system

Abstract

Provide a system. 【Solution means】Means for providing a user interface for inputting the purpose, goal, participant list, and time allocation of a meeting, Means for providing a human-machine interface for inputting the purpose, goal, participant list, and time management of a meeting, Means for analyzing the speeches of participants during a meeting, monitoring the time management for each topic, and generating a notification when a topic exceeds the scheduled time, Means for automatically generating minutes based on the speech content at the end of a meeting and highlighting the decisions made, Means for analyzing the tasks determined during a meeting, assigning tasks to participants, and sending the content as materials, Means for monitoring the progress and sending a reminder when the deadline approaches, Means for monitoring the progress of a meeting and sending a notification of deviation to a terminal when a deviation of the topic is detected, Means composed of a program installed in an information processing device for executing a meeting management application for stores, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Due to the skill differences of the facilitators in a meeting, there are variations in the efficiency of the meeting and the progress of tasks, which have an adverse impact on the working hours of all participants. In particular, digressions from the agenda, inappropriate time allocation, and unclear task assignments contribute to this. It is necessary to improve the efficiency of the meeting by solving these problems.

Means for Solving the Problems

[0005] To address this challenge, the present invention includes means for providing a user interface for inputting the purpose, goals, participant list, and time allocation of a meeting. During the meeting, it analyzes participants' contributions to monitor time allocation for each agenda item and generates notifications when the scheduled time is exceeded. It also includes means for automatically creating meeting minutes based on the contributions at the end of the meeting, highlighting decisions made, analyzing tasks decided during the meeting to assign tasks to participants, tracking progress, and sending reminders as deadlines approach. In addition, it includes a function to detect deviations from the agenda during the meeting and send notifications to terminals prompting a change of direction, as well as a function to send reminders to participant terminals to encourage review of pre-meeting materials. This makes it possible to compensate for differences in meeting management skills and achieve efficient meeting operation.

[0006] A "user interface" is a screen that allows a user to input information into a system or receive information from a system.

[0007] "Analysis" refers to the process of processing audio and text data to understand its content.

[0008] "Time allocation" refers to the time allotted to each agenda item or activity in a meeting, and serves as a standard for ensuring efficient progress.

[0009] A "notification" is a message or alert that a system uses to inform a user of important information or circumstances.

[0010] "Meeting minutes" are documents that record the content discussed and decisions made during a meeting, so that they can be reviewed later.

[0011] A "task" is a set of tasks or actions assigned to individual participants in order to achieve a specific objective.

[0012] A "reminder" is a notification sent to a user in advance to help them remember the deadline for an appointment or task.

[0013] "Progress" is an indicator that shows how far along a task or project is, or whether it is behind schedule or ahead of schedule.

[0014] "Changing direction" refers to the process of altering the direction of ongoing discussions or plans in order to adapt them to the schedule or objectives. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This 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 the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The present invention embodies an AI facilitator for efficiently managing meetings. This system is realized through the cooperation of users, servers, and terminals.

[0037] First, the user provides information through a user interface to input the meeting's purpose, goals, participant list, and time allocation into the system. The server then receives this information, automatically generates a meeting template, and saves it in the database.

[0038] Once the meeting begins, the terminal connects participants to the meeting application, and the server converts the audio data into text in real time and analyzes what is being said. Based on this analysis, the server monitors the progress of the agenda and sends notifications to the terminal if the meeting exceeds the scheduled time or goes off-topic. This allows users to conduct the meeting efficiently.

[0039] At the end of the meeting, the server automatically generates meeting minutes, summarizing the points made and clarifying key issues. It also analyzes the tasks for the next steps and assigns them to each participant. The server sends the documented meeting minutes and task allocation sheet to the terminals and follows up on the progress.

[0040] As a concrete example, let's consider a project progress review meeting. In this case, the project manager, as the user, inputs the purpose and goals, and the server supports the meeting's progress in real time. The server monitors the time allocation for each agenda item, and if progress is behind schedule or there are tangents, an alert is sent from the server to the participant's terminal. After the meeting ends, the server automatically generates meeting minutes, assigns each participant tasks to be completed by the next meeting, and tracks their progress.

[0041] In this way, by utilizing this system, it is possible to improve the efficiency of meetings and optimize the work time of all participants.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user, acting as the meeting organizer, accesses the system's user interface and enters the meeting's purpose, goals, participant list, and time allocation. The entered information is sent to the server and stored in the database.

[0045] Step 2:

[0046] The server generates a meeting template based on the received meeting information. As the meeting start time approaches, a reminder is sent to all participants' devices.

[0047] Step 3:

[0048] The terminal allows participants to access the application necessary to join the meeting, and the server checks everyone's readiness. Once everyone is ready, a notification to start the meeting is sent to the users.

[0049] Step 4:

[0050] During the meeting, the server uses real-time speech recognition to transcribe participants' statements into text. This text is then analyzed to monitor the progress of discussions on each agenda item.

[0051] Step 5:

[0052] The server monitors the scheduled time for each agenda item and automatically sends notifications to terminals if any time is exceeded. Alerts for course correction are also sent as needed.

[0053] Step 6:

[0054] Once the meeting concludes, the server automatically generates meeting minutes based on the recorded statements. These minutes are compiled in a format that organizes and highlights decisions and key points.

[0055] Step 7:

[0056] The server analyzes the next steps and related tasks decided during the meeting and assigns appropriate tasks to each participant. A documented task allocation sheet is sent to each participant's terminal.

[0057] Step 8:

[0058] The server tracks the progress of tasks and sends reminders to users as deadlines approach. Furthermore, it sends follow-up alerts if progress is behind schedule.

[0059] (Example 1)

[0060] 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."

[0061] In meetings, time management and the organization of discussions are often done manually, making efficient meeting management difficult. Furthermore, there are problems with the time and effort required for creating meeting minutes and assigning tasks after the meeting. Additionally, it is difficult to identify digressions and delays in real time, hindering effective meeting management.

[0062] 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.

[0063] In this invention, the server includes means for exchanging information between humans and machines, means for analyzing participants' statements and generating attention-grabbing information, and means for automatically generating reports and clarifying decisions. This enables efficient meeting progress and management. Furthermore, by processing natural language using a generative model, real-time analysis and automated meeting minute generation are achieved, reducing the workload of participants.

[0064] "A means of exchanging information between humans and machines" refers to an interface that allows users to input meeting objectives and participant information, and then import that information into the system.

[0065] "A means of analyzing participants' statements and generating information to draw attention to them" refers to a method of assisting the progress of a meeting in real time by converting audio data acquired during the meeting into text and analyzing its content.

[0066] "A means of automatically generating reports and clarifying decisions" refers to a method of automatically organizing and recording meeting summaries and decisions based on what was said during the meeting.

[0067] "Processing natural language using generative models" is a technique that uses large-scale language models to analyze input speech and text data and generate output that corresponds to natural language.

[0068] The information sent to participants' devices includes meeting reminders and notifications regarding the progress of the agenda, thereby encouraging participants to prepare for the meeting and enabling efficient meeting management.

[0069] This invention is a system for efficiently managing meetings, primarily involving the collaboration of users, servers, and terminals. First, users input the meeting's purpose, goals, participant information, and time allocation using a dedicated user interface. This interface is provided as a web application and operates on a standard browser.

[0070] The server generates a meeting template based on the information received from the user and stores this information in a database system (e.g., MySQL®). Once the meeting begins, participants connect to a video conferencing tool (e.g., common online meeting software) via their terminals.

[0071] During the meeting, the server uses speech recognition software (e.g., a common speech recognition API) to convert audio data into text and analyzes the content of the speech using a generative AI model (e.g., a common language model). Through this analysis, the server monitors the progress of the meeting and sends notifications via the terminal if deviations or delays are detected.

[0072] At the end of the meeting, the server automatically generates meeting minutes and highlights key decisions. This process uses natural language processing techniques to organize the information. The server also analyzes the tasks decided during the meeting and assigns them to participants as the next steps. This information is sent to each participant via their terminal, and their progress is tracked.

[0073] As a concrete example, let's consider a project progress review meeting. In this case, the user, the project manager, enters, "At the next development meeting, we plan to review the progress of the new feature and finalize the release date." The server supports the meeting in real time and sends notifications to terminals as needed, such as, "The scheduled time has been exceeded by 5 minutes. Please move on to the next agenda item." After the meeting ends, each participant is provided with automatically generated minutes stating something like, "The new feature is two weeks behind schedule. This is due to a lack of resources. We need to complete the arrangement of additional resources before the next meeting."

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

[0075] Step 1:

[0076] Users use a dedicated user interface to input the meeting's purpose, goals, participant information, and time allocation. This input serves as the basis for generating a meeting template. The entered data is sent to the server and prepared for storage in the database.

[0077] Step 2:

[0078] The server analyzes the data received from the user and generates a meeting template. This template generation algorithm automatically determines the necessary time allocation and procedure for each agenda item. The generated template is stored in a database for later reference.

[0079] Step 3:

[0080] At the start of a meeting, the terminal connects participants to the meeting application. The terminal then sends connection requests to the participants' terminals and, in conjunction with the online meeting tool using webcams and microphones, opens the meeting room. This ensures that participants are ready to join the meeting smoothly.

[0081] Step 4:

[0082] During the meeting, the server uses speech recognition software to convert audio data into text in real time. The acquired text data is then analyzed using a generative AI model to verify that the meeting is proceeding as planned. If the agenda deviates or the time limit is exceeded, alerts are sent to participants via their devices.

[0083] Step 5:

[0084] At the end of the meeting, the server integrates the audio and text data and automatically generates meeting minutes. An AI model extracts key points and organizes them into a report, making the meeting's results clear. The generated minutes are then shared with participants via their devices.

[0085] Step 6:

[0086] The server analyzes the tasks decided during the meeting and assigns them to each participant. This task information is stored in a database and managed for tracking. The server then monitors the progress and sends reminders via the terminal as needed.

[0087] (Application Example 1)

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

[0089] Meetings in physical stores and within companies often suffer from poor time management and ambiguous decisions. Against this backdrop, there is a need for an effective system to improve meeting efficiency and reliably track the progress of work. This invention aims to provide a means to manage meeting progress in real time and ensure smooth business operations.

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

[0091] In this invention, the server includes means for providing a human-machine interface for inputting the purpose, objectives, participant list, and time management of a meeting; means for analyzing participants' statements during the meeting, monitoring time management for each agenda item, and generating notifications if an agenda item exceeds the scheduled time; and means for automatically generating a record of the meeting minutes based on the content of the statements at the end of the meeting and highlighting the decisions made. This enables efficient progress management of meetings and reliable tracking of work.

[0092] The "purpose of the meeting" is the basic goal that the meeting aims to achieve.

[0093] "Goals" refer to the specific results that participants want to achieve through the meeting.

[0094] A "list of participants" is a list containing information about the individuals attending the meeting.

[0095] "Time management" refers to the activity of allocating appropriate time to each agenda item in a meeting and efficiently managing its progress.

[0096] A "human-machine interface" refers to the means or devices that allow a user to input or receive information.

[0097] "Analyzing speech" refers to the act of understanding and processing the content of what participants said during a meeting.

[0098] "Monitoring time management" is the activity of observing the actual progress against a set time allocation in real time.

[0099] "Generating notifications" is the process of informing participants of important information as the project progresses.

[0100] A "meeting record" is a document that organizes and preserves the statements and decisions made during a meeting.

[0101] "Distributing tasks" is the process of assigning the tasks decided in a meeting to each participant.

[0102] "Sending as reference material" means providing participants with a document containing the necessary information.

[0103] "Monitoring progress" refers to the activity of regularly checking how far each task has progressed.

[0104] "Derailment notification to terminal" is the process of sending a message to alert users if they deviate from the scheduled agenda.

[0105] A "program installed on an information processing device" is software embedded in a computer to perform a specific function.

[0106] This system utilizes an application designed to provide effective meeting management for physical stores. The server stores meeting purpose, objectives, participant list, and time management information entered by users in a cloud-based database, supporting efficient meeting management. The hardware used is smartphones and tablets, while the software consists of server-side processing using Node.js, a mobile application using React Native, and speech analysis using Google® Cloud Speech-to-Text.

[0107] After the meeting begins, the terminal uses Google Cloud Speech-to-Text to transcribe participants' speech in real time. The server then analyzes this text data to check for time management and any deviations from the agenda. If the meeting exceeds the scheduled time, the server sends a notification to the terminal. Furthermore, it automatically generates a meeting record, analyzes the tasks based on the decisions made, and sends the tasks to participants as reference material.

[0108] Users can utilize this system when holding meetings to introduce new products to their stores. For example, a user can enter a prompt into the system such as, "We will hold an in-store meeting regarding the introduction of new products. The agenda will include explaining the features of the new products and confirming display methods. Please ensure that we do not exceed the time limit and automatically distribute the task along with the meeting minutes." This allows for efficient meeting management and automated follow-up. Through this specific example, participants can smoothly carry out the new product introduction process, and the progress is tracked in real time.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] Users enter meeting objectives, goals, participant list, and time management information into a smartphone or tablet interface. This information is sent to a server and stored in a cloud database. The data includes meeting name, agenda, participant email addresses, and agenda content. The server stores this data and prepares it for use in subsequent steps.

[0112] Step 2:

[0113] Once the meeting begins, the terminal records audio in real time and converts it to text data using the Google Cloud Speech-to-Text API. The input is the audio from the meeting, and the output is the corresponding transcribed speech. By transcribing the audio data into text, the server can prepare for the next analysis.

