System

Abstract

A system is provided.SOLUTION: This system includes a means for inputting character information, a means for inputting the scenario of a conference, a means for inputting the position and opinion of a character, a means for inputting instructions and restrictions to the progress of the conference, a means for preparing explanation materials based on generated data, a means for preparing supposed questions and answers for questions and answers, a means for automatically adjusting the schedule of the conference, and a means for progressing the conference in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's business environment, numerous meetings are held frequently, creating a burden with scheduling and preparation. Executives and managers, in particular, are busy and spend a lot of their time in meetings. This results in inefficient use of human resources, and an unavoidable increase in labor costs. Therefore, it is necessary to automate efficient meeting management and scheduling, thereby realizing efficient use of human resources. [Means for solving the problem]

[0005] The present invention solves the problem by providing a system including a means for inputting participant information, a means for inputting a meeting scenario, a means for inputting the positions and opinions of the participants, a means for inputting instructions and constraints for the progress of the meeting, a means for creating explanatory materials based on the generated data, a means for preparing anticipated questions and answers for a question and answer session, a means for automatically adjusting the meeting schedule, and a means for conducting the meeting in real time. This system automates the process from meeting preparation to progress, enabling efficient use of human resources and reduction of labor costs.

[0006] "Participant information" refers to information such as the names, positions, and affiliations of individuals, such as executives and managers, who will be participating in the meeting.

[0007] A "meeting scenario" is the planning and setting information necessary for the progress of a meeting, such as the purpose and agenda of the meeting.

[0008] "Positions and opinions" refers to information that indicates the views and opinions that the participants have regarding the content of the meeting.

[0009] "Instructions and constraints" are the conditions and constraints that must be observed in the proceedings of the meeting, as well as specific instructions regarding the proceedings and decisions.

[0010] "Presentation materials" are documents and slides containing presentations and related information used during a meeting.

[0011] "Anticipated questions and answers" is data used to prepare questions and answers that are expected to be asked during a meeting.

[0012] "Scheduling" is the process of determining the date and time of a meeting and automatically adjusting it to fit the schedules of participants.

[0013] "Means for conducting the meeting in real time" refers to functions and methods for actually conducting the meeting in real time and supporting interactive activities by participants. [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 that includes inputting participant information, setting the meeting scenario, setting positions and opinions, setting instructions and constraints, generating explanatory materials, preparing anticipated questions and answers, adjusting schedules, and conducting the meeting in real time. This system automates the preparation and progress of meetings, improving work efficiency and optimizing resource utilization.

[0036] First, the user inputs the participant information. This includes entering personal information (such as names, positions, and affiliations) of the executives and managers participating in the meeting into the system. The terminal collects this information and sends it to the server. The server stores the received information in a database.

[0037] Next, the user sets the meeting scenario (objective, agenda, etc.). The user inputs this scenario information and sends it to the server via the terminal. The server receives the scenario information and stores it in a database.

[0038] Next, the user sets the position and opinion of each person. This information indicates the participants' views and opinions on the meeting agenda. The user enters this information into the terminal and sends it to the server. The server stores the received information in a database.

[0039] The user then sets the meeting instructions and constraints, including specific conditions and constraints for the meeting's progress, and the designation of a moderator. The user enters these instructions and constraints into the terminal and sends them to the server, which stores the received information in a database.

[0040] The server automatically generates explanatory materials by linking with various AI tools. Specifically, ChatPDF reviews related materials, gamma creates slides, Writesonic generates text, and Fliki creates actions. The generated explanatory materials are stored on the server.

[0041] For Q&A, the server prepares anticipated questions and answers in advance. This retrieves anticipated questions and their answers from a database and prepares for real-time responses. The server creates and stores this anticipated question and answer data.

[0042] The server uses an automatic scheduling function to adjust the schedule, arranging the optimal meeting date and time based on the schedule information of the participants and notifying all participants.

[0043] Finally, the server is responsible for the real-time progress of the meeting, using automatically generated explanatory materials and anticipated question and answer data to facilitate the real-time progress of the meeting and support the interactive activities of participants.

[0044] As a concrete example, consider a meeting to decide on a marketing strategy for a new product. The user enters information such as the president's name and title, and sets the purpose of the meeting as "deciding on a marketing strategy for a new product." Then, the user sets the opinions of each participant and enters constraints on the progress of the meeting (e.g., the meeting must last no more than one hour). Based on these settings, the server generates explanatory materials and prepares anticipated questions and answers. At the same time, the server automatically adjusts the optimal date and time for the meeting, taking into account the schedules of the participants. Finally, once the meeting begins, the server supports the progress in real time and also handles questions and answers.

[0045] In this way, the system of the present invention allows for efficient and effective conference conduct with minimal human intervention.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The user enters character information. Specifically, the user enters information such as the name, position, and affiliation of an executive or manager into a web form and then presses the submit button.

[0049] Step 2:

[0050] The terminal transmits the character information input by the user to the server, and the transmitted information reaches the server via the network.

[0051] Step 3:

[0052] The server stores the received character information in a database, which allows the information to be retained for later use.

[0053] Step 4:

[0054] The user enters the meeting scenario information, including details such as the purpose of the meeting and the agenda. The user completes the input and presses the send button.

[0055] Step 5:

[0056] The terminal transmits the scenario information input by the user to the server, and the transmitted information reaches the server via the network.

[0057] Step 6:

[0058] The server stores the received scenario information in a database, thereby storing the information necessary for the progress of the conference.

[0059] Step 7:

[0060] The user inputs the position and opinion of each character, and then presses the send button.

[0061] Step 8:

[0062] The terminal transmits the positions and opinions input by the user to the server, and the transmitted information reaches the server via the network.

[0063] Step 9:

[0064] The server stores the received positions and opinions in a database, thereby maintaining participants' views and opinions on the meeting agenda.

[0065] Step 10:

[0066] The user inputs instructions and restrictions for the progress of the conference, and then presses the send button.

[0067] Step 11:

[0068] The terminal transmits the instructions and constraints input by the user to the server, and the transmitted information reaches the server via the network.

[0069] Step 12:

[0070] The server stores the received instructions and constraints in a database, thereby maintaining the conditions for the progress of the conference.

[0071] Step 13:

[0072] The server works with various generative AI tools to prepare explanatory materials. Specifically, it reviews related documents with ChatPDF, creates slides with gamma, generates text with Writesonic, and creates actions with Fliki.

[0073] Step 14:

[0074] The server stores the generated explanatory materials in a database, thereby maintaining the materials used in the conference.

[0075] Step 15:

[0076] The server generates data on anticipated questions and answers. The server retrieves information necessary to prepare anticipated questions and answers from the database and creates the anticipated questions and answers.

[0077] Step 16:

[0078] The server stores the generated anticipated question and answer data in a database, thereby storing data in preparation for the question and answer session.

[0079] Step 17:

[0080] The server uses an automatic scheduling function to adjust the meeting date and time, calculating the optimal meeting date and time based on the schedules of the participants, and then determines the schedule.

[0081] Step 18:

[0082] The server notifies all participants of the adjusted meeting schedule, allowing them to check the meeting details.

[0083] Step 19:

[0084] The server runs the meeting in real time. When the meeting start time arrives, explanatory materials are displayed and, if necessary, the server answers questions based on the expected questions and answers.

[0085] Example 1

[0086] 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."

[0087] Conventional meeting preparation and execution requires a lot of time and effort, resulting in reduced work efficiency. Specifically, multiple processes are cumbersome, including manually entering participant information, setting the meeting scenario, gathering participants' positions and opinions, setting instructions and constraints for the meeting, creating explanatory materials, preparing anticipated questions and answers, and coordinating schedules. It is also difficult to properly conduct Q&A sessions and interactive activities on the day of the meeting.

[0088] 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.

[0089] In this invention, the server includes means for inputting participant information, means for inputting a meeting scenario, means for inputting the positions and opinions of the participants, means for inputting instructions and constraints for the progress of the meeting, means for automatically generating explanatory materials based on generated data, means for saving the explanatory materials, means for preparing anticipated questions and answers for a question and answer session, means for saving anticipated question and answer data, means for automatically adjusting the meeting schedule based on schedule information of the participants, means for notifying scheduling information, and means for conducting the meeting in real time. This consistently automates the process from preparation to progress of the meeting, enabling improved work efficiency and optimal use of resources.

[0090] "Participant information" refers to personal information such as names, positions, and affiliations of people participating in a meeting.

[0091] A "meeting scenario" refers to a plan of the meeting's purpose, agenda, and how it will proceed.

[0092] "Position" refers to the attributes and role of each participant in a meeting.

[0093] "Opinions" refers to each participant's views and opinions on the meeting agenda.

[0094] "Instructions" refer to specific conditions and constraints regarding the conduct of the meeting, as well as instructions from the facilitator.

[0095] "Constraints" refer to specific conditions or limitations on the progress of a meeting.

[0096] "Explanatory materials" refers to slides, text, charts, animations, etc. that are necessary to understand the content of the meeting.

[0097] A "generative AI tool" refers to a software tool that uses artificial intelligence to automatically generate materials and text.

[0098] "Storage means" refers to a mechanism for storing generated data and materials in a storage device.

[0099] "Anticipated questions and answers" refers to preparing questions and answers that are expected to be asked at a meeting in advance.

[0100] "Scheduling" refers to setting the optimal meeting date and time based on the participants' schedules.

[0101] "Automatic scheduling function" refers to the function that automatically calculates the optimal date and time based on the participants' schedule information.

[0102] "Real-time progress" refers to managing and supporting the progress of a meeting in real time.

[0103] The present invention is a system that automates the preparation and progress of a meeting, and covers a series of processes including inputting participant information, setting the meeting scenario, inputting positions and opinions, setting instructions and constraints, generating explanatory materials, preparing anticipated questions and answers, adjusting schedules, and proceeding with the meeting in real time. Specific embodiments of this system are described in detail below.

[0104] First, the user inputs information about the participants. This includes inputting information such as the names, positions, and affiliations of the executives and managers participating in the meeting into the terminal. The terminal collects this information and sends it to the server. The server stores the received information in a database.

[0105] Next, the user sets up a meeting scenario, which defines the purpose and agenda of the meeting, and inputs it from the user's terminal and sends it to the server, which then stores this scenario information in a database.

[0106] Next, the user sets the position and opinion of each participant. This involves inputting their views and opinions on the meeting agenda, which the user enters into their terminal and sends to the server, which then stores this information in a database.

[0107] The user then sets instructions and constraints for the conference, including specific conditions and constraints for the conference, as well as instructions from the moderator. The user enters these on the terminal and sends them to the server, which stores the received information in a database.

[0108] The server automatically generates explanatory materials using AI tools. Specifically, it uses ChatPDF to review relevant materials, gamma to create slides, Writesonic to generate text, and Fliki to create animations. The generated explanatory materials are stored on the server.

[0109] Regarding the preparation of Q&A sessions, the server prepares anticipated questions and answers in advance. This is done by retrieving anticipated questions and their answers from a database. The server creates and saves these anticipated question and answer data.

[0110] Regarding schedule adjustment, the server uses an automatic scheduling function to find the optimal meeting date and time based on the schedules of the participants and notifies all participants. For example, it determines that "September 10th from 2:00 to 3:00 PM is optimal" and notifies all participants.

[0111] Finally, the server uses automatically generated explanatory materials and anticipated question and answer data to facilitate real-time progress of the meeting. During the meeting, the server supports participants in Q&A sessions and other interactive activities.

[0112] As a concrete example, imagine a meeting to decide on a marketing strategy for a new product. The user enters the name and title of the president, and sets the purpose of the meeting as "deciding on a marketing strategy for a new product," for example. Then, the user sets the opinions of each participant and enters constraints on the progress of the meeting (e.g., the meeting must last no more than one hour). Based on these settings, the server generates explanatory materials and prepares anticipated questions and answers. At the same time, the server automatically adjusts the optimal date and time for the meeting, taking into account the schedules of the participants. Finally, once the meeting begins, the server provides real-time support for the progress and also handles questions and answers.

[0113] In this way, the system of the present invention allows for efficient and effective meeting preparation and conduct with minimal human intervention.

[0114] An example of a prompt sentence might be, "Please set up scenario information for a marketing strategy meeting for a new product. For example, please enter specific information about the characters, the purpose of the scenario, their positions and opinions, and instructions and constraints."

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1:

[0117] The user inputs the participant information. The user inputs the names, positions, and affiliations of the people participating in the conference into the terminal, and the terminal sends this information to the server. The server stores the received information in a database.

[0118] Input: Character's personal information (name, position, affiliation)

[0119] Data processing: The device organizes the input information and sends it to the server

[0120] Output: Character information is saved in the database

[0121] Specific operation: The user enters "Taro Tanaka, President, Sales Department," and the server records this information in the database.

[0122] Step 2:

[0123] The user sets up a meeting scenario. The user inputs the purpose and agenda of the meeting into the terminal, and the terminal sends this to the server. The server stores the scenario information in a database.

[0124] Input: Meeting purpose, agenda

[0125] Data processing: The device organizes the input information and sends it to the server

[0126] Output: Meeting scenario information is saved in the database

[0127] Specific operation: The user inputs "Deciding on marketing strategy for new product, budget consideration, market analysis," and the server records this information in the database.

[0128] Step 3:

[0129] The user sets the position and opinion of each character. The user inputs the corresponding information into the terminal, and the terminal sends it to the server. The server stores the position and opinion information in a database.

[0130] Input: Position and opinion of each character

[0131] Data processing: The device organizes the input information and sends it to the server

[0132] Output: The positions and opinions of the characters are stored in a database.

[0133] Specific operation: The user enters "Taro Tanaka, prioritizes market expansion" and "Jiro Suzuki, prioritizes cost reduction," and the server records the information in the database.

[0134] Step 4:

[0135] Users set instructions and constraints for the conference proceedings. The users input specific instructions and constraints into their terminals, which then transmit them to the server, which stores the received information in a database.

[0136] Input: Meeting instructions, constraints

[0137] Data processing: The device organizes the input information and sends it to the server

[0138] Output: Meeting instructions and constraints stored in a database

[0139] Specific operation: The user inputs "meeting time within 1 hour" and "presentation of materials within 5 minutes," and the server records this information in the database.

[0140] Step 5:

[0141] The server automatically generates explanatory materials using generative AI tools. Specifically, materials are reviewed with ChatPDF, slides are created with gamma, text is generated with Writesonic, and motion animations are added with Fliki.

[0142] Input: Scenario information, characters' positions and opinions, instructions and constraints

[0143] Data processing: Automatically generate materials using generative AI tools

[0144] Output: explanatory materials are generated and stored in a database

[0145] Specific Action: The server generates slides with the central theme of "New Product Features," supplements them with text, and adds action animations.

[0146] Step 6:

[0147] The server prepares anticipated questions and answers. The server retrieves anticipated questions and answers from the database and prepares them in advance.

[0148] Input: Past meeting data in the database

[0149] Data processing: The server extracts and prepares expected questions and answers

[0150] Output: Expected question and answer data is generated and saved in a database

[0151] What happens next: The server prepares questions and answers such as "What is the price of the new product?" and "What are your sales targets for the first year?"

[0152] Step 7:

[0153] The server coordinates the schedule. Based on the schedule information of the participants, the server uses an automatic scheduling function to determine the optimal meeting date and time and notifies all participants.

[0154] Input: Character schedule information

[0155] Data processing: Automatic scheduling algorithm calculates optimal date and time

[0156] Output: The best meeting time is determined and notified

[0157] Specific operation: The server determines that "September 10th, between 2:00 and 3:00 PM is optimal" and notifies participants of that date and time.

[0158] Step 8:

[0159] The server manages the real-time progress of the meeting using automatically generated explanatory materials and anticipated question and answer data. When a participant asks a question during the meeting, the server responds using anticipated questions and answers prepared in advance.

[0160] Input: explanatory materials, expected question and answer data

[0161] Data processing: Real-time presentation of materials and management of questions and answers

[0162] Output: Meetings run smoothly

[0163] Specific operation: The server conducts a meeting in real time, and answers the question "What is the target market for the product?" with "The target markets are North America and Europe."

[0164] (Application example 1)

[0165] 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."

[0166] Preparing and conducting meetings in modern corporate activities is extremely complicated, requiring a great deal of effort and time. In particular, in factories and other workplaces, where executives and workers are busy, there is a need to improve the efficiency and quality of meetings. The frequent time spent by employees and managers engaged in workplace work preparing for meetings is a serious problem that reduces productivity. Furthermore, insufficient preparation for explanatory materials and Q&A sessions can reduce the effectiveness of meetings, delay decision-making, and lead to misunderstandings. This creates a need for work efficiency and real-time support.

[0167] 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.

[0168] In this invention, the server includes a means for inputting participant information, a means for inputting a meeting scenario, a means for inputting the participants' positions and opinions, a means for automatically generating explanatory materials based on the generated data, a means for preparing anticipated questions and answers for a Q&A session, a means for adjusting the meeting schedule using an automatic scheduling function, and a means for conducting the meeting in real time. This makes it possible to improve the efficiency and quality of meeting preparation and conduct. Particularly in factories, this can improve the quality of meetings while maintaining productivity.

[0169] "Participant information" refers to information such as the name, position, and department of each person participating in the conference.

[0170] A "meeting scenario" is a plan that includes the purpose and agenda of a meeting and serves as a guide for the progress of the meeting.

[0171] "Positions and opinions" refers to each participant's views and suggestions on the agenda of the meeting.

[0172] "Instructions and Restrictions" indicates specific instructions and restrictions regarding the progress of the meeting.

[0173] "Explanatory materials" are documents and presentation materials used to clearly explain the contents of a meeting.

