System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a meeting request and determining a need using a generated AI; means for optimizing a participant list included in the meeting request; means for reviewing meeting materials and generating improvement suggestions; means for facilitating a meeting in real-time while the meeting is in progress; and means for receiving and analyzing feedback after the meeting has ended.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, the productivity of meetings has declined in many organizations, with delays in decision-making and unnecessary time wastage becoming problems. In particular, insufficient preparation, incorrect participant selection, and inappropriate progress can lead to poor meeting efficiency and a decline in the work efficiency of the entire organization. The present invention aims to solve these problems and improve the productivity of meetings. [Means for solving the problem]

[0005] The present invention provides the following system using generative AI:

[0006] a means for receiving and determining the necessity of a meeting request using a generative AI;

[0007] A means for optimizing the participant list included in a meeting request;

[0008] A means for reviewing meeting materials and generating improvement suggestions;

[0009] A means of real-time facilitation during the meeting,

[0010] A means to accept and analyze feedback after the meeting;

[0011] It is a system including:

[0012] This system carefully examines the need for meetings in advance and selects appropriate participants, thereby reducing unnecessary meetings. It also reviews meeting materials in advance and suggests improvements, ensuring meetings begin with all the necessary information. Furthermore, during the meeting, the generative AI adjusts the order of comments, speaking style, and speaking speed in real time, promoting important discussions. Analyzing feedback obtained after the meeting makes clear areas for improvement in future meetings. These features are expected to improve meeting productivity and operational efficiency across the organization.

[0013] "Generative AI" is an artificial intelligence system that uses natural language processing and machine learning techniques to automatically respond based on human instructions.

[0014] A "meeting request" is a document or data that is submitted to propose the holding of a meeting and includes information such as the purpose of the meeting, the agenda, and potential participants.

[0015] The "means for determining necessity" is an algorithm or method for analyzing the content of a received conference request and determining whether the conference is worth conducting.

[0016] A "means for optimizing the participant list" is an algorithm or method for selecting participants from the list of potential participants in a conference request that are most suitable for the purpose and agenda of the conference.

[0017] "Meeting materials" are documents and digital files such as presentations, reports, data sheets, etc. used in meetings.

[0018] A "means for reviewing and generating improvement suggestions" is an algorithm or method for analyzing the contents of meeting materials, checking whether necessary information is included, and presenting missing information and areas for improvement.

[0019] "Facilitation" refers to techniques and methods for smoothly advancing discussions during a meeting and adjusting the speaking style and speed of speakers as appropriate.

[0020] A "means for receiving and analyzing feedback" is an algorithm or method for collecting opinions and ratings provided by meeting participants and analyzing them to identify areas for improvement in the meeting. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] The present invention relates to a system for improving the efficiency of meetings using generative AI. Specific embodiments of the system of the present invention will be described below. In particular, the operations of the server, terminals, and users will be described in detail.

[0043] Overall system configuration

[0044] The system mainly consists of the following modules:

[0045] 1. Meeting Request Analysis Module

[0046] 2. Participant List Optimization Module

[0047] 3. Document Review and Improvement Suggestion Module

[0048] 4. Real-time Facilitation Module

[0049] 5. Feedback Collection and Analysis Module

[0050] System Operation

[0051] 1. Meeting request analysis

[0052] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The server uses the received information to determine the necessity of the meeting. At this time, it analyzes the request content using natural language processing technology and determines whether the meeting needs to be held.

[0053] 2. Optimize your attendee list

[0054] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device, and the user can confirm the final participant list. The user can then modify or add to the list as needed.

[0055] 3. Document review and improvement suggestions

[0056] Users upload meeting materials from their devices to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. Based on the results of the analysis, it generates specific suggestions for improving the materials and notifies the user via their device. This ensures that all necessary information is gathered before the meeting.

[0057] 4. Real-time facilitation

[0058] When a user starts a meeting, the server monitors the progress of the meeting in real time. The server adjusts the order, style, and speed of speech to help ensure important discussions proceed smoothly. For example, if a particular participant speaks for a long time, the server will suggest shortening their speech via the user's device. It also provides timely advice to prevent the discussion from going off track.

[0059] 5. Feedback Collection and Analysis

[0060] After the meeting, users fill out a feedback form on their device and send their evaluation of the meeting to the server. The server analyzes this feedback and identifies the effectiveness of the meeting and areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[0061] Specific examples

[0062] For example, this system is used when a company wants to hold a meeting to discuss sales strategies for a new product.

[0063] 1. A user enters a meeting request and submits it to the server.

[0064] 2. The server parses the request and determines whether a meeting is required.

[0065] 3. The server generates an optimal participant list and notifies the user through the terminal.

[0066] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[0067] 5. The user initiates the conference, which the server facilitates in real time.

[0068] 6. After the meeting, users enter their feedback, which the server analyzes and notifies them of improvements for the next meeting.

[0069] This series of processes ensures that meetings are carried out consistently and efficiently, from preparation to progress and evaluation, improving meeting productivity and the operational efficiency of the entire organization.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The input form contains specific meeting details.

[0073] Step 2:

[0074] The server analyzes the received meeting request and uses natural language processing technology to understand the request content, determine whether the purpose, agenda, and expected outcomes are appropriate, and evaluate the necessity of the meeting.

[0075] Step 3:

[0076] After the server confirms the need for a conference, it analyzes the participant list in the conference request, analyzes each participant's job title and area of ​​expertise, and selects the most suitable participants. At this time, it narrows down the participants to the minimum necessary.

[0077] Step 4:

[0078] The terminal notifies the user of the optimized participant list, and the user confirms it. The user can add or remove participants as needed and confirm the final list.

[0079] Step 5:

[0080] Users upload meeting materials from their devices to the server, including presentations, reports, data sheets, and more.

[0081] Step 6:

[0082] The server analyzes the uploaded materials, checks the completeness of the information, identifies missing information and areas that need improvement, and generates specific improvement proposals.

[0083] Step 7:

[0084] The terminal notifies the user of the improvement suggestions from the server, and the user corrects the materials. If necessary, the materials are uploaded to the server again and rechecked.

[0085] Step 8:

[0086] Once a user starts a meeting, the server monitors the progress of the meeting in real time, and algorithms are put into motion to adjust the order, style and speed of speech.

[0087] Step 9:

[0088] The server generates appropriate advice to support the progress of important discussions and notifies the users through their terminals, thereby enabling discussions to proceed actively and efficiently.

[0089] Step 10:

[0090] After the meeting, users fill out a feedback form on their devices and send it to the server. The feedback includes the quality of the meeting, how it was conducted, and areas for improvement.

[0091] Step 11:

[0092] The server analyzes the received feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are then sent to the user via their device, and specific suggestions for improvement are provided for the next meeting.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] In traditional meetings, selecting the best participants, preparing materials, managing the meeting, and collecting and analyzing feedback requires a great deal of time and effort. As a result, meetings are less efficient and discussions are often ineffective. In addition, because multiple methods are used individually, it is difficult to integrate and manage the entire meeting, which prevents the productivity of meetings from improving. An integrated system is needed to solve this problem.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0097] In this invention, the server includes: means for receiving a conference request using a generation AI and determining the necessity; means for optimizing the participant list included in the conference request; means for reviewing conference materials and generating improvement suggestions; means for facilitating in real time while the conference is in progress; means for accepting and analyzing feedback after the conference has ended; means for a user to input a conference request from a terminal and send it to the server; means for the server to analyze the request content using natural language processing technology; means for the server to generate an optimal participant list based on the participants' positions and areas of expertise; means for the server to analyze uploaded conference materials and identify missing information and areas for improvement; means for the server to monitor the progress of the conference in real time and adjust the order of speech, speaking style, and speaking speed; and means for the server to analyze feedback sent from users and identify areas for improvement next time.

[0098] This makes it possible to consistently manage meeting preparation, progress, and evaluation, significantly improving meeting productivity.

[0099] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data.

[0100] A "meeting request" is data including information such as the purpose of the meeting, the agenda, potential participants, and expected outcomes.

[0101] A "terminal" is an electronic device such as a computer or smartphone that a user uses to input, operate, and confirm information.

[0102] A "server" is a computer system that receives, analyzes, manages, and processes data sent by users.

[0103] "Natural language processing technology" is a technology for understanding and analyzing human language.

[0104] A "participant list" is a list of people who are expected to attend a conference.

[0105] "Position" refers to the participant's role or position in the workplace.

[0106] "Area of ​​expertise" refers to the area in which a participant has specialized knowledge or skills.

[0107] "Meeting materials" are documents such as reports and presentations used in meetings.

[0108] An "improvement proposal" is a specific proposal to point out problems with the meeting materials or progress and improve them.

[0109] "Facilitation" is the act of coordinating a meeting to ensure that it proceeds smoothly and that important discussions take place.

[0110] "Feedback" is information used to gather evaluations and opinions about the meeting.

[0111] "Analysis" is the process of analyzing data to find meaning and patterns.

[0112] The present invention is a system for improving the efficiency of meetings using generative AI. Specific embodiments for implementing the present invention are described below. In particular, the operations of the server, terminals, and users are described in detail.

[0113] Overall system configuration

[0114] The system mainly consists of the following modules:

[0115] 1. Meeting Request Analysis Module

[0116] 2. Participant List Optimization Module

[0117] 3. Document Review and Improvement Suggestion Module

[0118] 4. Real-time Facilitation Module

[0119] 5. Feedback Collection and Analysis Module

[0120] Filling out a meeting request

[0121] Users input meeting requests through their devices and send them to the server. Users use a form to enter the purpose of the meeting, the agenda, potential participants, and expected outcomes. By clicking the send button, the input data is sent to the server. The devices used here are general electronic devices such as PCs and smartphones, and the software used is a web form or a mobile app.

[0122] Parsing a meeting request

[0123] The server analyzes the received meeting request data using natural language processing technology. Specifically, it uses a generative AI model (e.g., GPT-3 or spaCy) to convert the request content into structured data and determine the necessity of the meeting. This makes it possible to understand the purpose and importance of the meeting.

[0124] Participant list optimization

[0125] After the server determines the need for a meeting, it analyzes the candidate participant data included in the request. The server generates an optimal participant list taking into account each participant's job title and area of ​​expertise. Pandas and scikit-learn are used as data analysis tools. The participant list generated by the server is displayed to the user via their terminal, allowing them to review and modify it.

[0126] Uploading meeting materials

[0127] Users upload meeting materials from their devices to the server. Using the file upload system, documents such as Word and PDF files can be easily uploaded. Users select the materials and click the upload button to send them to the server.

[0128] Document review and improvement suggestions

[0129] The server retrieves the uploaded meeting materials and analyzes their contents using a generative AI model (e.g., GPT-3 or BERT). The server identifies missing information and areas for improvement, generates specific improvement suggestions, and notifies the user via their device. This improves the quality of the meeting materials and increases the effectiveness of the meeting.

[0130] Real-time facilitation

[0131] When a user starts a meeting, the server monitors the progress of the meeting in real time. The server uses WebRTC to adjust the order, speaking style, and speaking speed of each participant. The server also provides timely advice to prevent discussions from going off track, helping to ensure important discussions proceed smoothly.

[0132] Feedback collection and analysis

[0133] After the meeting, users fill out a feedback form on their device and send it to the server. The server analyzes the feedback data to identify the effectiveness of the meeting and areas for improvement. Software used includes text analysis tools (e.g., sentiment analysis libraries like VADER and TextBlob). The analysis results are notified to users via their device, and suggestions for improvement are provided for the next meeting.

[0134] Specific examples

[0135] For example, when a company wants to hold a meeting to discuss sales strategies for a new product, the following sequence is executed.

[0136] 1. A user enters a meeting request and submits it to the server.

[0137] Example prompt: "We'd like to hold a sales strategy meeting for new product X next Thursday. Potential attendees will be the sales manager, marketing manager, and finance manager. Topics to discuss include market analysis, sales targets, and budget planning."

[0138] 2. The server parses the request and determines whether a meeting is required.

[0139] Example prompt: "Based on the information provided, determine whether a meeting is necessary."

[0140] 3. The server generates an optimal participant list and notifies the user through the terminal.

[0141] Example prompt: "Generate and display a list of participants that best fit your request."

[0142] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[0143] Example prompt: "Based on this data, please review the materials and suggest improvements."

[0144] 5. The user initiates the conference, which the server facilitates in real time.

[0145] Example prompt: "Please help us organize the order of speaking and keep the discussion flowing smoothly."

[0146] 6. After the meeting, users enter their feedback, which the server analyzes and notifies them of improvements for the next meeting.

[0147] Example prompt: "Based on your feedback, please rate the effectiveness of the meeting and provide suggestions for improvement for the next meeting."

[0148] In this way, the system can efficiently manage and support the entire meeting process, greatly improving meeting productivity.

[0149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0150] Step 1:

[0151] A user inputs a conference request through a terminal and sends it to the server.

[0152] Input: Information such as the purpose of the meeting, agenda, potential attendees, and expected outcomes.

[0153] Data processing: The information entered by the user is sent to the server in text format.

[0154] Output: The conference request data received by the server.

[0155] Specific operation: The user enters the following into the input form on the terminal: "We would like to hold a sales strategy meeting for new product X. Possible participants are the sales manager, marketing manager, and finance manager. The agenda will include market analysis, sales targets, and budget planning." and presses the send button.

[0156] Step 2:

[0157] The server analyzes the received conference request data using natural language processing technology.

[0158] Input: Meeting request data.

[0159] Data processing: Using natural language processing technology (e.g., GPT-3, spaCy), the request content is analyzed and converted into structured data.

[0160] Output: Structured data such as the purpose of the meeting, importance, and a list of potential attendees.

[0161] Specific operation: The server extracts keywords such as "new product," "sales strategy," and "meeting" from the request content and determines the need for a meeting.

[0162] Step 3:

[0163] The server analyzes the participant candidate data included in the request and generates an optimal participant list.

[0164] Input: Structured data (e.g., a list of potential participants, each candidate's job title, and area of ​​expertise).

[0165] Data processing: Using data analysis tools (e.g., pandas, scikit-learn), we evaluated each participant's job title and area of ​​expertise to generate the optimal list.

[0166] Output: Optimized participant list.

[0167] Specific operation: The server evaluates the data of "Sales Manager," "Marketing Manager," and "Finance Manager," and displays the optimal participant list to the user via the terminal.

[0168] Step 4:

[0169] The user uploads the meeting materials from the terminal to the server.

[0170] Input: Meeting materials (e.g. Word, PDF files).

[0171] Data processing: Send materials to the server using a file upload system.

[0172] Output: Meeting materials uploaded to the server.

[0173] Specific operation: The user selects the meeting materials (e.g., market analysis report) using the file upload system of the terminal and presses the upload button.

[0174] Step 5:

[0175] The server analyzes the uploaded meeting materials and identifies missing information and areas for improvement.

[0176] Input: Uploaded meeting materials.

[0177] Data processing: Analyze materials using generative AI models (e.g., GPT-3, BERT) to generate improvement suggestions.

[0178] Output: Specific suggestions for improving the material.

[0179] Specific operation: The server analyzes the "Market Analysis Report" and generates an improvement suggestion saying "Market data is insufficient. Please add more." and notifies the user.

[0180] Step 6:

[0181] A user initiates a conference and the server monitors the progress of the conference in real time.

[0182] Input: Meeting start signal and real-time data (e.g. what was said, who spoke, and when).

[0183] Data processing: Using WebRTC, the order of speech, speaking style, and speaking speed are analyzed and necessary adjustments are made.

[0184] Output: Adjusted instructions on speaking order, speaking style, and speaking speed.

[0185] Specific operation: The server monitors the order of comments and sends notifications via the device saying, "Please keep your comments brief." If the discussion goes off track, the server advises, "Please get back on topic."

[0186] Step 7:

[0187] After the conference ends, the user fills in a feedback form from the terminal and sends it to the server.

[0188] Input: Feedback data (e.g., meeting ratings, opinions).

[0189] Data processing: The feedback data was analyzed using text analysis tools (e.g., VADER, TextBlob).

[0190] Output: Analysis of the effectiveness of the meeting and areas for improvement.

[0191] Specific operation: The user inputs feedback such as "The meeting went smoothly" or "There were some parts where materials were lacking" from their device and sends it to the server. Based on the analysis results, the system notifies the user of an improvement suggestion such as "Please prepare more materials in advance."

[0192] (Application example 1)

[0193] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0194] Traditional meetings in factory operations often hinder efficient decision-making because they do not adequately optimize participants, suggest improvements to materials, or manage progress in real time. Furthermore, there is a problem with not gathering feedback after meetings and presenting areas for improvement for the next meeting, making it difficult to establish a continuous improvement cycle.

[0195] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0196] In this invention, the server includes means for receiving a conference request using a generation AI and determining its necessity, means for optimizing the participant list included in the conference request, means for reviewing conference materials and generating improvement proposals, means for facilitating the conference in real time while the conference is in progress, means for accepting and analyzing feedback after the conference ends, means for optimizing the operator list for the factory operations conference and managing the progress in real time, means for reviewing the operations conference materials and providing improvement proposals, and means for analyzing the feedback after the conference ends and presenting points for improving operations for the next time. This improves the efficiency of factory operations meetings and enables the creation of a continuous improvement cycle.

[0197] "Generative AI" is an artificial intelligence system that uses a generative approach to automate and optimize specific tasks.

[0198] A "meeting request" is a request that includes the information necessary to propose a meeting.

[0199] A "means for determining necessity" is a technical device or algorithm that analyzes and determines whether holding a meeting is truly necessary.

[0200] A "participant list optimization method" is a technological device or algorithm that selects the participants best suited to achieving the objectives of the meeting.

[0201] A "means for reviewing meeting materials and generating improvement suggestions" is a technical device or algorithm that analyzes the content of meeting materials and identifies and suggests improvements.

[0202] "Means for real-time facilitation during a meeting" refers to technological devices or algorithms that support and facilitate the progress of a meeting.

[0203] A "means for receiving and analyzing feedback" is a technological device or algorithm that collects and analyzes feedback from participants after the meeting has concluded.

[0204] The "means for optimizing the operator list and managing the progress in real time" refers to a technical device or algorithm that generates an optimal operator list in factory operations and manages the progress of meetings in real time.

[0205] A "means for reviewing operations meeting materials and providing improvement suggestions" is a technological device or algorithm that analyzes the content of factory operations meeting materials and identifies and suggests improvements.

[0206] The "means for analyzing feedback after the meeting and proposing improvements for the next operation" refers to a technological device or algorithm that collects and analyzes feedback from participants after the factory operations meeting and proposes specific improvements for the next operation.

[0207] To implement this invention, it is necessary to build a system for improving the efficiency of factory operation meetings. This system uses hardware including high-performance servers, smart devices (smartphones and tablets), and industrial robots, and software that utilizes generative AI and NLP (natural language processing) technologies.

[0208] Overall system flow

[0209] 1. Receiving and parsing meeting requests

[0210] A user inputs a meeting request on a smart device and sends it to the server. The request includes the purpose of the meeting, the agenda, potential participants, and the expected outcomes. The server uses an NLP model (e.g., spaCy) to analyze the request and determine the need for the meeting.

[0211] Examples:

[0212] The user types, "I'm proposing an operations meeting tomorrow regarding production line adjustments. The agenda will be about improving the efficiency of the manufacturing process and introducing new machines."

[0213] 2. Optimize your attendee list

[0214] If the need for a conference is confirmed, the server generates a list of participants that are optimally suited to the needs of the operator based on their job title and area of ​​expertise. This list is displayed to the user via their smart device, and they can make modifications or additions as needed.

[0215] 3. Reviewing meeting materials and making suggestions for improvement

[0216] When a user uploads meeting materials, the server analyzes them using generative AI to identify areas where information is missing or where improvements are needed. Based on the results of the analysis, specific improvement suggestions are generated and notified to the user via their smart device.

[0217] 4. Real-time facilitation

[0218] Once the meeting begins, the server monitors the progress of the meeting in real time, adjusting speaking style and speed to help ensure important discussions are carried out smoothly, and if a particular speech is long, the server suggests shortening it and provides appropriate advice to prevent the discussion from going off track.

[0219] 5. Feedback Collection and Analysis

[0220] After the meeting, the user fills out a feedback form and sends it to the server. The server analyzes this feedback and identifies the effectiveness of the meeting and areas for improvement. The analysis results are notified to the user via their smart device, and specific improvement suggestions are provided for the next meeting.

[0221] The specific hardware and software used

[0222] Hardware: high-performance servers, smart devices (smartphones and tablets), industrial robots.

[0223] Software: TensorFlow, spaCy, NLTK, Node.js, WebSockets.

[0224] Example prompt

[0225] If a user inputs something like, "I'd like to propose an operations meeting regarding tomorrow's production line adjustments. The agenda will be about improving the efficiency of the manufacturing process and introducing new machines," the system will analyze this prompt, determine the need for the meeting, and automatically generate an appropriate participant list. The generation AI uses NLP technology to proceed with work based on such user requests.

[0226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0227] Step 1:

[0228] A user inputs a meeting request from a smart device and sends information including the purpose of the meeting, the agenda, potential participants, and expected outcomes to the server. The input text is sent to the server, which receives this data.