[0114] Step 3:

[0115] The server analyzes the received text data and monitors time management for each agenda item. The input is the text data generated in step 2, and the output is a progress report for each agenda item. The server automatically generates and sends a notification to the terminal if an agenda item exceeds its scheduled time or goes off track. This allows the user to take timely action.

[0116] Step 4:

[0117] At the end of the meeting, the server automatically generates a transcript of the proceedings based on all the text data. The input is the text of all participants' statements, and the output is a well-organized transcript. A generation AI model is used to highlight important statements and decisions to create the final document.

[0118] Step 5:

[0119] The server analyzes the assigned tasks and automatically sends the corresponding tasks as documents to each participant. The input consists of meeting minutes and task lists generated during the meeting, while the output is a document detailing the specific tasks for each participant. Information is communicated via email and in-app notifications, clearly instructing each user on the necessary actions.

[0120] Step 6:

[0121] The server continuously monitors progress and sends reminders as the deadline approaches. The input is progress information for the task assigned in step 5, and the output is a reminder notification as the deadline approaches. This helps participants complete their work on schedule.

[0122] 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.

[0123] This invention combines an AI facilitator system that supports the efficient progress of meetings with an emotion engine, thereby enabling meeting management that takes into account the emotional state of participants. This system functions through the coordinated efforts of users, servers, and terminals.

[0124] First, the user enters the meeting's purpose, goals, participant list, and time allocation into the system. Based on this information, the server configures the meeting and generates a template. The system then sends meeting reminders to participants and prompts them to review any necessary pre-meeting materials.

[0125] Once the meeting begins, the server uses speech recognition technology to transcribe participants' statements into text in real time and analyzes the content. During this process, an emotion engine detects each participant's emotions and collects emotional data. For example, if a participant is feeling anxious, this information is recorded by the server.

[0126] The server provides feedback to adjust the meeting's progress based on the analyzed sentiment data. For example, if a participant's concentration is waning, it sends a notification to their device and suggests appropriate measures. It also monitors the time allocation for each agenda item and immediately sends a notification to the device if the meeting exceeds the allotted time or goes off-topic, prompting a course correction.

[0127] After the meeting ends, the server automatically generates meeting minutes, highlighting key points based on spoken content and sentiment data. Furthermore, it identifies the next steps and tasks decided during the meeting, assigns them to participants, and tracks their progress. Leveraging the sentiment engine, it also sends reminders to participants' devices to support their motivation and notifications to improve engagement.

[0128] As a concrete example, consider a meeting to advance an important project. In this case, the system would detect participants who are feeling particularly stressed and prompt them to take steps to alleviate that stress. This would improve the overall atmosphere of the meeting and allow for a more cohesive process.

[0129] This system enables more effective decision-making and team building compared to conventional, mechanical meeting management, by simultaneously utilizing participants' emotions and intellectual information.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user logs into the system as the meeting organizer and enters the meeting's purpose, goals, participant list, and time allocation. The server receives this information, generates a meeting template, and saves it to the database.

[0133] Step 2:

[0134] As the scheduled meeting time approaches, the server sends a reminder to all participants' devices. This reminder includes meeting details and a request to review any necessary pre-meeting materials.

[0135] Step 3:

[0136] Once the meeting begins, the terminal connects participants to the meeting, and the server converts audio data into text in real time and analyzes what is being said. At this time, an emotion engine operates, recognizing the participants' emotions from the camera and audio, and sending that data to the server.

[0137] Step 4:

[0138] The server monitors the progress of the meeting based on real-time analysis of spoken content and emotional data. If a participant's emotional state indicates a decrease in concentration, it sends a notification to their device prompting them to refresh. It also monitors the time allocation for each agenda item and sends notifications for course correction as needed.

[0139] Step 5:

[0140] During the meeting, the server analyzes emotional data such as anxiety and tension experienced by participants and adjusts the meeting's pace accordingly. For example, it might display suggestions on the participants' devices to adjust the tempo, encouraging better engagement.

[0141] Step 6:

[0142] At the end of the meeting, the server automatically generates meeting minutes based on the statements and sentiment data. This includes a summary of the statements as well as an analysis of changes in sentiment.

[0143] Step 7:

[0144] The server analyzes the tasks decided during the meeting and assigns them to participants. Progress is tracked in real time by the server, and reminders are sent to devices as needed. The emotion engine also provides notifications, especially to prevent a decline in motivation.

[0145] (Example 2)

[0146] 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".

[0147] In today's business environment, there is a demand for both increased meeting efficiency and participant satisfaction. However, conventional meeting facilitation systems often focus only on time management and agenda progression, without considering the emotional state of participants. As a result, maintaining participant focus and conducting smooth meetings has been a challenge.

[0148] 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.

[0149] In this invention, the server includes means for providing an information input device for inputting the purpose of the meeting and participant information, means including a device for transcribing speech in real time and analyzing emotions, and means for monitoring the time allocation of agenda items and generating notifications when time is exceeded. This makes it possible to facilitate efficient agenda progression and decision-making while taking into account the emotional state of participants during the meeting.

[0150] An "information input device" is a device that provides an interface for users to input the purpose, goals, participant list, and time allocation of a meeting.

[0151] "Speech recognition technology" is a technology that converts participants' speech into text data in real time.

[0152] An "emotion analysis device" is a device that analyzes and detects the emotional state of meeting participants from the content of their speech, which has been transcribed into text using speech recognition.

[0153] A "time allocation monitoring system" is a system that tracks the time allocation for each agenda item during a meeting and generates a notification if the scheduled time is exceeded.

[0154] A "meeting minutes automatic generation device" is a device that automatically creates meeting minutes at the end of a meeting, highlighting important points based on the content of the discussion and sentiment data.

[0155] A "task assignment device" is a device that analyzes tasks decided during a meeting, assigns them to each participant, and notifies them accordingly.

[0156] A "progress tracking system" is a system that tracks the progress of tasks decided in a meeting and sends reminders when the task deadline is approaching.

[0157] A "feedback notification system" is a system that generates suggestions for improving the meeting's progress based on participants' emotional data and notifies participants of these suggestions on their devices.

[0158] The present invention provides a system for efficient meeting management, particularly one that takes into account the emotional state of participants. This system includes an information input device, speech recognition technology, an emotion analysis device, time allocation monitoring means, an automatic meeting minutes generation device, a task assignment device, a progress tracking means, and a feedback notification means.

[0159] The user inputs the meeting's purpose, goals, participant list, and time allocation into an information input device. This device can be configured using, for example, a standard computer or smart device. The entered information is sent to a server and set as a meeting template.

[0160] During the meeting, the server uses speech recognition technology to transcribe speech in real time. For example, cloud-based speech recognition software is used. The audio data is then analyzed by an emotion analysis device to identify the emotions of each participant.

[0161] The time allocation monitoring system monitors the time elapsed for each agenda item and generates a notification if the scheduled time is exceeded. This notification is sent to the terminal, allowing the user to take appropriate action.

[0162] After the meeting ends, the server uses an automated minutes generation system to create minutes based on the content of the discussion and sentiment data. These minutes are then sent to the terminals with important points highlighted.

[0163] Furthermore, the task assignment device analyzes the action items decided during the meeting and assigns them appropriately to each participant. This allows participants to clearly understand their individual responsibilities through their devices.

[0164] Progress tracking measures track the progress of tasks and send reminders to participants as deadlines approach. This reinforces activity toward completing each task.

[0165] As an example, consider a scenario in a large-scale project meeting where the system detects participants who are experiencing particular stress and notifies them of measures to alleviate their condition. This method can facilitate the smooth running of the meeting and contribute to the success of the project.

[0166] An example of a prompt sentence generated using an AI model is, "What relaxation methods should be suggested to participants who are feeling stressed at the next project meeting?"

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user inputs the meeting's purpose, goals, participant list, and time allocation into an information input device. This input is sent to the server, which uses it to construct the meeting settings. As part of the data processing, the server formats each piece of information into a template and saves it as a schedule. As output, meeting template data is generated.

[0170] Step 2:

[0171] When the meeting begins, the server activates speech recognition software and converts participants' speech into text data in real time. The input is audio data, and the output is the transcribed speech. The server prepares this text data for analysis.

[0172] Step 3:

[0173] The server processes the generated text data into an emotion analysis device. This device uses natural language processing techniques to analyze the emotional aspects of each statement (e.g., anxiety or joy). The input is text data, and the output is participant emotion data. At this stage, emotion-based tagging is performed.

[0174] Step 4:

[0175] The server tracks the progress of each agenda item using a time allocation monitoring system. The input is real-time timestamp data, and the output is monitoring data for elapsed time. If any agenda item exceeds its scheduled time, the server generates a notification message.

[0176] Step 5:

[0177] After the meeting ends, the server uses an automated minutes generation system to create meeting minutes based on the recorded text and sentiment data. The input consists of spoken words and sentiment data, and the output is a meeting minute with key points highlighted. The server then sends this meeting minute to the terminal, making it accessible to participants.

[0178] Step 6:

[0179] The server operates a task assignment device, analyzing the action items decided during the meeting. The input is the meeting minutes, and the output generates specific tasks and the assigned person responsible for each task. Task information is then sent to each participant's terminal.

[0180] Step 7:

[0181] Using a progress tracking system, the server periodically updates and monitors the progress of each task. Input is progress data reported by participants, and output generates task completion status and reminders for tasks approaching their deadlines. Reminders are delivered to terminals and notified to participants.

[0182] (Application Example 2)

[0183] 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".

[0184] Modern meetings suffer from a lack of efficient progress management and disregard for participants' emotional states. This leads to challenges such as lapses in focus, derailment, and difficulty in making effective decisions. Furthermore, insufficient follow-up on decisions and tasks prevents meetings from maximizing their effectiveness.

[0185] 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.

[0186] In this invention, the server includes means for providing an input device for inputting meeting objectives, achievement criteria, participant information, and schedule; means for analyzing participant statements during the meeting, monitoring the schedule for each agenda item, and generating notifications if an agenda item exceeds the scheduled time; and means for detecting participants' emotional states in real time, adjusting the meeting's progress based on emotional data, and proposing countermeasures. This enables meeting management that takes participants' emotions into consideration, resulting in efficient and engaging meeting operations.

[0187] An "input device" is an interface for entering meeting objectives, achievement criteria, participant information, and schedules into the system.

[0188] "Statement information" refers to data used to transcribe and analyze the content of participants' statements during a meeting.

[0189] A "timetable" refers to the scheduled time allocated to each agenda item within a meeting, and is used to manage the progress of the meeting.

[0190] A "notification" is a message used in meeting management to inform participants when the scheduled time is exceeded or the agenda is deviated from.

[0191] "Emotional state" refers to information that detects changes in participants' emotions in real time and serves as an important indicator for the progress of the meeting.

[0192] "Emotional data" refers to digital data about the emotional state of participants, collected by the emotion engine.

[0193] "Measures" refer to analyzing the emotional state of participants during a meeting and proposing adjustments to the meeting's progress or suggesting breaks as needed.

[0194] This system is initiated by an interface that includes input devices for the user to enter meeting objectives, success criteria, participant information, and schedule. Once the user enters this information into the system, the server configures the meeting and generates a meeting flow template.

[0195] Once the meeting begins, the server utilizes voice input technology to transcribe participants' statements in real time and analyzes the data. This analysis includes a process that uses an emotion engine to detect participants' emotional states. Based on this emotion data, the server adjusts the flow of the meeting and sends notifications to participants' devices as needed. These notifications include managing the time allocation for agenda items, preventing digressions, and even suggesting breaks for participants.

[0196] As a concrete example, let's consider a citizens' meeting regarding local traffic issues. In this meeting, the server analyzes participants' statements, and if it determines that a participant's emotional state is one of tension, it notifies the participant's information terminal with relaxing videos or instructions for deep breathing to promote relaxation. In this way, the efficiency of the meeting can be maintained while increasing participants' concentration and motivation.

[0197] A typical example of a prompt would be asking a generating AI model a question like, "If participants are feeling stressed during a meeting, what should we do to offer them something to help them relax?" This would primarily lead to obtaining appropriate solutions.

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

[0199] Step 1:

[0200] The user enters the meeting's objectives, achievement criteria, participant information, and schedule. This data is received by the server and used as the basis for generating a meeting template. The server then structures and stores this data, making it available for use in the next step.

[0201] Step 2:

[0202] Once the meeting begins, the server records participants' speech and converts it to text using speech recognition technology. It receives the audio data as input and processes it into text data. This text data is used for analyzing the content of the speech and for processing with the sentiment engine.

[0203] Step 3:

[0204] The server uses an emotion engine to analyze participants' emotional states from text data. Specifically, it uses an emotion analysis algorithm to identify emotions in the text and outputs them as emotion data. This allows for an understanding of the meeting's atmosphere and the state of each participant.

[0205] Step 4:

[0206] The server adjusts the flow and time allocation of the meeting based on emotional data. It analyzes the input emotional data and, if it detects agenda items exceeding the scheduled time or a decline in participants' concentration, sends a notification to the terminal to prompt course correction. The output includes specific instructions and suggestions sent to the terminal.

[0207] Step 5:

[0208] If a meeting is ongoing and changes in participants' emotional states are observed, the server sends suggestions to terminals to encourage rest and refreshment. Taking emotional stress and tension data as input, the server generates suggestions to promote relaxation as output. Specific examples of such suggestions include links to relaxation videos and breathing exercises.