[0174] "Anticipated questions and answers" are a set of questions and answers that are predicted to occur during the meeting, and are preparatory materials to guide participants smoothly.

[0175] "Automatic scheduling" is the process of automatically scheduling a meeting in an optimal way based on the schedules of the participants.

[0176] "Real-time progress" refers to the process of presenting explanatory materials and answering questions in real time as the meeting progresses.

[0177] A "generative AI model" is an algorithm or tool that uses artificial intelligence to automatically generate documents or presentation materials.

[0178] The "automatic scheduling function" is a function that automatically arranges the optimal meeting date and time taking into account the schedules of the participants.

[0179] The present invention is a system for improving the efficiency of meeting preparation and progress at a workplace such as a factory. Specifically, the system automates the input of participant information, setting of a meeting scenario, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, scheduling, and real-time progress of the meeting. An embodiment of the present invention is described below.

[0180] First, the user uses a terminal to input information about the people participating in the meeting, including their names, job titles, and departments. The terminal collects this information and sends it to the server, which then stores it in a database.

[0181] Next, the user sets up a meeting scenario, which is a plan that includes the purpose and agenda of the meeting. The user inputs this scenario information and sends it to the server via their terminal. The server receives the scenario information and stores it in a database.

[0182] Next, the user sets the position and opinion of each person. This information represents the participants' views and suggestions on the meeting agenda. The user enters this information into the terminal and sends it to the server. The server stores the received information in a database.

[0183] The user then sets the meeting instructions and constraints, including specific instructions and constraints for the meeting and the designation of the moderator. The user enters these instructions and constraints into the terminal and sends them to the server, which stores the received information in a database.

[0184] The server automatically generates explanatory materials in cooperation with various generative AI models. Specifically, ChatPDF reviews related materials, gamma creates slides, Writesonic generates text, and Fliki creates actions. The generated explanatory materials are stored on the server.

[0185] For the Q&A session, the server prepares anticipated questions and answers in advance. This anticipated question and answer data includes predicted questions and answers based on the participants' positions and opinions. The server creates this anticipated question and answer data and stores it in a database.

[0186] Regarding scheduling, the server automatically adjusts the optimal meeting date and time based on the schedule information of all participants. For example, it manages the schedule using the Google Calendar API and notifies all participants.

[0187] Finally, the server is responsible for the real-time progress of the meeting. Using automatically generated explanatory materials and anticipated question and answer data, the server supports the progress of the meeting in real time and supports the interactive activities of participants.

[0188] As a concrete example, consider setting up a "maintenance meeting for a new production line at a factory." When entering the characters, enter the names, positions, and departments of the participants. When setting the scenario, set the agenda with the goal of "optimizing the maintenance of the new production line." When setting the opinions, enter the views of each participant. When setting the constraints, enter constraints such as "meeting time must be within one hour." ChatPDF, gamma, Writesonic, and Fliki are used to generate explanatory materials.

[0189] Example prompt sentence:

[0190] "Enter the information of all members participating in a factory maintenance meeting. Set the meeting purpose and agenda, and record each participant's opinions and positions. Set instructions and constraints for the meeting, use AI tools to generate briefing materials and anticipated questions and answers, and use the auto-scheduling function to arrange the optimal meeting date and time."

[0191] In this way, the system of the present invention can support efficient and effective preparation and conduct of meetings even at work sites such as factories.

[0192] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0193] Step 1:

[0194] The user uses a terminal to input information about the people participating in the meeting, including their names, job titles, and departments. The terminal collects this information and sends it to the server, which then stores it in a database.

[0195] Input: Name, position, department

[0196] Output: Character information stored in the database

[0197] Step 2:

[0198] The user sets up a meeting scenario, which is a plan that includes the purpose and agenda of the meeting. The user inputs this scenario information and sends it to the server via their terminal. The server receives the scenario information and stores it in a database.

[0199] Input: Meeting purpose, agenda

[0200] Output: Scenario information stored in the database

[0201] Step 3:

[0202] The user sets the position and opinion of each person involved. This information indicates the participants' views and suggestions regarding the meeting agenda. The user enters this information into the terminal and sends it to the server. The server stores the received information in a database.

[0203] Input: Position and opinion of each character

[0204] Output: Positions and opinions stored in a database

[0205] Step 4:

[0206] The user sets the meeting instructions and constraints, including specific instructions and constraints for the meeting's progress and the designation of the moderator. The user enters these instructions and constraints into the terminal and sends them to the server, which stores the received information in a database.

[0207] Input: Meeting instructions, constraints, and facilitator designation

[0208] Output: Instructions and constraints stored in a database

[0209] Step 5:

[0210] The server works with various generative AI models to automatically generate explanatory materials. Specifically, ChatPDF reviews related materials, gamma creates slides, Writesonic generates text, and Fliki creates videos. The generated explanatory materials are stored on the server.

[0211] Input: Meeting information stored in the database

[0212] Output: Generated explanatory material

[0213] Step 6:

[0214] The server prepares anticipated questions and answers for the Q&A session. This anticipated question and answer data includes predicted questions and answers based on the positions and opinions of the participants. The server creates this anticipated question and answer data and stores it in a database.

[0215] Input: Participants' positions and opinions

[0216] Output: Expected question and answer data

[0217] Step 7:

[0218] The server automatically adjusts the optimal meeting date and time based on the schedule information of all participants. It manages the schedule using the Google Calendar API and notifies all participants.

[0219] Input: Participant schedule information

[0220] Output: Determining the best meeting time and date and notifying

[0221] Step 8:

[0222] The server is responsible for the real-time progress of the meeting, using automatically generated explanatory materials and anticipated question and answer data to support the progress of the meeting in real time and to support the interactive activities of participants.

[0223] Input: explanatory materials, expected question and answer data

[0224] Output: Real-time meeting progress and Q&A

[0225] This series of steps will enable users to effectively prepare for and conduct a meeting.

[0226] 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.

[0227] The present invention is a system that includes input of participant information, setting of meeting scenarios, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, scheduling, real-time progress of meetings, and an emotion engine that recognizes user emotions. This system automates the preparation and progress of meetings, minimizing human intervention and realizing work efficiency and optimal use of resources.

[0228] First, the user inputs the participant information. This includes entering personal information (such as name, position, and affiliation) of the people in positions of authority who will be participating in the meeting into the system. The terminal collects this information and sends it to the server. The server then stores the received information in a database.

[0229] Next, the user sets the meeting scenario (objective, agenda, etc.). The user inputs this scenario information and sends it to the server via the terminal. The server receives the scenario information and stores it in a database.

[0230] The user then sets the position and opinion of each person involved. This information reflects the views and opinions of each participant regarding the content of the meeting. The user inputs this information and sends it to the server via their device. The server then stores the received information in a database.

[0231] Next, the user sets instructions and constraints for the progress of the meeting, including specific conditions and constraints for the progress of the meeting, and the designation of the moderator. The user inputs this information and sends it to the server via their terminal. The server stores the received instructions and constraints in a database.

[0232] The server works with various AI tools to generate the explanatory materials. Specifically, ChatPDF reviews the relevant materials, gamma creates slides, Writesonic generates text, and Fliki creates actions. The generated explanatory materials are stored on the server.

[0233] For Q&A, the server prepares anticipated questions and answers in advance. This retrieves anticipated questions and their answers from a database and prepares for responses in real time. The server creates and stores this anticipated question and answer data.

[0234] The server uses an automatic scheduling function to adjust the schedule, arranging the optimal meeting date and time based on the schedule information of the participants and notifying all participants.

[0235] Furthermore, an emotion engine is combined to recognize the user's emotions. The server uses the emotion engine to analyze emotions from the user's voice and facial expressions in real time. The analyzed emotion data can be used to dynamically adjust the progress of the meeting and Q&A. For example, if a user shows anxiety during a meeting, the server can temporarily slow down the progress of the meeting and provide more detailed explanations.

[0236] Finally, the meeting proceeds in real time. The server starts the meeting based on the adjusted meeting schedule and displays the generated explanatory materials. Furthermore, based on the user's emotions recognized by the emotion engine, the server dynamically changes its response measures to ensure smooth meeting operation. It also conducts Q&A based on anticipated questions and answers, providing appropriate responses.

[0237] For example, consider a meeting to decide on a marketing strategy for a new product. The user inputs the personal information of the executives and sets the purpose of the meeting as "deciding on a marketing strategy for a new product." They then set the opinions of the participants and input constraints on the progress of the meeting (e.g., the meeting time must be within one hour). Based on these settings, the server generates explanatory materials and prepares anticipated questions and answers. At the same time, the emotion engine recognizes the user's emotions and provides appropriate feedback. Finally, once the meeting begins, the server will proceed with the meeting in real time and also handle questions and answers.

[0238] In this way, the system of the present invention, by incorporating emotion recognition technology, achieves more advanced conference management and provides interactive conferences that take into consideration the emotions of participants.

[0239] The processing flow will be explained below.

[0240] Step 1:

[0241] The user enters the character information. This involves entering the personal information of the person in a position of authority (such as name, position, affiliation, etc.) into a web form and pressing the submit button.

[0242] Step 2:

[0243] The terminal transmits the character information input by the user to the server, and the transmitted information reaches the server via the network.

[0244] Step 3:

[0245] The server stores the received character information in a database, which allows the information to be retained for later use.

[0246] Step 4:

[0247] The user enters the meeting scenario information, including details such as the purpose of the meeting and the agenda. The user completes the input and presses the send button.

[0248] Step 5:

[0249] The terminal transmits the scenario information input by the user to the server, and the transmitted information reaches the server via the network.

[0250] Step 6:

[0251] The server stores the received scenario information in a database, thereby storing the information necessary for the progress of the conference.

[0252] Step 7:

[0253] The user inputs the position and opinion of each character, and then presses the send button.

[0254] Step 8:

[0255] The terminal transmits the positions and opinions input by the user to the server, and the transmitted information reaches the server via the network.

[0256] Step 9:

[0257] The server stores the received positions and opinions in a database, thereby maintaining participants' views and opinions on the meeting agenda.

[0258] Step 10:

[0259] The user inputs instructions and restrictions for the progress of the conference, and then presses the send button.

[0260] Step 11:

[0261] The terminal transmits the instructions and constraints input by the user to the server, and the transmitted information reaches the server via the network.

[0262] Step 12:

[0263] The server stores the received instructions and constraints in a database, thereby maintaining the conditions for the progress of the conference.

[0264] Step 13:

[0265] The server works with various generative AI tools to prepare explanatory materials: ChatPDF reviews related documents, Gamma creates slides, Writesonic generates text, and Fliki creates actions.

[0266] Step 14:

[0267] The server stores the generated explanatory materials in a database, thereby maintaining the materials used in the conference.

[0268] Step 15:

[0269] The server generates data on anticipated questions and answers. The server retrieves information necessary to prepare anticipated questions and answers from the database and creates the anticipated questions and answers.

[0270] Step 16:

[0271] The server stores the generated anticipated question and answer data in a database, thereby storing data in preparation for the question and answer session.

[0272] Step 17:

[0273] The server uses an automatic scheduling function to adjust the meeting date and time, calculating the optimal meeting date and time based on the schedules of the participants, and then determines the schedule.

[0274] Step 18:

[0275] The server notifies all participants of the adjusted meeting schedule, allowing them to check the meeting details.

[0276] Step 19:

[0277] The server uses an emotion engine to analyze the user's emotions in real time, and obtains the user's emotional data through voice analysis and facial expression analysis.

[0278] Step 20:

[0279] The server dynamically adjusts the progress of the meeting based on the emotion data. For example, if the user shows anxiety, the server may slow down the progress of the meeting and add detailed explanations.

[0280] Step 21:

[0281] The server runs the meeting in real time, displays explanatory materials when the meeting starts, and if necessary, holds a Q&A session based on anticipated questions and answers. The server provides appropriate responses based on emotions recognized by the emotion engine.

[0282] Example 2

[0283] 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."

[0284] Conventional meeting preparation and proceeding systems require a lot of manual human effort to set up participant information and meeting scenarios, create explanatory materials, prepare Q&A sessions, coordinate schedules, and proceed with the meeting, resulting in issues with efficiency and accuracy. It is also difficult to recognize participants' emotions during a meeting and respond appropriately, which can sometimes prevent the meeting from proceeding smoothly. It is necessary to solve these issues in order to improve meeting efficiency and participant satisfaction.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0286] In this invention, the server includes means for inputting participant information, means for inputting a meeting scenario, means for inputting the positions and opinions of the participants, means for inputting instructions and constraints for the progress of the meeting, means for creating explanatory materials based on data generated using a generative AI model, means for preparing anticipated questions and answers for a question and answer session, means for automatically adjusting the meeting schedule, means for recognizing user emotions in real time using an emotion analysis engine, means for dynamically adjusting the progress of the meeting based on the emotion data, and means for progressing the meeting in real time. This automates the preparation and progress of the meeting, minimizes human intervention, and enables smooth meeting management and efficient resource utilization.

[0287] "Participant information" is personal information such as the names, positions, and affiliations of individuals participating in the conference.

[0288] A "meeting scenario" is detailed planning information necessary for the progress of a meeting, such as the purpose and agenda of the meeting.

[0289] "Positions and opinions" is information that represents the views and opinions of each person participating in the meeting.

[0290] "Instructions and constraints" refers to information including specific conditions and constraints regarding the progress of the meeting and the designation of the facilitator.

[0291] A "generative AI model" is an artificial intelligence technology that automatically performs tasks such as natural language processing and image recognition based on large amounts of data.

[0292] "Explanatory materials" are materials created to support the content of a conference, and may include slides, documents, and actions.

[0293] "Anticipated Questions and Answers" is a list of anticipated questions and their answers prepared in advance.

[0294] "Automatically adjusting the schedule" means automatically determining the optimal date and time for a meeting based on the schedule information of the people participating in the meeting.

[0295] An "emotion analysis engine" is a system that analyzes emotions in real time based on a user's voice and facial expressions.

[0296] "Emotion data" is data about the user's emotional state obtained by the emotion analysis engine.

[0297] "Real-time progress" refers to responding immediately, providing information, and adjusting the flow of the meeting while the meeting is in progress.

[0298] This invention is a system that automates the preparation and progress of meetings, including the input of participant information, setting of the meeting scenario, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, scheduling, real-time recognition of emotions, and progress of the meeting. This system minimizes human intervention, realizes work efficiency, and optimal use of resources.

[0299] First, the user inputs the participant information through the terminal. This includes personal information (such as name, position, and affiliation) of the executives participating in the meeting. The terminal then sends this information to the server, which then stores the received information in a database.

[0300] Next, the user sets the meeting scenario (objectives, agenda, etc.). The scenario information entered through the terminal is sent to the server and stored in the database. When the user sets the position and opinion of each character, they input the views and opinions of each participant regarding the meeting content. This information is also sent to the server through the terminal and stored in the database.

[0301] The user then sets instructions and constraints for the conference, including specific conditions and constraints for the conference and the designation of a moderator. The input information is sent from the terminal to the server and stored in a database.

[0302] The server uses a generative AI model to generate explanatory materials. Specifically, it uses a natural language processing engine to review related materials, presentation software to create slides, a text generation engine to generate text, and an animation generation tool to create animations. The generated explanatory materials are stored on the server.

[0303] For Q&A sessions, the server prepares anticipated questions and answers in advance. This retrieves anticipated questions and their answers from a database and prepares for the Q&A session. The server creates and saves this anticipated question and answer data.

[0304] The server uses an automatic scheduling function to adjust the schedule, arranging the optimal meeting date and time based on the schedule information of the participants and notifying all participants.

[0305] The emotion analysis engine recognizes emotions by analyzing the voice and facial expression data input by the user. The server uses the emotion analysis engine and uses the analyzed data to dynamically adjust the progress of the meeting and Q&A in real time. For example, if a user shows signs of anxiety during a meeting, the server may temporarily slow down the meeting and provide more detailed explanations.

[0306] Finally, the server starts the meeting based on the adjusted schedule and displays the generated explanatory materials. Furthermore, the server dynamically changes its response based on the user's emotions recognized by the emotion analysis engine. This allows the meeting to proceed smoothly and questions and answers to be answered appropriately.

[0307] As a concrete example, consider a meeting to decide on a marketing strategy for a new product. The user inputs the personal information of the person in charge and sets the purpose of the meeting as "deciding on a marketing strategy for a new product." They then set their positions and opinions and input constraints on the progress of the meeting (e.g., the meeting time must be within one hour). The server generates explanatory materials based on these settings and prepares anticipated questions and answers. At the same time, an emotion analysis engine recognizes the user's emotions and provides appropriate feedback. Finally, once the meeting begins, the server proceeds with the meeting in real time and also handles questions and answers. This system enables highly efficient, interactive meetings that take emotions into consideration.

[0308] An example prompt is:

[0309] "I want to schedule a marketing strategy meeting for a new product. First, let's create a meeting scenario by entering the personal information of the executives."

[0310] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0311] Step 1:

[0312] The user inputs character information. Specifically, the user enters their name, position, and affiliation into the system's input form and clicks the "Submit" button. The device collects the input information and sends it to the server. The server stores the received information in a database. The input is the character's personal information, and the output is the information recorded in the database.

[0313] Step 2:

[0314] The user sets up a meeting scenario. The user inputs the purpose and agenda of the meeting and clicks the "Save Scenario" button. The terminal sends the scenario information to the server. The server saves the scenario information in a database. The input is the scenario information, and the output is the scenario information recorded in the database.

[0315] Step 3:

[0316] The user sets the position and opinion of each character. The user inputs the views and opinions of each character and clicks the "Save Opinion" button. The device sends the information to the server. The server stores the received positions and opinions in a database. The input is the positions and opinions, and the output is the positions and opinions recorded in the database.