[0229] Step 2:

[0230] The server processes the meeting request text with a generative AI model and uses NLP techniques (e.g., spaCy) to analyze the request. To determine whether a meeting is necessary, it extracts specific keywords and phrases and compares them with existing meeting criteria. The analysis results in a decision on whether a meeting is necessary (output).

[0231] Step 3:

[0232] When the need for a conference is confirmed, the server searches a database based on the operator's job title and area of ​​expertise to generate an optimal participant list, which is then sent to the user's smart device (output).

[0233] Step 4:

[0234] The user uploads meeting materials from their smart device to the server. The server then analyzes the materials using a generative AI to identify missing information and areas for improvement. Each section of the materials is analyzed, and the generative AI generates specific improvement suggestions. The improvement suggestions are then sent to the user's smart device (output).

[0235] Step 5:

[0236] When a user starts a conference, the server monitors the progress of the conference in real time. The server receives the conference audio and text data in real time via WebSockets, adjusts the order of speech and speaking speed, and, if necessary, suggests shortening speech or redirecting the discussion to specific participants (output).

[0237] Step 6:

[0238] After the meeting, the user fills out a feedback form on their smart device and sends it to the server. The server analyzes the feedback data using generative AI to identify the effectiveness of the meeting and areas for improvement. The analysis results are generated as specific improvement suggestions for the next meeting and are notified to the user's smart device (output).

[0239] Specific actions at each step

[0240] 1. In step 1, the user enters, "I would like to propose an operations meeting tomorrow regarding production line adjustments. The agenda will be about improving the efficiency of the manufacturing process and introducing new machines."

[0241] 2. In step 2, this text data is tokenized and analyzed by spaCy, and keywords such as "production line adjustment," "efficiency," and "new machine" are extracted.

[0242] 3. In step 3, appropriate operators are selected from the database to generate the optimal participant list, and the result is notified to the user as the "optimal participant list."

[0243] 4. In step 4, the generation AI analyzes the meeting materials uploaded by the user and notifies them of "areas where materials are lacking" and "specific improvement suggestions."

[0244] 5. In step 5, the server monitors in real time via WebSockets and adjusts the order and speed of speech, for example suggesting "move to the next topic" if the conversation is getting too long.

[0245] 6. In step 6, the user feedback is analyzed and specific improvement suggestions for the next meeting are notified to the user.

[0246] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0247] The present invention relates to a system that improves the efficiency of meetings by combining a generative AI and an emotion engine. Specific embodiments of the system of the present invention will be described below. In particular, the operations of the server, terminals, and users will be described in detail.

[0248] Overall system configuration

[0249] The system mainly consists of the following modules:

[0250] 1. Meeting Request Analysis Module

[0251] 2. Participant List Optimization Module

[0252] 3. Document Review and Improvement Suggestion Module

[0253] 4. Real-time Facilitation Module

[0254] 5. Emotion Recognition Module

[0255] 6. Feedback Collection and Analysis Module

[0256] System Operation

[0257] 1. Meeting request analysis

[0258] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The server uses the received information to determine the necessity of the meeting. At this time, it analyzes the request content using natural language processing technology and determines whether the meeting needs to be held.

[0259] 2. Optimize your attendee list

[0260] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device, and the user can confirm the final participant list. The user can then modify or add to the list as needed.

[0261] 3. Document review and improvement suggestions

[0262] Users upload meeting materials from their devices to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. Based on the results of the analysis, it generates specific suggestions for improving the materials and notifies the user via their device. This ensures that all necessary information is gathered before the meeting.

[0263] 4. Real-time facilitation and emotion recognition

[0264] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses image processing and voice analysis technology to analyze the user's facial expressions and tone of voice to recognize their emotions. The server adjusts the order, speaking style, and speaking speed of each comment, and provides appropriate advice based on the user's emotional state.

[0265] For example, if the user is nervous, the emotion engine can detect this and the server can notify the user through the terminal of advice to help them relax.

[0266] 5. Feedback Collection and Analysis

[0267] After the meeting, the user fills out a feedback form on their device and sends it to the server. The server analyzes this feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[0268] Specific examples

[0269] For example, this system is used when a company wants to hold a meeting to discuss sales strategies for a new product.

[0270] 1. A user enters a meeting request and submits it to the server.

[0271] 2. The server parses the request and determines whether a meeting is required.

[0272] 3. The server generates an optimal participant list and notifies the user through the terminal.

[0273] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[0274] 5. The user starts the conference, and the server facilitates in real time while monitoring the user's emotions using the emotion recognition module.

[0275] 6. Based on the emotion recognition results, the server provides appropriate advice.

[0276] 7. After the meeting, users enter their feedback, which is analyzed by the server and used to inform the server of improvements for the next meeting.

[0277] This series of processes ensures that meetings are carried out consistently and efficiently, from preparation to progress and evaluation, improving meeting productivity and the operational efficiency of the entire organization.

[0278] The processing flow will be explained below.

[0279] Step 1:

[0280] A user inputs a meeting request from a terminal. The input form includes fields for describing the purpose of the meeting, the agenda, potential participants, and the expected outcomes. Once the input is complete, the request data is sent to the server.

[0281] Step 2:

[0282] The server analyzes the received meeting request, uses natural language processing technology to evaluate the appropriateness of the meeting's purpose, agenda, and expected outcomes, and determines the need for the meeting based on this evaluation.

[0283] Step 3:

[0284] If the need for a meeting is confirmed, the server analyzes the participant list included in the meeting request, analyzes each participant's job title and area of ​​expertise, and generates an optimal participant list using an algorithm to select participants who will best contribute to the meeting's objectives.

[0285] Step 4:

[0286] The device displays the optimized participant list to the user, who can then review the list and add or remove participants as needed. Once the final list is finalized, it is sent to the server.

[0287] Step 5:

[0288] Users upload meeting materials (presentations, reports, data sheets, etc.) from their terminals to the server. These materials contain information necessary for preparing and conducting the meeting.

[0289] Step 6:

[0290] The server reviews the uploaded meeting materials, analyzes the contents of the materials, and determines whether the information is sufficient or if there is any information missing or needs improvement. Based on the analysis results, it generates specific improvement proposals.

[0291] Step 7:

[0292] The terminal notifies the user of the improvement suggestions from the server, and the user corrects the materials based on the suggestions and uploads them back to the server if necessary.

[0293] Step 8:

[0294] Once a user starts a meeting, the server monitors the progress of the meeting in real time, and algorithms are activated to adjust the order, style and speed of speech as the meeting progresses.

[0295] Step 9:

[0296] The server uses an emotion recognition module to analyze the user's facial expressions and tone of voice using image processing and voice analysis technology, thereby recognizing the user's emotional state in real time.

[0297] Step 10:

[0298] Based on the emotion recognition results, the server generates appropriate advice and notifies the user via the device. For example, if the user is feeling nervous, the server provides advice encouraging them to relax.

[0299] Step 11:

[0300] After the meeting, users fill out a feedback form on their device and send their evaluation of the meeting to the server. The feedback includes the quality of the meeting, how it was conducted, and areas for improvement.

[0301] Step 12:

[0302] The server analyzes the received feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are then sent to the user via their device, and specific suggestions for improvement are provided for the next meeting.

[0303] Example 2

[0304] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0305] In conventional conferencing systems, the process from preparing for a meeting to proceeding and evaluating it was often done manually, resulting in inefficiency and reduced productivity. Furthermore, there was no way to properly manage the order of speaking, speaking style, or emotions of speakers during the meeting, which often resulted in meetings not proceeding smoothly. This could result in a decline in the quality of the meeting and in participants being unable to fully discuss the matter.

[0306] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a user to input a conference request and determine the necessity of the conference using a generation AI, a means for analyzing the participant list included in the conference request and generating an optimized list, a means for performing text analysis of conference materials and generating improvement suggestions, a means for supporting the progress of the conference in real time and providing appropriate advice based on emotion recognition, and a means for receiving and analyzing feedback after the conference ends. This makes it possible to efficiently carry out the preparation, progress, and evaluation of the conference.

[0307] "User" means a person or entity that operates the System and enters and submits meeting requests and materials.

[0308] "Generative AI" refers to artificial intelligence that analyzes the content of a meeting request and determines its necessity.

[0309] A "meeting request" refers to information entered by a user, including the purpose of the meeting, the agenda, potential participants, and expected outcomes.

[0310] A "participant list" is a list of people and roles scheduled to attend a meeting.

[0311] "Optimization" refers to the process of selecting the most suitable participants for a conference.

[0312] "Text analysis" is the process of analyzing the content of meeting materials using artificial intelligence and software.

[0313] "Improvement proposals" refer to proposals to improve the quality of meeting materials and proceedings.

[0314] "Supporting meeting progress in real time" refers to technologies and functions that support the progress of a meeting while it is taking place.

[0315] "Emotion recognition" refers to the technology of analyzing a user's facial expressions and tone of voice to determine their emotions.

[0316] "Feedback" refers to information about evaluations, impressions, and areas for improvement submitted by users after the end of a conference.

[0317] "Analysis" refers to the process of using artificial intelligence and software to analyze feedback and other data in detail.

[0318] The present invention relates to a system for improving meeting efficiency using generative AI and an emotion engine. Implementation of this system primarily requires interactions between a server, a terminal, and a user. An embodiment of the system is described in detail below.

[0319] Overall system configuration

[0320] The system mainly consists of the following modules:

[0321] 1. Meeting Request Analysis Module

[0322] 2. Participant List Optimization Module

[0323] 3. Document Review and Improvement Suggestion Module

[0324] 4. Real-time Facilitation Module

[0325] 5. Emotion Recognition Module

[0326] 6. Feedback Collection and Analysis Module

[0327] Hardware and Software Configuration

[0328] The server uses the following software and databases:

[0329] Parsing a Meeting Request: Google Cloud Natural Language API

[0330] Participant list optimization: Internal database

[0331] Document Review: IBM Watson Natural Language Understanding

[0332] Real-time facilitation: OpenCV (image processing), Google Cloud Speech-to-Text (audio analysis)

[0333] A terminal is a typical computer or mobile device that users use to enter and submit meeting requests and materials.

[0334] Operation procedures and function details

[0335] 1. Entering and parsing meeting requests

[0336] The user inputs the purpose of the meeting, the agenda, potential participants, and the expected outcomes using a terminal, and sends the input to the server. The server then uses the Google Cloud Natural Language API to analyze the input request and determine the need for the meeting.

[0337] 2. Optimize your attendee list

[0338] After determining the need for a meeting, the server retrieves data on each account's job title and area of ​​expertise from an internal database. Based on this, it generates an optimal participant list and displays it to the user via their device. The user can then review and modify the list and finalize it.

[0339] 3. Document review and improvement suggestions

[0340] Users upload meeting materials from their devices to the server. The server then analyzes the materials using IBM Watson Natural Language Understanding to identify areas where information is missing or where improvements are needed. Based on the analysis results, specific improvement suggestions are generated and notified to the user via their device.

[0341] 4. Real-time facilitation and emotion recognition

[0342] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses OpenCV (image processing) and Google Cloud Speech-to-Text (audio analysis) to analyze the user's facial expressions and tone of voice to recognize their emotions. The server adjusts the order, speaking style, and speed of speech and provides advice based on the user's emotional state.

[0343] 5. Feedback Collection and Analysis

[0344] After the meeting, users fill out a feedback form on their device and send it to the server. The server analyzes the feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[0345] Specific examples

[0346] For example, this system is used when a company wants to hold a meeting to discuss sales strategies for a new product.

[0347] 1. A user inputs a meeting request stating, "I would like to hold a meeting to discuss the market strategy for a new product," and sends it to the server.

[0348] 2. The server parses the request and determines whether a meeting is required.

[0349] 3. The server generates a list of optimal participants, such as "marketing staff," "sales managers," and "technology development leaders," and notifies the user via their terminal.

[0350] 4. The user uploads meeting materials, and the server reviews the materials and makes specific suggestions for improvement, such as "Please add market analysis data."

[0351] 5. The user starts the conference, the server monitors the progress of the conference, and the emotion recognition module monitors the user's emotions.

[0352] 6. Based on the emotion recognition results, the server provides advice such as "Take a deep breath to relax."

[0353] 7. After the meeting ends, the user sends feedback that "the meeting is progressing too fast," and the server analyzes this and suggests adjusting the speed of the next meeting.

[0354] Prompt Sentence Examples

[0355] As an example of a prompt sentence, you can give instructions to the generative AI model as follows:

[0356] "Based on the participant list entered by the user, please create a list of the people who are best suited to the agenda. Then, generate a command to notify the user when the list is complete."

[0357] In this way, the system of the present invention allows efficient preparation, progress, and evaluation of a meeting, resulting in improved productivity of the meeting.

[0358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0359] Step 1:

[0360] A user inputs a meeting request from a terminal, describing the purpose, agenda, potential participants, and expected results, and then submits the request. The input request information is sent to the server.

[0361] The server receives this information and analyzes it using the Google Cloud Natural Language API. The analysis results determine whether a meeting is necessary. The input at this stage is the meeting request information, and the output is a determination of whether a meeting is necessary.

[0362] Specific behavior:

[0363] A user inputs and submits a request to "hold a market research meeting for a new product."

[0364] The server analyzes the request and determines that there is a need for a meeting.

[0365] Step 2:

[0366] After the necessity of the meeting is confirmed, the server retrieves data on each participant's job title and area of ​​expertise from an internal database to optimize the participant list. Based on this data, a generative AI is used to generate an optimal participant list. The generated list is displayed to the user via their device for confirmation and modification.

[0367] The input is participant information based on the conference request, and the output is an optimized participant list.

[0368] Specific behavior:

[0369] The server obtains data such as "project managers," "marketing staff," and "technical staff" and generates the optimal list.

[0370] The user reviews the list, adds "Designer" and confirms.

[0371] Step 3:

[0372] Users upload meeting materials from their devices to the server. The server then uses IBM Watson Natural Language Understanding to analyze the text of the materials and identify missing information or areas that need improvement. Based on the results of this analysis, specific improvement suggestions are generated and notified to the user via their device.

[0373] The input is the meeting materials and the output is the improvement proposals.

[0374] Specific behavior:

[0375] A user uploads a presentation.

[0376] The server analyzes the data and notifies you that "market analysis data is insufficient."

[0377] Step 4:

[0378] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses OpenCV and Google Cloud Speech-to-Text to analyze the user's facial expressions and tone of voice to recognize their emotional state. The server adjusts the order, style, and speed of speech and provides advice based on the user's emotional state.

[0379] The input is audio and video data from the meeting, and the output is real-time advice.

[0380] Specific behavior:

[0381] A user starts a conference and the server analyzes the audio and video.

[0382] The server offers advice such as "Take a deep breath to relax."

[0383] Step 5:

[0384] After the meeting, users fill out a feedback form on their device and send it to the server. The server then uses analysis software to analyze this feedback, evaluate the meeting, and identify areas for improvement. The analysis results are then sent to the user via their device, and specific suggestions for improvement are provided for the next meeting.

[0385] The input is a feedback form, and the output is the analysis results and improvement suggestions.

[0386] Specific behavior:

[0387] A user enters and submits feedback saying, "The meeting is moving too fast."

[0388] The server analyzes the feedback and suggests adjusting the pace of the next meeting.

[0389] This will clearly show the detailed process flow of how the entire system effectively supports and improves meetings.

[0390] (Application example 2)

[0391] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0392] In traditional brick-and-mortar store operations, meetings and conferences between store staff and managers are often extremely inefficient. The main causes are unclear judgment of the need for a meeting, a lack of appropriate participants, and insufficient materials that lead to confusing discussions. Another problem is that there are few ways to grasp the emotions and stress levels of participants during the meeting, which can easily create an awkward atmosphere. Furthermore, the productivity of meetings is hindered by the lack of a system in place to collect feedback and use it to improve the next meeting.

[0393] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0394] In this invention, the server includes means for receiving a conference request using a generation AI and determining its necessity, means for optimizing the participant list included in the conference request, means for reviewing conference materials and generating improvement proposals, means for facilitation in real time while the conference is in progress, means for recognizing emotions while the conference is in progress and providing appropriate advice according to the user's emotional state, and means for receiving and analyzing feedback after the conference ends. This enables conferences and meetings in physical stores to be conducted more efficiently and effectively.

[0395] "Generative AI" is artificial intelligence used to analyze meeting requests and suggest improvements to materials.

[0396] The "means for receiving a conference request and determining necessity" is a system element that has the function of analyzing a conference request sent by a user and determining whether the conference is truly necessary.

[0397] The "means for optimizing the participant list" is an element of the system that has the function of automatically selecting participants required for a meeting based on their job titles and areas of expertise, and generating an optimal list.

[0398] The "means for reviewing meeting materials and generating improvement proposals" refers to a system element that has the function of analyzing meeting materials uploaded by users, pointing out areas where information is lacking or areas for improvement, and providing specific improvement proposals.

[0399] "Means for real-time facilitation while a meeting is in progress" refers to a system element that has the function of monitoring the progress of a meeting while it is in progress and facilitating in real time, such as adjusting the order of comments, speaking style, and speaking speed.

[0400] "Means for recognizing emotions and providing appropriate advice according to the user's emotional state" refers to a system element that has the function of analyzing the facial expressions and tone of voice of participants during the meeting, understanding their emotional state, and providing advice to help them relax as needed.

[0401] The "means for receiving and analyzing feedback" is a system element that has the function of receiving feedback from users after the meeting, analyzing the content, and generating specific improvement suggestions for the next meeting.

[0402] This invention provides a system that combines generative AI and emotion recognition technology to efficiently manage conferences and meetings in brick-and-mortar stores. Specific embodiments are as follows.

[0403] System Configuration

[0404] The system mainly consists of the following modules:

[0405] 1. Meeting Request Analysis Module

[0406] 2. Participant List Optimization Module

[0407] 3. Document Review and Improvement Suggestion Module

[0408] 4. Real-time Facilitation Module

[0409] 5. Emotion Recognition Module

[0410] 6. Feedback Collection and Analysis Module

[0411] DETAILED DESCRIPTION OF THE EMBODIMENTS

[0412] 1. Meeting request analysis

[0413] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The server uses the received information to determine the need for the meeting, using natural language processing technology.

[0414] 2. Optimize your attendee list

[0415] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device, and the user can confirm the final participant list. The user can then modify or add to the list as needed.

[0416] 3. Document review and improvement suggestions

[0417] Users upload meeting materials from their devices to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. Based on the results of the analysis, it generates specific suggestions for improving the materials and notifies the user via their device.

[0418] 4. Real-time facilitation and emotion recognition

[0419] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses image processing and voice analysis technology to analyze the user's facial expressions and tone of voice to recognize their emotions. The server adjusts the order, speaking style, and speaking speed of what is said and provides appropriate advice based on the user's emotional state. For example, if the user is nervous, the emotion engine can detect this and the server can notify the user via their device with advice to help them relax.

[0420] 5. Feedback Collection and Analysis

[0421] After the meeting, the user fills out a feedback form on their device and sends it to the server. The server analyzes this feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[0422] Specific examples

[0423] For example, this system is used when store managers at a brick-and-mortar store chain hold monthly review meetings.

[0424] 1. A user enters a meeting request and submits it to the server.

[0425] 2. The server parses the request and determines whether a meeting is required.

[0426] 3. The server generates an optimal participant list and notifies the user through the terminal.

[0427] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[0428] 5. The user starts the conference, and the server facilitates in real time while monitoring the user's emotions using the emotion recognition module.

[0429] 6. Based on the emotion recognition results, the server provides appropriate advice.

[0430] 7. After the meeting, users enter their feedback, which is analyzed by the server and used to inform the server of improvements for the next meeting.

[0431] Prompt Sentence Examples

[0432] Below are some example prompts to input to a generative AI model:

[0433] text

[0434] A user wants to hold a monthly review meeting for a physical store. The purpose of the meeting is to analyze sales and plan for the next month, and the agenda is a performance review of each staff member. Participants include all store staff and the area manager. Based on this information, the user needs to analyze the meeting materials and make suggestions for improvement.

[0435] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0436] Step 1:

[0437] A user inputs a meeting request from a terminal and sends the purpose, agenda, potential participants, and expected outcomes to the server. The input data (meeting request information) is analyzed by the server using natural language processing technology. As a result of this data analysis, the need for a meeting is determined.

[0438] Step 2:

[0439] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device. The user can review the displayed list and modify or add participants as necessary. The input participant list data is output as the optimal list.

[0440] Step 3:

[0441] The user uploads meeting materials from their device to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. This analysis uses machine learning technology to evaluate the content of the materials. Specific improvement suggestions are generated by the server and notified to the user via their device.

[0442] Step 4:

[0443] When a user starts a meeting, the server monitors the progress of the meeting in real time. The server adjusts the order of speech, speaking style, and speaking speed, and uses an emotion recognition module to analyze the user's facial expressions and tone of voice. This allows it to recognize the emotions of participants and provide advice to help them relax if they are nervous. The server outputs the adjusted speaking order and advice.

