system

Abstract

Provide a system. 【Solution means】 Means for obtaining the user's schedule information and determining the optimal meeting time, Means for checking the reservation information of the meeting room and automatically making a reservation, Means for notifying the obtained meeting information, Means for recording the meeting content using voice recognition and automatically generating minutes, Means for analyzing the speech during the meeting and giving a warning when deviating from the topic, Means for monitoring the progress time of the meeting and notifying the end, Means for collecting and analyzing the feedback information after the meeting, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern business environment, the time spent in meetings is increasing, and the tasks related to the preparation, operation, and recording of meetings are becoming a burden. Also, the schedule adjustment among participants and the progress management of meetings are inefficient, and the meeting content often deviates from the agenda. Furthermore, it takes a lot of time to create meeting minutes and collect feedback after meetings, which reduces the efficiency of business. This invention aims to solve these problems using automation technology and improve the efficiency of meetings.

Means for Solving the Problems

[0005] This invention provides a means for acquiring user schedule information and determining the optimal meeting time for all participants. It also proposes a system equipped with a function to automatically reserve an available meeting room and notify users of meeting information. Furthermore, it incorporates a means for recording meeting content using speech recognition technology and automatically generating meeting minutes. During the meeting, it analyzes speech in real time and issues warnings if the discussion deviates from the agenda, enabling efficient progress management. It monitors the meeting time during the meeting, notifies the participant of its end at the appropriate time, and collects feedback after the meeting to support suggestions for improvement in the next meeting. Through this series of means, various tasks related to meetings can be streamlined, leading to improved overall business productivity.

[0006] A "user" refers to an individual or organization that operates the system to set up and manage meetings, primarily by entering schedules and submitting feedback.

[0007] "Schedule information" refers to information about other activities and appointments that users and participants already have, and is data collected to determine an appropriate date and time for the meeting.

[0008] The term "meeting room" refers to the physical or virtual space necessary for conducting a meeting, and is a concept that includes reservable facilities and online meeting platforms.

[0009] "Speech recognition" is a technology that analyzes and transcribes the speech of participants during a meeting in real time, and is the foundational process for the automatic generation of meeting minutes.

[0010] "Meeting minutes" refers to a document that summarizes the statements and decisions made during a meeting, and it serves to provide a record of the meeting's results by being distributed to participants.

[0011] A "generative AI engine" refers to an artificial intelligence-based function that summarizes and automatically generates meeting minutes based on speech recognition results, and is a technology that contributes to the efficiency of meetings.

[0012] "Feedback" refers to the opinions and evaluations gathered from participants after a meeting, and is collected as information to improve future meeting management.

[0013] A "warning" is a notification issued when a participant deviates from the agenda during a meeting, serving as a reminder to help them focus on the topic.

[0014] "Progress management" refers to the process of adjusting the time and content of a meeting to ensure it proceeds as scheduled, and is a management technique that supports efficient meeting operation. [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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.

Modes 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 a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

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

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

[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] This invention is a system for efficiently managing meetings, and mainly consists of a server, terminals, a generative AI engine, and speech recognition technology. By automating each step from the preparation stage to the progress and post-meeting processing of a meeting, this system provides an environment in which participants can concentrate on the meeting content.

[0037] First, the user uses their device to request a meeting. This request includes specifying the participants, preferred date and time, and the purpose of the meeting. Based on the information received from the user, the server retrieves each participant's schedule data from the database and identifies the availability of all participants.

[0038] Once an available time slot is identified, the server checks the availability of meeting rooms and automatically reserves a physical or online meeting room as needed. The server notifies all participants of the reservation result, and the meeting details are displayed on their devices.

[0039] On the day of the meeting, the device will support the meeting's progress. Once the meeting begins, the device will activate its speech recognition engine and transcribe participants' statements in real time. A generative AI engine will analyze these texts, and AI voice warnings will be issued for any deviant statements. This ensures that the meeting stays on schedule.

[0040] Furthermore, the server monitors the meeting time and notifies all participants when the end time is approaching. After the meeting ends, meeting minutes are automatically generated on the server using a speech recognition engine and a generative AI engine and distributed to participants. The minutes clearly state the key points of the meeting and the next steps to take.

[0041] Finally, there's a step where users input their evaluation of the meeting and suggestions for improvement through a feedback form. The server collects this feedback and makes suggestions for improvement to be used in the next meeting. In this way, the entire system works together to improve the quality and efficiency of meetings.

[0042] As a concrete example, consider a project meeting at a certain company. The project manager logs into a terminal and coordinates the meeting using the availability of all team members. The user clearly states the purpose of the meeting, and the system automatically reserves a meeting room. During the meeting, AI manages the progress hour by hour and notifies participants of important tasks in real time. This process significantly streamlines the coordination work that was previously done manually.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user enters a meeting request using their device. They specify the list of participants, preferred date and time, and the purpose of the meeting.

[0046] Step 2:

[0047] Based on the meeting request received by the user, the server retrieves participant schedule information from the database. It then identifies the availability of all participants.

[0048] Step 3:

[0049] The server checks the availability of meeting rooms and automatically reserves them during identified available times. Once a meeting room is secured, a reservation confirmation notification is generated.

[0050] Step 4:

[0051] The server notifies each participant via email or scheduling app of the confirmed meeting date and time, location, participant list, and meeting purpose.

[0052] Step 5:

[0053] On the day of the meeting, the device activates its speech recognition engine at the designated time. During the meeting, the device collects participants' speech in real time and converts the audio data into text.

[0054] Step 6:

[0055] The AI ​​generation engine analyzes the transcribed speech. If the conversation deviates from the topic, the device uses AI voice to prompt the user to return to the topic.

[0056] Step 7:

[0057] The server monitors the meeting time and notifies participants via their devices five minutes before the end of the meeting, stating, "There are 5 minutes left until the end of the meeting."

[0058] Step 8:

[0059] After the meeting ends, the server automatically creates meeting minutes using speech recognition data and a generative AI engine.

[0060] Step 9:

[0061] The server will email the meeting minutes to all participants. The minutes will include the key points of the meeting and action items.

[0062] Step 10:

[0063] Users access a feedback form through their device to input their evaluation of the meeting and suggestions for improvement.

[0064] Step 11:

[0065] The server collects and analyzes feedback information and stores the data to suggest improvements for the next meeting.

[0066] (Example 1)

[0067] 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."

[0068] In modern meeting management, tasks such as scheduling, booking meeting rooms, and recording meeting content are complex and time-consuming. In particular, issues such as discussions straying from the agenda and unclear meeting end times are problematic. Furthermore, there is a lack of mechanisms for effectively utilizing post-meeting feedback. These challenges highlight the need for a comprehensive system to improve meeting efficiency and productivity.

[0069] 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.

[0070] In this invention, the server includes means for a computing device to determine the optimal meeting time from the user's schedule, means for a computing device to check the reservation status of the meeting space and automate the reservation, and means for providing meeting information using notifications. This enables efficient scheduling and reservation of meetings, as well as the provision of detailed information to participants.

[0071] A "computing device" is an electronic device used for handling and calculating digital data, and can also be used for scheduling and coordinating meetings.

[0072] A "meeting space" refers to a specific area or platform used for conducting meetings, either physically or online.

[0073] A "notification" is a means of transmitting information, used to inform recipients of important information or events.

[0074] "Speech processing technology" is a general term for technologies that acquire, analyze, and convert speech data, and is used when converting speech to text.

[0075] A "document" is an electronic or physical document that records information and is intended to organize the content of a meeting and provide it to participants.

[0076] "Evaluation information" refers to feedback and reviews provided by users, which can be used to improve products and services.

[0077] This invention is a system for efficiently and effectively managing meetings. The system comprises a server, terminals, voice processing technology, and a generative AI model. The system's objective is to provide an environment where meeting participants can focus on key agenda items by automating scheduling, meeting room reservations, meeting content recording, and minute-taking.

[0078] Users use their devices to request meetings. These requests include participants, preferred dates and times, and the purpose of the meeting. The devices run a dedicated application that transmits this information to the server. For example, entering a prompt such as "Please add the next project meeting's availability to my schedule" into the device initiates meeting preparation.

[0079] Based on the received information, the server retrieves participants' schedule information from the database and identifies everyone's availability. The server also accesses the online booking system to check meeting room availability and automatically completes the necessary booking procedures. Through this process, the optimal conditions for holding a meeting can be ensured.

[0080] On the day of the meeting, the device utilizes speech processing technology to convert meeting comments into text in real time. This text data is analyzed by a generative AI model, and if any comments deviate from the agenda, an automatic warning is issued. In this way, the meeting itself is conducted efficiently and effectively.

[0081] After the meeting concludes, meeting minutes are automatically generated by the server and distributed to participants. The minutes summarize key points of the meeting and future action points based on text data obtained using speech processing technology.

[0082] After a meeting, users input feedback using their devices, which is then collected and analyzed by the server. The analyzed information is used to suggest improvements for the next meeting, thereby improving the overall quality of meetings within the system.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user enters a meeting request using their device. This input includes a participant list, preferred date and time, and the purpose of the meeting. The information entered by the user is output and sent to the server. Specifically, the user enters information into the input form on their device and presses the "Submit" button.

[0086] Step 2:

[0087] The server processes the received request data and retrieves participant schedule information from the database. The server then analyzes this schedule data to identify everyone's free time. The input is the request data from the user, and the output is the common free time for all participants. Specifically, the server uses SQL queries to access the database and check each participant's schedule.

[0088] Step 3:

[0089] The server accesses the meeting space reservation system based on availability information and reserves a suitable meeting room. The server checks the availability of meeting rooms via API and, if the reservation is successful, obtains reservation confirmation information. The input is the common availability time, and the output is reservation confirmation information. Specifically, the server sends an API request and receives a reservation response.

[0090] Step 4:

[0091] The server sends a notification to all participants regarding the meeting details. The server uses a notification system to provide all participants with the meeting date, time, and location, and this information is displayed on their devices. The input is reservation confirmation information, and the output is the notification to participants. Specifically, the server distributes the information via email or push notifications.

[0092] Step 5:

[0093] On the day of the meeting, the terminal uses speech processing technology to transcribe participants' speech in real time. The terminal analyzes the collected audio data with a speech recognition engine and outputs it as text data. The input is audio data, and the output is the transcribed speech. Specifically, the terminal captures audio with a microphone and performs text conversion using software.

[0094] Step 6:

[0095] The generating AI model analyzes the obtained text data and issues a warning if any statements deviate from the topic. The model compares the text to the topic content, performs data calculations to detect deviations, and outputs a warning command as needed. The input is text data, and the output is warning information. Specifically, it analyzes the text content as appropriate and displays a warning via voice or message from the terminal.

[0096] Step 7:

[0097] The server automatically generates meeting minutes after the meeting ends and distributes them to all participants. The server processes the text obtained using speech processing technology to create a summary. The input is the text data of the entire meeting, and the output is the completed meeting minutes. Specifically, the server sends the meeting minutes via email or uploads them to a dedicated portal.

[0098] Step 8:

[0099] Users enter feedback from their terminal after a meeting. The feedback is sent to the server and used for analysis to improve future meetings. The input is data from an evaluation form, and the output is the analysis results on the server. Specifically, the user answers questions in a survey format and presses a submit button.

[0100] (Application Example 1)

[0101] 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."

[0102] In smart cities, efficient and smooth operation of participatory meetings and public forums for citizens is hampered by the complexities of coordinating schedules among participants, booking venues, preventing deviations from the ongoing agenda, recording meeting content, and sharing information with participants. Therefore, a system is needed that enhances transparency and efficiency, ensuring that all participants receive information smoothly.

[0103] 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.

[0104] In this invention, the server includes means for acquiring user schedule information and determining the optimal activity time, means for checking space reservation information and automatically making reservations, and means for automatically distributing generated record documents to multiple participants. This makes it possible for citizens to efficiently prepare for participatory meetings and forums, carry out activities in line with the agenda, and easily share record documents.

[0105] "User schedule information" refers to data about each participant's individual schedule and available time.

[0106] The "optimal activity time" is the time period that allows all participants to participate without difficulty and to carry out activities efficiently.

[0107] "Space reservation information" refers to data regarding the reservation status and available times for a location where an activity will take place.

[0108] "Speech recognition" is a technology that converts speech into text, accurately recording what is said as written characters.

[0109] A "record document" is a document automatically generated based on what was said during an activity, and serves as an official record for later reference.

[0110] "Analyzing a statement" is the process of analyzing the content of a statement and identifying any deviations from its purpose or topic.

[0111] "Notifying participants of the end of the activity" is the process of informing participants that the allotted activity time is nearing its end.

[0112] "Opinion information" refers to data on evaluations, feedback, and suggestions for improvement provided by participants after the completion of the activity.

[0113] "Automated distribution" is the process of sending generated record documents to participants without human intervention.

[0114] "Presenting areas for improvement" means making suggestions to improve future activities based on the collected feedback.

[0115] The system to realize this application primarily consists of a server, a terminal, a speech recognition engine, and a generative AI engine. The server first acquires the user's schedule information and automatically determines the optimal activity time. Based on this, it checks the space reservation information and automatically reserves the space to ensure the activity proceeds smoothly.

[0116] Once the activity begins, the device activates its speech recognition engine and transcribes participants' statements into text in real time. This text data is then organized by a generative AI engine and automatically generated as a record document. Simultaneously, if a participant's statement deviates from the agenda, the generative AI issues a warning to alert them.

[0117] As the activity's end time approaches, the server notifies participants of its completion and provides support for extending or ending the activity as needed. After the activity, the server collects feedback and suggests improvements for the next activity. This ensures that the entire activity process is managed efficiently.

[0118] As a concrete example, consider a local community meeting. Residents register to participate in the meeting via their smartphones, a server determines the optimal date and time, and automatically reserves a room in the community hall. During the meeting, spoken content is transcribed into text via smart glasses, and after the meeting, a generating AI summarizes the content and distributes a transcript to participants. This process ensures that community meetings proceed quickly and systematically.

[0119] Examples of prompts for a generative AI model:

[0120] "Please schedule the next citizens' meeting and reserve the venue based on the participants' schedules."

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] Users use their devices to enter their interest in participating in an activity. The entered data includes the participant's name, the desired activity, and their available time slots. The device then sends this data to the server.

[0124] Step 2:

[0125] The server analyzes the received participation request data and aggregates each participant's schedule information. Based on this information, the server performs data processing to calculate the optimal activity time. Once the optimal time is determined, the information is passed on to the next step.

[0126] Step 3:

[0127] The server checks the availability of the space. It searches the database for a space available during the optimal activity time and automatically makes a reservation. The reservation result is notified to the user by the server.

[0128] Step 4:

[0129] At the start of the activity, the device activates its speech recognition engine and converts participants' speech to text in real time. The speech input is converted to text output and sent to the generation AI engine.

[0130] Step 5:

[0131] The generation AI engine analyzes the received text data and automatically generates a record document. In this process, it identifies statements that deviate from the agenda and generates warnings as needed. The generated warnings are displayed on the terminal to alert participants.