[0317] Step 4:

[0318] The user sets instructions and constraints for the progress of the conference. The user sets the conditions and constraints for the progress of the conference and clicks the "Save Constraints" button. The terminal sends the information to the server. The server saves the information in a database. The input is instructions and constraints, and the output is the instructions and constraints recorded in the database.

[0319] Step 5:

[0320] The server generates explanatory materials using a generative AI model. It uses a natural language processing engine to review related materials and presentation software to create slides. It also uses a text generation engine to generate text and an animation generation tool to create animations. The generated explanatory materials are stored on the server. The input is data such as the meeting scenario, positions and opinions, instructions and constraints, and the output is the generated explanatory materials.

[0321] Step 6:

[0322] The server prepares expected questions and answers. The server extracts relevant questions and answers from the database and creates a list of expected questions and answers. This list is used to prepare for Q&A sessions. The input is past meeting data and related documents, and the output is a list of expected questions and answers.

[0323] Step 7:

[0324] The server adjusts the schedule. Using its automatic scheduling function, the server calculates the optimal meeting date and time based on the schedule information of the participants and sends a notification email to all participants. The input is the schedule information of the participants, and the output is the adjusted meeting date and time and the notification email.

[0325] Step 8:

[0326] The server uses an emotion analysis engine to recognize the user's emotions in real time. It acquires voice and facial expression data from the user and analyzes it using the emotion analysis engine. The acquired emotional data is reflected in the progress of the conference. The input is the user's voice and facial expression data, and the output is the analyzed emotional data.

[0327] Step 9:

[0328] The server runs the meeting in real time. It starts the meeting based on the adjusted meeting schedule and displays explanatory materials. It dynamically adjusts the meeting progress based on emotional data obtained from the emotion analysis engine and also conducts appropriate Q&A. The inputs are the adjusted meeting schedule, generated explanatory materials, analyzed emotional data, and a list of anticipated questions and answers. The output is a smoothly progressing meeting and appropriate Q&A provided in real time.

[0329] (Application example 2)

[0330] 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."

[0331] Conventional conferencing systems require a lot of human intervention and are not fully automated, which means that preparing and running a meeting requires a lot of time and effort. Furthermore, when it comes to factory production management and task management, data collection and analysis are cumbersome, making efficient management difficult. Furthermore, it is difficult to conduct an interactive meeting that takes into account the emotions of the meeting participants, which can easily lead to misunderstandings and mistakes due to poor communication.

[0332] 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 inputting participant information, means for inputting a meeting scenario, means for inputting the positions and opinions of the participants, means for inputting instructions and constraints for the progress of the meeting, means for creating explanatory materials based on the generated data, means for preparing anticipated questions and answers for a question-and-answer session, means for automatically adjusting the meeting schedule, emotion analysis means for recognizing the user's emotions, means for conducting the meeting in real time, and means for collecting and analyzing data for production management and task management. This not only automates the preparation and progress of the meeting, but also improves the efficiency of production management and task management in factories, and further enables smooth meeting management that takes into account the emotions of the participants by using an emotion recognition function.

[0333] The "means for inputting participant information" refers to a device or system for inputting and managing information (such as name, position, affiliation, etc.) about each person participating in the conference.

[0334] The "means for inputting a meeting scenario" refers to a device or system for inputting and setting scenario information necessary for the progress of a meeting, such as the purpose and agenda of the meeting.

[0335] The "means for inputting the positions and opinions of participants" is a device or system for inputting and managing the views and opinions of conference participants.

[0336] The "means for inputting instructions and constraints for the progress of the meeting" refers to a device or system for inputting specific conditions and constraints for the progress of the meeting and the designation of the facilitator.

[0337] The "means for creating explanatory materials based on the generated data" refers to a device or system for automatically creating explanatory materials, slides, etc. based on the collected and generated data.

[0338] The "means for preparing anticipated questions and answers for a question and answer session" refers to a device or system for preparing anticipated questions and answers in advance and storing them in a database.

[0339] The "means for automatically adjusting the schedule of a meeting" is a device or system for automatically adjusting the optimal date of a meeting based on the schedule information of the meeting participants.

[0340] The "emotion analysis means for recognizing the user's emotions" is a device or system for analyzing the emotions of the user from their voice and facial expressions and providing appropriate feedback based on the analysis.

[0341] A "means for conducting a meeting in real time" is a device or system for conducting and managing a meeting in real time based on a pre-prepared scenario, materials, participants' opinions, and sentiment analysis data.

[0342] "Means for collecting and analyzing data for production management and task management" refers to a device or system that collects and analyzes data on production lines and tasks in a factory to perform efficient production management and task management.

[0343] This invention provides a system that combines the input of participant information, setting of meeting scenarios, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, schedule adjustment, real-time progress of meetings, and emotion recognition.The purpose is to apply this system to factory robots to improve the efficiency of production management and task management.

[0344] First, users enter information about each person participating in the meeting (such as name, position, and affiliation). This is done using a device such as a smartphone or tablet. The device collects this information and sends it to the server, which then stores the received information in a database.

[0345] Next, the user sets the meeting scenario (objective, agenda, etc.). The user inputs this scenario information and sends it from the terminal to the server. The server receives the scenario information and stores it in a database.

[0346] Furthermore, the user can set the position and opinion of each person, which reflects the participants' views and opinions on the content of the meeting. The information is sent from the terminal to the server, which stores it in a database.

[0347] Users also set specific conditions and constraints for the progress of the meeting, including the selection of a moderator and the duration of the meeting. This information is also sent from the device to the server, which stores it in a database.

[0348] The generation of explanatory materials is performed on the server using a generative AI model (e.g., gamma, Writesonic, Fliki, ChatPDF), and the server stores the generated materials in a database.

[0349] For Q&A, the server prepares anticipated questions and answers in advance by retrieving frequently asked questions and their answers from a database and saving them.

[0350] The server uses an automatic scheduling algorithm to schedule meetings, and it determines the optimal meeting date and time based on the participants' schedule information and notifies them.

[0351] The server uses an emotion analysis engine with TensorFlow for emotion recognition, which is a technology that analyzes emotions from the user's voice and facial expressions in real time.

[0352] Finally, the meeting proceeds in real time. The server displays the generated materials, adjusts the progress of the meeting based on the sentiment analysis data, and also conducts Q&A based on anticipated questions and answers.

[0353] When applied to factory robots, the robots collect data for production management and task management and analyze it on a server, thereby improving work efficiency within the factory.

[0354] As a concrete example, consider a meeting to introduce a new manufacturing line. The user inputs information about the manager and engineers and sets the purpose and content of the meeting. They then input the opinions of the participants and set the constraints of the meeting. A generative AI tool creates explanatory materials and prepares anticipated questions and answers. The server automatically adjusts the schedule and notifies everyone. An emotion analysis engine analyzes the voices and facial expressions of the participants and adjusts the progress of the meeting.

[0355] An example of a prompt is as follows:

[0356] Meeting objective: Introduction of a new production line

[0357] Characters:

[0358] Name: Administrator

[0359] Position: Production Department

[0360] Name: Technician

[0361] Position: Engineering Department

[0362] agenda:

[0363] 1. Issues with the current line

[0364] 2. Advantages and disadvantages of introducing a new line

[0365] 3. Implementation Schedule

[0366] 4. Cost-effectiveness

[0367] Expected Q&A:

[0368] Q: What is the cost of installing a new line?

[0369] A: The implementation cost is approximately 100 million yen, and the expected payback period is two years.

[0370] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0371] Step 1:

[0372] Users use devices such as smartphones or tablets to input information about each person participating in the meeting (such as name, position, and affiliation). This input data is sent from the device to a server. The server stores the received information in a database. The input data is personal information, and the output data is the personal information stored in the database.

[0373] Step 2:

[0374] The user inputs information to set up the meeting scenario (purpose, agenda, etc.) into the terminal and sends it to the server. The server receives the scenario information and stores it in a database. The input data is the scenario information, and the output data is the scenario information stored in the database.

[0375] Step 3:

[0376] The user inputs the position and opinion of each character. The information entered by the user is sent from the terminal to the server, which stores it in a database. The input data is the position and opinion information, and the output data is the position and opinion information stored in the database.

[0377] Step 4:

[0378] Users input instructions and constraints for the progress of the conference and send them to the server via their terminals. The server receives these instructions and constraints and stores them in a database. The input data is the instructions and constraints information, and the output data is the instructions and constraints information stored in the database.

[0379] Step 5:

[0380] The server automatically creates explanatory materials using a generation AI tool (gamma, Writesonic, Fliki, ChatPDF) based on the various input information. The server saves the generated materials in a database. The input data is the various input information, and the output data is the generated explanatory materials.

[0381] Step 6:

[0382] The server prepares expected questions and answers for the Q&A session. It retrieves frequently asked questions and their answers from the database, prepares them in advance, and saves them. The input data is the format of frequently asked questions and answers, and the output data is the expected questions and answers saved in the database.

[0383] Step 7:

[0384] The server adjusts the meeting schedule. It collects the schedule information of the participants and calculates the optimal meeting schedule using an automatic scheduling algorithm. The calculated schedule information is notified to all participants. The input data is the schedule information of the participants, and the output data is the adjusted meeting schedule.

[0385] Step 8:

[0386] The server uses an emotion analysis engine based on TensorFlow to analyze emotions from the user's voice and facial expressions in real time. The analysis results are stored in a database and used to adjust the progress of the meeting. The input data is voice and facial expression data, and the output data is analyzed emotional data.

[0387] Step 9:

[0388] The server conducts the meeting in real time, displays the generated explanatory materials, adjusts the progress based on emotion analysis data, and conducts Q&A based on anticipated questions and answers. The input data is the various generated materials and analysis data, and the output data is the meeting information conducted in real time.

[0389] Step 10:

[0390] Factory robots collect data for production management and task management. The server analyzes this data and helps improve work efficiency. The input data is production management and task management data, and the output data is analyzed management information.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] [Second embodiment]

[0395] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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).

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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."

[0407] The present invention is a system that includes inputting participant information, setting the meeting scenario, setting positions and opinions, setting instructions and constraints, generating explanatory materials, preparing anticipated questions and answers, adjusting schedules, and conducting the meeting in real time. This system automates the preparation and progress of meetings, improving work efficiency and optimizing resource utilization.

[0408] First, the user inputs the participant information. This includes entering personal information (such as names, positions, and affiliations) of the executives and managers participating in the meeting into the system. The terminal collects this information and sends it to the server. The server stores the received information in a database.

[0409] Next, the user sets the meeting scenario (objective, agenda, etc.). The user inputs this scenario information and sends it to the server via the terminal. The server receives the scenario information and stores it in a database.

[0410] Next, the user sets the position and opinion of each person. This information indicates the participants' views and opinions on the meeting agenda. The user enters this information into the terminal and sends it to the server. The server stores the received information in a database.

[0411] The user then sets the meeting instructions and constraints, including specific conditions and constraints for the meeting's progress, and the designation of a moderator. The user enters these instructions and constraints into the terminal and sends them to the server, which stores the received information in a database.

[0412] The server automatically generates explanatory materials by linking with various AI tools. Specifically, ChatPDF reviews related materials, gamma creates slides, Writesonic generates text, and Fliki creates actions. The generated explanatory materials are stored on the server.

[0413] For Q&A, the server prepares anticipated questions and answers in advance. This retrieves anticipated questions and their answers from a database and prepares for real-time responses. The server creates and stores this anticipated question and answer data.

[0414] The server uses an automatic scheduling function to adjust the schedule, arranging the optimal meeting date and time based on the schedule information of the participants and notifying all participants.

[0415] Finally, the server is responsible for the real-time progress of the meeting, using automatically generated explanatory materials and anticipated question and answer data to facilitate the real-time progress of the meeting and support the interactive activities of participants.

[0416] As a concrete example, consider a meeting to decide on a marketing strategy for a new product. The user enters information such as the president's name and title, and sets the purpose of the meeting as "deciding on a marketing strategy for a new product." Then, the user sets the opinions of each participant and enters constraints on the progress of the meeting (e.g., the meeting must last no more than one hour). Based on these settings, the server generates explanatory materials and prepares anticipated questions and answers. At the same time, the server automatically adjusts the optimal date and time for the meeting, taking into account the schedules of the participants. Finally, once the meeting begins, the server supports the progress in real time and also handles questions and answers.

[0417] In this way, the system of the present invention allows for efficient and effective conference conduct with minimal human intervention.

[0418] The processing flow will be explained below.

[0419] Step 1:

[0420] The user enters character information. Specifically, the user enters information such as the name, position, and affiliation of an executive or manager into a web form and then presses the submit button.

[0421] Step 2:

[0422] The terminal transmits the character information input by the user to the server, and the transmitted information reaches the server via the network.

[0423] Step 3:

[0424] The server stores the received character information in a database, which allows the information to be retained for later use.

[0425] Step 4:

[0426] The user enters the meeting scenario information, including details such as the purpose of the meeting and the agenda. The user completes the input and presses the send button.

[0427] Step 5:

[0428] The terminal transmits the scenario information input by the user to the server, and the transmitted information reaches the server via the network.

[0429] Step 6:

[0430] The server stores the received scenario information in a database, thereby storing the information necessary for the progress of the conference.

[0431] Step 7:

[0432] The user inputs the position and opinion of each character, and then presses the send button.

[0433] Step 8:

[0434] The terminal transmits the positions and opinions input by the user to the server, and the transmitted information reaches the server via the network.

[0435] Step 9:

[0436] The server stores the received positions and opinions in a database, thereby maintaining participants' views and opinions on the meeting agenda.

[0437] Step 10:

[0438] The user inputs instructions and restrictions for the progress of the conference, and then presses the send button.

[0439] Step 11:

[0440] The terminal transmits the instructions and constraints input by the user to the server, and the transmitted information reaches the server via the network.

[0441] Step 12:

[0442] The server stores the received instructions and constraints in a database, thereby maintaining the conditions for the progress of the conference.

[0443] Step 13:

[0444] The server works with various generative AI tools to prepare explanatory materials. Specifically, it reviews related documents with ChatPDF, creates slides with gamma, generates text with Writesonic, and creates actions with Fliki.

[0445] Step 14:

[0446] The server stores the generated explanatory materials in a database, thereby maintaining the materials used in the conference.

[0447] Step 15:

[0448] The server generates data on anticipated questions and answers. The server retrieves information necessary to prepare anticipated questions and answers from the database and creates the anticipated questions and answers.

[0449] Step 16:

[0450] The server stores the generated anticipated question and answer data in a database, thereby storing data in preparation for the question and answer session.

[0451] Step 17:

[0452] The server uses an automatic scheduling function to adjust the meeting date and time, calculating the optimal meeting date and time based on the schedules of the participants, and then determines the schedule.

[0453] Step 18:

[0454] The server notifies all participants of the adjusted meeting schedule, allowing them to check the meeting details.

[0455] Step 19:

[0456] The server runs the meeting in real time. When the meeting start time arrives, explanatory materials are displayed and, if necessary, the server answers questions based on the expected questions and answers.

[0457] Example 1

[0458] 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."

[0459] Conventional meeting preparation and execution requires a lot of time and effort, resulting in reduced work efficiency. Specifically, multiple processes are cumbersome, including manually entering participant information, setting the meeting scenario, gathering participants' positions and opinions, setting instructions and constraints for the meeting, creating explanatory materials, preparing anticipated questions and answers, and coordinating schedules. It is also difficult to properly conduct Q&A sessions and interactive activities on the day of the meeting.

[0460] 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.

[0461] In this invention, the server includes means for inputting participant information, means for inputting a meeting scenario, means for inputting the positions and opinions of the participants, means for inputting instructions and constraints for the progress of the meeting, means for automatically generating explanatory materials based on generated data, means for saving the explanatory materials, means for preparing anticipated questions and answers for a question and answer session, means for saving anticipated question and answer data, means for automatically adjusting the meeting schedule based on schedule information of the participants, means for notifying scheduling information, and means for conducting the meeting in real time. This consistently automates the process from preparation to progress of the meeting, enabling improved work efficiency and optimal use of resources.

[0462] "Participant information" refers to personal information such as names, positions, and affiliations of people participating in a meeting.

[0463] A "meeting scenario" refers to a plan of the meeting's purpose, agenda, and how it will proceed.

[0464] "Position" refers to the attributes and role of each participant in a meeting.

[0465] "Opinions" refers to each participant's views and opinions on the meeting agenda.

[0466] "Instructions" refer to specific conditions and constraints regarding the conduct of the meeting, as well as instructions from the facilitator.

[0467] "Constraints" refer to specific conditions or limitations on the progress of a meeting.

[0468] "Explanatory materials" refers to slides, text, charts, animations, etc. that are necessary to understand the content of the meeting.

[0469] A "generative AI tool" refers to a software tool that uses artificial intelligence to automatically generate materials and text.

[0470] "Storage means" refers to a mechanism for storing generated data and materials in a storage device.

[0471] "Anticipated questions and answers" refers to preparing questions and answers that are expected to be asked at a meeting in advance.

[0472] "Scheduling" refers to setting the optimal meeting date and time based on the participants' schedules.

[0473] "Automatic scheduling function" refers to the function that automatically calculates the optimal date and time based on the participants' schedule information.

[0474] "Real-time progress" refers to managing and supporting the progress of a meeting in real time.

[0475] The present invention is a system that automates the preparation and progress of a meeting, and covers a series of processes including inputting participant information, setting the meeting scenario, inputting positions and opinions, setting instructions and constraints, generating explanatory materials, preparing anticipated questions and answers, adjusting schedules, and proceeding with the meeting in real time. Specific embodiments of this system are described in detail below.