[0444] Step 5:

[0445] After the meeting, users fill out a feedback form on their device and send it to the server. The server analyzes this feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are generated as a report and notified to the user via their device. The feedback analysis results are output, and specific improvement suggestions for the next meeting are provided.

[0446] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0447] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0448] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0449] [Second embodiment]

[0450] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0451] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0452] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0453] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0454] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0455] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0456] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0457] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0458] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0459] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0460] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0461] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0462] The present invention relates to a system for improving the efficiency of meetings using generative AI. Specific embodiments of the system of the present invention will be described below. In particular, the operations of the server, terminals, and users will be described in detail.

[0463] Overall system configuration

[0464] The system mainly consists of the following modules:

[0465] 1. Meeting Request Analysis Module

[0466] 2. Participant List Optimization Module

[0467] 3. Document Review and Improvement Suggestion Module

[0468] 4. Real-time Facilitation Module

[0469] 5. Feedback Collection and Analysis Module

[0470] System Operation

[0471] 1. Meeting request analysis

[0472] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The server uses the received information to determine the necessity of the meeting. At this time, it analyzes the request content using natural language processing technology and determines whether the meeting needs to be held.

[0473] 2. Optimize your attendee list

[0474] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device, and the user can confirm the final participant list. The user can then modify or add to the list as needed.

[0475] 3. Document review and improvement suggestions

[0476] Users upload meeting materials from their devices to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. Based on the results of the analysis, it generates specific suggestions for improving the materials and notifies the user via their device. This ensures that all necessary information is gathered before the meeting.

[0477] 4. Real-time facilitation

[0478] When a user starts a meeting, the server monitors the progress of the meeting in real time. The server adjusts the order, style, and speed of speech to help ensure important discussions proceed smoothly. For example, if a particular participant speaks for a long time, the server will suggest shortening their speech via the user's device. It also provides timely advice to prevent the discussion from going off track.

[0479] 5. Feedback Collection and Analysis

[0480] After the meeting, users fill out a feedback form on their device and send their evaluation of the meeting to the server. The server analyzes this feedback and identifies the effectiveness of the meeting and areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[0481] Specific examples

[0482] For example, this system is used when a company wants to hold a meeting to discuss sales strategies for a new product.

[0483] 1. A user enters a meeting request and submits it to the server.

[0484] 2. The server parses the request and determines whether a meeting is required.

[0485] 3. The server generates an optimal participant list and notifies the user through the terminal.

[0486] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[0487] 5. The user initiates the conference, which the server facilitates in real time.

[0488] 6. After the meeting, users enter their feedback, which the server analyzes and notifies them of improvements for the next meeting.

[0489] This series of processes ensures that meetings are carried out consistently and efficiently, from preparation to progress and evaluation, improving meeting productivity and the operational efficiency of the entire organization.

[0490] The processing flow will be explained below.

[0491] Step 1:

[0492] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The input form contains specific meeting details.

[0493] Step 2:

[0494] The server analyzes the received meeting request and uses natural language processing technology to understand the request content, determine whether the purpose, agenda, and expected outcomes are appropriate, and evaluate the necessity of the meeting.

[0495] Step 3:

[0496] After the server confirms the need for a conference, it analyzes the participant list in the conference request, analyzes each participant's job title and area of ​​expertise, and selects the most suitable participants. At this time, it narrows down the participants to the minimum necessary.

[0497] Step 4:

[0498] The terminal notifies the user of the optimized participant list, and the user confirms it. The user can add or remove participants as needed and confirm the final list.

[0499] Step 5:

[0500] Users upload meeting materials from their devices to the server, including presentations, reports, data sheets, and more.

[0501] Step 6:

[0502] The server analyzes the uploaded materials, checks the completeness of the information, identifies missing information and areas that need improvement, and generates specific improvement proposals.

[0503] Step 7:

[0504] The terminal notifies the user of the improvement suggestions from the server, and the user corrects the materials. If necessary, the materials are uploaded to the server again and rechecked.

[0505] Step 8:

[0506] Once a user starts a meeting, the server monitors the progress of the meeting in real time, and algorithms are put into motion to adjust the order, style and speed of speech.

[0507] Step 9:

[0508] The server generates appropriate advice to support the progress of important discussions and notifies the users through their terminals, thereby enabling discussions to proceed actively and efficiently.

[0509] Step 10:

[0510] After the meeting, users fill out a feedback form on their devices and send it to the server. The feedback includes the quality of the meeting, how it was conducted, and areas for improvement.

[0511] Step 11:

[0512] The server analyzes the received feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are then sent to the user via their device, and specific suggestions for improvement are provided for the next meeting.

[0513] Example 1

[0514] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0515] In traditional meetings, selecting the best participants, preparing materials, managing the meeting, and collecting and analyzing feedback requires a great deal of time and effort. As a result, meetings are less efficient and discussions are often ineffective. In addition, because multiple methods are used individually, it is difficult to integrate and manage the entire meeting, which prevents the productivity of meetings from improving. An integrated system is needed to solve this problem.

[0516] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0517] In this invention, the server includes: means for receiving a conference request using a generation AI and determining the necessity; means for optimizing the participant list included in the conference request; means for reviewing conference materials and generating improvement suggestions; means for facilitating in real time while the conference is in progress; means for accepting and analyzing feedback after the conference has ended; means for a user to input a conference request from a terminal and send it to the server; means for the server to analyze the request content using natural language processing technology; means for the server to generate an optimal participant list based on the participants' positions and areas of expertise; means for the server to analyze uploaded conference materials and identify missing information and areas for improvement; means for the server to monitor the progress of the conference in real time and adjust the order of speech, speaking style, and speaking speed; and means for the server to analyze feedback sent from users and identify areas for improvement next time.

[0518] This makes it possible to consistently manage meeting preparation, progress, and evaluation, significantly improving meeting productivity.

[0519] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data.

[0520] A "meeting request" is data including information such as the purpose of the meeting, the agenda, potential participants, and expected outcomes.

[0521] A "terminal" is an electronic device such as a computer or smartphone that a user uses to input, operate, and confirm information.

[0522] A "server" is a computer system that receives, analyzes, manages, and processes data sent by users.

[0523] "Natural language processing technology" is a technology for understanding and analyzing human language.

[0524] A "participant list" is a list of people who are expected to attend a conference.

[0525] "Position" refers to the participant's role or position in the workplace.

[0526] "Area of ​​expertise" refers to the area in which a participant has specialized knowledge or skills.

[0527] "Meeting materials" are documents such as reports and presentations used in meetings.

[0528] An "improvement proposal" is a specific proposal to point out problems with the meeting materials or progress and improve them.

[0529] "Facilitation" is the act of coordinating a meeting to ensure that it proceeds smoothly and that important discussions take place.

[0530] "Feedback" is information used to gather evaluations and opinions about the meeting.

[0531] "Analysis" is the process of analyzing data to find meaning and patterns.

[0532] The present invention is a system for improving the efficiency of meetings using generative AI. Specific embodiments for implementing the present invention are described below. In particular, the operations of the server, terminals, and users are described in detail.

[0533] Overall system configuration

[0534] The system mainly consists of the following modules:

[0535] 1. Meeting Request Analysis Module

[0536] 2. Participant List Optimization Module

[0537] 3. Document Review and Improvement Suggestion Module

[0538] 4. Real-time Facilitation Module

[0539] 5. Feedback Collection and Analysis Module

[0540] Filling out a meeting request

[0541] Users input meeting requests through their devices and send them to the server. Users use a form to enter the purpose of the meeting, the agenda, potential participants, and expected outcomes. By clicking the send button, the input data is sent to the server. The devices used here are general electronic devices such as PCs and smartphones, and the software used is a web form or a mobile app.

[0542] Parsing a meeting request

[0543] The server analyzes the received meeting request data using natural language processing technology. Specifically, it uses a generative AI model (e.g., GPT-3 or spaCy) to convert the request content into structured data and determine the necessity of the meeting. This makes it possible to understand the purpose and importance of the meeting.

[0544] Participant list optimization

[0545] After the server determines the need for a meeting, it analyzes the candidate participant data included in the request. The server generates an optimal participant list taking into account each participant's job title and area of ​​expertise. Pandas and scikit-learn are used as data analysis tools. The participant list generated by the server is displayed to the user via their terminal, allowing them to review and modify it.

[0546] Uploading meeting materials

[0547] Users upload meeting materials from their devices to the server. Using the file upload system, documents such as Word and PDF files can be easily uploaded. Users select the materials and click the upload button to send them to the server.

[0548] Document review and improvement suggestions

[0549] The server retrieves the uploaded meeting materials and analyzes their contents using a generative AI model (e.g., GPT-3 or BERT). The server identifies missing information and areas for improvement, generates specific improvement suggestions, and notifies the user via their device. This improves the quality of the meeting materials and increases the effectiveness of the meeting.

[0550] Real-time facilitation

[0551] When a user starts a meeting, the server monitors the progress of the meeting in real time. The server uses WebRTC to adjust the order, speaking style, and speaking speed of each participant. The server also provides timely advice to prevent discussions from going off track, helping to ensure important discussions proceed smoothly.

[0552] Feedback collection and analysis

[0553] After the meeting, users fill out a feedback form on their device and send it to the server. The server analyzes the feedback data to identify the effectiveness of the meeting and areas for improvement. Software used includes text analysis tools (e.g., sentiment analysis libraries like VADER and TextBlob). The analysis results are notified to users via their device, and suggestions for improvement are provided for the next meeting.

[0554] Specific examples

[0555] For example, when a company wants to hold a meeting to discuss sales strategies for a new product, the following sequence is executed.

[0556] 1. A user enters a meeting request and submits it to the server.

[0557] Example prompt: "We'd like to hold a sales strategy meeting for new product X next Thursday. Potential attendees will be the sales manager, marketing manager, and finance manager. Topics to discuss include market analysis, sales targets, and budget planning."

[0558] 2. The server parses the request and determines whether a meeting is required.

[0559] Example prompt: "Based on the information provided, determine whether a meeting is necessary."

[0560] 3. The server generates an optimal participant list and notifies the user through the terminal.

[0561] Example prompt: "Generate and display a list of participants that best fit your request."

[0562] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[0563] Example prompt: "Based on this data, please review the materials and suggest improvements."

[0564] 5. The user initiates the conference, which the server facilitates in real time.

[0565] Example prompt: "Please help us organize the order of speaking and keep the discussion flowing smoothly."

[0566] 6. After the meeting, users enter their feedback, which the server analyzes and notifies them of improvements for the next meeting.

[0567] Example prompt: "Based on your feedback, please rate the effectiveness of the meeting and provide suggestions for improvement for the next meeting."

[0568] In this way, the system can efficiently manage and support the entire meeting process, greatly improving meeting productivity.

[0569] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0570] Step 1:

[0571] A user inputs a conference request through a terminal and sends it to the server.

[0572] Input: Information such as the purpose of the meeting, agenda, potential attendees, and expected outcomes.

[0573] Data processing: The information entered by the user is sent to the server in text format.

[0574] Output: The conference request data received by the server.

[0575] Specific operation: The user enters the following into the input form on the terminal: "We would like to hold a sales strategy meeting for new product X. Possible participants are the sales manager, marketing manager, and finance manager. The agenda will include market analysis, sales targets, and budget planning." and presses the send button.

[0576] Step 2:

[0577] The server analyzes the received conference request data using natural language processing technology.

[0578] Input: Meeting request data.

[0579] Data processing: Using natural language processing technology (e.g., GPT-3, spaCy), the request content is analyzed and converted into structured data.

[0580] Output: Structured data such as the purpose of the meeting, importance, and a list of potential attendees.

[0581] Specific operation: The server extracts keywords such as "new product," "sales strategy," and "meeting" from the request content and determines the need for a meeting.

[0582] Step 3:

[0583] The server analyzes the participant candidate data included in the request and generates an optimal participant list.

[0584] Input: Structured data (e.g., a list of potential participants, each candidate's job title, and area of ​​expertise).

[0585] Data processing: Using data analysis tools (e.g., pandas, scikit-learn), we evaluated each participant's job title and area of ​​expertise to generate the optimal list.

[0586] Output: Optimized participant list.

[0587] Specific operation: The server evaluates the data of "Sales Manager," "Marketing Manager," and "Finance Manager," and displays the optimal participant list to the user via the terminal.

[0588] Step 4:

[0589] The user uploads the meeting materials from the terminal to the server.

[0590] Input: Meeting materials (e.g. Word, PDF files).

[0591] Data processing: Send materials to the server using a file upload system.

[0592] Output: Meeting materials uploaded to the server.

[0593] Specific operation: The user selects the meeting materials (e.g., market analysis report) using the file upload system of the terminal and presses the upload button.

[0594] Step 5:

[0595] The server analyzes the uploaded meeting materials and identifies missing information and areas for improvement.

[0596] Input: Uploaded meeting materials.

[0597] Data processing: Analyze materials using generative AI models (e.g., GPT-3, BERT) to generate improvement suggestions.

[0598] Output: Specific suggestions for improving the material.

[0599] Specific operation: The server analyzes the "Market Analysis Report" and generates an improvement suggestion saying "Market data is insufficient. Please add more." and notifies the user.

[0600] Step 6:

[0601] A user initiates a conference and the server monitors the progress of the conference in real time.

[0602] Input: Meeting start signal and real-time data (e.g. what was said, who spoke, and when).

[0603] Data processing: Using WebRTC, the order of speech, speaking style, and speaking speed are analyzed and necessary adjustments are made.

[0604] Output: Adjusted instructions on speaking order, speaking style, and speaking speed.

[0605] Specific operation: The server monitors the order of comments and sends notifications via the device saying, "Please keep your comments brief." If the discussion goes off track, the server advises, "Please get back on topic."

[0606] Step 7:

[0607] After the conference ends, the user fills in a feedback form from the terminal and sends it to the server.

[0608] Input: Feedback data (e.g., meeting ratings, opinions).

[0609] Data processing: The feedback data was analyzed using text analysis tools (e.g., VADER, TextBlob).

[0610] Output: Analysis of the effectiveness of the meeting and areas for improvement.

[0611] Specific operation: The user inputs feedback such as "The meeting went smoothly" or "There were some parts where materials were lacking" from their device and sends it to the server. Based on the analysis results, the system notifies the user of an improvement suggestion such as "Please prepare more materials in advance."

[0612] (Application example 1)

[0613] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0614] Traditional meetings in factory operations often hinder efficient decision-making because they do not adequately optimize participants, suggest improvements to materials, or manage progress in real time. Furthermore, there is a problem with not gathering feedback after meetings and presenting areas for improvement for the next meeting, making it difficult to establish a continuous improvement cycle.

[0615] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0616] In this invention, the server includes means for receiving a conference request using a generation AI and determining its necessity, means for optimizing the participant list included in the conference request, means for reviewing conference materials and generating improvement proposals, means for facilitating the conference in real time while the conference is in progress, means for accepting and analyzing feedback after the conference ends, means for optimizing the operator list for the factory operations conference and managing the progress in real time, means for reviewing the operations conference materials and providing improvement proposals, and means for analyzing the feedback after the conference ends and presenting points for improving operations for the next time. This improves the efficiency of factory operations meetings and enables the creation of a continuous improvement cycle.

[0617] "Generative AI" is an artificial intelligence system that uses a generative approach to automate and optimize specific tasks.

[0618] A "meeting request" is a request that includes the information necessary to propose a meeting.

[0619] A "means for determining necessity" is a technical device or algorithm that analyzes and determines whether holding a meeting is truly necessary.

[0620] A "participant list optimization method" is a technological device or algorithm that selects the participants best suited to achieving the objectives of the meeting.

[0621] A "means for reviewing meeting materials and generating improvement suggestions" is a technical device or algorithm that analyzes the content of meeting materials and identifies and suggests improvements.

[0622] "Means for real-time facilitation during a meeting" refers to technological devices or algorithms that support and facilitate the progress of a meeting.

[0623] A "means for receiving and analyzing feedback" is a technological device or algorithm that collects and analyzes feedback from participants after the meeting has concluded.

[0624] The "means for optimizing the operator list and managing the progress in real time" refers to a technical device or algorithm that generates an optimal operator list in factory operations and manages the progress of meetings in real time.

[0625] A "means for reviewing operations meeting materials and providing improvement suggestions" is a technological device or algorithm that analyzes the content of factory operations meeting materials and identifies and suggests improvements.

[0626] The "means for analyzing feedback after the meeting and proposing improvements for the next operation" refers to a technological device or algorithm that collects and analyzes feedback from participants after the factory operations meeting and proposes specific improvements for the next operation.

[0627] To implement this invention, it is necessary to build a system for improving the efficiency of factory operation meetings. This system uses hardware including high-performance servers, smart devices (smartphones and tablets), and industrial robots, and software that utilizes generative AI and NLP (natural language processing) technologies.

[0628] Overall system flow

[0629] 1. Receiving and parsing meeting requests

[0630] A user inputs a meeting request on a smart device and sends it to the server. The request includes the purpose of the meeting, the agenda, potential participants, and the expected outcomes. The server uses an NLP model (e.g., spaCy) to analyze the request and determine the need for the meeting.

[0631] Examples:

[0632] The user types, "I'm proposing an operations meeting tomorrow regarding production line adjustments. The agenda will be about improving the efficiency of the manufacturing process and introducing new machines."

[0633] 2. Optimize your attendee list

[0634] If the need for a conference is confirmed, the server generates a list of participants that are optimally suited to the needs of the operator based on their job title and area of ​​expertise. This list is displayed to the user via their smart device, and they can make modifications or additions as needed.

[0635] 3. Reviewing meeting materials and making suggestions for improvement

[0636] When a user uploads meeting materials, the server analyzes them using generative AI to identify areas where information is missing or where improvements are needed. Based on the results of the analysis, specific improvement suggestions are generated and notified to the user via their smart device.

[0637] 4. Real-time facilitation

[0638] Once the meeting begins, the server monitors the progress of the meeting in real time, adjusting speaking style and speed to help ensure important discussions are carried out smoothly, and if a particular speech is long, the server suggests shortening it and provides appropriate advice to prevent the discussion from going off track.

[0639] 5. Feedback Collection and Analysis

[0640] After the meeting, the user fills out a feedback form and sends it to the server. The server analyzes this feedback and identifies the effectiveness of the meeting and areas for improvement. The analysis results are notified to the user via their smart device, and specific improvement suggestions are provided for the next meeting.

[0641] The specific hardware and software used

[0642] Hardware: high-performance servers, smart devices (smartphones and tablets), industrial robots.

[0643] Software: TensorFlow, spaCy, NLTK, Node.js, WebSockets.

[0644] Example prompt

[0645] If a user inputs something like, "I'd like to propose an operations meeting regarding tomorrow's production line adjustments. The agenda will be about improving the efficiency of the manufacturing process and introducing new machines," the system will analyze this prompt, determine the need for the meeting, and automatically generate an appropriate participant list. The generation AI uses NLP technology to proceed with work based on such user requests.

[0646] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0647] Step 1:

[0648] A user inputs a meeting request from a smart device and sends information including the purpose of the meeting, the agenda, potential participants, and expected outcomes to the server. The input text is sent to the server, which receives this data.

[0649] Step 2:

[0650] The server processes the meeting request text with a generative AI model and uses NLP techniques (e.g., spaCy) to analyze the request. To determine whether a meeting is necessary, it extracts specific keywords and phrases and compares them with existing meeting criteria. The analysis results in a decision on whether a meeting is necessary (output).

[0651] Step 3:

[0652] When the need for a conference is confirmed, the server searches a database based on the operator's job title and area of ​​expertise to generate an optimal participant list, which is then sent to the user's smart device (output).

[0653] Step 4:

[0654] The user uploads meeting materials from their smart device to the server. The server then analyzes the materials using a generative AI to identify missing information and areas for improvement. Each section of the materials is analyzed, and the generative AI generates specific improvement suggestions. The improvement suggestions are then sent to the user's smart device (output).

[0655] Step 5:

[0656] When a user starts a conference, the server monitors the progress of the conference in real time. The server receives the conference audio and text data in real time via WebSockets, adjusts the order of speech and speaking speed, and, if necessary, suggests shortening speech or redirecting the discussion to specific participants (output).

[0657] Step 6:

[0658] After the meeting, the user fills out a feedback form on their smart device and sends it to the server. The server analyzes the feedback data using generative AI to identify the effectiveness of the meeting and areas for improvement. The analysis results are generated as specific improvement suggestions for the next meeting and are notified to the user's smart device (output).

[0659] Specific actions at each step

[0660] 1. In step 1, the user enters, "I would like to propose an operations meeting tomorrow regarding production line adjustments. The agenda will be about improving the efficiency of the manufacturing process and introducing new machines."

[0661] 2. In step 2, this text data is tokenized and analyzed by spaCy, and keywords such as "production line adjustment," "efficiency," and "new machine" are extracted.

[0662] 3. In step 3, appropriate operators are selected from the database to generate the optimal participant list, and the result is notified to the user as the "optimal participant list."

[0663] 4. In step 4, the generation AI analyzes the meeting materials uploaded by the user and notifies them of "areas where materials are lacking" and "specific improvement suggestions."