[0132] Step 6:

[0133] As the activity's end time approaches, the server will notify the user of its completion. This notification is sent to the user to help them decide whether to end or extend the activity. This ensures that the activity is managed to stay within the allotted time.

[0134] Step 7:

[0135] After the activity concludes, the server collects feedback and analyzes it using a generative AI engine. Based on the analysis, suggestions for improving the next activity are generated. These suggestions are then provided to the user, who can use them to prepare for the next activity.

[0136] 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.

[0137] This invention is a meeting automation system incorporating an emotion engine that recognizes emotions in real time from participants' statements and facial expressions, aiming to improve the efficiency and quality of meeting management. The system consists of a server, terminals, a generative AI engine, speech recognition technology, and an emotion engine.

[0138] First, the user requests a meeting using their device. At this stage, the list of participants and the purpose of the meeting are specified. Based on the information received from the user, the server retrieves the participants' schedules from the database and analyzes their availability.

[0139] The server reserves a meeting room according to the identified available time slot. Once this reservation process is complete, the server sends a meeting notification to all participants. The notification will be sent via email or a scheduling app and will include the meeting details.

[0140] On the day of the meeting, the terminal activates its speech recognition engine and emotion engine in conjunction. Participants' remarks are collected in real time and converted into audio data. This audio data is analyzed by a generating AI engine to support the efficient progress of the meeting.

[0141] In addition, the emotion engine analyzes participants' emotions based on their statements and facial expressions. If emotions indicating discord or stress are detected, the terminal will take appropriate action via the server, such as "Please wait a moment while we check the situation." This feature helps ensure the smooth running of meetings.

[0142] As the meeting nears its end, the server issues an end-of-meeting notification, and the meeting content is recorded by a speech recognition engine. After the meeting, a generative AI engine automatically generates meeting minutes, which are then distributed to participants. Furthermore, emotional data collected by an emotion engine is added to the feedback, indicating areas for improvement for the next meeting.

[0143] As a concrete example, consider a team meeting in a remote environment. The team leader coordinates the members' availability and sets up an online meeting. During the meeting, the emotion engine analyzes the participants' facial expressions, and if a member shows dissatisfaction with a particular topic, it immediately provides support to alleviate the situation. This makes the meeting more effective and harmonious.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The user uses their device to request a meeting. Here, they enter a list of participants, preferred date and time, and the purpose of the meeting.

[0147] Step 2:

[0148] The server receives a request from the user and retrieves the schedule information of all participants from the database. It identifies available time slots and determines the optimal meeting time.

[0149] Step 3:

[0150] The server automatically reserves a meeting room based on the identified meeting time. This process takes into account the availability of both physical meeting rooms and online meeting platforms.

[0151] Step 4:

[0152] The server will notify all participants of the meeting details. The notification will be sent via email or a scheduling app and will include the date, time, location, and agenda.

[0153] Step 5:

[0154] At the start of the meeting, the device activates its speech recognition engine and emotion engine. The speech recognition engine collects participants' speech in real time and converts it into text.

[0155] Step 6:

[0156] The emotion engine analyzes speech and facial expression data to monitor the emotional state of participants. If a specific emotion is identified, it suggests appropriate countermeasures via the server.

[0157] Step 7:

[0158] During the meeting, a generative AI engine analyzes the speech-to-text format to help the discussion stay on track with the agenda. If the discussion deviates from the topic, the device will issue a warning.

[0159] Step 8:

[0160] As the meeting nears its end, the server displays a notification on the terminal stating, "There are 5 minutes left until the end."

[0161] Step 9:

[0162] After the meeting ends, the server automatically generates meeting minutes based on data collected through speech recognition and sentiment analysis.

[0163] Step 10:

[0164] The terminal will email the meeting minutes created to the participants, ensuring information is shared. Furthermore, a feedback report based on sentiment analysis results will also be provided.

[0165] Step 11:

[0166] Users use a feedback form to input their evaluation of the meeting and suggestions for improvement. The server collects and analyzes this information and saves it as data to suggest improvements for the next meeting.

[0167] (Example 2)

[0168] 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".

[0169] Traditional meeting systems often resulted in decreased meeting efficiency and hindered smooth communication among participants due to issues such as scheduling, meeting room reservations, agenda management during meetings, and a lack of emotionally conscious intervention. Furthermore, the lack of meeting minutes and emotionally-based feedback after meetings made it difficult to obtain information that could improve future meetings.

[0170] 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.

[0171] In this invention, the server includes means for acquiring user schedule information and determining the optimal meeting time, means for recording meeting content using speech recognition and automatically generating documents, and means for analyzing speech during the meeting, performing emotion analysis based on facial expressions, and intervening as necessary. This enables efficient and smooth meeting management, from scheduling participants' schedules to managing the meeting and providing feedback afterward.

[0172] A "user" is an individual or organization that uses this system to request a meeting and enters participant information and the purpose of the meeting.

[0173] "Schedule information" refers to data about participants' availability and schedules, and is used to determine the optimal meeting time.

[0174] A "meeting space" is a physical or virtual location reserved for holding a meeting, and is the entity whose reservation information is managed.

[0175] A "notification" is a message that provides participants with detailed information about a meeting, and is sent via email or scheduling apps.

[0176] "Speech recognition" is a technology that converts speech during a meeting into text and records the content.

[0177] "Documents" refer to meeting minutes and records that are automatically generated based on text data obtained through speech recognition.

[0178] "Speech analysis" is a technique for analyzing the content of conversations during meetings, and is used for agenda management and sentiment analysis.

[0179] "Emotional analysis" is the process of determining the emotional state of participants from their statements and facial expressions, and adjusting the flow of the meeting as needed.

[0180] "Intervention" refers to actions taken by the system during a meeting, based on the results of sentiment analysis, to facilitate the smooth running of the meeting.

[0181] "Feedback" refers to the information and suggestions provided after a meeting, including analytical results that can help improve future meetings.

[0182] This invention presents a meeting management system that combines sentiment analysis to improve the efficiency and quality of meeting operations. The system is comprised of users, terminals, a server, a generative AI engine, speech recognition technology, and a sentiment engine.

[0183] First, the user enters a meeting request using their device. A dedicated application is installed on the device, and the user uses this application to specify the list of participants and the purpose of the meeting. The entered information is then sent to the server.

[0184] The server retrieves participant schedule information from the database based on the information received. Analyzing this information, the server calculates the most optimal meeting time. Furthermore, the server automatically accesses the meeting space reservation system and reserves a meeting room. Once the reservation is complete, the server sends a meeting notification to all participants. This notification is delivered via email or scheduling apps and includes the meeting date and details.

[0185] On the day of the meeting, the terminal activates its speech recognition engine and emotion engine in conjunction. The speech recognition engine converts participants' speech into audio data in real time and sends it to the generative AI engine. The generative AI engine analyzes the meeting content and automatically generates documents, supporting efficient meeting progress. The emotion engine analyzes participants' speech and facial expressions and provides appropriate interventions according to the flow of the meeting.

[0186] As a concrete example, if there is a progress meeting for a project, the user sends a notification from their device to the participants, and everyone gathers online at the time specified in their schedule. During the meeting, the speech recognition engine records all statements as text, and the emotion engine monitors the participants' reactions. For example, if one participant expresses dissatisfaction with a certain agenda item, that emotion is immediately detected, and the device intervenes via the server to mitigate the situation.

[0187] As the meeting nears its end, the server notifies participants of the conclusion, and a generative AI engine automatically creates meeting minutes based on the content recorded by the speech recognition engine and distributes them to participants. In addition, sentiment data is analyzed and provided as feedback to indicate areas for improvement for the next meeting.

[0188] Example of a prompt message: "Please schedule the next project progress meeting. All team members will be attending, and the purpose is to review the development status."

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] The user requests a meeting using their device. Through a dedicated application on the device, the user enters the participant list and the purpose of the meeting. This input data is sent to the server as initial data.

[0192] Step 2:

[0193] Based on the request received from the user, the server retrieves participant schedule information from the database. During this process, the server performs a data calculation to determine the optimal meeting time by comparing the participants' calendar information. The output is information on alternative meeting times.

[0194] Step 3:

[0195] The server reserves a meeting space based on the processed meeting time information. During this process, the server accesses the reservation system API and automatically secures a meeting room. The output of this step is reservation confirmation information.

[0196] Step 4:

[0197] The server notifies all participants of the meeting time and location. This notification is sent via email or a scheduling app and is displayed to participants who receive the meeting details. Specifically, this involves sending messages using the notification system.

[0198] Step 5:

[0199] On the day of the meeting, the terminal activates its speech recognition engine and converts participants' speech into text data in real time during the meeting. This converted data is sent to a generative AI engine and used to document the meeting content.

[0200] Step 6:

[0201] Simultaneously, the terminal uses an emotion engine to collect participants' facial expression data. Based on this data, emotion analysis is performed, and if discord or stress is detected, appropriate intervention is initiated via the server. The output of this step is an adaptive meeting intervention instruction.

[0202] Step 7:

[0203] As the meeting nears its end, the server issues a meeting completion notification. This notification is sent to participants along with meeting minutes automatically generated by a generation AI engine based on data recorded by the speech recognition engine.

[0204] Step 8:

[0205] The server compiles feedback based on emotional data generated by the emotion engine and identifies areas for improvement for the next meeting. This feedback information is distributed to participants. The output is a feedback report that includes the analysis results.

[0206] (Application Example 2)

[0207] 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".

[0208] In modern meetings, coordinating schedules for all participants and managing the meeting's progress are time-consuming and labor-intensive. Furthermore, emotional disagreements and stress that arise during meetings can hinder their smooth operation. Solutions to these challenges are needed.

[0209] 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.

[0210] In this invention, the server includes means for acquiring user schedule information and determining the optimal meeting time, means for checking meeting room reservation information and automatically making a reservation, means for notifying the acquired meeting information, means for recording meeting content using speech recognition and automatically generating meeting minutes, means for analyzing statements made during the meeting and issuing a warning when the topic deviates, means for monitoring the progress of the meeting and notifying when it ends, means for collecting and analyzing feedback information after the meeting, and means for analyzing participants' emotions during the meeting and suggesting actions to alleviate the situation if emotions indicating stress are detected. This automates everything from meeting preparation to progress and post-meeting feedback, and further enables the smooth progress of meetings while taking into consideration the emotions of the participants.

[0211] "User schedule information" refers to information showing the schedules and availability of individual participants attending a meeting, and is data necessary for efficiently scheduling meetings.

[0212] "Meeting room reservation information" refers to information about the location and time of a meeting, and is data referenced to automate the reservation of meeting rooms.

[0213] "Acquired meeting information" refers to detailed information about the meeting, such as the date and time of the meeting, the list of participants, and the agenda, which is necessary for notifying participants.

[0214] "A method for recording meeting content and automatically generating meeting minutes using speech recognition" refers to a method of recording what is said during a meeting as audio, processing it into text data, and then creating meeting minutes.

[0215] "A means of analyzing comments made during a meeting and issuing warnings when they deviate from the agenda" refers to a system that analyzes the content of comments made during a meeting in real time and alerts participants if content that deviates from the set agenda is detected.

[0216] "Means for monitoring meeting progress and notifying participants of its end" refers to a function that tracks the time from the start to the end of a meeting and notifies participants of the meeting's end when a predetermined time has been reached.

[0217] "Methods for collecting and analyzing post-meeting feedback information" refers to the process of collecting opinions and impressions from participants after a meeting, analyzing them, and using that information to improve future meetings.

[0218] "A means of analyzing participants' emotions and proposing actions to alleviate the situation when stressful emotions are detected" refers to a system that analyzes participants' emotions from their facial expressions and statements during a meeting, and when emotions such as stress or dissatisfaction are detected, it presents action plans to reduce them.

[0219] This invention is a system that combines automated meetings with sentiment analysis. The server, upon receiving instructions from the user, retrieves participant schedule information from a database and determines the optimal meeting time. Next, it checks the availability of meeting rooms and automatically reserves them. Once the reservation is complete, it notifies participants of the retrieved meeting information. This notification is sent via email or a scheduling application.

[0220] On the day of the meeting, the terminal activates speech recognition software and records speech in real time as the meeting progresses. This uses Google® Speech-to-Text as the speech recognition engine to convert the meeting content into text data. A generative AI model (e.g., OpenAI® GPT series) is used to automatically generate meeting minutes and summarize the recorded content.

[0221] Furthermore, the device performs emotion analysis during the meeting. This utilizes Affectiva Emotion AI to analyze participants' facial expressions and collect emotional data. If the device detects that a participant is experiencing stress or dissatisfaction, it suggests actions to alleviate the situation using prompt messages. This information is then fed back to the participants in real time via the server.

[0222] After the meeting concludes, the generated meeting minutes and sentiment feedback data are automatically distributed to all participants from the server. For the next meeting, the server analyzes the collected feedback information and uses a generative AI model to suggest areas for improvement.

[0223] As a concrete example, let's say a family holds a meeting to discuss whether or not they should get a pet. If the analysis engine detects that the eldest daughter is showing dissatisfaction during the meeting, it will provide a prompt such as, "Why don't we discuss a solution that everyone can agree on?" An example of a prompt might be, "The eldest daughter is feeling unhappy in this family meeting. Please suggest how we can resolve this situation."

[0224] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0225] Step 1:

[0226] Upon receiving instructions from the user, the server retrieves the schedule information of all meeting participants from the database. This input includes participant IDs, and the output generates information about each participant's availability. Based on this data, the server executes an algorithm to calculate the optimal meeting time.

[0227] Step 2:

[0228] The server checks the database for meeting room availability based on the optimal meeting time and automatically reserves an available meeting room. In this step, the desired date and time and a list of meeting rooms are entered, and the output returns information about the successfully reserved meeting room.

[0229] Step 3:

[0230] The server retrieves meeting information (date, time, meeting room, participants) and sends notifications to participants. Notifications are sent via email or scheduling apps, using meeting details as input data. The output data is the notification information sent to participants.

[0231] Step 4:

[0232] On the day of the meeting, the terminal activates its speech recognition engine and collects the content spoken during the meeting as audio data in real time. In this step, the raw audio acquired through the microphone is used as input, and the information is converted into text data and output. Google Speech-to-Text is used at this stage.

[0233] Step 5:

[0234] The generative AI model automatically generates meeting minutes based on text output from the speech recognition engine. In this step, text data obtained during the meeting is used as input, and a summarized meeting minutes text is output. This process efficiently organizes the meeting content.

[0235] Step 6:

[0236] Simultaneously, the device monitors the participant's facial expressions with its camera, and an emotion analysis engine analyzes the participant's emotional state. This step uses video data as input and outputs data indicating the emotional state. Affectiva Emotion AI performs the emotion detection.

[0237] Step 7:

[0238] Based on the results of sentiment analysis, the server generates prompts to alleviate stress and frustration if the user is experiencing them, and proposes them to participants in real time. In this step, sentiment data is input, and situation-appropriate prompts are generated as output. A generative AI model is used to construct appropriate suggestion statements.

