list of participants into the system. The server retrieves the schedule information of the specified participants via a calendar API, analyzes it, and suggests the optimal meeting date and time. The suggested date and time are displayed on the user's device, allowing for confirmation and adjustment.

[0433] Step 4:

[0434] As the meeting start time approaches, the terminal notifies the user that the meeting has begun. During the meeting, the server uses speech recognition technology to transcribe and analyze the spoken content in real time.

[0435] Step 5:

[0436] The server generates comments and questions from the analysis results to stimulate discussion and provides them to users via their terminals. This facilitates the progress of the meeting.

[0437] Step 6:

[0438] After the meeting ends, the server summarizes the audio data and automatically creates meeting minutes. Users can then review the minutes on their devices and make any necessary corrections.

[0439] Step 7:

[0440] The server will send the meeting minutes, as finalized by the user, to the designated participants via email. Users can check the delivery status through their devices.

[0441] (Example 1)

[0442] 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."

[0443] In modern businesses, the processes of preparing, running, and following up on meetings are labor-intensive and time-consuming, making them difficult to manage efficiently. In particular, there is a growing need to automate a series of tasks, such as agenda creation, participant scheduling, active discussion during meetings, and meeting minute creation and distribution.

[0444] 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.

[0445] In this invention, the server includes means for inputting meeting parameters and analyzing relevant items, means for automatically generating an agenda using a generative artificial intelligence model, and means for obtaining the schedules of meeting participants and calculating the optimal meeting time. This makes it possible to efficiently automate the entire process of meeting management, reduce the burden on stakeholders, and achieve effective communication.

[0446] "Meeting parameters" refer to the basic information necessary to plan and run a meeting, such as the meeting's theme, purpose, time, location, and participant list.

[0447] "Related topics" refer to information and topics related to the theme and objectives of the meeting, extracted from past meeting records and literature data.

[0448] A "generative artificial intelligence model" is an algorithm that learns from vast amounts of data and generates, suggests, or classifies information by mimicking human thought and patterns.

[0449] An "agenda" is a plan that lists the items to be discussed in a meeting, and it is necessary for the smooth running of the meeting.

[0450] "Action schedule" refers to the schedule information of the dates and activities planned by the parties involved, and is used to determine the optimal date and time for a meeting.

[0451] "Audio information" refers to sound data collected in an analyzable digital format from statements and discussions made during a meeting.

[0452] "Textual information" refers to text data that is transcribed from audio information and used for creating meeting minutes.

[0453] "Meeting minutes" are documents that summarize the statements and decisions made during a meeting, and are used for later reference and verification.

[0454] "To transmit via communication" refers to the act of delivering information to a remote location using the internet or network technologies, such as email or messaging.

[0455] The invention is a system for automating and streamlining the preparation, operation, and follow-up of meetings. The system consists of a server, user terminals, and a data storage device.

[0456] Users input basic meeting information, such as the theme and purpose, via their device. The input devices used at this stage are typical computers or mobile devices. Users can input specific meeting themes, such as "marketing strategy."

[0457] The server uses a generative artificial intelligence model to search for relevant historical information in the database based on the input information and automatically generates an agenda using natural language processing techniques. The software used includes natural language processing libraries and a platform for running the AI ​​model. For example, the server can generate an agenda in response to a prompt such as, "Please suggest relevant agenda topics for the next marketing meeting."

[0458] Next, the server retrieves participants' schedules through their calendar applications and calculates the optimal meeting date and time for all participants. Specifically, it analyzes the schedule information using the Google Calendar API or an equivalent API.

[0459] During the meeting, the server uses speech recognition technology to analyze the conversation in real time and record important content. This process can utilize tools such as the Google Cloud Speech-to-Text API. The recorded audio information is converted into text, and then comments are generated to stimulate the discussion and displayed on the user's device.

[0460] After the meeting, the server automatically summarizes the data collected and creates a meeting record. This record is then sent directly to all participants via communication from the server. Participants use a standard email client as their means of communication.

[0461] In this way, the system streamlines the entire process from meeting preparation to follow-up, reducing the burden on users.

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

[0463] Step 1:

[0464] The user enters the meeting theme and purpose via their terminal. This input is sent to the server as text data, such as "Marketing Strategy Meeting." The server receives this input and stores it in a database as basic meeting information. Specifically, the user enters a series of pieces of information in text format on a dedicated input screen.

[0465] Step 2:

[0466] The server searches for relevant information in the database based on the entered theme and objective. It analyzes related items using natural language processing technology and automatically generates an agenda using a generative artificial intelligence model. The input is theme information, and the output is the generated agenda. This agenda is sent to the terminal, where the user can preview it. Specifically, the server generates an agenda that includes topics such as "Target Market Analysis" and "Competitor Research."

[0467] Step 3:

[0468] The server retrieves each participant's schedule through a calendar application based on the participant list specified by the user. It uses an API to collect schedule data and calculate the optimal meeting date and time. Specifically, it uses the Google Calendar API to check each participant's available meeting time slots. The input is the participant list and each participant's schedule, and the output is the optimal meeting date and time.

[0469] Step 4:

[0470] The server suggests the optimal meeting date and time to the user's terminal. The user can review this suggestion and make modifications as needed. The input is the suggested optimal date and time information, and the output is the user's confirmation and modification information. Specifically, the user can drag the suggested date and time to a different day through the terminal's calendar interface.

[0471] Step 5:

[0472] During the meeting, the terminal notifies the user of the start time, and the server analyzes the conversation in real time using speech recognition technology. The input is audio data from the meeting, and the output is the meeting content recorded in text format. Specifically, the server converts the audio via the Google Cloud Speech-to-Text API and automatically records important statements.

[0473] Step 6:

[0474] Once the meeting concludes, the server summarizes the meeting content based on the recorded audio data and automatically creates a meeting transcript. The input is the recorded text data, and the output is a summarized meeting transcript document. Specifically, the server uses a generation AI model to shorten the recording and extract the key points.

[0475] Step 7:

[0476] Finally, the server sends the created meeting minutes to all participants via email. Users can review the meeting minutes in their email clients and make corrections as needed. The input is the summarized meeting minutes, and the output is the mailboxes of the recipient participants. Specifically, the server uses the email protocol to quickly distribute the meeting minutes.

[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] Improving the efficiency of meeting preparation, management, and follow-up is a critical challenge for many organizations. However, responding quickly to emergencies and grasping and sharing key points of discussions in real time has been difficult with traditional methods.

[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 a meeting agenda, means for analyzing participants' schedules and proposing an optimal meeting date and time, and means for quickly adjusting participants' schedules and re-proposing an optimal meeting date and time in the event of an emergency. This makes it possible to streamline the entire process from meeting preparation to execution, rapid response in emergency situations, and follow-up.