[0664] 5. In step 5, the server monitors in real time via WebSockets and adjusts the order and speed of speech, for example suggesting "move to the next topic" if the conversation is getting too long.

[0665] 6. In step 6, the user feedback is analyzed and specific improvement suggestions for the next meeting are notified to the user.

[0666] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0667] The present invention relates to a system that improves the efficiency of meetings by combining a generative AI and an emotion engine. Specific embodiments of the system of the present invention will be described below. In particular, the operations of the server, terminals, and users will be described in detail.

[0668] Overall system configuration

[0669] The system mainly consists of the following modules:

[0670] 1. Meeting Request Analysis Module

[0671] 2. Participant List Optimization Module

[0672] 3. Document Review and Improvement Suggestion Module

[0673] 4. Real-time Facilitation Module

[0674] 5. Emotion Recognition Module

[0675] 6. Feedback Collection and Analysis Module

[0676] System Operation

[0677] 1. Meeting request analysis

[0678] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The server uses the received information to determine the necessity of the meeting. At this time, it analyzes the request content using natural language processing technology and determines whether the meeting needs to be held.

[0679] 2. Optimize your attendee list

[0680] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device, and the user can confirm the final participant list. The user can then modify or add to the list as needed.

[0681] 3. Document review and improvement suggestions

[0682] Users upload meeting materials from their devices to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. Based on the results of the analysis, it generates specific suggestions for improving the materials and notifies the user via their device. This ensures that all necessary information is gathered before the meeting.

[0683] 4. Real-time facilitation and emotion recognition

[0684] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses image processing and voice analysis technology to analyze the user's facial expressions and tone of voice to recognize their emotions. The server adjusts the order, speaking style, and speaking speed of each comment, and provides appropriate advice based on the user's emotional state.

[0685] For example, if the user is nervous, the emotion engine can detect this and the server can notify the user through the terminal of advice to help them relax.

[0686] 5. Feedback Collection and Analysis

[0687] After the meeting, the user fills out a feedback form on their device and sends it to the server. The server analyzes this feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[0688] Specific examples

[0689] For example, this system is used when a company wants to hold a meeting to discuss sales strategies for a new product.

[0690] 1. A user enters a meeting request and submits it to the server.

[0691] 2. The server parses the request and determines whether a meeting is required.

[0692] 3. The server generates an optimal participant list and notifies the user through the terminal.

[0693] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[0694] 5. The user starts the conference, and the server facilitates in real time while monitoring the user's emotions using the emotion recognition module.

[0695] 6. Based on the emotion recognition results, the server provides appropriate advice.

[0696] 7. After the meeting, users enter their feedback, which is analyzed by the server and used to inform the server of improvements for the next meeting.

[0697] This series of processes ensures that meetings are carried out consistently and efficiently, from preparation to progress and evaluation, improving meeting productivity and the operational efficiency of the entire organization.

[0698] The processing flow will be explained below.

[0699] Step 1:

[0700] A user inputs a meeting request from a terminal. The input form includes fields for describing the purpose of the meeting, the agenda, potential participants, and the expected outcomes. Once the input is complete, the request data is sent to the server.

[0701] Step 2:

[0702] The server analyzes the received meeting request, uses natural language processing technology to evaluate the appropriateness of the meeting's purpose, agenda, and expected outcomes, and determines the need for the meeting based on this evaluation.

[0703] Step 3:

[0704] If the need for a meeting is confirmed, the server analyzes the participant list included in the meeting request, analyzes each participant's job title and area of ​​expertise, and generates an optimal participant list using an algorithm to select participants who will best contribute to the meeting's objectives.

[0705] Step 4:

[0706] The device displays the optimized participant list to the user, who can then review the list and add or remove participants as needed. Once the final list is finalized, it is sent to the server.

[0707] Step 5:

[0708] Users upload meeting materials (presentations, reports, data sheets, etc.) from their terminals to the server. These materials contain information necessary for preparing and conducting the meeting.

[0709] Step 6:

[0710] The server reviews the uploaded meeting materials, analyzes the contents of the materials, and determines whether the information is sufficient or if there is any information missing or needs improvement. Based on the analysis results, it generates specific improvement proposals.

[0711] Step 7:

[0712] The terminal notifies the user of the improvement suggestions from the server, and the user corrects the materials based on the suggestions and uploads them back to the server if necessary.

[0713] Step 8:

[0714] Once a user starts a meeting, the server monitors the progress of the meeting in real time, and algorithms are activated to adjust the order, style and speed of speech as the meeting progresses.

[0715] Step 9:

[0716] The server uses an emotion recognition module to analyze the user's facial expressions and tone of voice using image processing and voice analysis technology, thereby recognizing the user's emotional state in real time.

[0717] Step 10:

[0718] Based on the emotion recognition results, the server generates appropriate advice and notifies the user via the device. For example, if the user is feeling nervous, the server provides advice encouraging them to relax.

[0719] Step 11:

[0720] After the meeting, users fill out a feedback form on their device and send their evaluation of the meeting to the server. The feedback includes the quality of the meeting, how it was conducted, and areas for improvement.

[0721] Step 12:

[0722] The server analyzes the received feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are then sent to the user via their device, and specific suggestions for improvement are provided for the next meeting.

[0723] Example 2

[0724] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0725] In conventional conferencing systems, the process from preparing for a meeting to proceeding and evaluating it was often done manually, resulting in inefficiency and reduced productivity. Furthermore, there was no way to properly manage the order of speaking, speaking style, or emotions of speakers during the meeting, which often resulted in meetings not proceeding smoothly. This could result in a decline in the quality of the meeting and in participants being unable to fully discuss the matter.

[0726] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a user to input a conference request and determine the necessity of the conference using a generation AI, a means for analyzing the participant list included in the conference request and generating an optimized list, a means for performing text analysis of conference materials and generating improvement suggestions, a means for supporting the progress of the conference in real time and providing appropriate advice based on emotion recognition, and a means for receiving and analyzing feedback after the conference ends. This makes it possible to efficiently carry out the preparation, progress, and evaluation of the conference.

[0727] "User" means a person or entity that operates the System and enters and submits meeting requests and materials.

[0728] "Generative AI" refers to artificial intelligence that analyzes the content of a meeting request and determines its necessity.

[0729] A "meeting request" refers to information entered by a user, including the purpose of the meeting, the agenda, potential participants, and expected outcomes.

[0730] A "participant list" is a list of people and roles scheduled to attend a meeting.

[0731] "Optimization" refers to the process of selecting the most suitable participants for a conference.

[0732] "Text analysis" is the process of analyzing the content of meeting materials using artificial intelligence and software.

[0733] "Improvement proposals" refer to proposals to improve the quality of meeting materials and proceedings.

[0734] "Supporting meeting progress in real time" refers to technologies and functions that support the progress of a meeting while it is taking place.

[0735] "Emotion recognition" refers to the technology of analyzing a user's facial expressions and tone of voice to determine their emotions.

[0736] "Feedback" refers to information about evaluations, impressions, and areas for improvement submitted by users after the end of a conference.

[0737] "Analysis" refers to the process of using artificial intelligence and software to analyze feedback and other data in detail.

[0738] The present invention relates to a system for improving meeting efficiency using generative AI and an emotion engine. Implementation of this system primarily requires interactions between a server, a terminal, and a user. An embodiment of the system is described in detail below.

[0739] Overall system configuration

[0740] The system mainly consists of the following modules:

[0741] 1. Meeting Request Analysis Module

[0742] 2. Participant List Optimization Module

[0743] 3. Document Review and Improvement Suggestion Module

[0744] 4. Real-time Facilitation Module

[0745] 5. Emotion Recognition Module

[0746] 6. Feedback Collection and Analysis Module

[0747] Hardware and Software Configuration

[0748] The server uses the following software and databases:

[0749] Parsing a Meeting Request: Google Cloud Natural Language API

[0750] Participant list optimization: Internal database

[0751] Document Review: IBM Watson Natural Language Understanding

[0752] Real-time facilitation: OpenCV (image processing), Google Cloud Speech-to-Text (audio analysis)

[0753] A terminal is a typical computer or mobile device that users use to enter and submit meeting requests and materials.

[0754] Operation procedures and function details

[0755] 1. Entering and parsing meeting requests

[0756] The user inputs the purpose of the meeting, the agenda, potential participants, and the expected outcomes using a terminal, and sends the input to the server. The server then uses the Google Cloud Natural Language API to analyze the input request and determine the need for the meeting.

[0757] 2. Optimize your attendee list

[0758] After determining the need for a meeting, the server retrieves data on each account's job title and area of ​​expertise from an internal database. Based on this, it generates an optimal participant list and displays it to the user via their device. The user can then review and modify the list and finalize it.

[0759] 3. Document review and improvement suggestions

[0760] Users upload meeting materials from their devices to the server. The server then analyzes the materials using IBM Watson Natural Language Understanding to identify areas where information is missing or where improvements are needed. Based on the analysis results, specific improvement suggestions are generated and notified to the user via their device.

[0761] 4. Real-time facilitation and emotion recognition

[0762] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses OpenCV (image processing) and Google Cloud Speech-to-Text (audio analysis) to analyze the user's facial expressions and tone of voice to recognize their emotions. The server adjusts the order, speaking style, and speed of speech and provides advice based on the user's emotional state.

[0763] 5. Feedback Collection and Analysis

[0764] After the meeting, users fill out a feedback form on their device and send it to the server. The server analyzes the feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[0765] Specific examples

[0766] For example, this system is used when a company wants to hold a meeting to discuss sales strategies for a new product.

[0767] 1. A user inputs a meeting request stating, "I would like to hold a meeting to discuss the market strategy for a new product," and sends it to the server.

[0768] 2. The server parses the request and determines whether a meeting is required.

[0769] 3. The server generates a list of optimal participants, such as "marketing staff," "sales managers," and "technology development leaders," and notifies the user via their terminal.

[0770] 4. The user uploads meeting materials, and the server reviews the materials and makes specific suggestions for improvement, such as "Please add market analysis data."

[0771] 5. The user starts the conference, the server monitors the progress of the conference, and the emotion recognition module monitors the user's emotions.

[0772] 6. Based on the emotion recognition results, the server provides advice such as "Take a deep breath to relax."

[0773] 7. After the meeting ends, the user sends feedback that "the meeting is progressing too fast," and the server analyzes this and suggests adjusting the speed of the next meeting.

[0774] Prompt Sentence Examples

[0775] As an example of a prompt sentence, you can give instructions to the generative AI model as follows:

[0776] "Based on the participant list entered by the user, please create a list of the people who are best suited to the agenda. Then, generate a command to notify the user when the list is complete."

[0777] In this way, the system of the present invention allows efficient preparation, progress, and evaluation of a meeting, resulting in improved productivity of the meeting.

[0778] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0779] Step 1:

[0780] A user inputs a meeting request from a terminal, describing the purpose, agenda, potential participants, and expected results, and then submits the request. The input request information is sent to the server.

[0781] The server receives this information and analyzes it using the Google Cloud Natural Language API. The analysis results determine whether a meeting is necessary. The input at this stage is the meeting request information, and the output is a determination of whether a meeting is necessary.

[0782] Specific behavior:

[0783] A user inputs and submits a request to "hold a market research meeting for a new product."

[0784] The server analyzes the request and determines that there is a need for a meeting.

[0785] Step 2:

[0786] After the necessity of the meeting is confirmed, the server retrieves data on each participant's job title and area of ​​expertise from an internal database to optimize the participant list. Based on this data, a generative AI is used to generate an optimal participant list. The generated list is displayed to the user via their device for confirmation and modification.

[0787] The input is participant information based on the conference request, and the output is an optimized participant list.

[0788] Specific behavior:

[0789] The server obtains data such as "project managers," "marketing staff," and "technical staff" and generates the optimal list.

[0790] The user reviews the list, adds "Designer" and confirms.

[0791] Step 3:

[0792] Users upload meeting materials from their devices to the server. The server then uses IBM Watson Natural Language Understanding to analyze the text of the materials and identify missing information or areas that need improvement. Based on the results of this analysis, specific improvement suggestions are generated and notified to the user via their device.

[0793] The input is the meeting materials and the output is the improvement proposals.

[0794] Specific behavior:

[0795] A user uploads a presentation.

[0796] The server analyzes the data and notifies you that "market analysis data is insufficient."

[0797] Step 4:

[0798] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses OpenCV and Google Cloud Speech-to-Text to analyze the user's facial expressions and tone of voice to recognize their emotional state. The server adjusts the order, style, and speed of speech and provides advice based on the user's emotional state.

[0799] The input is audio and video data from the meeting, and the output is real-time advice.

[0800] Specific behavior:

[0801] A user starts a conference and the server analyzes the audio and video.

[0802] The server offers advice such as "Take a deep breath to relax."

[0803] Step 5:

[0804] After the meeting, users fill out a feedback form on their device and send it to the server. The server then uses analysis software to analyze this feedback, evaluate the meeting, and identify areas for improvement. The analysis results are then sent to the user via their device, and specific suggestions for improvement are provided for the next meeting.

[0805] The input is a feedback form, and the output is the analysis results and improvement suggestions.

[0806] Specific behavior:

[0807] A user enters and submits feedback saying, "The meeting is moving too fast."

[0808] The server analyzes the feedback and suggests adjusting the pace of the next meeting.

[0809] This will clearly show the detailed process flow of how the entire system effectively supports and improves meetings.

[0810] (Application example 2)

[0811] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0812] In traditional brick-and-mortar store operations, meetings and conferences between store staff and managers are often extremely inefficient. The main causes are unclear judgment of the need for a meeting, a lack of appropriate participants, and insufficient materials that lead to confusing discussions. Another problem is that there are few ways to grasp the emotions and stress levels of participants during the meeting, which can easily create an awkward atmosphere. Furthermore, the productivity of meetings is hindered by the lack of a system in place to collect feedback and use it to improve the next meeting.

[0813] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0814] In this invention, the server includes means for receiving a conference request using a generation AI and determining its necessity, means for optimizing the participant list included in the conference request, means for reviewing conference materials and generating improvement proposals, means for facilitation in real time while the conference is in progress, means for recognizing emotions while the conference is in progress and providing appropriate advice according to the user's emotional state, and means for receiving and analyzing feedback after the conference ends. This enables conferences and meetings in physical stores to be conducted more efficiently and effectively.

[0815] "Generative AI" is artificial intelligence used to analyze meeting requests and suggest improvements to materials.

[0816] The "means for receiving a conference request and determining necessity" is a system element that has the function of analyzing a conference request sent by a user and determining whether the conference is truly necessary.

[0817] The "means for optimizing the participant list" is an element of the system that has the function of automatically selecting participants required for a meeting based on their job titles and areas of expertise, and generating an optimal list.

[0818] The "means for reviewing meeting materials and generating improvement proposals" refers to a system element that has the function of analyzing meeting materials uploaded by users, pointing out areas where information is lacking or areas for improvement, and providing specific improvement proposals.

[0819] "Means for real-time facilitation while a meeting is in progress" refers to a system element that has the function of monitoring the progress of a meeting while it is in progress and facilitating in real time, such as adjusting the order of comments, speaking style, and speaking speed.

[0820] "Means for recognizing emotions and providing appropriate advice according to the user's emotional state" refers to a system element that has the function of analyzing the facial expressions and tone of voice of participants during the meeting, understanding their emotional state, and providing advice to help them relax as needed.

[0821] The "means for receiving and analyzing feedback" is a system element that has the function of receiving feedback from users after the meeting, analyzing the content, and generating specific improvement suggestions for the next meeting.

[0822] This invention provides a system that combines generative AI and emotion recognition technology to efficiently manage conferences and meetings in brick-and-mortar stores. Specific embodiments are as follows.

[0823] System Configuration

[0824] The system mainly consists of the following modules:

[0825] 1. Meeting Request Analysis Module

[0826] 2. Participant List Optimization Module

[0827] 3. Document Review and Improvement Suggestion Module

[0828] 4. Real-time Facilitation Module

[0829] 5. Emotion Recognition Module

[0830] 6. Feedback Collection and Analysis Module

[0831] DETAILED DESCRIPTION OF THE EMBODIMENTS

[0832] 1. Meeting request analysis

[0833] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The server uses the received information to determine the need for the meeting, using natural language processing technology.

[0834] 2. Optimize your attendee list

[0835] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device, and the user can confirm the final participant list. The user can then modify or add to the list as needed.

[0836] 3. Document review and improvement suggestions

[0837] Users upload meeting materials from their devices to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. Based on the results of the analysis, it generates specific suggestions for improving the materials and notifies the user via their device.

[0838] 4. Real-time facilitation and emotion recognition

[0839] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses image processing and voice analysis technology to analyze the user's facial expressions and tone of voice to recognize their emotions. The server adjusts the order, speaking style, and speaking speed of what is said and provides appropriate advice based on the user's emotional state. For example, if the user is nervous, the emotion engine can detect this and the server can notify the user via their device with advice to help them relax.

[0840] 5. Feedback Collection and Analysis

[0841] After the meeting, the user fills out a feedback form on their device and sends it to the server. The server analyzes this feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[0842] Specific examples

[0843] For example, this system is used when store managers at a brick-and-mortar store chain hold monthly review meetings.

[0844] 1. A user enters a meeting request and submits it to the server.

[0845] 2. The server parses the request and determines whether a meeting is required.

[0846] 3. The server generates an optimal participant list and notifies the user through the terminal.

[0847] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[0848] 5. The user starts the conference, and the server facilitates in real time while monitoring the user's emotions using the emotion recognition module.

[0849] 6. Based on the emotion recognition results, the server provides appropriate advice.

[0850] 7. After the meeting, users enter their feedback, which is analyzed by the server and used to inform the server of improvements for the next meeting.

[0851] Prompt Sentence Examples

[0852] Below are some example prompts to input to a generative AI model:

[0853] text

[0854] A user wants to hold a monthly review meeting for a physical store. The purpose of the meeting is to analyze sales and plan for the next month, and the agenda is a performance review of each staff member. Participants include all store staff and the area manager. Based on this information, the user needs to analyze the meeting materials and make suggestions for improvement.

[0855] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0856] Step 1:

[0857] A user inputs a meeting request from a terminal and sends the purpose, agenda, potential participants, and expected outcomes to the server. The input data (meeting request information) is analyzed by the server using natural language processing technology. As a result of this data analysis, the need for a meeting is determined.

[0858] Step 2:

[0859] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device. The user can review the displayed list and modify or add participants as necessary. The input participant list data is output as the optimal list.

[0860] Step 3:

[0861] The user uploads meeting materials from their device to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. This analysis uses machine learning technology to evaluate the content of the materials. Specific improvement suggestions are generated by the server and notified to the user via their device.

[0862] Step 4:

[0863] When a user starts a meeting, the server monitors the progress of the meeting in real time. The server adjusts the order of speech, speaking style, and speaking speed, and uses an emotion recognition module to analyze the user's facial expressions and tone of voice. This allows it to recognize the emotions of participants and provide advice to help them relax if they are nervous. The server outputs the adjusted speaking order and advice.

[0864] Step 5:

[0865] After the meeting, users fill out a feedback form on their device and send it to the server. The server analyzes this feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are generated as a report and notified to the user via their device. The feedback analysis results are output, and specific improvement suggestions for the next meeting are provided.

[0866] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0867] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0868] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0869] [Third embodiment]

[0870] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0871] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0872] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0873] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0874] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0875] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0876] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0877] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0878] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0879] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0880] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0881] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0882] The present invention relates to a system for improving the efficiency of meetings using generative AI. Specific embodiments of the system of the present invention will be described below. In particular, the operations of the server, terminals, and users will be described in detail.

[0883] Overall system configuration

[0884] The system mainly consists of the following modules:

[0885] 1. Meeting Request Analysis Module

[0886] 2. Participant List Optimization Module

[0887] 3. Document Review and Improvement Suggestion Module

[0888] 4. Real-time Facilitation Module

[0889] 5. Feedback Collection and Analysis Module

[0890] System Operation

[0891] 1. Meeting request analysis

[0892] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The server uses the received information to determine the necessity of the meeting. At this time, it analyzes the request content using natural language processing technology and determines whether the meeting needs to be held.

[0893] 2. Optimize your attendee list

[0894] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device, and the user can confirm the final participant list. The user can then modify or add to the list as needed.

[0895] 3. Document review and improvement suggestions

[0896] Users upload meeting materials from their devices to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. Based on the results of the analysis, it generates specific suggestions for improving the materials and notifies the user via their device. This ensures that all necessary information is gathered before the meeting.

[0897] 4. Real-time facilitation

[0898] When a user starts a meeting, the server monitors the progress of the meeting in real time. The server adjusts the order, style, and speed of speech to help ensure important discussions proceed smoothly. For example, if a particular participant speaks for a long time, the server will suggest shortening their speech via the user's device. It also provides timely advice to prevent the discussion from going off track.

[0899] 5. Feedback Collection and Analysis

[0900] After the meeting, users fill out a feedback form on their device and send their evaluation of the meeting to the server. The server analyzes this feedback and identifies the effectiveness of the meeting and areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[0901] Specific examples

[0902] For example, this system is used when a company wants to hold a meeting to discuss sales strategies for a new product.

