system

Abstract

Provide a system. A method for setting meeting goals in advance; A means to collect audio data during meetings and transcribe it in real time, A method for analyzing the transcription data and scoring the contribution level based on the number of comments, speaking time, and content of each participant's comments. A means for displaying a list of scored data and providing feedback on the effectiveness of the meeting and the suitability of the participants; A system including:

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 the Japanese business environment, meetings are held frequently, but many of them are long and without a clear purpose, which is a problem. As a result, long working hours tend to occur and productivity tends to stagnate. In addition, there is no way to objectively evaluate the effectiveness of meetings or the contributions of each participant, making it difficult to improve. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes: a means for setting meeting goals in advance; a means for collecting audio data during a meeting and transcribing it in real time; a means for analyzing the transcribed data and scoring each participant's contribution based on the number of comments, duration, and content of their comments; a means for displaying a list of the scored data and providing feedback on the effectiveness of the meeting and the suitability of the participants; a means for evaluating the progress of the meeting and the achievement of goals; and a means for providing feedback to reduce unnecessary meetings and participants. Furthermore, this system includes a means for storing the meeting goals, content of comments, and contribution scores in a database and comparing them with past meeting data, and a means for providing improvement suggestions for the next meeting based on past meeting data. In this way, meeting productivity can be improved, leading to overall labor productivity.

[0006] "Means for pre-setting meeting objectives" refers to a device or program that allows you to input and save the specific objectives and goals that the meeting should achieve before the meeting begins.

[0007] "Means for collecting audio data during meetings and transcribing it in real time" refers to devices and software that record audio occurring during meetings and instantly convert the recording into text format.

[0008] "Means of analyzing transcription data and scoring each participant's contribution based on the number of comments, duration of comments, and content of comments" refers to algorithms and software that evaluate transcription data generated in real time and quantify how much each participant spoke and how involved they were in the meeting.

[0009] "Means for displaying a list of scored data and providing feedback on the effectiveness of the meeting and the suitability of participants" refers to a device and program for visually organizing the calculated scores and providing the user with feedback on the efficiency of the meeting and the contribution of each participant.

[0010] "Means for assessing the progress of meetings toward their goals" refers to algorithms and software that evaluate and quantify how much progress a meeting has made toward its set objectives and goals.

[0011] "Means for providing feedback to reduce unnecessary meetings and unnecessary participants" refers to devices and programs for suggesting the reduction of unnecessary agenda items and unnecessary participants in the next meeting based on the evaluation data.

[0012] "Means for comparing with past meeting data" refers to algorithms and software that compare stored previous meeting data with current meeting data to identify areas for performance improvement.

[0013] The "means for providing improvement suggestions for the next meeting" refers to a device and program that proposes specific improvement suggestions to the user regarding the method of conducting the next meeting and the selection of participants based on an analysis of past data. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a system aimed at improving meeting productivity, and has the functions of setting meeting goals, transcribing in real time, analyzing speech content, and providing feedback.

[0036] System configuration

[0037] 1. User Device

[0038] Provides an interface for users to input and save meeting goals.

[0039] It has the function of collecting audio data during a meeting and sending it to a server in real time.

[0040] 2. Server

[0041] Transcription is performed based on the received audio data.

[0042] The transcription data is analyzed and each participant's number of comments, speaking time, and content of comments are scored as a contribution.

[0043] The scored data is visualized and feedback is provided to the user.

[0044] Program processing

[0045] Setting meeting goals

[0046] User Action:

[0047] A user logs in to the system using a terminal and accesses the new meeting page, where they enter the title and purpose of the meeting and press the confirm button, which sends the goal of the meeting to the server and records it.

[0048] Natural language description:

[0049] A user logs into the system, enters the title and goal of the meeting, and sends it to the server.

[0050] Audio data collection and transcription

[0051] The device:

[0052] When a meeting starts, the device starts recording audio and sends the audio data in real time to the server, which then transcribes the received audio data in real time and saves it as text data.

[0053] Natural language description:

[0054] The device records the audio during the meeting in real time and sends the audio data to the server, which then transcribes the audio data and converts it into text data.

[0055] Analysis and scoring of speech content

[0056] The server:

[0057] The server analyzes the transcribed data, identifies the speakers, and assigns a score to each participant based on the number of comments, the duration of their comments, and the content of their comments. This also evaluates the progress toward the meeting's goals.

[0058] Natural language description:

[0059] The server analyzes the transcripts, counts each participant's contributions, and then assigns a score based on the results, as well as assessing progress toward the meeting's goals.

[0060] Score display and feedback

[0061] The server:

[0062] The server visually organizes and displays the scored data in a list, allowing users to easily check the effectiveness of the meeting and the contribution of each participant. If necessary, it provides feedback on the selection of participants for the next meeting and suggestions for improving the meeting method.

[0063] Natural language description:

[0064] The server visually organizes the scores so that users can easily check them, and also provides feedback to users on suggestions for improvement for the next meeting.

[0065] Specific examples

[0066] 1. Example of setting meeting goals:

[0067] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[0068] 2. Audio data collection and transcription example:

[0069] When a meeting starts, the device records audio and transmits the data in real time to a server, which transcribes the audio, converts it into text, and stores it.

[0070] 3. Example of analysis and scoring of speech content:

[0071] The server analyzes the transcription data and generates a score such as "Mr. Tanaka spoke 200 words, making a significant contribution to progress" or "Mr. Sato spoke 50 words, making a small contribution."

[0072] 4. Example of score display and feedback:

[0073] The server displays the scores in a list and provides feedback to the user, such as "Tanaka's participation is valid, Sato's participation is unnecessary." This allows the user to improve the selection of participants and the progress of the next meeting.

[0074] As described above, the present invention is a system that provides specific means for improving the productivity of meetings and supports users in managing meetings efficiently.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] User actions

[0078] A user logs in to the system using a terminal. They access the new meeting page and enter the title and goal of the meeting. For example, they might enter "Decide on the task allocation for a new project." Once they have completed the entry, they press the confirm button to send the meeting goal to the server.

[0079] Step 2:

[0080] Processing performed by the server

[0081] The server stores the received meeting goals in a database, generating a unique ID for the meeting and linking the goal information to that ID.

[0082] Step 3:

[0083] Processing performed by the device

[0084] When a conference is started, the user presses the conference start button on the terminal, and the terminal starts recording audio and transmitting the audio data to the server in real time.

[0085] Step 4:

[0086] Processing performed by the server

[0087] The server transcribes the received audio data in real time, and the transcribed text data is linked to a unique conference ID and stored in a database.

[0088] Step 5:

[0089] Processing performed by the server

[0090] The server analyzes the transcripts and starts counting the amount of speech, the duration of speech, and the content of speech from each speaker. Based on this data, it runs an algorithm to score each participant's contribution.

[0091] Step 6:

[0092] Processing performed by the server

[0093] The progress towards the goal is analyzed and the extent to which the meeting objectives have been achieved is evaluated. This progress is also scored and stored in the database.

[0094] Step 7:

[0095] Processing performed by the server

[0096] Visually organize the scored data and generate a feedback report that includes each participant's contribution and the meeting progress.

[0097] Step 8:

[0098] Processing performed by the server

[0099] The generated feedback report is sent to the user's device, allowing the user to consider improvements for the next meeting based on this data.

[0100] Step 9:

[0101] User actions

[0102] Users can check the feedback report on their devices and evaluate the effectiveness of the meeting and the contribution of each participant. Based on this evaluation, the system can improve the selection of participants and the progress of the next meeting.

[0103] In this way, the present invention provides a concrete means for improving the productivity of a meeting, and supports users in managing a meeting efficiently.

[0104] Example 1

[0105] 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."

[0106] In modern companies and organizations, meetings are a forum for important decision-making and information sharing, but low productivity is a major problem. Participants' opinions are often not properly reflected, progress is delayed, and meetings often end without reaching their goals. Another problem is when certain participants speak excessively, burying the opinions of others. In such situations, it becomes difficult to achieve the purpose of the meeting, which can lead to a loss of efficiency for the entire organization.

[0107] 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.

[0108] In this invention, the server includes a means for the user to set a goal for the meeting, a means for the terminal to collect voice data during the meeting and send it to the server in real time, a means for the server to transcribe the voice data in real time, a means for the server to analyze the transcription data and score the contribution level based on the number of comments, the duration of comments, and the content of comments made by each participant, and a means for visualizing the scored data and providing feedback to the user. This makes it possible to improve the productivity of the meeting, accurately evaluate the contribution level of each participant, and specifically suggest improvements for the next meeting.

[0109] A "user" is an individual or member of an organization who operates the system.

[0110] A "terminal" is an electronic device that is operated by a user and that performs data communication with a server.

[0111] "Meeting objectives" refer to the purpose of the meeting and the items to be achieved, which are set at the beginning of the meeting.

[0112] "Audio data" refers to data collected in digital format from voices spoken during a meeting.

[0113] "Server" refers to a computer system for processing and transcribing received audio data.

[0114] "Transcription" refers to the process of converting audio data into text data.

[0115] "Transcription Data" refers to data in converted text format.

[0116] "Analysis" refers to the process of analyzing the content of data to extract useful information.

[0117] "Number of remarks" refers to the number of remarks made by each participant during the meeting.

[0118] "Speaking time" refers to the total amount of time each participant spoke during the conference.

[0119] "Speech content" refers to the specific content that each participant spoke during the meeting.

[0120] "Contribution" refers to a score that evaluates each participant's usefulness in a meeting.

[0121] "Scoring" refers to the process of expressing evaluation results as quantitative numbers.

[0122] "Visualization" refers to the process of displaying data in the form of graphs, charts, etc., to make it easier to understand.

[0123] "Feedback" refers to providing users with information such as areas for improvement based on analysis and evaluation results.

[0124] "Goal achievement" is a measure of how close the meeting came to achieving its goals.

[0125] "Unnecessary meetings and unnecessary participants" refer to factors that reduce the productivity and efficiency of meetings.

[0126] A "database" refers to a system that manages a collection of systematically organized data.

[0127] "Improvement proposals" refer to suggestions for improving the next meeting based on past data.

[0128] The present invention is a system for improving the productivity of meetings, and has functions for setting meeting goals, transcribing real-time information, analyzing speech content, and providing feedback. Specific embodiments for implementing the present invention will be described below.

[0129] Hardware and Software

[0130] This system mainly uses the following hardware and software:

[0131] 1. User Device

[0132] A computer or smartphone that provides an interface for users to input and save meeting goals.

[0133] Microphone and network connection capabilities to collect audio data during meetings and send it to a server in real time.

[0134] 2. Server

[0135] A computer system for transcribing received audio data.

[0136] A processing unit that analyzes the transcription data and scores each participant's number of comments, speaking time, and content of comments as a contribution.

[0137] Data visualization software (e.g., D3.js, Chart.js) to visualize the scored data and provide feedback to the user.

[0138] 3. External Services

[0139] A speech recognition service for converting voice data into text (e.g., Google® Cloud Speech-to-Text API).

[0140] Natural language processing (NLP) models for analyzing text data (e.g., OpenAI® GPT-3®).

[0141] Setting meeting goals

[0142] User Action:

[0143] The user logs in to the system using a terminal and accesses the new meeting page, where they enter the title and goal of the meeting and press the confirm button, which sends the goal of the meeting to the server and records it.

[0144] Examples:

[0145] The user opens a browser on their device, accesses the "Create a new meeting" page, enters "Decide task allocation for new project" and presses the "Confirm" button. The server saves this information in a database.

[0146] Audio data collection and transcription

[0147] The device:

[0148] When a meeting starts, the device starts recording audio and sends the audio data in real time to the server, which then transcribes the received audio data in real time and saves it as text data.

[0149] Examples:

[0150] To start a meeting, the user clicks the "Start Meeting" button, which activates the device's microphone and streams audio in real time to the server, which uses the Google Cloud Speech-to-Text API to convert the audio into text and store it in a database.

[0151] Analysis and scoring of speech content

[0152] The server:

[0153] The server analyzes the transcribed data, identifies the speakers, and assigns a score to each participant based on the number of comments, the duration of their comments, and the content of their comments. This also evaluates the progress toward the meeting's goals.

[0154] Examples:

[0155] The server analyzes the text data using an NLP model (e.g., OpenAI GPT-3) and generates a score such as "Tanaka-san spoke 200 words, contributed greatly to progress," or "Sato-san spoke 50 words, contributed less."

[0156] Score display and feedback

[0157] The server:

[0158] The server visually organizes and displays the scored data in a list, allowing users to easily check the effectiveness of the meeting and the contribution of each participant. If necessary, it provides feedback on the selection of participants for the next meeting and suggestions for improving the meeting method.

[0159] Examples:

[0160] The server uses visualization libraries (e.g., D3.js, Chart.js) to display the scores in a graph. Users can see feedback on the dashboard, such as "Tanaka's participation is valid, Sato's participation is unnecessary." Suggestions for improvement for the next meeting, such as "Next time, Tanaka's comments should be reduced a little and Sato's opinions should be drawn out," are also displayed.

[0161] As described above, the present invention is a system that provides specific means for improving the productivity of meetings and supports users in managing meetings efficiently.

[0162] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0163] Step 1: Log in your users and set meeting goals

[0164] Specific details:

[0165] 1. User actions: The user starts a terminal, opens a browser and accesses the system login page.

[0166] 2. Input: The user enters the ID and password to log in to the system.

[0167] 3. Data processing: The server checks the entered authentication information against the database and performs authentication.

[0168] 4. Output: Once authentication is complete, the user is redirected to the "Create New Meeting" page.

[0169] 5. User action: The user creates a new meeting, enters its title and goal, and presses the "Confirm" button.

[0170] 6. Input: User-entered meeting title and purpose.

[0171] 7. Data processing: The server stores the entered conference title and purpose in a database.

[0172] 8. Output: The server displays a confirmation message to the user indicating a successful save.

[0173] Step 2: Collect and send audio data

[0174] Specific details:

[0175] 1. User action: The user clicks the "Start Meeting" button.

[0176] 2. Input: Triggers the "Start Meeting" button clicked by the user.

[0177] 3. Data processing: The device activates the microphone and starts recording audio.

[0178] 4. Output: The recorded audio data is generated.

[0179] 5. Device operation: Streams the recorded audio data to the server in real time.

[0180] 6. Input: Collected audio data.

[0181] 7. Data processing: The device splits the voice data into small chunks and sends them to the server.

[0182] 8. Output: The divided audio data chunks arrive at the server.

[0183] Step 3: Real-time transcription

[0184] Specific details:

[0185] 1. Server operation: Sends the received voice data to the voice recognition API.

[0186] 2. Input: Audio data chunks.

[0187] 3. Data processing: The server converts the voice data into text using the Google Cloud Speech-to-Text API.

[0188] 4. Output: Text data.

[0189] 5. Server operation: The converted text data is linked to the conference ID and saved in the database.

[0190] 6. Input: Text data and meeting ID.

[0191] 7. Data processing: The server converts the text data into an appropriate format and stores it in a database.

[0192] 8. Output: Text data stored in the database.

[0193] Step 4: Analyze and score the speech

[0194] Specific details:

[0195] 1. Server action: Retrieve the transcription data from the database and start analyzing it with the natural language processing model.

[0196] 2. Input: Transcription data from the meeting.

[0197] 3. Data processing: The server uses an NLP model (such as OpenAI GPT-3) to analyze the text data and extract the number of comments, the duration of each comment, and the content of each comment.

[0198] 4. Output: Analysis results include the number of statements made by each speaker, the duration of the statements, and the content of the statements.

[0199] 5. Server action: Score each participant's contribution based on the extracted data.

[0200] 6. Input: Analysis results.

[0201] 7. Data processing: The server uses a scoring algorithm to quantify the contribution.

[0202] 8. Output: Scored data.

[0203] Step 5: View scores and receive feedback

[0204] Specific details:

[0205] 1. Server action: Convert the scored data into graphs and charts using a visualization library.

[0206] 2. Input: Scored data.

[0207] 3. Data processing: The server visualizes the data using D3.js, Chart.js, etc.

[0208] 4. Output: Visualized data (graphs and charts).

[0209] 5. Server Actions: Provide feedback to the user and generate improvement suggestions for the next meeting.

[0210] 6. Input: Visualization data and analysis results.

[0211] 7. Data processing: The server uses the AI ​​model to generate improvement suggestions for the next meeting.

[0212] 8. Output: Feedback message and improvement suggestions to the user.

[0213] This concludes the detailed explanation of each processing step. Through these steps, productivity and efficient management of meetings are achieved.

[0214] (Application example 1)

[0215] 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."

[0216] Conventional conferencing systems often only transcribe audio data and analyze the number and duration of participants' speech, and do not provide feedback that directly translates into improved productivity or work performance in the actual workplace. This has led to a demand for effective methods that lead to specific improvement measures and next action plans in factories and workplaces. Furthermore, improvement suggestions based on comparisons with past meeting data are insufficient, making them less convenient for continuously improving productivity.

[0217] 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.

[0218] In this invention, the server includes a means for setting meeting goals in advance, a means for collecting voice data during the meeting and transcribing it in real time, a means for analyzing the transcribed data and scoring the contribution level based on the number of comments, duration of comments, and content of comments made by each participant, a means for displaying a list of the scored data and providing feedback on the effectiveness of the meeting and the suitability of the participants, and a means for evaluating the work performance of on-site workers and automated machines based on the data collected in real time. This makes it possible to accurately evaluate the degree of achievement of the meeting's goals and the contribution levels of participants, as well as to make specific work improvement proposals.

[0219] "Meeting objectives" are specific items or purposes to be achieved that are set before the meeting begins.

[0220] "Audio data" refers to data recorded in audio format of participants' remarks and discussions collected during a meeting.

[0221] "Real-time" refers to the fact that data collection and processing are immediate, with results being fed back almost instantly.

[0222] "Transcription" is the process of converting collected audio data into text data.

[0223] "Participants" are people who attend a meeting and take part in speaking and discussions.

[0224] The "number of statements" refers to the number of times each participant made a statement during the conference.

[0225] "Speaking time" is the cumulative time each participant has spoken during the conference.

[0226] "Content of remarks" refers to the specific content and topics of remarks made by each participant during the meeting.

[0227] "Scoring" refers to making a quantitative evaluation based on analyzed data and expressing it as a number.

[0228] "Work performance" is an indicator of the efficiency and effectiveness of work performed by workers and automated machines.

[0229] "Feedback" refers to improvement suggestions and evaluation information provided based on the analyzed results.

[0230] "Field workers" are workers who actually perform work in factories or on-site.

[0231] An "automatic machine" is a machine or robot that performs tasks automatically in a factory or on a work site.

[0232] A "database" is a system for storing and managing past meeting data and work performance data.

[0233] Comparison is the process of comparing data sets from different points in time to identify differences and areas for improvement.

[0234] "Improvement proposals" are specific proposals and measures for the next meeting or work that are provided based on the analysis results.

[0235] "Past meeting data" refers to information and recorded data relating to meetings that have taken place in the past.

[0236] MODE FOR CARRYING OUT THE INVENTION

[0237] This invention is an improvement conference system aimed at improving productivity within factories, and it sets meeting goals, collects and transcribes real-time audio, analyzes work performance, and provides feedback. This system is realized through the cooperation of user terminals, a server, on-site workers, and automated machines.

[0238] System configuration

[0239] 1. User Device

[0240] Setting meeting goals: Before the meeting, the manager logs in to the system and sets the meeting goals, such as "confirming new work procedures and proposing improvements."

[0241] Audio data collection: During the meeting, the device collects audio data in real time and sends it to the server. The hardware used is a high-performance microphone (e.g., Blue Yeti USB Microphone).

[0242] Real-time transcription: Audio data collected during the meeting is sent to a server and transcribed in real time.

[0243] 2. Server

[0244] Analysis of the transcribed data: The server analyzes the transcribed data and scores each participant's contribution based on the number of comments, the duration of their comments, and the content of their comments.

[0245] Work performance evaluation: The server evaluates the work performance of field workers and automated machines based on the data collected in real time, using the Python programming language and necessary libraries (e.g., speech_recognition, matplotlib).

[0246] Providing feedback: The server visualizes the scored data and provides feedback to the user's device, which then provides suggestions for improving the next meeting.

[0247] 3. Field workers and automated machinery

[0248] Data collection: Operation logs and performance data of field workers and automated machines are stored in a database.

[0249] Improved work performance: Based on feedback from the server, field workers and managers can implement specific work improvement measures.

[0250] Add specific examples to the description

[0251] Examples:

[0252] To improve the efficiency of a new production line, a site manager sets the goal of "confirming new work procedures and proposing improvements." During the meeting, a device collects voice data, which is transcribed and analyzed in real time on a server. For example, if Worker A speaks 200 words and makes specific suggestions for improvement, that will be evaluated as a high contribution and included in the feedback at the next meeting.

[0253] Example prompt sentence:

[0254] I would like to define the requirements for a system that sets meeting goals, transcribes meeting audio in real time, and analyzes and scores what participants say. Please design a system that includes specific goal setting, real-time transcription, scoring of contribution based on number of comments and time spent, and visualization processes.

[0255] As a result, the present invention aims to improve the productivity of meetings and optimize work performance in the field.

[0256] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0257] Step 1:

[0258] Setting meeting goals

[0259] Input: The user (administrator) inputs the meeting goal and sends it to the system.

[0260] Specific operation: A form is displayed on the user's device, and the user is asked to enter the title and goal of the meeting. When the user presses the "Confirm" button, the data is sent to the server and saved in the database.

[0261] Output: Meeting goal data stored on the server.

[0262] Step 2:

[0263] Audio data collection

[0264] Input: Audio data collected during the meeting.

[0265] Specific operation: The device (e.g., PC or tablet) uses a high-performance microphone to collect audio during the meeting and transmits it to the server in real time.

[0266] Output: Real-time audio data sent to the server.

[0267] Step 3:

[0268] Real-time transcription

[0269] Input: The audio data sent to the server.

[0270] What happens: The server transcribes the received audio data in real time, specifically by converting it into text using speech recognition software (e.g., the speech_recognition library).

[0271] Output: Transcription data generated in real time.

[0272] Step 4:

[0273] Analysis and scoring of speech data

[0274] Input: Transcription data generated in real time.

[0275] Specific operation: The server analyzes the transcription data and extracts the number of comments, duration, and content of each participant's comments. Based on this data, each participant's contribution is scored. Analysis is performed using NLP technology and statistical methods.

[0276] Output: Analyzed data and score data for each participant.

[0277] Step 5:

[0278] Work performance evaluation

[0279] Input: Real-time monitoring data and historical work log data.