[0482] "Methods for automatically generating agendas" refer to functions that automatically organize the main topics and agenda items based on the meeting content and plan the order in which the meeting will proceed.

[0483] "A means of analyzing participants' schedules and suggesting the optimal meeting date and time" refers to a function that analyzes participants' schedules and automatically calculates and presents dates and times when everyone is available.

[0484] "A means of recognizing and analyzing speech in real time" refers to a technology that converts audio from a meeting into digital data and performs text analysis on the spot.

[0485] "Means of providing comments to support the progress of meetings" refers to a function that provides instructions and suggestions in real time to stimulate discussion based on the analyzed meeting content.

[0486] "A means of summarizing the content of speeches and automatically creating meeting minutes" refers to a function that summarizes the important points of a meeting and compiles them into an official record.

[0487] "Methods for distributing meeting minutes to participants" refers to a function that quickly distributes the created meeting minutes to relevant parties and promotes information sharing.

[0488] "A means to quickly adjust participants' schedules and propose the optimal meeting date and time in the event of an emergency" refers to a function that immediately re-evaluates participants' schedules and proposes a new meeting date and time in response to unforeseen circumstances.

[0489] "A means of summarizing the key points of a meeting in real time and providing them to participants" refers to a function that summarizes the content of an ongoing meeting and shares its key points with relevant parties in real time.

[0490] The system implementing this invention consists of a server, a user terminal, and a database for storing necessary data. The user uses the terminal to input the meeting topic and purpose. The server receives this information, retrieves past meeting minutes and related documents from the database, and automatically generates an agenda using natural language processing technology. The generated agenda is sent to the user's terminal, where the user can review and modify it.

[0491] Furthermore, the server retrieves each participant's schedule via the calendar API based on the list of participants. The server then suggests an optimal meeting date and time considering all participants' schedules and sends this information to the user's device. The user can review the suggested date and time and readjust if necessary.

[0492] During the meeting, the server uses speech recognition technology to analyze the conversation in real time and automatically generates comments and questions based on the content. These are displayed on the user's device to help stimulate discussion.

[0493] After the meeting ends, the server automatically generates a summary and meeting minutes based on the analyzed audio data and sends them to the user's device. After the user reviews and makes any necessary corrections, the final version of the meeting minutes is distributed to all participants.

[0494] This system uses Google Speech-to-Text for speech recognition and Python's NLTK library for natural language processing. Data analysis is performed on cloud servers and local databases.

[0495] As a concrete example, let's consider an implementation of the system in a security enhancement meeting at a certain facility. For instance, based on the input of the meeting theme, it extracts relevant topics such as "intrusion detection technology" and "employee training programs" and automatically generates an agenda. A possible prompt to the generating AI model would be, "What topics should we automatically generate the agenda for the next security meeting?"

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

[0497] Step 1:

[0498] Users input the meeting topic and purpose via their terminal. This input data is sent to the server, which searches its database for past meeting minutes and related documents. As a result, relevant information is collected.

[0499] Step 2:

[0500] The server automatically generates an agenda using natural language processing technology based on the collected relevant information. Specifically, it identifies topics through a text analysis engine and creates a structured agenda. The generated agenda is sent to the user's terminal and displayed to the user.

[0501] Step 3:

[0502] The user reviews the agenda displayed on their device and makes corrections as needed. The corrected information is then sent back to the server and recorded in the database.

[0503] Step 4:

[0504] The server uses the Calendar API to retrieve each participant's schedule based on the list of participants. It then analyzes the time information obtained from this API call to calculate the optimal meeting date and time. The calculated date and time are sent to the user's device as a suggestion.

[0505] Step 5:

[0506] During the meeting, the server acquires audio data and converts it to text in real time using speech recognition technology. Based on the acquired text data, it performs natural language processing to analyze important agenda items and generate comments and questions. These generated comments are displayed on the user's device.

[0507] Step 6:

[0508] After the meeting ends, the server summarizes the text data and automatically generates meeting minutes. This process includes keyword extraction and summarization of key points. The generated minutes are sent to the user's device, where they can review and edit them. The final reviewed minutes are then distributed to all 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] This invention relates to a system using an AI agent to streamline meeting management, which in particular includes means for recognizing the emotions of meeting participants and using this to stimulate and facilitate discussion. The main components of the system are a server, user terminals, and an emotion engine.

[0511] Users input the meeting topic and objectives via their device. The server receives this information, analyzes past data, and collects relevant information. Based on the collected data, it automatically generates an agenda using natural language processing and proposes it to the user's device. Users can review the proposed agenda and modify it as needed.

[0512] When a user enters a list of participants, the server analyzes each participant's schedule and suggests the most suitable meeting time. The suggested time is displayed on the user's terminal, and can be approved or rescheduled.

[0513] During the meeting, the server uses speech recognition technology to analyze what is being said in real time, and in addition, an emotion engine analyzes the emotions of the participants. The emotion engine extracts emotional data from the audio and video and provides information to support the progress of the meeting.

[0514] The server generates comments and questions based on spoken content and sentiment data to optimize the flow of the meeting, and displays them through the terminal. This helps to ensure smooth and engaging meetings.

[0515] After the meeting, the server summarizes the conversation content and sentiment data, and automatically generates meeting minutes. Users can review the minutes on their devices, make corrections as needed, and the finalized minutes are distributed to participants by the server.

[0516] For example, in a meeting about marketing strategy, if participants' emotions are leaning towards the negative, the server, based on the analysis results of the emotion engine, displays positive comments on their terminals to boost motivation and correct the flow of the meeting. In this way, comprehensive support can be provided, significantly improving the quality and efficiency of meetings.

[0517] The following describes the processing flow.

[0518] Step 1:

[0519] The user enters the meeting topic and purpose via their device. The server retrieves this information and collects meeting-related data from its database.

[0520] Step 2:

[0521] The server uses collected data to execute a natural language processing algorithm and automatically generates a meeting agenda. The generated agenda is sent to the terminal, where users can review and modify it.

[0522] Step 3:

[0523] The user enters a list of participants on their device. The server retrieves and analyzes each participant's schedule information via a calendar API. The optimal meeting date and time are suggested and displayed on the user's device.

[0524] Step 4:

[0525] Before the meeting begins, the terminal sends a reminder to the user. During the meeting, the server uses speech recognition technology to transcribe what is said in real time, and an emotion engine analyzes the participants' emotions.

[0526] Step 5:

[0527] The server generates comments and questions to optimize the meeting's progress based on analyzed speech content and sentiment data. These comments are provided to users via their terminals to support and stimulate discussion.

[0528] Step 6:

[0529] After the meeting ends, the server analyzes the audio and sentiment data together, summarizes the key points, and automatically creates meeting minutes. The created minutes are then reviewed on the user's device and modified as needed.

[0530] Step 7:

[0531] Finalized meeting minutes are distributed to participants via email by the server. Users can check the delivery status and follow up via their devices.