[0476] First, the user inputs information about the participants. This includes inputting information such as the names, positions, and affiliations of the executives and managers participating in the meeting into the terminal. The terminal collects this information and sends it to the server. The server stores the received information in a database.

[0477] Next, the user sets up a meeting scenario, which defines the purpose and agenda of the meeting, and inputs it from the user's terminal and sends it to the server, which then stores this scenario information in a database.

[0478] Next, the user sets the position and opinion of each participant. This involves inputting their views and opinions on the meeting agenda, which the user enters into their terminal and sends to the server, which then stores this information in a database.

[0479] The user then sets instructions and constraints for the conference, including specific conditions and constraints for the conference, as well as instructions from the moderator. The user enters these on the terminal and sends them to the server, which stores the received information in a database.

[0480] The server automatically generates explanatory materials using AI tools. Specifically, it uses ChatPDF to review relevant materials, gamma to create slides, Writesonic to generate text, and Fliki to create animations. The generated explanatory materials are stored on the server.

[0481] Regarding the preparation of Q&A sessions, the server prepares anticipated questions and answers in advance. This is done by retrieving anticipated questions and their answers from a database. The server creates and saves these anticipated question and answer data.

[0482] Regarding schedule adjustment, the server uses an automatic scheduling function to find the optimal meeting date and time based on the schedules of the participants and notifies all participants. For example, it determines that "September 10th from 2:00 to 3:00 PM is optimal" and notifies all participants.

[0483] Finally, the server uses automatically generated explanatory materials and anticipated question and answer data to facilitate real-time progress of the meeting. During the meeting, the server supports participants in Q&A sessions and other interactive activities.

[0484] As a concrete example, imagine a meeting to decide on a marketing strategy for a new product. The user enters the name and title of the president, and sets the purpose of the meeting as "deciding on a marketing strategy for a new product," for example. Then, the user sets the opinions of each participant and enters constraints on the progress of the meeting (e.g., the meeting must last no more than one hour). Based on these settings, the server generates explanatory materials and prepares anticipated questions and answers. At the same time, the server automatically adjusts the optimal date and time for the meeting, taking into account the schedules of the participants. Finally, once the meeting begins, the server provides real-time support for the progress and also handles questions and answers.

[0485] In this way, the system of the present invention allows for efficient and effective meeting preparation and conduct with minimal human intervention.

[0486] An example of a prompt sentence might be, "Please set up scenario information for a marketing strategy meeting for a new product. For example, please enter specific information about the characters, the purpose of the scenario, their positions and opinions, and instructions and constraints."

[0487] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0488] Step 1:

[0489] The user inputs the participant information. The user inputs the names, positions, and affiliations of the people participating in the conference into the terminal, and the terminal sends this information to the server. The server stores the received information in a database.

[0490] Input: Character's personal information (name, position, affiliation)

[0491] Data processing: The device organizes the input information and sends it to the server

[0492] Output: Character information is saved in the database

[0493] Specific operation: The user enters "Taro Tanaka, President, Sales Department," and the server records this information in the database.

[0494] Step 2:

[0495] The user sets up a meeting scenario. The user inputs the purpose and agenda of the meeting into the terminal, and the terminal sends this to the server. The server stores the scenario information in a database.

[0496] Input: Meeting purpose, agenda

[0497] Data processing: The device organizes the input information and sends it to the server

[0498] Output: Meeting scenario information is saved in the database

[0499] Specific operation: The user inputs "Deciding on marketing strategy for new product, budget consideration, market analysis," and the server records this information in the database.

[0500] Step 3:

[0501] The user sets the position and opinion of each character. The user inputs the corresponding information into the terminal, and the terminal sends it to the server. The server stores the position and opinion information in a database.

[0502] Input: Position and opinion of each character

[0503] Data processing: The device organizes the input information and sends it to the server

[0504] Output: The positions and opinions of the characters are stored in a database.

[0505] Specific operation: The user enters "Taro Tanaka, prioritizes market expansion" and "Jiro Suzuki, prioritizes cost reduction," and the server records the information in the database.

[0506] Step 4:

[0507] Users set instructions and constraints for the conference proceedings. The users input specific instructions and constraints into their terminals, which then transmit them to the server, which stores the received information in a database.

[0508] Input: Meeting instructions, constraints

[0509] Data processing: The device organizes the input information and sends it to the server

[0510] Output: Meeting instructions and constraints stored in a database

[0511] Specific operation: The user inputs "meeting time within 1 hour" and "presentation of materials within 5 minutes," and the server records this information in the database.

[0512] Step 5:

[0513] The server automatically generates explanatory materials using generative AI tools. Specifically, materials are reviewed with ChatPDF, slides are created with gamma, text is generated with Writesonic, and motion animations are added with Fliki.

[0514] Input: Scenario information, characters' positions and opinions, instructions and constraints

[0515] Data processing: Automatically generate materials using generative AI tools

[0516] Output: explanatory materials are generated and stored in a database

[0517] Specific Action: The server generates slides with the central theme of "New Product Features," supplements them with text, and adds action animations.

[0518] Step 6:

[0519] The server prepares anticipated questions and answers. The server retrieves anticipated questions and answers from the database and prepares them in advance.

[0520] Input: Past meeting data in the database

[0521] Data processing: The server extracts and prepares expected questions and answers

[0522] Output: Expected question and answer data is generated and saved in a database

[0523] What happens next: The server prepares questions and answers such as "What is the price of the new product?" and "What are your sales targets for the first year?"

[0524] Step 7:

[0525] The server coordinates the schedule. Based on the schedule information of the participants, the server uses an automatic scheduling function to determine the optimal meeting date and time and notifies all participants.

[0526] Input: Character schedule information

[0527] Data processing: Automatic scheduling algorithm calculates optimal date and time

[0528] Output: The best meeting time is determined and notified

[0529] Specific operation: The server determines that "September 10th, between 2:00 and 3:00 PM is optimal" and notifies participants of that date and time.

[0530] Step 8:

[0531] The server manages the real-time progress of the meeting using automatically generated explanatory materials and anticipated question and answer data. When a participant asks a question during the meeting, the server responds using anticipated questions and answers prepared in advance.

[0532] Input: explanatory materials, expected question and answer data

[0533] Data processing: Real-time presentation of materials and management of questions and answers

[0534] Output: Meetings run smoothly

[0535] Specific operation: The server conducts a meeting in real time, and answers the question "What is the target market for the product?" with "The target markets are North America and Europe."

[0536] (Application example 1)

[0537] 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."

[0538] Preparing and conducting meetings in modern corporate activities is extremely complicated, requiring a great deal of effort and time. In particular, in factories and other workplaces, where executives and workers are busy, there is a need to improve the efficiency and quality of meetings. The frequent time spent by employees and managers engaged in workplace work preparing for meetings is a serious problem that reduces productivity. Furthermore, insufficient preparation for explanatory materials and Q&A sessions can reduce the effectiveness of meetings, delay decision-making, and lead to misunderstandings. This creates a need for work efficiency and real-time support.

[0539] 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.

[0540] In this invention, the server includes a means for inputting participant information, a means for inputting a meeting scenario, a means for inputting the participants' positions and opinions, a means for automatically generating explanatory materials based on the generated data, a means for preparing anticipated questions and answers for a Q&A session, a means for adjusting the meeting schedule using an automatic scheduling function, and a means for conducting the meeting in real time. This makes it possible to improve the efficiency and quality of meeting preparation and conduct. Particularly in factories, this can improve the quality of meetings while maintaining productivity.

[0541] "Participant information" refers to information such as the name, position, and department of each person participating in the conference.

[0542] A "meeting scenario" is a plan that includes the purpose and agenda of a meeting and serves as a guide for the progress of the meeting.

[0543] "Positions and opinions" refers to each participant's views and suggestions on the agenda of the meeting.

[0544] "Instructions and Restrictions" indicates specific instructions and restrictions regarding the progress of the meeting.

[0545] "Explanatory materials" are documents and presentation materials used to clearly explain the contents of a meeting.

[0546] "Anticipated questions and answers" are a set of questions and answers that are predicted to occur during the meeting, and are preparatory materials to guide participants smoothly.

[0547] "Automatic scheduling" is the process of automatically scheduling a meeting in an optimal way based on the schedules of the participants.

[0548] "Real-time progress" refers to the process of presenting explanatory materials and answering questions in real time as the meeting progresses.

[0549] A "generative AI model" is an algorithm or tool that uses artificial intelligence to automatically generate documents or presentation materials.

[0550] The "automatic scheduling function" is a function that automatically arranges the optimal meeting date and time taking into account the schedules of the participants.

[0551] The present invention is a system for improving the efficiency of meeting preparation and progress at a workplace such as a factory. Specifically, the system automates the input of participant information, setting of a meeting scenario, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, scheduling, and real-time progress of the meeting. An embodiment of the present invention is described below.

[0552] First, the user uses a terminal to input information about the people participating in the meeting, including their names, job titles, and departments. The terminal collects this information and sends it to the server, which then stores it in a database.

[0553] Next, the user sets up a meeting scenario, which is a plan that includes the purpose and agenda of the meeting. The user inputs this scenario information and sends it to the server via their terminal. The server receives the scenario information and stores it in a database.

[0554] Next, the user sets the position and opinion of each person. This information represents the participants' views and suggestions on the meeting agenda. The user enters this information into the terminal and sends it to the server. The server stores the received information in a database.

[0555] The user then sets the meeting instructions and constraints, including specific instructions and constraints for the meeting and the designation of the moderator. The user enters these instructions and constraints into the terminal and sends them to the server, which stores the received information in a database.

[0556] The server automatically generates explanatory materials in cooperation with various generative AI models. Specifically, ChatPDF reviews related materials, gamma creates slides, Writesonic generates text, and Fliki creates actions. The generated explanatory materials are stored on the server.

[0557] For the Q&A session, the server prepares anticipated questions and answers in advance. This anticipated question and answer data includes predicted questions and answers based on the participants' positions and opinions. The server creates this anticipated question and answer data and stores it in a database.

[0558] Regarding scheduling, the server automatically adjusts the optimal meeting date and time based on the schedule information of all participants. For example, it manages the schedule using the Google Calendar API and notifies all participants.

[0559] Finally, the server is responsible for the real-time progress of the meeting. Using automatically generated explanatory materials and anticipated question and answer data, the server supports the progress of the meeting in real time and supports the interactive activities of participants.

[0560] As a concrete example, consider setting up a "maintenance meeting for a new production line at a factory." When entering the characters, enter the names, positions, and departments of the participants. When setting the scenario, set the agenda with the goal of "optimizing the maintenance of the new production line." When setting the opinions, enter the views of each participant. When setting the constraints, enter constraints such as "meeting time must be within one hour." ChatPDF, gamma, Writesonic, and Fliki are used to generate explanatory materials.

[0561] Example prompt sentence:

[0562] "Enter the information of all members participating in a factory maintenance meeting. Set the meeting purpose and agenda, and record each participant's opinions and positions. Set instructions and constraints for the meeting, use AI tools to generate briefing materials and anticipated questions and answers, and use the auto-scheduling function to arrange the optimal meeting date and time."

[0563] In this way, the system of the present invention can support efficient and effective preparation and conduct of meetings even at work sites such as factories.

[0564] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0565] Step 1:

[0566] The user uses a terminal to input information about the people participating in the meeting, including their names, job titles, and departments. The terminal collects this information and sends it to the server, which then stores it in a database.

[0567] Input: Name, position, department

[0568] Output: Character information stored in the database

[0569] Step 2:

[0570] The user sets up a meeting scenario, which is a plan that includes the purpose and agenda of the meeting. The user inputs this scenario information and sends it to the server via their terminal. The server receives the scenario information and stores it in a database.

[0571] Input: Meeting purpose, agenda

[0572] Output: Scenario information stored in the database

[0573] Step 3:

[0574] The user sets the position and opinion of each person involved. This information indicates the participants' views and suggestions regarding the meeting agenda. The user enters this information into the terminal and sends it to the server. The server stores the received information in a database.

[0575] Input: Position and opinion of each character

[0576] Output: Positions and opinions stored in a database

[0577] Step 4:

[0578] The user sets the meeting instructions and constraints, including specific instructions and constraints for the meeting's progress and the designation of the moderator. The user enters these instructions and constraints into the terminal and sends them to the server, which stores the received information in a database.

[0579] Input: Meeting instructions, constraints, and facilitator designation

[0580] Output: Instructions and constraints stored in a database

[0581] Step 5:

[0582] The server works with various generative AI models to automatically generate explanatory materials. Specifically, ChatPDF reviews related materials, gamma creates slides, Writesonic generates text, and Fliki creates videos. The generated explanatory materials are stored on the server.

[0583] Input: Meeting information stored in the database

[0584] Output: Generated explanatory material

[0585] Step 6:

[0586] The server prepares anticipated questions and answers for the Q&A session. This anticipated question and answer data includes predicted questions and answers based on the positions and opinions of the participants. The server creates this anticipated question and answer data and stores it in a database.

[0587] Input: Participants' positions and opinions

[0588] Output: Expected question and answer data

[0589] Step 7:

[0590] The server automatically adjusts the optimal meeting date and time based on the schedule information of all participants. It manages the schedule using the Google Calendar API and notifies all participants.

[0591] Input: Participant schedule information

[0592] Output: Determining the best meeting time and date and notifying

[0593] Step 8:

[0594] The server is responsible for the real-time progress of the meeting, using automatically generated explanatory materials and anticipated question and answer data to support the progress of the meeting in real time and to support the interactive activities of participants.

[0595] Input: explanatory materials, expected question and answer data

[0596] Output: Real-time meeting progress and Q&A

[0597] This series of steps will enable users to effectively prepare for and conduct a meeting.

[0598] 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.

[0599] The present invention is a system that includes input of participant information, setting of meeting scenarios, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, scheduling, real-time progress of meetings, and an emotion engine that recognizes user emotions. This system automates the preparation and progress of meetings, minimizing human intervention and realizing work efficiency and optimal use of resources.

[0600] First, the user inputs the participant information. This includes entering personal information (such as name, position, and affiliation) of the people in positions of authority who will be participating in the meeting into the system. The terminal collects this information and sends it to the server. The server then stores the received information in a database.

[0601] Next, the user sets the meeting scenario (objective, agenda, etc.). The user inputs this scenario information and sends it to the server via the terminal. The server receives the scenario information and stores it in a database.

[0602] The user then sets the position and opinion of each person involved. This information reflects the views and opinions of each participant regarding the content of the meeting. The user inputs this information and sends it to the server via their device. The server then stores the received information in a database.

[0603] Next, the user sets instructions and constraints for the progress of the meeting, including specific conditions and constraints for the progress of the meeting, and the designation of the moderator. The user inputs this information and sends it to the server via their terminal. The server stores the received instructions and constraints in a database.

[0604] The server works with various AI tools to generate the explanatory materials. Specifically, ChatPDF reviews the relevant materials, gamma creates slides, Writesonic generates text, and Fliki creates actions. The generated explanatory materials are stored on the server.

[0605] For Q&A, the server prepares anticipated questions and answers in advance. This retrieves anticipated questions and their answers from a database and prepares for responses in real time. The server creates and stores this anticipated question and answer data.

[0606] The server uses an automatic scheduling function to adjust the schedule, arranging the optimal meeting date and time based on the schedule information of the participants and notifying all participants.

[0607] Furthermore, an emotion engine is combined to recognize the user's emotions. The server uses the emotion engine to analyze emotions from the user's voice and facial expressions in real time. The analyzed emotion data can be used to dynamically adjust the progress of the meeting and Q&A. For example, if a user shows anxiety during a meeting, the server can temporarily slow down the progress of the meeting and provide more detailed explanations.

[0608] Finally, the meeting proceeds in real time. The server starts the meeting based on the adjusted meeting schedule and displays the generated explanatory materials. Furthermore, based on the user's emotions recognized by the emotion engine, the server dynamically changes its response measures to ensure smooth meeting operation. It also conducts Q&A based on anticipated questions and answers, providing appropriate responses.

[0609] For example, consider a meeting to decide on a marketing strategy for a new product. The user inputs the personal information of the executives and sets the purpose of the meeting as "deciding on a marketing strategy for a new product." They then set the opinions of the participants and input constraints on the progress of the meeting (e.g., the meeting time must be within one hour). Based on these settings, the server generates explanatory materials and prepares anticipated questions and answers. At the same time, the emotion engine recognizes the user's emotions and provides appropriate feedback. Finally, once the meeting begins, the server will proceed with the meeting in real time and also handle questions and answers.

[0610] In this way, the system of the present invention, by incorporating emotion recognition technology, achieves more advanced conference management and provides interactive conferences that take into consideration the emotions of participants.

[0611] The processing flow will be explained below.

[0612] Step 1:

[0613] The user enters the character information. This involves entering the personal information of the person in a position of authority (such as name, position, affiliation, etc.) into a web form and pressing the submit button.

[0614] Step 2:

[0615] The terminal transmits the character information input by the user to the server, and the transmitted information reaches the server via the network.

[0616] Step 3:

[0617] The server stores the received character information in a database, which allows the information to be retained for later use.

[0618] Step 4:

[0619] The user enters the meeting scenario information, including details such as the purpose of the meeting and the agenda. The user completes the input and presses the send button.

[0620] Step 5:

[0621] The terminal transmits the scenario information input by the user to the server, and the transmitted information reaches the server via the network.

[0622] Step 6:

[0623] The server stores the received scenario information in a database, thereby storing the information necessary for the progress of the conference.