[0239] Step 8:

[0240] After the meeting ends, the server distributes the generated meeting minutes and sentiment data to all participants and collects post-meeting feedback. At this stage, the meeting minutes and sentiment data function as inputs, and feedback information is obtained as output.

[0241] Step 9:

[0242] The server analyzes the collected feedback information and suggests improvements for the next meeting. In this step, the feedback information is input, and data suggesting improvements is output. The improvement suggestions are optimized by a generative AI model.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] [Second Embodiment]

[0247] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0248] 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.

[0249] 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).

[0250] 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.

[0251] 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.

[0252] 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).

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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".

[0259] This invention is a system for efficiently managing meetings, and mainly consists of a server, terminals, a generative AI engine, and speech recognition technology. By automating each step from the preparation stage to the progress and post-meeting processing of a meeting, this system provides an environment in which participants can concentrate on the meeting content.

[0260] First, the user uses their device to request a meeting. This request includes specifying the participants, preferred date and time, and the purpose of the meeting. Based on the information received from the user, the server retrieves each participant's schedule data from the database and identifies the availability of all participants.

[0261] Once an available time slot is identified, the server checks the availability of meeting rooms and automatically reserves a physical or online meeting room as needed. The server notifies all participants of the reservation result, and the meeting details are displayed on their devices.

[0262] On the day of the meeting, the device will support the meeting's progress. Once the meeting begins, the device will activate its speech recognition engine and transcribe participants' statements in real time. A generative AI engine will analyze these texts, and AI voice warnings will be issued for any deviant statements. This ensures that the meeting stays on schedule.

[0263] Furthermore, the server monitors the meeting time and notifies all participants when the end time is approaching. After the meeting ends, meeting minutes are automatically generated on the server using a speech recognition engine and a generative AI engine and distributed to participants. The minutes clearly state the key points of the meeting and the next steps to take.

[0264] Finally, there's a step where users input their evaluation of the meeting and suggestions for improvement through a feedback form. The server collects this feedback and makes suggestions for improvement to be used in the next meeting. In this way, the entire system works together to improve the quality and efficiency of meetings.

[0265] As a concrete example, consider a project meeting at a certain company. The project manager logs into a terminal and coordinates the meeting using the availability of all team members. The user clearly states the purpose of the meeting, and the system automatically reserves a meeting room. During the meeting, AI manages the progress hour by hour and notifies participants of important tasks in real time. This process significantly streamlines the coordination work that was previously done manually.

[0266] The following describes the processing flow.

[0267] Step 1:

[0268] The user enters a meeting request using their device. They specify the list of participants, preferred date and time, and the purpose of the meeting.

[0269] Step 2:

[0270] Based on the meeting request received by the user, the server retrieves participant schedule information from the database. It then identifies the availability of all participants.

[0271] Step 3:

[0272] The server checks the availability of meeting rooms and automatically reserves them during identified available times. Once a meeting room is secured, a reservation confirmation notification is generated.

[0273] Step 4:

[0274] The server notifies each participant via email or scheduling app of the confirmed meeting date and time, location, participant list, and meeting purpose.

[0275] Step 5:

[0276] On the day of the meeting, the device activates its speech recognition engine at the designated time. During the meeting, the device collects participants' speech in real time and converts the audio data into text.

[0277] Step 6:

[0278] The AI ​​generation engine analyzes the transcribed speech. If the conversation deviates from the topic, the device uses AI voice to prompt the user to return to the topic.

[0279] Step 7:

[0280] The server monitors the meeting time and notifies participants via their devices five minutes before the end of the meeting, stating, "There are 5 minutes left until the end of the meeting."

[0281] Step 8:

[0282] After the meeting ends, the server automatically creates minutes of the meeting using the speech recognition data and the generative AI engine.

[0283] Step 9:

[0284] The server distributes the minutes of the meeting created to all participants by email. The minutes of the meeting include the key points and action items of the meeting.

[0285] Step 10:

[0286] The user accesses the feedback form through the terminal and enters evaluations and improvement suggestions for the meeting.

[0287] Step 11:

[0288] The server collects and analyzes the feedback information, stores the data, and proposes improvement points for the next meeting.

[0289] (Example 1)

[0290] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] In modern meeting management, tasks such as schedule adjustment, meeting room reservation, and recording of meeting content are complex and consume a lot of time and labor. In particular, the problem is that the speech during the meeting deviates from the topic and the end time becomes unclear. In addition, there is also a lack of a mechanism for effectively utilizing the feedback after the meeting. Due to such problems, an integrated system for improving the efficiency and productivity of meetings is required.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0293] In this invention, the server includes means for a computing device to determine the optimal meeting time from the user's schedule, means for a computing device to check the reservation status of the meeting space and automate the reservation, and means for providing meeting information using notifications. This enables efficient scheduling and reservation of meetings, as well as the provision of detailed information to participants.

[0294] A "computing device" is an electronic device used for handling and calculating digital data, and can also be used for scheduling and coordinating meetings.

[0295] A "meeting space" refers to a specific area or platform used for conducting meetings, either physically or online.

[0296] A "notification" is a means of transmitting information, used to inform recipients of important information or events.

[0297] "Speech processing technology" is a general term for technologies that acquire, analyze, and convert speech data, and is used when converting speech to text.

[0298] A "document" is an electronic or physical document that records information and is intended to organize the content of a meeting and provide it to participants.

[0299] "Evaluation information" refers to feedback and reviews provided by users, which can be used to improve products and services.

[0300] This invention is a system for efficiently and effectively managing meetings. The system comprises a server, terminals, voice processing technology, and a generative AI model. The system's objective is to provide an environment where meeting participants can focus on key agenda items by automating scheduling, meeting room reservations, meeting content recording, and minute-taking.

[0301] The user requests a meeting using the terminal. This request includes participants, desired date and time, purpose of the meeting, etc. The terminal runs a dedicated application and transmits information to the server via this application. As a specific example, by inputting a prompt sentence such as "Please add the available time of the next project meeting to the schedule" into the terminal, the preparation for the meeting is started.

[0302] Based on the received information, the server retrieves the schedule information of the participants from the database and identifies the available time for all. The server also accesses the online reservation system, checks the availability of the meeting rooms, and automatically performs the necessary reservation procedures. Through this procedure, the optimal conditions for holding the meeting can be prepared.

[0303] On the day of the meeting, the terminal utilizes voice processing technology to convert the meeting speech into text in real time. These text data are analyzed by the generative AI model, and if there is a speech that deviates from the topic, a warning is automatically issued. In this way, the meeting itself can be operated efficiently and effectively.

[0304] After the meeting ends, the minutes automatically generated by the server are distributed to the participants. The minutes summarize the key points of the meeting and the future action points based on the text data obtained by the voice processing technology.

[0305] After the meeting, the user inputs feedback using the terminal, which is collected and analyzed by the server. The analyzed information is reflected in the improvement proposals for the next meeting, and the quality of the meeting as a whole system can be improved.

[0306] The flow of the specific process in Example 1 will be described using FIG.

[0307] Step 1:

[0308] The user enters a meeting request using their device. This input includes a participant list, preferred date and time, and the purpose of the meeting. The information entered by the user is output and sent to the server. Specifically, the user enters information into the input form on their device and presses the "Submit" button.

[0309] Step 2:

[0310] The server processes the received request data and retrieves participant schedule information from the database. The server then analyzes this schedule data to identify everyone's free time. The input is the request data from the user, and the output is the common free time for all participants. Specifically, the server uses SQL queries to access the database and check each participant's schedule.

[0311] Step 3:

[0312] The server accesses the meeting space reservation system based on availability information and reserves a suitable meeting room. The server checks the availability of meeting rooms via API and, if the reservation is successful, obtains reservation confirmation information. The input is the common availability time, and the output is reservation confirmation information. Specifically, the server sends an API request and receives a reservation response.

[0313] Step 4:

[0314] The server sends a notification to all participants regarding the meeting details. The server uses a notification system to provide all participants with the meeting date, time, and location, and this information is displayed on their devices. The input is reservation confirmation information, and the output is the notification to participants. Specifically, the server distributes the information via email or push notifications.

[0315] Step 5:

[0316] On the day of the meeting, the terminal uses speech processing technology to transcribe participants' speech in real time. The terminal analyzes the collected audio data with a speech recognition engine and outputs it as text data. The input is audio data, and the output is the transcribed speech. Specifically, the terminal captures audio with a microphone and performs text conversion using software.

[0317] Step 6:

[0318] The generating AI model analyzes the obtained text data and issues a warning if any statements deviate from the topic. The model compares the text to the topic content, performs data calculations to detect deviations, and outputs a warning command as needed. The input is text data, and the output is warning information. Specifically, it analyzes the text content as appropriate and displays a warning via voice or message from the terminal.

[0319] Step 7:

[0320] The server automatically generates meeting minutes after the meeting ends and distributes them to all participants. The server processes the text obtained using speech processing technology to create a summary. The input is the text data of the entire meeting, and the output is the completed meeting minutes. Specifically, the server sends the meeting minutes via email or uploads them to a dedicated portal.

[0321] Step 8:

[0322] Users enter feedback from their terminal after a meeting. The feedback is sent to the server and used for analysis to improve future meetings. The input is data from an evaluation form, and the output is the analysis results on the server. Specifically, the user answers questions in a survey format and presses a submit button.

[0323] (Application Example 1)

[0324] 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."

[0325] In smart cities, efficient and smooth operation of participatory meetings and public forums for citizens is hampered by the complexities of coordinating schedules among participants, booking venues, preventing deviations from the ongoing agenda, recording meeting content, and sharing information with participants. Therefore, a system is needed that enhances transparency and efficiency, ensuring that all participants receive information smoothly.

[0326] 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.

[0327] In this invention, the server includes means for acquiring user schedule information and determining the optimal activity time, means for checking space reservation information and automatically making reservations, and means for automatically distributing generated record documents to multiple participants. This makes it possible for citizens to efficiently prepare for participatory meetings and forums, carry out activities in line with the agenda, and easily share record documents.

[0328] "User schedule information" refers to data about each participant's individual schedule and available time.

[0329] The "optimal activity time" is the time period that allows all participants to participate without difficulty and to carry out activities efficiently.

[0330] "Space reservation information" refers to data regarding the reservation status and available times for a location where an activity will take place.

[0331] "Speech recognition" is a technology that converts speech into text, accurately recording what is said as written characters.

[0332] A "record document" is a document automatically generated based on what was said during an activity, and serves as an official record for later reference.

[0333] "Analyzing a statement" is the process of analyzing the content of a statement and identifying any deviations from its purpose or topic.

[0334] "Notifying participants of the end of the activity" is the process of informing participants that the allotted activity time is nearing its end.

[0335] "Opinion information" refers to data on evaluations, feedback, and suggestions for improvement provided by participants after the completion of the activity.

[0336] "Automated distribution" is the process of sending generated record documents to participants without human intervention.

[0337] "Presenting areas for improvement" means making suggestions to improve future activities based on the collected feedback.

[0338] The system to realize this application primarily consists of a server, a terminal, a speech recognition engine, and a generative AI engine. The server first acquires the user's schedule information and automatically determines the optimal activity time. Based on this, it checks the space reservation information and automatically reserves the space to ensure the activity proceeds smoothly.

[0339] Once the activity begins, the device activates its speech recognition engine and transcribes participants' statements into text in real time. This text data is then organized by a generative AI engine and automatically generated as a record document. Simultaneously, if a participant's statement deviates from the agenda, the generative AI issues a warning to alert them.

[0340] As the activity's end time approaches, the server notifies participants of its completion and provides support for extending or ending the activity as needed. After the activity, the server collects feedback and suggests improvements for the next activity. This ensures that the entire activity process is managed efficiently.

[0341] As a concrete example, consider a local community meeting. Residents register to participate in the meeting via their smartphones, a server determines the optimal date and time, and automatically reserves a room in the community hall. During the meeting, spoken content is transcribed into text via smart glasses, and after the meeting, a generating AI summarizes the content and distributes a transcript to participants. This process ensures that community meetings proceed quickly and systematically.

[0342] Examples of prompts for a generative AI model:

[0343] "Please schedule the next citizens' meeting and reserve the venue based on the participants' schedules."

[0344] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0345] Step 1:

[0346] Users use their devices to enter their interest in participating in an activity. The entered data includes the participant's name, the desired activity, and their available time slots. The device then sends this data to the server.

[0347] Step 2:

[0348] The server analyzes the received participation request data and aggregates each participant's schedule information. Based on this information, the server performs data processing to calculate the optimal activity time. Once the optimal time is determined, the information is passed on to the next step.

[0349] Step 3:

[0350] The server checks the availability of the space. It searches the database for a space available during the optimal activity time and automatically makes a reservation. The reservation result is notified to the user by the server.

[0351] Step 4:

[0352] At the start of the activity, the device activates its speech recognition engine and converts participants' speech to text in real time. The speech input is converted to text output and sent to the generation AI engine.

[0353] Step 5:

[0354] The generation AI engine analyzes the received text data and automatically generates a record document. In this process, it identifies statements that deviate from the agenda and generates warnings as needed. The generated warnings are displayed on the terminal to alert participants.

[0355] Step 6:

[0356] As the activity's end time approaches, the server will notify the user of its completion. This notification is sent to the user to help them decide whether to end or extend the activity. This ensures that the activity is managed to stay within the allotted time.

[0357] Step 7:

[0358] After the activity concludes, the server collects feedback and analyzes it using a generative AI engine. Based on the analysis, suggestions for improving the next activity are generated. These suggestions are then provided to the user, who can use them to prepare for the next activity.

[0359] 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.

[0360] This invention is a meeting automation system incorporating an emotion engine that recognizes emotions in real time from participants' statements and facial expressions, aiming to improve the efficiency and quality of meeting management. The system consists of a server, terminals, a generative AI engine, speech recognition technology, and an emotion engine.

[0361] First, the user requests a meeting using their device. At this stage, the list of participants and the purpose of the meeting are specified. Based on the information received from the user, the server retrieves the participants' schedules from the database and analyzes their availability.

[0362] The server reserves a meeting room according to the identified available time slot. Once this reservation process is complete, the server sends a meeting notification to all participants. The notification will be sent via email or a scheduling app and will include the meeting details.

[0363] On the day of the meeting, the terminal activates its speech recognition engine and emotion engine in conjunction. Participants' remarks are collected in real time and converted into audio data. This audio data is analyzed by a generating AI engine to support the efficient progress of the meeting.

[0364] In addition, the emotion engine analyzes participants' emotions based on their statements and facial expressions. If emotions indicating discord or stress are detected, the terminal will take appropriate action via the server, such as "Please wait a moment while we check the situation." This feature helps ensure the smooth running of meetings.

[0365] As the meeting nears its end, the server issues an end-of-meeting notification, and the meeting content is recorded by a speech recognition engine. After the meeting, a generative AI engine automatically generates meeting minutes, which are then distributed to participants. Furthermore, emotional data collected by an emotion engine is added to the feedback, indicating areas for improvement for the next meeting.