[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] Managing a meeting requires considerable preparation and effort, particularly creating an agenda, coordinating participants' schedules, facilitating the meeting during it, and preparing meeting minutes afterward. Furthermore, effective facilitation through a precise understanding of participants' emotions and conversations is crucial, something difficult for humans to do in real time. There is a need to reduce the burden of meeting management and improve the quality of meetings.

[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 automatically creating an agenda based on the meeting plan, means for analyzing participants' schedules to suggest the optimal meeting time, and means for recognizing and immediately analyzing audio acquired during the meeting. This enables more efficient meeting management and optimization of the meeting's progress in accordance with the participants' emotions.

[0537] "Meeting planning" is the process of setting agenda items and clarifying objectives necessary for conducting a meeting effectively.

[0538] An "automatic agenda creation system" is a system that uses machine learning techniques and algorithms to construct an agenda based on the meeting's theme and objectives, minimizing human intervention.

[0539] "Analyzing participants' schedules" means analyzing participants' schedule data to identify the optimal meeting time that everyone can attend.

[0540] A "device that suggests the optimal meeting time" refers to a system that, based on analysis results, proposes the most suitable date and time slot for a meeting.

[0541] "Recognizing audio acquired during a meeting" refers to the process of capturing the audio spoken during a meeting as digital data and converting its content into an analyzable format.

[0542] "Methods for immediate analysis" refers to technologies that process acquired audio data and other information from meetings in real time and generate analysis results instantly.

[0543] A "device for automatically creating record documents" is a system that automatically generates meeting minutes and reports based on audio and text data accumulated during a meeting.

[0544] A "device for transmitting recorded documents to participants" is a system equipped with communication means for electronically distributing the created meeting minutes to participants.

[0545] This invention is a system for automating and streamlining the entire process of meeting planning, implementation, and follow-up. The system primarily consists of a server, user terminals, and an emotion engine.

[0546] First, the user inputs the meeting topic and objectives through their device. The device then sends this information to the server. The server uses this information to search its past database and collect and analyze relevant information. For this purpose, the server utilizes natural language processing technology and a generative AI model to automatically generate the agenda. An example of input for this generation process is a prompt such as, "Please create the agenda for the next marketing meeting."

[0547] The user enters a list of meeting participants and their schedule information into their terminal. The server analyzes this schedule information and suggests the optimal meeting date and time when all participants can attend. A calendar API is often used for this analysis.

[0548] During the meeting, the server's speech recognition technology transcribes spoken content into text in real time. Simultaneously, the server uses an emotion engine to analyze participants' emotions and uses the results to assist in the meeting's progress. Based on this analysis, the server generates appropriate comments and questions to maintain the flow of the meeting and displays them on the participants' terminals.

[0549] After the meeting, the server summarizes the content and sentiment of the participants and automatically generates meeting minutes. Users can review the generated minutes on their terminals and make corrections as needed. The revised minutes are then distributed to each participant from the server.

[0550] For example, in a meeting about marketing strategy, if the server detects a situation where participants' emotions tend to become negative, it can improve the mood of the meeting by offering positive comments. This kind of support dramatically improves the quality and efficiency of the meeting.

[0551] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0552] Step 1:

[0553] Users input the purpose and theme of the meeting using a terminal. This information serves as the starting data for the system. The data entered by the user is sent to the server by the terminal. The terminal's specific operations include receiving input through the user interface and sending the data to the server via network communication.

[0554] Step 2:

[0555] The server accesses internal databases or external sources to collect relevant information based on the received meeting theme and objectives. The collected data includes past meeting data and relevant literature. Using this information, the server creates prompts for a generative AI model and generates an appropriate agenda. The input is the meeting theme, and the output is the generated agenda. Specifically, the server executes database queries via an API and sends the collected results to the AI ​​model.

[0556] Step 3:

[0557] The user enters a list of meeting participants via a terminal. The terminal then sends this list to the server. The input is a list of participant names, and the output is participant information in digital format. The specific actions include creating the list and sending the data to the server.

[0558] Step 4:

[0559] The server retrieves and analyzes each participant's schedule data from an external calendar service based on the received participant list. Based on the analysis, it proposes a meeting time that allows the most participants to attend. The input is the participants' schedules, and the output is the optimal meeting date and time. Specifically, the process involves fetching calendar data via an API and applying an analysis algorithm.

[0560] Step 5:

[0561] During the meeting, the server uses speech recognition technology to convert spoken content into text in real time. The server also works in conjunction with an emotion engine to analyze participants' speech and facial expressions to generate emotion data. The input is the audio and video from the meeting, and the output is the transcribed conversation and emotion data. Specifically, the server performs tasks such as capturing and transcribing audio data and analyzing video data.

[0562] Step 6:

[0563] The server generates comments and questions to assist in meeting progress based on the acquired speech content and sentiment data. The generated comments are sent to the terminal and displayed to the user. Input is real-time conversation text and sentiment data, while output is comments to assist in progress. Specifically, reply generation is performed using natural language generation technology.

[0564] Step 7:

[0565] After the meeting ends, the server summarizes the conversation content and sentiment data to automatically generate meeting minutes. Users can review and edit the generated minutes on their terminals. Finally, the minutes are distributed from the server to the participants. The input is the conversation content and sentiment data, and the output is the finalized meeting minutes. The specific operation involves the use of a text summarization algorithm and a distribution system.

[0566] (Application Example 2)

[0567] 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."

[0568] In modern cities, meetings involving diverse stakeholders are held frequently, requiring efficient management and smooth collection of participants' opinions. However, optimizing the progress of a meeting in real time, accurately understanding participants' emotions, and facilitating active discussion are not easy. As a result, the quality and efficiency of meetings decline, and important discussions may end without taking place. This invention aims to solve these problems.

[0569] 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.

[0570] In this invention, the server includes means for automatically generating a meeting agenda, means for analyzing participants' schedules and suggesting the optimal meeting date and time, and means for recognizing and analyzing voice and emotions during the meeting in real time. This makes it possible to significantly improve the quality and efficiency of meetings.

[0571] "Methods for automatically generating meeting agendas" refer to functions that automatically create necessary topics and schedules based on the content and purpose of the meeting.

[0572] "A method for analyzing participants' schedules and suggesting the optimal meeting date and time" refers to a function that analyzes each participant's schedule and automatically selects the most suitable date and time for everyone to attend.

[0573] "Means for recognizing and analyzing audio and emotions during meetings in real time" refers to a function that uses speech recognition technology to transcribe the content of speech during meetings into text, and simultaneously detects and analyzes the emotions of participants in real time using emotion analysis technology.

[0574] "Means for providing comments to support the progress of a meeting based on analysis results and sentiment data" refers to a function that automatically generates appropriate comments and suggestions to ensure that a meeting proceeds effectively, based on the results of voice and sentiment analysis.