[0280] How it works: The server analyzes real-time and historical work data to evaluate the work performance of on-site workers and automated machines, using statistical methods to calculate efficiency and productivity indicators.

[0281] Output: Work performance evaluation report.

[0282] Step 6:

[0283] Providing feedback

[0284] Input: Analyzed data and score data, work performance evaluation report.

[0285] Specific operation: The server visualizes the scored data and work performance evaluation reports and provides feedback to the user's device. The feedback is displayed in the form of graphs and reports, and includes suggestions for next improvement.

[0286] Output: A feedback report provided to the user and suggestions for improvement for the next meeting.

[0287] Through these steps, the system helps improve productivity and optimize work performance during and after meetings.

[0288] 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.

[0289] This invention is a system that supports the improvement of productivity and efficient management of meetings, and in particular, combines an emotion engine that recognizes user emotions. This system not only sets meeting goals, transcribes speech in real time, analyzes speech content, and provides feedback, but also recognizes and monitors emotions using the emotion engine.

[0290] System configuration

[0291] 1. User Device

[0292] Provides an interface for users to input and save meeting goals.

[0293] It has the function of collecting voice data and emotional data during a meeting and sending it to a server in real time.

[0294] 2. Server

[0295] Transcription is performed based on the received audio data.

[0296] The transcription data is analyzed and each participant's number of comments, speaking time, and content of comments are scored as a contribution.

[0297] The emotional data recognized by the emotion engine is analyzed to monitor the atmosphere during the meeting and the stress level of each participant.

[0298] The scored and emotional data are visualized to provide feedback to the user.

[0299] Program processing

[0300] Setting meeting goals

[0301] User Action:

[0302] The user logs in to the system using a terminal and accesses the new meeting page. They enter the title and objective of the meeting and set a goal such as "Decide on the task allocation for the new project." Once the input is complete, they press the confirm button to send the meeting goal to the server.

[0303] Natural language description:

[0304] A user logs into the system, enters the title and goal of the meeting, and sends it to the server.

[0305] Collecting voice and emotion data

[0306] The device:

[0307] When a conference starts, the user presses the conference start button on the device. The device starts recording audio and transmits the audio data to the server in real time. At the same time, the device's built-in camera and sensors are used to collect emotional data from the user's facial expressions and tone of voice, which is also transmitted to the server in real time.

[0308] Natural language description:

[0309] The device records the audio during the meeting in real time and transmits the data along with the user's emotional data to the server.

[0310] Analysis and scoring of speech content

[0311] The server:

[0312] The server transcribes the received audio data in real time, analyzes the transcript, counts the amount of speech, speaking time, and content of each speaker, and assigns a score to their contribution.

[0313] Natural language description:

[0314] The server transcribes the received audio data and analyzes the amount and duration of each speaker's speech to score their contribution.

[0315] Emotional data analysis and monitoring

[0316] The server:

[0317] The server uses an emotion engine to analyze the received emotion data, monitor the atmosphere during the meeting and the stress level of each participant, and evaluate the atmosphere and stress level of the meeting based on this.

[0318] Natural language description:

[0319] The server uses an emotion engine to analyze the received emotion data and monitor the atmosphere of the meeting and the stress levels of the participants.

[0320] Displaying scores and emotional data at a glance and providing feedback

[0321] The server:

[0322] The server visually organizes and displays the score data and emotional data. This allows users to easily check the effectiveness of the meeting, the contribution of each participant, and their emotional state. It also makes suggestions for improving the selection of participants for the next meeting and how the meeting will proceed.

[0323] Natural language description:

[0324] The server visualizes the scores and emotion data so that users can easily check them, and also provides feedback on suggestions for improvement for the next meeting.

[0325] Specific examples

[0326] 1. Example of setting meeting goals:

[0327] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[0328] 2. Example of collecting voice and emotion data:

[0329] When a meeting starts, the device records audio and sends the data to the server in real time. At the same time, it analyzes facial expressions and tone of voice to send emotional data to the server.

[0330] 3. Example of analysis and scoring of speech content:

[0331] The server analyzes the transcription data and generates a score such as "Mr. Tanaka spoke 200 words, making a great contribution to progress" or "Mr. Sato spoke 50 words, making a low contribution."

[0332] 4. Emotion data analysis and monitoring example:

[0333] The server uses an emotion engine to make evaluations such as "the stress level of all participants decreases at the start of the meeting" and "the stress level of a certain participant increases during the meeting."

[0334] 5. Example of score and emotion data list display and feedback:

[0335] The server displays a list of scores and emotional data, and provides feedback to the user, such as "Tanaka's participation is valid, Sato's participation is unnecessary," and "Be careful of rising stress levels during the meeting."

[0336] In this way, the present invention provides a concrete means for realizing efficient conference management that takes into consideration both the productivity of the conference and the emotional state of the participants.

[0337] The processing flow will be explained below.

[0338] Step 1:

[0339] User actions

[0340] The user logs in to the system using a terminal and accesses the new meeting page. There, they input the title and objective of the meeting, setting a goal such as "Decide on the allocation of tasks for a new project," and press the confirm button to send it to the server.

[0341] Step 2:

[0342] Processing performed by the server

[0343] The server stores the received meeting goals in a database, generating a unique ID for the meeting and linking the goal information to that ID so that the meeting can be identified and tracked.

[0344] Step 3:

[0345] Processing performed by the device

[0346] When a conference call begins, the user presses the conference start button on the device. The device starts recording audio and begins sending the audio data to the server in real time. At the same time, the device's built-in camera and sensors are used to capture facial expressions and tone of voice, and emotional data is also sent to the server in real time.

[0347] Step 4:

[0348] Processing performed by the server

[0349] The server transcribes the received audio data in real time. The transcribed text data is linked to a unique conference ID and stored in a database. Emotion data is also linked and stored in the same way.

[0350] Step 5:

[0351] Processing performed by the server

[0352] The server analyzes the transcript data, identifies each speaker, counts the amount of speech, speaking time, and content, and scores each participant's contribution. It also analyzes emotional data to evaluate the atmosphere during the meeting and the stress levels of participants.

[0353] Step 6:

[0354] Processing performed by the server

[0355] Progress toward the goal is evaluated, and emotional data is collected and stored in a database along with the scored data. An algorithm is applied to evaluate the effectiveness of the meeting based on the progress.

[0356] Step 7:

[0357] Processing performed by the server

[0358] It visually organizes the scored and emotional data and generates feedback reports that include each speaker's contribution, meeting progress, and emotional state.

[0359] Step 8:

[0360] Processing performed by the server

[0361] The generated feedback report is sent to the user's device, and the user can use this data to consider improvements for the next meeting.

[0362] Step 9:

[0363] User actions

[0364] Users can review the feedback report on their devices and evaluate the effectiveness of the meeting, the contributions of each participant, and their emotional state. Based on this evaluation, the system can improve the selection of participants and the way the meeting will proceed for the next meeting.

[0365] In this way, the present invention not only improves the productivity of a meeting, but also realizes efficient meeting management that takes into account the emotional states of the participants.

[0366] Example 2

[0367] 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."

[0368] In today's business environment, the productivity and efficiency of meetings are being questioned. However, many meetings are unable to effectively evaluate participants' emotions or real-time contributions, and do not provide appropriate feedback. This can lead to inefficient meetings and a decline in participants' motivation. Furthermore, meetings held in high-stress environments can increase participants' psychological burden. To address these issues, a system combining the collection and analysis of real-time emotional data is needed.

[0369] 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 setting a goal of the conference in advance, a means for collecting voice data and emotional data during the conference and transcribing them in real time, a means for analyzing the transcribed data and scoring the contribution level based on the number of comments, the duration of comments, and the content of comments made by each participant, a means for analyzing the emotional data and monitoring the emotional state and atmosphere of the participants during the conference, and a means for displaying a list of scored data and emotional data to provide feedback on the effectiveness of the conference and the suitability of the participants. This makes it possible to improve the productivity of the conference and reduce the psychological burden on the participants.

[0370] "Meeting objectives" refers to the purpose of the meeting and the specific goals to be achieved.

[0371] "Audio data" refers to data that is a digital recording of audio information spoken during a conference.

[0372] "Emotional data" refers to data that indicates the emotional state and psychological reactions of participants during a meeting, and is obtained from facial expressions, tone of voice, physical movements, etc.

[0373] "Real-time transcription" refers to the process of instantly converting spoken audio data into text data.

[0374] "Transcription data" refers to data that has been converted from audio into text.

[0375] "Number of remarks" refers to the number of remarks made by each participant during the meeting.

[0376] "Speaking time" refers to the total amount of time each participant spoke during a meeting.

[0377] "Speech content" refers to the specific themes or topics that each participant spoke about during the meeting.

[0378] "Contribution score" refers to a numerical value that indicates each participant's contribution to the conference, evaluated based on the number of comments, speaking time, and content of comments.

[0379] "Scored data" refers to data that has been calculated and organized as a contribution score.

[0380] An "emotion engine" is a system that analyzes emotional data and uses that data to evaluate and monitor the emotional state and atmosphere of participants.

[0381] "Monitoring" refers to the process of observing, recording, and analyzing the situation during a meeting in real time.

[0382] "List display" refers to visually organizing the analysis results and displaying them in a format that can be checked at a glance.

[0383] "Feedback" refers to information and suggestions generated based on analyzed data, and refers to guidance for improving the progress of the meeting and the behavior of participants.

[0384] MODE FOR CARRYING OUT THE INVENTION

[0385] This invention is a system that supports the improvement of productivity and efficient management of meetings, and in particular, combines an emotion engine that recognizes user emotions. This system not only sets meeting goals, transcribes speech in real time, analyzes speech content, and provides feedback, but also recognizes and monitors emotions using the emotion engine.

[0386] System configuration:

[0387] 1. User Device

[0388] Provides an interface for users to input and save meeting goals.

[0389] It has the function of collecting voice data and emotional data during a meeting and sending it to a server in real time.

[0390] 2. Server

[0391] Transcription is performed based on the received audio data.

[0392] The transcription data is analyzed and each participant's number of comments, speaking time, and content of comments are scored as a contribution.

[0393] The emotional data recognized by the emotion engine is analyzed to monitor the atmosphere during the meeting and the stress level of each participant.

[0394] The scored and emotional data are visualized to provide feedback to the user.

[0395] Program processing:

[0396] Setting meeting goals

[0397] User Action:

[0398] The user logs in to the system using a terminal and accesses the new meeting page. They enter the title and objective of the meeting and set a goal such as "Decide on the task allocation for the new project." Once the input is complete, they press the confirm button to send the meeting goal to the server.

[0399] The server:

[0400] The server saves the meeting title and goal sent by the user in the database, and if the save is successful, gives feedback to the user.

[0401] Collecting voice and emotion data

[0402] User Action:

[0403] At the start of a conference, the user presses a conference start button on the terminal.

[0404] The device:

[0405] The device starts recording and sends the audio data to the server in real time. The device also uses cameras and sensors to detect the user's facial expressions and tone of voice, generating emotion data. The generated emotion data is also sent to the server in real time.

[0406] Analysis and scoring of speech content

[0407] The server:

[0408] The server converts the received audio data into text using transcription software (e.g., Google Speech-to-Text API). This text data is analyzed to count the amount of speech and the duration of speech for each speaker. The usefulness and contribution of the speech content are then analyzed to calculate the speaker's score.

[0409] Emotional data analysis and monitoring

[0410] The server:

[0411] The server analyzes the received emotional data using an emotion analysis engine (e.g., Microsoft® Azure® Emotion API) to evaluate the atmosphere during the meeting and the stress levels of the participants. Based on the analysis results, the atmosphere of the meeting and the stress levels of each participant are monitored in real time.

[0412] Displaying scores and emotional data at a glance and providing feedback

[0413] The server:

[0414] The server uses visualization tools such as dashboards (e.g., Tableau) to format the score data and emotion data and display them in real time. After the meeting, users can access the dashboard to see at a glance each participant's contribution to the discussion, the atmosphere during the meeting, and their stress level. It also displays suggestions for improvement for the next meeting and feedback on participant selection.

[0415] Examples:

[0416] 1. Example of setting meeting goals:

[0417] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[0418] 2. Example of collecting voice and emotion data:

[0419] When a meeting starts, the device records audio and sends the data to the server in real time. At the same time, it analyzes facial expressions and tone of voice to send emotional data to the server.

[0420] 3. Example of analysis and scoring of speech content:

[0421] The server analyzes the transcription data and generates a score such as "Participant A spoke 200 words and contributed greatly to the progress" or "Participant B spoke 50 words and contributed less."

[0422] 4. Emotion data analysis and monitoring example:

[0423] The server uses an emotion engine to make evaluations such as "the stress level of all participants decreases at the start of the meeting" and "the stress level of a certain participant increases during the meeting."

[0424] 5. Example of score and emotion data list display and feedback:

[0425] The server displays a list of scores and emotional data and provides feedback to the user, such as "Participant A's participation is valid, but Participant B's participation is unnecessary," and "Be careful of rising stress levels during the meeting."

[0426] In this way, the present invention provides a concrete means for realizing efficient conference management that takes into consideration both the productivity of the conference and the emotional state of the participants.

[0427] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0428] Step 1: Set meeting goals

[0429] Input: The meeting title and goal entered by the user on the meeting goal setting screen

[0430] Specific behavior:

[0431] 1. A user logs into the system using a terminal.

[0432] 2. The user visits the new meeting page.

[0433] 3. The user enters the meeting title (e.g., "Project Kickoff") and goal (e.g., "Determine key task assignments").

[0434] 4. The user presses the confirm button to send the information to the server.

[0435] Data processing / calculation: The server verifies the received conference title and objectives, and stores them in the database if there are no errors.

[0436] Output: If the save is successful, the server will provide feedback to the user.

[0437] Step 2: Collect speech and emotion data at the start of the meeting

[0438] Input: User presses start conference button

[0439] Specific behavior:

[0440] 1. At the start of a conference, the user presses the conference start button on the terminal.

[0441] 2. The device starts the recording function.

[0442] 3. The device uses cameras and sensors to detect the user's facial expressions and tone of voice.

[0443] Data processing / calculation: The terminal compresses and packets the recorded voice data in real time, and simultaneously records facial expressions and tone of voice data as emotion data.

[0444] Output: Compressed audio data and emotion data are sent to the server in real time.

[0445] Step 3: Transcribe and analyze the audio data

[0446] Input: Audio data received by the server

[0447] Specific behavior:

[0448] 1. The server converts the received voice data into text using transcription software (e.g., Google Speech-to-Text API).

[0449] 2. The server analyzes the text data and counts the amount of speech and the duration of speech for each speaker.

[0450] Data processing / calculation: The server extracts specific keywords and phrases from the text data and scores the usefulness of the comments based on that.

[0451] Output: The scoring results for each participant's speech volume, speech time, and content are output.

[0452] Step 4: Analyze and evaluate emotion data

[0453] Input: Emotion data received by the server

[0454] Specific behavior:

[0455] 1. The server analyzes the received emotion data using an emotion analysis engine (e.g., Microsoft Azure Emotion API).

[0456] 2. Based on the analysis results, the server evaluates and monitors the atmosphere during the meeting and the stress level of each participant in real time.

[0457] Data processing / calculation: The results of emotion data analysis are quantified and combined with related time series data.

[0458] Output: The measured mood score and stress level evaluation results are output.

[0459] Step 5: Integrating and visualizing scores and sentiment data

[0460] Input: Analysis results of speech score data and emotion data

[0461] Specific behavior:

[0462] 1. The server aggregates the score data and emotion data and formats it using a visualization tool such as a dashboard (e.g., Tableau).

[0463] 2. The visualized data is displayed in a user-viewable format.

[0464] Data processing / calculation: Link score data and emotion data and arrange the overall display format.

[0465] Output: A visual dashboard is provided to the user, visualizing each participant's contribution, emotional state, and meeting effectiveness, as well as providing feedback on improvement suggestions for the next meeting.

[0466] In this way, by combining analysis of speech content and emotional state monitoring, the present invention improves meeting productivity and properly evaluates participants.

[0467] (Application example 2)

[0468] 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."

[0469] Conventional conference systems only record the content and duration of comments made without considering the emotions or mental state of participants. This has led to problems such as reduced productivity and stress accumulation. While improvements in production efficiency and communication within factories are also required, conventional systems have made it difficult to comprehensively improve these. The objective of this invention is to improve productivity and reduce stress by analyzing the progress of conferences and work meetings in real time, monitoring the emotional state of participants, and providing feedback.

[0470] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for setting a conference goal in advance; means for collecting voice data during the conference and transcribing it in real time; means for analyzing the transcribed data and scoring the contribution level based on the number of comments, duration, and content of each participant's comments; means for displaying a list of the scored data and providing feedback on the productivity of the conference and the contribution levels of the participants; means for collecting emotional data from users' facial expressions and tone of voice; and means for analyzing the collected emotional data and monitoring the emotional states and stress levels of the participants. This makes it possible to comprehensively grasp the progress of a conference or work meeting and analyze and display the contribution level and emotional state of each participant in real time, thereby improving productivity and reducing stress.

[0471] "Means for setting meeting objectives in advance" is a function for defining specific objectives and goals for meetings and work meetings in advance and managing them within the system.

[0472] "Means for collecting audio data during meetings and transcribing it in real time" refers to a technology that records participants' speech using a microphone or other device and instantly converts the recorded data into text format.

[0473] "A means of analyzing transcribed data and scoring contribution based on the number of comments, speaking time, and content of each participant" is a function that analyzes transcribed text data and evaluates and quantifies the content, frequency, and duration of each participant's comments.

[0474] "A means of displaying scored data in a list and providing feedback on meeting productivity and participant contributions" is a function that visually displays the analyzed contribution scores and provides feedback on the efficiency of the overall meeting and the contributions of each participant.

[0475] "Means for collecting emotional data from a user's facial expressions and tone of voice" refers to technology that uses a camera or microphone to analyze a user's facial expressions and tone of voice, and collects their emotional state as data.

[0476] "Means for analyzing collected emotional data and monitoring participants' emotional states and stress levels" refers to a function that evaluates each participant's mental state based on collected emotional data and monitors fluctuations in real time.

[0477] "A means of monitoring fluctuations in mental state based on emotional data during work meetings and making suggestions to reduce tension and stress" is a function that analyzes emotional data during meetings, grasps fluctuations in participants' tension and stress levels, and suggests improvement measures accordingly.

[0478] "Means for assessing long-term mental health status based on emotional data and proposing improvements" refers to a technology that analyzes accumulated emotional data, assesses participants' long-term mental health status, and proposes specific improvement measures.

[0479] This invention is a system that supports the improvement of productivity and efficient management of work meetings using factory robots, and in particular, it combines an emotion engine that recognizes the user's emotions. This system consists of the following elements:

[0480] System configuration

[0481] 1. User Device

[0482] Provides an interface for users to enter and save work meeting goals.

[0483] It has the ability to collect voice and emotional data during meetings and send it to a server in real time.

[0484] 2. Server

[0485] Transcription is performed based on the received audio data.

[0486] The transcription data is analyzed, and each participant's contribution is scored based on the number of comments, the duration of their comments, and the content of their comments.

[0487] The emotional data recognized by the emotion engine is analyzed to monitor the atmosphere during the meeting and the stress level of each participant.

[0488] The scored and emotional data are visualized to provide feedback to the user.

[0489] Program processing

[0490] Setting meeting goals

[0491] The user logs in to the system using a terminal and accesses the new meeting page. They enter the meeting title and objective, and set a goal such as "Decide on the task allocation for a new project." Once the input is complete, they press the confirm button to send the meeting objective to the server.

[0492] Collecting voice and emotion data

[0493] When a meeting starts, the user presses the start meeting button on the device. The device starts recording audio and sends the audio data to the server in real time. At the same time, the device's built-in camera and sensors are used to collect emotional data from the user's facial expressions and tone of voice, which is also sent to the server in real time.

[0494] Analysis and scoring of speech content

[0495] The server transcribes the received audio data in real time, analyzes the transcript, counts the amount of speech, speaking time, and content of each speaker, and assigns a score to their contribution.

[0496] Emotional data analysis and monitoring

[0497] The server uses an emotion engine to analyze the received emotion data, monitor the atmosphere during the meeting and the stress level of each participant, and evaluate the atmosphere and stress level of the meeting based on this.

[0498] Displaying scores and emotional data at a glance and providing feedback

[0499] The server visually organizes and displays the score data and emotional data, allowing users to easily check the effectiveness of the meeting, the contribution of each participant, and their emotional state. It also makes suggestions for improving the selection of participants for the next meeting and how the meeting will proceed.

[0500] Hardware and software used

[0501] Hardware: Smart helmet (audio microphone, camera)

[0502] Software: Python, speech_recognition, emotion_recognition

[0503] Specific examples

[0504] 1. Example of setting meeting goals

[0505] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[0506] 2. Example of collecting voice data and emotion data

[0507] When a meeting starts, the device records audio and sends the data to the server in real time. At the same time, it analyzes facial expressions and tone of voice to send emotional data to the server.

[0508] 3. Analysis of speech content and scoring example

[0509] The server analyzes the transcription data and generates a score such as "Participant A spoke 200 words and contributed greatly to the progress" or "Participant B spoke 50 words and contributed less."

[0510] 4. Analysis and Monitoring of Emotion Data

[0511] The server uses an emotion engine to make evaluations such as "the stress level of all participants decreases at the start of the meeting" or "the stress level of a certain participant increases during the meeting."

[0512] 5. Example of score and emotion data display and feedback

[0513] The server displays a list of scores and emotional data and provides feedback to the user, such as "Participant A's participation is valid, but participant B's participation is unnecessary" and "Be careful of rising stress levels during the meeting."

[0514] Prompt Sentence Examples

[0515] "Transcribe audio data from project meetings in real time and analyze the conversations. Also, use video data to recognize participants' emotions and monitor their stress levels and tension. Based on stored goals, provide feedback and suggestions for improvement after the meeting."

[0516] With the above configuration, the present invention realizes efficient management of conferences and work meetings, taking into consideration both the productivity of the conferences and work meetings and the emotional state of the participants.

[0517] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0518] Step 1:

[0519] A user logs in to the system using a terminal and accesses the new meeting page. There, they input the meeting title and goal, and set a specific goal such as "Decide the task allocation for a new project." Once the input is complete, they press the confirm button to send the meeting goal to the server. The input is the meeting title and goal, and the output is the meeting goal data sent to the server.