[0366] As a concrete example, consider a team meeting in a remote environment. The team leader coordinates the members' availability and sets up an online meeting. During the meeting, the emotion engine analyzes the participants' facial expressions, and if a member shows dissatisfaction with a particular topic, it immediately provides support to alleviate the situation. This makes the meeting more effective and harmonious.

[0367] The following describes the processing flow.

[0368] Step 1:

[0369] The user uses their device to request a meeting. Here, they enter a list of participants, preferred date and time, and the purpose of the meeting.

[0370] Step 2:

[0371] The server receives a request from the user and retrieves the schedule information of all participants from the database. It identifies available time slots and determines the optimal meeting time.

[0372] Step 3:

[0373] The server automatically reserves a meeting room based on the identified meeting time. This process takes into account the availability of both physical meeting rooms and online meeting platforms.

[0374] Step 4:

[0375] The server will notify all participants of the meeting details. The notification will be sent via email or a scheduling app and will include the date, time, location, and agenda.

[0376] Step 5:

[0377] At the start of the meeting, the device activates its speech recognition engine and emotion engine. The speech recognition engine collects participants' speech in real time and converts it into text.

[0378] Step 6:

[0379] The emotion engine analyzes speech and facial expression data to monitor the emotional state of participants. If a specific emotion is identified, it suggests appropriate countermeasures via the server.

[0380] Step 7:

[0381] During the meeting, a generative AI engine analyzes the speech-to-text format to help the discussion stay on track with the agenda. If the discussion deviates from the topic, the device will issue a warning.

[0382] Step 8:

[0383] As the meeting nears its end, the server displays a notification on the terminal stating, "There are 5 minutes left until the end."

[0384] Step 9:

[0385] After the meeting ends, the server automatically generates meeting minutes based on data collected through speech recognition and sentiment analysis.

[0386] Step 10:

[0387] The terminal will email the meeting minutes created to the participants, ensuring information is shared. Furthermore, a feedback report based on sentiment analysis results will also be provided.

[0388] Step 11:

[0389] Users use a feedback form to input their evaluation of the meeting and suggestions for improvement. The server collects and analyzes this information and saves it as data to suggest improvements for the next meeting.

[0390] (Example 2)

[0391] 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".

[0392] Traditional meeting systems often resulted in decreased meeting efficiency and hindered smooth communication among participants due to issues such as scheduling, meeting room reservations, agenda management during meetings, and a lack of emotionally conscious intervention. Furthermore, the lack of meeting minutes and emotionally-based feedback after meetings made it difficult to obtain information that could improve future meetings.

[0393] 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.

[0394] In this invention, the server includes means for acquiring user schedule information and determining the optimal meeting time, means for recording meeting content using speech recognition and automatically generating documents, and means for analyzing speech during the meeting, performing emotion analysis based on facial expressions, and intervening as necessary. This enables efficient and smooth meeting management, from scheduling participants' schedules to managing the meeting and providing feedback afterward.

[0395] A "user" is an individual or organization that uses this system to request a meeting and enters participant information and the purpose of the meeting.

[0396] "Schedule information" refers to data about participants' availability and schedules, and is used to determine the optimal meeting time.

[0397] A "meeting space" is a physical or virtual location reserved for holding a meeting, and is the entity whose reservation information is managed.

[0398] A "notification" is a message that provides participants with detailed information about a meeting, and is sent via email or scheduling apps.

[0399] "Speech recognition" is a technology that converts speech during a meeting into text and records the content.

[0400] "Documents" refer to meeting minutes and records that are automatically generated based on text data obtained through speech recognition.

[0401] "Speech analysis" is a technique for analyzing the content of conversations during meetings, and is used for agenda management and sentiment analysis.

[0402] "Emotional analysis" is the process of determining the emotional state of participants from their statements and facial expressions, and adjusting the flow of the meeting as needed.

[0403] "Intervention" refers to actions taken by the system during a meeting, based on the results of sentiment analysis, to facilitate the smooth running of the meeting.

[0404] "Feedback" refers to the information and suggestions provided after a meeting, including analytical results that can help improve future meetings.

[0405] This invention presents a meeting management system that combines sentiment analysis to improve the efficiency and quality of meeting operations. The system is comprised of users, terminals, a server, a generative AI engine, speech recognition technology, and a sentiment engine.

[0406] First, the user enters a meeting request using their device. A dedicated application is installed on the device, and the user uses this application to specify the list of participants and the purpose of the meeting. The entered information is then sent to the server.

[0407] The server retrieves participant schedule information from the database based on the information received. Analyzing this information, the server calculates the most optimal meeting time. Furthermore, the server automatically accesses the meeting space reservation system and reserves a meeting room. Once the reservation is complete, the server sends a meeting notification to all participants. This notification is delivered via email or scheduling apps and includes the meeting date and details.

[0408] On the day of the meeting, the terminal activates its speech recognition engine and emotion engine in conjunction. The speech recognition engine converts participants' speech into audio data in real time and sends it to the generative AI engine. The generative AI engine analyzes the meeting content and automatically generates documents, supporting efficient meeting progress. The emotion engine analyzes participants' speech and facial expressions and provides appropriate interventions according to the flow of the meeting.

[0409] As a concrete example, if there is a progress meeting for a project, the user sends a notification from their device to the participants, and everyone gathers online at the time specified in their schedule. During the meeting, the speech recognition engine records all statements as text, and the emotion engine monitors the participants' reactions. For example, if one participant expresses dissatisfaction with a certain agenda item, that emotion is immediately detected, and the device intervenes via the server to mitigate the situation.

[0410] As the meeting nears its end, the server notifies participants of the conclusion, and a generative AI engine automatically creates meeting minutes based on the content recorded by the speech recognition engine and distributes them to participants. In addition, sentiment data is analyzed and provided as feedback to indicate areas for improvement for the next meeting.

[0411] Example of a prompt message: "Please schedule the next project progress meeting. All team members will be attending, and the purpose is to review the development status."

[0412] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0413] Step 1:

[0414] The user requests a meeting using their device. Through a dedicated application on the device, the user enters the participant list and the purpose of the meeting. This input data is sent to the server as initial data.

[0415] Step 2:

[0416] Based on the request received from the user, the server retrieves participant schedule information from the database. During this process, the server performs a data calculation to determine the optimal meeting time by comparing the participants' calendar information. The output is information on alternative meeting times.

[0417] Step 3:

[0418] The server reserves a meeting space based on the processed meeting time information. During this process, the server accesses the reservation system API and automatically secures a meeting room. The output of this step is reservation confirmation information.

[0419] Step 4:

[0420] The server notifies all participants of the meeting time and location. This notification is sent via email or a scheduling app and is displayed to participants who receive the meeting details. Specifically, this involves sending messages using the notification system.

[0421] Step 5:

[0422] On the day of the meeting, the terminal activates its speech recognition engine and converts participants' speech into text data in real time during the meeting. This converted data is sent to a generative AI engine and used to document the meeting content.

[0423] Step 6:

[0424] Simultaneously, the terminal uses an emotion engine to collect participants' facial expression data. Based on this data, emotion analysis is performed, and if discord or stress is detected, appropriate intervention is initiated via the server. The output of this step is an adaptive meeting intervention instruction.

[0425] Step 7:

[0426] As the meeting nears its end, the server issues a meeting completion notification. This notification is sent to participants along with meeting minutes automatically generated by a generation AI engine based on data recorded by the speech recognition engine.

[0427] Step 8:

[0428] The server compiles feedback based on emotional data generated by the emotion engine and identifies areas for improvement for the next meeting. This feedback information is distributed to participants. The output is a feedback report that includes the analysis results.

[0429] (Application Example 2)

[0430] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0431] In modern meetings, coordinating schedules for all participants and managing the meeting's progress are time-consuming and labor-intensive. Furthermore, emotional disagreements and stress that arise during meetings can hinder their smooth operation. Solutions to these challenges are needed.

[0432] 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.

[0433] In this invention, the server includes means for acquiring user schedule information and determining the optimal meeting time, means for checking meeting room reservation information and automatically making a reservation, means for notifying the acquired meeting information, means for recording meeting content using speech recognition and automatically generating meeting minutes, means for analyzing statements made during the meeting and issuing a warning when the topic deviates, means for monitoring the progress of the meeting and notifying when it ends, means for collecting and analyzing feedback information after the meeting, and means for analyzing participants' emotions during the meeting and suggesting actions to alleviate the situation if emotions indicating stress are detected. This automates everything from meeting preparation to progress and post-meeting feedback, and further enables the smooth progress of meetings while taking into consideration the emotions of the participants.

[0434] "User schedule information" refers to information showing the schedules and availability of individual participants attending a meeting, and is data necessary for efficiently scheduling meetings.

[0435] "Meeting room reservation information" refers to information about the location and time of a meeting, and is data referenced to automate the reservation of meeting rooms.

[0436] "Acquired meeting information" refers to detailed information about the meeting, such as the date and time of the meeting, the list of participants, and the agenda, which is necessary for notifying participants.

[0437] "A method for recording meeting content and automatically generating meeting minutes using speech recognition" refers to a method of recording what is said during a meeting as audio, processing it into text data, and then creating meeting minutes.

[0438] "A means of analyzing comments made during a meeting and issuing warnings when they deviate from the agenda" refers to a system that analyzes the content of comments made during a meeting in real time and alerts participants if content that deviates from the set agenda is detected.

[0439] "Means for monitoring meeting progress and notifying participants of its end" refers to a function that tracks the time from the start to the end of a meeting and notifies participants of the meeting's end when a predetermined time has been reached.

[0440] "Methods for collecting and analyzing post-meeting feedback information" refers to the process of collecting opinions and impressions from participants after a meeting, analyzing them, and using that information to improve future meetings.

[0441] "A means of analyzing participants' emotions and proposing actions to alleviate the situation when stressful emotions are detected" refers to a system that analyzes participants' emotions from their facial expressions and statements during a meeting, and when emotions such as stress or dissatisfaction are detected, it presents action plans to reduce them.

[0442] This invention is a system that combines automated meetings with sentiment analysis. The server, upon receiving instructions from the user, retrieves participant schedule information from a database and determines the optimal meeting time. Next, it checks the availability of meeting rooms and automatically reserves them. Once the reservation is complete, it notifies participants of the retrieved meeting information. This notification is sent via email or a scheduling application.

[0443] On the day of the meeting, the terminal activates speech recognition software and records speech in real time as the meeting progresses. This uses Google Speech-to-Text as the speech recognition engine to convert the meeting content into text data. A generative AI model (e.g., OpenAI's GPT series) is used to automatically generate meeting minutes and summarize the recorded content.

[0444] Furthermore, the device performs emotion analysis during the meeting. This utilizes Affectiva Emotion AI to analyze participants' facial expressions and collect emotional data. If the device detects that a participant is experiencing stress or dissatisfaction, it suggests actions to alleviate the situation using prompt messages. This information is then fed back to the participants in real time via the server.

[0445] After the meeting concludes, the generated meeting minutes and sentiment feedback data are automatically distributed to all participants from the server. For the next meeting, the server analyzes the collected feedback information and uses a generative AI model to suggest areas for improvement.

[0446] As a concrete example, let's say a family holds a meeting to discuss whether or not they should get a pet. If the analysis engine detects that the eldest daughter is showing dissatisfaction during the meeting, it will provide a prompt such as, "Why don't we discuss a solution that everyone can agree on?" An example of a prompt might be, "The eldest daughter is feeling unhappy in this family meeting. Please suggest how we can resolve this situation."

[0447] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0448] Step 1:

[0449] Upon receiving instructions from the user, the server retrieves the schedule information of all meeting participants from the database. This input includes participant IDs, and the output generates information about each participant's availability. Based on this data, the server executes an algorithm to calculate the optimal meeting time.

[0450] Step 2:

[0451] The server checks the database for meeting room availability based on the optimal meeting time and automatically reserves an available meeting room. In this step, the desired date and time and a list of meeting rooms are entered, and the output returns information about the successfully reserved meeting room.

[0452] Step 3:

[0453] The server retrieves meeting information (date, time, meeting room, participants) and sends notifications to participants. Notifications are sent via email or scheduling apps, using meeting details as input data. The output data is the notification information sent to participants.

[0454] Step 4:

[0455] On the day of the meeting, the terminal activates its speech recognition engine and collects the content spoken during the meeting as audio data in real time. In this step, the raw audio acquired through the microphone is used as input, and the information is converted into text data and output. Google Speech-to-Text is used at this stage.

[0456] Step 5:

[0457] The generative AI model automatically generates meeting minutes based on text output from the speech recognition engine. In this step, text data obtained during the meeting is used as input, and a summarized meeting minutes text is output. This process efficiently organizes the meeting content.

[0458] Step 6:

[0459] Simultaneously, the device monitors the participant's facial expressions with its camera, and an emotion analysis engine analyzes the participant's emotional state. This step uses video data as input and outputs data indicating the emotional state. Affectiva Emotion AI performs the emotion detection.

[0460] Step 7:

[0461] Based on the results of sentiment analysis, the server generates prompts to alleviate stress and frustration if the user is experiencing them, and proposes them to participants in real time. In this step, sentiment data is input, and situation-appropriate prompts are generated as output. A generative AI model is used to construct appropriate suggestion statements.

[0462] Step 8:

[0463] After the meeting ends, the server distributes the generated meeting minutes and sentiment data to all participants and collects post-meeting feedback. At this stage, the meeting minutes and sentiment data function as inputs, and feedback information is obtained as output.

[0464] Step 9:

[0465] The server analyzes the collected feedback information and suggests improvements for the next meeting. In this step, the feedback information is input, and data suggesting improvements is output. The improvement suggestions are optimized by a generative AI model.

[0466] 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.

[0467] 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.

[0468] 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.

[0469] [Third Embodiment]

[0470] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0471] 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.

[0472] 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).

[0473] 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.

[0474] 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.

[0475] 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).

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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".

[0482] This invention is a system for efficiently managing meetings, and mainly consists of a server, terminals, a generative AI engine, and speech recognition technology. By automating each step from the preparation stage to the progress and post-meeting processing of a meeting, this system provides an environment in which participants can concentrate on the meeting content.

[0483] First, the user uses their device to request a meeting. This request includes specifying the participants, preferred date and time, and the purpose of the meeting. Based on the information received from the user, the server retrieves each participant's schedule data from the database and identifies the availability of all participants.

[0484] Once an available time slot is identified, the server checks the availability of meeting rooms and automatically reserves a physical or online meeting room as needed. The server notifies all participants of the reservation result, and the meeting details are displayed on their devices.

[0485] On the day of the meeting, the device will support the meeting's progress. Once the meeting begins, the device will activate its speech recognition engine and transcribe participants' statements in real time. A generative AI engine will analyze these texts, and AI voice warnings will be issued for any deviant statements. This ensures that the meeting stays on schedule.

[0486] Furthermore, the server monitors the meeting time and notifies all participants when the end time is approaching. After the meeting ends, meeting minutes are automatically generated on the server using a speech recognition engine and a generative AI engine and distributed to participants. The minutes clearly state the key points of the meeting and the next steps to take.