[0575] "A means of summarizing the content of discussions and sentiment data after a meeting and automatically creating meeting minutes" refers to a function that extracts important points based on the content of discussions and sentiment data from the meeting and automatically creates meeting minutes.

[0576] "Method for distributing meeting minutes to participants" refers to a function that electronically distributes automatically generated meeting minutes to meeting participants.

[0577] "A means of analyzing emotions and supporting the operation of city council meetings" refers to a function that analyzes the emotions of participants to help city council meetings run more actively and smoothly.

[0578] This system consists of a server and user terminals. The server receives and analyzes information about the meeting's theme, purpose, and participants, which is entered by the user using their terminal.

[0579] The server automatically generates the meeting agenda using natural language processing technology. The software used could be Google Cloud's natural language processing API. The server also has the functionality to analyze participant schedules received from users' devices and calculate the optimal meeting date and time. This requires computing resources to process schedule information in real time.

[0580] During the meeting, the server uses speech recognition technology to convert participants' speech into text in real time. Text conversion of audio data is possible using the Google Cloud Speech-to-Text API. Simultaneously, an emotion analysis engine analyzes audio and video data to determine participants' emotional states and obtain analytical information.

[0581] To support the progress of the meeting, the server generates appropriate comments and questions based on speech and sentiment analysis results and displays them on the user's terminal. IBM Watson's Natural Language Understanding API can be used for this application.

[0582] After the meeting ends, the server summarizes the collected statements and sentiment data, automatically creates meeting minutes, and distributes them to participants after they have been reviewed on their user terminals.

[0583] For example, if a city council meeting is held to discuss the construction of a new public facility, the server can generate questions such as, "What kind of community contributions can be expected from the construction of this facility?" when the discussion stalls, thereby helping to stimulate the discussion. An example of a prompt from the generating AI model would be, "Please tell me how to analyze the statements and sentiments of meeting participants in real time and generate comments and questions to stimulate the discussion."

[0584] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0585] Step 1:

[0586] The user uses a terminal to input the meeting's theme, purpose, and participant information. The entered data is sent to the server. The server records the received data and uses natural language processing technology to organize it into data necessary for automatic agenda generation. This organized data then becomes the input for the next step.

[0587] Step 2:

[0588] The server uses the organized data to call Google Cloud's natural language processing API to automatically generate the meeting agenda. Using the API, the agenda and schedule, tailored to the meeting's theme, are output as text. The generated agenda is sent to the user's device for approval or modification.

[0589] Step 3:

[0590] The user's device sends participant schedule information to the server. The server analyzes this information and calculates the optimal meeting date and time. The analysis uses an algorithm that takes into account available time slots and avoids overlaps in the schedule. The calculated optimal date and time is proposed to the user's device, and the user can choose to accept or reschedule.

[0591] Step 4:

[0592] During the meeting, the server converts audio acquired from the user's device into text in real time using the Google Cloud Speech-to-Text API. The input is audio data, and the output is the transcribed text of that audio. Furthermore, an emotion analysis engine analyzes emotion data from the audio and video to classify the emotional state. The analysis results are stored on the server.

[0593] Step 5:

[0594] The server uses IBM Watson's Natural Language Understanding API to generate comments and questions to support the meeting, based on real-time text and sentiment data. This involves generating prompts using a generative AI model. The generated comments and questions are displayed on the user's device.

[0595] Step 6:

[0596] After the meeting ends, the server summarizes the recorded statements and sentiment data, extracts key points, and automatically creates meeting minutes. This is done by the server's internal algorithm and output as text. After users review the created minutes on their devices, they are electronically distributed to participants.

[0597] 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.

[0598] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0599] 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.

[0600] [Fourth Embodiment]

[0601] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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".

[0614] This invention relates to a system using an AI agent configured to streamline the preparation, conduct, and follow-up of meetings. The system consists of a server, user terminals, and a database for storing necessary data.

[0615] The user enters the meeting topic and objectives via their terminal. The server receives this information and collects relevant topics by analyzing past meeting minutes and related documents in its database. The server automatically generates an agenda using natural language processing technology and proposes it to the user's terminal. The user can review, approve, or modify this agenda.

[0616] Next, based on the list of participants specified by the user, the server retrieves each participant's schedule via the calendar API. The server then suggests an optimal meeting date and time and sends it to the user's device. The user can review the suggested date and time and take appropriate action if necessary to reschedule.

[0617] When a meeting begins, the terminal notifies the user of the start of the meeting. The server uses speech recognition technology to analyze the conversation in real time during the meeting and records its content. Based on the analyzed data, the server generates comments and questions to assist in the progress of the meeting and displays them on the user's terminal to stimulate discussion.

[0618] After the meeting ends, the server automatically generates a summary and meeting minutes based on the recorded audio data. These minutes are sent to the user's device, where they can review and edit them. The server then emails the finalized minutes to all participants.

[0619] For example, in a meeting about marketing strategy, this system automatically generates an agenda from topics such as "target market analysis" and "competitor research," and proposes the optimal meeting date. During the meeting, the server provides specific comments to stimulate discussion, and after the meeting, the server efficiently creates meeting minutes and distributes them quickly. This entire process can significantly reduce the time and effort required to manage meetings.

[0620] The following describes the processing flow.

[0621] Step 1:

[0622] The user enters the meeting topic and purpose via their device. The server receives this information and retrieves relevant past information by referring to its database.

[0623] Step 2:

[0624] Based on the information collected by the server, a meeting agenda is automatically generated using natural language processing. The generated agenda is then sent to the user's device in an editable format.

[0625] Step 3:

[0626] The user enters a list of participants into the system. The server retrieves the schedule information of the specified participants via a calendar API, analyzes it, and suggests the optimal meeting date and time. The suggested date and time are displayed on the user's device, allowing for confirmation and adjustment.

[0627] Step 4:

[0628] As the meeting start time approaches, the terminal notifies the user that the meeting has begun. During the meeting, the server uses speech recognition technology to transcribe and analyze the spoken content in real time.

[0629] Step 5:

[0630] The server generates comments and questions from the analysis results to stimulate discussion and provides them to users via their terminals. This facilitates the progress of the meeting.

[0631] Step 6:

[0632] After the meeting ends, the server summarizes the audio data and automatically creates meeting minutes. Users can then review the minutes on their devices and make any necessary corrections.

[0633] Step 7:

[0634] The server will send the meeting minutes, as finalized by the user, to the designated participants via email. Users can check the delivery status through their devices.

[0635] (Example 1)

[0636] 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".

[0637] In modern businesses, the processes of preparing, running, and following up on meetings are labor-intensive and time-consuming, making them difficult to manage efficiently. In particular, there is a growing need to automate a series of tasks, such as agenda creation, participant scheduling, active discussion during meetings, and meeting minute creation and distribution.