[0520] Step 2:

[0521] When a meeting starts, the user presses the start meeting button on the device, which causes the device to start recording audio. The device collects the user's audio data in real time and sends it to the server. At the same time, the device uses cameras and sensors to collect emotional data from the user's facial expressions and tone of voice, which is also sent to the server in real time. The input is audio data and emotional data, and the output is the collected data sent to the server.

[0522] Step 3:

[0523] The server transcribes the received audio data in real time. The software used is the Python speech_recognition library. The audio data is converted into text format and saved as transcription data. The input is audio data, and the output is transcribed text data.

[0524] Step 4:

[0525] The server analyzes the transcription data. Specifically, it counts the amount of speech, speech duration, and speech content of each participant, and then scores their contribution based on these. The analyzed data is then stored in a database and used for later feedback and contribution evaluation. The input is the transcription data, and the output is a contribution score.

[0526] Step 5:

[0527] The server uses an emotion engine to analyze the received emotion data. The software used is the Python emotion_recognition library. The server evaluates the user's emotional state and stress level from their facial expressions and tone of voice, and monitors the atmosphere during the meeting and the mental state of each participant. The input is emotion data, and the output is the analysis results, such as emotional state and stress level data.

[0528] Step 6:

[0529] The server displays a list of the scored data and emotional data, providing visual feedback to the user. This allows the user to easily check the effectiveness of the meeting, the contribution of each participant, and their emotional state. The server also uses this data to select participants for the next meeting and make suggestions for improving how the meeting is conducted. The input is the scored data and emotional data, and the output is displayed feedback and suggestions for improvement.

[0530] This completes a series of processes, improving production efficiency and communication within the factory.

[0531] 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.

[0532] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0533] 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.

[0534] [Second embodiment]

[0535] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0536] 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.

[0537] 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).

[0538] 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.

[0539] 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.

[0540] 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).

[0541] 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.

[0542] 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.

[0543] 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.

[0544] 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.

[0545] 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.

[0546] 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."

[0547] The present invention is a system aimed at improving meeting productivity, and has the functions of setting meeting goals, transcribing in real time, analyzing speech content, and providing feedback.

[0548] System configuration

[0549] 1. User Device

[0550] Provides an interface for users to input and save meeting goals.

[0551] It has the function of collecting audio data during a meeting and sending it to a server in real time.

[0552] 2. Server

[0553] Transcription is performed based on the received audio data.

[0554] The transcription data is analyzed and each participant's number of comments, speaking time, and content of comments are scored as a contribution.

[0555] The scored data is visualized and feedback is provided to the user.

[0556] Program processing

[0557] Setting meeting goals

[0558] User Action:

[0559] A user logs in to the system using a terminal and accesses the new meeting page, where they enter the title and purpose of the meeting and press the confirm button, which sends the goal of the meeting to the server and records it.

[0560] Natural language description:

[0561] A user logs into the system, enters the title and goal of the meeting, and sends it to the server.

[0562] Audio data collection and transcription

[0563] The device:

[0564] When a meeting starts, the device starts recording audio and sends the audio data in real time to the server, which then transcribes the received audio data in real time and saves it as text data.

[0565] Natural language description:

[0566] The device records the audio during the meeting in real time and sends the audio data to the server, which then transcribes the audio data and converts it into text data.

[0567] Analysis and scoring of speech content

[0568] The server:

[0569] The server analyzes the transcribed data, identifies the speakers, and assigns a score to each participant based on the number of comments, the duration of their comments, and the content of their comments. This also evaluates the progress toward the meeting's goals.

[0570] Natural language description:

[0571] The server analyzes the transcripts, counts each participant's contributions, and then assigns a score based on the results, as well as assessing progress toward the meeting's goals.

[0572] Score display and feedback

[0573] The server:

[0574] The server visually organizes and displays the scored data in a list, allowing users to easily check the effectiveness of the meeting and the contribution of each participant. If necessary, it provides feedback on the selection of participants for the next meeting and suggestions for improving the meeting method.

[0575] Natural language description:

[0576] The server visually organizes the scores so that users can easily check them, and also provides feedback to users on suggestions for improvement for the next meeting.

[0577] Specific examples

[0578] 1. Example of setting meeting goals:

[0579] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[0580] 2. Audio data collection and transcription example:

[0581] When a meeting starts, the device records audio and transmits the data in real time to a server, which transcribes the audio, converts it into text, and stores it.

[0582] 3. Example of analysis and scoring of speech content:

[0583] The server analyzes the transcription data and generates a score such as "Mr. Tanaka spoke 200 words, making a significant contribution to progress" or "Mr. Sato spoke 50 words, making a small contribution."

[0584] 4. Example of score display and feedback:

[0585] The server displays the scores in a list and provides feedback to the user, such as "Tanaka's participation is valid, Sato's participation is unnecessary." This allows the user to improve the selection of participants and the progress of the next meeting.

[0586] As described above, the present invention is a system that provides specific means for improving the productivity of meetings and supports users in managing meetings efficiently.

[0587] The processing flow will be explained below.

[0588] Step 1:

[0589] User actions

[0590] A user logs in to the system using a terminal. They access the new meeting page and enter the title and goal of the meeting. For example, they might enter "Decide on the task allocation for a new project." Once they have completed the entry, they press the confirm button to send the meeting goal to the server.

[0591] Step 2:

[0592] Processing performed by the server

[0593] The server stores the received meeting goals in a database, generating a unique ID for the meeting and linking the goal information to that ID.

[0594] Step 3:

[0595] Processing performed by the device

[0596] When a conference is started, the user presses the conference start button on the terminal, and the terminal starts recording audio and transmitting the audio data to the server in real time.

[0597] Step 4:

[0598] Processing performed by the server

[0599] The server transcribes the received audio data in real time, and the transcribed text data is linked to a unique conference ID and stored in a database.

[0600] Step 5:

[0601] Processing performed by the server

[0602] The server analyzes the transcripts and starts counting the amount of speech, the duration of speech, and the content of speech from each speaker. Based on this data, it runs an algorithm to score each participant's contribution.

[0603] Step 6:

[0604] Processing performed by the server

[0605] The progress towards the goal is analyzed and the extent to which the meeting objectives have been achieved is evaluated. This progress is also scored and stored in the database.

[0606] Step 7:

[0607] Processing performed by the server

[0608] Visually organize the scored data and generate a feedback report that includes each participant's contribution and the meeting progress.

[0609] Step 8:

[0610] Processing performed by the server

[0611] The generated feedback report is sent to the user's device, allowing the user to consider improvements for the next meeting based on this data.

[0612] Step 9:

[0613] User actions

[0614] Users can check the feedback report on their devices and evaluate the effectiveness of the meeting and the contribution of each participant. Based on this evaluation, the system can improve the selection of participants and the progress of the next meeting.

[0615] In this way, the present invention provides a concrete means for improving the productivity of a meeting, and supports users in managing a meeting efficiently.

[0616] Example 1

[0617] 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."

[0618] In modern companies and organizations, meetings are a forum for important decision-making and information sharing, but low productivity is a major problem. Participants' opinions are often not properly reflected, progress is delayed, and meetings often end without reaching their goals. Another problem is when certain participants speak excessively, burying the opinions of others. In such situations, it becomes difficult to achieve the purpose of the meeting, which can lead to a loss of efficiency for the entire organization.

[0619] 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.

[0620] In this invention, the server includes a means for the user to set a goal for the meeting, a means for the terminal to collect voice data during the meeting and send it to the server in real time, a means for the server to transcribe the voice data in real time, a means for the server to analyze the transcription data and score the contribution level based on the number of comments, the duration of comments, and the content of comments made by each participant, and a means for visualizing the scored data and providing feedback to the user. This makes it possible to improve the productivity of the meeting, accurately evaluate the contribution level of each participant, and specifically suggest improvements for the next meeting.

[0621] A "user" is an individual or member of an organization who operates the system.

[0622] A "terminal" is an electronic device that is operated by a user and that performs data communication with a server.

[0623] "Meeting objectives" refer to the purpose of the meeting and the items to be achieved, which are set at the beginning of the meeting.

[0624] "Audio data" refers to data collected in digital format from voices spoken during a meeting.

[0625] "Server" refers to a computer system for processing and transcribing received audio data.

[0626] "Transcription" refers to the process of converting audio data into text data.

[0627] "Transcription Data" refers to data in converted text format.

[0628] "Analysis" refers to the process of analyzing the content of data to extract useful information.

[0629] "Number of remarks" refers to the number of remarks made by each participant during the meeting.

[0630] "Speaking time" refers to the total amount of time each participant spoke during the conference.

[0631] "Speech content" refers to the specific content that each participant spoke during the meeting.

[0632] "Contribution" refers to a score that evaluates each participant's usefulness in a meeting.

[0633] "Scoring" refers to the process of expressing evaluation results as quantitative numbers.

[0634] "Visualization" refers to the process of displaying data in the form of graphs, charts, etc., to make it easier to understand.

[0635] "Feedback" refers to providing users with information such as areas for improvement based on analysis and evaluation results.

[0636] "Goal achievement" is a measure of how close the meeting came to achieving its goals.

[0637] "Unnecessary meetings and unnecessary participants" refer to factors that reduce the productivity and efficiency of meetings.

[0638] A "database" refers to a system that manages a collection of systematically organized data.

[0639] "Improvement proposals" refer to suggestions for improving the next meeting based on past data.

[0640] The present invention is a system for improving the productivity of meetings, and has functions for setting meeting goals, transcribing real-time information, analyzing speech content, and providing feedback. Specific embodiments for implementing the present invention will be described below.

[0641] Hardware and Software

[0642] This system mainly uses the following hardware and software:

[0643] 1. User Device

[0644] A computer or smartphone that provides an interface for users to input and save meeting goals.

[0645] Microphone and network connection capabilities to collect audio data during meetings and send it to a server in real time.

[0646] 2. Server

[0647] A computer system for transcribing received audio data.

[0648] A processing unit that analyzes the transcription data and scores each participant's number of comments, speaking time, and content of comments as a contribution.

[0649] Data visualization software (e.g., D3.js, Chart.js) to visualize the scored data and provide feedback to the user.

[0650] 3. External Services

[0651] A speech recognition service to convert voice data into text (e.g., Google Cloud Speech-to-Text API).

[0652] Natural language processing (NLP) models (e.g., OpenAI GPT-3) for analyzing text data.

[0653] Setting meeting goals

[0654] User Action:

[0655] The user logs in to the system using a terminal and accesses the new meeting page, where they enter the title and goal of the meeting and press the confirm button, which sends the goal of the meeting to the server and records it.

[0656] Examples:

[0657] The user opens a browser on their device, accesses the "Create a new meeting" page, enters "Decide task allocation for new project" and presses the "Confirm" button. The server saves this information in a database.

[0658] Audio data collection and transcription

[0659] The device:

[0660] When a meeting starts, the device starts recording audio and sends the audio data in real time to the server, which then transcribes the received audio data in real time and saves it as text data.

[0661] Examples:

[0662] To start a meeting, the user clicks the "Start Meeting" button, which activates the device's microphone and streams audio in real time to the server, which uses the Google Cloud Speech-to-Text API to convert the audio into text and store it in a database.

[0663] Analysis and scoring of speech content

[0664] The server:

[0665] The server analyzes the transcribed data, identifies the speakers, and assigns a score to each participant based on the number of comments, the duration of their comments, and the content of their comments. This also evaluates the progress toward the meeting's goals.

[0666] Examples:

[0667] The server analyzes the text data using an NLP model (e.g., OpenAI GPT-3) and generates a score such as "Tanaka-san spoke 200 words, contributed greatly to progress," or "Sato-san spoke 50 words, contributed less."

[0668] Score display and feedback

[0669] The server:

[0670] The server visually organizes and displays the scored data in a list, allowing users to easily check the effectiveness of the meeting and the contribution of each participant. If necessary, it provides feedback on the selection of participants for the next meeting and suggestions for improving the meeting method.

[0671] Examples:

[0672] The server uses visualization libraries (e.g., D3.js, Chart.js) to display the scores in a graph. Users can see feedback on the dashboard, such as "Tanaka's participation is valid, Sato's participation is unnecessary." Suggestions for improvement for the next meeting, such as "Next time, Tanaka's comments should be reduced a little and Sato's opinions should be drawn out," are also displayed.

[0673] As described above, the present invention is a system that provides specific means for improving the productivity of meetings and supports users in managing meetings efficiently.

[0674] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0675] Step 1: Log in your users and set meeting goals

[0676] Specific details:

[0677] 1. User actions: The user starts a terminal, opens a browser and accesses the system login page.

[0678] 2. Input: The user enters the ID and password to log in to the system.

[0679] 3. Data processing: The server checks the entered authentication information against the database and performs authentication.

[0680] 4. Output: Once authentication is complete, the user is redirected to the "Create New Meeting" page.

[0681] 5. User action: The user creates a new meeting, enters its title and goal, and presses the "Confirm" button.

[0682] 6. Input: User-entered meeting title and purpose.

[0683] 7. Data processing: The server stores the entered conference title and purpose in a database.

[0684] 8. Output: The server displays a confirmation message to the user indicating a successful save.

[0685] Step 2: Collect and send audio data

[0686] Specific details:

[0687] 1. User action: The user clicks the "Start Meeting" button.

[0688] 2. Input: Triggers the "Start Meeting" button clicked by the user.

[0689] 3. Data processing: The device activates the microphone and starts recording audio.

[0690] 4. Output: The recorded audio data is generated.

[0691] 5. Device operation: Streams the recorded audio data to the server in real time.

[0692] 6. Input: Collected audio data.

[0693] 7. Data processing: The device splits the voice data into small chunks and sends them to the server.

[0694] 8. Output: The divided audio data chunks arrive at the server.

[0695] Step 3: Real-time transcription

[0696] Specific details:

[0697] 1. Server operation: Sends the received voice data to the voice recognition API.

[0698] 2. Input: Audio data chunks.

[0699] 3. Data processing: The server converts the voice data into text using the Google Cloud Speech-to-Text API.

[0700] 4. Output: Text data.

[0701] 5. Server operation: The converted text data is linked to the conference ID and saved in the database.

[0702] 6. Input: Text data and meeting ID.

[0703] 7. Data processing: The server converts the text data into an appropriate format and stores it in a database.

[0704] 8. Output: Text data stored in the database.

[0705] Step 4: Analyze and score the speech

[0706] Specific details:

[0707] 1. Server action: Retrieve the transcription data from the database and start analyzing it with the natural language processing model.

[0708] 2. Input: Transcription data from the meeting.

[0709] 3. Data processing: The server uses an NLP model (such as OpenAI GPT-3) to analyze the text data and extract the number of comments, the duration of each comment, and the content of each comment.

[0710] 4. Output: Analysis results include the number of statements made by each speaker, the duration of the statements, and the content of the statements.

[0711] 5. Server action: Score each participant's contribution based on the extracted data.

[0712] 6. Input: Analysis results.

[0713] 7. Data processing: The server uses a scoring algorithm to quantify the contribution.

[0714] 8. Output: Scored data.

[0715] Step 5: View scores and receive feedback

[0716] Specific details:

[0717] 1. Server action: Convert the scored data into graphs and charts using a visualization library.

[0718] 2. Input: Scored data.

[0719] 3. Data processing: The server visualizes the data using D3.js, Chart.js, etc.

[0720] 4. Output: Visualized data (graphs and charts).

[0721] 5. Server Actions: Provide feedback to the user and generate improvement suggestions for the next meeting.

[0722] 6. Input: Visualization data and analysis results.

[0723] 7. Data processing: The server uses the AI ​​model to generate improvement suggestions for the next meeting.

[0724] 8. Output: Feedback message and improvement suggestions to the user.

[0725] This concludes the detailed explanation of each processing step. Through these steps, productivity and efficient management of meetings are achieved.

[0726] (Application example 1)

[0727] 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."

[0728] Conventional conferencing systems often only transcribe audio data and analyze the number and duration of participants' speech, and do not provide feedback that directly translates into improved productivity or work performance in the actual workplace. This has led to a demand for effective methods that lead to specific improvement measures and next action plans in factories and workplaces. Furthermore, improvement suggestions based on comparisons with past meeting data are insufficient, making them less convenient for continuously improving productivity.

[0729] 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.

[0730] In this invention, the server includes a means for setting meeting goals in advance, a means for collecting voice data during the meeting and transcribing it in real time, a means for analyzing the transcribed data and scoring the contribution level based on the number of comments, duration of comments, and content of comments made by each participant, a means for displaying a list of the scored data and providing feedback on the effectiveness of the meeting and the suitability of the participants, and a means for evaluating the work performance of on-site workers and automated machines based on the data collected in real time. This makes it possible to accurately evaluate the degree of achievement of the meeting's goals and the contribution levels of participants, as well as to make specific work improvement proposals.

[0731] "Meeting objectives" are specific items or purposes to be achieved that are set before the meeting begins.

[0732] "Audio data" refers to data recorded in audio format of participants' remarks and discussions collected during a meeting.

[0733] "Real-time" refers to the fact that data collection and processing are immediate, with results being fed back almost instantly.

[0734] "Transcription" is the process of converting collected audio data into text data.

[0735] "Participants" are people who attend a meeting and take part in speaking and discussions.

[0736] The "number of statements" refers to the number of times each participant made a statement during the conference.

[0737] "Speaking time" is the cumulative time each participant has spoken during the conference.

[0738] "Content of remarks" refers to the specific content and topics of remarks made by each participant during the meeting.

[0739] "Scoring" refers to making a quantitative evaluation based on analyzed data and expressing it as a number.

[0740] "Work performance" is an indicator of the efficiency and effectiveness of work performed by workers and automated machines.

[0741] "Feedback" refers to improvement suggestions and evaluation information provided based on the analyzed results.

[0742] "Field workers" are workers who actually perform work in factories or on-site.

[0743] An "automatic machine" is a machine or robot that performs tasks automatically in a factory or on a work site.

[0744] A "database" is a system for storing and managing past meeting data and work performance data.

[0745] Comparison is the process of comparing data sets from different points in time to identify differences and areas for improvement.

[0746] "Improvement proposals" are specific proposals and measures for the next meeting or work that are provided based on the analysis results.

[0747] "Past meeting data" refers to information and recorded data relating to meetings that have taken place in the past.

[0748] MODE FOR CARRYING OUT THE INVENTION

[0749] This invention is an improvement conference system aimed at improving productivity within factories, and it sets meeting goals, collects and transcribes real-time audio, analyzes work performance, and provides feedback. This system is realized through the cooperation of user terminals, a server, on-site workers, and automated machines.

[0750] System configuration

[0751] 1. User Device

[0752] Setting meeting goals: Before the meeting, the manager logs in to the system and sets the meeting goals, such as "confirming new work procedures and proposing improvements."

[0753] Audio data collection: During the meeting, the device collects audio data in real time and sends it to the server. The hardware used is a high-performance microphone (e.g., Blue Yeti USB Microphone).

[0754] Real-time transcription: Audio data collected during the meeting is sent to a server and transcribed in real time.

[0755] 2. Server

[0756] Analysis of the transcribed data: The server analyzes the transcribed data and scores each participant's contribution based on the number of comments, the duration of their comments, and the content of their comments.

[0757] Work performance evaluation: The server evaluates the work performance of field workers and automated machines based on the data collected in real time, using the Python programming language and necessary libraries (e.g., speech_recognition, matplotlib).

[0758] Providing feedback: The server visualizes the scored data and provides feedback to the user's device, which then provides suggestions for improving the next meeting.

[0759] 3. Field workers and automated machinery

[0760] Data collection: Operation logs and performance data of field workers and automated machines are stored in a database.

[0761] Improved work performance: Based on feedback from the server, field workers and managers can implement specific work improvement measures.

[0762] Add specific examples to the description

[0763] Examples:

[0764] To improve the efficiency of a new production line, a site manager sets the goal of "confirming new work procedures and proposing improvements." During the meeting, a device collects voice data, which is transcribed and analyzed in real time on a server. For example, if Worker A speaks 200 words and makes specific suggestions for improvement, that will be evaluated as a high contribution and included in the feedback at the next meeting.

[0765] Example prompt sentence:

[0766] I would like to define the requirements for a system that sets meeting goals, transcribes meeting audio in real time, and analyzes and scores what participants say. Please design a system that includes specific goal setting, real-time transcription, scoring of contribution based on number of comments and time spent, and visualization processes.

[0767] As a result, the present invention aims to improve the productivity of meetings and optimize work performance in the field.

[0768] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0769] Step 1:

[0770] Setting meeting goals

[0771] Input: The user (administrator) inputs the meeting goal and sends it to the system.

[0772] Specific operation: A form is displayed on the user's device, and the user is asked to enter the title and goal of the meeting. When the user presses the "Confirm" button, the data is sent to the server and saved in the database.

[0773] Output: Meeting goal data stored on the server.

[0774] Step 2:

[0775] Audio data collection

[0776] Input: Audio data collected during the meeting.

[0777] Specific operation: The device (e.g., PC or tablet) uses a high-performance microphone to collect audio during the meeting and transmits it to the server in real time.

[0778] Output: Real-time audio data sent to the server.

[0779] Step 3:

[0780] Real-time transcription

[0781] Input: The audio data sent to the server.

[0782] What happens: The server transcribes the received audio data in real time, specifically by converting it into text using speech recognition software (e.g., the speech_recognition library).

[0783] Output: Transcription data generated in real time.

[0784] Step 4:

[0785] Analysis and scoring of speech data

[0786] Input: Transcription data generated in real time.

[0787] Specific operation: The server analyzes the transcription data and extracts the number of comments, duration, and content of each participant's comments. Based on this data, each participant's contribution is scored. Analysis is performed using NLP technology and statistical methods.

[0788] Output: Analyzed data and score data for each participant.

[0789] Step 5:

[0790] Work performance evaluation

[0791] Input: Real-time monitoring data and historical work log data.

[0792] How it works: The server analyzes real-time and historical work data to evaluate the work performance of on-site workers and automated machines, using statistical methods to calculate efficiency and productivity indicators.

[0793] Output: Work performance evaluation report.

[0794] Step 6:

[0795] Providing feedback

[0796] Input: Analyzed data and score data, work performance evaluation report.

[0797] Specific operation: The server visualizes the scored data and work performance evaluation reports and provides feedback to the user's device. The feedback is displayed in the form of graphs and reports, and includes suggestions for next improvement.

[0798] Output: A feedback report provided to the user and suggestions for improvement for the next meeting.

[0799] Through these steps, the system helps improve productivity and optimize work performance during and after meetings.

[0800] 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.

[0801] This invention is a system that supports the improvement of productivity and efficient management of meetings, and in particular, combines an emotion engine that recognizes user emotions. This system not only sets meeting goals, transcribes speech in real time, analyzes speech content, and provides feedback, but also recognizes and monitors emotions using the emotion engine.