[0487] Finally, there's a step where users input their evaluation of the meeting and suggestions for improvement through a feedback form. The server collects this feedback and makes suggestions for improvement to be used in the next meeting. In this way, the entire system works together to improve the quality and efficiency of meetings.

[0488] As a concrete example, consider a project meeting at a certain company. The project manager logs into a terminal and coordinates the meeting using the availability of all team members. The user clearly states the purpose of the meeting, and the system automatically reserves a meeting room. During the meeting, AI manages the progress hour by hour and notifies participants of important tasks in real time. This process significantly streamlines the coordination work that was previously done manually.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] The user enters a meeting request using their device. They specify the list of participants, preferred date and time, and the purpose of the meeting.

[0492] Step 2:

[0493] Based on the meeting request received by the user, the server retrieves participant schedule information from the database. It then identifies the availability of all participants.

[0494] Step 3:

[0495] The server checks the availability of meeting rooms and automatically reserves them during identified available times. Once a meeting room is secured, a reservation confirmation notification is generated.

[0496] Step 4:

[0497] The server notifies each participant via email or scheduling app of the confirmed meeting date and time, location, participant list, and meeting purpose.

[0498] Step 5:

[0499] On the day of the meeting, the device activates its speech recognition engine at the designated time. During the meeting, the device collects participants' speech in real time and converts the audio data into text.

[0500] Step 6:

[0501] The AI ​​generation engine analyzes the transcribed speech. If the conversation deviates from the topic, the device uses AI voice to prompt the user to return to the topic.

[0502] Step 7:

[0503] The server monitors the meeting time and notifies participants via their devices five minutes before the end of the meeting, stating, "There are 5 minutes left until the end of the meeting."

[0504] Step 8:

[0505] After the meeting ends, the server automatically creates meeting minutes using speech recognition data and a generative AI engine.

[0506] Step 9:

[0507] The server will email the meeting minutes to all participants. The minutes will include the key points of the meeting and action items.

[0508] Step 10:

[0509] Users access a feedback form through their device to input their evaluation of the meeting and suggestions for improvement.

[0510] Step 11:

[0511] The server collects and analyzes feedback information and stores the data to suggest improvements for the next meeting.

[0512] (Example 1)

[0513] 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."

[0514] In modern meeting management, tasks such as scheduling, booking meeting rooms, and recording meeting content are complex and time-consuming. In particular, issues such as discussions straying from the agenda and unclear meeting end times are problematic. Furthermore, there is a lack of mechanisms for effectively utilizing post-meeting feedback. These challenges highlight the need for a comprehensive system to improve meeting efficiency and productivity.

[0515] 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.

[0516] In this invention, the server includes means for a computing device to determine the optimal meeting time from the user's schedule, means for a computing device to check the reservation status of the meeting space and automate the reservation, and means for providing meeting information using notifications. This enables efficient scheduling and reservation of meetings, as well as the provision of detailed information to participants.

[0517] A "computing device" is an electronic device used for handling and calculating digital data, and can also be used for scheduling and coordinating meetings.

[0518] A "meeting space" refers to a specific area or platform used for conducting meetings, either physically or online.

[0519] A "notification" is a means of transmitting information, used to inform recipients of important information or events.

[0520] "Speech processing technology" is a general term for technologies that acquire, analyze, and convert speech data, and is used when converting speech to text.

[0521] A "document" is an electronic or physical document that records information and is intended to organize the content of a meeting and provide it to participants.

[0522] "Evaluation information" refers to feedback and reviews provided by users, which can be used to improve products and services.

[0523] This invention is a system for efficiently and effectively managing meetings. The system comprises a server, terminals, voice processing technology, and a generative AI model. The system's objective is to provide an environment where meeting participants can focus on key agenda items by automating scheduling, meeting room reservations, meeting content recording, and minute-taking.

[0524] Users use their devices to request meetings. These requests include participants, preferred dates and times, and the purpose of the meeting. The devices run a dedicated application that transmits this information to the server. For example, entering a prompt such as "Please add the next project meeting's availability to my schedule" into the device initiates meeting preparation.

[0525] Based on the received information, the server retrieves participants' schedule information from the database and identifies everyone's availability. The server also accesses the online booking system to check meeting room availability and automatically completes the necessary booking procedures. Through this process, the optimal conditions for holding a meeting can be ensured.

[0526] On the day of the meeting, the device utilizes speech processing technology to convert meeting comments into text in real time. This text data is analyzed by a generative AI model, and if any comments deviate from the agenda, an automatic warning is issued. In this way, the meeting itself is conducted efficiently and effectively.

[0527] After the meeting concludes, meeting minutes are automatically generated by the server and distributed to participants. The minutes summarize key points of the meeting and future action points based on text data obtained using speech processing technology.

[0528] After a meeting, users input feedback using their devices, which is then collected and analyzed by the server. The analyzed information is used to suggest improvements for the next meeting, thereby improving the overall quality of meetings within the system.

[0529] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0530] Step 1:

[0531] The user enters a meeting request using their device. This input includes a participant list, preferred date and time, and the purpose of the meeting. The information entered by the user is output and sent to the server. Specifically, the user enters information into the input form on their device and presses the "Submit" button.

[0532] Step 2:

[0533] The server processes the received request data and retrieves participant schedule information from the database. The server then analyzes this schedule data to identify everyone's free time. The input is the request data from the user, and the output is the common free time for all participants. Specifically, the server uses SQL queries to access the database and check each participant's schedule.

[0534] Step 3:

[0535] The server accesses the meeting space reservation system based on availability information and reserves a suitable meeting room. The server checks the availability of meeting rooms via API and, if the reservation is successful, obtains reservation confirmation information. The input is the common availability time, and the output is reservation confirmation information. Specifically, the server sends an API request and receives a reservation response.

[0536] Step 4:

[0537] The server sends a notification to all participants regarding the meeting details. The server uses a notification system to provide all participants with the meeting date, time, and location, and this information is displayed on their devices. The input is reservation confirmation information, and the output is the notification to participants. Specifically, the server distributes the information via email or push notifications.

[0538] Step 5:

[0539] On the day of the meeting, the terminal uses speech processing technology to transcribe participants' speech in real time. The terminal analyzes the collected audio data with a speech recognition engine and outputs it as text data. The input is audio data, and the output is the transcribed speech. Specifically, the terminal captures audio with a microphone and performs text conversion using software.

[0540] Step 6:

[0541] The generating AI model analyzes the obtained text data and issues a warning if any statements deviate from the topic. The model compares the text to the topic content, performs data calculations to detect deviations, and outputs a warning command as needed. The input is text data, and the output is warning information. Specifically, it analyzes the text content as appropriate and displays a warning via voice or message from the terminal.

[0542] Step 7:

[0543] The server automatically generates meeting minutes after the meeting ends and distributes them to all participants. The server processes the text obtained using speech processing technology to create a summary. The input is the text data of the entire meeting, and the output is the completed meeting minutes. Specifically, the server sends the meeting minutes via email or uploads them to a dedicated portal.

[0544] Step 8:

[0545] Users enter feedback from their terminal after a meeting. The feedback is sent to the server and used for analysis to improve future meetings. The input is data from an evaluation form, and the output is the analysis results on the server. Specifically, the user answers questions in a survey format and presses a submit button.

[0546] (Application Example 1)

[0547] 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."

[0548] In smart cities, efficient and smooth operation of participatory meetings and public forums for citizens is hampered by the complexities of coordinating schedules among participants, booking venues, preventing deviations from the ongoing agenda, recording meeting content, and sharing information with participants. Therefore, a system is needed that enhances transparency and efficiency, ensuring that all participants receive information smoothly.

[0549] 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.

[0550] In this invention, the server includes means for acquiring user schedule information and determining the optimal activity time, means for checking space reservation information and automatically making reservations, and means for automatically distributing generated record documents to multiple participants. This makes it possible for citizens to efficiently prepare for participatory meetings and forums, carry out activities in line with the agenda, and easily share record documents.

[0551] "User schedule information" refers to data about each participant's individual schedule and available time.

[0552] The "optimal activity time" is the time period that allows all participants to participate without difficulty and to carry out activities efficiently.

[0553] "Space reservation information" refers to data regarding the reservation status and available times for a location where an activity will take place.

[0554] "Speech recognition" is a technology that converts speech into text, accurately recording what is said as written characters.

[0555] A "record document" is a document automatically generated based on what was said during an activity, and serves as an official record for later reference.

[0556] "Analyzing a statement" is the process of analyzing the content of a statement and identifying any deviations from its purpose or topic.

[0557] "Notifying participants of the end of the activity" is the process of informing participants that the allotted activity time is nearing its end.

[0558] "Opinion information" refers to data on evaluations, feedback, and suggestions for improvement provided by participants after the completion of the activity.

[0559] "Automated distribution" is the process of sending generated record documents to participants without human intervention.

[0560] "Presenting areas for improvement" means making suggestions to improve future activities based on the collected feedback.

[0561] The system to realize this application primarily consists of a server, a terminal, a speech recognition engine, and a generative AI engine. The server first acquires the user's schedule information and automatically determines the optimal activity time. Based on this, it checks the space reservation information and automatically reserves the space to ensure the activity proceeds smoothly.

[0562] Once the activity begins, the device activates its speech recognition engine and transcribes participants' statements into text in real time. This text data is then organized by a generative AI engine and automatically generated as a record document. Simultaneously, if a participant's statement deviates from the agenda, the generative AI issues a warning to alert them.

[0563] As the activity's end time approaches, the server notifies participants of its completion and provides support for extending or ending the activity as needed. After the activity, the server collects feedback and suggests improvements for the next activity. This ensures that the entire activity process is managed efficiently.

[0564] As a concrete example, consider a local community meeting. Residents register to participate in the meeting via their smartphones, a server determines the optimal date and time, and automatically reserves a room in the community hall. During the meeting, spoken content is transcribed into text via smart glasses, and after the meeting, a generating AI summarizes the content and distributes a transcript to participants. This process ensures that community meetings proceed quickly and systematically.

[0565] Examples of prompts for a generative AI model:

[0566] "Please schedule the next citizens' meeting and reserve the venue based on the participants' schedules."

[0567] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0568] Step 1:

[0569] Users use their devices to enter their interest in participating in an activity. The entered data includes the participant's name, the desired activity, and their available time slots. The device then sends this data to the server.

[0570] Step 2:

[0571] The server analyzes the received participation request data and aggregates each participant's schedule information. Based on this information, the server performs data processing to calculate the optimal activity time. Once the optimal time is determined, the information is passed on to the next step.

[0572] Step 3:

[0573] The server checks the availability of the space. It searches the database for a space available during the optimal activity time and automatically makes a reservation. The reservation result is notified to the user by the server.

[0574] Step 4:

[0575] At the start of the activity, the device activates its speech recognition engine and converts participants' speech to text in real time. The speech input is converted to text output and sent to the generation AI engine.

[0576] Step 5:

[0577] The generation AI engine analyzes the received text data and automatically generates a record document. In this process, it identifies statements that deviate from the agenda and generates warnings as needed. The generated warnings are displayed on the terminal to alert participants.

[0578] Step 6:

[0579] As the activity's end time approaches, the server will notify the user of its completion. This notification is sent to the user to help them decide whether to end or extend the activity. This ensures that the activity is managed to stay within the allotted time.

[0580] Step 7:

[0581] After the activity concludes, the server collects feedback and analyzes it using a generative AI engine. Based on the analysis, suggestions for improving the next activity are generated. These suggestions are then provided to the user, who can use them to prepare for the next activity.

[0582] 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.

[0583] This invention is a meeting automation system incorporating an emotion engine that recognizes emotions in real time from participants' statements and facial expressions, aiming to improve the efficiency and quality of meeting management. The system consists of a server, terminals, a generative AI engine, speech recognition technology, and an emotion engine.

[0584] First, the user requests a meeting using their device. At this stage, the list of participants and the purpose of the meeting are specified. Based on the information received from the user, the server retrieves the participants' schedules from the database and analyzes their availability.

[0585] The server reserves a meeting room according to the identified available time slot. Once this reservation process is complete, the server sends a meeting notification to all participants. The notification will be sent via email or a scheduling app and will include the meeting details.

[0586] On the day of the meeting, the terminal activates its speech recognition engine and emotion engine in conjunction. Participants' remarks are collected in real time and converted into audio data. This audio data is analyzed by a generating AI engine to support the efficient progress of the meeting.

[0587] In addition, the emotion engine analyzes participants' emotions based on their statements and facial expressions. If emotions indicating discord or stress are detected, the terminal will take appropriate action via the server, such as "Please wait a moment while we check the situation." This feature helps ensure the smooth running of meetings.

[0588] As the meeting nears its end, the server issues an end-of-meeting notification, and the meeting content is recorded by a speech recognition engine. After the meeting, a generative AI engine automatically generates meeting minutes, which are then distributed to participants. Furthermore, emotional data collected by an emotion engine is added to the feedback, indicating areas for improvement for the next meeting.

[0589] As a concrete example, consider a team meeting in a remote environment. The team leader coordinates the members' availability and sets up an online meeting. During the meeting, the emotion engine analyzes the participants' facial expressions, and if a member shows dissatisfaction with a particular topic, it immediately provides support to alleviate the situation. This makes the meeting more effective and harmonious.

[0590] The following describes the processing flow.

[0591] Step 1:

[0592] The user uses their device to request a meeting. Here, they enter a list of participants, preferred date and time, and the purpose of the meeting.

[0593] Step 2:

[0594] The server receives a request from the user and retrieves the schedule information of all participants from the database. It identifies available time slots and determines the optimal meeting time.

[0595] Step 3:

[0596] The server automatically reserves a meeting room based on the identified meeting time. This process takes into account the availability of both physical meeting rooms and online meeting platforms.

[0597] Step 4:

[0598] The server will notify all participants of the meeting details. The notification will be sent via email or a scheduling app and will include the date, time, location, and agenda.

[0599] Step 5:

[0600] At the start of the meeting, the device activates its speech recognition engine and emotion engine. The speech recognition engine collects participants' speech in real time and converts it into text.

[0601] Step 6:

[0602] The emotion engine analyzes speech and facial expression data to monitor the emotional state of participants. If a specific emotion is identified, it suggests appropriate countermeasures via the server.

[0603] Step 7:

[0604] During the meeting, a generative AI engine analyzes the speech-to-text format to help the discussion stay on track with the agenda. If the discussion deviates from the topic, the device will issue a warning.

[0605] Step 8:

[0606] As the meeting nears its end, the server displays a notification on the terminal stating, "There are 5 minutes left until the end."

[0607] Step 9:

[0608] After the meeting ends, the server automatically generates meeting minutes based on data collected through speech recognition and sentiment analysis.

[0609] Step 10:

[0610] The terminal will email the meeting minutes created to the participants, ensuring information is shared. Furthermore, a feedback report based on sentiment analysis results will also be provided.

[0611] Step 11:

[0612] Users use a feedback form to input their evaluation of the meeting and suggestions for improvement. The server collects and analyzes this information and saves it as data to suggest improvements for the next meeting.