[0638] 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.

[0639] In this invention, the server includes means for inputting meeting parameters and analyzing relevant items, means for automatically generating an agenda using a generative artificial intelligence model, and means for obtaining the schedules of meeting participants and calculating the optimal meeting time. This makes it possible to efficiently automate the entire process of meeting management, reduce the burden on stakeholders, and achieve effective communication.

[0640] "Meeting parameters" refer to the basic information necessary to plan and run a meeting, such as the meeting's theme, purpose, time, location, and participant list.

[0641] "Related topics" refer to information and topics related to the theme and objectives of the meeting, extracted from past meeting records and literature data.

[0642] A "generative artificial intelligence model" is an algorithm that learns from vast amounts of data and generates, suggests, or classifies information by mimicking human thought and patterns.

[0643] An "agenda" is a plan that lists the items to be discussed in a meeting, and it is necessary for the smooth running of the meeting.

[0644] "Action schedule" refers to the schedule information of the dates and activities planned by the parties involved, and is used to determine the optimal date and time for a meeting.

[0645] "Audio information" refers to sound data collected in an analyzable digital format from statements and discussions made during a meeting.

[0646] "Textual information" refers to text data that is transcribed from audio information and used for creating meeting minutes.

[0647] "Meeting minutes" are documents that summarize the statements and decisions made during a meeting, and are used for later reference and verification.

[0648] "To transmit via communication" refers to the act of delivering information to a remote location using the internet or network technologies, such as email or messaging.

[0649] The invention is a system for automating and streamlining the preparation, operation, and follow-up of meetings. The system consists of a server, user terminals, and a data storage device.

[0650] Users input basic meeting information, such as the theme and purpose, via their device. The input devices used at this stage are typical computers or mobile devices. Users can input specific meeting themes, such as "marketing strategy."

[0651] The server uses a generative artificial intelligence model to search for relevant historical information in the database based on the input information and automatically generates an agenda using natural language processing techniques. The software used includes natural language processing libraries and a platform for running the AI ​​model. For example, the server can generate an agenda in response to a prompt such as, "Please suggest relevant agenda topics for the next marketing meeting."

[0652] Next, the server retrieves participants' schedules through their calendar applications and calculates the optimal meeting date and time for all participants. Specifically, it analyzes the schedule information using the Google Calendar API or an equivalent API.

[0653] During the meeting, the server uses speech recognition technology to analyze the conversation in real time and record important content. This process can utilize tools such as the Google Cloud Speech-to-Text API. The recorded audio information is converted into text, and then comments are generated to stimulate the discussion and displayed on the user's device.

[0654] After the meeting, the server automatically summarizes the data collected and creates a meeting record. This record is then sent directly to all participants via communication from the server. Participants use a standard email client as their means of communication.

[0655] In this way, the system streamlines the entire process from meeting preparation to follow-up, reducing the burden on users.

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] The user enters the meeting theme and purpose via their terminal. This input is sent to the server as text data, such as "Marketing Strategy Meeting." The server receives this input and stores it in a database as basic meeting information. Specifically, the user enters a series of pieces of information in text format on a dedicated input screen.

[0659] Step 2:

[0660] The server searches for relevant information in the database based on the entered theme and objective. It analyzes related items using natural language processing technology and automatically generates an agenda using a generative artificial intelligence model. The input is theme information, and the output is the generated agenda. This agenda is sent to the terminal, where the user can preview it. Specifically, the server generates an agenda that includes topics such as "Target Market Analysis" and "Competitor Research."

[0661] Step 3:

[0662] The server retrieves each participant's schedule through a calendar application based on the participant list specified by the user. It uses an API to collect schedule data and calculate the optimal meeting date and time. Specifically, it uses the Google Calendar API to check each participant's available meeting time slots. The input is the participant list and each participant's schedule, and the output is the optimal meeting date and time.

[0663] Step 4:

[0664] The server suggests the optimal meeting date and time to the user's terminal. The user can review this suggestion and make modifications as needed. The input is the suggested optimal date and time information, and the output is the user's confirmation and modification information. Specifically, the user can drag the suggested date and time to a different day through the terminal's calendar interface.

[0665] Step 5:

[0666] During the meeting, the terminal notifies the user of the start time, and the server analyzes the conversation in real time using speech recognition technology. The input is audio data from the meeting, and the output is the meeting content recorded in text format. Specifically, the server converts the audio via the Google Cloud Speech-to-Text API and automatically records important statements.

[0667] Step 6:

[0668] Once the meeting concludes, the server summarizes the meeting content based on the recorded audio data and automatically creates a meeting transcript. The input is the recorded text data, and the output is a summarized meeting transcript document. Specifically, the server uses a generation AI model to shorten the recording and extract the key points.

[0669] Step 7:

[0670] Finally, the server sends the created meeting minutes to all participants via email. Users can review the meeting minutes in their email clients and make corrections as needed. The input is the summarized meeting minutes, and the output is the mailboxes of the recipient participants. Specifically, the server uses the email protocol to quickly distribute the meeting minutes.

[0671] (Application Example 1)

[0672] 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".

[0673] Improving the efficiency of meeting preparation, management, and follow-up is a critical challenge for many organizations. However, responding quickly to emergencies and grasping and sharing key points of discussions in real time has been difficult with traditional methods.

[0674] 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.

[0675] In this invention, the server includes means for automatically generating a meeting agenda, means for analyzing participants' schedules and proposing an optimal meeting date and time, and means for quickly adjusting participants' schedules and re-proposing an optimal meeting date and time in the event of an emergency. This makes it possible to streamline the entire process from meeting preparation to execution, rapid response in emergency situations, and follow-up.

[0676] "Methods for automatically generating agendas" refer to functions that automatically organize the main topics and agenda items based on the meeting content and plan the order in which the meeting will proceed.

[0677] "A means of analyzing participants' schedules and suggesting the optimal meeting date and time" refers to a function that analyzes participants' schedules and automatically calculates and presents dates and times when everyone is available.

[0678] "A means of recognizing and analyzing speech in real time" refers to a technology that converts audio from a meeting into digital data and performs text analysis on the spot.

[0679] "Means of providing comments to support the progress of meetings" refers to a function that provides instructions and suggestions in real time to stimulate discussion based on the analyzed meeting content.

[0680] "A means of summarizing the content of speeches and automatically creating meeting minutes" refers to a function that summarizes the important points of a meeting and compiles them into an official record.

[0681] "Methods for distributing meeting minutes to participants" refers to a function that quickly distributes the created meeting minutes to relevant parties and promotes information sharing.

[0682] "A means to quickly adjust participants' schedules and propose the optimal meeting date and time in the event of an emergency" refers to a function that immediately re-evaluates participants' schedules and proposes a new meeting date and time in response to unforeseen circumstances.