[0209] Step 6:

[0210] After the meeting concludes, the server automatically generates a meeting record, highlighting particularly important decisions. This process integrates the input speech information and sentiment data to produce a formatted meeting transcript. This output is distributed to participants after the meeting.

[0211] Step 7:

[0212] The server analyzes the next steps and tasks and assigns them to each participant. Output is generated based on the input task information and the participant's role, and reminders are sent to the terminal as needed to track progress. In this way, post-meeting follow-up is also ensured.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] The present invention embodies an AI facilitator for efficiently managing meetings. This system is realized through the cooperation of users, servers, and terminals.

[0230] First, the user provides information through a user interface to input the meeting's purpose, goals, participant list, and time allocation into the system. The server then receives this information, automatically generates a meeting template, and saves it in the database.

[0231] Once the meeting begins, the terminal connects participants to the meeting application, and the server converts the audio data into text in real time and analyzes what is being said. Based on this analysis, the server monitors the progress of the agenda and sends notifications to the terminal if the meeting exceeds the scheduled time or goes off-topic. This allows users to conduct the meeting efficiently.

[0232] At the end of the meeting, the server automatically generates meeting minutes, summarizing the points made and clarifying key issues. It also analyzes the tasks for the next steps and assigns them to each participant. The server sends the documented meeting minutes and task allocation sheet to the terminals and follows up on the progress.

[0233] As a concrete example, let's consider a project progress review meeting. In this case, the project manager, as the user, inputs the purpose and goals, and the server supports the meeting's progress in real time. The server monitors the time allocation for each agenda item, and if progress is behind schedule or there are tangents, an alert is sent from the server to the participant's terminal. After the meeting ends, the server automatically generates meeting minutes, assigns each participant tasks to be completed by the next meeting, and tracks their progress.

[0234] In this way, by utilizing this system, it is possible to improve the efficiency of meetings and optimize the work time of all participants.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The user, acting as the meeting organizer, accesses the system's user interface and enters the meeting's purpose, goals, participant list, and time allocation. The entered information is sent to the server and stored in the database.

[0238] Step 2:

[0239] The server generates a meeting template based on the received meeting information. As the meeting start time approaches, a reminder is sent to all participants' devices.

[0240] Step 3:

[0241] The terminal allows participants to access the application necessary to join the meeting, and the server checks everyone's readiness. Once everyone is ready, a notification to start the meeting is sent to the users.

[0242] Step 4:

[0243] During the meeting, the server uses real-time speech recognition to transcribe participants' statements into text. This text is then analyzed to monitor the progress of discussions on each agenda item.

[0244] Step 5:

[0245] The server monitors the scheduled time for each agenda item and automatically sends notifications to terminals if any time is exceeded. Alerts for course correction are also sent as needed.

[0246] Step 6:

[0247] Once the meeting concludes, the server automatically generates meeting minutes based on the recorded statements. These minutes are compiled in a format that organizes and highlights decisions and key points.

[0248] Step 7:

[0249] The server analyzes the next steps and related tasks decided during the meeting and assigns appropriate tasks to each participant. A documented task allocation sheet is sent to each participant's terminal.

[0250] Step 8:

[0251] The server tracks the progress of tasks and sends reminders to users as deadlines approach. Furthermore, it sends follow-up alerts if progress is behind schedule.

[0252] (Example 1)

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

[0254] In meetings, time management and the organization of discussions are often done manually, making efficient meeting management difficult. Furthermore, there are problems with the time and effort required for creating meeting minutes and assigning tasks after the meeting. Additionally, it is difficult to identify digressions and delays in real time, hindering effective meeting management.

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

[0256] In this invention, the server includes means for exchanging information between humans and machines, means for analyzing participants' statements and generating attention-grabbing information, and means for automatically generating reports and clarifying decisions. This enables efficient meeting progress and management. Furthermore, by processing natural language using a generative model, real-time analysis and automated meeting minute generation are achieved, reducing the workload of participants.

[0257] "A means of exchanging information between humans and machines" refers to an interface that allows users to input meeting objectives and participant information, and then import that information into the system.

[0258] "A means of analyzing participants' statements and generating information to draw attention to them" refers to a method of assisting the progress of a meeting in real time by converting audio data acquired during the meeting into text and analyzing its content.

[0259] "A means of automatically generating reports and clarifying decisions" refers to a method of automatically organizing and recording meeting summaries and decisions based on what was said during the meeting.

[0260] "Processing natural language using generative models" is a technique that uses large-scale language models to analyze input speech and text data and generate output that corresponds to natural language.

[0261] The information sent to participants' devices includes meeting reminders and notifications regarding the progress of the agenda, thereby encouraging participants to prepare for the meeting and enabling efficient meeting management.

[0262] This invention is a system for efficiently managing meetings, primarily involving the collaboration of users, servers, and terminals. First, users input the meeting's purpose, goals, participant information, and time allocation using a dedicated user interface. This interface is provided as a web application and operates on a standard browser.

[0263] The server generates a meeting template based on the information received from the user and saves this information in a database system (e.g., MySQL). When the meeting starts, participants connect to a video conferencing tool (e.g., common online meeting software) via their terminals.

[0264] During the meeting, the server uses speech recognition software (e.g., a common speech recognition API) to convert audio data into text and analyzes the content of the speech using a generative AI model (e.g., a common language model). Through this analysis, the server monitors the progress of the meeting and sends notifications via the terminal if deviations or delays are detected.

[0265] At the end of the meeting, the server automatically generates meeting minutes and highlights key decisions. This process uses natural language processing techniques to organize the information. The server also analyzes the tasks decided during the meeting and assigns them to participants as the next steps. This information is sent to each participant via their terminal, and their progress is tracked.

[0266] As a concrete example, let's consider a project progress review meeting. In this case, the user, the project manager, enters, "At the next development meeting, we plan to review the progress of the new feature and finalize the release date." The server supports the meeting in real time and sends notifications to terminals as needed, such as, "The scheduled time has been exceeded by 5 minutes. Please move on to the next agenda item." After the meeting ends, each participant is provided with automatically generated minutes stating something like, "The new feature is two weeks behind schedule. This is due to a lack of resources. We need to complete the arrangement of additional resources before the next meeting."

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] Users use a dedicated user interface to input the meeting's purpose, goals, participant information, and time allocation. This input serves as the basis for generating a meeting template. The entered data is sent to the server and prepared for storage in the database.

[0270] Step 2:

[0271] The server analyzes the data received from the user and generates a meeting template. This template generation algorithm automatically determines the necessary time allocation and procedure for each agenda item. The generated template is stored in a database for later reference.

[0272] Step 3:

[0273] At the start of a meeting, the terminal connects participants to the meeting application. The terminal then sends connection requests to the participants' terminals and, in conjunction with the online meeting tool using webcams and microphones, opens the meeting room. This ensures that participants are ready to join the meeting smoothly.

[0274] Step 4:

[0275] During the meeting, the server uses speech recognition software to convert audio data into text in real time. The acquired text data is then analyzed using a generative AI model to verify that the meeting is proceeding as planned. If the agenda deviates or the time limit is exceeded, alerts are sent to participants via their devices.

[0276] Step 5:

[0277] At the end of the meeting, the server integrates the audio and text data and automatically generates meeting minutes. An AI model extracts key points and organizes them into a report, making the meeting's results clear. The generated minutes are then shared with participants via their devices.

[0278] Step 6:

[0279] The server analyzes the tasks decided during the meeting and assigns them to each participant. This task information is stored in a database and managed for tracking. The server then monitors the progress and sends reminders via the terminal as needed.

[0280] (Application Example 1)

[0281] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0282] Meetings in physical stores or within enterprises often have inappropriate time management and unclear decision-making items. Against this background, there is a need for an effective system to improve meeting efficiency and reliably track business progress. The present invention aims to provide a means for managing the progress of meetings in real time and smoothly advancing business operations.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes means for providing a human-machine interface for inputting the purpose, goals, participant list, and time management of a meeting, means for analyzing the speech of participants during the meeting, monitoring time management for each topic, and generating a notification when a topic exceeds the scheduled time, and means for automatically generating a record of the minutes based on the speech content at the end of the meeting and highlighting the decisions made. This enables efficient progress management of meetings and reliable tracking of business operations.

[0285] The "purpose of the meeting" is the basic goal that the meeting aims to achieve.

[0286] The "goals" refer to the specific results that participants want to achieve through the meeting.

[0287] The "participant list" is a list that collects information on individuals attending the meeting.

[0288] "Time management" is an activity of allocating appropriate time to each topic of the meeting and efficiently managing the progress.

[0289] The "human-machine interface" refers to means or devices for users to input or receive information.

[0290] "Analyzing the speech" is an act of understanding the content of what participants said during the meeting and processing the information.

[0291] "Monitoring time management" is the activity of observing the actual progress against a set time allocation in real time.

[0292] "Generating notifications" is the process of informing participants of important information as the project progresses.

[0293] A "meeting record" is a document that organizes and preserves the statements and decisions made during a meeting.

[0294] "Distributing tasks" is the process of assigning the tasks decided in a meeting to each participant.

[0295] "Sending as reference material" means providing participants with a document containing the necessary information.

[0296] "Monitoring progress" refers to the activity of regularly checking how far each task has progressed.

[0297] "Derailment notification to terminal" is the process of sending a message to alert users if they deviate from the scheduled agenda.

[0298] A "program installed on an information processing device" is software embedded in a computer to perform a specific function.

[0299] This system utilizes an application designed to provide effective meeting management for physical stores. The server stores meeting purpose, objectives, participant list, and time management information entered by users in a cloud-based database, supporting efficient meeting management. The hardware used is smartphones and tablets, while the software consists of server-side processing using Node.js, a mobile application using React Native, and speech analysis using Google Cloud Speech-to-Text.

[0300] After the meeting begins, the terminal uses Google Cloud Speech-to-Text to transcribe participants' speech in real time. The server then analyzes this text data to check for time management and any deviations from the agenda. If the meeting exceeds the scheduled time, the server sends a notification to the terminal. Furthermore, it automatically generates a meeting record, analyzes the tasks based on the decisions made, and sends the tasks to participants as reference material.

[0301] Users can utilize this system when holding meetings to introduce new products to their stores. For example, a user can enter a prompt into the system such as, "We will hold an in-store meeting regarding the introduction of new products. The agenda will include explaining the features of the new products and confirming display methods. Please ensure that we do not exceed the time limit and automatically distribute the task along with the meeting minutes." This allows for efficient meeting management and automated follow-up. Through this specific example, participants can smoothly carry out the new product introduction process, and the progress is tracked in real time.

[0302] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0303] Step 1:

[0304] Users enter meeting objectives, goals, participant list, and time management information into a smartphone or tablet interface. This information is sent to a server and stored in a cloud database. The data includes meeting name, agenda, participant email addresses, and agenda content. The server stores this data and prepares it for use in subsequent steps.

[0305] Step 2:

[0306] When the meeting starts, the terminal records the audio in real time and converts it into text data using the Google Cloud Speech-to-Text API. The input is the audio during the meeting, and the output is the corresponding text-based speech. By converting the audio data into text, the server is ready for the following analysis.

[0307] Step 3:

[0308] The server analyzes the received text data and monitors the time management for each topic. The input is the text data generated in Step 2, and the output is the progress report for each topic. When the server detects that a topic exceeds the scheduled time or goes off track, it automatically generates a notification and sends it to the terminal. This enables the user to take timely actions.

[0309] Step 4:

[0310] At the end of the meeting, the server automatically generates a meeting record based on all the text data. The input is the speech text of all participants, and the output is the organized meeting minutes. An AI model is used to highlight important speeches and decisions to create the final document.

[0311] Step 5:

[0312] The server analyzes the determined tasks and automatically sends the corresponding tasks to each participant as materials. The input is the meeting record and task list generated during the meeting, and the output is the materials with specific tasks for each participant. Mail or in-app notifications are used to convey the information and clearly instruct the necessary actions for each user.

[0313] Step 6:

[0314] The server continuously monitors progress and sends reminders as the deadline approaches. The input is progress information for the task assigned in step 5, and the output is a reminder notification as the deadline approaches. This helps participants complete their work on schedule.

[0315] 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.

[0316] This invention combines an AI facilitator system that supports the efficient progress of meetings with an emotion engine, thereby enabling meeting management that takes into account the emotional state of participants. This system functions through the coordinated efforts of users, servers, and terminals.

[0317] First, the user enters the meeting's purpose, goals, participant list, and time allocation into the system. Based on this information, the server configures the meeting and generates a template. The system then sends meeting reminders to participants and prompts them to review any necessary pre-meeting materials.

[0318] Once the meeting begins, the server uses speech recognition technology to transcribe participants' statements into text in real time and analyzes the content. During this process, an emotion engine detects each participant's emotions and collects emotional data. For example, if a participant is feeling anxious, this information is recorded by the server.

[0319] The server provides feedback to adjust the meeting's progress based on the analyzed sentiment data. For example, if a participant's concentration is waning, it sends a notification to their device and suggests appropriate measures. It also monitors the time allocation for each agenda item and immediately sends a notification to the device if the meeting exceeds the allotted time or goes off-topic, prompting a course correction.

[0320] After the meeting ends, the server automatically generates meeting minutes, highlighting key points based on spoken content and sentiment data. Furthermore, it identifies the next steps and tasks decided during the meeting, assigns them to participants, and tracks their progress. Leveraging the sentiment engine, it also sends reminders to participants' devices to support their motivation and notifications to improve engagement.