[0802] System configuration

[0803] 1. User Device

[0804] Provides an interface for users to input and save meeting goals.

[0805] It has the function of collecting voice data and emotional data during a meeting and sending it to a server in real time.

[0806] 2. Server

[0807] Transcription is performed based on the received audio data.

[0808] The transcription data is analyzed and each participant's number of comments, speaking time, and content of comments are scored as a contribution.

[0809] The emotional data recognized by the emotion engine is analyzed to monitor the atmosphere during the meeting and the stress level of each participant.

[0810] The scored and emotional data are visualized to provide feedback to the user.

[0811] Program processing

[0812] Setting meeting goals

[0813] User Action:

[0814] The user logs in to the system using a terminal and accesses the new meeting page. They enter the title and objective of the meeting and set a goal such as "Decide on the task allocation for the new project." Once the input is complete, they press the confirm button to send the meeting goal to the server.

[0815] Natural language description:

[0816] A user logs into the system, enters the title and goal of the meeting, and sends it to the server.

[0817] Collecting voice and emotion data

[0818] The device:

[0819] When a conference starts, the user presses the conference start button on the device. The device starts recording audio and transmits the audio data to the server in real time. At the same time, the device's built-in camera and sensors are used to collect emotional data from the user's facial expressions and tone of voice, which is also transmitted to the server in real time.

[0820] Natural language description:

[0821] The device records the audio during the meeting in real time and transmits the data along with the user's emotional data to the server.

[0822] Analysis and scoring of speech content

[0823] The server:

[0824] The server transcribes the received audio data in real time, analyzes the transcript, counts the amount of speech, speaking time, and content of each speaker, and assigns a score to their contribution.

[0825] Natural language description:

[0826] The server transcribes the received audio data and analyzes the amount and duration of each speaker's speech to score their contribution.

[0827] Emotional data analysis and monitoring

[0828] The server:

[0829] The server uses an emotion engine to analyze the received emotion data, monitor the atmosphere during the meeting and the stress level of each participant, and evaluate the atmosphere and stress level of the meeting based on this.

[0830] Natural language description:

[0831] The server uses an emotion engine to analyze the received emotion data and monitor the atmosphere of the meeting and the stress levels of the participants.

[0832] Displaying scores and emotional data at a glance and providing feedback

[0833] The server:

[0834] The server visually organizes and displays the score data and emotional data. This allows users to easily check the effectiveness of the meeting, the contribution of each participant, and their emotional state. It also makes suggestions for improving the selection of participants for the next meeting and how the meeting will proceed.

[0835] Natural language description:

[0836] The server visualizes the scores and emotion data so that users can easily check them, and also provides feedback on suggestions for improvement for the next meeting.

[0837] Specific examples

[0838] 1. Example of setting meeting goals:

[0839] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[0840] 2. Example of collecting voice and emotion data:

[0841] When a meeting starts, the device records audio and sends the data to the server in real time. At the same time, it analyzes facial expressions and tone of voice to send emotional data to the server.

[0842] 3. Example of analysis and scoring of speech content:

[0843] The server analyzes the transcription data and generates a score such as "Mr. Tanaka spoke 200 words, making a great contribution to progress" or "Mr. Sato spoke 50 words, making a low contribution."

[0844] 4. Emotion data analysis and monitoring example:

[0845] The server uses an emotion engine to make evaluations such as "the stress level of all participants decreases at the start of the meeting" and "the stress level of a certain participant increases during the meeting."

[0846] 5. Example of score and emotion data list display and feedback:

[0847] The server displays a list of scores and emotional data, and provides feedback to the user, such as "Tanaka's participation is valid, Sato's participation is unnecessary," and "Be careful of rising stress levels during the meeting."

[0848] In this way, the present invention provides a concrete means for realizing efficient conference management that takes into consideration both the productivity of the conference and the emotional state of the participants.

[0849] The processing flow will be explained below.

[0850] Step 1:

[0851] User actions

[0852] The user logs in to the system using a terminal and accesses the new meeting page. There, they input the title and objective of the meeting, setting a goal such as "Decide on the allocation of tasks for a new project," and press the confirm button to send it to the server.

[0853] Step 2:

[0854] Processing performed by the server

[0855] The server stores the received meeting goals in a database, generating a unique ID for the meeting and linking the goal information to that ID so that the meeting can be identified and tracked.

[0856] Step 3:

[0857] Processing performed by the device

[0858] When a conference call begins, the user presses the conference start button on the device. The device starts recording audio and begins sending the audio data to the server in real time. At the same time, the device's built-in camera and sensors are used to capture facial expressions and tone of voice, and emotional data is also sent to the server in real time.

[0859] Step 4:

[0860] Processing performed by the server

[0861] The server transcribes the received audio data in real time. The transcribed text data is linked to a unique conference ID and stored in a database. Emotion data is also linked and stored in the same way.

[0862] Step 5:

[0863] Processing performed by the server

[0864] The server analyzes the transcript data, identifies each speaker, counts the amount of speech, speaking time, and content, and scores each participant's contribution. It also analyzes emotional data to evaluate the atmosphere during the meeting and the stress levels of participants.

[0865] Step 6:

[0866] Processing performed by the server

[0867] Progress toward the goal is evaluated, and emotional data is collected and stored in a database along with the scored data. An algorithm is applied to evaluate the effectiveness of the meeting based on the progress.

[0868] Step 7:

[0869] Processing performed by the server

[0870] It visually organizes the scored and emotional data and generates feedback reports that include each speaker's contribution, meeting progress, and emotional state.

[0871] Step 8:

[0872] Processing performed by the server

[0873] The generated feedback report is sent to the user's device, and the user can use this data to consider improvements for the next meeting.

[0874] Step 9:

[0875] User actions

[0876] Users can review the feedback report on their devices and evaluate the effectiveness of the meeting, the contributions of each participant, and their emotional state. Based on this evaluation, the system can improve the selection of participants and the way the meeting will proceed for the next meeting.

[0877] In this way, the present invention not only improves the productivity of a meeting, but also realizes efficient meeting management that takes into account the emotional states of the participants.

[0878] Example 2

[0879] 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."

[0880] In today's business environment, the productivity and efficiency of meetings are being questioned. However, many meetings are unable to effectively evaluate participants' emotions or real-time contributions, and do not provide appropriate feedback. This can lead to inefficient meetings and a decline in participants' motivation. Furthermore, meetings held in high-stress environments can increase participants' psychological burden. To address these issues, a system combining the collection and analysis of real-time emotional data is needed.

[0881] 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 setting a goal of the conference in advance, a means for collecting voice data and emotional data during the conference and transcribing them in real time, a means for analyzing the transcribed data and scoring the contribution level based on the number of comments, the duration of comments, and the content of comments made by each participant, a means for analyzing the emotional data and monitoring the emotional state and atmosphere of the participants during the conference, and a means for displaying a list of scored data and emotional data to provide feedback on the effectiveness of the conference and the suitability of the participants. This makes it possible to improve the productivity of the conference and reduce the psychological burden on the participants.

[0882] "Meeting objectives" refers to the purpose of the meeting and the specific goals to be achieved.

[0883] "Audio data" refers to data that is a digital recording of audio information spoken during a conference.

[0884] "Emotional data" refers to data that indicates the emotional state and psychological reactions of participants during a meeting, and is obtained from facial expressions, tone of voice, physical movements, etc.

[0885] "Real-time transcription" refers to the process of instantly converting spoken audio data into text data.

[0886] "Transcription data" refers to data that has been converted from audio into text.

[0887] "Number of remarks" refers to the number of remarks made by each participant during the meeting.

[0888] "Speaking time" refers to the total amount of time each participant spoke during a meeting.

[0889] "Speech content" refers to the specific themes or topics that each participant spoke about during the meeting.

[0890] "Contribution score" refers to a numerical value that indicates each participant's contribution to the conference, evaluated based on the number of comments, speaking time, and content of comments.

[0891] "Scored data" refers to data that has been calculated and organized as a contribution score.

[0892] An "emotion engine" is a system that analyzes emotional data and uses that data to evaluate and monitor the emotional state and atmosphere of participants.

[0893] "Monitoring" refers to the process of observing, recording, and analyzing the situation during a meeting in real time.

[0894] "List display" refers to visually organizing the analysis results and displaying them in a format that can be checked at a glance.

[0895] "Feedback" refers to information and suggestions generated based on analyzed data, and refers to guidance for improving the progress of the meeting and the behavior of participants.

[0896] MODE FOR CARRYING OUT THE INVENTION

[0897] This invention is a system that supports the improvement of productivity and efficient management of meetings, and in particular, combines an emotion engine that recognizes user emotions. This system not only sets meeting goals, transcribes speech in real time, analyzes speech content, and provides feedback, but also recognizes and monitors emotions using the emotion engine.

[0898] System configuration:

[0899] 1. User Device

[0900] Provides an interface for users to input and save meeting goals.

[0901] It has the function of collecting voice data and emotional data during a meeting and sending it to a server in real time.

[0902] 2. Server

[0903] Transcription is performed based on the received audio data.

[0904] The transcription data is analyzed and each participant's number of comments, speaking time, and content of comments are scored as a contribution.

[0905] The emotional data recognized by the emotion engine is analyzed to monitor the atmosphere during the meeting and the stress level of each participant.

[0906] The scored and emotional data are visualized to provide feedback to the user.

[0907] Program processing:

[0908] Setting meeting goals

[0909] User Action:

[0910] The user logs in to the system using a terminal and accesses the new meeting page. They enter the title and objective of the meeting and set a goal such as "Decide on the task allocation for the new project." Once the input is complete, they press the confirm button to send the meeting goal to the server.

[0911] The server:

[0912] The server saves the meeting title and goal sent by the user in the database, and if the save is successful, gives feedback to the user.

[0913] Collecting voice and emotion data

[0914] User Action:

[0915] At the start of a conference, the user presses a conference start button on the terminal.

[0916] The device:

[0917] The device starts recording and sends the audio data to the server in real time. The device also uses cameras and sensors to detect the user's facial expressions and tone of voice, generating emotion data. The generated emotion data is also sent to the server in real time.

[0918] Analysis and scoring of speech content

[0919] The server:

[0920] The server converts the received audio data into text using transcription software (e.g., Google Speech-to-Text API). This text data is analyzed to count the amount of speech and the duration of speech for each speaker. The usefulness and contribution of the speech content are then analyzed to calculate the speaker's score.

[0921] Emotional data analysis and monitoring

[0922] The server:

[0923] The server analyzes the received emotional data using an emotion analysis engine (e.g., Microsoft Azure Emotion API) to evaluate the atmosphere during the meeting and the stress levels of the participants. Based on the analysis results, the atmosphere of the meeting and the stress levels of each participant are monitored in real time.

[0924] Displaying scores and emotional data at a glance and providing feedback

[0925] The server:

[0926] The server uses visualization tools such as dashboards (e.g., Tableau) to format the score data and emotion data and display them in real time. After the meeting, users can access the dashboard to see at a glance each participant's contribution to the discussion, the atmosphere during the meeting, and their stress level. It also displays suggestions for improvement for the next meeting and feedback on participant selection.

[0927] Examples:

[0928] 1. Example of setting meeting goals:

[0929] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[0930] 2. Example of collecting voice and emotion data:

[0931] When a meeting starts, the device records audio and sends the data to the server in real time. At the same time, it analyzes facial expressions and tone of voice to send emotional data to the server.

[0932] 3. Example of analysis and scoring of speech content:

[0933] The server analyzes the transcription data and generates a score such as "Participant A spoke 200 words and contributed greatly to the progress" or "Participant B spoke 50 words and contributed less."

[0934] 4. Emotion data analysis and monitoring example:

[0935] The server uses an emotion engine to make evaluations such as "the stress level of all participants decreases at the start of the meeting" and "the stress level of a certain participant increases during the meeting."

[0936] 5. Example of score and emotion data list display and feedback:

[0937] The server displays a list of scores and emotional data and provides feedback to the user, such as "Participant A's participation is valid, but Participant B's participation is unnecessary," and "Be careful of rising stress levels during the meeting."

[0938] In this way, the present invention provides a concrete means for realizing efficient conference management that takes into consideration both the productivity of the conference and the emotional state of the participants.

[0939] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0940] Step 1: Set meeting goals

[0941] Input: The meeting title and goal entered by the user on the meeting goal setting screen

[0942] Specific behavior:

[0943] 1. A user logs into the system using a terminal.

[0944] 2. The user visits the new meeting page.

[0945] 3. The user enters the meeting title (e.g., "Project Kickoff") and goal (e.g., "Determine key task assignments").

[0946] 4. The user presses the confirm button to send the information to the server.

[0947] Data processing / calculation: The server verifies the received conference title and objectives, and stores them in the database if there are no errors.

[0948] Output: If the save is successful, the server will provide feedback to the user.

[0949] Step 2: Collect speech and emotion data at the start of the meeting

[0950] Input: User presses start conference button

[0951] Specific behavior:

[0952] 1. At the start of a conference, the user presses the conference start button on the terminal.

[0953] 2. The device starts the recording function.

[0954] 3. The device uses cameras and sensors to detect the user's facial expressions and tone of voice.

[0955] Data processing / calculation: The terminal compresses and packets the recorded voice data in real time, and simultaneously records facial expressions and tone of voice data as emotion data.

[0956] Output: Compressed audio data and emotion data are sent to the server in real time.

[0957] Step 3: Transcribe and analyze the audio data

[0958] Input: Audio data received by the server

[0959] Specific behavior:

[0960] 1. The server converts the received voice data into text using transcription software (e.g., Google Speech-to-Text API).

[0961] 2. The server analyzes the text data and counts the amount of speech and the duration of speech for each speaker.

[0962] Data processing / calculation: The server extracts specific keywords and phrases from the text data and scores the usefulness of the comments based on that.

[0963] Output: The scoring results for each participant's speech volume, speech time, and content are output.

[0964] Step 4: Analyze and evaluate emotion data

[0965] Input: Emotion data received by the server

[0966] Specific behavior:

[0967] 1. The server analyzes the received emotion data using an emotion analysis engine (e.g., Microsoft Azure Emotion API).

[0968] 2. Based on the analysis results, the server evaluates and monitors the atmosphere during the meeting and the stress level of each participant in real time.

[0969] Data processing / calculation: The results of emotion data analysis are quantified and combined with related time series data.

[0970] Output: The measured mood score and stress level evaluation results are output.

[0971] Step 5: Integrating and visualizing scores and sentiment data

[0972] Input: Analysis results of speech score data and emotion data

[0973] Specific behavior:

[0974] 1. The server aggregates the score data and emotion data and formats it using a visualization tool such as a dashboard (e.g., Tableau).

[0975] 2. The visualized data is displayed in a user-viewable format.

[0976] Data processing / calculation: Link score data and emotion data and arrange the overall display format.

[0977] Output: A visual dashboard is provided to the user, visualizing each participant's contribution, emotional state, and meeting effectiveness, as well as providing feedback on improvement suggestions for the next meeting.

[0978] In this way, by combining analysis of speech content and emotional state monitoring, the present invention improves meeting productivity and properly evaluates participants.

[0979] (Application example 2)

[0980] 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."

[0981] Conventional conference systems only record the content and duration of comments made without considering the emotions or mental state of participants. This has led to problems such as reduced productivity and stress accumulation. While improvements in production efficiency and communication within factories are also required, conventional systems have made it difficult to comprehensively improve these. The objective of this invention is to improve productivity and reduce stress by analyzing the progress of conferences and work meetings in real time, monitoring the emotional state of participants, and providing feedback.

[0982] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for setting a conference goal in advance; means for collecting voice data during the conference and transcribing it in real time; means for analyzing the transcribed data and scoring the contribution level based on the number of comments, duration, and content of each participant's comments; means for displaying a list of the scored data and providing feedback on the productivity of the conference and the contribution levels of the participants; means for collecting emotional data from users' facial expressions and tone of voice; and means for analyzing the collected emotional data and monitoring the emotional states and stress levels of the participants. This makes it possible to comprehensively grasp the progress of a conference or work meeting and analyze and display the contribution level and emotional state of each participant in real time, thereby improving productivity and reducing stress.

[0983] "Means for setting meeting objectives in advance" is a function for defining specific objectives and goals for meetings and work meetings in advance and managing them within the system.

[0984] "Means for collecting audio data during meetings and transcribing it in real time" refers to a technology that records participants' speech using a microphone or other device and instantly converts the recorded data into text format.

[0985] "A means of analyzing transcribed data and scoring contribution based on the number of comments, speaking time, and content of each participant" is a function that analyzes transcribed text data and evaluates and quantifies the content, frequency, and duration of each participant's comments.

[0986] "A means of displaying scored data in a list and providing feedback on meeting productivity and participant contributions" is a function that visually displays the analyzed contribution scores and provides feedback on the efficiency of the overall meeting and the contributions of each participant.

[0987] "Means for collecting emotional data from a user's facial expressions and tone of voice" refers to technology that uses a camera or microphone to analyze a user's facial expressions and tone of voice, and collects their emotional state as data.

[0988] "Means for analyzing collected emotional data and monitoring participants' emotional states and stress levels" refers to a function that evaluates each participant's mental state based on collected emotional data and monitors fluctuations in real time.

[0989] "A means of monitoring fluctuations in mental state based on emotional data during work meetings and making suggestions to reduce tension and stress" is a function that analyzes emotional data during meetings, grasps fluctuations in participants' tension and stress levels, and suggests improvement measures accordingly.

[0990] "Means for assessing long-term mental health status based on emotional data and proposing improvements" refers to a technology that analyzes accumulated emotional data, assesses participants' long-term mental health status, and proposes specific improvement measures.

[0991] This invention is a system that supports the improvement of productivity and efficient management of work meetings using factory robots, and in particular, it combines an emotion engine that recognizes the user's emotions. This system consists of the following elements:

[0992] System configuration

[0993] 1. User Device

[0994] Provides an interface for users to enter and save work meeting goals.

[0995] It has the ability to collect voice and emotional data during meetings and send it to a server in real time.

[0996] 2. Server

[0997] Transcription is performed based on the received audio data.

[0998] The transcription data is analyzed, and each participant's contribution is scored based on the number of comments, the duration of their comments, and the content of their comments.

[0999] The emotional data recognized by the emotion engine is analyzed to monitor the atmosphere during the meeting and the stress level of each participant.

[1000] The scored and emotional data are visualized to provide feedback to the user.

[1001] Program processing

[1002] Setting meeting goals

[1003] The user logs in to the system using a terminal and accesses the new meeting page. They enter the meeting title and objective, and set a goal such as "Decide on the task allocation for a new project." Once the input is complete, they press the confirm button to send the meeting objective to the server.

[1004] Collecting voice and emotion data

[1005] When a meeting starts, the user presses the start meeting button on the device. The device starts recording audio and sends the audio data to the server in real time. At the same time, the device's built-in camera and sensors are used to collect emotional data from the user's facial expressions and tone of voice, which is also sent to the server in real time.

[1006] Analysis and scoring of speech content

[1007] The server transcribes the received audio data in real time, analyzes the transcript, counts the amount of speech, speaking time, and content of each speaker, and assigns a score to their contribution.

[1008] Emotional data analysis and monitoring

[1009] The server uses an emotion engine to analyze the received emotion data, monitor the atmosphere during the meeting and the stress level of each participant, and evaluate the atmosphere and stress level of the meeting based on this.

[1010] Displaying scores and emotional data at a glance and providing feedback

[1011] The server visually organizes and displays the score data and emotional data, allowing users to easily check the effectiveness of the meeting, the contribution of each participant, and their emotional state. It also makes suggestions for improving the selection of participants for the next meeting and how the meeting will proceed.

[1012] Hardware and software used

[1013] Hardware: Smart helmet (audio microphone, camera)

[1014] Software: Python, speech_recognition, emotion_recognition

[1015] Specific examples

[1016] 1. Example of setting meeting goals

[1017] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[1018] 2. Example of collecting voice data and emotion data

[1019] When a meeting starts, the device records audio and sends the data to the server in real time. At the same time, it analyzes facial expressions and tone of voice to send emotional data to the server.

[1020] 3. Analysis of speech content and scoring example

[1021] The server analyzes the transcription data and generates a score such as "Participant A spoke 200 words and contributed greatly to the progress" or "Participant B spoke 50 words and contributed less."

[1022] 4. Analysis and Monitoring of Emotion Data

[1023] The server uses an emotion engine to make evaluations such as "the stress level of all participants decreases at the start of the meeting" or "the stress level of a certain participant increases during the meeting."

[1024] 5. Example of score and emotion data display and feedback

[1025] The server displays a list of scores and emotional data and provides feedback to the user, such as "Participant A's participation is valid, but participant B's participation is unnecessary" and "Be careful of rising stress levels during the meeting."

[1026] Prompt Sentence Examples

[1027] "Transcribe audio data from project meetings in real time and analyze the conversations. Also, use video data to recognize participants' emotions and monitor their stress levels and tension. Based on stored goals, provide feedback and suggestions for improvement after the meeting."

[1028] With the above configuration, the present invention realizes efficient management of conferences and work meetings, taking into consideration both the productivity of the conferences and work meetings and the emotional state of the participants.

[1029] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1030] Step 1:

[1031] A user logs in to the system using a terminal and accesses the new meeting page. There, they input the meeting title and goal, and set a specific goal such as "Decide the task allocation for a new project." Once the input is complete, they press the confirm button to send the meeting goal to the server. The input is the meeting title and goal, and the output is the meeting goal data sent to the server.

[1032] Step 2:

[1033] When a meeting starts, the user presses the start meeting button on the device, which causes the device to start recording audio. The device collects the user's audio data in real time and sends it to the server. At the same time, the device uses cameras and sensors to collect emotional data from the user's facial expressions and tone of voice, which is also sent to the server in real time. The input is audio data and emotional data, and the output is the collected data sent to the server.

[1034] Step 3:

[1035] The server transcribes the received audio data in real time. The software used is the Python speech_recognition library. The audio data is converted into text format and saved as transcription data. The input is audio data, and the output is transcribed text data.

[1036] Step 4:

[1037] The server analyzes the transcription data. Specifically, it counts the amount of speech, speech duration, and speech content of each participant, and then scores their contribution based on these. The analyzed data is then stored in a database and used for later feedback and contribution evaluation. The input is the transcription data, and the output is a contribution score.