[0683] "A means of summarizing the key points of a meeting in real time and providing them to participants" refers to a function that summarizes the content of an ongoing meeting and shares its key points with relevant parties in real time.

[0684] The system implementing this invention consists of a server, a user terminal, and a database for storing necessary data. The user uses the terminal to input the meeting topic and purpose. The server receives this information, retrieves past meeting minutes and related documents from the database, and automatically generates an agenda using natural language processing technology. The generated agenda is sent to the user's terminal, where the user can review and modify it.

[0685] Furthermore, the server retrieves each participant's schedule via the calendar API based on the list of participants. The server then suggests an optimal meeting date and time considering all participants' schedules and sends this information to the user's device. The user can review the suggested date and time and readjust if necessary.

[0686] During the meeting, the server uses speech recognition technology to analyze the conversation in real time and automatically generates comments and questions based on the content. These are displayed on the user's device to help stimulate discussion.

[0687] After the meeting ends, the server automatically generates a summary and meeting minutes based on the analyzed audio data and sends them to the user's device. After the user reviews and makes any necessary corrections, the final version of the meeting minutes is distributed to all participants.

[0688] This system uses Google Speech-to-Text for speech recognition and Python's NLTK library for natural language processing. Data analysis is performed on cloud servers and local databases.

[0689] As a concrete example, let's consider an implementation of the system in a security enhancement meeting at a certain facility. For instance, based on the input of the meeting theme, it extracts relevant topics such as "intrusion detection technology" and "employee training programs" and automatically generates an agenda. A possible prompt to the generating AI model would be, "What topics should we automatically generate the agenda for the next security meeting?"

[0690] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0691] Step 1:

[0692] Users input the meeting topic and purpose via their terminal. This input data is sent to the server, which searches its database for past meeting minutes and related documents. As a result, relevant information is collected.

[0693] Step 2:

[0694] The server automatically generates an agenda using natural language processing technology based on the collected relevant information. Specifically, it identifies topics through a text analysis engine and creates a structured agenda. The generated agenda is sent to the user's terminal and displayed to the user.

[0695] Step 3:

[0696] The user reviews the agenda displayed on their device and makes corrections as needed. The corrected information is then sent back to the server and recorded in the database.

[0697] Step 4:

[0698] The server uses the Calendar API to retrieve each participant's schedule based on the list of participants. It then analyzes the time information obtained from this API call to calculate the optimal meeting date and time. The calculated date and time are sent to the user's device as a suggestion.

[0699] Step 5:

[0700] During the meeting, the server acquires audio data and converts it to text in real time using speech recognition technology. Based on the acquired text data, it performs natural language processing to analyze important agenda items and generate comments and questions. These generated comments are displayed on the user's device.

[0701] Step 6:

[0702] After the meeting ends, the server summarizes the text data and automatically generates meeting minutes. This process includes keyword extraction and summarization of key points. The generated minutes are sent to the user's device, where they can review and edit them. The final reviewed minutes are then distributed to all participants.

[0703] 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.

[0704] This invention relates to a system using an AI agent to streamline meeting management, which in particular includes means for recognizing the emotions of meeting participants and using this to stimulate and facilitate discussion. The main components of the system are a server, user terminals, and an emotion engine.

[0705] Users input the meeting topic and objectives via their device. The server receives this information, analyzes past data, and collects relevant information. Based on the collected data, it automatically generates an agenda using natural language processing and proposes it to the user's device. Users can review the proposed agenda and modify it as needed.

[0706] When a user enters a list of participants, the server analyzes each participant's schedule and suggests the most suitable meeting time. The suggested time is displayed on the user's terminal, and can be approved or rescheduled.

[0707] During the meeting, the server uses speech recognition technology to analyze what is being said in real time, and in addition, an emotion engine analyzes the emotions of the participants. The emotion engine extracts emotional data from the audio and video and provides information to support the progress of the meeting.

[0708] The server generates comments and questions based on spoken content and sentiment data to optimize the flow of the meeting, and displays them through the terminal. This helps to ensure smooth and engaging meetings.

[0709] After the meeting, the server summarizes the conversation content and sentiment data, and automatically generates meeting minutes. Users can review the minutes on their devices, make corrections as needed, and the finalized minutes are distributed to participants by the server.

[0710] For example, in a meeting about marketing strategy, if participants' emotions are leaning towards the negative, the server, based on the analysis results of the emotion engine, displays positive comments on their terminals to boost motivation and correct the flow of the meeting. In this way, comprehensive support can be provided, significantly improving the quality and efficiency of meetings.

[0711] The following describes the processing flow.

[0712] Step 1:

[0713] The user enters the meeting topic and purpose via their device. The server retrieves this information and collects meeting-related data from its database.

[0714] Step 2:

[0715] The server uses collected data to execute a natural language processing algorithm and automatically generates a meeting agenda. The generated agenda is sent to the terminal, where users can review and modify it.

[0716] Step 3:

[0717] The user enters a list of participants on their device. The server retrieves and analyzes each participant's schedule information via a calendar API. The optimal meeting date and time are suggested and displayed on the user's device.

[0718] Step 4:

[0719] Before the meeting begins, the terminal sends a reminder to the user. During the meeting, the server uses speech recognition technology to transcribe what is said in real time, and an emotion engine analyzes the participants' emotions.

[0720] Step 5:

[0721] The server generates comments and questions to optimize the meeting's progress based on analyzed speech content and sentiment data. These comments are provided to users via their terminals to support and stimulate discussion.

[0722] Step 6:

[0723] After the meeting ends, the server analyzes the audio and sentiment data together, summarizes the key points, and automatically creates meeting minutes. The created minutes are then reviewed on the user's device and modified as needed.

[0724] Step 7:

[0725] Finalized meeting minutes are distributed to participants via email by the server. Users can check the delivery status and follow up via their devices.

[0726] (Example 2)

[0727] 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".

[0728] Managing a meeting requires considerable preparation and effort, particularly creating an agenda, coordinating participants' schedules, facilitating the meeting during it, and preparing meeting minutes afterward. Furthermore, effective facilitation through a precise understanding of participants' emotions and conversations is crucial, something difficult for humans to do in real time. There is a need to reduce the burden of meeting management and improve the quality of meetings.

[0729] 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.

[0730] In this invention, the server includes means for automatically creating an agenda based on the meeting plan, means for analyzing participants' schedules to suggest the optimal meeting time, and means for recognizing and immediately analyzing audio acquired during the meeting. This enables more efficient meeting management and optimization of the meeting's progress in accordance with the participants' emotions.

[0731] "Meeting planning" is the process of setting agenda items and clarifying objectives necessary for conducting a meeting effectively.

[0732] An "automatic agenda creation system" is a system that uses machine learning techniques and algorithms to construct an agenda based on the meeting's theme and objectives, minimizing human intervention.

[0733] "Analyzing participants' schedules" means analyzing participants' schedule data to identify the optimal meeting time that everyone can attend.