[0321] As a concrete example, consider a meeting to advance an important project. In this case, the system would detect participants who are feeling particularly stressed and prompt them to take steps to alleviate that stress. This would improve the overall atmosphere of the meeting and allow for a more cohesive process.

[0322] This system enables more effective decision-making and team building compared to conventional, mechanical meeting management, by simultaneously utilizing participants' emotions and intellectual information.

[0323] The following describes the processing flow.

[0324] Step 1:

[0325] The user logs into the system as the meeting organizer and enters the meeting's purpose, goals, participant list, and time allocation. The server receives this information, generates a meeting template, and saves it to the database.

[0326] Step 2:

[0327] As the scheduled meeting time approaches, the server sends a reminder to all participants' devices. This reminder includes meeting details and a request to review any necessary pre-meeting materials.

[0328] Step 3:

[0329] Once the meeting begins, the terminal connects participants to the meeting, and the server converts audio data into text in real time and analyzes what is being said. At this time, an emotion engine operates, recognizing the participants' emotions from the camera and audio, and sending that data to the server.

[0330] Step 4:

[0331] The server monitors the progress of the meeting based on real-time analysis of spoken content and emotional data. If a participant's emotional state indicates a decrease in concentration, it sends a notification to their device prompting them to refresh. It also monitors the time allocation for each agenda item and sends notifications for course correction as needed.

[0332] Step 5:

[0333] During the meeting, the server analyzes emotional data such as anxiety and tension experienced by participants and adjusts the meeting's pace accordingly. For example, it might display suggestions on the participants' devices to adjust the tempo, encouraging better engagement.

[0334] Step 6:

[0335] At the end of the meeting, the server automatically generates meeting minutes based on the statements and sentiment data. This includes a summary of the statements as well as an analysis of changes in sentiment.

[0336] Step 7:

[0337] The server analyzes the tasks decided during the meeting and assigns them to participants. Progress is tracked in real time by the server, and reminders are sent to devices as needed. The emotion engine also provides notifications, especially to prevent a decline in motivation.

[0338] (Example 2)

[0339] 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".

[0340] In today's business environment, there is a demand for both increased meeting efficiency and participant satisfaction. However, conventional meeting facilitation systems often focus only on time management and agenda progression, without considering the emotional state of participants. As a result, maintaining participant focus and conducting smooth meetings has been a challenge.

[0341] 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.

[0342] In this invention, the server includes means for providing an information input device for inputting the purpose of the meeting and participant information, means including a device for transcribing speech in real time and analyzing emotions, and means for monitoring the time allocation of agenda items and generating notifications when time is exceeded. This makes it possible to facilitate efficient agenda progression and decision-making while taking into account the emotional state of participants during the meeting.

[0343] An "information input device" is a device that provides an interface for users to input the purpose, goals, participant list, and time allocation of a meeting.

[0344] "Speech recognition technology" is a technology that converts participants' speech into text data in real time.

[0345] An "emotion analysis device" is a device that analyzes and detects the emotional state of meeting participants from the content of their speech, which has been transcribed into text using speech recognition.

[0346] A "time allocation monitoring system" is a system that tracks the time allocation for each agenda item during a meeting and generates a notification if the scheduled time is exceeded.

[0347] A "meeting minutes automatic generation device" is a device that automatically creates meeting minutes at the end of a meeting, highlighting important points based on the content of the discussion and sentiment data.

[0348] A "task assignment device" is a device that analyzes tasks decided during a meeting, assigns them to each participant, and notifies them accordingly.

[0349] A "progress tracking system" is a system that tracks the progress of tasks decided in a meeting and sends reminders when the task deadline is approaching.

[0350] A "feedback notification system" is a system that generates suggestions for improving the meeting's progress based on participants' emotional data and notifies participants of these suggestions on their devices.

[0351] The present invention provides a system for efficient meeting management, particularly one that takes into account the emotional state of participants. This system includes an information input device, speech recognition technology, an emotion analysis device, time allocation monitoring means, an automatic meeting minutes generation device, a task assignment device, a progress tracking means, and a feedback notification means.

[0352] The user inputs the meeting's purpose, goals, participant list, and time allocation into an information input device. This device can be configured using, for example, a standard computer or smart device. The entered information is sent to a server and set as a meeting template.

[0353] During the meeting, the server uses speech recognition technology to transcribe speech in real time. For example, cloud-based speech recognition software is used. The audio data is then analyzed by an emotion analysis device to identify the emotions of each participant.

[0354] The time allocation monitoring system monitors the time elapsed for each agenda item and generates a notification if the scheduled time is exceeded. This notification is sent to the terminal, allowing the user to take appropriate action.

[0355] After the meeting ends, the server uses an automated minutes generation system to create minutes based on the content of the discussion and sentiment data. These minutes are then sent to the terminals with important points highlighted.

[0356] Furthermore, the task assignment device analyzes the action items decided during the meeting and assigns them appropriately to each participant. This allows participants to clearly understand their individual responsibilities through their devices.

[0357] Progress tracking measures track the progress of tasks and send reminders to participants as deadlines approach. This reinforces activity toward completing each task.

[0358] As an example, consider a scenario in a large-scale project meeting where the system detects participants who are experiencing particular stress and notifies them of measures to alleviate their condition. This method can facilitate the smooth running of the meeting and contribute to the success of the project.

[0359] An example of a prompt sentence generated using an AI model is, "What relaxation methods should be suggested to participants who are feeling stressed at the next project meeting?"

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The user inputs the meeting's purpose, goals, participant list, and time allocation into an information input device. This input is sent to the server, which uses it to construct the meeting settings. As part of the data processing, the server formats each piece of information into a template and saves it as a schedule. As output, meeting template data is generated.

[0363] Step 2:

[0364] When the meeting begins, the server activates speech recognition software and converts participants' speech into text data in real time. The input is audio data, and the output is the transcribed speech. The server prepares this text data for analysis.

[0365] Step 3:

[0366] The server processes the generated text data into an emotion analysis device. This device uses natural language processing techniques to analyze the emotional aspects of each statement (e.g., anxiety or joy). The input is text data, and the output is participant emotion data. At this stage, emotion-based tagging is performed.

[0367] Step 4:

[0368] The server tracks the progress of each agenda item using a time allocation monitoring system. The input is real-time timestamp data, and the output is monitoring data for elapsed time. If any agenda item exceeds its scheduled time, the server generates a notification message.

[0369] Step 5:

[0370] After the meeting ends, the server uses an automated minutes generation system to create meeting minutes based on the recorded text and sentiment data. The input consists of spoken words and sentiment data, and the output is a meeting minute with key points highlighted. The server then sends this meeting minute to the terminal, making it accessible to participants.

[0371] Step 6:

[0372] The server operates a task assignment device, analyzing the action items decided during the meeting. The input is the meeting minutes, and the output generates specific tasks and the assigned person responsible for each task. Task information is then sent to each participant's terminal.

[0373] Step 7:

[0374] Using a progress tracking system, the server periodically updates and monitors the progress of each task. Input is progress data reported by participants, and output generates task completion status and reminders for tasks approaching their deadlines. Reminders are delivered to terminals and notified to participants.

[0375] (Application Example 2)

[0376] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0377] Modern meetings suffer from a lack of efficient progress management and disregard for participants' emotional states. This leads to challenges such as lapses in focus, derailment, and difficulty in making effective decisions. Furthermore, insufficient follow-up on decisions and tasks prevents meetings from maximizing their effectiveness.

[0378] 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.

[0379] In this invention, the server includes means for providing an input device for inputting meeting objectives, achievement criteria, participant information, and schedule; means for analyzing participant statements during the meeting, monitoring the schedule for each agenda item, and generating notifications if an agenda item exceeds the scheduled time; and means for detecting participants' emotional states in real time, adjusting the meeting's progress based on emotional data, and proposing countermeasures. This enables meeting management that takes participants' emotions into consideration, resulting in efficient and engaging meeting operations.

[0380] An "input device" is an interface for entering meeting objectives, achievement criteria, participant information, and schedules into the system.

[0381] "Statement information" refers to data used to transcribe and analyze the content of participants' statements during a meeting.

[0382] A "timetable" refers to the scheduled time allocated to each agenda item within a meeting, and is used to manage the progress of the meeting.

[0383] A "notification" is a message used in meeting management to inform participants when the scheduled time is exceeded or the agenda is deviated from.

[0384] "Emotional state" refers to information that detects changes in participants' emotions in real time and serves as an important indicator for the progress of the meeting.

[0385] "Emotional data" refers to digital data about the emotional state of participants, collected by the emotion engine.

[0386] "Measures" refer to analyzing the emotional state of participants during a meeting and proposing adjustments to the meeting's progress or suggesting breaks as needed.

[0387] This system is initiated by an interface that includes input devices for the user to enter meeting objectives, success criteria, participant information, and schedule. Once the user enters this information into the system, the server configures the meeting and generates a meeting flow template.

[0388] Once the meeting begins, the server utilizes voice input technology to transcribe participants' statements in real time and analyzes the data. This analysis includes a process that uses an emotion engine to detect participants' emotional states. Based on this emotion data, the server adjusts the flow of the meeting and sends notifications to participants' devices as needed. These notifications include managing the time allocation for agenda items, preventing digressions, and even suggesting breaks for participants.

[0389] As a concrete example, let's consider a citizens' meeting regarding local traffic issues. In this meeting, the server analyzes participants' statements, and if it determines that a participant's emotional state is one of tension, it notifies the participant's information terminal with relaxing videos or instructions for deep breathing to promote relaxation. In this way, the efficiency of the meeting can be maintained while increasing participants' concentration and motivation.

[0390] A typical example of a prompt would be asking a generating AI model a question like, "If participants are feeling stressed during a meeting, what should we do to offer them something to help them relax?" This would primarily lead to obtaining appropriate solutions.

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

[0392] Step 1:

[0393] The user enters the meeting's objectives, achievement criteria, participant information, and schedule. This data is received by the server and used as the basis for generating a meeting template. The server then structures and stores this data, making it available for use in the next step.

[0394] Step 2:

[0395] Once the meeting begins, the server records participants' speech and converts it to text using speech recognition technology. It receives the audio data as input and processes it into text data. This text data is used for analyzing the content of the speech and for processing with the sentiment engine.

[0396] Step 3:

[0397] The server uses an emotion engine to analyze participants' emotional states from text data. Specifically, it uses an emotion analysis algorithm to identify emotions in the text and outputs them as emotion data. This allows for an understanding of the meeting's atmosphere and the state of each participant.

[0398] Step 4:

[0399] The server adjusts the flow and time allocation of the meeting based on emotional data. It analyzes the input emotional data and, if it detects agenda items exceeding the scheduled time or a decline in participants' concentration, sends a notification to the terminal to prompt course correction. The output includes specific instructions and suggestions sent to the terminal.

[0400] Step 5:

[0401] If a meeting is ongoing and changes in participants' emotional states are observed, the server sends suggestions to terminals to encourage rest and refreshment. Taking emotional stress and tension data as input, the server generates suggestions to promote relaxation as output. Specific examples of such suggestions include links to relaxation videos and breathing exercises.

[0402] Step 6:

[0403] After the meeting concludes, the server automatically generates a meeting record, highlighting particularly important decisions. This process integrates the input speech information and sentiment data to produce a formatted meeting transcript. This output is distributed to participants after the meeting.

[0404] Step 7:

[0405] The server analyzes the next steps and tasks and assigns them to each participant. Output is generated based on the input task information and the participant's role, and reminders are sent to the terminal as needed to track progress. In this way, post-meeting follow-up is also ensured.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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".

[0422] The present invention embodies an AI facilitator for efficiently managing meetings. This system is realized through the cooperation of users, servers, and terminals.

[0423] First, the user provides information through a user interface to input the meeting's purpose, goals, participant list, and time allocation into the system. The server then receives this information, automatically generates a meeting template, and saves it in the database.

[0424] Once the meeting begins, the terminal connects participants to the meeting application, and the server converts the audio data into text in real time and analyzes what is being said. Based on this analysis, the server monitors the progress of the agenda and sends notifications to the terminal if the meeting exceeds the scheduled time or goes off-topic. This allows users to conduct the meeting efficiently.

[0425] At the end of the meeting, the server automatically generates meeting minutes, summarizing the points made and clarifying key issues. It also analyzes the tasks for the next steps and assigns them to each participant. The server sends the documented meeting minutes and task allocation sheet to the terminals and follows up on the progress.

[0426] As a concrete example, let's consider a project progress review meeting. In this case, the project manager, as the user, inputs the purpose and goals, and the server supports the meeting's progress in real time. The server monitors the time allocation for each agenda item, and if progress is behind schedule or there are tangents, an alert is sent from the server to the participant's terminal. After the meeting ends, the server automatically generates meeting minutes, assigns each participant tasks to be completed by the next meeting, and tracks their progress.

[0427] In this way, by utilizing this system, it is possible to improve the efficiency of meetings and optimize the work time of all participants.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The user, acting as the meeting organizer, accesses the system's user interface and enters the meeting's purpose, goals, participant list, and time allocation. The entered information is sent to the server and stored in the database.

[0431] Step 2:

[0432] The server generates a meeting template based on the received meeting information. As the meeting start time approaches, a reminder is sent to all participants' devices.