[1038] Step 5:

[1039] The server uses an emotion engine to analyze the received emotion data. The software used is the Python emotion_recognition library. The server evaluates the user's emotional state and stress level from their facial expressions and tone of voice, and monitors the atmosphere during the meeting and the mental state of each participant. The input is emotion data, and the output is the analysis results, such as emotional state and stress level data.

[1040] Step 6:

[1041] The server displays a list of the scored data and emotional data, providing visual feedback to the user. This allows the user to easily check the effectiveness of the meeting, the contribution of each participant, and their emotional state. The server also uses this data to select participants for the next meeting and make suggestions for improving how the meeting is conducted. The input is the scored data and emotional data, and the output is displayed feedback and suggestions for improvement.

[1042] This completes a series of processes, improving production efficiency and communication within the factory.

[1043] 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.

[1044] 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.

[1045] 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.

[1046] [Third embodiment]

[1047] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1048] 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.

[1049] 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).

[1050] 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.

[1051] 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.

[1052] 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).

[1053] 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.

[1054] 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.

[1055] 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.

[1056] 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.

[1057] 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.

[1058] 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."

[1059] The present invention is a system aimed at improving meeting productivity, and has the functions of setting meeting goals, transcribing in real time, analyzing speech content, and providing feedback.

[1060] System configuration

[1061] 1. User Device

[1062] Provides an interface for users to input and save meeting goals.

[1063] It has the function of collecting audio data during a meeting and sending it to a server in real time.

[1064] 2. Server

[1065] Transcription is performed based on the received audio data.

[1066] The transcription data is analyzed and each participant's number of comments, speaking time, and content of comments are scored as a contribution.

[1067] The scored data is visualized and feedback is provided to the user.

[1068] Program processing

[1069] Setting meeting goals

[1070] User Action:

[1071] A user logs in to the system using a terminal and accesses the new meeting page, where they enter the title and purpose of the meeting and press the confirm button, which sends the goal of the meeting to the server and records it.

[1072] Natural language description:

[1073] A user logs into the system, enters the title and goal of the meeting, and sends it to the server.

[1074] Audio data collection and transcription

[1075] The device:

[1076] When a meeting starts, the device starts recording audio and sends the audio data in real time to the server, which then transcribes the received audio data in real time and saves it as text data.

[1077] Natural language description:

[1078] The device records the audio during the meeting in real time and sends the audio data to the server, which then transcribes the audio data and converts it into text data.

[1079] Analysis and scoring of speech content

[1080] The server:

[1081] The server analyzes the transcribed data, identifies the speakers, and assigns a score to each participant based on the number of comments, the duration of their comments, and the content of their comments. This also evaluates the progress toward the meeting's goals.

[1082] Natural language description:

[1083] The server analyzes the transcripts, counts each participant's contributions, and then assigns a score based on the results, as well as assessing progress toward the meeting's goals.

[1084] Score display and feedback

[1085] The server:

[1086] The server visually organizes and displays the scored data in a list, allowing users to easily check the effectiveness of the meeting and the contribution of each participant. If necessary, it provides feedback on the selection of participants for the next meeting and suggestions for improving the meeting method.

[1087] Natural language description:

[1088] The server visually organizes the scores so that users can easily check them, and also provides feedback to users on suggestions for improvement for the next meeting.

[1089] Specific examples

[1090] 1. Example of setting meeting goals:

[1091] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[1092] 2. Audio data collection and transcription example:

[1093] When a meeting starts, the device records audio and transmits the data in real time to a server, which transcribes the audio, converts it into text, and stores it.

[1094] 3. Example of analysis and scoring of speech content:

[1095] The server analyzes the transcription data and generates a score such as "Mr. Tanaka spoke 200 words, making a significant contribution to progress" or "Mr. Sato spoke 50 words, making a small contribution."

[1096] 4. Example of score display and feedback:

[1097] The server displays the scores in a list and provides feedback to the user, such as "Tanaka's participation is valid, Sato's participation is unnecessary." This allows the user to improve the selection of participants and the progress of the next meeting.

[1098] As described above, the present invention is a system that provides specific means for improving the productivity of meetings and supports users in managing meetings efficiently.

[1099] The processing flow will be explained below.

[1100] Step 1:

[1101] User actions

[1102] A user logs in to the system using a terminal. They access the new meeting page and enter the title and goal of the meeting. For example, they might enter "Decide on the task allocation for a new project." Once they have completed the entry, they press the confirm button to send the meeting goal to the server.

[1103] Step 2:

[1104] Processing performed by the server

[1105] The server stores the received meeting goals in a database, generating a unique ID for the meeting and linking the goal information to that ID.

[1106] Step 3:

[1107] Processing performed by the device

[1108] When a conference is started, the user presses the conference start button on the terminal, and the terminal starts recording audio and transmitting the audio data to the server in real time.

[1109] Step 4:

[1110] Processing performed by the server

[1111] The server transcribes the received audio data in real time, and the transcribed text data is linked to a unique conference ID and stored in a database.

[1112] Step 5:

[1113] Processing performed by the server

[1114] The server analyzes the transcripts and starts counting the amount of speech, the duration of speech, and the content of speech from each speaker. Based on this data, it runs an algorithm to score each participant's contribution.

[1115] Step 6:

[1116] Processing performed by the server

[1117] The progress towards the goal is analyzed and the extent to which the meeting objectives have been achieved is evaluated. This progress is also scored and stored in the database.

[1118] Step 7:

[1119] Processing performed by the server

[1120] Visually organize the scored data and generate a feedback report that includes each participant's contribution and the meeting progress.

[1121] Step 8:

[1122] Processing performed by the server

[1123] The generated feedback report is sent to the user's device, allowing the user to consider improvements for the next meeting based on this data.

[1124] Step 9:

[1125] User actions

[1126] Users can check the feedback report on their devices and evaluate the effectiveness of the meeting and the contribution of each participant. Based on this evaluation, the system can improve the selection of participants and the progress of the next meeting.

[1127] In this way, the present invention provides a concrete means for improving the productivity of a meeting, and supports users in managing a meeting efficiently.

[1128] Example 1

[1129] 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."

[1130] In modern companies and organizations, meetings are a forum for important decision-making and information sharing, but low productivity is a major problem. Participants' opinions are often not properly reflected, progress is delayed, and meetings often end without reaching their goals. Another problem is when certain participants speak excessively, burying the opinions of others. In such situations, it becomes difficult to achieve the purpose of the meeting, which can lead to a loss of efficiency for the entire organization.

[1131] 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.

[1132] In this invention, the server includes a means for the user to set a goal for the meeting, a means for the terminal to collect voice data during the meeting and send it to the server in real time, a means for the server to transcribe the voice data in real time, a means for the server to analyze the transcription data and score the contribution level based on the number of comments, the duration of comments, and the content of comments made by each participant, and a means for visualizing the scored data and providing feedback to the user. This makes it possible to improve the productivity of the meeting, accurately evaluate the contribution level of each participant, and specifically suggest improvements for the next meeting.

[1133] A "user" is an individual or member of an organization who operates the system.

[1134] A "terminal" is an electronic device that is operated by a user and that performs data communication with a server.

[1135] "Meeting objectives" refer to the purpose of the meeting and the items to be achieved, which are set at the beginning of the meeting.

[1136] "Audio data" refers to data collected in digital format from voices spoken during a meeting.

[1137] "Server" refers to a computer system for processing and transcribing received audio data.

[1138] "Transcription" refers to the process of converting audio data into text data.

[1139] "Transcription Data" refers to data in converted text format.

[1140] "Analysis" refers to the process of analyzing the content of data to extract useful information.

[1141] "Number of remarks" refers to the number of remarks made by each participant during the meeting.

[1142] "Speaking time" refers to the total amount of time each participant spoke during the conference.

[1143] "Speech content" refers to the specific content that each participant spoke during the meeting.

[1144] "Contribution" refers to a score that evaluates each participant's usefulness in a meeting.

[1145] "Scoring" refers to the process of expressing evaluation results as quantitative numbers.

[1146] "Visualization" refers to the process of displaying data in the form of graphs, charts, etc., to make it easier to understand.

[1147] "Feedback" refers to providing users with information such as areas for improvement based on analysis and evaluation results.

[1148] "Goal achievement" is a measure of how close the meeting came to achieving its goals.

[1149] "Unnecessary meetings and unnecessary participants" refer to factors that reduce the productivity and efficiency of meetings.

[1150] A "database" refers to a system that manages a collection of systematically organized data.

[1151] "Improvement proposals" refer to suggestions for improving the next meeting based on past data.

[1152] The present invention is a system for improving the productivity of meetings, and has functions for setting meeting goals, transcribing real-time information, analyzing speech content, and providing feedback. Specific embodiments for implementing the present invention will be described below.

[1153] Hardware and Software

[1154] This system mainly uses the following hardware and software:

[1155] 1. User Device

[1156] A computer or smartphone that provides an interface for users to input and save meeting goals.

[1157] Microphone and network connection capabilities to collect audio data during meetings and send it to a server in real time.

[1158] 2. Server

[1159] A computer system for transcribing received audio data.

[1160] A processing unit that analyzes the transcription data and scores each participant's number of comments, speaking time, and content of comments as a contribution.

[1161] Data visualization software (e.g., D3.js, Chart.js) to visualize the scored data and provide feedback to the user.

[1162] 3. External Services

[1163] A speech recognition service to convert voice data into text (e.g., Google Cloud Speech-to-Text API).

[1164] Natural language processing (NLP) models (e.g., OpenAI GPT-3) for analyzing text data.

[1165] Setting meeting goals

[1166] User Action:

[1167] The user logs in to the system using a terminal and accesses the new meeting page, where they enter the title and goal of the meeting and press the confirm button, which sends the goal of the meeting to the server and records it.

[1168] Examples:

[1169] The user opens a browser on their device, accesses the "Create a new meeting" page, enters "Decide task allocation for new project" and presses the "Confirm" button. The server saves this information in a database.

[1170] Audio data collection and transcription

[1171] The device:

[1172] When a meeting starts, the device starts recording audio and sends the audio data in real time to the server, which then transcribes the received audio data in real time and saves it as text data.

[1173] Examples:

[1174] To start a meeting, the user clicks the "Start Meeting" button, which activates the device's microphone and streams audio in real time to the server, which uses the Google Cloud Speech-to-Text API to convert the audio into text and store it in a database.

[1175] Analysis and scoring of speech content

[1176] The server:

[1177] The server analyzes the transcribed data, identifies the speakers, and assigns a score to each participant based on the number of comments, the duration of their comments, and the content of their comments. This also evaluates the progress toward the meeting's goals.

[1178] Examples:

[1179] The server analyzes the text data using an NLP model (e.g., OpenAI GPT-3) and generates a score such as "Tanaka-san spoke 200 words, contributed greatly to progress," or "Sato-san spoke 50 words, contributed less."

[1180] Score display and feedback

[1181] The server:

[1182] The server visually organizes and displays the scored data in a list, allowing users to easily check the effectiveness of the meeting and the contribution of each participant. If necessary, it provides feedback on the selection of participants for the next meeting and suggestions for improving the meeting method.

[1183] Examples:

[1184] The server uses visualization libraries (e.g., D3.js, Chart.js) to display the scores in a graph. Users can see feedback on the dashboard, such as "Tanaka's participation is valid, Sato's participation is unnecessary." Suggestions for improvement for the next meeting, such as "Next time, Tanaka's comments should be reduced a little and Sato's opinions should be drawn out," are also displayed.

[1185] As described above, the present invention is a system that provides specific means for improving the productivity of meetings and supports users in managing meetings efficiently.

[1186] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1187] Step 1: Log in your users and set meeting goals

[1188] Specific details:

[1189] 1. User actions: The user starts a terminal, opens a browser and accesses the system login page.

[1190] 2. Input: The user enters the ID and password to log in to the system.

[1191] 3. Data processing: The server checks the entered authentication information against the database and performs authentication.

[1192] 4. Output: Once authentication is complete, the user is redirected to the "Create New Meeting" page.

[1193] 5. User action: The user creates a new meeting, enters its title and goal, and presses the "Confirm" button.

[1194] 6. Input: User-entered meeting title and purpose.

[1195] 7. Data processing: The server stores the entered conference title and purpose in a database.

[1196] 8. Output: The server displays a confirmation message to the user indicating a successful save.

[1197] Step 2: Collect and send audio data

[1198] Specific details:

[1199] 1. User action: The user clicks the "Start Meeting" button.

[1200] 2. Input: Triggers the "Start Meeting" button clicked by the user.

[1201] 3. Data processing: The device activates the microphone and starts recording audio.

[1202] 4. Output: The recorded audio data is generated.

[1203] 5. Device operation: Streams the recorded audio data to the server in real time.

[1204] 6. Input: Collected audio data.

[1205] 7. Data processing: The device splits the voice data into small chunks and sends them to the server.

[1206] 8. Output: The divided audio data chunks arrive at the server.

[1207] Step 3: Real-time transcription

[1208] Specific details:

[1209] 1. Server operation: Sends the received voice data to the voice recognition API.

[1210] 2. Input: Audio data chunks.

[1211] 3. Data processing: The server converts the voice data into text using the Google Cloud Speech-to-Text API.

[1212] 4. Output: Text data.

[1213] 5. Server operation: The converted text data is linked to the conference ID and saved in the database.

[1214] 6. Input: Text data and meeting ID.

[1215] 7. Data processing: The server converts the text data into an appropriate format and stores it in a database.

[1216] 8. Output: Text data stored in the database.

[1217] Step 4: Analyze and score the speech

[1218] Specific details:

[1219] 1. Server action: Retrieve the transcription data from the database and start analyzing it with the natural language processing model.

[1220] 2. Input: Transcription data from the meeting.

[1221] 3. Data processing: The server uses an NLP model (such as OpenAI GPT-3) to analyze the text data and extract the number of comments, the duration of each comment, and the content of each comment.

[1222] 4. Output: Analysis results include the number of statements made by each speaker, the duration of the statements, and the content of the statements.

[1223] 5. Server action: Score each participant's contribution based on the extracted data.

[1224] 6. Input: Analysis results.

[1225] 7. Data processing: The server uses a scoring algorithm to quantify the contribution.

[1226] 8. Output: Scored data.

[1227] Step 5: View scores and receive feedback

[1228] Specific details:

[1229] 1. Server action: Convert the scored data into graphs and charts using a visualization library.

[1230] 2. Input: Scored data.

[1231] 3. Data processing: The server visualizes the data using D3.js, Chart.js, etc.

[1232] 4. Output: Visualized data (graphs and charts).

[1233] 5. Server Actions: Provide feedback to the user and generate improvement suggestions for the next meeting.

[1234] 6. Input: Visualization data and analysis results.

[1235] 7. Data processing: The server uses the AI ​​model to generate improvement suggestions for the next meeting.

[1236] 8. Output: Feedback message and improvement suggestions to the user.

[1237] This concludes the detailed explanation of each processing step. Through these steps, productivity and efficient management of meetings are achieved.

[1238] (Application example 1)

[1239] 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."

[1240] Conventional conferencing systems often only transcribe audio data and analyze the number and duration of participants' speech, and do not provide feedback that directly translates into improved productivity or work performance in the actual workplace. This has led to a demand for effective methods that lead to specific improvement measures and next action plans in factories and workplaces. Furthermore, improvement suggestions based on comparisons with past meeting data are insufficient, making them less convenient for continuously improving productivity.

[1241] 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.

[1242] In this invention, the server includes a means for setting meeting goals in advance, a means for collecting voice data during the meeting and transcribing it in real time, a means for analyzing the transcribed data and scoring the contribution level based on the number of comments, duration of comments, and content of comments made by each participant, a means for displaying a list of the scored data and providing feedback on the effectiveness of the meeting and the suitability of the participants, and a means for evaluating the work performance of on-site workers and automated machines based on the data collected in real time. This makes it possible to accurately evaluate the degree of achievement of the meeting's goals and the contribution levels of participants, as well as to make specific work improvement proposals.

[1243] "Meeting objectives" are specific items or purposes to be achieved that are set before the meeting begins.

[1244] "Audio data" refers to data recorded in audio format of participants' remarks and discussions collected during a meeting.

[1245] "Real-time" refers to the fact that data collection and processing are immediate, with results being fed back almost instantly.

[1246] "Transcription" is the process of converting collected audio data into text data.

[1247] "Participants" are people who attend a meeting and take part in speaking and discussions.

[1248] The "number of statements" refers to the number of times each participant made a statement during the conference.

[1249] "Speaking time" is the cumulative time each participant has spoken during the conference.

[1250] "Content of remarks" refers to the specific content and topics of remarks made by each participant during the meeting.

[1251] "Scoring" refers to making a quantitative evaluation based on analyzed data and expressing it as a number.

[1252] "Work performance" is an indicator of the efficiency and effectiveness of work performed by workers and automated machines.

[1253] "Feedback" refers to improvement suggestions and evaluation information provided based on the analyzed results.

[1254] "Field workers" are workers who actually perform work in factories or on-site.

[1255] An "automatic machine" is a machine or robot that performs tasks automatically in a factory or on a work site.

[1256] A "database" is a system for storing and managing past meeting data and work performance data.

[1257] Comparison is the process of comparing data sets from different points in time to identify differences and areas for improvement.

[1258] "Improvement proposals" are specific proposals and measures for the next meeting or work that are provided based on the analysis results.

[1259] "Past meeting data" refers to information and recorded data relating to meetings that have taken place in the past.

[1260] MODE FOR CARRYING OUT THE INVENTION

[1261] This invention is an improvement conference system aimed at improving productivity within factories, and it sets meeting goals, collects and transcribes real-time audio, analyzes work performance, and provides feedback. This system is realized through the cooperation of user terminals, a server, on-site workers, and automated machines.

[1262] System configuration

[1263] 1. User Device

[1264] Setting meeting goals: Before the meeting, the manager logs in to the system and sets the meeting goals, such as "confirming new work procedures and proposing improvements."

[1265] Audio data collection: During the meeting, the device collects audio data in real time and sends it to the server. The hardware used is a high-performance microphone (e.g., Blue Yeti USB Microphone).

[1266] Real-time transcription: Audio data collected during the meeting is sent to a server and transcribed in real time.

[1267] 2. Server

[1268] Analysis of the transcribed data: The server analyzes the transcribed data and scores each participant's contribution based on the number of comments, the duration of their comments, and the content of their comments.

[1269] Work performance evaluation: The server evaluates the work performance of field workers and automated machines based on the data collected in real time, using the Python programming language and necessary libraries (e.g., speech_recognition, matplotlib).

[1270] Providing feedback: The server visualizes the scored data and provides feedback to the user's device, which then provides suggestions for improving the next meeting.

[1271] 3. Field workers and automated machinery

[1272] Data collection: Operation logs and performance data of field workers and automated machines are stored in a database.

[1273] Improved work performance: Based on feedback from the server, field workers and managers can implement specific work improvement measures.

[1274] Add specific examples to the description

[1275] Examples:

[1276] To improve the efficiency of a new production line, a site manager sets the goal of "confirming new work procedures and proposing improvements." During the meeting, a device collects voice data, which is transcribed and analyzed in real time on a server. For example, if Worker A speaks 200 words and makes specific suggestions for improvement, that will be evaluated as a high contribution and included in the feedback at the next meeting.

[1277] Example prompt sentence:

[1278] I would like to define the requirements for a system that sets meeting goals, transcribes meeting audio in real time, and analyzes and scores what participants say. Please design a system that includes specific goal setting, real-time transcription, scoring of contribution based on number of comments and time spent, and visualization processes.

[1279] As a result, the present invention aims to improve the productivity of meetings and optimize work performance in the field.

[1280] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1281] Step 1:

[1282] Setting meeting goals

[1283] Input: The user (administrator) inputs the meeting goal and sends it to the system.

[1284] Specific operation: A form is displayed on the user's device, and the user is asked to enter the title and goal of the meeting. When the user presses the "Confirm" button, the data is sent to the server and saved in the database.

[1285] Output: Meeting goal data stored on the server.

[1286] Step 2:

[1287] Audio data collection

[1288] Input: Audio data collected during the meeting.

[1289] Specific operation: The device (e.g., PC or tablet) uses a high-performance microphone to collect audio during the meeting and transmits it to the server in real time.

[1290] Output: Real-time audio data sent to the server.

[1291] Step 3:

[1292] Real-time transcription

[1293] Input: The audio data sent to the server.

[1294] What happens: The server transcribes the received audio data in real time, specifically by converting it into text using speech recognition software (e.g., the speech_recognition library).

[1295] Output: Transcription data generated in real time.

[1296] Step 4:

[1297] Analysis and scoring of speech data

[1298] Input: Transcription data generated in real time.

[1299] Specific operation: The server analyzes the transcription data and extracts the number of comments, duration, and content of each participant's comments. Based on this data, each participant's contribution is scored. Analysis is performed using NLP technology and statistical methods.

[1300] Output: Analyzed data and score data for each participant.

[1301] Step 5:

[1302] Work performance evaluation

[1303] Input: Real-time monitoring data and historical work log data.

[1304] How it works: The server analyzes real-time and historical work data to evaluate the work performance of on-site workers and automated machines, using statistical methods to calculate efficiency and productivity indicators.

[1305] Output: Work performance evaluation report.

[1306] Step 6:

[1307] Providing feedback

[1308] Input: Analyzed data and score data, work performance evaluation report.

[1309] Specific operation: The server visualizes the scored data and work performance evaluation reports and provides feedback to the user's device. The feedback is displayed in the form of graphs and reports, and includes suggestions for next improvement.

[1310] Output: A feedback report provided to the user and suggestions for improvement for the next meeting.

[1311] Through these steps, the system helps improve productivity and optimize work performance during and after meetings.

[1312] 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.

[1313] This invention is a system that supports the improvement of productivity and efficient management of meetings, and in particular, combines an emotion engine that recognizes user emotions. This system not only sets meeting goals, transcribes speech in real time, analyzes speech content, and provides feedback, but also recognizes and monitors emotions using the emotion engine.

[1314] System configuration

[1315] 1. User Device

[1316] Provides an interface for users to input and save meeting goals.

[1317] It has the function of collecting voice data and emotional data during a meeting and sending it to a server in real time.

[1318] 2. Server

[1319] Transcription is performed based on the received audio data.

[1320] The transcription data is analyzed and each participant's number of comments, speaking time, and content of comments are scored as a contribution.

[1321] The emotional data recognized by the emotion engine is analyzed to monitor the atmosphere during the meeting and the stress level of each participant.

[1322] The scored and emotional data are visualized to provide feedback to the user.

[1323] Program processing

[1324] Setting meeting goals

[1325] User Action:

[1326] The user logs in to the system using a terminal and accesses the new meeting page. They enter the title and objective of the meeting and set a goal such as "Decide on the task allocation for the new project." Once the input is complete, they press the confirm button to send the meeting goal to the server.