[0613] (Example 2)

[0614] 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."

[0615] Traditional meeting systems often resulted in decreased meeting efficiency and hindered smooth communication among participants due to issues such as scheduling, meeting room reservations, agenda management during meetings, and a lack of emotionally conscious intervention. Furthermore, the lack of meeting minutes and emotionally-based feedback after meetings made it difficult to obtain information that could improve future meetings.

[0616] 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.

[0617] In this invention, the server includes means for acquiring user schedule information and determining the optimal meeting time, means for recording meeting content using speech recognition and automatically generating documents, and means for analyzing speech during the meeting, performing emotion analysis based on facial expressions, and intervening as necessary. This enables efficient and smooth meeting management, from scheduling participants' schedules to managing the meeting and providing feedback afterward.

[0618] A "user" is an individual or organization that uses this system to request a meeting and enters participant information and the purpose of the meeting.

[0619] "Schedule information" refers to data about participants' availability and schedules, and is used to determine the optimal meeting time.

[0620] A "meeting space" is a physical or virtual location reserved for holding a meeting, and is the entity whose reservation information is managed.

[0621] A "notification" is a message that provides participants with detailed information about a meeting, and is sent via email or scheduling apps.

[0622] "Speech recognition" is a technology that converts speech during a meeting into text and records the content.

[0623] "Documents" refer to meeting minutes and records that are automatically generated based on text data obtained through speech recognition.

[0624] "Speech analysis" is a technique for analyzing the content of conversations during meetings, and is used for agenda management and sentiment analysis.

[0625] "Emotional analysis" is the process of determining the emotional state of participants from their statements and facial expressions, and adjusting the flow of the meeting as needed.

[0626] "Intervention" refers to actions taken by the system during a meeting, based on the results of sentiment analysis, to facilitate the smooth running of the meeting.

[0627] "Feedback" refers to the information and suggestions provided after a meeting, including analytical results that can help improve future meetings.

[0628] This invention presents a meeting management system that combines sentiment analysis to improve the efficiency and quality of meeting operations. The system is comprised of users, terminals, a server, a generative AI engine, speech recognition technology, and a sentiment engine.

[0629] First, the user enters a meeting request using their device. A dedicated application is installed on the device, and the user uses this application to specify the list of participants and the purpose of the meeting. The entered information is then sent to the server.

[0630] The server retrieves participant schedule information from the database based on the information received. Analyzing this information, the server calculates the most optimal meeting time. Furthermore, the server automatically accesses the meeting space reservation system and reserves a meeting room. Once the reservation is complete, the server sends a meeting notification to all participants. This notification is delivered via email or scheduling apps and includes the meeting date and details.

[0631] On the day of the meeting, the terminal activates its speech recognition engine and emotion engine in conjunction. The speech recognition engine converts participants' speech into audio data in real time and sends it to the generative AI engine. The generative AI engine analyzes the meeting content and automatically generates documents, supporting efficient meeting progress. The emotion engine analyzes participants' speech and facial expressions and provides appropriate interventions according to the flow of the meeting.

[0632] As a concrete example, if there is a progress meeting for a project, the user sends a notification from their device to the participants, and everyone gathers online at the time specified in their schedule. During the meeting, the speech recognition engine records all statements as text, and the emotion engine monitors the participants' reactions. For example, if one participant expresses dissatisfaction with a certain agenda item, that emotion is immediately detected, and the device intervenes via the server to mitigate the situation.

[0633] As the meeting nears its end, the server notifies participants of the conclusion, and a generative AI engine automatically creates meeting minutes based on the content recorded by the speech recognition engine and distributes them to participants. In addition, sentiment data is analyzed and provided as feedback to indicate areas for improvement for the next meeting.

[0634] Example of a prompt message: "Please schedule the next project progress meeting. All team members will be attending, and the purpose is to review the development status."

[0635] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0636] Step 1:

[0637] The user requests a meeting using their device. Through a dedicated application on the device, the user enters the participant list and the purpose of the meeting. This input data is sent to the server as initial data.

[0638] Step 2:

[0639] Based on the request received from the user, the server retrieves participant schedule information from the database. During this process, the server performs a data calculation to determine the optimal meeting time by comparing the participants' calendar information. The output is information on alternative meeting times.

[0640] Step 3:

[0641] The server reserves a meeting space based on the processed meeting time information. During this process, the server accesses the reservation system API and automatically secures a meeting room. The output of this step is reservation confirmation information.

[0642] Step 4:

[0643] The server notifies all participants of the meeting time and location. This notification is sent via email or a scheduling app and is displayed to participants who receive the meeting details. Specifically, this involves sending messages using the notification system.

[0644] Step 5:

[0645] On the day of the meeting, the terminal activates its speech recognition engine and converts participants' speech into text data in real time during the meeting. This converted data is sent to a generative AI engine and used to document the meeting content.

[0646] Step 6:

[0647] Simultaneously, the terminal uses an emotion engine to collect participants' facial expression data. Based on this data, emotion analysis is performed, and if discord or stress is detected, appropriate intervention is initiated via the server. The output of this step is an adaptive meeting intervention instruction.

[0648] Step 7:

[0649] As the meeting nears its end, the server issues a meeting completion notification. This notification is sent to participants along with meeting minutes automatically generated by a generation AI engine based on data recorded by the speech recognition engine.

[0650] Step 8:

[0651] The server compiles feedback based on emotional data generated by the emotion engine and identifies areas for improvement for the next meeting. This feedback information is distributed to participants. The output is a feedback report that includes the analysis results.

[0652] (Application Example 2)

[0653] 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."

[0654] In modern meetings, coordinating schedules for all participants and managing the meeting's progress are time-consuming and labor-intensive. Furthermore, emotional disagreements and stress that arise during meetings can hinder their smooth operation. Solutions to these challenges are needed.

[0655] 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.

[0656] In this invention, the server includes means for acquiring user schedule information and determining the optimal meeting time, means for checking meeting room reservation information and automatically making a reservation, means for notifying the acquired meeting information, means for recording meeting content using speech recognition and automatically generating meeting minutes, means for analyzing statements made during the meeting and issuing a warning when the topic deviates, means for monitoring the progress of the meeting and notifying when it ends, means for collecting and analyzing feedback information after the meeting, and means for analyzing participants' emotions during the meeting and suggesting actions to alleviate the situation if emotions indicating stress are detected. This automates everything from meeting preparation to progress and post-meeting feedback, and further enables the smooth progress of meetings while taking into consideration the emotions of the participants.

[0657] "User schedule information" refers to information showing the schedules and availability of individual participants attending a meeting, and is data necessary for efficiently scheduling meetings.

[0658] "Meeting room reservation information" refers to information about the location and time of a meeting, and is data referenced to automate the reservation of meeting rooms.

[0659] "Acquired meeting information" refers to detailed information about the meeting, such as the date and time of the meeting, the list of participants, and the agenda, which is necessary for notifying participants.

[0660] "A method for recording meeting content and automatically generating meeting minutes using speech recognition" refers to a method of recording what is said during a meeting as audio, processing it into text data, and then creating meeting minutes.

[0661] "A means of analyzing comments made during a meeting and issuing warnings when they deviate from the agenda" refers to a system that analyzes the content of comments made during a meeting in real time and alerts participants if content that deviates from the set agenda is detected.

[0662] "Means for monitoring meeting progress and notifying participants of its end" refers to a function that tracks the time from the start to the end of a meeting and notifies participants of the meeting's end when a predetermined time has been reached.

[0663] "Methods for collecting and analyzing post-meeting feedback information" refers to the process of collecting opinions and impressions from participants after a meeting, analyzing them, and using that information to improve future meetings.

[0664] "A means of analyzing participants' emotions and proposing actions to alleviate the situation when stressful emotions are detected" refers to a system that analyzes participants' emotions from their facial expressions and statements during a meeting, and when emotions such as stress or dissatisfaction are detected, it presents action plans to reduce them.

[0665] This invention is a system that combines automated meetings with sentiment analysis. The server, upon receiving instructions from the user, retrieves participant schedule information from a database and determines the optimal meeting time. Next, it checks the availability of meeting rooms and automatically reserves them. Once the reservation is complete, it notifies participants of the retrieved meeting information. This notification is sent via email or a scheduling application.

[0666] On the day of the meeting, the terminal activates speech recognition software and records speech in real time as the meeting progresses. This uses Google Speech-to-Text as the speech recognition engine to convert the meeting content into text data. A generative AI model (e.g., OpenAI's GPT series) is used to automatically generate meeting minutes and summarize the recorded content.

[0667] Furthermore, the device performs emotion analysis during the meeting. This utilizes Affectiva Emotion AI to analyze participants' facial expressions and collect emotional data. If the device detects that a participant is experiencing stress or dissatisfaction, it suggests actions to alleviate the situation using prompt messages. This information is then fed back to the participants in real time via the server.

[0668] After the meeting concludes, the generated meeting minutes and sentiment feedback data are automatically distributed to all participants from the server. For the next meeting, the server analyzes the collected feedback information and uses a generative AI model to suggest areas for improvement.

[0669] As a concrete example, let's say a family holds a meeting to discuss whether or not they should get a pet. If the analysis engine detects that the eldest daughter is showing dissatisfaction during the meeting, it will provide a prompt such as, "Why don't we discuss a solution that everyone can agree on?" An example of a prompt might be, "The eldest daughter is feeling unhappy in this family meeting. Please suggest how we can resolve this situation."

[0670] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0671] Step 1:

[0672] Upon receiving instructions from the user, the server retrieves the schedule information of all meeting participants from the database. This input includes participant IDs, and the output generates information about each participant's availability. Based on this data, the server executes an algorithm to calculate the optimal meeting time.

[0673] Step 2:

[0674] The server checks the database for meeting room availability based on the optimal meeting time and automatically reserves an available meeting room. In this step, the desired date and time and a list of meeting rooms are entered, and the output returns information about the successfully reserved meeting room.

[0675] Step 3:

[0676] The server retrieves meeting information (date, time, meeting room, participants) and sends notifications to participants. Notifications are sent via email or scheduling apps, using meeting details as input data. The output data is the notification information sent to participants.

[0677] Step 4:

[0678] On the day of the meeting, the terminal activates its speech recognition engine and collects the content spoken during the meeting as audio data in real time. In this step, the raw audio acquired through the microphone is used as input, and the information is converted into text data and output. Google Speech-to-Text is used at this stage.

[0679] Step 5:

[0680] The generative AI model automatically generates meeting minutes based on text output from the speech recognition engine. In this step, text data obtained during the meeting is used as input, and a summarized meeting minutes text is output. This process efficiently organizes the meeting content.

[0681] Step 6:

[0682] Simultaneously, the device monitors the participant's facial expressions with its camera, and an emotion analysis engine analyzes the participant's emotional state. This step uses video data as input and outputs data indicating the emotional state. Affectiva Emotion AI performs the emotion detection.

[0683] Step 7:

[0684] Based on the results of sentiment analysis, the server generates prompts to alleviate stress and frustration if the user is experiencing them, and proposes them to participants in real time. In this step, sentiment data is input, and situation-appropriate prompts are generated as output. A generative AI model is used to construct appropriate suggestion statements.

[0685] Step 8:

[0686] After the meeting ends, the server distributes the generated meeting minutes and sentiment data to all participants and collects post-meeting feedback. At this stage, the meeting minutes and sentiment data function as inputs, and feedback information is obtained as output.

[0687] Step 9:

[0688] The server analyzes the collected feedback information and suggests improvements for the next meeting. In this step, the feedback information is input, and data suggesting improvements is output. The improvement suggestions are optimized by a generative AI model.

[0689] 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.

[0690] 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.

[0691] 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.

[0692] [Fourth Embodiment]

[0693] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0694] 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.

[0695] 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).

[0696] 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.

[0697] 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.

[0698] 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).

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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".

[0706] This invention is a system for efficiently managing meetings, and mainly consists of a server, terminals, a generative AI engine, and speech recognition technology. By automating each step from the preparation stage to the progress and post-meeting processing of a meeting, this system provides an environment in which participants can concentrate on the meeting content.

[0707] First, the user uses their device to request a meeting. This request includes specifying the participants, preferred date and time, and the purpose of the meeting. Based on the information received from the user, the server retrieves each participant's schedule data from the database and identifies the availability of all participants.

[0708] Once an available time slot is identified, the server checks the availability of meeting rooms and automatically reserves a physical or online meeting room as needed. The server notifies all participants of the reservation result, and the meeting details are displayed on their devices.

[0709] On the day of the meeting, the device will support the meeting's progress. Once the meeting begins, the device will activate its speech recognition engine and transcribe participants' statements in real time. A generative AI engine will analyze these texts, and AI voice warnings will be issued for any deviant statements. This ensures that the meeting stays on schedule.

[0710] Furthermore, the server monitors the meeting time and notifies all participants when the end time is approaching. After the meeting ends, meeting minutes are automatically generated on the server using a speech recognition engine and a generative AI engine and distributed to participants. The minutes clearly state the key points of the meeting and the next steps to take.

[0711] Finally, there's a step where users input their evaluation of the meeting and suggestions for improvement through a feedback form. The server collects this feedback and makes suggestions for improvement to be used in the next meeting. In this way, the entire system works together to improve the quality and efficiency of meetings.

[0712] As a concrete example, consider a project meeting at a certain company. The project manager logs into a terminal and coordinates the meeting using the availability of all team members. The user clearly states the purpose of the meeting, and the system automatically reserves a meeting room. During the meeting, AI manages the progress hour by hour and notifies participants of important tasks in real time. This process significantly streamlines the coordination work that was previously done manually.

[0713] The following describes the processing flow.

[0714] Step 1:

[0715] The user enters a meeting request using their device. They specify the list of participants, preferred date and time, and the purpose of the meeting.

[0716] Step 2:

[0717] Based on the meeting request received by the user, the server retrieves participant schedule information from the database. It then identifies the availability of all participants.

[0718] Step 3:

[0719] The server checks the availability of meeting rooms and automatically reserves them during identified available times. Once a meeting room is secured, a reservation confirmation notification is generated.

[0720] Step 4:

[0721] The server notifies each participant via email or scheduling app of the confirmed meeting date and time, location, participant list, and meeting purpose.

[0722] Step 5:

[0723] On the day of the meeting, the device activates its speech recognition engine at the designated time. During the meeting, the device collects participants' speech in real time and converts the audio data into text.

[0724] Step 6:

[0725] The AI ​​generation engine analyzes the transcribed speech. If the conversation deviates from the topic, the device uses AI voice to prompt the user to return to the topic.

[0726] Step 7:

[0727] The server monitors the meeting time and notifies participants via their devices five minutes before the end of the meeting, stating, "There are 5 minutes left until the end of the meeting."

[0728] Step 8:

[0729] After the meeting ends, the server automatically creates meeting minutes using speech recognition data and a generative AI engine.

[0730] Step 9:

[0731] The server will email the meeting minutes to all participants. The minutes will include the key points of the meeting and action items.