[0433] Step 3:

[0434] The terminal allows participants to access the application necessary to join the meeting, and the server checks everyone's readiness. Once everyone is ready, a notification to start the meeting is sent to the users.

[0435] Step 4:

[0436] During the meeting, the server uses real-time speech recognition to transcribe participants' statements into text. This text is then analyzed to monitor the progress of discussions on each agenda item.

[0437] Step 5:

[0438] The server monitors the scheduled time for each agenda item and automatically sends notifications to terminals if any time is exceeded. Alerts for course correction are also sent as needed.

[0439] Step 6:

[0440] Once the meeting concludes, the server automatically generates meeting minutes based on the recorded statements. These minutes are compiled in a format that organizes and highlights decisions and key points.

[0441] Step 7:

[0442] The server analyzes the next steps and related tasks decided during the meeting and assigns appropriate tasks to each participant. A documented task allocation sheet is sent to each participant's terminal.

[0443] Step 8:

[0444] The server tracks the progress of tasks and sends reminders to users as deadlines approach. Furthermore, it sends follow-up alerts if progress is behind schedule.

[0445] (Example 1)

[0446] 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."

[0447] In meetings, time management and the organization of discussions are often done manually, making efficient meeting management difficult. Furthermore, there are problems with the time and effort required for creating meeting minutes and assigning tasks after the meeting. Additionally, it is difficult to identify digressions and delays in real time, hindering effective meeting management.

[0448] 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.

[0449] In this invention, the server includes means for exchanging information between humans and machines, means for analyzing participants' statements and generating attention-grabbing information, and means for automatically generating reports and clarifying decisions. This enables efficient meeting progress and management. Furthermore, by processing natural language using a generative model, real-time analysis and automated meeting minute generation are achieved, reducing the workload of participants.

[0450] "A means of exchanging information between humans and machines" refers to an interface that allows users to input meeting objectives and participant information, and then import that information into the system.

[0451] "A means of analyzing participants' statements and generating information to draw attention to them" refers to a method of assisting the progress of a meeting in real time by converting audio data acquired during the meeting into text and analyzing its content.

[0452] "A means of automatically generating reports and clarifying decisions" refers to a method of automatically organizing and recording meeting summaries and decisions based on what was said during the meeting.

[0453] "Processing natural language using generative models" is a technique that uses large-scale language models to analyze input speech and text data and generate output that corresponds to natural language.

[0454] The information sent to participants' devices includes meeting reminders and notifications regarding the progress of the agenda, thereby encouraging participants to prepare for the meeting and enabling efficient meeting management.

[0455] This invention is a system for efficiently managing meetings, primarily involving the collaboration of users, servers, and terminals. First, users input the meeting's purpose, goals, participant information, and time allocation using a dedicated user interface. This interface is provided as a web application and operates on a standard browser.

[0456] The server generates a meeting template based on the information received from the user and saves this information in a database system (e.g., MySQL). When the meeting starts, participants connect to a video conferencing tool (e.g., common online meeting software) via their terminals.

[0457] During the meeting, the server uses speech recognition software (e.g., a common speech recognition API) to convert audio data into text and analyzes the content of the speech using a generative AI model (e.g., a common language model). Through this analysis, the server monitors the progress of the meeting and sends notifications via the terminal if deviations or delays are detected.

[0458] At the end of the meeting, the server automatically generates meeting minutes and highlights key decisions. This process uses natural language processing techniques to organize the information. The server also analyzes the tasks decided during the meeting and assigns them to participants as the next steps. This information is sent to each participant via their terminal, and their progress is tracked.

[0459] As a concrete example, let's consider a project progress review meeting. In this case, the user, the project manager, enters, "At the next development meeting, we plan to review the progress of the new feature and finalize the release date." The server supports the meeting in real time and sends notifications to terminals as needed, such as, "The scheduled time has been exceeded by 5 minutes. Please move on to the next agenda item." After the meeting ends, each participant is provided with automatically generated minutes stating something like, "The new feature is two weeks behind schedule. This is due to a lack of resources. We need to complete the arrangement of additional resources before the next meeting."

[0460] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0461] Step 1:

[0462] Users use a dedicated user interface to input the meeting's purpose, goals, participant information, and time allocation. This input serves as the basis for generating a meeting template. The entered data is sent to the server and prepared for storage in the database.

[0463] Step 2:

[0464] The server analyzes the data received from the user and generates a meeting template. This template generation algorithm automatically determines the necessary time allocation and procedure for each agenda item. The generated template is stored in a database for later reference.

[0465] Step 3:

[0466] At the start of a meeting, the terminal connects participants to the meeting application. The terminal then sends connection requests to the participants' terminals and, in conjunction with the online meeting tool using webcams and microphones, opens the meeting room. This ensures that participants are ready to join the meeting smoothly.

[0467] Step 4:

[0468] During the meeting, the server uses speech recognition software to convert audio data into text in real time. The acquired text data is then analyzed using a generative AI model to verify that the meeting is proceeding as planned. If the agenda deviates or the time limit is exceeded, alerts are sent to participants via their devices.

[0469] Step 5:

[0470] At the end of the meeting, the server integrates the audio and text data and automatically generates meeting minutes. An AI model extracts key points and organizes them into a report, making the meeting's results clear. The generated minutes are then shared with participants via their devices.

[0471] Step 6:

[0472] The server analyzes the tasks decided during the meeting and assigns them to each participant. This task information is stored in a database and managed for tracking. The server then monitors the progress and sends reminders via the terminal as needed.

[0473] (Application Example 1)

[0474] 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."

[0475] Meetings in physical stores and within companies often suffer from poor time management and ambiguous decisions. Against this backdrop, there is a need for an effective system to improve meeting efficiency and reliably track the progress of work. This invention aims to provide a means to manage meeting progress in real time and ensure smooth business operations.

[0476] 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.

[0477] In this invention, the server includes means for providing a human-machine interface for inputting the purpose, objectives, participant list, and time management of a meeting; means for analyzing participants' statements during the meeting, monitoring time management for each agenda item, and generating notifications if an agenda item exceeds the scheduled time; and means for automatically generating a record of the meeting minutes based on the content of the statements at the end of the meeting and highlighting the decisions made. This enables efficient progress management of meetings and reliable tracking of work.

[0478] The "purpose of the meeting" is the basic goal that the meeting aims to achieve.

[0479] "Goals" refer to the specific results that participants want to achieve through the meeting.

[0480] A "list of participants" is a list containing information about the individuals attending the meeting.

[0481] "Time management" refers to the activity of allocating appropriate time to each agenda item in a meeting and efficiently managing its progress.

[0482] A "human-machine interface" refers to the means or devices that allow a user to input or receive information.

[0483] "Analyzing speech" refers to the act of understanding and processing the content of what participants said during a meeting.

[0484] "Monitoring time management" is the activity of observing the actual progress against a set time allocation in real time.

[0485] "Generating notifications" is the process of informing participants of important information as the project progresses.

[0486] A "meeting record" is a document that organizes and preserves the statements and decisions made during a meeting.

[0487] "Distributing tasks" is the process of assigning the tasks decided in a meeting to each participant.

[0488] "Sending as reference material" means providing participants with a document containing the necessary information.

[0489] "Monitoring progress" refers to the activity of regularly checking how far each task has progressed.

[0490] "Derailment notification to terminal" is the process of sending a message to alert users if they deviate from the scheduled agenda.

[0491] A "program installed on an information processing device" is software embedded in a computer to perform a specific function.

[0492] This system utilizes an application designed to provide effective meeting management for physical stores. The server stores meeting purpose, objectives, participant list, and time management information entered by users in a cloud-based database, supporting efficient meeting management. The hardware used is smartphones and tablets, while the software consists of server-side processing using Node.js, a mobile application using React Native, and speech analysis using Google Cloud Speech-to-Text.

[0493] After the meeting begins, the terminal uses Google Cloud Speech-to-Text to transcribe participants' speech in real time. The server then analyzes this text data to check for time management and any deviations from the agenda. If the meeting exceeds the scheduled time, the server sends a notification to the terminal. Furthermore, it automatically generates a meeting record, analyzes the tasks based on the decisions made, and sends the tasks to participants as reference material.

[0494] Users can utilize this system when holding meetings to introduce new products to their stores. For example, a user can enter a prompt into the system such as, "We will hold an in-store meeting regarding the introduction of new products. The agenda will include explaining the features of the new products and confirming display methods. Please ensure that we do not exceed the time limit and automatically distribute the task along with the meeting minutes." This allows for efficient meeting management and automated follow-up. Through this specific example, participants can smoothly carry out the new product introduction process, and the progress is tracked in real time.

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] Users enter meeting objectives, goals, participant list, and time management information into a smartphone or tablet interface. This information is sent to a server and stored in a cloud database. The data includes meeting name, agenda, participant email addresses, and agenda content. The server stores this data and prepares it for use in subsequent steps.

[0498] Step 2:

[0499] Once the meeting begins, the terminal records audio in real time and converts it to text data using the Google Cloud Speech-to-Text API. The input is the audio from the meeting, and the output is the corresponding transcribed speech. By transcribing the audio data into text, the server can prepare for the next analysis.

[0500] Step 3:

[0501] The server analyzes the received text data and monitors time management for each agenda item. The input is the text data generated in step 2, and the output is a progress report for each agenda item. The server automatically generates and sends a notification to the terminal if an agenda item exceeds its scheduled time or goes off track. This allows the user to take timely action.

[0502] Step 4:

[0503] At the end of the meeting, the server automatically generates a transcript of the proceedings based on all the text data. The input is the text of all participants' statements, and the output is a well-organized transcript. A generation AI model is used to highlight important statements and decisions to create the final document.

[0504] Step 5:

[0505] The server analyzes the assigned tasks and automatically sends the corresponding tasks as documents to each participant. The input consists of meeting minutes and task lists generated during the meeting, while the output is a document detailing the specific tasks for each participant. Information is communicated via email and in-app notifications, clearly instructing each user on the necessary actions.

[0506] Step 6:

[0507] The server continuously monitors progress and sends reminders as the deadline approaches. The input is progress information for the task assigned in step 5, and the output is a reminder notification as the deadline approaches. This helps participants complete their work on schedule.

[0508] 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.

[0509] This invention combines an AI facilitator system that supports the efficient progress of meetings with an emotion engine, thereby enabling meeting management that takes into account the emotional state of participants. This system functions through the coordinated efforts of users, servers, and terminals.

[0510] First, the user enters the meeting's purpose, goals, participant list, and time allocation into the system. Based on this information, the server configures the meeting and generates a template. The system then sends meeting reminders to participants and prompts them to review any necessary pre-meeting materials.

[0511] Once the meeting begins, the server uses speech recognition technology to transcribe participants' statements into text in real time and analyzes the content. During this process, an emotion engine detects each participant's emotions and collects emotional data. For example, if a participant is feeling anxious, this information is recorded by the server.

[0512] The server provides feedback to adjust the meeting's progress based on the analyzed sentiment data. For example, if a participant's concentration is waning, it sends a notification to their device and suggests appropriate measures. It also monitors the time allocation for each agenda item and immediately sends a notification to the device if the meeting exceeds the allotted time or goes off-topic, prompting a course correction.

[0513] After the meeting ends, the server automatically generates meeting minutes, highlighting key points based on spoken content and sentiment data. Furthermore, it identifies the next steps and tasks decided during the meeting, assigns them to participants, and tracks their progress. Leveraging the sentiment engine, it also sends reminders to participants' devices to support their motivation and notifications to improve engagement.

[0514] As a concrete example, consider a meeting to advance an important project. In this case, the system would detect participants who are feeling particularly stressed and prompt them to take steps to alleviate that stress. This would improve the overall atmosphere of the meeting and allow for a more cohesive process.

[0515] This system enables more effective decision-making and team building compared to conventional, mechanical meeting management, by simultaneously utilizing participants' emotions and intellectual information.

[0516] The following describes the processing flow.

[0517] Step 1:

[0518] The user logs into the system as the meeting organizer and enters the meeting's purpose, goals, participant list, and time allocation. The server receives this information, generates a meeting template, and saves it to the database.

[0519] Step 2:

[0520] As the scheduled meeting time approaches, the server sends a reminder to all participants' devices. This reminder includes meeting details and a request to review any necessary pre-meeting materials.

[0521] Step 3:

[0522] Once the meeting begins, the terminal connects participants to the meeting, and the server converts audio data into text in real time and analyzes what is being said. At this time, an emotion engine operates, recognizing the participants' emotions from the camera and audio, and sending that data to the server.

[0523] Step 4:

[0524] The server monitors the progress of the meeting based on real-time analysis of spoken content and emotional data. If a participant's emotional state indicates a decrease in concentration, it sends a notification to their device prompting them to refresh. It also monitors the time allocation for each agenda item and sends notifications for course correction as needed.

[0525] Step 5:

[0526] During the meeting, the server analyzes emotional data such as anxiety and tension experienced by participants and adjusts the meeting's pace accordingly. For example, it might display suggestions on the participants' devices to adjust the tempo, encouraging better engagement.

[0527] Step 6:

[0528] At the end of the meeting, the server automatically generates meeting minutes based on the statements and sentiment data. This includes a summary of the statements as well as an analysis of changes in sentiment.

[0529] Step 7:

[0530] The server analyzes the tasks decided during the meeting and assigns them to participants. Progress is tracked in real time by the server, and reminders are sent to devices as needed. The emotion engine also provides notifications, especially to prevent a decline in motivation.