[0624] Step 7:

[0625] The user inputs the position and opinion of each character, and then presses the send button.

[0626] Step 8:

[0627] The terminal transmits the positions and opinions input by the user to the server, and the transmitted information reaches the server via the network.

[0628] Step 9:

[0629] The server stores the received positions and opinions in a database, thereby maintaining participants' views and opinions on the meeting agenda.

[0630] Step 10:

[0631] The user inputs instructions and restrictions for the progress of the conference, and then presses the send button.

[0632] Step 11:

[0633] The terminal transmits the instructions and constraints input by the user to the server, and the transmitted information reaches the server via the network.

[0634] Step 12:

[0635] The server stores the received instructions and constraints in a database, thereby maintaining the conditions for the progress of the conference.

[0636] Step 13:

[0637] The server works with various generative AI tools to prepare explanatory materials: ChatPDF reviews related documents, Gamma creates slides, Writesonic generates text, and Fliki creates actions.

[0638] Step 14:

[0639] The server stores the generated explanatory materials in a database, thereby maintaining the materials used in the conference.

[0640] Step 15:

[0641] The server generates data on anticipated questions and answers. The server retrieves information necessary to prepare anticipated questions and answers from the database and creates the anticipated questions and answers.

[0642] Step 16:

[0643] The server stores the generated anticipated question and answer data in a database, thereby storing data in preparation for the question and answer session.

[0644] Step 17:

[0645] The server uses an automatic scheduling function to adjust the meeting date and time, calculating the optimal meeting date and time based on the schedules of the participants, and then determines the schedule.

[0646] Step 18:

[0647] The server notifies all participants of the adjusted meeting schedule, allowing them to check the meeting details.

[0648] Step 19:

[0649] The server uses an emotion engine to analyze the user's emotions in real time, and obtains the user's emotional data through voice analysis and facial expression analysis.

[0650] Step 20:

[0651] The server dynamically adjusts the progress of the meeting based on the emotion data. For example, if the user shows anxiety, the server may slow down the progress of the meeting and add detailed explanations.

[0652] Step 21:

[0653] The server runs the meeting in real time, displays explanatory materials when the meeting starts, and if necessary, holds a Q&A session based on anticipated questions and answers. The server provides appropriate responses based on emotions recognized by the emotion engine.

[0654] Example 2

[0655] 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."

[0656] Conventional meeting preparation and proceeding systems require a lot of manual human effort to set up participant information and meeting scenarios, create explanatory materials, prepare Q&A sessions, coordinate schedules, and proceed with the meeting, resulting in issues with efficiency and accuracy. It is also difficult to recognize participants' emotions during a meeting and respond appropriately, which can sometimes prevent the meeting from proceeding smoothly. It is necessary to solve these issues in order to improve meeting efficiency and participant satisfaction.

[0657] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0658] In this invention, the server includes means for inputting participant information, means for inputting a meeting scenario, means for inputting the positions and opinions of the participants, means for inputting instructions and constraints for the progress of the meeting, means for creating explanatory materials based on data generated using a generative AI model, means for preparing anticipated questions and answers for a question and answer session, means for automatically adjusting the meeting schedule, means for recognizing user emotions in real time using an emotion analysis engine, means for dynamically adjusting the progress of the meeting based on the emotion data, and means for progressing the meeting in real time. This automates the preparation and progress of the meeting, minimizes human intervention, and enables smooth meeting management and efficient resource utilization.

[0659] "Participant information" is personal information such as the names, positions, and affiliations of individuals participating in the conference.

[0660] A "meeting scenario" is detailed planning information necessary for the progress of a meeting, such as the purpose and agenda of the meeting.

[0661] "Positions and opinions" is information that represents the views and opinions of each person participating in the meeting.

[0662] "Instructions and constraints" refers to information including specific conditions and constraints regarding the progress of the meeting and the designation of the facilitator.

[0663] A "generative AI model" is an artificial intelligence technology that automatically performs tasks such as natural language processing and image recognition based on large amounts of data.

[0664] "Explanatory materials" are materials created to support the content of a conference, and may include slides, documents, and actions.

[0665] "Anticipated Questions and Answers" is a list of anticipated questions and their answers prepared in advance.

[0666] "Automatically adjusting the schedule" means automatically determining the optimal date and time for a meeting based on the schedule information of the people participating in the meeting.

[0667] An "emotion analysis engine" is a system that analyzes emotions in real time based on a user's voice and facial expressions.

[0668] "Emotion data" is data about the user's emotional state obtained by the emotion analysis engine.

[0669] "Real-time progress" refers to responding immediately, providing information, and adjusting the flow of the meeting while the meeting is in progress.

[0670] This invention is a system that automates the preparation and progress of meetings, including the input of participant information, setting of the meeting scenario, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, scheduling, real-time recognition of emotions, and progress of the meeting. This system minimizes human intervention, realizes work efficiency, and optimal use of resources.

[0671] First, the user inputs the participant information through the terminal. This includes personal information (such as name, position, and affiliation) of the executives participating in the meeting. The terminal then sends this information to the server, which then stores the received information in a database.

[0672] Next, the user sets the meeting scenario (objectives, agenda, etc.). The scenario information entered through the terminal is sent to the server and stored in the database. When the user sets the position and opinion of each character, they input the views and opinions of each participant regarding the meeting content. This information is also sent to the server through the terminal and stored in the database.

[0673] The user then sets instructions and constraints for the conference, including specific conditions and constraints for the conference and the designation of a moderator. The input information is sent from the terminal to the server and stored in a database.

[0674] The server uses a generative AI model to generate explanatory materials. Specifically, it uses a natural language processing engine to review related materials, presentation software to create slides, a text generation engine to generate text, and an animation generation tool to create animations. The generated explanatory materials are stored on the server.

[0675] For Q&A sessions, the server prepares anticipated questions and answers in advance. This retrieves anticipated questions and their answers from a database and prepares for the Q&A session. The server creates and saves this anticipated question and answer data.

[0676] The server uses an automatic scheduling function to adjust the schedule, arranging the optimal meeting date and time based on the schedule information of the participants and notifying all participants.

[0677] The emotion analysis engine recognizes emotions by analyzing the voice and facial expression data input by the user. The server uses the emotion analysis engine and uses the analyzed data to dynamically adjust the progress of the meeting and Q&A in real time. For example, if a user shows signs of anxiety during a meeting, the server may temporarily slow down the meeting and provide more detailed explanations.

[0678] Finally, the server starts the meeting based on the adjusted schedule and displays the generated explanatory materials. Furthermore, the server dynamically changes its response based on the user's emotions recognized by the emotion analysis engine. This allows the meeting to proceed smoothly and questions and answers to be answered appropriately.

[0679] As a concrete example, consider a meeting to decide on a marketing strategy for a new product. The user inputs the personal information of the person in charge and sets the purpose of the meeting as "deciding on a marketing strategy for a new product." They then set their positions and opinions and input constraints on the progress of the meeting (e.g., the meeting time must be within one hour). The server generates explanatory materials based on these settings and prepares anticipated questions and answers. At the same time, an emotion analysis engine recognizes the user's emotions and provides appropriate feedback. Finally, once the meeting begins, the server proceeds with the meeting in real time and also handles questions and answers. This system enables highly efficient, interactive meetings that take emotions into consideration.

[0680] An example prompt is:

[0681] "I want to schedule a marketing strategy meeting for a new product. First, let's create a meeting scenario by entering the personal information of the executives."

[0682] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0683] Step 1:

[0684] The user inputs character information. Specifically, the user enters their name, position, and affiliation into the system's input form and clicks the "Submit" button. The device collects the input information and sends it to the server. The server stores the received information in a database. The input is the character's personal information, and the output is the information recorded in the database.

[0685] Step 2:

[0686] The user sets up a meeting scenario. The user inputs the purpose and agenda of the meeting and clicks the "Save Scenario" button. The terminal sends the scenario information to the server. The server saves the scenario information in a database. The input is the scenario information, and the output is the scenario information recorded in the database.

[0687] Step 3:

[0688] The user sets the position and opinion of each character. The user inputs the views and opinions of each character and clicks the "Save Opinion" button. The device sends the information to the server. The server stores the received positions and opinions in a database. The input is the positions and opinions, and the output is the positions and opinions recorded in the database.

[0689] Step 4:

[0690] The user sets instructions and constraints for the progress of the conference. The user sets the conditions and constraints for the progress of the conference and clicks the "Save Constraints" button. The terminal sends the information to the server. The server saves the information in a database. The input is instructions and constraints, and the output is the instructions and constraints recorded in the database.

[0691] Step 5:

[0692] The server generates explanatory materials using a generative AI model. It uses a natural language processing engine to review related materials and presentation software to create slides. It also uses a text generation engine to generate text and an animation generation tool to create animations. The generated explanatory materials are stored on the server. The input is data such as the meeting scenario, positions and opinions, instructions and constraints, and the output is the generated explanatory materials.

[0693] Step 6:

[0694] The server prepares expected questions and answers. The server extracts relevant questions and answers from the database and creates a list of expected questions and answers. This list is used to prepare for Q&A sessions. The input is past meeting data and related documents, and the output is a list of expected questions and answers.

[0695] Step 7:

[0696] The server adjusts the schedule. Using its automatic scheduling function, the server calculates the optimal meeting date and time based on the schedule information of the participants and sends a notification email to all participants. The input is the schedule information of the participants, and the output is the adjusted meeting date and time and the notification email.

[0697] Step 8:

[0698] The server uses an emotion analysis engine to recognize the user's emotions in real time. It acquires voice and facial expression data from the user and analyzes it using the emotion analysis engine. The acquired emotional data is reflected in the progress of the conference. The input is the user's voice and facial expression data, and the output is the analyzed emotional data.

[0699] Step 9:

[0700] The server runs the meeting in real time. It starts the meeting based on the adjusted meeting schedule and displays explanatory materials. It dynamically adjusts the meeting progress based on emotional data obtained from the emotion analysis engine and also conducts appropriate Q&A. The inputs are the adjusted meeting schedule, generated explanatory materials, analyzed emotional data, and a list of anticipated questions and answers. The output is a smoothly progressing meeting and appropriate Q&A provided in real time.

[0701] (Application example 2)

[0702] 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."

[0703] Conventional conferencing systems require a lot of human intervention and are not fully automated, which means that preparing and running a meeting requires a lot of time and effort. Furthermore, when it comes to factory production management and task management, data collection and analysis are cumbersome, making efficient management difficult. Furthermore, it is difficult to conduct an interactive meeting that takes into account the emotions of the meeting participants, which can easily lead to misunderstandings and mistakes due to poor communication.

[0704] 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 inputting participant information, means for inputting a meeting scenario, means for inputting the positions and opinions of the participants, means for inputting instructions and constraints for the progress of the meeting, means for creating explanatory materials based on the generated data, means for preparing anticipated questions and answers for a question-and-answer session, means for automatically adjusting the meeting schedule, emotion analysis means for recognizing the user's emotions, means for conducting the meeting in real time, and means for collecting and analyzing data for production management and task management. This not only automates the preparation and progress of the meeting, but also improves the efficiency of production management and task management in factories, and further enables smooth meeting management that takes into account the emotions of the participants by using an emotion recognition function.

[0705] The "means for inputting participant information" refers to a device or system for inputting and managing information (such as name, position, affiliation, etc.) about each person participating in the conference.

[0706] The "means for inputting a meeting scenario" refers to a device or system for inputting and setting scenario information necessary for the progress of a meeting, such as the purpose and agenda of the meeting.

[0707] The "means for inputting the positions and opinions of participants" is a device or system for inputting and managing the views and opinions of conference participants.

[0708] The "means for inputting instructions and constraints for the progress of the meeting" refers to a device or system for inputting specific conditions and constraints for the progress of the meeting and the designation of the facilitator.

[0709] The "means for creating explanatory materials based on the generated data" refers to a device or system for automatically creating explanatory materials, slides, etc. based on the collected and generated data.

[0710] The "means for preparing anticipated questions and answers for a question and answer session" refers to a device or system for preparing anticipated questions and answers in advance and storing them in a database.

[0711] The "means for automatically adjusting the schedule of a meeting" is a device or system for automatically adjusting the optimal date of a meeting based on the schedule information of the meeting participants.

[0712] The "emotion analysis means for recognizing the user's emotions" is a device or system for analyzing the emotions of the user from their voice and facial expressions and providing appropriate feedback based on the analysis.

[0713] A "means for conducting a meeting in real time" is a device or system for conducting and managing a meeting in real time based on a pre-prepared scenario, materials, participants' opinions, and sentiment analysis data.

[0714] "Means for collecting and analyzing data for production management and task management" refers to a device or system that collects and analyzes data on production lines and tasks in a factory to perform efficient production management and task management.

[0715] This invention provides a system that combines the input of participant information, setting of meeting scenarios, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, schedule adjustment, real-time progress of meetings, and emotion recognition.The purpose is to apply this system to factory robots to improve the efficiency of production management and task management.

[0716] First, users enter information about each person participating in the meeting (such as name, position, and affiliation). This is done using a device such as a smartphone or tablet. The device collects this information and sends it to the server, which then stores the received information in a database.

[0717] Next, the user sets the meeting scenario (objective, agenda, etc.). The user inputs this scenario information and sends it from the terminal to the server. The server receives the scenario information and stores it in a database.

[0718] Furthermore, the user can set the position and opinion of each person, which reflects the participants' views and opinions on the content of the meeting. The information is sent from the terminal to the server, which stores it in a database.

[0719] Users also set specific conditions and constraints for the progress of the meeting, including the selection of a moderator and the duration of the meeting. This information is also sent from the device to the server, which stores it in a database.

[0720] The generation of explanatory materials is performed on the server using a generative AI model (e.g., gamma, Writesonic, Fliki, ChatPDF), and the server stores the generated materials in a database.

[0721] For Q&A, the server prepares anticipated questions and answers in advance by retrieving frequently asked questions and their answers from a database and saving them.

[0722] The server uses an automatic scheduling algorithm to schedule meetings, and it determines the optimal meeting date and time based on the participants' schedule information and notifies them.

[0723] The server uses an emotion analysis engine with TensorFlow for emotion recognition, which is a technology that analyzes emotions from the user's voice and facial expressions in real time.

[0724] Finally, the meeting proceeds in real time. The server displays the generated materials, adjusts the progress of the meeting based on the sentiment analysis data, and also conducts Q&A based on anticipated questions and answers.

[0725] When applied to factory robots, the robots collect data for production management and task management and analyze it on a server, thereby improving work efficiency within the factory.

[0726] As a concrete example, consider a meeting to introduce a new manufacturing line. The user inputs information about the manager and engineers and sets the purpose and content of the meeting. They then input the opinions of the participants and set the constraints of the meeting. A generative AI tool creates explanatory materials and prepares anticipated questions and answers. The server automatically adjusts the schedule and notifies everyone. An emotion analysis engine analyzes the voices and facial expressions of the participants and adjusts the progress of the meeting.

[0727] An example of a prompt is as follows:

[0728] Meeting objective: Introduction of a new production line

[0729] Characters:

[0730] Name: Administrator

[0731] Position: Production Department

[0732] Name: Technician

[0733] Position: Engineering Department

[0734] agenda:

[0735] 1. Issues with the current line

[0736] 2. Advantages and disadvantages of introducing a new line

[0737] 3. Implementation Schedule

[0738] 4. Cost-effectiveness

[0739] Expected Q&A:

[0740] Q: What is the cost of installing a new line?

[0741] A: The implementation cost is approximately 100 million yen, and the expected payback period is two years.

[0742] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0743] Step 1:

[0744] Users use devices such as smartphones or tablets to input information about each person participating in the meeting (such as name, position, and affiliation). This input data is sent from the device to a server. The server stores the received information in a database. The input data is personal information, and the output data is the personal information stored in the database.

[0745] Step 2:

[0746] The user inputs information to set up the meeting scenario (purpose, agenda, etc.) into the terminal and sends it to the server. The server receives the scenario information and stores it in a database. The input data is the scenario information, and the output data is the scenario information stored in the database.

[0747] Step 3:

[0748] The user inputs the position and opinion of each character. The information entered by the user is sent from the terminal to the server, which stores it in a database. The input data is the position and opinion information, and the output data is the position and opinion information stored in the database.

[0749] Step 4:

[0750] Users input instructions and constraints for the progress of the conference and send them to the server via their terminals. The server receives these instructions and constraints and stores them in a database. The input data is the instructions and constraints information, and the output data is the instructions and constraints information stored in the database.

[0751] Step 5:

[0752] The server automatically creates explanatory materials using a generation AI tool (gamma, Writesonic, Fliki, ChatPDF) based on the various input information. The server saves the generated materials in a database. The input data is the various input information, and the output data is the generated explanatory materials.

[0753] Step 6:

[0754] The server prepares expected questions and answers for the Q&A session. It retrieves frequently asked questions and their answers from the database, prepares them in advance, and saves them. The input data is the format of frequently asked questions and answers, and the output data is the expected questions and answers saved in the database.

[0755] Step 7:

[0756] The server adjusts the meeting schedule. It collects the schedule information of the participants and calculates the optimal meeting schedule using an automatic scheduling algorithm. The calculated schedule information is notified to all participants. The input data is the schedule information of the participants, and the output data is the adjusted meeting schedule.

[0757] Step 8:

[0758] The server uses an emotion analysis engine based on TensorFlow to analyze emotions from the user's voice and facial expressions in real time. The analysis results are stored in a database and used to adjust the progress of the meeting. The input data is voice and facial expression data, and the output data is analyzed emotional data.