[0903] 1. A user enters a meeting request and submits it to the server.

[0904] 2. The server parses the request and determines whether a meeting is required.

[0905] 3. The server generates an optimal participant list and notifies the user through the terminal.

[0906] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[0907] 5. The user initiates the conference, which the server facilitates in real time.

[0908] 6. After the meeting, users enter their feedback, which the server analyzes and notifies them of improvements for the next meeting.

[0909] This series of processes ensures that meetings are carried out consistently and efficiently, from preparation to progress and evaluation, improving meeting productivity and the operational efficiency of the entire organization.

[0910] The processing flow will be explained below.

[0911] Step 1:

[0912] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The input form contains specific meeting details.

[0913] Step 2:

[0914] The server analyzes the received meeting request and uses natural language processing technology to understand the request content, determine whether the purpose, agenda, and expected outcomes are appropriate, and evaluate the necessity of the meeting.

[0915] Step 3:

[0916] After the server confirms the need for a conference, it analyzes the participant list in the conference request, analyzes each participant's job title and area of ​​expertise, and selects the most suitable participants. At this time, it narrows down the participants to the minimum necessary.

[0917] Step 4:

[0918] The terminal notifies the user of the optimized participant list, and the user confirms it. The user can add or remove participants as needed and confirm the final list.

[0919] Step 5:

[0920] Users upload meeting materials from their devices to the server, including presentations, reports, data sheets, and more.

[0921] Step 6:

[0922] The server analyzes the uploaded materials, checks the completeness of the information, identifies missing information and areas that need improvement, and generates specific improvement proposals.

[0923] Step 7:

[0924] The terminal notifies the user of the improvement suggestions from the server, and the user corrects the materials. If necessary, the materials are uploaded to the server again and rechecked.

[0925] Step 8:

[0926] Once a user starts a meeting, the server monitors the progress of the meeting in real time, and algorithms are put into motion to adjust the order, style and speed of speech.

[0927] Step 9:

[0928] The server generates appropriate advice to support the progress of important discussions and notifies the users through their terminals, thereby enabling discussions to proceed actively and efficiently.

[0929] Step 10:

[0930] After the meeting, users fill out a feedback form on their devices and send it to the server. The feedback includes the quality of the meeting, how it was conducted, and areas for improvement.

[0931] Step 11:

[0932] The server analyzes the received feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are then sent to the user via their device, and specific suggestions for improvement are provided for the next meeting.

[0933] Example 1

[0934] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0935] In traditional meetings, selecting the best participants, preparing materials, managing the meeting, and collecting and analyzing feedback requires a great deal of time and effort. As a result, meetings are less efficient and discussions are often ineffective. In addition, because multiple methods are used individually, it is difficult to integrate and manage the entire meeting, which prevents the productivity of meetings from improving. An integrated system is needed to solve this problem.

[0936] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0937] In this invention, the server includes: means for receiving a conference request using a generation AI and determining the necessity; means for optimizing the participant list included in the conference request; means for reviewing conference materials and generating improvement suggestions; means for facilitating in real time while the conference is in progress; means for accepting and analyzing feedback after the conference has ended; means for a user to input a conference request from a terminal and send it to the server; means for the server to analyze the request content using natural language processing technology; means for the server to generate an optimal participant list based on the participants' positions and areas of expertise; means for the server to analyze uploaded conference materials and identify missing information and areas for improvement; means for the server to monitor the progress of the conference in real time and adjust the order of speech, speaking style, and speaking speed; and means for the server to analyze feedback sent from users and identify areas for improvement next time.

[0938] This makes it possible to consistently manage meeting preparation, progress, and evaluation, significantly improving meeting productivity.

[0939] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data.

[0940] A "meeting request" is data including information such as the purpose of the meeting, the agenda, potential participants, and expected outcomes.

[0941] A "terminal" is an electronic device such as a computer or smartphone that a user uses to input, operate, and confirm information.

[0942] A "server" is a computer system that receives, analyzes, manages, and processes data sent by users.

[0943] "Natural language processing technology" is a technology for understanding and analyzing human language.

[0944] A "participant list" is a list of people who are expected to attend a conference.

[0945] "Position" refers to the participant's role or position in the workplace.

[0946] "Area of ​​expertise" refers to the area in which a participant has specialized knowledge or skills.

[0947] "Meeting materials" are documents such as reports and presentations used in meetings.

[0948] An "improvement proposal" is a specific proposal to point out problems with the meeting materials or progress and improve them.

[0949] "Facilitation" is the act of coordinating a meeting to ensure that it proceeds smoothly and that important discussions take place.

[0950] "Feedback" is information used to gather evaluations and opinions about the meeting.

[0951] "Analysis" is the process of analyzing data to find meaning and patterns.

[0952] The present invention is a system for improving the efficiency of meetings using generative AI. Specific embodiments for implementing the present invention are described below. In particular, the operations of the server, terminals, and users are described in detail.

[0953] Overall system configuration

[0954] The system mainly consists of the following modules:

[0955] 1. Meeting Request Analysis Module

[0956] 2. Participant List Optimization Module

[0957] 3. Document Review and Improvement Suggestion Module

[0958] 4. Real-time Facilitation Module

[0959] 5. Feedback Collection and Analysis Module

[0960] Filling out a meeting request

[0961] Users input meeting requests through their devices and send them to the server. Users use a form to enter the purpose of the meeting, the agenda, potential participants, and expected outcomes. By clicking the send button, the input data is sent to the server. The devices used here are general electronic devices such as PCs and smartphones, and the software used is a web form or a mobile app.

[0962] Parsing a meeting request

[0963] The server analyzes the received meeting request data using natural language processing technology. Specifically, it uses a generative AI model (e.g., GPT-3 or spaCy) to convert the request content into structured data and determine the necessity of the meeting. This makes it possible to understand the purpose and importance of the meeting.

[0964] Participant list optimization

[0965] After the server determines the need for a meeting, it analyzes the candidate participant data included in the request. The server generates an optimal participant list taking into account each participant's job title and area of ​​expertise. Pandas and scikit-learn are used as data analysis tools. The participant list generated by the server is displayed to the user via their terminal, allowing them to review and modify it.

[0966] Uploading meeting materials

[0967] Users upload meeting materials from their devices to the server. Using the file upload system, documents such as Word and PDF files can be easily uploaded. Users select the materials and click the upload button to send them to the server.

[0968] Document review and improvement suggestions

[0969] The server retrieves the uploaded meeting materials and analyzes their contents using a generative AI model (e.g., GPT-3 or BERT). The server identifies missing information and areas for improvement, generates specific improvement suggestions, and notifies the user via their device. This improves the quality of the meeting materials and increases the effectiveness of the meeting.

[0970] Real-time facilitation

[0971] When a user starts a meeting, the server monitors the progress of the meeting in real time. The server uses WebRTC to adjust the order, speaking style, and speaking speed of each participant. The server also provides timely advice to prevent discussions from going off track, helping to ensure important discussions proceed smoothly.

[0972] Feedback collection and analysis

[0973] After the meeting, users fill out a feedback form on their device and send it to the server. The server analyzes the feedback data to identify the effectiveness of the meeting and areas for improvement. Software used includes text analysis tools (e.g., sentiment analysis libraries like VADER and TextBlob). The analysis results are notified to users via their device, and suggestions for improvement are provided for the next meeting.

[0974] Specific examples

[0975] For example, when a company wants to hold a meeting to discuss sales strategies for a new product, the following sequence is executed.

[0976] 1. A user enters a meeting request and submits it to the server.

[0977] Example prompt: "We'd like to hold a sales strategy meeting for new product X next Thursday. Potential attendees will be the sales manager, marketing manager, and finance manager. Topics to discuss include market analysis, sales targets, and budget planning."

[0978] 2. The server parses the request and determines whether a meeting is required.

[0979] Example prompt: "Based on the information provided, determine whether a meeting is necessary."

[0980] 3. The server generates an optimal participant list and notifies the user through the terminal.

[0981] Example prompt: "Generate and display a list of participants that best fit your request."

[0982] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[0983] Example prompt: "Based on this data, please review the materials and suggest improvements."

[0984] 5. The user initiates the conference, which the server facilitates in real time.

[0985] Example prompt: "Please help us organize the order of speaking and keep the discussion flowing smoothly."

[0986] 6. After the meeting, users enter their feedback, which the server analyzes and notifies them of improvements for the next meeting.

[0987] Example prompt: "Based on your feedback, please rate the effectiveness of the meeting and provide suggestions for improvement for the next meeting."

[0988] In this way, the system can efficiently manage and support the entire meeting process, greatly improving meeting productivity.

[0989] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0990] Step 1:

[0991] A user inputs a conference request through a terminal and sends it to the server.

[0992] Input: Information such as the purpose of the meeting, agenda, potential attendees, and expected outcomes.

[0993] Data processing: The information entered by the user is sent to the server in text format.

[0994] Output: The conference request data received by the server.

[0995] Specific operation: The user enters the following into the input form on the terminal: "We would like to hold a sales strategy meeting for new product X. Possible participants are the sales manager, marketing manager, and finance manager. The agenda will include market analysis, sales targets, and budget planning." and presses the send button.

[0996] Step 2:

[0997] The server analyzes the received conference request data using natural language processing technology.

[0998] Input: Meeting request data.

[0999] Data processing: Using natural language processing technology (e.g., GPT-3, spaCy), the request content is analyzed and converted into structured data.

[1000] Output: Structured data such as the purpose of the meeting, importance, and a list of potential attendees.

[1001] Specific operation: The server extracts keywords such as "new product," "sales strategy," and "meeting" from the request content and determines the need for a meeting.

[1002] Step 3:

[1003] The server analyzes the participant candidate data included in the request and generates an optimal participant list.

[1004] Input: Structured data (e.g., a list of potential participants, each candidate's job title, and area of ​​expertise).

[1005] Data processing: Using data analysis tools (e.g., pandas, scikit-learn), we evaluated each participant's job title and area of ​​expertise to generate the optimal list.

[1006] Output: Optimized participant list.

[1007] Specific operation: The server evaluates the data of "Sales Manager," "Marketing Manager," and "Finance Manager," and displays the optimal participant list to the user via the terminal.

[1008] Step 4:

[1009] The user uploads the meeting materials from the terminal to the server.

[1010] Input: Meeting materials (e.g. Word, PDF files).

[1011] Data processing: Send materials to the server using a file upload system.

[1012] Output: Meeting materials uploaded to the server.

[1013] Specific operation: The user selects the meeting materials (e.g., market analysis report) using the file upload system of the terminal and presses the upload button.

[1014] Step 5:

[1015] The server analyzes the uploaded meeting materials and identifies missing information and areas for improvement.

[1016] Input: Uploaded meeting materials.

[1017] Data processing: Analyze materials using generative AI models (e.g., GPT-3, BERT) to generate improvement suggestions.

[1018] Output: Specific suggestions for improving the material.

[1019] Specific operation: The server analyzes the "Market Analysis Report" and generates an improvement suggestion saying "Market data is insufficient. Please add more." and notifies the user.

[1020] Step 6:

[1021] A user initiates a conference and the server monitors the progress of the conference in real time.

[1022] Input: Meeting start signal and real-time data (e.g. what was said, who spoke, and when).

[1023] Data processing: Using WebRTC, the order of speech, speaking style, and speaking speed are analyzed and necessary adjustments are made.

[1024] Output: Adjusted instructions on speaking order, speaking style, and speaking speed.

[1025] Specific operation: The server monitors the order of comments and sends notifications via the device saying, "Please keep your comments brief." If the discussion goes off track, the server advises, "Please get back on topic."

[1026] Step 7:

[1027] After the conference ends, the user fills in a feedback form from the terminal and sends it to the server.

[1028] Input: Feedback data (e.g., meeting ratings, opinions).

[1029] Data processing: The feedback data was analyzed using text analysis tools (e.g., VADER, TextBlob).

[1030] Output: Analysis of the effectiveness of the meeting and areas for improvement.

[1031] Specific operation: The user inputs feedback such as "The meeting went smoothly" or "There were some parts where materials were lacking" from their device and sends it to the server. Based on the analysis results, the system notifies the user of an improvement suggestion such as "Please prepare more materials in advance."

[1032] (Application example 1)

[1033] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1034] Traditional meetings in factory operations often hinder efficient decision-making because they do not adequately optimize participants, suggest improvements to materials, or manage progress in real time. Furthermore, there is a problem with not gathering feedback after meetings and presenting areas for improvement for the next meeting, making it difficult to establish a continuous improvement cycle.

[1035] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1036] In this invention, the server includes means for receiving a conference request using a generation AI and determining its necessity, means for optimizing the participant list included in the conference request, means for reviewing conference materials and generating improvement proposals, means for facilitating the conference in real time while the conference is in progress, means for accepting and analyzing feedback after the conference ends, means for optimizing the operator list for the factory operations conference and managing the progress in real time, means for reviewing the operations conference materials and providing improvement proposals, and means for analyzing the feedback after the conference ends and presenting points for improving operations for the next time. This improves the efficiency of factory operations meetings and enables the creation of a continuous improvement cycle.

[1037] "Generative AI" is an artificial intelligence system that uses a generative approach to automate and optimize specific tasks.

[1038] A "meeting request" is a request that includes the information necessary to propose a meeting.

[1039] A "means for determining necessity" is a technical device or algorithm that analyzes and determines whether holding a meeting is truly necessary.

[1040] A "participant list optimization method" is a technological device or algorithm that selects the participants best suited to achieving the objectives of the meeting.

[1041] A "means for reviewing meeting materials and generating improvement suggestions" is a technical device or algorithm that analyzes the content of meeting materials and identifies and suggests improvements.

[1042] "Means for real-time facilitation during a meeting" refers to technological devices or algorithms that support and facilitate the progress of a meeting.

[1043] A "means for receiving and analyzing feedback" is a technological device or algorithm that collects and analyzes feedback from participants after the meeting has concluded.

[1044] The "means for optimizing the operator list and managing the progress in real time" refers to a technical device or algorithm that generates an optimal operator list in factory operations and manages the progress of meetings in real time.

[1045] A "means for reviewing operations meeting materials and providing improvement suggestions" is a technological device or algorithm that analyzes the content of factory operations meeting materials and identifies and suggests improvements.

[1046] The "means for analyzing feedback after the meeting and proposing improvements for the next operation" refers to a technological device or algorithm that collects and analyzes feedback from participants after the factory operations meeting and proposes specific improvements for the next operation.

[1047] To implement this invention, it is necessary to build a system for improving the efficiency of factory operation meetings. This system uses hardware including high-performance servers, smart devices (smartphones and tablets), and industrial robots, and software that utilizes generative AI and NLP (natural language processing) technologies.

[1048] Overall system flow

[1049] 1. Receiving and parsing meeting requests

[1050] A user inputs a meeting request on a smart device and sends it to the server. The request includes the purpose of the meeting, the agenda, potential participants, and the expected outcomes. The server uses an NLP model (e.g., spaCy) to analyze the request and determine the need for the meeting.

[1051] Examples:

[1052] The user types, "I'm proposing an operations meeting tomorrow regarding production line adjustments. The agenda will be about improving the efficiency of the manufacturing process and introducing new machines."

[1053] 2. Optimize your attendee list

[1054] If the need for a conference is confirmed, the server generates a list of participants that are optimally suited to the needs of the operator based on their job title and area of ​​expertise. This list is displayed to the user via their smart device, and they can make modifications or additions as needed.

[1055] 3. Reviewing meeting materials and making suggestions for improvement

[1056] When a user uploads meeting materials, the server analyzes them using generative AI to identify areas where information is missing or where improvements are needed. Based on the results of the analysis, specific improvement suggestions are generated and notified to the user via their smart device.

[1057] 4. Real-time facilitation

[1058] Once the meeting begins, the server monitors the progress of the meeting in real time, adjusting speaking style and speed to help ensure important discussions are carried out smoothly, and if a particular speech is long, the server suggests shortening it and provides appropriate advice to prevent the discussion from going off track.

[1059] 5. Feedback Collection and Analysis

[1060] After the meeting, the user fills out a feedback form and sends it to the server. The server analyzes this feedback and identifies the effectiveness of the meeting and areas for improvement. The analysis results are notified to the user via their smart device, and specific improvement suggestions are provided for the next meeting.

[1061] The specific hardware and software used

[1062] Hardware: high-performance servers, smart devices (smartphones and tablets), industrial robots.

[1063] Software: TensorFlow, spaCy, NLTK, Node.js, WebSockets.

[1064] Example prompt

[1065] If a user inputs something like, "I'd like to propose an operations meeting regarding tomorrow's production line adjustments. The agenda will be about improving the efficiency of the manufacturing process and introducing new machines," the system will analyze this prompt, determine the need for the meeting, and automatically generate an appropriate participant list. The generation AI uses NLP technology to proceed with work based on such user requests.

[1066] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1067] Step 1:

[1068] A user inputs a meeting request from a smart device and sends information including the purpose of the meeting, the agenda, potential participants, and expected outcomes to the server. The input text is sent to the server, which receives this data.

[1069] Step 2:

[1070] The server processes the meeting request text with a generative AI model and uses NLP techniques (e.g., spaCy) to analyze the request. To determine whether a meeting is necessary, it extracts specific keywords and phrases and compares them with existing meeting criteria. The analysis results in a decision on whether a meeting is necessary (output).

[1071] Step 3:

[1072] When the need for a conference is confirmed, the server searches a database based on the operator's job title and area of ​​expertise to generate an optimal participant list, which is then sent to the user's smart device (output).

[1073] Step 4:

[1074] The user uploads meeting materials from their smart device to the server. The server then analyzes the materials using a generative AI to identify missing information and areas for improvement. Each section of the materials is analyzed, and the generative AI generates specific improvement suggestions. The improvement suggestions are then sent to the user's smart device (output).

[1075] Step 5:

[1076] When a user starts a conference, the server monitors the progress of the conference in real time. The server receives the conference audio and text data in real time via WebSockets, adjusts the order of speech and speaking speed, and, if necessary, suggests shortening speech or redirecting the discussion to specific participants (output).

[1077] Step 6:

[1078] After the meeting, the user fills out a feedback form on their smart device and sends it to the server. The server analyzes the feedback data using generative AI to identify the effectiveness of the meeting and areas for improvement. The analysis results are generated as specific improvement suggestions for the next meeting and are notified to the user's smart device (output).

[1079] Specific actions at each step

[1080] 1. In step 1, the user enters, "I would like to propose an operations meeting tomorrow regarding production line adjustments. The agenda will be about improving the efficiency of the manufacturing process and introducing new machines."

[1081] 2. In step 2, this text data is tokenized and analyzed by spaCy, and keywords such as "production line adjustment," "efficiency," and "new machine" are extracted.

[1082] 3. In step 3, appropriate operators are selected from the database to generate the optimal participant list, and the result is notified to the user as the "optimal participant list."

[1083] 4. In step 4, the generation AI analyzes the meeting materials uploaded by the user and notifies them of "areas where materials are lacking" and "specific improvement suggestions."

[1084] 5. In step 5, the server monitors in real time via WebSockets and adjusts the order and speed of speech, for example suggesting "move to the next topic" if the conversation is getting too long.

[1085] 6. In step 6, the user feedback is analyzed and specific improvement suggestions for the next meeting are notified to the user.

[1086] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1087] The present invention relates to a system that improves the efficiency of meetings by combining a generative AI and an emotion engine. Specific embodiments of the system of the present invention will be described below. In particular, the operations of the server, terminals, and users will be described in detail.

[1088] Overall system configuration

[1089] The system mainly consists of the following modules:

[1090] 1. Meeting Request Analysis Module

[1091] 2. Participant List Optimization Module

[1092] 3. Document Review and Improvement Suggestion Module

[1093] 4. Real-time Facilitation Module

[1094] 5. Emotion Recognition Module

[1095] 6. Feedback Collection and Analysis Module

[1096] System Operation

[1097] 1. Meeting request analysis

[1098] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The server uses the received information to determine the necessity of the meeting. At this time, it analyzes the request content using natural language processing technology and determines whether the meeting needs to be held.

[1099] 2. Optimize your attendee list

[1100] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device, and the user can confirm the final participant list. The user can then modify or add to the list as needed.

[1101] 3. Document review and improvement suggestions

[1102] Users upload meeting materials from their devices to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. Based on the results of the analysis, it generates specific suggestions for improving the materials and notifies the user via their device. This ensures that all necessary information is gathered before the meeting.

[1103] 4. Real-time facilitation and emotion recognition

[1104] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses image processing and voice analysis technology to analyze the user's facial expressions and tone of voice to recognize their emotions. The server adjusts the order, speaking style, and speaking speed of each comment, and provides appropriate advice based on the user's emotional state.

[1105] For example, if the user is nervous, the emotion engine can detect this and the server can notify the user through the terminal of advice to help them relax.

[1106] 5. Feedback Collection and Analysis

[1107] After the meeting, the user fills out a feedback form on their device and sends it to the server. The server analyzes this feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[1108] Specific examples

[1109] For example, this system is used when a company wants to hold a meeting to discuss sales strategies for a new product.