[0531] (Example 2)

[0532] 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."

[0533] In today's business environment, there is a demand for both increased meeting efficiency and participant satisfaction. However, conventional meeting facilitation systems often focus only on time management and agenda progression, without considering the emotional state of participants. As a result, maintaining participant focus and conducting smooth meetings has been a challenge.

[0534] 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.

[0535] In this invention, the server includes means for providing an information input device for inputting the purpose of the meeting and participant information, means including a device for transcribing speech in real time and analyzing emotions, and means for monitoring the time allocation of agenda items and generating notifications when time is exceeded. This makes it possible to facilitate efficient agenda progression and decision-making while taking into account the emotional state of participants during the meeting.

[0536] An "information input device" is a device that provides an interface for users to input the purpose, goals, participant list, and time allocation of a meeting.

[0537] "Speech recognition technology" is a technology that converts participants' speech into text data in real time.

[0538] An "emotion analysis device" is a device that analyzes and detects the emotional state of meeting participants from the content of their speech, which has been transcribed into text using speech recognition.

[0539] A "time allocation monitoring system" is a system that tracks the time allocation for each agenda item during a meeting and generates a notification if the scheduled time is exceeded.

[0540] A "meeting minutes automatic generation device" is a device that automatically creates meeting minutes at the end of a meeting, highlighting important points based on the content of the discussion and sentiment data.

[0541] A "task assignment device" is a device that analyzes tasks decided during a meeting, assigns them to each participant, and notifies them accordingly.

[0542] A "progress tracking system" is a system that tracks the progress of tasks decided in a meeting and sends reminders when the task deadline is approaching.

[0543] A "feedback notification system" is a system that generates suggestions for improving the meeting's progress based on participants' emotional data and notifies participants of these suggestions on their devices.

[0544] The present invention provides a system for efficient meeting management, particularly one that takes into account the emotional state of participants. This system includes an information input device, speech recognition technology, an emotion analysis device, time allocation monitoring means, an automatic meeting minutes generation device, a task assignment device, a progress tracking means, and a feedback notification means.

[0545] The user inputs the meeting's purpose, goals, participant list, and time allocation into an information input device. This device can be configured using, for example, a standard computer or smart device. The entered information is sent to a server and set as a meeting template.

[0546] During the meeting, the server uses speech recognition technology to transcribe speech in real time. For example, cloud-based speech recognition software is used. The audio data is then analyzed by an emotion analysis device to identify the emotions of each participant.

[0547] The time allocation monitoring system monitors the time elapsed for each agenda item and generates a notification if the scheduled time is exceeded. This notification is sent to the terminal, allowing the user to take appropriate action.

[0548] After the meeting ends, the server uses an automated minutes generation system to create minutes based on the content of the discussion and sentiment data. These minutes are then sent to the terminals with important points highlighted.

[0549] Furthermore, the task assignment device analyzes the action items decided during the meeting and assigns them appropriately to each participant. This allows participants to clearly understand their individual responsibilities through their devices.

[0550] Progress tracking measures track the progress of tasks and send reminders to participants as deadlines approach. This reinforces activity toward completing each task.

[0551] As an example, consider a scenario in a large-scale project meeting where the system detects participants who are experiencing particular stress and notifies them of measures to alleviate their condition. This method can facilitate the smooth running of the meeting and contribute to the success of the project.

[0552] An example of a prompt sentence generated using an AI model is, "What relaxation methods should be suggested to participants who are feeling stressed at the next project meeting?"

[0553] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0554] Step 1:

[0555] The user inputs the meeting's purpose, goals, participant list, and time allocation into an information input device. This input is sent to the server, which uses it to construct the meeting settings. As part of the data processing, the server formats each piece of information into a template and saves it as a schedule. As output, meeting template data is generated.

[0556] Step 2:

[0557] When the meeting begins, the server activates speech recognition software and converts participants' speech into text data in real time. The input is audio data, and the output is the transcribed speech. The server prepares this text data for analysis.

[0558] Step 3:

[0559] The server processes the generated text data into an emotion analysis device. This device uses natural language processing techniques to analyze the emotional aspects of each statement (e.g., anxiety or joy). The input is text data, and the output is participant emotion data. At this stage, emotion-based tagging is performed.

[0560] Step 4:

[0561] The server tracks the progress of each agenda item using a time allocation monitoring system. The input is real-time timestamp data, and the output is monitoring data for elapsed time. If any agenda item exceeds its scheduled time, the server generates a notification message.

[0562] Step 5:

[0563] After the meeting ends, the server uses an automated minutes generation system to create meeting minutes based on the recorded text and sentiment data. The input consists of spoken words and sentiment data, and the output is a meeting minute with key points highlighted. The server then sends this meeting minute to the terminal, making it accessible to participants.

[0564] Step 6:

[0565] The server operates a task assignment device, analyzing the action items decided during the meeting. The input is the meeting minutes, and the output generates specific tasks and the assigned person responsible for each task. Task information is then sent to each participant's terminal.

[0566] Step 7:

[0567] Using a progress tracking system, the server periodically updates and monitors the progress of each task. Input is progress data reported by participants, and output generates task completion status and reminders for tasks approaching their deadlines. Reminders are delivered to terminals and notified to participants.

[0568] (Application Example 2)

[0569] 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."

[0570] Modern meetings suffer from a lack of efficient progress management and disregard for participants' emotional states. This leads to challenges such as lapses in focus, derailment, and difficulty in making effective decisions. Furthermore, insufficient follow-up on decisions and tasks prevents meetings from maximizing their effectiveness.

[0571] 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.

[0572] In this invention, the server includes means for providing an input device for inputting meeting objectives, achievement criteria, participant information, and schedule; means for analyzing participant statements during the meeting, monitoring the schedule for each agenda item, and generating notifications if an agenda item exceeds the scheduled time; and means for detecting participants' emotional states in real time, adjusting the meeting's progress based on emotional data, and proposing countermeasures. This enables meeting management that takes participants' emotions into consideration, resulting in efficient and engaging meeting operations.

[0573] An "input device" is an interface for entering meeting objectives, achievement criteria, participant information, and schedules into the system.

[0574] "Statement information" refers to data used to transcribe and analyze the content of participants' statements during a meeting.

[0575] A "timetable" refers to the scheduled time allocated to each agenda item within a meeting, and is used to manage the progress of the meeting.

[0576] A "notification" is a message used in meeting management to inform participants when the scheduled time is exceeded or the agenda is deviated from.

[0577] "Emotional state" refers to information that detects changes in participants' emotions in real time and serves as an important indicator for the progress of the meeting.

[0578] "Emotional data" refers to digital data about the emotional state of participants, collected by the emotion engine.

[0579] "Measures" refer to analyzing the emotional state of participants during a meeting and proposing adjustments to the meeting's progress or suggesting breaks as needed.

[0580] This system is initiated by an interface that includes input devices for the user to enter meeting objectives, success criteria, participant information, and schedule. Once the user enters this information into the system, the server configures the meeting and generates a meeting flow template.

[0581] Once the meeting begins, the server utilizes voice input technology to transcribe participants' statements in real time and analyzes the data. This analysis includes a process that uses an emotion engine to detect participants' emotional states. Based on this emotion data, the server adjusts the flow of the meeting and sends notifications to participants' devices as needed. These notifications include managing the time allocation for agenda items, preventing digressions, and even suggesting breaks for participants.

[0582] As a concrete example, let's consider a citizens' meeting regarding local traffic issues. In this meeting, the server analyzes participants' statements, and if it determines that a participant's emotional state is one of tension, it notifies the participant's information terminal with relaxing videos or instructions for deep breathing to promote relaxation. In this way, the efficiency of the meeting can be maintained while increasing participants' concentration and motivation.

[0583] A typical example of a prompt would be asking a generating AI model a question like, "If participants are feeling stressed during a meeting, what should we do to offer them something to help them relax?" This would primarily lead to obtaining appropriate solutions.

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

[0585] Step 1:

[0586] The user enters the meeting's objectives, achievement criteria, participant information, and schedule. This data is received by the server and used as the basis for generating a meeting template. The server then structures and stores this data, making it available for use in the next step.

[0587] Step 2:

[0588] Once the meeting begins, the server records participants' speech and converts it to text using speech recognition technology. It receives the audio data as input and processes it into text data. This text data is used for analyzing the content of the speech and for processing with the sentiment engine.

[0589] Step 3:

[0590] The server uses an emotion engine to analyze participants' emotional states from text data. Specifically, it uses an emotion analysis algorithm to identify emotions in the text and outputs them as emotion data. This allows for an understanding of the meeting's atmosphere and the state of each participant.

[0591] Step 4:

[0592] The server adjusts the flow and time allocation of the meeting based on emotional data. It analyzes the input emotional data and, if it detects agenda items exceeding the scheduled time or a decline in participants' concentration, sends a notification to the terminal to prompt course correction. The output includes specific instructions and suggestions sent to the terminal.

[0593] Step 5:

[0594] If a meeting is ongoing and changes in participants' emotional states are observed, the server sends suggestions to terminals to encourage rest and refreshment. Taking emotional stress and tension data as input, the server generates suggestions to promote relaxation as output. Specific examples of such suggestions include links to relaxation videos and breathing exercises.

[0595] Step 6:

[0596] After the meeting concludes, the server automatically generates a meeting record, highlighting particularly important decisions. This process integrates the input speech information and sentiment data to produce a formatted meeting transcript. This output is distributed to participants after the meeting.

[0597] Step 7:

[0598] The server analyzes the next steps and tasks and assigns them to each participant. Output is generated based on the input task information and the participant's role, and reminders are sent to the terminal as needed to track progress. In this way, post-meeting follow-up is also ensured.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] [Fourth Embodiment]

[0603] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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).

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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".

[0616] The present invention embodies an AI facilitator for efficiently managing meetings. This system is realized through the cooperation of users, servers, and terminals.

[0617] First, the user provides information through a user interface to input the meeting's purpose, goals, participant list, and time allocation into the system. The server then receives this information, automatically generates a meeting template, and saves it in the database.

[0618] Once the meeting begins, the terminal connects participants to the meeting application, and the server converts the audio data into text in real time and analyzes what is being said. Based on this analysis, the server monitors the progress of the agenda and sends notifications to the terminal if the meeting exceeds the scheduled time or goes off-topic. This allows users to conduct the meeting efficiently.

[0619] At the end of the meeting, the server automatically generates meeting minutes, summarizing the points made and clarifying key issues. It also analyzes the tasks for the next steps and assigns them to each participant. The server sends the documented meeting minutes and task allocation sheet to the terminals and follows up on the progress.

[0620] As a concrete example, let's consider a project progress review meeting. In this case, the project manager, as the user, inputs the purpose and goals, and the server supports the meeting's progress in real time. The server monitors the time allocation for each agenda item, and if progress is behind schedule or there are tangents, an alert is sent from the server to the participant's terminal. After the meeting ends, the server automatically generates meeting minutes, assigns each participant tasks to be completed by the next meeting, and tracks their progress.

[0621] In this way, by utilizing this system, it is possible to improve the efficiency of meetings and optimize the work time of all participants.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] The user, acting as the meeting organizer, accesses the system's user interface and enters the meeting's purpose, goals, participant list, and time allocation. The entered information is sent to the server and stored in the database.

[0625] Step 2:

[0626] The server generates a meeting template based on the received meeting information. As the meeting start time approaches, a reminder is sent to all participants' devices.

[0627] Step 3:

[0628] The terminal allows participants to access the application necessary to join the meeting, and the server checks everyone's readiness. Once everyone is ready, a notification to start the meeting is sent to the users.

[0629] Step 4:

[0630] During the meeting, the server uses real-time speech recognition to transcribe participants' statements into text. This text is then analyzed to monitor the progress of discussions on each agenda item.

[0631] Step 5:

[0632] The server monitors the scheduled time for each agenda item and automatically sends notifications to terminals if any time is exceeded. Alerts for course correction are also sent as needed.

[0633] Step 6:

[0634] Once the meeting concludes, the server automatically generates meeting minutes based on the recorded statements. These minutes are compiled in a format that organizes and highlights decisions and key points.

[0635] Step 7:

[0636] The server analyzes the next steps and related tasks decided during the meeting and assigns appropriate tasks to each participant. A documented task allocation sheet is sent to each participant's terminal.

[0637] Step 8:

[0638] The server tracks the progress of tasks and sends reminders to users as deadlines approach. Furthermore, it sends follow-up alerts if progress is behind schedule.

[0639] (Example 1)

[0640] 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".

[0641] In meetings, time management and the organization of discussions are often done manually, making efficient meeting management difficult. Furthermore, there are problems with the time and effort required for creating meeting minutes and assigning tasks after the meeting. Additionally, it is difficult to identify digressions and delays in real time, hindering effective meeting management.

[0642] 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.

[0643] In this invention, the server includes means for exchanging information between humans and machines, means for analyzing participants' statements and generating attention-grabbing information, and means for automatically generating reports and clarifying decisions. This enables efficient meeting progress and management. Furthermore, by processing natural language using a generative model, real-time analysis and automated meeting minute generation are achieved, reducing the workload of participants.

[0644] "A means of exchanging information between humans and machines" refers to an interface that allows users to input meeting objectives and participant information, and then import that information into the system.

[0645] "A means of analyzing participants' statements and generating information to draw attention to them" refers to a method of assisting the progress of a meeting in real time by converting audio data acquired during the meeting into text and analyzing its content.