[0759] Step 9:

[0760] The server conducts the meeting in real time, displays the generated explanatory materials, adjusts the progress based on emotion analysis data, and conducts Q&A based on anticipated questions and answers. The input data is the various generated materials and analysis data, and the output data is the meeting information conducted in real time.

[0761] Step 10:

[0762] Factory robots collect data for production management and task management. The server analyzes this data and helps improve work efficiency. The input data is production management and task management data, and the output data is analyzed management information.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] [Third embodiment]

[0767] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0768] 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.

[0769] 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).

[0770] 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.

[0771] 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.

[0772] 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).

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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."

[0779] The present invention is a system that includes inputting participant information, setting the meeting scenario, setting positions and opinions, setting instructions and constraints, generating explanatory materials, preparing anticipated questions and answers, adjusting schedules, and conducting the meeting in real time. This system automates the preparation and progress of meetings, improving work efficiency and optimizing resource utilization.

[0780] First, the user inputs the participant information. This includes entering personal information (such as names, positions, and affiliations) of the executives and managers participating in the meeting into the system. The terminal collects this information and sends it to the server. The server stores the received information in a database.

[0781] Next, the user sets the meeting scenario (objective, agenda, etc.). The user inputs this scenario information and sends it to the server via the terminal. The server receives the scenario information and stores it in a database.

[0782] Next, the user sets the position and opinion of each person. This information indicates the participants' views and opinions on the meeting agenda. The user enters this information into the terminal and sends it to the server. The server stores the received information in a database.

[0783] The user then sets the meeting instructions and constraints, including specific conditions and constraints for the meeting's progress, and the designation of a moderator. The user enters these instructions and constraints into the terminal and sends them to the server, which stores the received information in a database.

[0784] The server automatically generates explanatory materials by linking with various AI tools. Specifically, ChatPDF reviews related materials, gamma creates slides, Writesonic generates text, and Fliki creates actions. The generated explanatory materials are stored on the server.

[0785] For Q&A, the server prepares anticipated questions and answers in advance. This retrieves anticipated questions and their answers from a database and prepares for real-time responses. The server creates and stores this anticipated question and answer data.

[0786] The server uses an automatic scheduling function to adjust the schedule, arranging the optimal meeting date and time based on the schedule information of the participants and notifying all participants.

[0787] Finally, the server is responsible for the real-time progress of the meeting, using automatically generated explanatory materials and anticipated question and answer data to facilitate the real-time progress of the meeting and support the interactive activities of participants.

[0788] As a concrete example, consider a meeting to decide on a marketing strategy for a new product. The user enters information such as the president's name and title, and sets the purpose of the meeting as "deciding on a marketing strategy for a new product." Then, the user sets the opinions of each participant and enters constraints on the progress of the meeting (e.g., the meeting must last no more than one hour). Based on these settings, the server generates explanatory materials and prepares anticipated questions and answers. At the same time, the server automatically adjusts the optimal date and time for the meeting, taking into account the schedules of the participants. Finally, once the meeting begins, the server supports the progress in real time and also handles questions and answers.

[0789] In this way, the system of the present invention allows for efficient and effective conference conduct with minimal human intervention.

[0790] The processing flow will be explained below.

[0791] Step 1:

[0792] The user enters character information. Specifically, the user enters information such as the name, position, and affiliation of an executive or manager into a web form and then presses the submit button.

[0793] Step 2:

[0794] The terminal transmits the character information input by the user to the server, and the transmitted information reaches the server via the network.

[0795] Step 3:

[0796] The server stores the received character information in a database, which allows the information to be retained for later use.

[0797] Step 4:

[0798] The user enters the meeting scenario information, including details such as the purpose of the meeting and the agenda. The user completes the input and presses the send button.

[0799] Step 5:

[0800] The terminal transmits the scenario information input by the user to the server, and the transmitted information reaches the server via the network.

[0801] Step 6:

[0802] The server stores the received scenario information in a database, thereby storing the information necessary for the progress of the conference.

[0803] Step 7:

[0804] The user inputs the position and opinion of each character, and then presses the send button.

[0805] Step 8:

[0806] The terminal transmits the positions and opinions input by the user to the server, and the transmitted information reaches the server via the network.

[0807] Step 9:

[0808] The server stores the received positions and opinions in a database, thereby maintaining participants' views and opinions on the meeting agenda.

[0809] Step 10:

[0810] The user inputs instructions and restrictions for the progress of the conference, and then presses the send button.

[0811] Step 11:

[0812] The terminal transmits the instructions and constraints input by the user to the server, and the transmitted information reaches the server via the network.

[0813] Step 12:

[0814] The server stores the received instructions and constraints in a database, thereby maintaining the conditions for the progress of the conference.

[0815] Step 13:

[0816] The server works with various generative AI tools to prepare explanatory materials. Specifically, it reviews related documents with ChatPDF, creates slides with gamma, generates text with Writesonic, and creates actions with Fliki.

[0817] Step 14:

[0818] The server stores the generated explanatory materials in a database, thereby maintaining the materials used in the conference.

[0819] Step 15:

[0820] The server generates data on anticipated questions and answers. The server retrieves information necessary to prepare anticipated questions and answers from the database and creates the anticipated questions and answers.

[0821] Step 16:

[0822] The server stores the generated anticipated question and answer data in a database, thereby storing data in preparation for the question and answer session.

[0823] Step 17:

[0824] The server uses an automatic scheduling function to adjust the meeting date and time, calculating the optimal meeting date and time based on the schedules of the participants, and then determines the schedule.

[0825] Step 18:

[0826] The server notifies all participants of the adjusted meeting schedule, allowing them to check the meeting details.

[0827] Step 19:

[0828] The server runs the meeting in real time. When the meeting start time arrives, explanatory materials are displayed and, if necessary, the server answers questions based on the expected questions and answers.

[0829] Example 1

[0830] 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."

[0831] Conventional meeting preparation and execution requires a lot of time and effort, resulting in reduced work efficiency. Specifically, multiple processes are cumbersome, including manually entering participant information, setting the meeting scenario, gathering participants' positions and opinions, setting instructions and constraints for the meeting, creating explanatory materials, preparing anticipated questions and answers, and coordinating schedules. It is also difficult to properly conduct Q&A sessions and interactive activities on the day of the meeting.

[0832] 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.

[0833] In this invention, the server includes means for inputting participant information, means for inputting a meeting scenario, means for inputting the positions and opinions of the participants, means for inputting instructions and constraints for the progress of the meeting, means for automatically generating explanatory materials based on generated data, means for saving the explanatory materials, means for preparing anticipated questions and answers for a question and answer session, means for saving anticipated question and answer data, means for automatically adjusting the meeting schedule based on schedule information of the participants, means for notifying scheduling information, and means for conducting the meeting in real time. This consistently automates the process from preparation to progress of the meeting, enabling improved work efficiency and optimal use of resources.

[0834] "Participant information" refers to personal information such as names, positions, and affiliations of people participating in a meeting.

[0835] A "meeting scenario" refers to a plan of the meeting's purpose, agenda, and how it will proceed.

[0836] "Position" refers to the attributes and role of each participant in a meeting.

[0837] "Opinions" refers to each participant's views and opinions on the meeting agenda.

[0838] "Instructions" refer to specific conditions and constraints regarding the conduct of the meeting, as well as instructions from the facilitator.

[0839] "Constraints" refer to specific conditions or limitations on the progress of a meeting.

[0840] "Explanatory materials" refers to slides, text, charts, animations, etc. that are necessary to understand the content of the meeting.

[0841] A "generative AI tool" refers to a software tool that uses artificial intelligence to automatically generate materials and text.

[0842] "Storage means" refers to a mechanism for storing generated data and materials in a storage device.

[0843] "Anticipated questions and answers" refers to preparing questions and answers that are expected to be asked at a meeting in advance.

[0844] "Scheduling" refers to setting the optimal meeting date and time based on the participants' schedules.

[0845] "Automatic scheduling function" refers to the function that automatically calculates the optimal date and time based on the participants' schedule information.

[0846] "Real-time progress" refers to managing and supporting the progress of a meeting in real time.

[0847] The present invention is a system that automates the preparation and progress of a meeting, and covers a series of processes including inputting participant information, setting the meeting scenario, inputting positions and opinions, setting instructions and constraints, generating explanatory materials, preparing anticipated questions and answers, adjusting schedules, and proceeding with the meeting in real time. Specific embodiments of this system are described in detail below.

[0848] First, the user inputs information about the participants. This includes inputting information such as the names, positions, and affiliations of the executives and managers participating in the meeting into the terminal. The terminal collects this information and sends it to the server. The server stores the received information in a database.

[0849] Next, the user sets up a meeting scenario, which defines the purpose and agenda of the meeting, and inputs it from the user's terminal and sends it to the server, which then stores this scenario information in a database.

[0850] Next, the user sets the position and opinion of each participant. This involves inputting their views and opinions on the meeting agenda, which the user enters into their terminal and sends to the server, which then stores this information in a database.

[0851] The user then sets instructions and constraints for the conference, including specific conditions and constraints for the conference, as well as instructions from the moderator. The user enters these on the terminal and sends them to the server, which stores the received information in a database.

[0852] The server automatically generates explanatory materials using AI tools. Specifically, it uses ChatPDF to review relevant materials, gamma to create slides, Writesonic to generate text, and Fliki to create animations. The generated explanatory materials are stored on the server.

[0853] Regarding the preparation of Q&A sessions, the server prepares anticipated questions and answers in advance. This is done by retrieving anticipated questions and their answers from a database. The server creates and saves these anticipated question and answer data.

[0854] Regarding schedule adjustment, the server uses an automatic scheduling function to find the optimal meeting date and time based on the schedules of the participants and notifies all participants. For example, it determines that "September 10th from 2:00 to 3:00 PM is optimal" and notifies all participants.

[0855] Finally, the server uses automatically generated explanatory materials and anticipated question and answer data to facilitate real-time progress of the meeting. During the meeting, the server supports participants in Q&A sessions and other interactive activities.

[0856] As a concrete example, imagine a meeting to decide on a marketing strategy for a new product. The user enters the name and title of the president, and sets the purpose of the meeting as "deciding on a marketing strategy for a new product," for example. Then, the user sets the opinions of each participant and enters constraints on the progress of the meeting (e.g., the meeting must last no more than one hour). Based on these settings, the server generates explanatory materials and prepares anticipated questions and answers. At the same time, the server automatically adjusts the optimal date and time for the meeting, taking into account the schedules of the participants. Finally, once the meeting begins, the server provides real-time support for the progress and also handles questions and answers.

[0857] In this way, the system of the present invention allows for efficient and effective meeting preparation and conduct with minimal human intervention.

[0858] An example of a prompt sentence might be, "Please set up scenario information for a marketing strategy meeting for a new product. For example, please enter specific information about the characters, the purpose of the scenario, their positions and opinions, and instructions and constraints."

[0859] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0860] Step 1:

[0861] The user inputs the participant information. The user inputs the names, positions, and affiliations of the people participating in the conference into the terminal, and the terminal sends this information to the server. The server stores the received information in a database.

[0862] Input: Character's personal information (name, position, affiliation)

[0863] Data processing: The device organizes the input information and sends it to the server

[0864] Output: Character information is saved in the database

[0865] Specific operation: The user enters "Taro Tanaka, President, Sales Department," and the server records this information in the database.

[0866] Step 2:

[0867] The user sets up a meeting scenario. The user inputs the purpose and agenda of the meeting into the terminal, and the terminal sends this to the server. The server stores the scenario information in a database.

[0868] Input: Meeting purpose, agenda

[0869] Data processing: The device organizes the input information and sends it to the server

[0870] Output: Meeting scenario information is saved in the database

[0871] Specific operation: The user inputs "Deciding on marketing strategy for new product, budget consideration, market analysis," and the server records this information in the database.

[0872] Step 3:

[0873] The user sets the position and opinion of each character. The user inputs the corresponding information into the terminal, and the terminal sends it to the server. The server stores the position and opinion information in a database.

[0874] Input: Position and opinion of each character

[0875] Data processing: The device organizes the input information and sends it to the server

[0876] Output: The positions and opinions of the characters are stored in a database.

[0877] Specific operation: The user enters "Taro Tanaka, prioritizes market expansion" and "Jiro Suzuki, prioritizes cost reduction," and the server records the information in the database.

[0878] Step 4:

[0879] Users set instructions and constraints for the conference proceedings. The users input specific instructions and constraints into their terminals, which then transmit them to the server, which stores the received information in a database.

[0880] Input: Meeting instructions, constraints

[0881] Data processing: The device organizes the input information and sends it to the server

[0882] Output: Meeting instructions and constraints stored in a database

[0883] Specific operation: The user inputs "meeting time within 1 hour" and "presentation of materials within 5 minutes," and the server records this information in the database.

[0884] Step 5:

[0885] The server automatically generates explanatory materials using generative AI tools. Specifically, materials are reviewed with ChatPDF, slides are created with gamma, text is generated with Writesonic, and motion animations are added with Fliki.

[0886] Input: Scenario information, characters' positions and opinions, instructions and constraints

[0887] Data processing: Automatically generate materials using generative AI tools

[0888] Output: explanatory materials are generated and stored in a database

[0889] Specific Action: The server generates slides with the central theme of "New Product Features," supplements them with text, and adds action animations.

[0890] Step 6:

[0891] The server prepares anticipated questions and answers. The server retrieves anticipated questions and answers from the database and prepares them in advance.

[0892] Input: Past meeting data in the database

[0893] Data processing: The server extracts and prepares expected questions and answers

[0894] Output: Expected question and answer data is generated and saved in a database

[0895] What happens next: The server prepares questions and answers such as "What is the price of the new product?" and "What are your sales targets for the first year?"

[0896] Step 7:

[0897] The server coordinates the schedule. Based on the schedule information of the participants, the server uses an automatic scheduling function to determine the optimal meeting date and time and notifies all participants.

[0898] Input: Character schedule information

[0899] Data processing: Automatic scheduling algorithm calculates optimal date and time

[0900] Output: The best meeting time is determined and notified

[0901] Specific operation: The server determines that "September 10th, between 2:00 and 3:00 PM is optimal" and notifies participants of that date and time.

[0902] Step 8:

[0903] The server manages the real-time progress of the meeting using automatically generated explanatory materials and anticipated question and answer data. When a participant asks a question during the meeting, the server responds using anticipated questions and answers prepared in advance.

[0904] Input: explanatory materials, expected question and answer data

[0905] Data processing: Real-time presentation of materials and management of questions and answers

[0906] Output: Meetings run smoothly

[0907] Specific operation: The server conducts a meeting in real time, and answers the question "What is the target market for the product?" with "The target markets are North America and Europe."

[0908] (Application example 1)

[0909] 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."

[0910] Preparing and conducting meetings in modern corporate activities is extremely complicated, requiring a great deal of effort and time. In particular, in factories and other workplaces, where executives and workers are busy, there is a need to improve the efficiency and quality of meetings. The frequent time spent by employees and managers engaged in workplace work preparing for meetings is a serious problem that reduces productivity. Furthermore, insufficient preparation for explanatory materials and Q&A sessions can reduce the effectiveness of meetings, delay decision-making, and lead to misunderstandings. This creates a need for work efficiency and real-time support.

[0911] 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.

[0912] In this invention, the server includes a means for inputting participant information, a means for inputting a meeting scenario, a means for inputting the participants' positions and opinions, a means for automatically generating explanatory materials based on the generated data, a means for preparing anticipated questions and answers for a Q&A session, a means for adjusting the meeting schedule using an automatic scheduling function, and a means for conducting the meeting in real time. This makes it possible to improve the efficiency and quality of meeting preparation and conduct. Particularly in factories, this can improve the quality of meetings while maintaining productivity.

[0913] "Participant information" refers to information such as the name, position, and department of each person participating in the conference.

[0914] A "meeting scenario" is a plan that includes the purpose and agenda of a meeting and serves as a guide for the progress of the meeting.

[0915] "Positions and opinions" refers to each participant's views and suggestions on the agenda of the meeting.

[0916] "Instructions and Restrictions" indicates specific instructions and restrictions regarding the progress of the meeting.

[0917] "Explanatory materials" are documents and presentation materials used to clearly explain the contents of a meeting.

[0918] "Anticipated questions and answers" are a set of questions and answers that are predicted to occur during the meeting, and are preparatory materials to guide participants smoothly.

[0919] "Automatic scheduling" is the process of automatically scheduling a meeting in an optimal way based on the schedules of the participants.

[0920] "Real-time progress" refers to the process of presenting explanatory materials and answering questions in real time as the meeting progresses.

[0921] A "generative AI model" is an algorithm or tool that uses artificial intelligence to automatically generate documents or presentation materials.

[0922] The "automatic scheduling function" is a function that automatically arranges the optimal meeting date and time taking into account the schedules of the participants.

[0923] The present invention is a system for improving the efficiency of meeting preparation and progress at a workplace such as a factory. Specifically, the system automates the input of participant information, setting of a meeting scenario, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, scheduling, and real-time progress of the meeting. An embodiment of the present invention is described below.

[0924] First, the user uses a terminal to input information about the people participating in the meeting, including their names, job titles, and departments. The terminal collects this information and sends it to the server, which then stores it in a database.

[0925] Next, the user sets up a meeting scenario, which is a plan that includes the purpose and agenda of the meeting. The user inputs this scenario information and sends it to the server via their terminal. The server receives the scenario information and stores it in a database.

[0926] Next, the user sets the position and opinion of each person. This information represents the participants' views and suggestions on the meeting agenda. The user enters this information into the terminal and sends it to the server. The server stores the received information in a database.

[0927] The user then sets the meeting instructions and constraints, including specific instructions and constraints for the meeting and the designation of the moderator. The user enters these instructions and constraints into the terminal and sends them to the server, which stores the received information in a database.