[1110] 1. A user enters a meeting request and submits it to the server.

[1111] 2. The server parses the request and determines whether a meeting is required.

[1112] 3. The server generates an optimal participant list and notifies the user through the terminal.

[1113] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[1114] 5. The user starts the conference, and the server facilitates in real time while monitoring the user's emotions using the emotion recognition module.

[1115] 6. Based on the emotion recognition results, the server provides appropriate advice.

[1116] 7. After the meeting, users enter their feedback, which is analyzed by the server and used to inform the server of improvements for the next meeting.

[1117] This series of processes ensures that meetings are carried out consistently and efficiently, from preparation to progress and evaluation, improving meeting productivity and the operational efficiency of the entire organization.

[1118] The processing flow will be explained below.

[1119] Step 1:

[1120] A user inputs a meeting request from a terminal. The input form includes fields for describing the purpose of the meeting, the agenda, potential participants, and the expected outcomes. Once the input is complete, the request data is sent to the server.

[1121] Step 2:

[1122] The server analyzes the received meeting request, uses natural language processing technology to evaluate the appropriateness of the meeting's purpose, agenda, and expected outcomes, and determines the need for the meeting based on this evaluation.

[1123] Step 3:

[1124] If the need for a meeting is confirmed, the server analyzes the participant list included in the meeting request, analyzes each participant's job title and area of ​​expertise, and generates an optimal participant list using an algorithm to select participants who will best contribute to the meeting's objectives.

[1125] Step 4:

[1126] The device displays the optimized participant list to the user, who can then review the list and add or remove participants as needed. Once the final list is finalized, it is sent to the server.

[1127] Step 5:

[1128] Users upload meeting materials (presentations, reports, data sheets, etc.) from their terminals to the server. These materials contain information necessary for preparing and conducting the meeting.

[1129] Step 6:

[1130] The server reviews the uploaded meeting materials, analyzes the contents of the materials, and determines whether the information is sufficient or if there is any information missing or needs improvement. Based on the analysis results, it generates specific improvement proposals.

[1131] Step 7:

[1132] The terminal notifies the user of the improvement suggestions from the server, and the user corrects the materials based on the suggestions and uploads them back to the server if necessary.

[1133] Step 8:

[1134] Once a user starts a meeting, the server monitors the progress of the meeting in real time, and algorithms are activated to adjust the order, style and speed of speech as the meeting progresses.

[1135] Step 9:

[1136] The server uses an emotion recognition module to analyze the user's facial expressions and tone of voice using image processing and voice analysis technology, thereby recognizing the user's emotional state in real time.

[1137] Step 10:

[1138] Based on the emotion recognition results, the server generates appropriate advice and notifies the user via the device. For example, if the user is feeling nervous, the server provides advice encouraging them to relax.

[1139] Step 11:

[1140] After the meeting, users fill out a feedback form on their device and send their evaluation of the meeting to the server. The feedback includes the quality of the meeting, how it was conducted, and areas for improvement.

[1141] Step 12:

[1142] The server analyzes the received feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are then sent to the user via their device, and specific suggestions for improvement are provided for the next meeting.

[1143] Example 2

[1144] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1145] In conventional conferencing systems, the process from preparing for a meeting to proceeding and evaluating it was often done manually, resulting in inefficiency and reduced productivity. Furthermore, there was no way to properly manage the order of speaking, speaking style, or emotions of speakers during the meeting, which often resulted in meetings not proceeding smoothly. This could result in a decline in the quality of the meeting and in participants being unable to fully discuss the matter.

[1146] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a user to input a conference request and determine the necessity of the conference using a generation AI, a means for analyzing the participant list included in the conference request and generating an optimized list, a means for performing text analysis of conference materials and generating improvement suggestions, a means for supporting the progress of the conference in real time and providing appropriate advice based on emotion recognition, and a means for receiving and analyzing feedback after the conference ends. This makes it possible to efficiently carry out the preparation, progress, and evaluation of the conference.

[1147] "User" means a person or entity that operates the System and enters and submits meeting requests and materials.

[1148] "Generative AI" refers to artificial intelligence that analyzes the content of a meeting request and determines its necessity.

[1149] A "meeting request" refers to information entered by a user, including the purpose of the meeting, the agenda, potential participants, and expected outcomes.

[1150] A "participant list" is a list of people and roles scheduled to attend a meeting.

[1151] "Optimization" refers to the process of selecting the most suitable participants for a conference.

[1152] "Text analysis" is the process of analyzing the content of meeting materials using artificial intelligence and software.

[1153] "Improvement proposals" refer to proposals to improve the quality of meeting materials and proceedings.

[1154] "Supporting meeting progress in real time" refers to technologies and functions that support the progress of a meeting while it is taking place.

[1155] "Emotion recognition" refers to the technology of analyzing a user's facial expressions and tone of voice to determine their emotions.

[1156] "Feedback" refers to information about evaluations, impressions, and areas for improvement submitted by users after the end of a conference.

[1157] "Analysis" refers to the process of using artificial intelligence and software to analyze feedback and other data in detail.

[1158] The present invention relates to a system for improving meeting efficiency using generative AI and an emotion engine. Implementation of this system primarily requires interactions between a server, a terminal, and a user. An embodiment of the system is described in detail below.

[1159] Overall system configuration

[1160] The system mainly consists of the following modules:

[1161] 1. Meeting Request Analysis Module

[1162] 2. Participant List Optimization Module

[1163] 3. Document Review and Improvement Suggestion Module

[1164] 4. Real-time Facilitation Module

[1165] 5. Emotion Recognition Module

[1166] 6. Feedback Collection and Analysis Module

[1167] Hardware and Software Configuration

[1168] The server uses the following software and databases:

[1169] Parsing a Meeting Request: Google Cloud Natural Language API

[1170] Participant list optimization: Internal database

[1171] Document Review: IBM Watson Natural Language Understanding

[1172] Real-time facilitation: OpenCV (image processing), Google Cloud Speech-to-Text (audio analysis)

[1173] A terminal is a typical computer or mobile device that users use to enter and submit meeting requests and materials.

[1174] Operation procedures and function details

[1175] 1. Entering and parsing meeting requests

[1176] The user inputs the purpose of the meeting, the agenda, potential participants, and the expected outcomes using a terminal, and sends the input to the server. The server then uses the Google Cloud Natural Language API to analyze the input request and determine the need for the meeting.

[1177] 2. Optimize your attendee list

[1178] After determining the need for a meeting, the server retrieves data on each account's job title and area of ​​expertise from an internal database. Based on this, it generates an optimal participant list and displays it to the user via their device. The user can then review and modify the list and finalize it.

[1179] 3. Document review and improvement suggestions

[1180] Users upload meeting materials from their devices to the server. The server then analyzes the materials using IBM Watson Natural Language Understanding to identify areas where information is missing or where improvements are needed. Based on the analysis results, specific improvement suggestions are generated and notified to the user via their device.

[1181] 4. Real-time facilitation and emotion recognition

[1182] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses OpenCV (image processing) and Google Cloud Speech-to-Text (audio analysis) to analyze the user's facial expressions and tone of voice to recognize their emotions. The server adjusts the order, speaking style, and speed of speech and provides advice based on the user's emotional state.

[1183] 5. Feedback Collection and Analysis

[1184] After the meeting, users fill out a feedback form on their device and send it to the server. The server analyzes the feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[1185] Specific examples

[1186] For example, this system is used when a company wants to hold a meeting to discuss sales strategies for a new product.

[1187] 1. A user inputs a meeting request stating, "I would like to hold a meeting to discuss the market strategy for a new product," and sends it to the server.

[1188] 2. The server parses the request and determines whether a meeting is required.

[1189] 3. The server generates a list of optimal participants, such as "marketing staff," "sales managers," and "technology development leaders," and notifies the user via their terminal.

[1190] 4. The user uploads meeting materials, and the server reviews the materials and makes specific suggestions for improvement, such as "Please add market analysis data."

[1191] 5. The user starts the conference, the server monitors the progress of the conference, and the emotion recognition module monitors the user's emotions.

[1192] 6. Based on the emotion recognition results, the server provides advice such as "Take a deep breath to relax."

[1193] 7. After the meeting ends, the user sends feedback that "the meeting is progressing too fast," and the server analyzes this and suggests adjusting the speed of the next meeting.

[1194] Prompt Sentence Examples

[1195] As an example of a prompt sentence, you can give instructions to the generative AI model as follows:

[1196] "Based on the participant list entered by the user, please create a list of the people who are best suited to the agenda. Then, generate a command to notify the user when the list is complete."

[1197] In this way, the system of the present invention allows efficient preparation, progress, and evaluation of a meeting, resulting in improved productivity of the meeting.

[1198] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1199] Step 1:

[1200] A user inputs a meeting request from a terminal, describing the purpose, agenda, potential participants, and expected results, and then submits the request. The input request information is sent to the server.

[1201] The server receives this information and analyzes it using the Google Cloud Natural Language API. The analysis results determine whether a meeting is necessary. The input at this stage is the meeting request information, and the output is a determination of whether a meeting is necessary.

[1202] Specific behavior:

[1203] A user inputs and submits a request to "hold a market research meeting for a new product."

[1204] The server analyzes the request and determines that there is a need for a meeting.

[1205] Step 2:

[1206] After the necessity of the meeting is confirmed, the server retrieves data on each participant's job title and area of ​​expertise from an internal database to optimize the participant list. Based on this data, a generative AI is used to generate an optimal participant list. The generated list is displayed to the user via their device for confirmation and modification.

[1207] The input is participant information based on the conference request, and the output is an optimized participant list.

[1208] Specific behavior:

[1209] The server obtains data such as "project managers," "marketing staff," and "technical staff" and generates the optimal list.

[1210] The user reviews the list, adds "Designer" and confirms.

[1211] Step 3:

[1212] Users upload meeting materials from their devices to the server. The server then uses IBM Watson Natural Language Understanding to analyze the text of the materials and identify missing information or areas that need improvement. Based on the results of this analysis, specific improvement suggestions are generated and notified to the user via their device.

[1213] The input is the meeting materials and the output is the improvement proposals.

[1214] Specific behavior:

[1215] A user uploads a presentation.

[1216] The server analyzes the data and notifies you that "market analysis data is insufficient."

[1217] Step 4:

[1218] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses OpenCV and Google Cloud Speech-to-Text to analyze the user's facial expressions and tone of voice to recognize their emotional state. The server adjusts the order, style, and speed of speech and provides advice based on the user's emotional state.

[1219] The input is audio and video data from the meeting, and the output is real-time advice.

[1220] Specific behavior:

[1221] A user starts a conference and the server analyzes the audio and video.

[1222] The server offers advice such as "Take a deep breath to relax."

[1223] Step 5:

[1224] After the meeting, users fill out a feedback form on their device and send it to the server. The server then uses analysis software to analyze this feedback, evaluate the meeting, and identify areas for improvement. The analysis results are then sent to the user via their device, and specific suggestions for improvement are provided for the next meeting.

[1225] The input is a feedback form, and the output is the analysis results and improvement suggestions.

[1226] Specific behavior:

[1227] A user enters and submits feedback saying, "The meeting is moving too fast."

[1228] The server analyzes the feedback and suggests adjusting the pace of the next meeting.

[1229] This will clearly show the detailed process flow of how the entire system effectively supports and improves meetings.

[1230] (Application example 2)

[1231] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1232] In traditional brick-and-mortar store operations, meetings and conferences between store staff and managers are often extremely inefficient. The main causes are unclear judgment of the need for a meeting, a lack of appropriate participants, and insufficient materials that lead to confusing discussions. Another problem is that there are few ways to grasp the emotions and stress levels of participants during the meeting, which can easily create an awkward atmosphere. Furthermore, the productivity of meetings is hindered by the lack of a system in place to collect feedback and use it to improve the next meeting.

[1233] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1234] In this invention, the server includes means for receiving a conference request using a generation AI and determining its necessity, means for optimizing the participant list included in the conference request, means for reviewing conference materials and generating improvement proposals, means for facilitation in real time while the conference is in progress, means for recognizing emotions while the conference is in progress and providing appropriate advice according to the user's emotional state, and means for receiving and analyzing feedback after the conference ends. This enables conferences and meetings in physical stores to be conducted more efficiently and effectively.

[1235] "Generative AI" is artificial intelligence used to analyze meeting requests and suggest improvements to materials.

[1236] The "means for receiving a conference request and determining necessity" is a system element that has the function of analyzing a conference request sent by a user and determining whether the conference is truly necessary.

[1237] The "means for optimizing the participant list" is an element of the system that has the function of automatically selecting participants required for a meeting based on their job titles and areas of expertise, and generating an optimal list.

[1238] The "means for reviewing meeting materials and generating improvement proposals" refers to a system element that has the function of analyzing meeting materials uploaded by users, pointing out areas where information is lacking or areas for improvement, and providing specific improvement proposals.

[1239] "Means for real-time facilitation while a meeting is in progress" refers to a system element that has the function of monitoring the progress of a meeting while it is in progress and facilitating in real time, such as adjusting the order of comments, speaking style, and speaking speed.

[1240] "Means for recognizing emotions and providing appropriate advice according to the user's emotional state" refers to a system element that has the function of analyzing the facial expressions and tone of voice of participants during the meeting, understanding their emotional state, and providing advice to help them relax as needed.

[1241] The "means for receiving and analyzing feedback" is a system element that has the function of receiving feedback from users after the meeting, analyzing the content, and generating specific improvement suggestions for the next meeting.

[1242] This invention provides a system that combines generative AI and emotion recognition technology to efficiently manage conferences and meetings in brick-and-mortar stores. Specific embodiments are as follows.

[1243] System Configuration

[1244] The system mainly consists of the following modules:

[1245] 1. Meeting Request Analysis Module

[1246] 2. Participant List Optimization Module

[1247] 3. Document Review and Improvement Suggestion Module

[1248] 4. Real-time Facilitation Module

[1249] 5. Emotion Recognition Module

[1250] 6. Feedback Collection and Analysis Module

[1251] DETAILED DESCRIPTION OF THE EMBODIMENTS

[1252] 1. Meeting request analysis

[1253] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The server uses the received information to determine the need for the meeting, using natural language processing technology.

[1254] 2. Optimize your attendee list

[1255] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device, and the user can confirm the final participant list. The user can then modify or add to the list as needed.

[1256] 3. Document review and improvement suggestions

[1257] Users upload meeting materials from their devices to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. Based on the results of the analysis, it generates specific suggestions for improving the materials and notifies the user via their device.

[1258] 4. Real-time facilitation and emotion recognition

[1259] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses image processing and voice analysis technology to analyze the user's facial expressions and tone of voice to recognize their emotions. The server adjusts the order, speaking style, and speaking speed of what is said and provides appropriate advice based on the user's emotional state. For example, if the user is nervous, the emotion engine can detect this and the server can notify the user via their device with advice to help them relax.

[1260] 5. Feedback Collection and Analysis

[1261] After the meeting, the user fills out a feedback form on their device and sends it to the server. The server analyzes this feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[1262] Specific examples

[1263] For example, this system is used when store managers at a brick-and-mortar store chain hold monthly review meetings.

[1264] 1. A user enters a meeting request and submits it to the server.

[1265] 2. The server parses the request and determines whether a meeting is required.

[1266] 3. The server generates an optimal participant list and notifies the user through the terminal.

[1267] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[1268] 5. The user starts the conference, and the server facilitates in real time while monitoring the user's emotions using the emotion recognition module.

[1269] 6. Based on the emotion recognition results, the server provides appropriate advice.

[1270] 7. After the meeting, users enter their feedback, which is analyzed by the server and used to inform the server of improvements for the next meeting.

[1271] Prompt Sentence Examples

[1272] Below are some example prompts to input to a generative AI model:

[1273] text

[1274] A user wants to hold a monthly review meeting for a physical store. The purpose of the meeting is to analyze sales and plan for the next month, and the agenda is a performance review of each staff member. Participants include all store staff and the area manager. Based on this information, the user needs to analyze the meeting materials and make suggestions for improvement.

[1275] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1276] Step 1:

[1277] A user inputs a meeting request from a terminal and sends the purpose, agenda, potential participants, and expected outcomes to the server. The input data (meeting request information) is analyzed by the server using natural language processing technology. As a result of this data analysis, the need for a meeting is determined.

[1278] Step 2:

[1279] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device. The user can review the displayed list and modify or add participants as necessary. The input participant list data is output as the optimal list.

[1280] Step 3:

[1281] The user uploads meeting materials from their device to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. This analysis uses machine learning technology to evaluate the content of the materials. Specific improvement suggestions are generated by the server and notified to the user via their device.

[1282] Step 4:

[1283] When a user starts a meeting, the server monitors the progress of the meeting in real time. The server adjusts the order of speech, speaking style, and speaking speed, and uses an emotion recognition module to analyze the user's facial expressions and tone of voice. This allows it to recognize the emotions of participants and provide advice to help them relax if they are nervous. The server outputs the adjusted speaking order and advice.

[1284] Step 5:

[1285] After the meeting, users fill out a feedback form on their device and send it to the server. The server analyzes this feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are generated as a report and notified to the user via their device. The feedback analysis results are output, and specific improvement suggestions for the next meeting are provided.

[1286] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1287] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1288] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1289] [Fourth embodiment]

[1290] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1291] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1292] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1293] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1294] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1295] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1296] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1297] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1298] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1299] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1300] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1301] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1302] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1303] The present invention relates to a system for improving the efficiency of meetings using generative AI. Specific embodiments of the system of the present invention will be described below. In particular, the operations of the server, terminals, and users will be described in detail.

[1304] Overall system configuration

[1305] The system mainly consists of the following modules:

[1306] 1. Meeting Request Analysis Module

[1307] 2. Participant List Optimization Module

[1308] 3. Document Review and Improvement Suggestion Module

[1309] 4. Real-time Facilitation Module

[1310] 5. Feedback Collection and Analysis Module

[1311] System Operation

[1312] 1. Meeting request analysis

[1313] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The server uses the received information to determine the necessity of the meeting. At this time, it analyzes the request content using natural language processing technology and determines whether the meeting needs to be held.

[1314] 2. Optimize your attendee list

[1315] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device, and the user can confirm the final participant list. The user can then modify or add to the list as needed.

[1316] 3. Document review and improvement suggestions

[1317] Users upload meeting materials from their devices to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. Based on the results of the analysis, it generates specific suggestions for improving the materials and notifies the user via their device. This ensures that all necessary information is gathered before the meeting.

[1318] 4. Real-time facilitation

[1319] When a user starts a meeting, the server monitors the progress of the meeting in real time. The server adjusts the order, style, and speed of speech to help ensure important discussions proceed smoothly. For example, if a particular participant speaks for a long time, the server will suggest shortening their speech via the user's device. It also provides timely advice to prevent the discussion from going off track.

[1320] 5. Feedback Collection and Analysis

[1321] After the meeting, users fill out a feedback form on their device and send their evaluation of the meeting to the server. The server analyzes this feedback and identifies the effectiveness of the meeting and areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[1322] Specific examples

[1323] For example, this system is used when a company wants to hold a meeting to discuss sales strategies for a new product.

[1324] 1. A user enters a meeting request and submits it to the server.

[1325] 2. The server parses the request and determines whether a meeting is required.

[1326] 3. The server generates an optimal participant list and notifies the user through the terminal.

[1327] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[1328] 5. The user initiates the conference, which the server facilitates in real time.

[1329] 6. After the meeting, users enter their feedback, which the server analyzes and notifies them of improvements for the next meeting.

[1330] This series of processes ensures that meetings are carried out consistently and efficiently, from preparation to progress and evaluation, improving meeting productivity and the operational efficiency of the entire organization.

[1331] The processing flow will be explained below.

[1332] Step 1:

[1333] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The input form contains specific meeting details.

[1334] Step 2:

[1335] The server analyzes the received meeting request and uses natural language processing technology to understand the request content, determine whether the purpose, agenda, and expected outcomes are appropriate, and evaluate the necessity of the meeting.

[1336] Step 3:

[1337] After the server confirms the need for a conference, it analyzes the participant list in the conference request, analyzes each participant's job title and area of ​​expertise, and selects the most suitable participants. At this time, it narrows down the participants to the minimum necessary.

[1338] Step 4:

[1339] The terminal notifies the user of the optimized participant list, and the user confirms it. The user can add or remove participants as needed and confirm the final list.

[1340] Step 5:

[1341] Users upload meeting materials from their devices to the server, including presentations, reports, data sheets, and more.

[1342] Step 6:

[1343] The server analyzes the uploaded materials, checks the completeness of the information, identifies missing information and areas that need improvement, and generates specific improvement proposals.

[1344] Step 7:

[1345] The terminal notifies the user of the improvement suggestions from the server, and the user corrects the materials. If necessary, the materials are uploaded to the server again and rechecked.

[1346] Step 8:

[1347] Once a user starts a meeting, the server monitors the progress of the meeting in real time, and algorithms are put into motion to adjust the order, style and speed of speech.