[1327] Natural language description:

[1328] A user logs into the system, enters the title and goal of the meeting, and sends it to the server.

[1329] Collecting voice and emotion data

[1330] The device:

[1331] When a conference starts, the user presses the conference start button on the device. The device starts recording audio and transmits the audio data to the server in real time. At the same time, the device's built-in camera and sensors are used to collect emotional data from the user's facial expressions and tone of voice, which is also transmitted to the server in real time.

[1332] Natural language description:

[1333] The device records the audio during the meeting in real time and transmits the data along with the user's emotional data to the server.

[1334] Analysis and scoring of speech content

[1335] The server:

[1336] The server transcribes the received audio data in real time, analyzes the transcript, counts the amount of speech, speaking time, and content of each speaker, and assigns a score to their contribution.

[1337] Natural language description:

[1338] The server transcribes the received audio data and analyzes the amount and duration of each speaker's speech to score their contribution.

[1339] Emotional data analysis and monitoring

[1340] The server:

[1341] The server uses an emotion engine to analyze the received emotion data, monitor the atmosphere during the meeting and the stress level of each participant, and evaluate the atmosphere and stress level of the meeting based on this.

[1342] Natural language description:

[1343] The server uses an emotion engine to analyze the received emotion data and monitor the atmosphere of the meeting and the stress levels of the participants.

[1344] Displaying scores and emotional data at a glance and providing feedback

[1345] The server:

[1346] The server visually organizes and displays the score data and emotional data. This allows users to easily check the effectiveness of the meeting, the contribution of each participant, and their emotional state. It also makes suggestions for improving the selection of participants for the next meeting and how the meeting will proceed.

[1347] Natural language description:

[1348] The server visualizes the scores and emotion data so that users can easily check them, and also provides feedback on suggestions for improvement for the next meeting.

[1349] Specific examples

[1350] 1. Example of setting meeting goals:

[1351] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[1352] 2. Example of collecting voice and emotion data:

[1353] When a meeting starts, the device records audio and sends the data to the server in real time. At the same time, it analyzes facial expressions and tone of voice to send emotional data to the server.

[1354] 3. Example of analysis and scoring of speech content:

[1355] The server analyzes the transcription data and generates a score such as "Mr. Tanaka spoke 200 words, making a great contribution to progress" or "Mr. Sato spoke 50 words, making a low contribution."

[1356] 4. Emotion data analysis and monitoring example:

[1357] The server uses an emotion engine to make evaluations such as "the stress level of all participants decreases at the start of the meeting" and "the stress level of a certain participant increases during the meeting."

[1358] 5. Example of score and emotion data list display and feedback:

[1359] The server displays a list of scores and emotional data, and provides feedback to the user, such as "Tanaka's participation is valid, Sato's participation is unnecessary," and "Be careful of rising stress levels during the meeting."

[1360] In this way, the present invention provides a concrete means for realizing efficient conference management that takes into consideration both the productivity of the conference and the emotional state of the participants.

[1361] The processing flow will be explained below.

[1362] Step 1:

[1363] User actions

[1364] The user logs in to the system using a terminal and accesses the new meeting page. There, they input the title and objective of the meeting, setting a goal such as "Decide on the allocation of tasks for a new project," and press the confirm button to send it to the server.

[1365] Step 2:

[1366] Processing performed by the server

[1367] The server stores the received meeting goals in a database, generating a unique ID for the meeting and linking the goal information to that ID so that the meeting can be identified and tracked.

[1368] Step 3:

[1369] Processing performed by the device

[1370] When a conference call begins, the user presses the conference start button on the device. The device starts recording audio and begins sending the audio data to the server in real time. At the same time, the device's built-in camera and sensors are used to capture facial expressions and tone of voice, and emotional data is also sent to the server in real time.

[1371] Step 4:

[1372] Processing performed by the server

[1373] The server transcribes the received audio data in real time. The transcribed text data is linked to a unique conference ID and stored in a database. Emotion data is also linked and stored in the same way.

[1374] Step 5:

[1375] Processing performed by the server

[1376] The server analyzes the transcript data, identifies each speaker, counts the amount of speech, speaking time, and content, and scores each participant's contribution. It also analyzes emotional data to evaluate the atmosphere during the meeting and the stress levels of participants.

[1377] Step 6:

[1378] Processing performed by the server

[1379] Progress toward the goal is evaluated, and emotional data is collected and stored in a database along with the scored data. An algorithm is applied to evaluate the effectiveness of the meeting based on the progress.

[1380] Step 7:

[1381] Processing performed by the server

[1382] It visually organizes the scored and emotional data and generates feedback reports that include each speaker's contribution, meeting progress, and emotional state.

[1383] Step 8:

[1384] Processing performed by the server

[1385] The generated feedback report is sent to the user's device, and the user can use this data to consider improvements for the next meeting.

[1386] Step 9:

[1387] User actions

[1388] Users can review the feedback report on their devices and evaluate the effectiveness of the meeting, the contributions of each participant, and their emotional state. Based on this evaluation, the system can improve the selection of participants and the way the meeting will proceed for the next meeting.

[1389] In this way, the present invention not only improves the productivity of a meeting, but also realizes efficient meeting management that takes into account the emotional states of the participants.

[1390] Example 2

[1391] 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."

[1392] In today's business environment, the productivity and efficiency of meetings are being questioned. However, many meetings are unable to effectively evaluate participants' emotions or real-time contributions, and do not provide appropriate feedback. This can lead to inefficient meetings and a decline in participants' motivation. Furthermore, meetings held in high-stress environments can increase participants' psychological burden. To address these issues, a system combining the collection and analysis of real-time emotional data is needed.

[1393] 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 setting a goal of the conference in advance, a means for collecting voice data and emotional data during the conference and transcribing them in real time, a means for analyzing the transcribed data and scoring the contribution level based on the number of comments, the duration of comments, and the content of comments made by each participant, a means for analyzing the emotional data and monitoring the emotional state and atmosphere of the participants during the conference, and a means for displaying a list of scored data and emotional data to provide feedback on the effectiveness of the conference and the suitability of the participants. This makes it possible to improve the productivity of the conference and reduce the psychological burden on the participants.

[1394] "Meeting objectives" refers to the purpose of the meeting and the specific goals to be achieved.

[1395] "Audio data" refers to data that is a digital recording of audio information spoken during a conference.

[1396] "Emotional data" refers to data that indicates the emotional state and psychological reactions of participants during a meeting, and is obtained from facial expressions, tone of voice, physical movements, etc.

[1397] "Real-time transcription" refers to the process of instantly converting spoken audio data into text data.

[1398] "Transcription data" refers to data that has been converted from audio into text.

[1399] "Number of remarks" refers to the number of remarks made by each participant during the meeting.

[1400] "Speaking time" refers to the total amount of time each participant spoke during a meeting.

[1401] "Speech content" refers to the specific themes or topics that each participant spoke about during the meeting.

[1402] "Contribution score" refers to a numerical value that indicates each participant's contribution to the conference, evaluated based on the number of comments, speaking time, and content of comments.

[1403] "Scored data" refers to data that has been calculated and organized as a contribution score.

[1404] An "emotion engine" is a system that analyzes emotional data and uses that data to evaluate and monitor the emotional state and atmosphere of participants.

[1405] "Monitoring" refers to the process of observing, recording, and analyzing the situation during a meeting in real time.

[1406] "List display" refers to visually organizing the analysis results and displaying them in a format that can be checked at a glance.

[1407] "Feedback" refers to information and suggestions generated based on analyzed data, and refers to guidance for improving the progress of the meeting and the behavior of participants.

[1408] MODE FOR CARRYING OUT THE INVENTION

[1409] This invention is a system that supports the improvement of productivity and efficient management of meetings, and in particular, combines an emotion engine that recognizes user emotions. This system not only sets meeting goals, transcribes speech in real time, analyzes speech content, and provides feedback, but also recognizes and monitors emotions using the emotion engine.

[1410] System configuration:

[1411] 1. User Device

[1412] Provides an interface for users to input and save meeting goals.

[1413] It has the function of collecting voice data and emotional data during a meeting and sending it to a server in real time.

[1414] 2. Server

[1415] Transcription is performed based on the received audio data.

[1416] The transcription data is analyzed and each participant's number of comments, speaking time, and content of comments are scored as a contribution.

[1417] The emotional data recognized by the emotion engine is analyzed to monitor the atmosphere during the meeting and the stress level of each participant.

[1418] The scored and emotional data are visualized to provide feedback to the user.

[1419] Program processing:

[1420] Setting meeting goals

[1421] User Action:

[1422] The user logs in to the system using a terminal and accesses the new meeting page. They enter the title and objective of the meeting and set a goal such as "Decide on the task allocation for the new project." Once the input is complete, they press the confirm button to send the meeting goal to the server.

[1423] The server:

[1424] The server saves the meeting title and goal sent by the user in the database, and if the save is successful, gives feedback to the user.

[1425] Collecting voice and emotion data

[1426] User Action:

[1427] At the start of a conference, the user presses a conference start button on the terminal.

[1428] The device:

[1429] The device starts recording and sends the audio data to the server in real time. The device also uses cameras and sensors to detect the user's facial expressions and tone of voice, generating emotion data. The generated emotion data is also sent to the server in real time.

[1430] Analysis and scoring of speech content

[1431] The server:

[1432] The server converts the received audio data into text using transcription software (e.g., Google Speech-to-Text API). This text data is analyzed to count the amount of speech and the duration of speech for each speaker. The usefulness and contribution of the speech content are then analyzed to calculate the speaker's score.

[1433] Emotional data analysis and monitoring

[1434] The server:

[1435] The server analyzes the received emotional data using an emotion analysis engine (e.g., Microsoft Azure Emotion API) to evaluate the atmosphere during the meeting and the stress levels of the participants. Based on the analysis results, the atmosphere of the meeting and the stress levels of each participant are monitored in real time.

[1436] Displaying scores and emotional data at a glance and providing feedback

[1437] The server:

[1438] The server uses visualization tools such as dashboards (e.g., Tableau) to format the score data and emotion data and display them in real time. After the meeting, users can access the dashboard to see at a glance each participant's contribution to the discussion, the atmosphere during the meeting, and their stress level. It also displays suggestions for improvement for the next meeting and feedback on participant selection.

[1439] Examples:

[1440] 1. Example of setting meeting goals:

[1441] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[1442] 2. Example of collecting voice and emotion data:

[1443] When a meeting starts, the device records audio and sends the data to the server in real time. At the same time, it analyzes facial expressions and tone of voice to send emotional data to the server.

[1444] 3. Example of analysis and scoring of speech content:

[1445] The server analyzes the transcription data and generates a score such as "Participant A spoke 200 words and contributed greatly to the progress" or "Participant B spoke 50 words and contributed less."

[1446] 4. Emotion data analysis and monitoring example:

[1447] The server uses an emotion engine to make evaluations such as "the stress level of all participants decreases at the start of the meeting" and "the stress level of a certain participant increases during the meeting."

[1448] 5. Example of score and emotion data list display and feedback:

[1449] The server displays a list of scores and emotional data and provides feedback to the user, such as "Participant A's participation is valid, but Participant B's participation is unnecessary," and "Be careful of rising stress levels during the meeting."

[1450] In this way, the present invention provides a concrete means for realizing efficient conference management that takes into consideration both the productivity of the conference and the emotional state of the participants.

[1451] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1452] Step 1: Set meeting goals

[1453] Input: The meeting title and goal entered by the user on the meeting goal setting screen

[1454] Specific behavior:

[1455] 1. A user logs into the system using a terminal.

[1456] 2. The user visits the new meeting page.

[1457] 3. The user enters the meeting title (e.g., "Project Kickoff") and goal (e.g., "Determine key task assignments").

[1458] 4. The user presses the confirm button to send the information to the server.

[1459] Data processing / calculation: The server verifies the received conference title and objectives, and stores them in the database if there are no errors.

[1460] Output: If the save is successful, the server will provide feedback to the user.

[1461] Step 2: Collect speech and emotion data at the start of the meeting

[1462] Input: User presses start conference button

[1463] Specific behavior:

[1464] 1. At the start of a conference, the user presses the conference start button on the terminal.

[1465] 2. The device starts the recording function.

[1466] 3. The device uses cameras and sensors to detect the user's facial expressions and tone of voice.

[1467] Data processing / calculation: The terminal compresses and packets the recorded voice data in real time, and simultaneously records facial expressions and tone of voice data as emotion data.

[1468] Output: Compressed audio data and emotion data are sent to the server in real time.

[1469] Step 3: Transcribe and analyze the audio data

[1470] Input: Audio data received by the server

[1471] Specific behavior:

[1472] 1. The server converts the received voice data into text using transcription software (e.g., Google Speech-to-Text API).

[1473] 2. The server analyzes the text data and counts the amount of speech and the duration of speech for each speaker.

[1474] Data processing / calculation: The server extracts specific keywords and phrases from the text data and scores the usefulness of the comments based on that.

[1475] Output: The scoring results for each participant's speech volume, speech time, and content are output.

[1476] Step 4: Analyze and evaluate emotion data

[1477] Input: Emotion data received by the server

[1478] Specific behavior:

[1479] 1. The server analyzes the received emotion data using an emotion analysis engine (e.g., Microsoft Azure Emotion API).

[1480] 2. Based on the analysis results, the server evaluates and monitors the atmosphere during the meeting and the stress level of each participant in real time.

[1481] Data processing / calculation: The results of emotion data analysis are quantified and combined with related time series data.

[1482] Output: The measured mood score and stress level evaluation results are output.

[1483] Step 5: Integrating and visualizing scores and sentiment data

[1484] Input: Analysis results of speech score data and emotion data

[1485] Specific behavior:

[1486] 1. The server aggregates the score data and emotion data and formats it using a visualization tool such as a dashboard (e.g., Tableau).

[1487] 2. The visualized data is displayed in a user-viewable format.

[1488] Data processing / calculation: Link score data and emotion data and arrange the overall display format.

[1489] Output: A visual dashboard is provided to the user, visualizing each participant's contribution, emotional state, and meeting effectiveness, as well as providing feedback on improvement suggestions for the next meeting.

[1490] In this way, by combining analysis of speech content and emotional state monitoring, the present invention improves meeting productivity and properly evaluates participants.

[1491] (Application example 2)

[1492] 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."

[1493] Conventional conference systems only record the content and duration of comments made without considering the emotions or mental state of participants. This has led to problems such as reduced productivity and stress accumulation. While improvements in production efficiency and communication within factories are also required, conventional systems have made it difficult to comprehensively improve these. The objective of this invention is to improve productivity and reduce stress by analyzing the progress of conferences and work meetings in real time, monitoring the emotional state of participants, and providing feedback.

[1494] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for setting a conference goal in advance; means for collecting voice data during the conference and transcribing it in real time; means for analyzing the transcribed data and scoring the contribution level based on the number of comments, duration, and content of each participant's comments; means for displaying a list of the scored data and providing feedback on the productivity of the conference and the contribution levels of the participants; means for collecting emotional data from users' facial expressions and tone of voice; and means for analyzing the collected emotional data and monitoring the emotional states and stress levels of the participants. This makes it possible to comprehensively grasp the progress of a conference or work meeting and analyze and display the contribution level and emotional state of each participant in real time, thereby improving productivity and reducing stress.

[1495] "Means for setting meeting objectives in advance" is a function for defining specific objectives and goals for meetings and work meetings in advance and managing them within the system.

[1496] "Means for collecting audio data during meetings and transcribing it in real time" refers to a technology that records participants' speech using a microphone or other device and instantly converts the recorded data into text format.

[1497] "A means of analyzing transcribed data and scoring contribution based on the number of comments, speaking time, and content of each participant" is a function that analyzes transcribed text data and evaluates and quantifies the content, frequency, and duration of each participant's comments.

[1498] "A means of displaying scored data in a list and providing feedback on meeting productivity and participant contributions" is a function that visually displays the analyzed contribution scores and provides feedback on the efficiency of the overall meeting and the contributions of each participant.

[1499] "Means for collecting emotional data from a user's facial expressions and tone of voice" refers to technology that uses a camera or microphone to analyze a user's facial expressions and tone of voice, and collects their emotional state as data.

[1500] "Means for analyzing collected emotional data and monitoring participants' emotional states and stress levels" refers to a function that evaluates each participant's mental state based on collected emotional data and monitors fluctuations in real time.

[1501] "A means of monitoring fluctuations in mental state based on emotional data during work meetings and making suggestions to reduce tension and stress" is a function that analyzes emotional data during meetings, grasps fluctuations in participants' tension and stress levels, and suggests improvement measures accordingly.

[1502] "Means for assessing long-term mental health status based on emotional data and proposing improvements" refers to a technology that analyzes accumulated emotional data, assesses participants' long-term mental health status, and proposes specific improvement measures.

[1503] This invention is a system that supports the improvement of productivity and efficient management of work meetings using factory robots, and in particular, it combines an emotion engine that recognizes the user's emotions. This system consists of the following elements:

[1504] System configuration

[1505] 1. User Device

[1506] Provides an interface for users to enter and save work meeting goals.

[1507] It has the ability to collect voice and emotional data during meetings and send it to a server in real time.

[1508] 2. Server

[1509] Transcription is performed based on the received audio data.

[1510] The transcription data is analyzed, and each participant's contribution is scored based on the number of comments, the duration of their comments, and the content of their comments.

[1511] The emotional data recognized by the emotion engine is analyzed to monitor the atmosphere during the meeting and the stress level of each participant.

[1512] The scored and emotional data are visualized to provide feedback to the user.

[1513] Program processing

[1514] Setting meeting goals

[1515] The user logs in to the system using a terminal and accesses the new meeting page. They enter the meeting title and objective, and set a goal such as "Decide on the task allocation for a new project." Once the input is complete, they press the confirm button to send the meeting objective to the server.

[1516] Collecting voice and emotion data

[1517] When a meeting starts, the user presses the start meeting button on the device. The device starts recording audio and sends the audio data to the server in real time. At the same time, the device's built-in camera and sensors are used to collect emotional data from the user's facial expressions and tone of voice, which is also sent to the server in real time.

[1518] Analysis and scoring of speech content

[1519] The server transcribes the received audio data in real time, analyzes the transcript, counts the amount of speech, speaking time, and content of each speaker, and assigns a score to their contribution.

[1520] Emotional data analysis and monitoring

[1521] The server uses an emotion engine to analyze the received emotion data, monitor the atmosphere during the meeting and the stress level of each participant, and evaluate the atmosphere and stress level of the meeting based on this.

[1522] Displaying scores and emotional data at a glance and providing feedback

[1523] The server visually organizes and displays the score data and emotional data, allowing users to easily check the effectiveness of the meeting, the contribution of each participant, and their emotional state. It also makes suggestions for improving the selection of participants for the next meeting and how the meeting will proceed.

[1524] Hardware and software used

[1525] Hardware: Smart helmet (audio microphone, camera)

[1526] Software: Python, speech_recognition, emotion_recognition

[1527] Specific examples

[1528] 1. Example of setting meeting goals

[1529] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[1530] 2. Example of collecting voice data and emotion data

[1531] When a meeting starts, the device records audio and sends the data to the server in real time. At the same time, it analyzes facial expressions and tone of voice to send emotional data to the server.

[1532] 3. Analysis of speech content and scoring example

[1533] The server analyzes the transcription data and generates a score such as "Participant A spoke 200 words and contributed greatly to the progress" or "Participant B spoke 50 words and contributed less."

[1534] 4. Analysis and Monitoring of Emotion Data

[1535] The server uses an emotion engine to make evaluations such as "the stress level of all participants decreases at the start of the meeting" or "the stress level of a certain participant increases during the meeting."

[1536] 5. Example of score and emotion data display and feedback

[1537] The server displays a list of scores and emotional data and provides feedback to the user, such as "Participant A's participation is valid, but participant B's participation is unnecessary" and "Be careful of rising stress levels during the meeting."

[1538] Prompt Sentence Examples

[1539] "Transcribe audio data from project meetings in real time and analyze the conversations. Also, use video data to recognize participants' emotions and monitor their stress levels and tension. Based on stored goals, provide feedback and suggestions for improvement after the meeting."

[1540] With the above configuration, the present invention realizes efficient management of conferences and work meetings, taking into consideration both the productivity of the conferences and work meetings and the emotional state of the participants.

[1541] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1542] Step 1:

[1543] A user logs in to the system using a terminal and accesses the new meeting page. There, they input the meeting title and goal, and set a specific goal such as "Decide the task allocation for a new project." Once the input is complete, they press the confirm button to send the meeting goal to the server. The input is the meeting title and goal, and the output is the meeting goal data sent to the server.

[1544] Step 2:

[1545] When a meeting starts, the user presses the start meeting button on the device, which causes the device to start recording audio. The device collects the user's audio data in real time and sends it to the server. At the same time, the device uses cameras and sensors to collect emotional data from the user's facial expressions and tone of voice, which is also sent to the server in real time. The input is audio data and emotional data, and the output is the collected data sent to the server.

[1546] Step 3:

[1547] The server transcribes the received audio data in real time. The software used is the Python speech_recognition library. The audio data is converted into text format and saved as transcription data. The input is audio data, and the output is transcribed text data.

[1548] Step 4:

[1549] The server analyzes the transcription data. Specifically, it counts the amount of speech, speech duration, and speech content of each participant, and then scores their contribution based on these. The analyzed data is then stored in a database and used for later feedback and contribution evaluation. The input is the transcription data, and the output is a contribution score.

[1550] Step 5:

[1551] The server uses an emotion engine to analyze the received emotion data. The software used is the Python emotion_recognition library. The server evaluates the user's emotional state and stress level from their facial expressions and tone of voice, and monitors the atmosphere during the meeting and the mental state of each participant. The input is emotion data, and the output is the analysis results, such as emotional state and stress level data.

[1552] Step 6:

[1553] The server displays a list of the scored data and emotional data, providing visual feedback to the user. This allows the user to easily check the effectiveness of the meeting, the contribution of each participant, and their emotional state. The server also uses this data to select participants for the next meeting and make suggestions for improving how the meeting is conducted. The input is the scored data and emotional data, and the output is displayed feedback and suggestions for improvement.

[1554] This completes a series of processes, improving production efficiency and communication within the factory.

[1555] 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.

[1556] 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.

[1557] 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.

[1558] [Fourth embodiment]

[1559] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1560] 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.

[1561] 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).

[1562] 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.

[1563] 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.

[1564] 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).

[1565] 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.

[1566] 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.

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] 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."