[0734] A "device that suggests the optimal meeting time" refers to a system that, based on analysis results, proposes the most suitable date and time slot for a meeting.

[0735] "Recognizing audio acquired during a meeting" refers to the process of capturing the audio spoken during a meeting as digital data and converting its content into an analyzable format.

[0736] "Methods for immediate analysis" refers to technologies that process acquired audio data and other information from meetings in real time and generate analysis results instantly.

[0737] A "device for automatically creating record documents" is a system that automatically generates meeting minutes and reports based on audio and text data accumulated during a meeting.

[0738] A "device for transmitting recorded documents to participants" is a system equipped with communication means for electronically distributing the created meeting minutes to participants.

[0739] This invention is a system for automating and streamlining the entire process of meeting planning, implementation, and follow-up. The system primarily consists of a server, user terminals, and an emotion engine.

[0740] First, the user inputs the meeting topic and objectives through their device. The device then sends this information to the server. The server uses this information to search its past database and collect and analyze relevant information. For this purpose, the server utilizes natural language processing technology and a generative AI model to automatically generate the agenda. An example of input for this generation process is a prompt such as, "Please create the agenda for the next marketing meeting."

[0741] The user enters a list of meeting participants and their schedule information into their terminal. The server analyzes this schedule information and suggests the optimal meeting date and time when all participants can attend. A calendar API is often used for this analysis.

[0742] During the meeting, the server's speech recognition technology transcribes spoken content into text in real time. Simultaneously, the server uses an emotion engine to analyze participants' emotions and uses the results to assist in the meeting's progress. Based on this analysis, the server generates appropriate comments and questions to maintain the flow of the meeting and displays them on the participants' terminals.

[0743] After the meeting, the server summarizes the content and sentiment of the participants and automatically generates meeting minutes. Users can review the generated minutes on their terminals and make corrections as needed. The revised minutes are then distributed to each participant from the server.

[0744] For example, in a meeting about marketing strategy, if the server detects a situation where participants' emotions tend to become negative, it can improve the mood of the meeting by offering positive comments. This kind of support dramatically improves the quality and efficiency of the meeting.

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

[0746] Step 1:

[0747] Users input the purpose and theme of the meeting using a terminal. This information serves as the starting data for the system. The data entered by the user is sent to the server by the terminal. The terminal's specific operations include receiving input through the user interface and sending the data to the server via network communication.

[0748] Step 2:

[0749] The server accesses internal databases or external sources to collect relevant information based on the received meeting theme and objectives. The collected data includes past meeting data and relevant literature. Using this information, the server creates prompts for a generative AI model and generates an appropriate agenda. The input is the meeting theme, and the output is the generated agenda. Specifically, the server executes database queries via an API and sends the collected results to the AI ​​model.

[0750] Step 3:

[0751] The user enters a list of meeting participants via a terminal. The terminal then sends this list to the server. The input is a list of participant names, and the output is participant information in digital format. The specific actions include creating the list and sending the data to the server.

[0752] Step 4:

[0753] The server retrieves and analyzes each participant's schedule data from an external calendar service based on the received participant list. Based on the analysis, it proposes a meeting time that allows the most participants to attend. The input is the participants' schedules, and the output is the optimal meeting date and time. Specifically, the process involves fetching calendar data via an API and applying an analysis algorithm.

[0754] Step 5:

[0755] During the meeting, the server uses speech recognition technology to convert spoken content into text in real time. The server also works in conjunction with an emotion engine to analyze participants' speech and facial expressions to generate emotion data. The input is the audio and video from the meeting, and the output is the transcribed conversation and emotion data. Specifically, the server performs tasks such as capturing and transcribing audio data and analyzing video data.

[0756] Step 6:

[0757] The server generates comments and questions to assist in meeting progress based on the acquired speech content and sentiment data. The generated comments are sent to the terminal and displayed to the user. Input is real-time conversation text and sentiment data, while output is comments to assist in progress. Specifically, reply generation is performed using natural language generation technology.

[0758] Step 7:

[0759] After the meeting ends, the server summarizes the conversation content and sentiment data to automatically generate meeting minutes. Users can review and edit the generated minutes on their terminals. Finally, the minutes are distributed from the server to the participants. The input is the conversation content and sentiment data, and the output is the finalized meeting minutes. The specific operation involves the use of a text summarization algorithm and a distribution system.

[0760] (Application Example 2)

[0761] 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".

[0762] In modern cities, meetings involving diverse stakeholders are held frequently, requiring efficient management and smooth collection of participants' opinions. However, optimizing the progress of a meeting in real time, accurately understanding participants' emotions, and facilitating active discussion are not easy. As a result, the quality and efficiency of meetings decline, and important discussions may end without taking place. This invention aims to solve these problems.

[0763] 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.

[0764] In this invention, the server includes means for automatically generating a meeting agenda, means for analyzing participants' schedules and suggesting the optimal meeting date and time, and means for recognizing and analyzing voice and emotions during the meeting in real time. This makes it possible to significantly improve the quality and efficiency of meetings.

[0765] "Methods for automatically generating meeting agendas" refer to functions that automatically create necessary topics and schedules based on the content and purpose of the meeting.

[0766] "A method for analyzing participants' schedules and suggesting the optimal meeting date and time" refers to a function that analyzes each participant's schedule and automatically selects the most suitable date and time for everyone to attend.

[0767] "Means for recognizing and analyzing audio and emotions during meetings in real time" refers to a function that uses speech recognition technology to transcribe the content of speech during meetings into text, and simultaneously detects and analyzes the emotions of participants in real time using emotion analysis technology.

[0768] "Means for providing comments to support the progress of a meeting based on analysis results and sentiment data" refers to a function that automatically generates appropriate comments and suggestions to ensure that a meeting proceeds effectively, based on the results of voice and sentiment analysis.

[0769] "A means of summarizing the content of discussions and sentiment data after a meeting and automatically creating meeting minutes" refers to a function that extracts important points based on the content of discussions and sentiment data from the meeting and automatically creates meeting minutes.

[0770] "Method for distributing meeting minutes to participants" refers to a function that electronically distributes automatically generated meeting minutes to meeting participants.

[0771] "A means of analyzing emotions and supporting the operation of city council meetings" refers to a function that analyzes the emotions of participants to help city council meetings run more actively and smoothly.

[0772] This system consists of a server and user terminals. The server receives and analyzes information about the meeting's theme, purpose, and participants, which is entered by the user using their terminal.

[0773] The server automatically generates the meeting agenda using natural language processing technology. The software used could be Google Cloud's natural language processing API. The server also has the functionality to analyze participant schedules received from users' devices and calculate the optimal meeting date and time. This requires computing resources to process schedule information in real time.