[1348] Step 9:

[1349] The server generates appropriate advice to support the progress of important discussions and notifies the users through their terminals, thereby enabling discussions to proceed actively and efficiently.

[1350] Step 10:

[1351] After the meeting, users fill out a feedback form on their devices and send it to the server. The feedback includes the quality of the meeting, how it was conducted, and areas for improvement.

[1352] Step 11:

[1353] The server analyzes the received feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are then sent to the user via their device, and specific suggestions for improvement are provided for the next meeting.

[1354] Example 1

[1355] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1356] In traditional meetings, selecting the best participants, preparing materials, managing the meeting, and collecting and analyzing feedback requires a great deal of time and effort. As a result, meetings are less efficient and discussions are often ineffective. In addition, because multiple methods are used individually, it is difficult to integrate and manage the entire meeting, which prevents the productivity of meetings from improving. An integrated system is needed to solve this problem.

[1357] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1358] In this invention, the server includes: means for receiving a conference request using a generation AI and determining the necessity; means for optimizing the participant list included in the conference request; means for reviewing conference materials and generating improvement suggestions; means for facilitating in real time while the conference is in progress; means for accepting and analyzing feedback after the conference has ended; means for a user to input a conference request from a terminal and send it to the server; means for the server to analyze the request content using natural language processing technology; means for the server to generate an optimal participant list based on the participants' positions and areas of expertise; means for the server to analyze uploaded conference materials and identify missing information and areas for improvement; means for the server to monitor the progress of the conference in real time and adjust the order of speech, speaking style, and speaking speed; and means for the server to analyze feedback sent from users and identify areas for improvement next time.

[1359] This makes it possible to consistently manage meeting preparation, progress, and evaluation, significantly improving meeting productivity.

[1360] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data.

[1361] A "meeting request" is data including information such as the purpose of the meeting, the agenda, potential participants, and expected outcomes.

[1362] A "terminal" is an electronic device such as a computer or smartphone that a user uses to input, operate, and confirm information.

[1363] A "server" is a computer system that receives, analyzes, manages, and processes data sent by users.

[1364] "Natural language processing technology" is a technology for understanding and analyzing human language.

[1365] A "participant list" is a list of people who are expected to attend a conference.

[1366] "Position" refers to the participant's role or position in the workplace.

[1367] "Area of ​​expertise" refers to the area in which a participant has specialized knowledge or skills.

[1368] "Meeting materials" are documents such as reports and presentations used in meetings.

[1369] An "improvement proposal" is a specific proposal to point out problems with the meeting materials or progress and improve them.

[1370] "Facilitation" is the act of coordinating a meeting to ensure that it proceeds smoothly and that important discussions take place.

[1371] "Feedback" is information used to gather evaluations and opinions about the meeting.

[1372] "Analysis" is the process of analyzing data to find meaning and patterns.

[1373] The present invention is a system for improving the efficiency of meetings using generative AI. Specific embodiments for implementing the present invention are described below. In particular, the operations of the server, terminals, and users are described in detail.

[1374] Overall system configuration

[1375] The system mainly consists of the following modules:

[1376] 1. Meeting Request Analysis Module

[1377] 2. Participant List Optimization Module

[1378] 3. Document Review and Improvement Suggestion Module

[1379] 4. Real-time Facilitation Module

[1380] 5. Feedback Collection and Analysis Module

[1381] Filling out a meeting request

[1382] Users input meeting requests through their devices and send them to the server. Users use a form to enter the purpose of the meeting, the agenda, potential participants, and expected outcomes. By clicking the send button, the input data is sent to the server. The devices used here are general electronic devices such as PCs and smartphones, and the software used is a web form or a mobile app.

[1383] Parsing a meeting request

[1384] The server analyzes the received meeting request data using natural language processing technology. Specifically, it uses a generative AI model (e.g., GPT-3 or spaCy) to convert the request content into structured data and determine the necessity of the meeting. This makes it possible to understand the purpose and importance of the meeting.

[1385] Participant list optimization

[1386] After the server determines the need for a meeting, it analyzes the candidate participant data included in the request. The server generates an optimal participant list taking into account each participant's job title and area of ​​expertise. Pandas and scikit-learn are used as data analysis tools. The participant list generated by the server is displayed to the user via their terminal, allowing them to review and modify it.

[1387] Uploading meeting materials

[1388] Users upload meeting materials from their devices to the server. Using the file upload system, documents such as Word and PDF files can be easily uploaded. Users select the materials and click the upload button to send them to the server.

[1389] Document review and improvement suggestions

[1390] The server retrieves the uploaded meeting materials and analyzes their contents using a generative AI model (e.g., GPT-3 or BERT). The server identifies missing information and areas for improvement, generates specific improvement suggestions, and notifies the user via their device. This improves the quality of the meeting materials and increases the effectiveness of the meeting.

[1391] Real-time facilitation

[1392] When a user starts a meeting, the server monitors the progress of the meeting in real time. The server uses WebRTC to adjust the order, speaking style, and speaking speed of each participant. The server also provides timely advice to prevent discussions from going off track, helping to ensure important discussions proceed smoothly.

[1393] Feedback collection and analysis

[1394] After the meeting, users fill out a feedback form on their device and send it to the server. The server analyzes the feedback data to identify the effectiveness of the meeting and areas for improvement. Software used includes text analysis tools (e.g., sentiment analysis libraries like VADER and TextBlob). The analysis results are notified to users via their device, and suggestions for improvement are provided for the next meeting.

[1395] Specific examples

[1396] For example, when a company wants to hold a meeting to discuss sales strategies for a new product, the following sequence is executed.

[1397] 1. A user enters a meeting request and submits it to the server.

[1398] Example prompt: "We'd like to hold a sales strategy meeting for new product X next Thursday. Potential attendees will be the sales manager, marketing manager, and finance manager. Topics to discuss include market analysis, sales targets, and budget planning."

[1399] 2. The server parses the request and determines whether a meeting is required.

[1400] Example prompt: "Based on the information provided, determine whether a meeting is necessary."

[1401] 3. The server generates an optimal participant list and notifies the user through the terminal.

[1402] Example prompt: "Generate and display a list of participants that best fit your request."

[1403] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[1404] Example prompt: "Based on this data, please review the materials and suggest improvements."

[1405] 5. The user initiates the conference, which the server facilitates in real time.

[1406] Example prompt: "Please help us organize the order of speaking and keep the discussion flowing smoothly."

[1407] 6. After the meeting, users enter their feedback, which the server analyzes and notifies them of improvements for the next meeting.

[1408] Example prompt: "Based on your feedback, please rate the effectiveness of the meeting and provide suggestions for improvement for the next meeting."

[1409] In this way, the system can efficiently manage and support the entire meeting process, greatly improving meeting productivity.

[1410] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1411] Step 1:

[1412] A user inputs a conference request through a terminal and sends it to the server.

[1413] Input: Information such as the purpose of the meeting, agenda, potential attendees, and expected outcomes.

[1414] Data processing: The information entered by the user is sent to the server in text format.

[1415] Output: The conference request data received by the server.

[1416] Specific operation: The user enters the following into the input form on the terminal: "We would like to hold a sales strategy meeting for new product X. Possible participants are the sales manager, marketing manager, and finance manager. The agenda will include market analysis, sales targets, and budget planning." and presses the send button.

[1417] Step 2:

[1418] The server analyzes the received conference request data using natural language processing technology.

[1419] Input: Meeting request data.

[1420] Data processing: Using natural language processing technology (e.g., GPT-3, spaCy), the request content is analyzed and converted into structured data.

[1421] Output: Structured data such as the purpose of the meeting, importance, and a list of potential attendees.

[1422] Specific operation: The server extracts keywords such as "new product," "sales strategy," and "meeting" from the request content and determines the need for a meeting.

[1423] Step 3:

[1424] The server analyzes the participant candidate data included in the request and generates an optimal participant list.

[1425] Input: Structured data (e.g., a list of potential participants, each candidate's job title, and area of ​​expertise).

[1426] Data processing: Using data analysis tools (e.g., pandas, scikit-learn), we evaluated each participant's job title and area of ​​expertise to generate the optimal list.

[1427] Output: Optimized participant list.

[1428] Specific operation: The server evaluates the data of "Sales Manager," "Marketing Manager," and "Finance Manager," and displays the optimal participant list to the user via the terminal.

[1429] Step 4:

[1430] The user uploads the meeting materials from the terminal to the server.

[1431] Input: Meeting materials (e.g. Word, PDF files).

[1432] Data processing: Send materials to the server using a file upload system.

[1433] Output: Meeting materials uploaded to the server.

[1434] Specific operation: The user selects the meeting materials (e.g., market analysis report) using the file upload system of the terminal and presses the upload button.

[1435] Step 5:

[1436] The server analyzes the uploaded meeting materials and identifies missing information and areas for improvement.

[1437] Input: Uploaded meeting materials.

[1438] Data processing: Analyze materials using generative AI models (e.g., GPT-3, BERT) to generate improvement suggestions.

[1439] Output: Specific suggestions for improving the material.

[1440] Specific operation: The server analyzes the "Market Analysis Report" and generates an improvement suggestion saying "Market data is insufficient. Please add more." and notifies the user.

[1441] Step 6:

[1442] A user initiates a conference and the server monitors the progress of the conference in real time.

[1443] Input: Meeting start signal and real-time data (e.g. what was said, who spoke, and when).

[1444] Data processing: Using WebRTC, the order of speech, speaking style, and speaking speed are analyzed and necessary adjustments are made.

[1445] Output: Adjusted instructions on speaking order, speaking style, and speaking speed.

[1446] Specific operation: The server monitors the order of comments and sends notifications via the device saying, "Please keep your comments brief." If the discussion goes off track, the server advises, "Please get back on topic."

[1447] Step 7:

[1448] After the conference ends, the user fills in a feedback form from the terminal and sends it to the server.

[1449] Input: Feedback data (e.g., meeting ratings, opinions).

[1450] Data processing: The feedback data was analyzed using text analysis tools (e.g., VADER, TextBlob).

[1451] Output: Analysis of the effectiveness of the meeting and areas for improvement.

[1452] Specific operation: The user inputs feedback such as "The meeting went smoothly" or "There were some parts where materials were lacking" from their device and sends it to the server. Based on the analysis results, the system notifies the user of an improvement suggestion such as "Please prepare more materials in advance."

[1453] (Application example 1)

[1454] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1455] Traditional meetings in factory operations often hinder efficient decision-making because they do not adequately optimize participants, suggest improvements to materials, or manage progress in real time. Furthermore, there is a problem with not gathering feedback after meetings and presenting areas for improvement for the next meeting, making it difficult to establish a continuous improvement cycle.

[1456] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1457] In this invention, the server includes means for receiving a conference request using a generation AI and determining its necessity, means for optimizing the participant list included in the conference request, means for reviewing conference materials and generating improvement proposals, means for facilitating the conference in real time while the conference is in progress, means for accepting and analyzing feedback after the conference ends, means for optimizing the operator list for the factory operations conference and managing the progress in real time, means for reviewing the operations conference materials and providing improvement proposals, and means for analyzing the feedback after the conference ends and presenting points for improving operations for the next time. This improves the efficiency of factory operations meetings and enables the creation of a continuous improvement cycle.

[1458] "Generative AI" is an artificial intelligence system that uses a generative approach to automate and optimize specific tasks.

[1459] A "meeting request" is a request that includes the information necessary to propose a meeting.

[1460] A "means for determining necessity" is a technical device or algorithm that analyzes and determines whether holding a meeting is truly necessary.

[1461] A "participant list optimization method" is a technological device or algorithm that selects the participants best suited to achieving the objectives of the meeting.

[1462] A "means for reviewing meeting materials and generating improvement suggestions" is a technical device or algorithm that analyzes the content of meeting materials and identifies and suggests improvements.

[1463] "Means for real-time facilitation during a meeting" refers to technological devices or algorithms that support and facilitate the progress of a meeting.

[1464] A "means for receiving and analyzing feedback" is a technological device or algorithm that collects and analyzes feedback from participants after the meeting has concluded.

[1465] The "means for optimizing the operator list and managing the progress in real time" refers to a technical device or algorithm that generates an optimal operator list in factory operations and manages the progress of meetings in real time.

[1466] A "means for reviewing operations meeting materials and providing improvement suggestions" is a technological device or algorithm that analyzes the content of factory operations meeting materials and identifies and suggests improvements.

[1467] The "means for analyzing feedback after the meeting and proposing improvements for the next operation" refers to a technological device or algorithm that collects and analyzes feedback from participants after the factory operations meeting and proposes specific improvements for the next operation.

[1468] To implement this invention, it is necessary to build a system for improving the efficiency of factory operation meetings. This system uses hardware including high-performance servers, smart devices (smartphones and tablets), and industrial robots, and software that utilizes generative AI and NLP (natural language processing) technologies.

[1469] Overall system flow

[1470] 1. Receiving and parsing meeting requests

[1471] A user inputs a meeting request on a smart device and sends it to the server. The request includes the purpose of the meeting, the agenda, potential participants, and the expected outcomes. The server uses an NLP model (e.g., spaCy) to analyze the request and determine the need for the meeting.

[1472] Examples:

[1473] The user types, "I'm proposing an operations meeting tomorrow regarding production line adjustments. The agenda will be about improving the efficiency of the manufacturing process and introducing new machines."

[1474] 2. Optimize your attendee list

[1475] If the need for a conference is confirmed, the server generates a list of participants that are optimally suited to the needs of the operator based on their job title and area of ​​expertise. This list is displayed to the user via their smart device, and they can make modifications or additions as needed.

[1476] 3. Reviewing meeting materials and making suggestions for improvement

[1477] When a user uploads meeting materials, the server analyzes them using generative AI to identify areas where information is missing or where improvements are needed. Based on the results of the analysis, specific improvement suggestions are generated and notified to the user via their smart device.

[1478] 4. Real-time facilitation

[1479] Once the meeting begins, the server monitors the progress of the meeting in real time, adjusting speaking style and speed to help ensure important discussions are carried out smoothly, and if a particular speech is long, the server suggests shortening it and provides appropriate advice to prevent the discussion from going off track.

[1480] 5. Feedback Collection and Analysis

[1481] After the meeting, the user fills out a feedback form and sends it to the server. The server analyzes this feedback and identifies the effectiveness of the meeting and areas for improvement. The analysis results are notified to the user via their smart device, and specific improvement suggestions are provided for the next meeting.

[1482] The specific hardware and software used

[1483] Hardware: high-performance servers, smart devices (smartphones and tablets), industrial robots.

[1484] Software: TensorFlow, spaCy, NLTK, Node.js, WebSockets.

[1485] Example prompt

[1486] If a user inputs something like, "I'd like to propose an operations meeting regarding tomorrow's production line adjustments. The agenda will be about improving the efficiency of the manufacturing process and introducing new machines," the system will analyze this prompt, determine the need for the meeting, and automatically generate an appropriate participant list. The generation AI uses NLP technology to proceed with work based on such user requests.

[1487] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1488] Step 1:

[1489] A user inputs a meeting request from a smart device and sends information including the purpose of the meeting, the agenda, potential participants, and expected outcomes to the server. The input text is sent to the server, which receives this data.

[1490] Step 2:

[1491] The server processes the meeting request text with a generative AI model and uses NLP techniques (e.g., spaCy) to analyze the request. To determine whether a meeting is necessary, it extracts specific keywords and phrases and compares them with existing meeting criteria. The analysis results in a decision on whether a meeting is necessary (output).

[1492] Step 3:

[1493] When the need for a conference is confirmed, the server searches a database based on the operator's job title and area of ​​expertise to generate an optimal participant list, which is then sent to the user's smart device (output).

[1494] Step 4:

[1495] The user uploads meeting materials from their smart device to the server. The server then analyzes the materials using a generative AI to identify missing information and areas for improvement. Each section of the materials is analyzed, and the generative AI generates specific improvement suggestions. The improvement suggestions are then sent to the user's smart device (output).

[1496] Step 5:

[1497] When a user starts a conference, the server monitors the progress of the conference in real time. The server receives the conference audio and text data in real time via WebSockets, adjusts the order of speech and speaking speed, and, if necessary, suggests shortening speech or redirecting the discussion to specific participants (output).

[1498] Step 6:

[1499] After the meeting, the user fills out a feedback form on their smart device and sends it to the server. The server analyzes the feedback data using generative AI to identify the effectiveness of the meeting and areas for improvement. The analysis results are generated as specific improvement suggestions for the next meeting and are notified to the user's smart device (output).

[1500] Specific actions at each step

[1501] 1. In step 1, the user enters, "I would like to propose an operations meeting tomorrow regarding production line adjustments. The agenda will be about improving the efficiency of the manufacturing process and introducing new machines."

[1502] 2. In step 2, this text data is tokenized and analyzed by spaCy, and keywords such as "production line adjustment," "efficiency," and "new machine" are extracted.

[1503] 3. In step 3, appropriate operators are selected from the database to generate the optimal participant list, and the result is notified to the user as the "optimal participant list."

[1504] 4. In step 4, the generation AI analyzes the meeting materials uploaded by the user and notifies them of "areas where materials are lacking" and "specific improvement suggestions."

[1505] 5. In step 5, the server monitors in real time via WebSockets and adjusts the order and speed of speech, for example suggesting "move to the next topic" if the conversation is getting too long.

[1506] 6. In step 6, the user feedback is analyzed and specific improvement suggestions for the next meeting are notified to the user.

[1507] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1508] The present invention relates to a system that improves the efficiency of meetings by combining a generative AI and an emotion engine. Specific embodiments of the system of the present invention will be described below. In particular, the operations of the server, terminals, and users will be described in detail.

[1509] Overall system configuration

[1510] The system mainly consists of the following modules:

[1511] 1. Meeting Request Analysis Module

[1512] 2. Participant List Optimization Module

[1513] 3. Document Review and Improvement Suggestion Module

[1514] 4. Real-time Facilitation Module

[1515] 5. Emotion Recognition Module

[1516] 6. Feedback Collection and Analysis Module

[1517] System Operation

[1518] 1. Meeting request analysis

[1519] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The server uses the received information to determine the necessity of the meeting. At this time, it analyzes the request content using natural language processing technology and determines whether the meeting needs to be held.

[1520] 2. Optimize your attendee list

[1521] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device, and the user can confirm the final participant list. The user can then modify or add to the list as needed.

[1522] 3. Document review and improvement suggestions

[1523] Users upload meeting materials from their devices to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. Based on the results of the analysis, it generates specific suggestions for improving the materials and notifies the user via their device. This ensures that all necessary information is gathered before the meeting.

[1524] 4. Real-time facilitation and emotion recognition

[1525] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses image processing and voice analysis technology to analyze the user's facial expressions and tone of voice to recognize their emotions. The server adjusts the order, speaking style, and speaking speed of each comment, and provides appropriate advice based on the user's emotional state.

[1526] For example, if the user is nervous, the emotion engine can detect this and the server can notify the user through the terminal of advice to help them relax.

[1527] 5. Feedback Collection and Analysis

[1528] After the meeting, the user fills out a feedback form on their device and sends it to the server. The server analyzes this feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[1529] Specific examples

[1530] For example, this system is used when a company wants to hold a meeting to discuss sales strategies for a new product.

[1531] 1. A user enters a meeting request and submits it to the server.

[1532] 2. The server parses the request and determines whether a meeting is required.

[1533] 3. The server generates an optimal participant list and notifies the user through the terminal.

[1534] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[1535] 5. The user starts the conference, and the server facilitates in real time while monitoring the user's emotions using the emotion recognition module.

[1536] 6. Based on the emotion recognition results, the server provides appropriate advice.

[1537] 7. After the meeting, users enter their feedback, which is analyzed by the server and used to inform the server of improvements for the next meeting.

[1538] This series of processes ensures that meetings are carried out consistently and efficiently, from preparation to progress and evaluation, improving meeting productivity and the operational efficiency of the entire organization.

[1539] The processing flow will be explained below.

[1540] Step 1:

[1541] A user inputs a meeting request from a terminal. The input form includes fields for describing the purpose of the meeting, the agenda, potential participants, and the expected outcomes. Once the input is complete, the request data is sent to the server.

[1542] Step 2:

[1543] The server analyzes the received meeting request, uses natural language processing technology to evaluate the appropriateness of the meeting's purpose, agenda, and expected outcomes, and determines the need for the meeting based on this evaluation.

[1544] Step 3:

[1545] If the need for a meeting is confirmed, the server analyzes the participant list included in the meeting request, analyzes each participant's job title and area of ​​expertise, and generates an optimal participant list using an algorithm to select participants who will best contribute to the meeting's objectives.

[1546] Step 4:

[1547] The device displays the optimized participant list to the user, who can then review the list and add or remove participants as needed. Once the final list is finalized, it is sent to the server.

[1548] Step 5:

[1549] Users upload meeting materials (presentations, reports, data sheets, etc.) from their terminals to the server. These materials contain information necessary for preparing and conducting the meeting.

[1550] Step 6:

[1551] The server reviews the uploaded meeting materials, analyzes the contents of the materials, and determines whether the information is sufficient or if there is any information missing or needs improvement. Based on the analysis results, it generates specific improvement proposals.