[0646] "A means of automatically generating reports and clarifying decisions" refers to a method of automatically organizing and recording meeting summaries and decisions based on what was said during the meeting.

[0647] "Processing natural language using generative models" is a technique that uses large-scale language models to analyze input speech and text data and generate output that corresponds to natural language.

[0648] The information sent to participants' devices includes meeting reminders and notifications regarding the progress of the agenda, thereby encouraging participants to prepare for the meeting and enabling efficient meeting management.

[0649] This invention is a system for efficiently managing meetings, primarily involving the collaboration of users, servers, and terminals. First, users input the meeting's purpose, goals, participant information, and time allocation using a dedicated user interface. This interface is provided as a web application and operates on a standard browser.

[0650] The server generates a meeting template based on the information received from the user and saves this information in a database system (e.g., MySQL). When the meeting starts, participants connect to a video conferencing tool (e.g., common online meeting software) via their terminals.

[0651] During the meeting, the server uses speech recognition software (e.g., a common speech recognition API) to convert audio data into text and analyzes the content of the speech using a generative AI model (e.g., a common language model). Through this analysis, the server monitors the progress of the meeting and sends notifications via the terminal if deviations or delays are detected.

[0652] At the end of the meeting, the server automatically generates meeting minutes and highlights key decisions. This process uses natural language processing techniques to organize the information. The server also analyzes the tasks decided during the meeting and assigns them to participants as the next steps. This information is sent to each participant via their terminal, and their progress is tracked.

[0653] As a concrete example, let's consider a project progress review meeting. In this case, the user, the project manager, enters, "At the next development meeting, we plan to review the progress of the new feature and finalize the release date." The server supports the meeting in real time and sends notifications to terminals as needed, such as, "The scheduled time has been exceeded by 5 minutes. Please move on to the next agenda item." After the meeting ends, each participant is provided with automatically generated minutes stating something like, "The new feature is two weeks behind schedule. This is due to a lack of resources. We need to complete the arrangement of additional resources before the next meeting."

[0654] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0655] Step 1:

[0656] Users use a dedicated user interface to input the meeting's purpose, goals, participant information, and time allocation. This input serves as the basis for generating a meeting template. The entered data is sent to the server and prepared for storage in the database.

[0657] Step 2:

[0658] The server analyzes the data received from the user and generates a meeting template. This template generation algorithm automatically determines the necessary time allocation and procedure for each agenda item. The generated template is stored in a database for later reference.

[0659] Step 3:

[0660] At the start of a meeting, the terminal connects participants to the meeting application. The terminal then sends connection requests to the participants' terminals and, in conjunction with the online meeting tool using webcams and microphones, opens the meeting room. This ensures that participants are ready to join the meeting smoothly.

[0661] Step 4:

[0662] During the meeting, the server uses speech recognition software to convert audio data into text in real time. The acquired text data is then analyzed using a generative AI model to verify that the meeting is proceeding as planned. If the agenda deviates or the time limit is exceeded, alerts are sent to participants via their devices.

[0663] Step 5:

[0664] At the end of the meeting, the server integrates the audio and text data and automatically generates meeting minutes. An AI model extracts key points and organizes them into a report, making the meeting's results clear. The generated minutes are then shared with participants via their devices.

[0665] Step 6:

[0666] The server analyzes the tasks decided during the meeting and assigns them to each participant. This task information is stored in a database and managed for tracking. The server then monitors the progress and sends reminders via the terminal as needed.

[0667] (Application Example 1)

[0668] 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".

[0669] Meetings in physical stores and within companies often suffer from poor time management and ambiguous decisions. Against this backdrop, there is a need for an effective system to improve meeting efficiency and reliably track the progress of work. This invention aims to provide a means to manage meeting progress in real time and ensure smooth business operations.

[0670] 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.

[0671] In this invention, the server includes means for providing a human-machine interface for inputting the purpose, objectives, participant list, and time management of a meeting; means for analyzing participants' statements during the meeting, monitoring time management for each agenda item, and generating notifications if an agenda item exceeds the scheduled time; and means for automatically generating a record of the meeting minutes based on the content of the statements at the end of the meeting and highlighting the decisions made. This enables efficient progress management of meetings and reliable tracking of work.

[0672] The "purpose of the meeting" is the basic goal that the meeting aims to achieve.

[0673] "Goals" refer to the specific results that participants want to achieve through the meeting.

[0674] A "list of participants" is a list containing information about the individuals attending the meeting.

[0675] "Time management" refers to the activity of allocating appropriate time to each agenda item in a meeting and efficiently managing its progress.

[0676] A "human-machine interface" refers to the means or devices that allow a user to input or receive information.

[0677] "Analyzing speech" refers to the act of understanding and processing the content of what participants said during a meeting.

[0678] "Monitoring time management" is the activity of observing the actual progress against a set time allocation in real time.

[0679] "Generating notifications" is the process of informing participants of important information as the project progresses.

[0680] A "meeting record" is a document that organizes and preserves the statements and decisions made during a meeting.

[0681] "Distributing tasks" is the process of assigning the tasks decided in a meeting to each participant.

[0682] "Sending as reference material" means providing participants with a document containing the necessary information.

[0683] "Monitoring progress" refers to the activity of regularly checking how far each task has progressed.

[0684] "Derailment notification to terminal" is the process of sending a message to alert users if they deviate from the scheduled agenda.

[0685] A "program installed on an information processing device" is software embedded in a computer to perform a specific function.

[0686] This system utilizes an application designed to provide effective meeting management for physical stores. The server stores meeting purpose, objectives, participant list, and time management information entered by users in a cloud-based database, supporting efficient meeting management. The hardware used is smartphones and tablets, while the software consists of server-side processing using Node.js, a mobile application using React Native, and speech analysis using Google Cloud Speech-to-Text.

[0687] After the meeting begins, the terminal uses Google Cloud Speech-to-Text to transcribe participants' speech in real time. The server then analyzes this text data to check for time management and any deviations from the agenda. If the meeting exceeds the scheduled time, the server sends a notification to the terminal. Furthermore, it automatically generates a meeting record, analyzes the tasks based on the decisions made, and sends the tasks to participants as reference material.

[0688] Users can utilize this system when holding meetings to introduce new products to their stores. For example, a user can enter a prompt into the system such as, "We will hold an in-store meeting regarding the introduction of new products. The agenda will include explaining the features of the new products and confirming display methods. Please ensure that we do not exceed the time limit and automatically distribute the task along with the meeting minutes." This allows for efficient meeting management and automated follow-up. Through this specific example, participants can smoothly carry out the new product introduction process, and the progress is tracked in real time.

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] Users enter meeting objectives, goals, participant list, and time management information into a smartphone or tablet interface. This information is sent to a server and stored in a cloud database. The data includes meeting name, agenda, participant email addresses, and agenda content. The server stores this data and prepares it for use in subsequent steps.

[0692] Step 2:

[0693] Once the meeting begins, the terminal records audio in real time and converts it to text data using the Google Cloud Speech-to-Text API. The input is the audio from the meeting, and the output is the corresponding transcribed speech. By transcribing the audio data into text, the server can prepare for the next analysis.

[0694] Step 3:

[0695] The server analyzes the received text data and monitors time management for each agenda item. The input is the text data generated in step 2, and the output is a progress report for each agenda item. The server automatically generates and sends a notification to the terminal if an agenda item exceeds its scheduled time or goes off track. This allows the user to take timely action.

[0696] Step 4:

[0697] At the end of the meeting, the server automatically generates a transcript of the proceedings based on all the text data. The input is the text of all participants' statements, and the output is a well-organized transcript. A generation AI model is used to highlight important statements and decisions to create the final document.

[0698] Step 5:

[0699] The server analyzes the assigned tasks and automatically sends the corresponding tasks as documents to each participant. The input consists of meeting minutes and task lists generated during the meeting, while the output is a document detailing the specific tasks for each participant. Information is communicated via email and in-app notifications, clearly instructing each user on the necessary actions.

[0700] Step 6:

[0701] The server continuously monitors progress and sends reminders as the deadline approaches. The input is progress information for the task assigned in step 5, and the output is a reminder notification as the deadline approaches. This helps participants complete their work on schedule.

[0702] 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.

[0703] This invention combines an AI facilitator system that supports the efficient progress of meetings with an emotion engine, thereby enabling meeting management that takes into account the emotional state of participants. This system functions through the coordinated efforts of users, servers, and terminals.

[0704] First, the user enters the meeting's purpose, goals, participant list, and time allocation into the system. Based on this information, the server configures the meeting and generates a template. The system then sends meeting reminders to participants and prompts them to review any necessary pre-meeting materials.

[0705] Once the meeting begins, the server uses speech recognition technology to transcribe participants' statements into text in real time and analyzes the content. During this process, an emotion engine detects each participant's emotions and collects emotional data. For example, if a participant is feeling anxious, this information is recorded by the server.

[0706] The server provides feedback to adjust the meeting's progress based on the analyzed sentiment data. For example, if a participant's concentration is waning, it sends a notification to their device and suggests appropriate measures. It also monitors the time allocation for each agenda item and immediately sends a notification to the device if the meeting exceeds the allotted time or goes off-topic, prompting a course correction.

[0707] After the meeting ends, the server automatically generates meeting minutes, highlighting key points based on spoken content and sentiment data. Furthermore, it identifies the next steps and tasks decided during the meeting, assigns them to participants, and tracks their progress. Leveraging the sentiment engine, it also sends reminders to participants' devices to support their motivation and notifications to improve engagement.

[0708] As a concrete example, consider a meeting to advance an important project. In this case, the system would detect participants who are feeling particularly stressed and prompt them to take steps to alleviate that stress. This would improve the overall atmosphere of the meeting and allow for a more cohesive process.

[0709] This system enables more effective decision-making and team building compared to conventional, mechanical meeting management, by simultaneously utilizing participants' emotions and intellectual information.

[0710] The following describes the processing flow.

[0711] Step 1:

[0712] The user logs into the system as the meeting organizer and enters the meeting's purpose, goals, participant list, and time allocation. The server receives this information, generates a meeting template, and saves it to the database.

[0713] Step 2:

[0714] As the scheduled meeting time approaches, the server sends a reminder to all participants' devices. This reminder includes meeting details and a request to review any necessary pre-meeting materials.

[0715] Step 3:

[0716] Once the meeting begins, the terminal connects participants to the meeting, and the server converts audio data into text in real time and analyzes what is being said. At this time, an emotion engine operates, recognizing the participants' emotions from the camera and audio, and sending that data to the server.

[0717] Step 4:

[0718] The server monitors the progress of the meeting based on real-time analysis of spoken content and emotional data. If a participant's emotional state indicates a decrease in concentration, it sends a notification to their device prompting them to refresh. It also monitors the time allocation for each agenda item and sends notifications for course correction as needed.

[0719] Step 5:

[0720] During the meeting, the server analyzes emotional data such as anxiety and tension experienced by participants and adjusts the meeting's pace accordingly. For example, it might display suggestions on the participants' devices to adjust the tempo, encouraging better engagement.

[0721] Step 6:

[0722] At the end of the meeting, the server automatically generates meeting minutes based on the statements and sentiment data. This includes a summary of the statements as well as an analysis of changes in sentiment.

[0723] Step 7:

[0724] The server analyzes the tasks decided during the meeting and assigns them to participants. Progress is tracked in real time by the server, and reminders are sent to devices as needed. The emotion engine also provides notifications, especially to prevent a decline in motivation.

[0725] (Example 2)

[0726] 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".

[0727] In today's business environment, there is a demand for both increased meeting efficiency and participant satisfaction. However, conventional meeting facilitation systems often focus only on time management and agenda progression, without considering the emotional state of participants. As a result, maintaining participant focus and conducting smooth meetings has been a challenge.

[0728] 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.

[0729] In this invention, the server includes means for providing an information input device for inputting the purpose of the meeting and participant information, means including a device for transcribing speech in real time and analyzing emotions, and means for monitoring the time allocation of agenda items and generating notifications when time is exceeded. This makes it possible to facilitate efficient agenda progression and decision-making while taking into account the emotional state of participants during the meeting.

[0730] An "information input device" is a device that provides an interface for users to input the purpose, goals, participant list, and time allocation of a meeting.

[0731] "Speech recognition technology" is a technology that converts participants' speech into text data in real time.

[0732] An "emotion analysis device" is a device that analyzes and detects the emotional state of meeting participants from the content of their speech, which has been transcribed into text using speech recognition.

[0733] A "time allocation monitoring system" is a system that tracks the time allocation for each agenda item during a meeting and generates a notification if the scheduled time is exceeded.

[0734] A "meeting minutes automatic generation device" is a device that automatically creates meeting minutes at the end of a meeting, highlighting important points based on the content of the discussion and sentiment data.

[0735] A "task assignment device" is a device that analyzes tasks decided during a meeting, assigns them to each participant, and notifies them accordingly.

[0736] A "progress tracking system" is a system that tracks the progress of tasks decided in a meeting and sends reminders when the task deadline is approaching.

[0737] A "feedback notification system" is a system that generates suggestions for improving the meeting's progress based on participants' emotional data and notifies participants of these suggestions on their devices.

[0738] The present invention provides a system for efficient meeting management, particularly one that takes into account the emotional state of participants. This system includes an information input device, speech recognition technology, an emotion analysis device, time allocation monitoring means, an automatic meeting minutes generation device, a task assignment device, a progress tracking means, and a feedback notification means.