[1572] The present invention is a system aimed at improving meeting productivity, and has the functions of setting meeting goals, transcribing in real time, analyzing speech content, and providing feedback.

[1573] System configuration

[1574] 1. User Device

[1575] Provides an interface for users to input and save meeting goals.

[1576] It has the function of collecting audio data during a meeting and sending it to a server in real time.

[1577] 2. Server

[1578] Transcription is performed based on the received audio data.

[1579] The transcription data is analyzed and each participant's number of comments, speaking time, and content of comments are scored as a contribution.

[1580] The scored data is visualized and feedback is provided to the user.

[1581] Program processing

[1582] Setting meeting goals

[1583] User Action:

[1584] A user logs in to the system using a terminal and accesses the new meeting page, where they enter the title and purpose of the meeting and press the confirm button, which sends the goal of the meeting to the server and records it.

[1585] Natural language description:

[1586] A user logs into the system, enters the title and goal of the meeting, and sends it to the server.

[1587] Audio data collection and transcription

[1588] The device:

[1589] When a meeting starts, the device starts recording audio and sends the audio data in real time to the server, which then transcribes the received audio data in real time and saves it as text data.

[1590] Natural language description:

[1591] The device records the audio during the meeting in real time and sends the audio data to the server, which then transcribes the audio data and converts it into text data.

[1592] Analysis and scoring of speech content

[1593] The server:

[1594] The server analyzes the transcribed data, identifies the speakers, and assigns a score to each participant based on the number of comments, the duration of their comments, and the content of their comments. This also evaluates the progress toward the meeting's goals.

[1595] Natural language description:

[1596] The server analyzes the transcripts, counts each participant's contributions, and then assigns a score based on the results, as well as assessing progress toward the meeting's goals.

[1597] Score display and feedback

[1598] The server:

[1599] The server visually organizes and displays the scored data in a list, allowing users to easily check the effectiveness of the meeting and the contribution of each participant. If necessary, it provides feedback on the selection of participants for the next meeting and suggestions for improving the meeting method.

[1600] Natural language description:

[1601] The server visually organizes the scores so that users can easily check them, and also provides feedback to users on suggestions for improvement for the next meeting.

[1602] Specific examples

[1603] 1. Example of setting meeting goals:

[1604] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[1605] 2. Audio data collection and transcription example:

[1606] When a meeting starts, the device records audio and transmits the data in real time to a server, which transcribes the audio, converts it into text, and stores it.

[1607] 3. Example of analysis and scoring of speech content:

[1608] The server analyzes the transcription data and generates a score such as "Mr. Tanaka spoke 200 words, making a significant contribution to progress" or "Mr. Sato spoke 50 words, making a small contribution."

[1609] 4. Example of score display and feedback:

[1610] The server displays the scores in a list and provides feedback to the user, such as "Tanaka's participation is valid, Sato's participation is unnecessary." This allows the user to improve the selection of participants and the progress of the next meeting.

[1611] As described above, the present invention is a system that provides specific means for improving the productivity of meetings and supports users in managing meetings efficiently.

[1612] The processing flow will be explained below.

[1613] Step 1:

[1614] User actions

[1615] A user logs in to the system using a terminal. They access the new meeting page and enter the title and goal of the meeting. For example, they might enter "Decide on the task allocation for a new project." Once they have completed the entry, they press the confirm button to send the meeting goal to the server.

[1616] Step 2:

[1617] Processing performed by the server

[1618] The server stores the received meeting goals in a database, generating a unique ID for the meeting and linking the goal information to that ID.

[1619] Step 3:

[1620] Processing performed by the device

[1621] When a conference is started, the user presses the conference start button on the terminal, and the terminal starts recording audio and transmitting the audio data to the server in real time.

[1622] Step 4:

[1623] Processing performed by the server

[1624] The server transcribes the received audio data in real time, and the transcribed text data is linked to a unique conference ID and stored in a database.

[1625] Step 5:

[1626] Processing performed by the server

[1627] The server analyzes the transcripts and starts counting the amount of speech, the duration of speech, and the content of speech from each speaker. Based on this data, it runs an algorithm to score each participant's contribution.

[1628] Step 6:

[1629] Processing performed by the server

[1630] The progress towards the goal is analyzed and the extent to which the meeting objectives have been achieved is evaluated. This progress is also scored and stored in the database.

[1631] Step 7:

[1632] Processing performed by the server

[1633] Visually organize the scored data and generate a feedback report that includes each participant's contribution and the meeting progress.

[1634] Step 8:

[1635] Processing performed by the server

[1636] The generated feedback report is sent to the user's device, allowing the user to consider improvements for the next meeting based on this data.

[1637] Step 9:

[1638] User actions

[1639] Users can check the feedback report on their devices and evaluate the effectiveness of the meeting and the contribution of each participant. Based on this evaluation, the system can improve the selection of participants and the progress of the next meeting.

[1640] In this way, the present invention provides a concrete means for improving the productivity of a meeting, and supports users in managing a meeting efficiently.

[1641] Example 1

[1642] 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."

[1643] In modern companies and organizations, meetings are a forum for important decision-making and information sharing, but low productivity is a major problem. Participants' opinions are often not properly reflected, progress is delayed, and meetings often end without reaching their goals. Another problem is when certain participants speak excessively, burying the opinions of others. In such situations, it becomes difficult to achieve the purpose of the meeting, which can lead to a loss of efficiency for the entire organization.

[1644] 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.

[1645] In this invention, the server includes a means for the user to set a goal for the meeting, a means for the terminal to collect voice data during the meeting and send it to the server in real time, a means for the server to transcribe the voice data in real time, a means for the server to analyze the transcription data and score the contribution level based on the number of comments, the duration of comments, and the content of comments made by each participant, and a means for visualizing the scored data and providing feedback to the user. This makes it possible to improve the productivity of the meeting, accurately evaluate the contribution level of each participant, and specifically suggest improvements for the next meeting.

[1646] A "user" is an individual or member of an organization who operates the system.

[1647] A "terminal" is an electronic device that is operated by a user and that performs data communication with a server.

[1648] "Meeting objectives" refer to the purpose of the meeting and the items to be achieved, which are set at the beginning of the meeting.

[1649] "Audio data" refers to data collected in digital format from voices spoken during a meeting.

[1650] "Server" refers to a computer system for processing and transcribing received audio data.

[1651] "Transcription" refers to the process of converting audio data into text data.

[1652] "Transcription Data" refers to data in converted text format.

[1653] "Analysis" refers to the process of analyzing the content of data to extract useful information.

[1654] "Number of remarks" refers to the number of remarks made by each participant during the meeting.

[1655] "Speaking time" refers to the total amount of time each participant spoke during the conference.

[1656] "Speech content" refers to the specific content that each participant spoke during the meeting.

[1657] "Contribution" refers to a score that evaluates each participant's usefulness in a meeting.

[1658] "Scoring" refers to the process of expressing evaluation results as quantitative numbers.

[1659] "Visualization" refers to the process of displaying data in the form of graphs, charts, etc., to make it easier to understand.

[1660] "Feedback" refers to providing users with information such as areas for improvement based on analysis and evaluation results.

[1661] "Goal achievement" is a measure of how close the meeting came to achieving its goals.

[1662] "Unnecessary meetings and unnecessary participants" refer to factors that reduce the productivity and efficiency of meetings.

[1663] A "database" refers to a system that manages a collection of systematically organized data.

[1664] "Improvement proposals" refer to suggestions for improving the next meeting based on past data.

[1665] The present invention is a system for improving the productivity of meetings, and has functions for setting meeting goals, transcribing real-time information, analyzing speech content, and providing feedback. Specific embodiments for implementing the present invention will be described below.

[1666] Hardware and Software

[1667] This system mainly uses the following hardware and software:

[1668] 1. User Device

[1669] A computer or smartphone that provides an interface for users to input and save meeting goals.

[1670] Microphone and network connection capabilities to collect audio data during meetings and send it to a server in real time.

[1671] 2. Server

[1672] A computer system for transcribing received audio data.

[1673] A processing unit that analyzes the transcription data and scores each participant's number of comments, speaking time, and content of comments as a contribution.

[1674] Data visualization software (e.g., D3.js, Chart.js) to visualize the scored data and provide feedback to the user.

[1675] 3. External Services

[1676] A speech recognition service to convert voice data into text (e.g., Google Cloud Speech-to-Text API).

[1677] Natural language processing (NLP) models (e.g., OpenAI GPT-3) for analyzing text data.

[1678] Setting meeting goals

[1679] User Action:

[1680] The user logs in to the system using a terminal and accesses the new meeting page, where they enter the title and goal of the meeting and press the confirm button, which sends the goal of the meeting to the server and records it.

[1681] Examples:

[1682] The user opens a browser on their device, accesses the "Create a new meeting" page, enters "Decide task allocation for new project" and presses the "Confirm" button. The server saves this information in a database.

[1683] Audio data collection and transcription

[1684] The device:

[1685] When a meeting starts, the device starts recording audio and sends the audio data in real time to the server, which then transcribes the received audio data in real time and saves it as text data.

[1686] Examples:

[1687] To start a meeting, the user clicks the "Start Meeting" button, which activates the device's microphone and streams audio in real time to the server, which uses the Google Cloud Speech-to-Text API to convert the audio into text and store it in a database.

[1688] Analysis and scoring of speech content

[1689] The server:

[1690] The server analyzes the transcribed data, identifies the speakers, and assigns a score to each participant based on the number of comments, the duration of their comments, and the content of their comments. This also evaluates the progress toward the meeting's goals.

[1691] Examples:

[1692] The server analyzes the text data using an NLP model (e.g., OpenAI GPT-3) and generates a score such as "Tanaka-san spoke 200 words, contributed greatly to progress," or "Sato-san spoke 50 words, contributed less."

[1693] Score display and feedback

[1694] The server:

[1695] The server visually organizes and displays the scored data in a list, allowing users to easily check the effectiveness of the meeting and the contribution of each participant. If necessary, it provides feedback on the selection of participants for the next meeting and suggestions for improving the meeting method.

[1696] Examples:

[1697] The server uses visualization libraries (e.g., D3.js, Chart.js) to display the scores in a graph. Users can see feedback on the dashboard, such as "Tanaka's participation is valid, Sato's participation is unnecessary." Suggestions for improvement for the next meeting, such as "Next time, Tanaka's comments should be reduced a little and Sato's opinions should be drawn out," are also displayed.

[1698] As described above, the present invention is a system that provides specific means for improving the productivity of meetings and supports users in managing meetings efficiently.

[1699] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1700] Step 1: Log in your users and set meeting goals

[1701] Specific details:

[1702] 1. User actions: The user starts a terminal, opens a browser and accesses the system login page.

[1703] 2. Input: The user enters the ID and password to log in to the system.

[1704] 3. Data processing: The server checks the entered authentication information against the database and performs authentication.

[1705] 4. Output: Once authentication is complete, the user is redirected to the "Create New Meeting" page.

[1706] 5. User action: The user creates a new meeting, enters its title and goal, and presses the "Confirm" button.

[1707] 6. Input: User-entered meeting title and purpose.

[1708] 7. Data processing: The server stores the entered conference title and purpose in a database.

[1709] 8. Output: The server displays a confirmation message to the user indicating a successful save.

[1710] Step 2: Collect and send audio data

[1711] Specific details:

[1712] 1. User action: The user clicks the "Start Meeting" button.

[1713] 2. Input: Triggers the "Start Meeting" button clicked by the user.

[1714] 3. Data processing: The device activates the microphone and starts recording audio.

[1715] 4. Output: The recorded audio data is generated.

[1716] 5. Device operation: Streams the recorded audio data to the server in real time.

[1717] 6. Input: Collected audio data.

[1718] 7. Data processing: The device splits the voice data into small chunks and sends them to the server.

[1719] 8. Output: The divided audio data chunks arrive at the server.

[1720] Step 3: Real-time transcription

[1721] Specific details:

[1722] 1. Server operation: Sends the received voice data to the voice recognition API.

[1723] 2. Input: Audio data chunks.

[1724] 3. Data processing: The server converts the voice data into text using the Google Cloud Speech-to-Text API.

[1725] 4. Output: Text data.

[1726] 5. Server operation: The converted text data is linked to the conference ID and saved in the database.

[1727] 6. Input: Text data and meeting ID.

[1728] 7. Data processing: The server converts the text data into an appropriate format and stores it in a database.

[1729] 8. Output: Text data stored in the database.

[1730] Step 4: Analyze and score the speech

[1731] Specific details:

[1732] 1. Server action: Retrieve the transcription data from the database and start analyzing it with the natural language processing model.

[1733] 2. Input: Transcription data from the meeting.

[1734] 3. Data processing: The server uses an NLP model (such as OpenAI GPT-3) to analyze the text data and extract the number of comments, the duration of each comment, and the content of each comment.

[1735] 4. Output: Analysis results include the number of statements made by each speaker, the duration of the statements, and the content of the statements.

[1736] 5. Server action: Score each participant's contribution based on the extracted data.

[1737] 6. Input: Analysis results.

[1738] 7. Data processing: The server uses a scoring algorithm to quantify the contribution.

[1739] 8. Output: Scored data.

[1740] Step 5: View scores and receive feedback

[1741] Specific details:

[1742] 1. Server action: Convert the scored data into graphs and charts using a visualization library.

[1743] 2. Input: Scored data.

[1744] 3. Data processing: The server visualizes the data using D3.js, Chart.js, etc.

[1745] 4. Output: Visualized data (graphs and charts).

[1746] 5. Server Actions: Provide feedback to the user and generate improvement suggestions for the next meeting.

[1747] 6. Input: Visualization data and analysis results.

[1748] 7. Data processing: The server uses the AI ​​model to generate improvement suggestions for the next meeting.

[1749] 8. Output: Feedback message and improvement suggestions to the user.

[1750] This concludes the detailed explanation of each processing step. Through these steps, productivity and efficient management of meetings are achieved.

[1751] (Application example 1)

[1752] 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."

[1753] Conventional conferencing systems often only transcribe audio data and analyze the number and duration of participants' speech, and do not provide feedback that directly translates into improved productivity or work performance in the actual workplace. This has led to a demand for effective methods that lead to specific improvement measures and next action plans in factories and workplaces. Furthermore, improvement suggestions based on comparisons with past meeting data are insufficient, making them less convenient for continuously improving productivity.

[1754] 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.

[1755] In this invention, the server includes a means for setting meeting goals in advance, a means for collecting voice data during the meeting and transcribing it in real time, a means for analyzing the transcribed data and scoring the contribution level based on the number of comments, duration of comments, and content of comments made by each participant, a means for displaying a list of the scored data and providing feedback on the effectiveness of the meeting and the suitability of the participants, and a means for evaluating the work performance of on-site workers and automated machines based on the data collected in real time. This makes it possible to accurately evaluate the degree of achievement of the meeting's goals and the contribution levels of participants, as well as to make specific work improvement proposals.

[1756] "Meeting objectives" are specific items or purposes to be achieved that are set before the meeting begins.

[1757] "Audio data" refers to data recorded in audio format of participants' remarks and discussions collected during a meeting.

[1758] "Real-time" refers to the fact that data collection and processing are immediate, with results being fed back almost instantly.

[1759] "Transcription" is the process of converting collected audio data into text data.

[1760] "Participants" are people who attend a meeting and take part in speaking and discussions.

[1761] The "number of statements" refers to the number of times each participant made a statement during the conference.

[1762] "Speaking time" is the cumulative time each participant has spoken during the conference.

[1763] "Content of remarks" refers to the specific content and topics of remarks made by each participant during the meeting.

[1764] "Scoring" refers to making a quantitative evaluation based on analyzed data and expressing it as a number.

[1765] "Work performance" is an indicator of the efficiency and effectiveness of work performed by workers and automated machines.

[1766] "Feedback" refers to improvement suggestions and evaluation information provided based on the analyzed results.

[1767] "Field workers" are workers who actually perform work in factories or on-site.

[1768] An "automatic machine" is a machine or robot that performs tasks automatically in a factory or on a work site.

[1769] A "database" is a system for storing and managing past meeting data and work performance data.

[1770] Comparison is the process of comparing data sets from different points in time to identify differences and areas for improvement.

[1771] "Improvement proposals" are specific proposals and measures for the next meeting or work that are provided based on the analysis results.

[1772] "Past meeting data" refers to information and recorded data relating to meetings that have taken place in the past.

[1773] MODE FOR CARRYING OUT THE INVENTION

[1774] This invention is an improvement conference system aimed at improving productivity within factories, and it sets meeting goals, collects and transcribes real-time audio, analyzes work performance, and provides feedback. This system is realized through the cooperation of user terminals, a server, on-site workers, and automated machines.

[1775] System configuration

[1776] 1. User Device

[1777] Setting meeting goals: Before the meeting, the manager logs in to the system and sets the meeting goals, such as "confirming new work procedures and proposing improvements."

[1778] Audio data collection: During the meeting, the device collects audio data in real time and sends it to the server. The hardware used is a high-performance microphone (e.g., Blue Yeti USB Microphone).

[1779] Real-time transcription: Audio data collected during the meeting is sent to a server and transcribed in real time.

[1780] 2. Server

[1781] Analysis of the transcribed data: The server analyzes the transcribed data and scores each participant's contribution based on the number of comments, the duration of their comments, and the content of their comments.

[1782] Work performance evaluation: The server evaluates the work performance of field workers and automated machines based on the data collected in real time, using the Python programming language and necessary libraries (e.g., speech_recognition, matplotlib).

[1783] Providing feedback: The server visualizes the scored data and provides feedback to the user's device, which then provides suggestions for improving the next meeting.

[1784] 3. Field workers and automated machinery

[1785] Data collection: Operation logs and performance data of field workers and automated machines are stored in a database.

[1786] Improved work performance: Based on feedback from the server, field workers and managers can implement specific work improvement measures.

[1787] Add specific examples to the description

[1788] Examples:

[1789] To improve the efficiency of a new production line, a site manager sets the goal of "confirming new work procedures and proposing improvements." During the meeting, a device collects voice data, which is transcribed and analyzed in real time on a server. For example, if Worker A speaks 200 words and makes specific suggestions for improvement, that will be evaluated as a high contribution and included in the feedback at the next meeting.

[1790] Example prompt sentence:

[1791] I would like to define the requirements for a system that sets meeting goals, transcribes meeting audio in real time, and analyzes and scores what participants say. Please design a system that includes specific goal setting, real-time transcription, scoring of contribution based on number of comments and time spent, and visualization processes.

[1792] As a result, the present invention aims to improve the productivity of meetings and optimize work performance in the field.

[1793] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1794] Step 1:

[1795] Setting meeting goals

[1796] Input: The user (administrator) inputs the meeting goal and sends it to the system.

[1797] Specific operation: A form is displayed on the user's device, and the user is asked to enter the title and goal of the meeting. When the user presses the "Confirm" button, the data is sent to the server and saved in the database.

[1798] Output: Meeting goal data stored on the server.

[1799] Step 2:

[1800] Audio data collection

[1801] Input: Audio data collected during the meeting.

[1802] Specific operation: The device (e.g., PC or tablet) uses a high-performance microphone to collect audio during the meeting and transmits it to the server in real time.

[1803] Output: Real-time audio data sent to the server.

[1804] Step 3:

[1805] Real-time transcription

[1806] Input: The audio data sent to the server.

[1807] What happens: The server transcribes the received audio data in real time, specifically by converting it into text using speech recognition software (e.g., the speech_recognition library).

[1808] Output: Transcription data generated in real time.

[1809] Step 4:

[1810] Analysis and scoring of speech data

[1811] Input: Transcription data generated in real time.

[1812] Specific operation: The server analyzes the transcription data and extracts the number of comments, duration, and content of each participant's comments. Based on this data, each participant's contribution is scored. Analysis is performed using NLP technology and statistical methods.

[1813] Output: Analyzed data and score data for each participant.

[1814] Step 5:

[1815] Work performance evaluation

[1816] Input: Real-time monitoring data and historical work log data.

[1817] How it works: The server analyzes real-time and historical work data to evaluate the work performance of on-site workers and automated machines, using statistical methods to calculate efficiency and productivity indicators.

[1818] Output: Work performance evaluation report.

[1819] Step 6:

[1820] Providing feedback

[1821] Input: Analyzed data and score data, work performance evaluation report.

[1822] Specific operation: The server visualizes the scored data and work performance evaluation reports and provides feedback to the user's device. The feedback is displayed in the form of graphs and reports, and includes suggestions for next improvement.

[1823] Output: A feedback report provided to the user and suggestions for improvement for the next meeting.

[1824] Through these steps, the system helps improve productivity and optimize work performance during and after meetings.

[1825] 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.

[1826] This invention is a system that supports the improvement of productivity and efficient management of meetings, and in particular, combines an emotion engine that recognizes user emotions. This system not only sets meeting goals, transcribes speech in real time, analyzes speech content, and provides feedback, but also recognizes and monitors emotions using the emotion engine.

[1827] System configuration

[1828] 1. User Device

[1829] Provides an interface for users to input and save meeting goals.

[1830] It has the function of collecting voice data and emotional data during a meeting and sending it to a server in real time.

[1831] 2. Server

[1832] Transcription is performed based on the received audio data.

[1833] The transcription data is analyzed and each participant's number of comments, speaking time, and content of comments are scored as a contribution.

[1834] The emotional data recognized by the emotion engine is analyzed to monitor the atmosphere during the meeting and the stress level of each participant.

[1835] The scored and emotional data are visualized to provide feedback to the user.

[1836] Program processing

[1837] Setting meeting goals

[1838] User Action:

[1839] The user logs in to the system using a terminal and accesses the new meeting page. They enter the title and objective of the meeting and set a goal such as "Decide on the task allocation for the new project." Once the input is complete, they press the confirm button to send the meeting goal to the server.

[1840] Natural language description:

[1841] A user logs into the system, enters the title and goal of the meeting, and sends it to the server.

[1842] Collecting voice and emotion data

[1843] The device:

[1844] When a conference starts, the user presses the conference start button on the device. The device starts recording audio and transmits the audio data to the server in real time. At the same time, the device's built-in camera and sensors are used to collect emotional data from the user's facial expressions and tone of voice, which is also transmitted to the server in real time.

[1845] Natural language description:

[1846] The device records the audio during the meeting in real time and transmits the data along with the user's emotional data to the server.

[1847] Analysis and scoring of speech content

[1848] The server:

[1849] The server transcribes the received audio data in real time, analyzes the transcript, counts the amount of speech, speaking time, and content of each speaker, and assigns a score to their contribution.