[0774] During the meeting, the server uses speech recognition technology to convert participants' speech into text in real time. Text conversion of audio data is possible using the Google Cloud Speech-to-Text API. Simultaneously, an emotion analysis engine analyzes audio and video data to determine participants' emotional states and obtain analytical information.

[0775] To support the progress of the meeting, the server generates appropriate comments and questions based on speech and sentiment analysis results and displays them on the user's terminal. IBM Watson's Natural Language Understanding API can be used for this application.

[0776] After the meeting ends, the server summarizes the collected statements and sentiment data, automatically creates meeting minutes, and distributes them to participants after they have been reviewed on their user terminals.

[0777] For example, if a city council meeting is held to discuss the construction of a new public facility, the server can generate questions such as, "What kind of community contributions can be expected from the construction of this facility?" when the discussion stalls, thereby helping to stimulate the discussion. An example of a prompt from the generating AI model would be, "Please tell me how to analyze the statements and sentiments of meeting participants in real time and generate comments and questions to stimulate the discussion."

[0778] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0779] Step 1:

[0780] The user uses a terminal to input the meeting's theme, purpose, and participant information. The entered data is sent to the server. The server records the received data and uses natural language processing technology to organize it into data necessary for automatic agenda generation. This organized data then becomes the input for the next step.

[0781] Step 2:

[0782] The server uses the organized data to call Google Cloud's natural language processing API to automatically generate the meeting agenda. Using the API, the agenda and schedule, tailored to the meeting's theme, are output as text. The generated agenda is sent to the user's device for approval or modification.

[0783] Step 3:

[0784] The user's device sends participant schedule information to the server. The server analyzes this information and calculates the optimal meeting date and time. The analysis uses an algorithm that takes into account available time slots and avoids overlaps in the schedule. The calculated optimal date and time is proposed to the user's device, and the user can choose to accept or reschedule.

[0785] Step 4:

[0786] During the meeting, the server converts audio acquired from the user's device into text in real time using the Google Cloud Speech-to-Text API. The input is audio data, and the output is the transcribed text of that audio. Furthermore, an emotion analysis engine analyzes emotion data from the audio and video to classify the emotional state. The analysis results are stored on the server.

[0787] Step 5:

[0788] The server uses IBM Watson's Natural Language Understanding API to generate comments and questions to support the meeting, based on real-time text and sentiment data. This involves generating prompts using a generative AI model. The generated comments and questions are displayed on the user's device.

[0789] Step 6:

[0790] After the meeting ends, the server summarizes the recorded statements and sentiment data, extracts key points, and automatically creates meeting minutes. This is done by the server's internal algorithm and output as text. After users review the created minutes on their devices, they are electronically distributed to participants.

[0791] 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.

[0792] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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."

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] The following is further disclosed regarding the embodiments described above.

[0813] (Claim 1)

[0814] A method for automatically generating meeting agendas,

[0815] A method for analyzing participants' schedules and suggesting the optimal meeting date and time,

[0816] A method for recognizing and analyzing audio during a meeting in real time,

[0817] A means of providing comments to support the progress of the meeting based on the analysis results,

[0818] A method for summarizing the content of discussions after a meeting and automatically creating meeting minutes,

[0819] Methods for distributing meeting minutes to participants,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, further comprising means for collecting relevant information based on the theme of the meeting.

[0823] (Claim 3)

[0824] The system according to claim 1, further comprising means for converting audio data into text.

[0825] "Example 1"

[0826] (Claim 1)

[0827] A means of inputting meeting parameters and analyzing related items,

[0828] A means of automatically generating an agenda using a generative artificial intelligence model,

[0829] A method for obtaining the schedules of meeting participants and calculating the optimal meeting time,

[0830] A method for analyzing audio during a meeting and recognizing important points in real time,

[0831] A means of providing electronic devices with comments that assist the flow of a meeting based on the analysis results,

[0832] A means of summarizing recorded audio information and automatically creating meeting minutes,

[0833] A means of sending meeting minutes to all participants via communication,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, further comprising means for searching for and collecting information resources corresponding to the meeting theme.

[0837] (Claim 3)

[0838] The system according to claim 1, further comprising means for converting audio information into text information.

[0839] "Application Example 1"

[0840] (Claim 1)

[0841] A method for automatically generating meeting agendas,

[0842] A method for analyzing participants' schedules and suggesting the optimal meeting date and time,

[0843] A method for recognizing and analyzing audio during a meeting in real time,

[0844] A means of providing comments to support the progress of the meeting based on the analysis results,

[0845] A method for summarizing the content of discussions after a meeting and automatically creating meeting minutes,

[0846] Methods for distributing meeting minutes to participants,

[0847] A means to quickly adjust participants' schedules and propose the optimal meeting date and time in the event of an emergency,

[0848] A means of summarizing the key points of the meeting in real time and providing them to participants,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, further comprising means for collecting relevant information based on the theme of the meeting.

[0852] (Claim 3)

[0853] The system according to claim 1, further comprising means for converting audio data into text.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] A device that automatically creates an agenda based on the meeting plan,

[0857] A device that analyzes participants' schedules to suggest the optimal meeting time,

[0858] A device that recognizes and immediately analyzes audio acquired during a meeting,

[0859] A device that generates statements to facilitate a meeting based on the results of analysis,

[0860] A device that automatically summarizes the content of a meeting and creates a record document,

[0861] A device for sending recorded documents to participants,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, further comprising a device for collecting information related to the agenda of a meeting.

[0865] (Claim 3)

[0866] The system according to claim 1, further comprising a device for converting acoustic data into textual information.

[0867] "Application example 2 when combining with an emotional engine"

[0868] (Claim 1)

[0869] A method for automatically generating meeting agendas,

[0870] A method for analyzing participants' schedules and suggesting the optimal meeting date and time,

[0871] A method for recognizing and analyzing audio during a meeting in real time,

[0872] A means of providing comments to support the progress of a meeting based on analysis results and sentiment data,

[0873] A method for summarizing the content of discussions and sentiment data after a meeting and automatically creating meeting minutes,

[0874] Methods for distributing meeting minutes to participants,

[0875] A means to analyze emotions and support the management of city council meetings,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, further comprising means for collecting relevant information based on the subject of the meeting.

[0879] (Claim 3)

[0880] The system according to claim 1, further comprising means for converting audio data into documents. [Explanation of Symbols]

[0881] 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 method for automatically generating meeting agendas, A method for analyzing participants' schedules and suggesting the optimal meeting date and time, A method for recognizing and analyzing audio during a meeting in real time, A means of providing comments to support the progress of the meeting based on the analysis results, A method for summarizing the content of discussions after a meeting and automatically creating meeting minutes, Methods for distributing meeting minutes to participants, A system that includes this.

2. The system according to claim 1, further comprising means for collecting relevant information based on the theme of the meeting.

3. The system according to claim 1, further comprising means for converting audio data into text.

Images