[0732] Step 10:

[0733] Users access a feedback form through their device to input their evaluation of the meeting and suggestions for improvement.

[0734] Step 11:

[0735] The server collects and analyzes feedback information and stores the data to suggest improvements for the next meeting.

[0736] (Example 1)

[0737] 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".

[0738] In modern meeting management, tasks such as scheduling, booking meeting rooms, and recording meeting content are complex and time-consuming. In particular, issues such as discussions straying from the agenda and unclear meeting end times are problematic. Furthermore, there is a lack of mechanisms for effectively utilizing post-meeting feedback. These challenges highlight the need for a comprehensive system to improve meeting efficiency and productivity.

[0739] 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.

[0740] In this invention, the server includes means for a computing device to determine the optimal meeting time from the user's schedule, means for a computing device to check the reservation status of the meeting space and automate the reservation, and means for providing meeting information using notifications. This enables efficient scheduling and reservation of meetings, as well as the provision of detailed information to participants.

[0741] A "computing device" is an electronic device used for handling and calculating digital data, and can also be used for scheduling and coordinating meetings.

[0742] A "meeting space" refers to a specific area or platform used for conducting meetings, either physically or online.

[0743] A "notification" is a means of transmitting information, used to inform recipients of important information or events.

[0744] "Speech processing technology" is a general term for technologies that acquire, analyze, and convert speech data, and is used when converting speech to text.

[0745] A "document" is an electronic or physical document that records information and is intended to organize the content of a meeting and provide it to participants.

[0746] "Evaluation information" refers to feedback and reviews provided by users, which can be used to improve products and services.

[0747] This invention is a system for efficiently and effectively managing meetings. The system comprises a server, terminals, voice processing technology, and a generative AI model. The system's objective is to provide an environment where meeting participants can focus on key agenda items by automating scheduling, meeting room reservations, meeting content recording, and minute-taking.

[0748] Users use their devices to request meetings. These requests include participants, preferred dates and times, and the purpose of the meeting. The devices run a dedicated application that transmits this information to the server. For example, entering a prompt such as "Please add the next project meeting's availability to my schedule" into the device initiates meeting preparation.

[0749] Based on the received information, the server retrieves participants' schedule information from the database and identifies everyone's availability. The server also accesses the online booking system to check meeting room availability and automatically completes the necessary booking procedures. Through this process, the optimal conditions for holding a meeting can be ensured.

[0750] On the day of the meeting, the device utilizes speech processing technology to convert meeting comments into text in real time. This text data is analyzed by a generative AI model, and if any comments deviate from the agenda, an automatic warning is issued. In this way, the meeting itself is conducted efficiently and effectively.

[0751] After the meeting concludes, meeting minutes are automatically generated by the server and distributed to participants. The minutes summarize key points of the meeting and future action points based on text data obtained using speech processing technology.

[0752] After a meeting, users input feedback using their devices, which is then collected and analyzed by the server. The analyzed information is used to suggest improvements for the next meeting, thereby improving the overall quality of meetings within the system.

[0753] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0754] Step 1:

[0755] The user enters a meeting request using their device. This input includes a participant list, preferred date and time, and the purpose of the meeting. The information entered by the user is output and sent to the server. Specifically, the user enters information into the input form on their device and presses the "Submit" button.

[0756] Step 2:

[0757] The server processes the received request data and retrieves participant schedule information from the database. The server then analyzes this schedule data to identify everyone's free time. The input is the request data from the user, and the output is the common free time for all participants. Specifically, the server uses SQL queries to access the database and check each participant's schedule.

[0758] Step 3:

[0759] The server accesses the meeting space reservation system based on availability information and reserves a suitable meeting room. The server checks the availability of meeting rooms via API and, if the reservation is successful, obtains reservation confirmation information. The input is the common availability time, and the output is reservation confirmation information. Specifically, the server sends an API request and receives a reservation response.

[0760] Step 4:

[0761] The server sends a notification to all participants regarding the meeting details. The server uses a notification system to provide all participants with the meeting date, time, and location, and this information is displayed on their devices. The input is reservation confirmation information, and the output is the notification to participants. Specifically, the server distributes the information via email or push notifications.

[0762] Step 5:

[0763] On the day of the meeting, the terminal uses speech processing technology to transcribe participants' speech in real time. The terminal analyzes the collected audio data with a speech recognition engine and outputs it as text data. The input is audio data, and the output is the transcribed speech. Specifically, the terminal captures audio with a microphone and performs text conversion using software.

[0764] Step 6:

[0765] The generating AI model analyzes the obtained text data and issues a warning if any statements deviate from the topic. The model compares the text to the topic content, performs data calculations to detect deviations, and outputs a warning command as needed. The input is text data, and the output is warning information. Specifically, it analyzes the text content as appropriate and displays a warning via voice or message from the terminal.

[0766] Step 7:

[0767] The server automatically generates meeting minutes after the meeting ends and distributes them to all participants. The server processes the text obtained using speech processing technology to create a summary. The input is the text data of the entire meeting, and the output is the completed meeting minutes. Specifically, the server sends the meeting minutes via email or uploads them to a dedicated portal.

[0768] Step 8:

[0769] Users enter feedback from their terminal after a meeting. The feedback is sent to the server and used for analysis to improve future meetings. The input is data from an evaluation form, and the output is the analysis results on the server. Specifically, the user answers questions in a survey format and presses a submit button.

[0770] (Application Example 1)

[0771] 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".

[0772] In smart cities, efficient and smooth operation of participatory meetings and public forums for citizens is hampered by the complexities of coordinating schedules among participants, booking venues, preventing deviations from the ongoing agenda, recording meeting content, and sharing information with participants. Therefore, a system is needed that enhances transparency and efficiency, ensuring that all participants receive information smoothly.

[0773] 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.

[0774] In this invention, the server includes means for acquiring user schedule information and determining the optimal activity time, means for checking space reservation information and automatically making reservations, and means for automatically distributing generated record documents to multiple participants. This makes it possible for citizens to efficiently prepare for participatory meetings and forums, carry out activities in line with the agenda, and easily share record documents.

[0775] "User schedule information" refers to data about each participant's individual schedule and available time.

[0776] The "optimal activity time" is the time period that allows all participants to participate without difficulty and to carry out activities efficiently.

[0777] "Space reservation information" refers to data regarding the reservation status and available times for a location where an activity will take place.

[0778] "Speech recognition" is a technology that converts speech into text, accurately recording what is said as written characters.

[0779] A "record document" is a document automatically generated based on what was said during an activity, and serves as an official record for later reference.

[0780] "Analyzing a statement" is the process of analyzing the content of a statement and identifying any deviations from its purpose or topic.

[0781] "Notifying participants of the end of the activity" is the process of informing participants that the allotted activity time is nearing its end.

[0782] "Opinion information" refers to data on evaluations, feedback, and suggestions for improvement provided by participants after the completion of the activity.

[0783] "Automated distribution" is the process of sending generated record documents to participants without human intervention.

[0784] "Presenting areas for improvement" means making suggestions to improve future activities based on the collected feedback.

[0785] The system to realize this application primarily consists of a server, a terminal, a speech recognition engine, and a generative AI engine. The server first acquires the user's schedule information and automatically determines the optimal activity time. Based on this, it checks the space reservation information and automatically reserves the space to ensure the activity proceeds smoothly.

[0786] Once the activity begins, the device activates its speech recognition engine and transcribes participants' statements into text in real time. This text data is then organized by a generative AI engine and automatically generated as a record document. Simultaneously, if a participant's statement deviates from the agenda, the generative AI issues a warning to alert them.

[0787] As the activity's end time approaches, the server notifies participants of its completion and provides support for extending or ending the activity as needed. After the activity, the server collects feedback and suggests improvements for the next activity. This ensures that the entire activity process is managed efficiently.

[0788] As a concrete example, consider a local community meeting. Residents register to participate in the meeting via their smartphones, a server determines the optimal date and time, and automatically reserves a room in the community hall. During the meeting, spoken content is transcribed into text via smart glasses, and after the meeting, a generating AI summarizes the content and distributes a transcript to participants. This process ensures that community meetings proceed quickly and systematically.

[0789] Examples of prompts for a generative AI model:

[0790] "Please schedule the next citizens' meeting and reserve the venue based on the participants' schedules."

[0791] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0792] Step 1:

[0793] Users use their devices to enter their interest in participating in an activity. The entered data includes the participant's name, the desired activity, and their available time slots. The device then sends this data to the server.

[0794] Step 2:

[0795] The server analyzes the received participation request data and aggregates each participant's schedule information. Based on this information, the server performs data processing to calculate the optimal activity time. Once the optimal time is determined, the information is passed on to the next step.

[0796] Step 3:

[0797] The server checks the availability of the space. It searches the database for a space available during the optimal activity time and automatically makes a reservation. The reservation result is notified to the user by the server.

[0798] Step 4:

[0799] At the start of the activity, the device activates its speech recognition engine and converts participants' speech to text in real time. The speech input is converted to text output and sent to the generation AI engine.

[0800] Step 5:

[0801] The generation AI engine analyzes the received text data and automatically generates a record document. In this process, it identifies statements that deviate from the agenda and generates warnings as needed. The generated warnings are displayed on the terminal to alert participants.

[0802] Step 6:

[0803] As the activity's end time approaches, the server will notify the user of its completion. This notification is sent to the user to help them decide whether to end or extend the activity. This ensures that the activity is managed to stay within the allotted time.

[0804] Step 7:

[0805] After the activity concludes, the server collects feedback and analyzes it using a generative AI engine. Based on the analysis, suggestions for improving the next activity are generated. These suggestions are then provided to the user, who can use them to prepare for the next activity.

[0806] 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.

[0807] This invention is a meeting automation system incorporating an emotion engine that recognizes emotions in real time from participants' statements and facial expressions, aiming to improve the efficiency and quality of meeting management. The system consists of a server, terminals, a generative AI engine, speech recognition technology, and an emotion engine.

[0808] First, the user requests a meeting using their device. At this stage, the list of participants and the purpose of the meeting are specified. Based on the information received from the user, the server retrieves the participants' schedules from the database and analyzes their availability.

[0809] The server reserves a meeting room according to the identified available time slot. Once this reservation process is complete, the server sends a meeting notification to all participants. The notification will be sent via email or a scheduling app and will include the meeting details.

[0810] On the day of the meeting, the terminal activates its speech recognition engine and emotion engine in conjunction. Participants' remarks are collected in real time and converted into audio data. This audio data is analyzed by a generating AI engine to support the efficient progress of the meeting.

[0811] In addition, the emotion engine analyzes participants' emotions based on their statements and facial expressions. If emotions indicating discord or stress are detected, the terminal will take appropriate action via the server, such as "Please wait a moment while we check the situation." This feature helps ensure the smooth running of meetings.

[0812] As the meeting nears its end, the server issues an end-of-meeting notification, and the meeting content is recorded by a speech recognition engine. After the meeting, a generative AI engine automatically generates meeting minutes, which are then distributed to participants. Furthermore, emotional data collected by an emotion engine is added to the feedback, indicating areas for improvement for the next meeting.

[0813] As a concrete example, consider a team meeting in a remote environment. The team leader coordinates the members' availability and sets up an online meeting. During the meeting, the emotion engine analyzes the participants' facial expressions, and if a member shows dissatisfaction with a particular topic, it immediately provides support to alleviate the situation. This makes the meeting more effective and harmonious.

[0814] The following describes the processing flow.

[0815] Step 1:

[0816] The user uses their device to request a meeting. Here, they enter a list of participants, preferred date and time, and the purpose of the meeting.

[0817] Step 2:

[0818] The server receives a request from the user and retrieves the schedule information of all participants from the database. It identifies available time slots and determines the optimal meeting time.

[0819] Step 3:

[0820] The server automatically reserves a meeting room based on the identified meeting time. This process takes into account the availability of both physical meeting rooms and online meeting platforms.

[0821] Step 4:

[0822] The server will notify all participants of the meeting details. The notification will be sent via email or a scheduling app and will include the date, time, location, and agenda.

[0823] Step 5:

[0824] At the start of the meeting, the device activates its speech recognition engine and emotion engine. The speech recognition engine collects participants' speech in real time and converts it into text.

[0825] Step 6:

[0826] The emotion engine analyzes speech and facial expression data to monitor the emotional state of participants. If a specific emotion is identified, it suggests appropriate countermeasures via the server.

[0827] Step 7:

[0828] During the meeting, a generative AI engine analyzes the speech-to-text format to help the discussion stay on track with the agenda. If the discussion deviates from the topic, the device will issue a warning.

[0829] Step 8:

[0830] As the meeting nears its end, the server displays a notification on the terminal stating, "There are 5 minutes left until the end."

[0831] Step 9:

[0832] After the meeting ends, the server automatically generates meeting minutes based on data collected through speech recognition and sentiment analysis.

[0833] Step 10:

[0834] The terminal will email the meeting minutes created to the participants, ensuring information is shared. Furthermore, a feedback report based on sentiment analysis results will also be provided.

[0835] Step 11:

[0836] Users use a feedback form to input their evaluation of the meeting and suggestions for improvement. The server collects and analyzes this information and saves it as data to suggest improvements for the next meeting.

[0837] (Example 2)

[0838] 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".

[0839] Traditional meeting systems often resulted in decreased meeting efficiency and hindered smooth communication among participants due to issues such as scheduling, meeting room reservations, agenda management during meetings, and a lack of emotionally conscious intervention. Furthermore, the lack of meeting minutes and emotionally-based feedback after meetings made it difficult to obtain information that could improve future meetings.

[0840] 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.

[0841] In this invention, the server includes means for acquiring user schedule information and determining the optimal meeting time, means for recording meeting content using speech recognition and automatically generating documents, and means for analyzing speech during the meeting, performing emotion analysis based on facial expressions, and intervening as necessary. This enables efficient and smooth meeting management, from scheduling participants' schedules to managing the meeting and providing feedback afterward.

[0842] A "user" is an individual or organization that uses this system to request a meeting and enters participant information and the purpose of the meeting.

[0843] "Schedule information" refers to data about participants' availability and schedules, and is used to determine the optimal meeting time.

[0844] A "meeting space" is a physical or virtual location reserved for holding a meeting, and is the entity whose reservation information is managed.

[0845] A "notification" is a message that provides participants with detailed information about a meeting, and is sent via email or scheduling apps.

[0846] "Speech recognition" is a technology that converts speech during a meeting into text and records the content.

[0847] "Documents" refer to meeting minutes and records that are automatically generated based on text data obtained through speech recognition.

[0848] "Speech analysis" is a technique for analyzing the content of conversations during meetings, and is used for agenda management and sentiment analysis.

[0849] "Emotional analysis" is the process of determining the emotional state of participants from their statements and facial expressions, and adjusting the flow of the meeting as needed.

[0850] "Intervention" refers to actions taken by the system during a meeting, based on the results of sentiment analysis, to facilitate the smooth running of the meeting.

[0851] "Feedback" refers to the information and suggestions provided after a meeting, including analytical results that can help improve future meetings.