[0928] The server automatically generates explanatory materials in cooperation with various generative AI models. Specifically, ChatPDF reviews related materials, gamma creates slides, Writesonic generates text, and Fliki creates actions. The generated explanatory materials are stored on the server.

[0929] For the Q&A session, the server prepares anticipated questions and answers in advance. This anticipated question and answer data includes predicted questions and answers based on the participants' positions and opinions. The server creates this anticipated question and answer data and stores it in a database.

[0930] Regarding scheduling, the server automatically adjusts the optimal meeting date and time based on the schedule information of all participants. For example, it manages the schedule using the Google Calendar API and notifies all participants.

[0931] Finally, the server is responsible for the real-time progress of the meeting. Using automatically generated explanatory materials and anticipated question and answer data, the server supports the progress of the meeting in real time and supports the interactive activities of participants.

[0932] As a concrete example, consider setting up a "maintenance meeting for a new production line at a factory." When entering the characters, enter the names, positions, and departments of the participants. When setting the scenario, set the agenda with the goal of "optimizing the maintenance of the new production line." When setting the opinions, enter the views of each participant. When setting the constraints, enter constraints such as "meeting time must be within one hour." ChatPDF, gamma, Writesonic, and Fliki are used to generate explanatory materials.

[0933] Example prompt sentence:

[0934] "Enter the information of all members participating in a factory maintenance meeting. Set the meeting purpose and agenda, and record each participant's opinions and positions. Set instructions and constraints for the meeting, use AI tools to generate briefing materials and anticipated questions and answers, and use the auto-scheduling function to arrange the optimal meeting date and time."

[0935] In this way, the system of the present invention can support efficient and effective preparation and conduct of meetings even at work sites such as factories.

[0936] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0937] Step 1:

[0938] The user uses a terminal to input information about the people participating in the meeting, including their names, job titles, and departments. The terminal collects this information and sends it to the server, which then stores it in a database.

[0939] Input: Name, position, department

[0940] Output: Character information stored in the database

[0941] Step 2:

[0942] The user sets up a meeting scenario, which is a plan that includes the purpose and agenda of the meeting. The user inputs this scenario information and sends it to the server via their terminal. The server receives the scenario information and stores it in a database.

[0943] Input: Meeting purpose, agenda

[0944] Output: Scenario information stored in the database

[0945] Step 3:

[0946] The user sets the position and opinion of each person involved. This information indicates the participants' views and suggestions regarding the meeting agenda. The user enters this information into the terminal and sends it to the server. The server stores the received information in a database.

[0947] Input: Position and opinion of each character

[0948] Output: Positions and opinions stored in a database

[0949] Step 4:

[0950] The user sets the meeting instructions and constraints, including specific instructions and constraints for the meeting's progress and the designation of the moderator. The user enters these instructions and constraints into the terminal and sends them to the server, which stores the received information in a database.

[0951] Input: Meeting instructions, constraints, and facilitator designation

[0952] Output: Instructions and constraints stored in a database

[0953] Step 5:

[0954] The server works with various generative AI models to automatically generate explanatory materials. Specifically, ChatPDF reviews related materials, gamma creates slides, Writesonic generates text, and Fliki creates videos. The generated explanatory materials are stored on the server.

[0955] Input: Meeting information stored in the database

[0956] Output: Generated explanatory material

[0957] Step 6:

[0958] The server prepares anticipated questions and answers for the Q&A session. This anticipated question and answer data includes predicted questions and answers based on the positions and opinions of the participants. The server creates this anticipated question and answer data and stores it in a database.

[0959] Input: Participants' positions and opinions

[0960] Output: Expected question and answer data

[0961] Step 7:

[0962] The server automatically adjusts the optimal meeting date and time based on the schedule information of all participants. It manages the schedule using the Google Calendar API and notifies all participants.

[0963] Input: Participant schedule information

[0964] Output: Determining the best meeting time and date and notifying

[0965] Step 8:

[0966] The server is responsible for the real-time progress of the meeting, using automatically generated explanatory materials and anticipated question and answer data to support the progress of the meeting in real time and to support the interactive activities of participants.

[0967] Input: explanatory materials, expected question and answer data

[0968] Output: Real-time meeting progress and Q&A

[0969] This series of steps will enable users to effectively prepare for and conduct a meeting.

[0970] 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.

[0971] The present invention is a system that includes input of participant information, setting of meeting scenarios, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, scheduling, real-time progress of meetings, and an emotion engine that recognizes user emotions. This system automates the preparation and progress of meetings, minimizing human intervention and realizing work efficiency and optimal use of resources.

[0972] First, the user inputs the participant information. This includes entering personal information (such as name, position, and affiliation) of the people in positions of authority who will be participating in the meeting into the system. The terminal collects this information and sends it to the server. The server then stores the received information in a database.

[0973] Next, the user sets the meeting scenario (objective, agenda, etc.). The user inputs this scenario information and sends it to the server via the terminal. The server receives the scenario information and stores it in a database.

[0974] The user then sets the position and opinion of each person involved. This information reflects the views and opinions of each participant regarding the content of the meeting. The user inputs this information and sends it to the server via their device. The server then stores the received information in a database.

[0975] Next, the user sets instructions and constraints for the progress of the meeting, including specific conditions and constraints for the progress of the meeting, and the designation of the moderator. The user inputs this information and sends it to the server via their terminal. The server stores the received instructions and constraints in a database.

[0976] The server works with various AI tools to generate the explanatory materials. Specifically, ChatPDF reviews the relevant materials, gamma creates slides, Writesonic generates text, and Fliki creates actions. The generated explanatory materials are stored on the server.

[0977] For Q&A, the server prepares anticipated questions and answers in advance. This retrieves anticipated questions and their answers from a database and prepares for responses in real time. The server creates and stores this anticipated question and answer data.

[0978] The server uses an automatic scheduling function to adjust the schedule, arranging the optimal meeting date and time based on the schedule information of the participants and notifying all participants.

[0979] Furthermore, an emotion engine is combined to recognize the user's emotions. The server uses the emotion engine to analyze emotions from the user's voice and facial expressions in real time. The analyzed emotion data can be used to dynamically adjust the progress of the meeting and Q&A. For example, if a user shows anxiety during a meeting, the server can temporarily slow down the progress of the meeting and provide more detailed explanations.

[0980] Finally, the meeting proceeds in real time. The server starts the meeting based on the adjusted meeting schedule and displays the generated explanatory materials. Furthermore, based on the user's emotions recognized by the emotion engine, the server dynamically changes its response measures to ensure smooth meeting operation. It also conducts Q&A based on anticipated questions and answers, providing appropriate responses.

[0981] For example, consider a meeting to decide on a marketing strategy for a new product. The user inputs the personal information of the executives and sets the purpose of the meeting as "deciding on a marketing strategy for a new product." They then set the opinions of the participants and input constraints on the progress of the meeting (e.g., the meeting time must be within one hour). Based on these settings, the server generates explanatory materials and prepares anticipated questions and answers. At the same time, the emotion engine recognizes the user's emotions and provides appropriate feedback. Finally, once the meeting begins, the server will proceed with the meeting in real time and also handle questions and answers.

[0982] In this way, the system of the present invention, by incorporating emotion recognition technology, achieves more advanced conference management and provides interactive conferences that take into consideration the emotions of participants.

[0983] The processing flow will be explained below.

[0984] Step 1:

[0985] The user enters the character information. This involves entering the personal information of the person in a position of authority (such as name, position, affiliation, etc.) into a web form and pressing the submit button.

[0986] Step 2:

[0987] The terminal transmits the character information input by the user to the server, and the transmitted information reaches the server via the network.

[0988] Step 3:

[0989] The server stores the received character information in a database, which allows the information to be retained for later use.

[0990] Step 4:

[0991] The user enters the meeting scenario information, including details such as the purpose of the meeting and the agenda. The user completes the input and presses the send button.

[0992] Step 5:

[0993] The terminal transmits the scenario information input by the user to the server, and the transmitted information reaches the server via the network.

[0994] Step 6:

[0995] The server stores the received scenario information in a database, thereby storing the information necessary for the progress of the conference.

[0996] Step 7:

[0997] The user inputs the position and opinion of each character, and then presses the send button.

[0998] Step 8:

[0999] The terminal transmits the positions and opinions input by the user to the server, and the transmitted information reaches the server via the network.

[1000] Step 9:

[1001] The server stores the received positions and opinions in a database, thereby maintaining participants' views and opinions on the meeting agenda.

[1002] Step 10:

[1003] The user inputs instructions and restrictions for the progress of the conference, and then presses the send button.

[1004] Step 11:

[1005] The terminal transmits the instructions and constraints input by the user to the server, and the transmitted information reaches the server via the network.

[1006] Step 12:

[1007] The server stores the received instructions and constraints in a database, thereby maintaining the conditions for the progress of the conference.

[1008] Step 13:

[1009] The server works with various generative AI tools to prepare explanatory materials: ChatPDF reviews related documents, Gamma creates slides, Writesonic generates text, and Fliki creates actions.

[1010] Step 14:

[1011] The server stores the generated explanatory materials in a database, thereby maintaining the materials used in the conference.

[1012] Step 15:

[1013] The server generates data on anticipated questions and answers. The server retrieves information necessary to prepare anticipated questions and answers from the database and creates the anticipated questions and answers.

[1014] Step 16:

[1015] The server stores the generated anticipated question and answer data in a database, thereby storing data in preparation for the question and answer session.

[1016] Step 17:

[1017] The server uses an automatic scheduling function to adjust the meeting date and time, calculating the optimal meeting date and time based on the schedules of the participants, and then determines the schedule.

[1018] Step 18:

[1019] The server notifies all participants of the adjusted meeting schedule, allowing them to check the meeting details.

[1020] Step 19:

[1021] The server uses an emotion engine to analyze the user's emotions in real time, and obtains the user's emotional data through voice analysis and facial expression analysis.

[1022] Step 20:

[1023] The server dynamically adjusts the progress of the meeting based on the emotion data. For example, if the user shows anxiety, the server may slow down the progress of the meeting and add detailed explanations.

[1024] Step 21:

[1025] The server runs the meeting in real time, displays explanatory materials when the meeting starts, and if necessary, holds a Q&A session based on anticipated questions and answers. The server provides appropriate responses based on emotions recognized by the emotion engine.

[1026] Example 2

[1027] 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."

[1028] Conventional meeting preparation and proceeding systems require a lot of manual human effort to set up participant information and meeting scenarios, create explanatory materials, prepare Q&A sessions, coordinate schedules, and proceed with the meeting, resulting in issues with efficiency and accuracy. It is also difficult to recognize participants' emotions during a meeting and respond appropriately, which can sometimes prevent the meeting from proceeding smoothly. It is necessary to solve these issues in order to improve meeting efficiency and participant satisfaction.

[1029] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1030] In this invention, the server includes means for inputting participant information, means for inputting a meeting scenario, means for inputting the positions and opinions of the participants, means for inputting instructions and constraints for the progress of the meeting, means for creating explanatory materials based on data generated using a generative AI model, means for preparing anticipated questions and answers for a question and answer session, means for automatically adjusting the meeting schedule, means for recognizing user emotions in real time using an emotion analysis engine, means for dynamically adjusting the progress of the meeting based on the emotion data, and means for progressing the meeting in real time. This automates the preparation and progress of the meeting, minimizes human intervention, and enables smooth meeting management and efficient resource utilization.

[1031] "Participant information" is personal information such as the names, positions, and affiliations of individuals participating in the conference.

[1032] A "meeting scenario" is detailed planning information necessary for the progress of a meeting, such as the purpose and agenda of the meeting.

[1033] "Positions and opinions" is information that represents the views and opinions of each person participating in the meeting.

[1034] "Instructions and constraints" refers to information including specific conditions and constraints regarding the progress of the meeting and the designation of the facilitator.

[1035] A "generative AI model" is an artificial intelligence technology that automatically performs tasks such as natural language processing and image recognition based on large amounts of data.

[1036] "Explanatory materials" are materials created to support the content of a conference, and may include slides, documents, and actions.

[1037] "Anticipated Questions and Answers" is a list of anticipated questions and their answers prepared in advance.

[1038] "Automatically adjusting the schedule" means automatically determining the optimal date and time for a meeting based on the schedule information of the people participating in the meeting.

[1039] An "emotion analysis engine" is a system that analyzes emotions in real time based on a user's voice and facial expressions.

[1040] "Emotion data" is data about the user's emotional state obtained by the emotion analysis engine.

[1041] "Real-time progress" refers to responding immediately, providing information, and adjusting the flow of the meeting while the meeting is in progress.

[1042] This invention is a system that automates the preparation and progress of meetings, including the input of participant information, setting of the meeting scenario, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, scheduling, real-time recognition of emotions, and progress of the meeting. This system minimizes human intervention, realizes work efficiency, and optimal use of resources.

[1043] First, the user inputs the participant information through the terminal. This includes personal information (such as name, position, and affiliation) of the executives participating in the meeting. The terminal then sends this information to the server, which then stores the received information in a database.

[1044] Next, the user sets the meeting scenario (objectives, agenda, etc.). The scenario information entered through the terminal is sent to the server and stored in the database. When the user sets the position and opinion of each character, they input the views and opinions of each participant regarding the meeting content. This information is also sent to the server through the terminal and stored in the database.

[1045] The user then sets instructions and constraints for the conference, including specific conditions and constraints for the conference and the designation of a moderator. The input information is sent from the terminal to the server and stored in a database.

[1046] The server uses a generative AI model to generate explanatory materials. Specifically, it uses a natural language processing engine to review related materials, presentation software to create slides, a text generation engine to generate text, and an animation generation tool to create animations. The generated explanatory materials are stored on the server.

[1047] For Q&A sessions, the server prepares anticipated questions and answers in advance. This retrieves anticipated questions and their answers from a database and prepares for the Q&A session. The server creates and saves this anticipated question and answer data.

[1048] The server uses an automatic scheduling function to adjust the schedule, arranging the optimal meeting date and time based on the schedule information of the participants and notifying all participants.

[1049] The emotion analysis engine recognizes emotions by analyzing the voice and facial expression data input by the user. The server uses the emotion analysis engine and uses the analyzed data to dynamically adjust the progress of the meeting and Q&A in real time. For example, if a user shows signs of anxiety during a meeting, the server may temporarily slow down the meeting and provide more detailed explanations.

[1050] Finally, the server starts the meeting based on the adjusted schedule and displays the generated explanatory materials. Furthermore, the server dynamically changes its response based on the user's emotions recognized by the emotion analysis engine. This allows the meeting to proceed smoothly and questions and answers to be answered appropriately.

[1051] As a concrete example, consider a meeting to decide on a marketing strategy for a new product. The user inputs the personal information of the person in charge and sets the purpose of the meeting as "deciding on a marketing strategy for a new product." They then set their positions and opinions and input constraints on the progress of the meeting (e.g., the meeting time must be within one hour). The server generates explanatory materials based on these settings and prepares anticipated questions and answers. At the same time, an emotion analysis engine recognizes the user's emotions and provides appropriate feedback. Finally, once the meeting begins, the server proceeds with the meeting in real time and also handles questions and answers. This system enables highly efficient, interactive meetings that take emotions into consideration.

[1052] An example prompt is:

[1053] "I want to schedule a marketing strategy meeting for a new product. First, let's create a meeting scenario by entering the personal information of the executives."

[1054] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1055] Step 1:

[1056] The user inputs character information. Specifically, the user enters their name, position, and affiliation into the system's input form and clicks the "Submit" button. The device collects the input information and sends it to the server. The server stores the received information in a database. The input is the character's personal information, and the output is the information recorded in the database.

[1057] Step 2:

[1058] The user sets up a meeting scenario. The user inputs the purpose and agenda of the meeting and clicks the "Save Scenario" button. The terminal sends the scenario information to the server. The server saves the scenario information in a database. The input is the scenario information, and the output is the scenario information recorded in the database.

[1059] Step 3:

[1060] The user sets the position and opinion of each character. The user inputs the views and opinions of each character and clicks the "Save Opinion" button. The device sends the information to the server. The server stores the received positions and opinions in a database. The input is the positions and opinions, and the output is the positions and opinions recorded in the database.

[1061] Step 4:

[1062] The user sets instructions and constraints for the progress of the conference. The user sets the conditions and constraints for the progress of the conference and clicks the "Save Constraints" button. The terminal sends the information to the server. The server saves the information in a database. The input is instructions and constraints, and the output is the instructions and constraints recorded in the database.

[1063] Step 5:

[1064] The server generates explanatory materials using a generative AI model. It uses a natural language processing engine to review related materials and presentation software to create slides. It also uses a text generation engine to generate text and an animation generation tool to create animations. The generated explanatory materials are stored on the server. The input is data such as the meeting scenario, positions and opinions, instructions and constraints, and the output is the generated explanatory materials.

[1065] Step 6:

[1066] The server prepares expected questions and answers. The server extracts relevant questions and answers from the database and creates a list of expected questions and answers. This list is used to prepare for Q&A sessions. The input is past meeting data and related documents, and the output is a list of expected questions and answers.

[1067] Step 7:

[1068] The server adjusts the schedule. Using its automatic scheduling function, the server calculates the optimal meeting date and time based on the schedule information of the participants and sends a notification email to all participants. The input is the schedule information of the participants, and the output is the adjusted meeting date and time and the notification email.

[1069] Step 8:

[1070] The server uses an emotion analysis engine to recognize the user's emotions in real time. It acquires voice and facial expression data from the user and analyzes it using the emotion analysis engine. The acquired emotional data is reflected in the progress of the conference. The input is the user's voice and facial expression data, and the output is the analyzed emotional data.