[1850] Natural language description:

[1851] The server transcribes the received audio data and analyzes the amount and duration of each speaker's speech to score their contribution.

[1852] Emotional data analysis and monitoring

[1853] The server:

[1854] The server uses an emotion engine to analyze the received emotion data, monitor the atmosphere during the meeting and the stress level of each participant, and evaluate the atmosphere and stress level of the meeting based on this.

[1855] Natural language description:

[1856] The server uses an emotion engine to analyze the received emotion data and monitor the atmosphere of the meeting and the stress levels of the participants.

[1857] Displaying scores and emotional data at a glance and providing feedback

[1858] The server:

[1859] The server visually organizes and displays the score data and emotional data. This allows users to easily check the effectiveness of the meeting, the contribution of each participant, and their emotional state. It also makes suggestions for improving the selection of participants for the next meeting and how the meeting will proceed.

[1860] Natural language description:

[1861] The server visualizes the scores and emotion data so that users can easily check them, and also provides feedback on suggestions for improvement for the next meeting.

[1862] Specific examples

[1863] 1. Example of setting meeting goals:

[1864] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[1865] 2. Example of collecting voice and emotion data:

[1866] When a meeting starts, the device records audio and sends the data to the server in real time. At the same time, it analyzes facial expressions and tone of voice to send emotional data to the server.

[1867] 3. Example of analysis and scoring of speech content:

[1868] The server analyzes the transcription data and generates a score such as "Mr. Tanaka spoke 200 words, making a great contribution to progress" or "Mr. Sato spoke 50 words, making a low contribution."

[1869] 4. Emotion data analysis and monitoring example:

[1870] The server uses an emotion engine to make evaluations such as "the stress level of all participants decreases at the start of the meeting" and "the stress level of a certain participant increases during the meeting."

[1871] 5. Example of score and emotion data list display and feedback:

[1872] The server displays a list of scores and emotional data, and provides feedback to the user, such as "Tanaka's participation is valid, Sato's participation is unnecessary," and "Be careful of rising stress levels during the meeting."

[1873] In this way, the present invention provides a concrete means for realizing efficient conference management that takes into consideration both the productivity of the conference and the emotional state of the participants.

[1874] The processing flow will be explained below.

[1875] Step 1:

[1876] User actions

[1877] The user logs in to the system using a terminal and accesses the new meeting page. There, they input the title and objective of the meeting, setting a goal such as "Decide on the allocation of tasks for a new project," and press the confirm button to send it to the server.

[1878] Step 2:

[1879] Processing performed by the server

[1880] The server stores the received meeting goals in a database, generating a unique ID for the meeting and linking the goal information to that ID so that the meeting can be identified and tracked.

[1881] Step 3:

[1882] Processing performed by the device

[1883] When a conference call begins, the user presses the conference start button on the device. The device starts recording audio and begins sending the audio data to the server in real time. At the same time, the device's built-in camera and sensors are used to capture facial expressions and tone of voice, and emotional data is also sent to the server in real time.

[1884] Step 4:

[1885] Processing performed by the server

[1886] The server transcribes the received audio data in real time. The transcribed text data is linked to a unique conference ID and stored in a database. Emotion data is also linked and stored in the same way.

[1887] Step 5:

[1888] Processing performed by the server

[1889] The server analyzes the transcript data, identifies each speaker, counts the amount of speech, speaking time, and content, and scores each participant's contribution. It also analyzes emotional data to evaluate the atmosphere during the meeting and the stress levels of participants.

[1890] Step 6:

[1891] Processing performed by the server

[1892] Progress toward the goal is evaluated, and emotional data is collected and stored in a database along with the scored data. An algorithm is applied to evaluate the effectiveness of the meeting based on the progress.

[1893] Step 7:

[1894] Processing performed by the server

[1895] It visually organizes the scored and emotional data and generates feedback reports that include each speaker's contribution, meeting progress, and emotional state.

[1896] Step 8:

[1897] Processing performed by the server

[1898] The generated feedback report is sent to the user's device, and the user can use this data to consider improvements for the next meeting.

[1899] Step 9:

[1900] User actions

[1901] Users can review the feedback report on their devices and evaluate the effectiveness of the meeting, the contributions of each participant, and their emotional state. Based on this evaluation, the system can improve the selection of participants and the way the meeting will proceed for the next meeting.

[1902] In this way, the present invention not only improves the productivity of a meeting, but also realizes efficient meeting management that takes into account the emotional states of the participants.

[1903] Example 2

[1904] 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."

[1905] In today's business environment, the productivity and efficiency of meetings are being questioned. However, many meetings are unable to effectively evaluate participants' emotions or real-time contributions, and do not provide appropriate feedback. This can lead to inefficient meetings and a decline in participants' motivation. Furthermore, meetings held in high-stress environments can increase participants' psychological burden. To address these issues, a system combining the collection and analysis of real-time emotional data is needed.

[1906] 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 setting a goal of the conference in advance, a means for collecting voice data and emotional data during the conference and transcribing them in real time, a means for analyzing the transcribed data and scoring the contribution level based on the number of comments, the duration of comments, and the content of comments made by each participant, a means for analyzing the emotional data and monitoring the emotional state and atmosphere of the participants during the conference, and a means for displaying a list of scored data and emotional data to provide feedback on the effectiveness of the conference and the suitability of the participants. This makes it possible to improve the productivity of the conference and reduce the psychological burden on the participants.

[1907] "Meeting objectives" refers to the purpose of the meeting and the specific goals to be achieved.

[1908] "Audio data" refers to data that is a digital recording of audio information spoken during a conference.

[1909] "Emotional data" refers to data that indicates the emotional state and psychological reactions of participants during a meeting, and is obtained from facial expressions, tone of voice, physical movements, etc.

[1910] "Real-time transcription" refers to the process of instantly converting spoken audio data into text data.

[1911] "Transcription data" refers to data that has been converted from audio into text.

[1912] "Number of remarks" refers to the number of remarks made by each participant during the meeting.

[1913] "Speaking time" refers to the total amount of time each participant spoke during a meeting.

[1914] "Speech content" refers to the specific themes or topics that each participant spoke about during the meeting.

[1915] "Contribution score" refers to a numerical value that indicates each participant's contribution to the conference, evaluated based on the number of comments, speaking time, and content of comments.

[1916] "Scored data" refers to data that has been calculated and organized as a contribution score.

[1917] An "emotion engine" is a system that analyzes emotional data and uses that data to evaluate and monitor the emotional state and atmosphere of participants.

[1918] "Monitoring" refers to the process of observing, recording, and analyzing the situation during a meeting in real time.

[1919] "List display" refers to visually organizing the analysis results and displaying them in a format that can be checked at a glance.

[1920] "Feedback" refers to information and suggestions generated based on analyzed data, and refers to guidance for improving the progress of the meeting and the behavior of participants.

[1921] MODE FOR CARRYING OUT THE INVENTION

[1922] This invention is a system that supports the improvement of productivity and efficient management of meetings, and in particular, combines an emotion engine that recognizes user emotions. This system not only sets meeting goals, transcribes speech in real time, analyzes speech content, and provides feedback, but also recognizes and monitors emotions using the emotion engine.

[1923] System configuration:

[1924] 1. User Device

[1925] Provides an interface for users to input and save meeting goals.

[1926] It has the function of collecting voice data and emotional data during a meeting and sending it to a server in real time.

[1927] 2. Server

[1928] Transcription is performed based on the received audio data.

[1929] The transcription data is analyzed and each participant's number of comments, speaking time, and content of comments are scored as a contribution.

[1930] The emotional data recognized by the emotion engine is analyzed to monitor the atmosphere during the meeting and the stress level of each participant.

[1931] The scored and emotional data are visualized to provide feedback to the user.

[1932] Program processing:

[1933] Setting meeting goals

[1934] User Action:

[1935] The user logs in to the system using a terminal and accesses the new meeting page. They enter the title and objective of the meeting and set a goal such as "Decide on the task allocation for the new project." Once the input is complete, they press the confirm button to send the meeting goal to the server.

[1936] The server:

[1937] The server saves the meeting title and goal sent by the user in the database, and if the save is successful, gives feedback to the user.

[1938] Collecting voice and emotion data

[1939] User Action:

[1940] At the start of a conference, the user presses a conference start button on the terminal.

[1941] The device:

[1942] The device starts recording and sends the audio data to the server in real time. The device also uses cameras and sensors to detect the user's facial expressions and tone of voice, generating emotion data. The generated emotion data is also sent to the server in real time.

[1943] Analysis and scoring of speech content

[1944] The server:

[1945] The server converts the received audio data into text using transcription software (e.g., Google Speech-to-Text API). This text data is analyzed to count the amount of speech and the duration of speech for each speaker. The usefulness and contribution of the speech content are then analyzed to calculate the speaker's score.

[1946] Emotional data analysis and monitoring

[1947] The server:

[1948] The server analyzes the received emotional data using an emotion analysis engine (e.g., Microsoft Azure Emotion API) to evaluate the atmosphere during the meeting and the stress levels of the participants. Based on the analysis results, the atmosphere of the meeting and the stress levels of each participant are monitored in real time.

[1949] Displaying scores and emotional data at a glance and providing feedback

[1950] The server:

[1951] The server uses visualization tools such as dashboards (e.g., Tableau) to format the score data and emotion data and display them in real time. After the meeting, users can access the dashboard to see at a glance each participant's contribution to the discussion, the atmosphere during the meeting, and their stress level. It also displays suggestions for improvement for the next meeting and feedback on participant selection.

[1952] Examples:

[1953] 1. Example of setting meeting goals:

[1954] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[1955] 2. Example of collecting voice and emotion data:

[1956] When a meeting starts, the device records audio and sends the data to the server in real time. At the same time, it analyzes facial expressions and tone of voice to send emotional data to the server.

[1957] 3. Example of analysis and scoring of speech content:

[1958] The server analyzes the transcription data and generates a score such as "Participant A spoke 200 words and contributed greatly to the progress" or "Participant B spoke 50 words and contributed less."

[1959] 4. Emotion data analysis and monitoring example:

[1960] The server uses an emotion engine to make evaluations such as "the stress level of all participants decreases at the start of the meeting" and "the stress level of a certain participant increases during the meeting."

[1961] 5. Example of score and emotion data list display and feedback:

[1962] The server displays a list of scores and emotional data and provides feedback to the user, such as "Participant A's participation is valid, but Participant B's participation is unnecessary," and "Be careful of rising stress levels during the meeting."

[1963] In this way, the present invention provides a concrete means for realizing efficient conference management that takes into consideration both the productivity of the conference and the emotional state of the participants.

[1964] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1965] Step 1: Set meeting goals

[1966] Input: The meeting title and goal entered by the user on the meeting goal setting screen

[1967] Specific behavior:

[1968] 1. A user logs into the system using a terminal.

[1969] 2. The user visits the new meeting page.

[1970] 3. The user enters the meeting title (e.g., "Project Kickoff") and goal (e.g., "Determine key task assignments").

[1971] 4. The user presses the confirm button to send the information to the server.

[1972] Data processing / calculation: The server verifies the received conference title and objectives, and stores them in the database if there are no errors.

[1973] Output: If the save is successful, the server will provide feedback to the user.

[1974] Step 2: Collect speech and emotion data at the start of the meeting

[1975] Input: User presses start conference button

[1976] Specific behavior:

[1977] 1. At the start of a conference, the user presses the conference start button on the terminal.

[1978] 2. The device starts the recording function.

[1979] 3. The device uses cameras and sensors to detect the user's facial expressions and tone of voice.

[1980] Data processing / calculation: The terminal compresses and packets the recorded voice data in real time, and simultaneously records facial expressions and tone of voice data as emotion data.

[1981] Output: Compressed audio data and emotion data are sent to the server in real time.

[1982] Step 3: Transcribe and analyze the audio data

[1983] Input: Audio data received by the server

[1984] Specific behavior:

[1985] 1. The server converts the received voice data into text using transcription software (e.g., Google Speech-to-Text API).

[1986] 2. The server analyzes the text data and counts the amount of speech and the duration of speech for each speaker.

[1987] Data processing / calculation: The server extracts specific keywords and phrases from the text data and scores the usefulness of the comments based on that.

[1988] Output: The scoring results for each participant's speech volume, speech time, and content are output.

[1989] Step 4: Analyze and evaluate emotion data

[1990] Input: Emotion data received by the server

[1991] Specific behavior:

[1992] 1. The server analyzes the received emotion data using an emotion analysis engine (e.g., Microsoft Azure Emotion API).

[1993] 2. Based on the analysis results, the server evaluates and monitors the atmosphere during the meeting and the stress level of each participant in real time.

[1994] Data processing / calculation: The results of emotion data analysis are quantified and combined with related time series data.

[1995] Output: The measured mood score and stress level evaluation results are output.

[1996] Step 5: Integrating and visualizing scores and sentiment data

[1997] Input: Analysis results of speech score data and emotion data

[1998] Specific behavior:

[1999] 1. The server aggregates the score data and emotion data and formats it using a visualization tool such as a dashboard (e.g., Tableau).

[2000] 2. The visualized data is displayed in a user-viewable format.

[2001] Data processing / calculation: Link score data and emotion data and arrange the overall display format.

[2002] Output: A visual dashboard is provided to the user, visualizing each participant's contribution, emotional state, and meeting effectiveness, as well as providing feedback on improvement suggestions for the next meeting.

[2003] In this way, by combining analysis of speech content and emotional state monitoring, the present invention improves meeting productivity and properly evaluates participants.

[2004] (Application example 2)

[2005] 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."

[2006] Conventional conference systems only record the content and duration of comments made without considering the emotions or mental state of participants. This has led to problems such as reduced productivity and stress accumulation. While improvements in production efficiency and communication within factories are also required, conventional systems have made it difficult to comprehensively improve these. The objective of this invention is to improve productivity and reduce stress by analyzing the progress of conferences and work meetings in real time, monitoring the emotional state of participants, and providing feedback.

[2007] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for setting a conference goal in advance; means for collecting voice data during the conference and transcribing it in real time; means for analyzing the transcribed data and scoring the contribution level based on the number of comments, duration, and content of each participant's comments; means for displaying a list of the scored data and providing feedback on the productivity of the conference and the contribution levels of the participants; means for collecting emotional data from users' facial expressions and tone of voice; and means for analyzing the collected emotional data and monitoring the emotional states and stress levels of the participants. This makes it possible to comprehensively grasp the progress of a conference or work meeting and analyze and display the contribution level and emotional state of each participant in real time, thereby improving productivity and reducing stress.

[2008] "Means for setting meeting objectives in advance" is a function for defining specific objectives and goals for meetings and work meetings in advance and managing them within the system.

[2009] "Means for collecting audio data during meetings and transcribing it in real time" refers to a technology that records participants' speech using a microphone or other device and instantly converts the recorded data into text format.

[2010] "A means of analyzing transcribed data and scoring contribution based on the number of comments, speaking time, and content of each participant" is a function that analyzes transcribed text data and evaluates and quantifies the content, frequency, and duration of each participant's comments.

[2011] "A means of displaying scored data in a list and providing feedback on meeting productivity and participant contributions" is a function that visually displays the analyzed contribution scores and provides feedback on the efficiency of the overall meeting and the contributions of each participant.

[2012] "Means for collecting emotional data from a user's facial expressions and tone of voice" refers to technology that uses a camera or microphone to analyze a user's facial expressions and tone of voice, and collects their emotional state as data.

[2013] "Means for analyzing collected emotional data and monitoring participants' emotional states and stress levels" refers to a function that evaluates each participant's mental state based on collected emotional data and monitors fluctuations in real time.

[2014] "A means of monitoring fluctuations in mental state based on emotional data during work meetings and making suggestions to reduce tension and stress" is a function that analyzes emotional data during meetings, grasps fluctuations in participants' tension and stress levels, and suggests improvement measures accordingly.

[2015] "Means for assessing long-term mental health status based on emotional data and proposing improvements" refers to a technology that analyzes accumulated emotional data, assesses participants' long-term mental health status, and proposes specific improvement measures.

[2016] This invention is a system that supports the improvement of productivity and efficient management of work meetings using factory robots, and in particular, it combines an emotion engine that recognizes the user's emotions. This system consists of the following elements:

[2017] System configuration

[2018] 1. User Device

[2019] Provides an interface for users to enter and save work meeting goals.

[2020] It has the ability to collect voice and emotional data during meetings and send it to a server in real time.

[2021] 2. Server

[2022] Transcription is performed based on the received audio data.

[2023] The transcription data is analyzed, and each participant's contribution is scored based on the number of comments, the duration of their comments, and the content of their comments.

[2024] The emotional data recognized by the emotion engine is analyzed to monitor the atmosphere during the meeting and the stress level of each participant.

[2025] The scored and emotional data are visualized to provide feedback to the user.

[2026] Program processing

[2027] Setting meeting goals

[2028] The user logs in to the system using a terminal and accesses the new meeting page. They enter the meeting title and objective, and set a goal such as "Decide on the task allocation for a new project." Once the input is complete, they press the confirm button to send the meeting objective to the server.

[2029] Collecting voice and emotion data

[2030] When a meeting starts, the user presses the start meeting button on the device. The device starts recording audio and sends the audio data to the server in real time. At the same time, the device's built-in camera and sensors are used to collect emotional data from the user's facial expressions and tone of voice, which is also sent to the server in real time.

[2031] Analysis and scoring of speech content

[2032] The server transcribes the received audio data in real time, analyzes the transcript, counts the amount of speech, speaking time, and content of each speaker, and assigns a score to their contribution.

[2033] Emotional data analysis and monitoring

[2034] The server uses an emotion engine to analyze the received emotion data, monitor the atmosphere during the meeting and the stress level of each participant, and evaluate the atmosphere and stress level of the meeting based on this.

[2035] Displaying scores and emotional data at a glance and providing feedback

[2036] The server visually organizes and displays the score data and emotional data, allowing users to easily check the effectiveness of the meeting, the contribution of each participant, and their emotional state. It also makes suggestions for improving the selection of participants for the next meeting and how the meeting will proceed.

[2037] Hardware and software used

[2038] Hardware: Smart helmet (audio microphone, camera)

[2039] Software: Python, speech_recognition, emotion_recognition

[2040] Specific examples

[2041] 1. Example of setting meeting goals

[2042] The user inputs the goal "Decide on the allocation of tasks for a new project" into the terminal and sends it to the server, which saves this information and records it as the goal of the meeting.

[2043] 2. Example of collecting voice data and emotion data

[2044] When a meeting starts, the device records audio and sends the data to the server in real time. At the same time, it analyzes facial expressions and tone of voice to send emotional data to the server.

[2045] 3. Analysis of speech content and scoring example

[2046] The server analyzes the transcription data and generates a score such as "Participant A spoke 200 words and contributed greatly to the progress" or "Participant B spoke 50 words and contributed less."

[2047] 4. Analysis and Monitoring of Emotion Data

[2048] The server uses an emotion engine to make evaluations such as "the stress level of all participants decreases at the start of the meeting" or "the stress level of a certain participant increases during the meeting."

[2049] 5. Example of score and emotion data display and feedback

[2050] The server displays a list of scores and emotional data and provides feedback to the user, such as "Participant A's participation is valid, but participant B's participation is unnecessary" and "Be careful of rising stress levels during the meeting."

[2051] Prompt Sentence Examples

[2052] "Transcribe audio data from project meetings in real time and analyze the conversations. Also, use video data to recognize participants' emotions and monitor their stress levels and tension. Based on stored goals, provide feedback and suggestions for improvement after the meeting."

[2053] With the above configuration, the present invention realizes efficient management of conferences and work meetings, taking into consideration both the productivity of the conferences and work meetings and the emotional state of the participants.

[2054] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2055] Step 1:

[2056] A user logs in to the system using a terminal and accesses the new meeting page. There, they input the meeting title and goal, and set a specific goal such as "Decide the task allocation for a new project." Once the input is complete, they press the confirm button to send the meeting goal to the server. The input is the meeting title and goal, and the output is the meeting goal data sent to the server.

[2057] Step 2:

[2058] When a meeting starts, the user presses the start meeting button on the device, which causes the device to start recording audio. The device collects the user's audio data in real time and sends it to the server. At the same time, the device uses cameras and sensors to collect emotional data from the user's facial expressions and tone of voice, which is also sent to the server in real time. The input is audio data and emotional data, and the output is the collected data sent to the server.

[2059] Step 3:

[2060] The server transcribes the received audio data in real time. The software used is the Python speech_recognition library. The audio data is converted into text format and saved as transcription data. The input is audio data, and the output is transcribed text data.

[2061] Step 4:

[2062] The server analyzes the transcription data. Specifically, it counts the amount of speech, speech duration, and speech content of each participant, and then scores their contribution based on these. The analyzed data is then stored in a database and used for later feedback and contribution evaluation. The input is the transcription data, and the output is a contribution score.

[2063] Step 5:

[2064] The server uses an emotion engine to analyze the received emotion data. The software used is the Python emotion_recognition library. The server evaluates the user's emotional state and stress level from their facial expressions and tone of voice, and monitors the atmosphere during the meeting and the mental state of each participant. The input is emotion data, and the output is the analysis results, such as emotional state and stress level data.

[2065] Step 6:

[2066] The server displays a list of the scored data and emotional data, providing visual feedback to the user. This allows the user to easily check the effectiveness of the meeting, the contribution of each participant, and their emotional state. The server also uses this data to select participants for the next meeting and make suggestions for improving how the meeting is conducted. The input is the scored data and emotional data, and the output is displayed feedback and suggestions for improvement.

[2067] This completes a series of processes, improving production efficiency and communication within the factory.

[2068] 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.

[2069] 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.

[2070] 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.

[2071] 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.

[2072] 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.

[2073] 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.

[2074] 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).

[2075] 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, and motorcycles, 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.

[2076] 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."

[2077] 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.

[2078] 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).

[2079] 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.

[2080] 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.

[2081] 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.

[2082] 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.

[2083] 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.

[2084] 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.

[2085] 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 va...

Claims

1. A means of setting meeting goals in advance; A means to collect audio data during meetings and transcribe it in real time, A method for analyzing the transcription data and scoring the contribution level based on the number of comments, speaking time, and content of each participant's comments. A means for displaying a list of scored data and providing feedback on the effectiveness of the meeting and the suitability of the participants; A system including:

2. A means to evaluate the progress of meetings and provide feedback to reduce unnecessary meetings and unnecessary participants. The system of claim 1 further comprising:

3. A method for storing meeting goals, comments, and contribution scores in a database and comparing them with past meeting data. A means to provide improvement suggestions for the next meeting based on past meeting data; The system of claim 1 further comprising:

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