[0852] This invention presents a meeting management system that combines sentiment analysis to improve the efficiency and quality of meeting operations. The system is comprised of users, terminals, a server, a generative AI engine, speech recognition technology, and a sentiment engine.

[0853] First, the user enters a meeting request using their device. A dedicated application is installed on the device, and the user uses this application to specify the list of participants and the purpose of the meeting. The entered information is then sent to the server.

[0854] The server retrieves participant schedule information from the database based on the information received. Analyzing this information, the server calculates the most optimal meeting time. Furthermore, the server automatically accesses the meeting space reservation system and reserves a meeting room. Once the reservation is complete, the server sends a meeting notification to all participants. This notification is delivered via email or scheduling apps and includes the meeting date and details.

[0855] On the day of the meeting, the terminal activates its speech recognition engine and emotion engine in conjunction. The speech recognition engine converts participants' speech into audio data in real time and sends it to the generative AI engine. The generative AI engine analyzes the meeting content and automatically generates documents, supporting efficient meeting progress. The emotion engine analyzes participants' speech and facial expressions and provides appropriate interventions according to the flow of the meeting.

[0856] As a concrete example, if there is a progress meeting for a project, the user sends a notification from their device to the participants, and everyone gathers online at the time specified in their schedule. During the meeting, the speech recognition engine records all statements as text, and the emotion engine monitors the participants' reactions. For example, if one participant expresses dissatisfaction with a certain agenda item, that emotion is immediately detected, and the device intervenes via the server to mitigate the situation.

[0857] As the meeting nears its end, the server notifies participants of the conclusion, and a generative AI engine automatically creates meeting minutes based on the content recorded by the speech recognition engine and distributes them to participants. In addition, sentiment data is analyzed and provided as feedback to indicate areas for improvement for the next meeting.

[0858] Example of a prompt message: "Please schedule the next project progress meeting. All team members will be attending, and the purpose is to review the development status."

[0859] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0860] Step 1:

[0861] The user requests a meeting using their device. Through a dedicated application on the device, the user enters the participant list and the purpose of the meeting. This input data is sent to the server as initial data.

[0862] Step 2:

[0863] Based on the request received from the user, the server retrieves participant schedule information from the database. During this process, the server performs a data calculation to determine the optimal meeting time by comparing the participants' calendar information. The output is information on alternative meeting times.

[0864] Step 3:

[0865] The server reserves a meeting space based on the processed meeting time information. During this process, the server accesses the reservation system API and automatically secures a meeting room. The output of this step is reservation confirmation information.

[0866] Step 4:

[0867] The server notifies all participants of the meeting time and location. This notification is sent via email or a scheduling app and is displayed to participants who receive the meeting details. Specifically, this involves sending messages using the notification system.

[0868] Step 5:

[0869] On the day of the meeting, the terminal activates its speech recognition engine and converts participants' speech into text data in real time during the meeting. This converted data is sent to a generative AI engine and used to document the meeting content.

[0870] Step 6:

[0871] Simultaneously, the terminal uses an emotion engine to collect participants' facial expression data. Based on this data, emotion analysis is performed, and if discord or stress is detected, appropriate intervention is initiated via the server. The output of this step is an adaptive meeting intervention instruction.

[0872] Step 7:

[0873] As the meeting nears its end, the server issues a meeting completion notification. This notification is sent to participants along with meeting minutes automatically generated by a generation AI engine based on data recorded by the speech recognition engine.

[0874] Step 8:

[0875] The server compiles feedback based on emotional data generated by the emotion engine and identifies areas for improvement for the next meeting. This feedback information is distributed to participants. The output is a feedback report that includes the analysis results.

[0876] (Application Example 2)

[0877] 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".

[0878] In modern meetings, coordinating schedules for all participants and managing the meeting's progress are time-consuming and labor-intensive. Furthermore, emotional disagreements and stress that arise during meetings can hinder their smooth operation. Solutions to these challenges are needed.

[0879] 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.

[0880] In this invention, the server includes means for acquiring user schedule information and determining the optimal meeting time, means for checking meeting room reservation information and automatically making a reservation, means for notifying the acquired meeting information, means for recording meeting content using speech recognition and automatically generating meeting minutes, means for analyzing statements made during the meeting and issuing a warning when the topic deviates, means for monitoring the progress of the meeting and notifying when it ends, means for collecting and analyzing feedback information after the meeting, and means for analyzing participants' emotions during the meeting and suggesting actions to alleviate the situation if emotions indicating stress are detected. This automates everything from meeting preparation to progress and post-meeting feedback, and further enables the smooth progress of meetings while taking into consideration the emotions of the participants.

[0881] "User schedule information" refers to information showing the schedules and availability of individual participants attending a meeting, and is data necessary for efficiently scheduling meetings.

[0882] "Meeting room reservation information" refers to information about the location and time of a meeting, and is data referenced to automate the reservation of meeting rooms.

[0883] "Acquired meeting information" refers to detailed information about the meeting, such as the date and time of the meeting, the list of participants, and the agenda, which is necessary for notifying participants.

[0884] "A method for recording meeting content and automatically generating meeting minutes using speech recognition" refers to a method of recording what is said during a meeting as audio, processing it into text data, and then creating meeting minutes.

[0885] "A means of analyzing comments made during a meeting and issuing warnings when they deviate from the agenda" refers to a system that analyzes the content of comments made during a meeting in real time and alerts participants if content that deviates from the set agenda is detected.

[0886] "Means for monitoring meeting progress and notifying participants of its end" refers to a function that tracks the time from the start to the end of a meeting and notifies participants of the meeting's end when a predetermined time has been reached.

[0887] "Methods for collecting and analyzing post-meeting feedback information" refers to the process of collecting opinions and impressions from participants after a meeting, analyzing them, and using that information to improve future meetings.

[0888] "A means of analyzing participants' emotions and proposing actions to alleviate the situation when stressful emotions are detected" refers to a system that analyzes participants' emotions from their facial expressions and statements during a meeting, and when emotions such as stress or dissatisfaction are detected, it presents action plans to reduce them.

[0889] This invention is a system that combines automated meetings with sentiment analysis. The server, upon receiving instructions from the user, retrieves participant schedule information from a database and determines the optimal meeting time. Next, it checks the availability of meeting rooms and automatically reserves them. Once the reservation is complete, it notifies participants of the retrieved meeting information. This notification is sent via email or a scheduling application.

[0890] On the day of the meeting, the terminal activates speech recognition software and records speech in real time as the meeting progresses. This uses Google Speech-to-Text as the speech recognition engine to convert the meeting content into text data. A generative AI model (e.g., OpenAI's GPT series) is used to automatically generate meeting minutes and summarize the recorded content.

[0891] Furthermore, the device performs emotion analysis during the meeting. This utilizes Affectiva Emotion AI to analyze participants' facial expressions and collect emotional data. If the device detects that a participant is experiencing stress or dissatisfaction, it suggests actions to alleviate the situation using prompt messages. This information is then fed back to the participants in real time via the server.

[0892] After the meeting concludes, the generated meeting minutes and sentiment feedback data are automatically distributed to all participants from the server. For the next meeting, the server analyzes the collected feedback information and uses a generative AI model to suggest areas for improvement.

[0893] As a concrete example, let's say a family holds a meeting to discuss whether or not they should get a pet. If the analysis engine detects that the eldest daughter is showing dissatisfaction during the meeting, it will provide a prompt such as, "Why don't we discuss a solution that everyone can agree on?" An example of a prompt might be, "The eldest daughter is feeling unhappy in this family meeting. Please suggest how we can resolve this situation."

[0894] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0895] Step 1:

[0896] Upon receiving instructions from the user, the server retrieves the schedule information of all meeting participants from the database. This input includes participant IDs, and the output generates information about each participant's availability. Based on this data, the server executes an algorithm to calculate the optimal meeting time.

[0897] Step 2:

[0898] The server checks the database for meeting room availability based on the optimal meeting time and automatically reserves an available meeting room. In this step, the desired date and time and a list of meeting rooms are entered, and the output returns information about the successfully reserved meeting room.

[0899] Step 3:

[0900] The server retrieves meeting information (date, time, meeting room, participants) and sends notifications to participants. Notifications are sent via email or scheduling apps, using meeting details as input data. The output data is the notification information sent to participants.

[0901] Step 4:

[0902] On the day of the meeting, the terminal activates its speech recognition engine and collects the content spoken during the meeting as audio data in real time. In this step, the raw audio acquired through the microphone is used as input, and the information is converted into text data and output. Google Speech-to-Text is used at this stage.

[0903] Step 5:

[0904] The generative AI model automatically generates meeting minutes based on text output from the speech recognition engine. In this step, text data obtained during the meeting is used as input, and a summarized meeting minutes text is output. This process efficiently organizes the meeting content.

[0905] Step 6:

[0906] Simultaneously, the device monitors the participant's facial expressions with its camera, and an emotion analysis engine analyzes the participant's emotional state. This step uses video data as input and outputs data indicating the emotional state. Affectiva Emotion AI performs the emotion detection.

[0907] Step 7:

[0908] Based on the results of sentiment analysis, the server generates prompts to alleviate stress and frustration if the user is experiencing them, and proposes them to participants in real time. In this step, sentiment data is input, and situation-appropriate prompts are generated as output. A generative AI model is used to construct appropriate suggestion statements.

[0909] Step 8:

[0910] After the meeting ends, the server distributes the generated meeting minutes and sentiment data to all participants and collects post-meeting feedback. At this stage, the meeting minutes and sentiment data function as inputs, and feedback information is obtained as output.

[0911] Step 9:

[0912] The server analyzes the collected feedback information and suggests improvements for the next meeting. In this step, the feedback information is input, and data suggesting improvements is output. The improvement suggestions are optimized by a generative AI model.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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."

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] The following is further disclosed regarding the embodiments described above.

[0935] (Claim 1)

[0936] A means of obtaining user schedule information and determining the optimal meeting time,

[0937] A method for checking meeting room reservation information and making reservations automatically,

[0938] A means of notifying the acquired meeting information,

[0939] A method for recording meeting content and automatically generating meeting minutes using speech recognition,

[0940] A means of analyzing what is said during a meeting and issuing a warning when it deviates from the agenda,

[0941] A means of monitoring the progress of a meeting and notifying its end,

[0942] A means of collecting and analyzing feedback information after a meeting,

[0943] A system that includes this.

[0944] (Claim 2)

[0945] The system according to claim 1, characterized in that it includes means for automatically distributing generated meeting minutes to multiple participants.

[0946] (Claim 3)

[0947] The system according to claim 1, characterized in that it includes a means for suggesting improvements to the next meeting based on feedback information obtained after the meeting.

[0948] "Example 1"

[0949] (Claim 1)

[0950] A means by which a computing device determines the optimal meeting time from the user's schedule,

[0951] A computing device checks the reservation status of meeting spaces and automates the reservation process.

[0952] A means by which a computing device provides meeting information using notifications,

[0953] A method for recording meeting content and automatically generating documents using voice processing technology,

[0954] A means of analyzing comments made during a meeting and issuing warnings if they deviate from the agenda,

[0955] A means of measuring the progress of a meeting and signaling its conclusion,

[0956] A means of analyzing evaluation information collected after the meeting,

[0957] A system that includes this.

[0958] (Claim 2)

[0959] The system according to claim 1, further comprising means for automatically sending a generated document to multiple participants.

[0960] (Claim 3)

[0961] The system according to claim 1, comprising means for indicating improvements for the next meeting based on evaluation information obtained after the meeting.

[0962] "Application Example 1"

[0963] (Claim 1)

[0964] A means of obtaining user schedule information and determining the optimal activity time,

[0965] A method for checking space reservation information and making reservations automatically,

[0966] A means of notifying acquired activity information,

[0967] A means for recording activity details using speech recognition and automatically generating a record document,

[0968] A means of analyzing statements made during activities and issuing warnings when they deviate from the agenda,

[0969] A means of monitoring the progress of an activity and notifying its completion,

[0970] A means of collecting and analyzing opinion information after the activity,

[0971] A system that includes this.

[0972] (Claim 2)

[0973] The system according to claim 1, characterized by comprising means for automatically distributing generated record documents to multiple participants.

[0974] (Claim 3)

[0975] The system according to claim 1, characterized in that it includes a means for suggesting areas for improvement in the next activity based on the feedback information obtained after the activity.

[0976] "Example 2 of combining an emotion engine"

[0977] (Claim 1)

[0978] A means of obtaining user schedule information and determining the optimal meeting time,

[0979] A method for checking meeting space reservation information and making reservations automatically,

[0980] A means of notifying the acquired meeting information,

[0981] A means of recording meeting content using speech recognition and automatically generating documents,

[0982] This method involves analyzing what is said during a meeting, performing emotional analysis based on facial expressions, and intervening as needed.

[0983] A means of monitoring the progress of a meeting and notifying its end,

[0984] A means of collecting and analyzing feedback information after a meeting,

[0985] Emotional data is provided as feedback, and this serves as a means to indicate improvements for the next meeting.

[0986] A system that includes this.

[0987] (Claim 2)

[0988] The system according to claim 1, characterized in that it includes means for automatically distributing the generated document to multiple participants.

[0989] (Claim 3)

[0990] The system according to claim 1, characterized in that it includes a means to support the smooth progress of a meeting by performing emotional analysis from facial expressions during the meeting.

[0991] "Application example 2 when combining with an emotional engine"

[0992] (Claim 1)

[0993] A means of obtaining user schedule information and determining the optimal meeting time,

[0994] A method for checking meeting room reservation information and making reservations automatically,

[0995] A means of notifying the acquired meeting information,

[0996] A method for recording meeting content and automatically generating meeting minutes using speech recognition,

[0997] A means of analyzing what is said during a meeting and issuing a warning when it deviates from the agenda,

[0998] A means of monitoring the progress of a meeting and notifying its end,

[0999] A means of collecting and analyzing feedback information after a meeting,

[1000] A method for analyzing participants' emotions during a meeting and suggesting actions to alleviate the situation if stressful emotions are detected,

[1001] A system that includes this.

[1002] (Claim 2)

[1003] The system according to claim 1, characterized in that it includes means for automatically distributing generated meeting minutes to multiple participants.

[1004] (Claim 3)

[1005] The system according to claim 1, characterized in that it includes a means for suggesting improvements to the next meeting based on feedback information obtained after the meeting. [Explanation of Symbols]

[1006] 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 obtaining user schedule information and determining the optimal meeting time, A method for checking meeting room reservation information and making reservations automatically, A means of notifying the acquired meeting information, A method for recording meeting content and automatically generating meeting minutes using speech recognition, A means of analyzing what is said during a meeting and issuing a warning when it deviates from the agenda, A means of monitoring the progress of a meeting and notifying its end, A means of collecting and analyzing feedback information after a meeting, A system that includes this.

2. The system according to claim 1, characterized in that it includes means for automatically distributing the generated meeting minutes to multiple participants.

3. The system according to claim 1, characterized in that it includes a means for suggesting improvements to the next meeting based on feedback information obtained after the meeting.

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