[1552] Step 7:

[1553] The terminal notifies the user of the improvement suggestions from the server, and the user corrects the materials based on the suggestions and uploads them back to the server if necessary.

[1554] Step 8:

[1555] Once a user starts a meeting, the server monitors the progress of the meeting in real time, and algorithms are activated to adjust the order, style and speed of speech as the meeting progresses.

[1556] Step 9:

[1557] The server uses an emotion recognition module to analyze the user's facial expressions and tone of voice using image processing and voice analysis technology, thereby recognizing the user's emotional state in real time.

[1558] Step 10:

[1559] Based on the emotion recognition results, the server generates appropriate advice and notifies the user via the device. For example, if the user is feeling nervous, the server provides advice encouraging them to relax.

[1560] Step 11:

[1561] After the meeting, users fill out a feedback form on their device and send their evaluation of the meeting to the server. The feedback includes the quality of the meeting, how it was conducted, and areas for improvement.

[1562] Step 12:

[1563] The server analyzes the received feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are then sent to the user via their device, and specific suggestions for improvement are provided for the next meeting.

[1564] Example 2

[1565] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1566] In conventional conferencing systems, the process from preparing for a meeting to proceeding and evaluating it was often done manually, resulting in inefficiency and reduced productivity. Furthermore, there was no way to properly manage the order of speaking, speaking style, or emotions of speakers during the meeting, which often resulted in meetings not proceeding smoothly. This could result in a decline in the quality of the meeting and in participants being unable to fully discuss the matter.

[1567] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a user to input a conference request and determine the necessity of the conference using a generation AI, a means for analyzing the participant list included in the conference request and generating an optimized list, a means for performing text analysis of conference materials and generating improvement suggestions, a means for supporting the progress of the conference in real time and providing appropriate advice based on emotion recognition, and a means for receiving and analyzing feedback after the conference ends. This makes it possible to efficiently carry out the preparation, progress, and evaluation of the conference.

[1568] "User" means a person or entity that operates the System and enters and submits meeting requests and materials.

[1569] "Generative AI" refers to artificial intelligence that analyzes the content of a meeting request and determines its necessity.

[1570] A "meeting request" refers to information entered by a user, including the purpose of the meeting, the agenda, potential participants, and expected outcomes.

[1571] A "participant list" is a list of people and roles scheduled to attend a meeting.

[1572] "Optimization" refers to the process of selecting the most suitable participants for a conference.

[1573] "Text analysis" is the process of analyzing the content of meeting materials using artificial intelligence and software.

[1574] "Improvement proposals" refer to proposals to improve the quality of meeting materials and proceedings.

[1575] "Supporting meeting progress in real time" refers to technologies and functions that support the progress of a meeting while it is taking place.

[1576] "Emotion recognition" refers to the technology of analyzing a user's facial expressions and tone of voice to determine their emotions.

[1577] "Feedback" refers to information about evaluations, impressions, and areas for improvement submitted by users after the end of a conference.

[1578] "Analysis" refers to the process of using artificial intelligence and software to analyze feedback and other data in detail.

[1579] The present invention relates to a system for improving meeting efficiency using generative AI and an emotion engine. Implementation of this system primarily requires interactions between a server, a terminal, and a user. An embodiment of the system is described in detail below.

[1580] Overall system configuration

[1581] The system mainly consists of the following modules:

[1582] 1. Meeting Request Analysis Module

[1583] 2. Participant List Optimization Module

[1584] 3. Document Review and Improvement Suggestion Module

[1585] 4. Real-time Facilitation Module

[1586] 5. Emotion Recognition Module

[1587] 6. Feedback Collection and Analysis Module

[1588] Hardware and Software Configuration

[1589] The server uses the following software and databases:

[1590] Parsing a Meeting Request: Google Cloud Natural Language API

[1591] Participant list optimization: Internal database

[1592] Document Review: IBM Watson Natural Language Understanding

[1593] Real-time facilitation: OpenCV (image processing), Google Cloud Speech-to-Text (audio analysis)

[1594] A terminal is a typical computer or mobile device that users use to enter and submit meeting requests and materials.

[1595] Operation procedures and function details

[1596] 1. Entering and parsing meeting requests

[1597] The user inputs the purpose of the meeting, the agenda, potential participants, and the expected outcomes using a terminal, and sends the input to the server. The server then uses the Google Cloud Natural Language API to analyze the input request and determine the need for the meeting.

[1598] 2. Optimize your attendee list

[1599] After determining the need for a meeting, the server retrieves data on each account's job title and area of ​​expertise from an internal database. Based on this, it generates an optimal participant list and displays it to the user via their device. The user can then review and modify the list and finalize it.

[1600] 3. Document review and improvement suggestions

[1601] Users upload meeting materials from their devices to the server. The server then analyzes the materials using IBM Watson Natural Language Understanding to identify areas where information is missing or where improvements are needed. Based on the analysis results, specific improvement suggestions are generated and notified to the user via their device.

[1602] 4. Real-time facilitation and emotion recognition

[1603] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses OpenCV (image processing) and Google Cloud Speech-to-Text (audio analysis) to analyze the user's facial expressions and tone of voice to recognize their emotions. The server adjusts the order, speaking style, and speed of speech and provides advice based on the user's emotional state.

[1604] 5. Feedback Collection and Analysis

[1605] After the meeting, users fill out a feedback form on their device and send it to the server. The server analyzes the feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[1606] Specific examples

[1607] For example, this system is used when a company wants to hold a meeting to discuss sales strategies for a new product.

[1608] 1. A user inputs a meeting request stating, "I would like to hold a meeting to discuss the market strategy for a new product," and sends it to the server.

[1609] 2. The server parses the request and determines whether a meeting is required.

[1610] 3. The server generates a list of optimal participants, such as "marketing staff," "sales managers," and "technology development leaders," and notifies the user via their terminal.

[1611] 4. The user uploads meeting materials, and the server reviews the materials and makes specific suggestions for improvement, such as "Please add market analysis data."

[1612] 5. The user starts the conference, the server monitors the progress of the conference, and the emotion recognition module monitors the user's emotions.

[1613] 6. Based on the emotion recognition results, the server provides advice such as "Take a deep breath to relax."

[1614] 7. After the meeting ends, the user sends feedback that "the meeting is progressing too fast," and the server analyzes this and suggests adjusting the speed of the next meeting.

[1615] Prompt Sentence Examples

[1616] As an example of a prompt sentence, you can give instructions to the generative AI model as follows:

[1617] "Based on the participant list entered by the user, please create a list of the people who are best suited to the agenda. Then, generate a command to notify the user when the list is complete."

[1618] In this way, the system of the present invention allows efficient preparation, progress, and evaluation of a meeting, resulting in improved productivity of the meeting.

[1619] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1620] Step 1:

[1621] A user inputs a meeting request from a terminal, describing the purpose, agenda, potential participants, and expected results, and then submits the request. The input request information is sent to the server.

[1622] The server receives this information and analyzes it using the Google Cloud Natural Language API. The analysis results determine whether a meeting is necessary. The input at this stage is the meeting request information, and the output is a determination of whether a meeting is necessary.

[1623] Specific behavior:

[1624] A user inputs and submits a request to "hold a market research meeting for a new product."

[1625] The server analyzes the request and determines that there is a need for a meeting.

[1626] Step 2:

[1627] After the necessity of the meeting is confirmed, the server retrieves data on each participant's job title and area of ​​expertise from an internal database to optimize the participant list. Based on this data, a generative AI is used to generate an optimal participant list. The generated list is displayed to the user via their device for confirmation and modification.

[1628] The input is participant information based on the conference request, and the output is an optimized participant list.

[1629] Specific behavior:

[1630] The server obtains data such as "project managers," "marketing staff," and "technical staff" and generates the optimal list.

[1631] The user reviews the list, adds "Designer" and confirms.

[1632] Step 3:

[1633] Users upload meeting materials from their devices to the server. The server then uses IBM Watson Natural Language Understanding to analyze the text of the materials and identify missing information or areas that need improvement. Based on the results of this analysis, specific improvement suggestions are generated and notified to the user via their device.

[1634] The input is the meeting materials and the output is the improvement proposals.

[1635] Specific behavior:

[1636] A user uploads a presentation.

[1637] The server analyzes the data and notifies you that "market analysis data is insufficient."

[1638] Step 4:

[1639] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses OpenCV and Google Cloud Speech-to-Text to analyze the user's facial expressions and tone of voice to recognize their emotional state. The server adjusts the order, style, and speed of speech and provides advice based on the user's emotional state.

[1640] The input is audio and video data from the meeting, and the output is real-time advice.

[1641] Specific behavior:

[1642] A user starts a conference and the server analyzes the audio and video.

[1643] The server offers advice such as "Take a deep breath to relax."

[1644] Step 5:

[1645] After the meeting, users fill out a feedback form on their device and send it to the server. The server then uses analysis software to analyze this feedback, evaluate the meeting, and identify areas for improvement. The analysis results are then sent to the user via their device, and specific suggestions for improvement are provided for the next meeting.

[1646] The input is a feedback form, and the output is the analysis results and improvement suggestions.

[1647] Specific behavior:

[1648] A user enters and submits feedback saying, "The meeting is moving too fast."

[1649] The server analyzes the feedback and suggests adjusting the pace of the next meeting.

[1650] This will clearly show the detailed process flow of how the entire system effectively supports and improves meetings.

[1651] (Application example 2)

[1652] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1653] In traditional brick-and-mortar store operations, meetings and conferences between store staff and managers are often extremely inefficient. The main causes are unclear judgment of the need for a meeting, a lack of appropriate participants, and insufficient materials that lead to confusing discussions. Another problem is that there are few ways to grasp the emotions and stress levels of participants during the meeting, which can easily create an awkward atmosphere. Furthermore, the productivity of meetings is hindered by the lack of a system in place to collect feedback and use it to improve the next meeting.

[1654] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1655] In this invention, the server includes means for receiving a conference request using a generation AI and determining its necessity, means for optimizing the participant list included in the conference request, means for reviewing conference materials and generating improvement proposals, means for facilitation in real time while the conference is in progress, means for recognizing emotions while the conference is in progress and providing appropriate advice according to the user's emotional state, and means for receiving and analyzing feedback after the conference ends. This enables conferences and meetings in physical stores to be conducted more efficiently and effectively.

[1656] "Generative AI" is artificial intelligence used to analyze meeting requests and suggest improvements to materials.

[1657] The "means for receiving a conference request and determining necessity" is a system element that has the function of analyzing a conference request sent by a user and determining whether the conference is truly necessary.

[1658] The "means for optimizing the participant list" is an element of the system that has the function of automatically selecting participants required for a meeting based on their job titles and areas of expertise, and generating an optimal list.

[1659] The "means for reviewing meeting materials and generating improvement proposals" refers to a system element that has the function of analyzing meeting materials uploaded by users, pointing out areas where information is lacking or areas for improvement, and providing specific improvement proposals.

[1660] "Means for real-time facilitation while a meeting is in progress" refers to a system element that has the function of monitoring the progress of a meeting while it is in progress and facilitating in real time, such as adjusting the order of comments, speaking style, and speaking speed.

[1661] "Means for recognizing emotions and providing appropriate advice according to the user's emotional state" refers to a system element that has the function of analyzing the facial expressions and tone of voice of participants during the meeting, understanding their emotional state, and providing advice to help them relax as needed.

[1662] The "means for receiving and analyzing feedback" is a system element that has the function of receiving feedback from users after the meeting, analyzing the content, and generating specific improvement suggestions for the next meeting.

[1663] This invention provides a system that combines generative AI and emotion recognition technology to efficiently manage conferences and meetings in brick-and-mortar stores. Specific embodiments are as follows.

[1664] System Configuration

[1665] The system mainly consists of the following modules:

[1666] 1. Meeting Request Analysis Module

[1667] 2. Participant List Optimization Module

[1668] 3. Document Review and Improvement Suggestion Module

[1669] 4. Real-time Facilitation Module

[1670] 5. Emotion Recognition Module

[1671] 6. Feedback Collection and Analysis Module

[1672] DETAILED DESCRIPTION OF THE EMBODIMENTS

[1673] 1. Meeting request analysis

[1674] A user inputs a meeting request from a terminal and sends information including the purpose, agenda, potential participants, and expected outcomes to the server. The server uses the received information to determine the need for the meeting, using natural language processing technology.

[1675] 2. Optimize your attendee list

[1676] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device, and the user can confirm the final participant list. The user can then modify or add to the list as needed.

[1677] 3. Document review and improvement suggestions

[1678] Users upload meeting materials from their devices to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. Based on the results of the analysis, it generates specific suggestions for improving the materials and notifies the user via their device.

[1679] 4. Real-time facilitation and emotion recognition

[1680] When a user starts a meeting, the server monitors the progress of the meeting in real time. The emotion recognition module uses image processing and voice analysis technology to analyze the user's facial expressions and tone of voice to recognize their emotions. The server adjusts the order, speaking style, and speaking speed of what is said and provides appropriate advice based on the user's emotional state. For example, if the user is nervous, the emotion engine can detect this and the server can notify the user via their device with advice to help them relax.

[1681] 5. Feedback Collection and Analysis

[1682] After the meeting, the user fills out a feedback form on their device and sends it to the server. The server analyzes this feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are notified to the user via their device, and specific improvement suggestions are provided for the next meeting.

[1683] Specific examples

[1684] For example, this system is used when store managers at a brick-and-mortar store chain hold monthly review meetings.

[1685] 1. A user enters a meeting request and submits it to the server.

[1686] 2. The server parses the request and determines whether a meeting is required.

[1687] 3. The server generates an optimal participant list and notifies the user through the terminal.

[1688] 4. The user uploads the necessary meeting materials, and the server reviews the materials and makes suggestions for improvement.

[1689] 5. The user starts the conference, and the server facilitates in real time while monitoring the user's emotions using the emotion recognition module.

[1690] 6. Based on the emotion recognition results, the server provides appropriate advice.

[1691] 7. After the meeting, users enter their feedback, which is analyzed by the server and used to inform the server of improvements for the next meeting.

[1692] Prompt Sentence Examples

[1693] Below are some example prompts to input to a generative AI model:

[1694] text

[1695] A user wants to hold a monthly review meeting for a physical store. The purpose of the meeting is to analyze sales and plan for the next month, and the agenda is a performance review of each staff member. Participants include all store staff and the area manager. Based on this information, the user needs to analyze the meeting materials and make suggestions for improvement.

[1696] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1697] Step 1:

[1698] A user inputs a meeting request from a terminal and sends the purpose, agenda, potential participants, and expected outcomes to the server. The input data (meeting request information) is analyzed by the server using natural language processing technology. As a result of this data analysis, the need for a meeting is determined.

[1699] Step 2:

[1700] After the need for a meeting is confirmed, the server analyzes the participant list and generates an optimal participant list based on each participant's job title and area of ​​expertise. This list is displayed to the user via their device. The user can review the displayed list and modify or add participants as necessary. The input participant list data is output as the optimal list.

[1701] Step 3:

[1702] The user uploads meeting materials from their device to the server. The server analyzes the uploaded materials and identifies any missing information or areas that need improvement. This analysis uses machine learning technology to evaluate the content of the materials. Specific improvement suggestions are generated by the server and notified to the user via their device.

[1703] Step 4:

[1704] When a user starts a meeting, the server monitors the progress of the meeting in real time. The server adjusts the order of speech, speaking style, and speaking speed, and uses an emotion recognition module to analyze the user's facial expressions and tone of voice. This allows it to recognize the emotions of participants and provide advice to help them relax if they are nervous. The server outputs the adjusted speaking order and advice.

[1705] Step 5:

[1706] After the meeting, users fill out a feedback form on their device and send it to the server. The server analyzes this feedback, evaluates the meeting, and identifies areas for improvement. The analysis results are generated as a report and notified to the user via their device. The feedback analysis results are output, and specific improvement suggestions for the next meeting are provided.

[1707] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1708] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1709] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1710] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1711] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1712] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1713] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1714] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, motorcycles, and other devices, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1715] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1716] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1717] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1718] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1719] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1720] 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.

[1721] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1722] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1723] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1724] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1725] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1726] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1727] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1728] The following is further disclosed regarding the above embodiment.

[1729] (Claim 1)

[1730] a means for receiving and determining the necessity of a meeting request using a generative AI;

[1731] A means for optimizing the participant list included in a meeting request;

[1732] A means for reviewing meeting materials and generating improvement suggestions;

[1733] A means of real-time facilitation during the meeting,

[1734] A means to accept and analyze feedback after the meeting;

[1735] A system including:

[1736] (Claim 2)

[1737] 10. The system of claim 1, further comprising means for adjusting the order of speech and speaking style / speed during the conference.

[1738] (Claim 3)

[1739] 10. The system of claim 1, further comprising means for monitoring the progress of the meeting in real time and providing advice for promoting important discussions.

[1740] "Example 1"

[1741] (Claim 1)

[1742] a means for receiving and determining the necessity of a meeting request using a generative AI;

[1743] A means for optimizing the participant list included in a meeting request;

[1744] A means for reviewing meeting materials and generating improvement suggestions;

[1745] A means of real-time facilitation during the meeting,

[1746] A means to accept and analyze feedback after the meeting;

[1747] A means for a user to input a conference request from a terminal and transmit the request to a server;

[1748] A means for the server to analyze the request content using natural language processing technology;

[1749] A means for the server to generate an optimal participant list based on the participants' job titles and areas of expertise;

[1750] The server analyzes the uploaded meeting materials and identifies missing information and areas for improvement.

[1751] The server monitors the progress of the meeting in real time and adjusts the order, style and speed of speaking.

[1752] A means for the server to analyze user-submitted feedback and identify areas for improvement next time;

[1753] A system including:

[1754] (Claim 2)

[1755] 10. The system of claim 1, further comprising means for adjusting the order of speech and speaking style / speed during the conference.

[1756] (Claim 3)

[1757] 10. The system of claim 1, further comprising means for monitoring the progress of the meeting in real time and providing advice for promoting important discussions.

[1758] "Application Example 1"

[1759] (Claim 1)

[1760] a means for receiving and determining the necessity of a meeting request using a generative AI;

[1761] A means for optimizing the participant list included in a meeting request;

[1762] A means for reviewing meeting materials and generating improvement suggestions;

[1763] A means of real-time facilitation during the meeting,

[1764] A means to accept and analyze feedback after the meeting;

[1765] Optimize operator lists for factory operation meetings and manage progress in real time;

[1766] A means to review operational meeting materials and provide improvement suggestions;

[1767] After the meeting, we will analyze the feedback and suggest ways to improve operations for the next meeting.

[1768] A system including:

[1769] (Claim 2)

[1770] 10. The system of claim 1, further comprising means for adjusting the order of speech and speaking style / speed during the conference.

[1771] (Claim 3)

[1772] 10. The system of claim 1, further comprising means for monitoring the progress of the meeting in real time and providing advice for promoting important discussions.

[1773] "Example 2: Combining Emotion Engines"

[1774] (Claim 1)

[1775] A means for a user to input a meeting request and determine the need for the meeting using a generative AI;

[1776] means for parsing the participant list included in the conference request and generating an optimized list;

[1777] A means for analyzing the text of meeting materials and generating improvement proposals;

[1778] A means to support meeting progress in real time and provide appropriate advice based on emotion recognition,

[1779] A means to accept and analyze feedback after the meeting;

[1780] A system including:

[1781] (Claim 2)

[1782] 10. The system of claim 1, further comprising means for adjusting a user's order of speech and speaking style / speed during an ongoing conference.

[1783] (Claim 3)

[1784] 10. The system of claim 1, further comprising means for monitoring the progress of the meeting in real time and providing appropriate advice according to the user's emotional state.

[1785] "Application example 2 when combining emotion engines"

[1786] (Claim 1)

[1787] a means for receiving and determining the necessity of a meeting request using a generative AI;

[1788] A means for optimizing the participant list included in a meeting request;

[1789] A means for reviewing meeting materials and generating improvement suggestions;

[1790] A means of real-time facilitation during the meeting,

[1791] A means for recognizing emotions during a conference and providing appropriate advice according to the user's emotional state;

[1792] A means to accept and analyze feedback after the meeting;

[1793] A system including:

[1794] (Claim 2)

[1795] 10. The system of claim 1, further comprising means for adjusting the order of speech and speaking style / speed during the conference.

[1796] (Claim 3)

[1797] 10. The system of claim 1, further comprising means for monitoring the progress of the meeting in real time and providing advice for promoting important discussions. [Explanation of symbols]

[1798] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for receiving and determining the necessity of a meeting request using a generative AI; A means for optimizing the participant list included in a meeting request; A means for reviewing meeting materials and generating improvement suggestions; A means of real-time facilitation during the meeting, A means to accept and analyze feedback after the meeting; A system including:

2. 2. The system according to claim 1, further comprising means for adjusting the order of speech and the speaking style and speaking speed during the conference.

3. 10. The system according to claim 1, further comprising means for monitoring the progress of the meeting in real time and providing advice for promoting important discussions.

Images