[0739] The user inputs the meeting's purpose, goals, participant list, and time allocation into an information input device. This device can be configured using, for example, a standard computer or smart device. The entered information is sent to a server and set as a meeting template.

[0740] During the meeting, the server uses speech recognition technology to transcribe speech in real time. For example, cloud-based speech recognition software is used. The audio data is then analyzed by an emotion analysis device to identify the emotions of each participant.

[0741] The time allocation monitoring system monitors the time elapsed for each agenda item and generates a notification if the scheduled time is exceeded. This notification is sent to the terminal, allowing the user to take appropriate action.

[0742] After the meeting ends, the server uses an automated minutes generation system to create minutes based on the content of the discussion and sentiment data. These minutes are then sent to the terminals with important points highlighted.

[0743] Furthermore, the task assignment device analyzes the action items decided during the meeting and assigns them appropriately to each participant. This allows participants to clearly understand their individual responsibilities through their devices.

[0744] Progress tracking measures track the progress of tasks and send reminders to participants as deadlines approach. This reinforces activity toward completing each task.

[0745] As an example, consider a scenario in a large-scale project meeting where the system detects participants who are experiencing particular stress and notifies them of measures to alleviate their condition. This method can facilitate the smooth running of the meeting and contribute to the success of the project.

[0746] An example of a prompt sentence generated using an AI model is, "What relaxation methods should be suggested to participants who are feeling stressed at the next project meeting?"

[0747] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0748] Step 1:

[0749] The user inputs the meeting's purpose, goals, participant list, and time allocation into an information input device. This input is sent to the server, which uses it to construct the meeting settings. As part of the data processing, the server formats each piece of information into a template and saves it as a schedule. As output, meeting template data is generated.

[0750] Step 2:

[0751] When the meeting begins, the server activates speech recognition software and converts participants' speech into text data in real time. The input is audio data, and the output is the transcribed speech. The server prepares this text data for analysis.

[0752] Step 3:

[0753] The server processes the generated text data into an emotion analysis device. This device uses natural language processing techniques to analyze the emotional aspects of each statement (e.g., anxiety or joy). The input is text data, and the output is participant emotion data. At this stage, emotion-based tagging is performed.

[0754] Step 4:

[0755] The server tracks the progress of each agenda item using a time allocation monitoring system. The input is real-time timestamp data, and the output is monitoring data for elapsed time. If any agenda item exceeds its scheduled time, the server generates a notification message.

[0756] Step 5:

[0757] After the meeting ends, the server uses an automated minutes generation system to create meeting minutes based on the recorded text and sentiment data. The input consists of spoken words and sentiment data, and the output is a meeting minute with key points highlighted. The server then sends this meeting minute to the terminal, making it accessible to participants.

[0758] Step 6:

[0759] The server operates a task assignment device, analyzing the action items decided during the meeting. The input is the meeting minutes, and the output generates specific tasks and the assigned person responsible for each task. Task information is then sent to each participant's terminal.

[0760] Step 7:

[0761] Using a progress tracking system, the server periodically updates and monitors the progress of each task. Input is progress data reported by participants, and output generates task completion status and reminders for tasks approaching their deadlines. Reminders are delivered to terminals and notified to participants.

[0762] (Application Example 2)

[0763] 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".

[0764] Modern meetings suffer from a lack of efficient progress management and disregard for participants' emotional states. This leads to challenges such as lapses in focus, derailment, and difficulty in making effective decisions. Furthermore, insufficient follow-up on decisions and tasks prevents meetings from maximizing their effectiveness.

[0765] 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.

[0766] In this invention, the server includes means for providing an input device for inputting meeting objectives, achievement criteria, participant information, and schedule; means for analyzing participant statements during the meeting, monitoring the schedule for each agenda item, and generating notifications if an agenda item exceeds the scheduled time; and means for detecting participants' emotional states in real time, adjusting the meeting's progress based on emotional data, and proposing countermeasures. This enables meeting management that takes participants' emotions into consideration, resulting in efficient and engaging meeting operations.

[0767] An "input device" is an interface for entering meeting objectives, achievement criteria, participant information, and schedules into the system.

[0768] "Statement information" refers to data used to transcribe and analyze the content of participants' statements during a meeting.

[0769] A "timetable" refers to the scheduled time allocated to each agenda item within a meeting, and is used to manage the progress of the meeting.

[0770] A "notification" is a message used in meeting management to inform participants when the scheduled time is exceeded or the agenda is deviated from.

[0771] "Emotional state" refers to information that detects changes in participants' emotions in real time and serves as an important indicator for the progress of the meeting.

[0772] "Emotional data" refers to digital data about the emotional state of participants, collected by the emotion engine.

[0773] "Measures" refer to analyzing the emotional state of participants during a meeting and proposing adjustments to the meeting's progress or suggesting breaks as needed.

[0774] This system is initiated by an interface that includes input devices for the user to enter meeting objectives, success criteria, participant information, and schedule. Once the user enters this information into the system, the server configures the meeting and generates a meeting flow template.

[0775] Once the meeting begins, the server utilizes voice input technology to transcribe participants' statements in real time and analyzes the data. This analysis includes a process that uses an emotion engine to detect participants' emotional states. Based on this emotion data, the server adjusts the flow of the meeting and sends notifications to participants' devices as needed. These notifications include managing the time allocation for agenda items, preventing digressions, and even suggesting breaks for participants.

[0776] As a concrete example, let's consider a citizens' meeting regarding local traffic issues. In this meeting, the server analyzes participants' statements, and if it determines that a participant's emotional state is one of tension, it notifies the participant's information terminal with relaxing videos or instructions for deep breathing to promote relaxation. In this way, the efficiency of the meeting can be maintained while increasing participants' concentration and motivation.

[0777] A typical example of a prompt would be asking a generating AI model a question like, "If participants are feeling stressed during a meeting, what should we do to offer them something to help them relax?" This would primarily lead to obtaining appropriate solutions.

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

[0779] Step 1:

[0780] The user enters the meeting's objectives, achievement criteria, participant information, and schedule. This data is received by the server and used as the basis for generating a meeting template. The server then structures and stores this data, making it available for use in the next step.

[0781] Step 2:

[0782] Once the meeting begins, the server records participants' speech and converts it to text using speech recognition technology. It receives the audio data as input and processes it into text data. This text data is used for analyzing the content of the speech and for processing with the sentiment engine.

[0783] Step 3:

[0784] The server uses an emotion engine to analyze participants' emotional states from text data. Specifically, it uses an emotion analysis algorithm to identify emotions in the text and outputs them as emotion data. This allows for an understanding of the meeting's atmosphere and the state of each participant.

[0785] Step 4:

[0786] The server adjusts the flow and time allocation of the meeting based on emotional data. It analyzes the input emotional data and, if it detects agenda items exceeding the scheduled time or a decline in participants' concentration, sends a notification to the terminal to prompt course correction. The output includes specific instructions and suggestions sent to the terminal.

[0787] Step 5:

[0788] If a meeting is ongoing and changes in participants' emotional states are observed, the server sends suggestions to terminals to encourage rest and refreshment. Taking emotional stress and tension data as input, the server generates suggestions to promote relaxation as output. Specific examples of such suggestions include links to relaxation videos and breathing exercises.

[0789] Step 6:

[0790] After the meeting concludes, the server automatically generates a meeting record, highlighting particularly important decisions. This process integrates the input speech information and sentiment data to produce a formatted meeting transcript. This output is distributed to participants after the meeting.

[0791] Step 7:

[0792] The server analyzes the next steps and tasks and assigns them to each participant. Output is generated based on the input task information and the participant's role, and reminders are sent to the terminal as needed to track progress. In this way, post-meeting follow-up is also ensured.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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."

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] The following is further disclosed regarding the embodiments described above.

[0815] (Claim 1)

[0816] A means of providing a user interface for entering the purpose, goals, participant list, and time allocation of a meeting,

[0817] A means to analyze the content of participants' remarks during a meeting, monitor the time allocation for each agenda item, and generate a notification if an agenda item exceeds the scheduled time.

[0818] A method to automatically generate meeting minutes based on the content of the discussions at the end of the meeting and to highlight the decisions made,

[0819] A means of analyzing tasks decided during a meeting, assigning tasks to participants, and sending the details as documentation,

[0820] A means to track progress and send reminders as deadlines approach,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, comprising means for detecting a deviation from the agenda during a meeting and sending a notification to a terminal prompting a change in the direction of the agenda.

[0824] (Claim 3)

[0825] The system according to claim 1, comprising a reminder function that sends meeting reminders to participants' devices and prompts them to review pre-meeting materials.

[0826] "Example 1"

[0827] (Claim 1)

[0828] A means of exchanging information between humans and machines, inputting the purpose, goals, participant information, and time allocation of a meeting,

[0829] A means of analyzing participants' statements during a meeting, monitoring the time allocation for each agenda item, and generating warnings if an agenda item exceeds its scheduled time.

[0830] A means to automatically generate a report based on the information gathered at the end of a meeting to clarify the decisions made,

[0831] A means of analyzing the tasks decided during the meeting, assigning those tasks to participants, and transmitting the details as information,

[0832] A means to track progress and send notifications as deadlines approach,

[0833] A method for processing natural language using generative models,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, comprising means for detecting deviations from the agenda during a meeting and transmitting information to the device prompting a course correction of the agenda.

[0837] (Claim 3)

[0838] The system according to claim 1, further comprising a function to send meeting notifications to participants' devices and prompt them to confirm pre-meeting information.

[0839] "Application Example 1"

[0840] (Claim 1)

[0841] A means of providing a human-machine interface for inputting the purpose, objectives, participant list, and time management of a meeting,

[0842] A means to analyze participants' comments during a meeting, monitor time management for each agenda item, and generate notifications if an agenda item exceeds its scheduled time.

[0843] A method to automatically generate a record of the meeting minutes based on what was said at the end of the meeting and to highlight the decisions made,

[0844] A means of analyzing the tasks decided during the meeting, assigning those tasks to participants, and sending the details as documentation,

[0845] A means to monitor progress and send reminders as deadlines approach,

[0846] A means of monitoring the progress of a meeting and sending a notification to a terminal if a digression is detected,

[0847] A means comprising a program installed on an information processing device for running a meeting management application for stores,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, further comprising a function to send meeting reminders to participants' devices and prompt them to review materials in advance.

[0851] (Claim 3)

[0852] The system according to claim 1, which includes a function for efficiently managing meetings within a store, distributing tasks to participants after the meeting, and tracking their progress.

[0853] "Example 2 of combining an emotion engine"

[0854] (Claim 1)

[0855] A means for providing an information input device for entering the purpose, goals, participant list, and time allocation of a meeting,

[0856] A means including a device that transcribes spoken content into text in real time using speech recognition technology and analyzes the emotions of participants,

[0857] A means to monitor the time allocation for each agenda item and generate a notification if an agenda item exceeds its scheduled time,

[0858] A method to automatically generate meeting minutes at the end of the meeting based on the content of the discussion and sentiment data, and to highlight important points,

[0859] A means for analyzing the determined tasks, assigning tasks to participants, and transmitting the details to an information terminal,

[0860] A means to track progress and send notifications as deadlines approach,

[0861] A means of generating and notifying feedback to adjust meeting progress based on participant sentiment data,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, comprising means for detecting deviations from the agenda and transmitting suggestions for improving the meeting proceedings to an information terminal.

[0865] (Claim 3)

[0866] The system according to claim 1, which has a function to send meeting reminders to participants and prompt them to review necessary materials in advance.

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

[0868] (Claim 1)

[0869] A means for providing an input device for entering meeting objectives, achievement criteria, participant information, and schedule,

[0870] A means to analyze participants' statements during a meeting, monitor the time schedule for each agenda item, and generate a notification if an agenda item exceeds the scheduled time.

[0871] A method for automatically generating documents based on the information shared at the end of a meeting to highlight the decisions made,

[0872] A means of analyzing the tasks decided during the meeting, assigning those tasks to participants, and transmitting the details as information,

[0873] A means to track progress and send notifications as the deadline approaches,

[0874] A means to detect participants' emotional states in real time, adjust the meeting's progress based on emotional data, and propose countermeasures.

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, comprising means for detecting deviations from the agenda during a meeting, sending notifications to information terminals prompting a change in the direction of the agenda, and suggesting breaks in response to changes in the emotional state of participants.

[0878] (Claim 3)

[0879] The system according to claim 1, which includes a notification function that sends meeting notifications to participants' information terminals and prompts them to check pre-meeting information, and which suggests appropriate rest plans when participants' concentration decreases. [Explanation of Symbols]

[0880] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of providing a human-machine interface for inputting the purpose, objectives, participant list, and time management of a meeting, A means to analyze participants' comments during a meeting, monitor time management for each agenda item, and generate notifications if an agenda item exceeds its scheduled time. A method to automatically generate a record of the meeting minutes based on what was said at the end of the meeting and to highlight the decisions made, A means of analyzing the tasks decided during the meeting, assigning those tasks to participants, and sending the details as documentation, A means to monitor progress and send reminders as deadlines approach, A means of monitoring the progress of a meeting and sending a notification to a terminal if a digression is detected, A means comprising a program installed on an information processing device for running a meeting management application for stores, A system that includes this.

2. The system according to claim 1, further comprising a function to send meeting reminders to participants' devices and prompt them to review materials in advance.

3. The system according to claim 1, which includes a function for efficiently managing meetings within a store, distributing tasks to participants after the meeting, and tracking their progress.

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