[1071] Step 9:

[1072] The server runs the meeting in real time. It starts the meeting based on the adjusted meeting schedule and displays explanatory materials. It dynamically adjusts the meeting progress based on emotional data obtained from the emotion analysis engine and also conducts appropriate Q&A. The inputs are the adjusted meeting schedule, generated explanatory materials, analyzed emotional data, and a list of anticipated questions and answers. The output is a smoothly progressing meeting and appropriate Q&A provided in real time.

[1073] (Application example 2)

[1074] 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."

[1075] Conventional conferencing systems require a lot of human intervention and are not fully automated, which means that preparing and running a meeting requires a lot of time and effort. Furthermore, when it comes to factory production management and task management, data collection and analysis are cumbersome, making efficient management difficult. Furthermore, it is difficult to conduct an interactive meeting that takes into account the emotions of the meeting participants, which can easily lead to misunderstandings and mistakes due to poor communication.

[1076] 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 inputting participant information, means for inputting a meeting scenario, means for inputting the positions and opinions of the participants, means for inputting instructions and constraints for the progress of the meeting, means for creating explanatory materials based on the generated data, means for preparing anticipated questions and answers for a question-and-answer session, means for automatically adjusting the meeting schedule, emotion analysis means for recognizing the user's emotions, means for conducting the meeting in real time, and means for collecting and analyzing data for production management and task management. This not only automates the preparation and progress of the meeting, but also improves the efficiency of production management and task management in factories, and further enables smooth meeting management that takes into account the emotions of the participants by using an emotion recognition function.

[1077] The "means for inputting participant information" refers to a device or system for inputting and managing information (such as name, position, affiliation, etc.) about each person participating in the conference.

[1078] The "means for inputting a meeting scenario" refers to a device or system for inputting and setting scenario information necessary for the progress of a meeting, such as the purpose and agenda of the meeting.

[1079] The "means for inputting the positions and opinions of participants" is a device or system for inputting and managing the views and opinions of conference participants.

[1080] The "means for inputting instructions and constraints for the progress of the meeting" refers to a device or system for inputting specific conditions and constraints for the progress of the meeting and the designation of the facilitator.

[1081] The "means for creating explanatory materials based on the generated data" refers to a device or system for automatically creating explanatory materials, slides, etc. based on the collected and generated data.

[1082] The "means for preparing anticipated questions and answers for a question and answer session" refers to a device or system for preparing anticipated questions and answers in advance and storing them in a database.

[1083] The "means for automatically adjusting the schedule of a meeting" is a device or system for automatically adjusting the optimal date of a meeting based on the schedule information of the meeting participants.

[1084] The "emotion analysis means for recognizing the user's emotions" is a device or system for analyzing the emotions of the user from their voice and facial expressions and providing appropriate feedback based on the analysis.

[1085] A "means for conducting a meeting in real time" is a device or system for conducting and managing a meeting in real time based on a pre-prepared scenario, materials, participants' opinions, and sentiment analysis data.

[1086] "Means for collecting and analyzing data for production management and task management" refers to a device or system that collects and analyzes data on production lines and tasks in a factory to perform efficient production management and task management.

[1087] This invention provides a system that combines the input of participant information, setting of meeting scenarios, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, schedule adjustment, real-time progress of meetings, and emotion recognition.The purpose is to apply this system to factory robots to improve the efficiency of production management and task management.

[1088] First, users enter information about each person participating in the meeting (such as name, position, and affiliation). This is done using a device such as a smartphone or tablet. The device collects this information and sends it to the server, which then stores the received information in a database.

[1089] Next, the user sets the meeting scenario (objective, agenda, etc.). The user inputs this scenario information and sends it from the terminal to the server. The server receives the scenario information and stores it in a database.

[1090] Furthermore, the user can set the position and opinion of each person, which reflects the participants' views and opinions on the content of the meeting. The information is sent from the terminal to the server, which stores it in a database.

[1091] Users also set specific conditions and constraints for the progress of the meeting, including the selection of a moderator and the duration of the meeting. This information is also sent from the device to the server, which stores it in a database.

[1092] The generation of explanatory materials is performed on the server using a generative AI model (e.g., gamma, Writesonic, Fliki, ChatPDF), and the server stores the generated materials in a database.

[1093] For Q&A, the server prepares anticipated questions and answers in advance by retrieving frequently asked questions and their answers from a database and saving them.

[1094] The server uses an automatic scheduling algorithm to schedule meetings, and it determines the optimal meeting date and time based on the participants' schedule information and notifies them.

[1095] The server uses an emotion analysis engine with TensorFlow for emotion recognition, which is a technology that analyzes emotions from the user's voice and facial expressions in real time.

[1096] Finally, the meeting proceeds in real time. The server displays the generated materials, adjusts the progress of the meeting based on the sentiment analysis data, and also conducts Q&A based on anticipated questions and answers.

[1097] When applied to factory robots, the robots collect data for production management and task management and analyze it on a server, thereby improving work efficiency within the factory.

[1098] As a concrete example, consider a meeting to introduce a new manufacturing line. The user inputs information about the manager and engineers and sets the purpose and content of the meeting. They then input the opinions of the participants and set the constraints of the meeting. A generative AI tool creates explanatory materials and prepares anticipated questions and answers. The server automatically adjusts the schedule and notifies everyone. An emotion analysis engine analyzes the voices and facial expressions of the participants and adjusts the progress of the meeting.

[1099] An example of a prompt is as follows:

[1100] Meeting objective: Introduction of a new production line

[1101] Characters:

[1102] Name: Administrator

[1103] Position: Production Department

[1104] Name: Technician

[1105] Position: Engineering Department

[1106] agenda:

[1107] 1. Issues with the current line

[1108] 2. Advantages and disadvantages of introducing a new line

[1109] 3. Implementation Schedule

[1110] 4. Cost-effectiveness

[1111] Expected Q&A:

[1112] Q: What is the cost of installing a new line?

[1113] A: The implementation cost is approximately 100 million yen, and the expected payback period is two years.

[1114] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1115] Step 1:

[1116] Users use devices such as smartphones or tablets to input information about each person participating in the meeting (such as name, position, and affiliation). This input data is sent from the device to a server. The server stores the received information in a database. The input data is personal information, and the output data is the personal information stored in the database.

[1117] Step 2:

[1118] The user inputs information to set up the meeting scenario (purpose, agenda, etc.) into the terminal and sends it to the server. The server receives the scenario information and stores it in a database. The input data is the scenario information, and the output data is the scenario information stored in the database.

[1119] Step 3:

[1120] The user inputs the position and opinion of each character. The information entered by the user is sent from the terminal to the server, which stores it in a database. The input data is the position and opinion information, and the output data is the position and opinion information stored in the database.

[1121] Step 4:

[1122] Users input instructions and constraints for the progress of the conference and send them to the server via their terminals. The server receives these instructions and constraints and stores them in a database. The input data is the instructions and constraints information, and the output data is the instructions and constraints information stored in the database.

[1123] Step 5:

[1124] The server automatically creates explanatory materials using a generation AI tool (gamma, Writesonic, Fliki, ChatPDF) based on the various input information. The server saves the generated materials in a database. The input data is the various input information, and the output data is the generated explanatory materials.

[1125] Step 6:

[1126] The server prepares expected questions and answers for the Q&A session. It retrieves frequently asked questions and their answers from the database, prepares them in advance, and saves them. The input data is the format of frequently asked questions and answers, and the output data is the expected questions and answers saved in the database.

[1127] Step 7:

[1128] The server adjusts the meeting schedule. It collects the schedule information of the participants and calculates the optimal meeting schedule using an automatic scheduling algorithm. The calculated schedule information is notified to all participants. The input data is the schedule information of the participants, and the output data is the adjusted meeting schedule.

[1129] Step 8:

[1130] The server uses an emotion analysis engine based on TensorFlow to analyze emotions from the user's voice and facial expressions in real time. The analysis results are stored in a database and used to adjust the progress of the meeting. The input data is voice and facial expression data, and the output data is analyzed emotional data.

[1131] Step 9:

[1132] The server conducts the meeting in real time, displays the generated explanatory materials, adjusts the progress based on emotion analysis data, and conducts Q&A based on anticipated questions and answers. The input data is the various generated materials and analysis data, and the output data is the meeting information conducted in real time.

[1133] Step 10:

[1134] Factory robots collect data for production management and task management. The server analyzes this data and helps improve work efficiency. The input data is production management and task management data, and the output data is analyzed management information.

[1135] 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.

[1136] 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.

[1137] 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.

[1138] [Fourth embodiment]

[1139] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1140] 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.

[1141] 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).

[1142] 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.

[1143] 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.

[1144] 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).

[1145] 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.

[1146] 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.

[1147] 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.

[1148] 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.

[1149] 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.

[1150] 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.

[1151] 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."

[1152] The present invention is a system that includes inputting participant information, setting the meeting scenario, setting positions and opinions, setting instructions and constraints, generating explanatory materials, preparing anticipated questions and answers, adjusting schedules, and conducting the meeting in real time. This system automates the preparation and progress of meetings, improving work efficiency and optimizing resource utilization.

[1153] First, the user inputs the participant information. This includes entering personal information (such as names, positions, and affiliations) of the executives and managers participating in the meeting into the system. The terminal collects this information and sends it to the server. The server stores the received information in a database.

[1154] Next, the user sets the meeting scenario (objective, agenda, etc.). The user inputs this scenario information and sends it to the server via the terminal. The server receives the scenario information and stores it in a database.

[1155] Next, the user sets the position and opinion of each person. This information indicates the participants' views and opinions on the meeting agenda. The user enters this information into the terminal and sends it to the server. The server stores the received information in a database.

[1156] The user then sets the meeting instructions and constraints, including specific conditions and constraints for the meeting's progress, and the designation of a moderator. The user enters these instructions and constraints into the terminal and sends them to the server, which stores the received information in a database.

[1157] The server automatically generates explanatory materials by linking with various AI tools. Specifically, ChatPDF reviews related materials, gamma creates slides, Writesonic generates text, and Fliki creates actions. The generated explanatory materials are stored on the server.

[1158] For Q&A, the server prepares anticipated questions and answers in advance. This retrieves anticipated questions and their answers from a database and prepares for real-time responses. The server creates and stores this anticipated question and answer data.

[1159] The server uses an automatic scheduling function to adjust the schedule, arranging the optimal meeting date and time based on the schedule information of the participants and notifying all participants.

[1160] Finally, the server is responsible for the real-time progress of the meeting, using automatically generated explanatory materials and anticipated question and answer data to facilitate the real-time progress of the meeting and support the interactive activities of participants.

[1161] As a concrete example, consider a meeting to decide on a marketing strategy for a new product. The user enters information such as the president's name and title, and sets the purpose of the meeting as "deciding on a marketing strategy for a new product." Then, the user sets the opinions of each participant and enters constraints on the progress of the meeting (e.g., the meeting must last no more than one hour). Based on these settings, the server generates explanatory materials and prepares anticipated questions and answers. At the same time, the server automatically adjusts the optimal date and time for the meeting, taking into account the schedules of the participants. Finally, once the meeting begins, the server supports the progress in real time and also handles questions and answers.

[1162] In this way, the system of the present invention allows for efficient and effective conference conduct with minimal human intervention.

[1163] The processing flow will be explained below.

[1164] Step 1:

[1165] The user enters character information. Specifically, the user enters information such as the name, position, and affiliation of an executive or manager into a web form and then presses the submit button.

[1166] Step 2:

[1167] The terminal transmits the character information input by the user to the server, and the transmitted information reaches the server via the network.

[1168] Step 3:

[1169] The server stores the received character information in a database, which allows the information to be retained for later use.

[1170] Step 4:

[1171] The user enters the meeting scenario information, including details such as the purpose of the meeting and the agenda. The user completes the input and presses the send button.

[1172] Step 5:

[1173] The terminal transmits the scenario information input by the user to the server, and the transmitted information reaches the server via the network.

[1174] Step 6:

[1175] The server stores the received scenario information in a database, thereby storing the information necessary for the progress of the conference.

[1176] Step 7:

[1177] The user inputs the position and opinion of each character, and then presses the send button.

[1178] Step 8:

[1179] The terminal transmits the positions and opinions input by the user to the server, and the transmitted information reaches the server via the network.

[1180] Step 9:

[1181] The server stores the received positions and opinions in a database, thereby maintaining participants' views and opinions on the meeting agenda.

[1182] Step 10:

[1183] The user inputs instructions and restrictions for the progress of the conference, and then presses the send button.

[1184] Step 11:

[1185] The terminal transmits the instructions and constraints input by the user to the server, and the transmitted information reaches the server via the network.

[1186] Step 12:

[1187] The server stores the received instructions and constraints in a database, thereby maintaining the conditions for the progress of the conference.

[1188] Step 13:

[1189] The server works with various generative AI tools to prepare explanatory materials. Specifically, it reviews related documents with ChatPDF, creates slides with gamma, generates text with Writesonic, and creates actions with Fliki.

[1190] Step 14:

[1191] The server stores the generated explanatory materials in a database, thereby maintaining the materials used in the conference.

[1192] Step 15:

[1193] The server generates data on anticipated questions and answers. The server retrieves information necessary to prepare anticipated questions and answers from the database and creates the anticipated questions and answers.

[1194] Step 16:

[1195] The server stores the generated anticipated question and answer data in a database, thereby storing data in preparation for the question and answer session.

[1196] Step 17:

[1197] The server uses an automatic scheduling function to adjust the meeting date and time, calculating the optimal meeting date and time based on the schedules of the participants, and then determines the schedule.

[1198] Step 18:

[1199] The server notifies all participants of the adjusted meeting schedule, allowing them to check the meeting details.

[1200] Step 19:

[1201] The server runs the meeting in real time. When the meeting start time arrives, explanatory materials are displayed and, if necessary, the server answers questions based on the expected questions and answers.

[1202] Example 1

[1203] 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."

[1204] Conventional meeting preparation and execution requires a lot of time and effort, resulting in reduced work efficiency. Specifically, multiple processes are cumbersome, including manually entering participant information, setting the meeting scenario, gathering participants' positions and opinions, setting instructions and constraints for the meeting, creating explanatory materials, preparing anticipated questions and answers, and coordinating schedules. It is also difficult to properly conduct Q&A sessions and interactive activities on the day of the meeting.

[1205] 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.

[1206] In this invention, the server includes means for inputting participant information, means for inputting a meeting scenario, means for inputting the positions and opinions of the participants, means for inputting instructions and constraints for the progress of the meeting, means for automatically generating explanatory materials based on generated data, means for saving the explanatory materials, means for preparing anticipated questions and answers for a question and answer session, means for saving anticipated question and answer data, means for automatically adjusting the meeting schedule based on schedule information of the participants, means for notifying scheduling information, and means for conducting the meeting in real time. This consistently automates the process from preparation to progress of the meeting, enabling improved work efficiency and optimal use of resources.

[1207] "Participant information" refers to personal information such as names, positions, and affiliations of people participating in a meeting.

[1208] A "meeting scenario" refers to a plan of the meeting's purpose, agenda, and how it will proceed.

[1209] "Position" refers to the attributes and role of each participant in a meeting.

[1210] "Opinions" refers to each participant's views and opinions on the meeting agenda.

[1211] "Instructions" refer to specific conditions and constraints regarding the conduct of the meeting, as well as instructions from the facilitator.

[1212] "Constraints" refer to specific conditions or limitations on the progress of a meeting.

[1213] "Explanatory materials" refers to slides, text, charts, animations, etc. that are necessary to understand the content of the meeting.

[1214] A "generative AI tool" refers to a software tool that uses artificial intelligence to automatically generate materials and text.

[1215] "Storage means" refers to a mechanism for storing generated data and materials in a storage device.

[1216] "Anticipated questions and answers" refers to preparing questions and answers that are expected to be asked at a meeting in advance.

[1217] "Scheduling" refers to setting the optimal meeting date and time based on the participants' schedules.

[1218] "Automatic scheduling function" refers to the function that automatically calculates the optimal date and time based on the participants' schedule information.

[1219] "Real-time progress" refers to managing and supporting the progress of a meeting in real time.

[1220] The present invention is a system that automates the preparation and progress of a meeting, and covers a series of processes including inputting participant information, setting the meeting scenario, inputting positions and opinions, setting instructions and constraints, generating explanatory materials, preparing anticipated questions and answers, adjusting schedules, and proceeding with the meeting in real time. Specific embodiments of this system are described in detail below.

[1221] First, the user inputs information about the participants. This includes inputting information such as the names, positions, and affiliations of the executives and managers participating in the meeting into the terminal. The terminal collects this information and sends it to the server. The server stores the received information in a database.

[1222] Next, the user sets up a meeting scenario, which defines the purpose and agenda of the meeting, and inputs it from the user's terminal and sends it to the server, which then stores this scenario information in a database.

[1223] Next, the user sets the position and opinion of each participant. This involves inputting their views and opinions on the meeting agenda, which the user enters into their terminal and sends to the server, which then stores this information in a database.

[1224] The user then sets instructions and constraints for the conference, including specific conditions and constraints for the conference, as well as instructions from the moderator. The user enters these on the terminal and sends them to the server, which stores the received information in a database.

[1225] The server automatically generates explanatory materials using AI tools. Specifically, it uses ChatPDF to review relevant materials, gamma to create slides, Writesonic to generate text, and Fliki to create animations. The generated explanatory materials are stored on the server.

[1226] Regarding the preparation of Q&A sessions, the server prepares anticipated questions and answers in advance. This is done by retrieving anticipated questions and their answers from a database. The server creates and saves these anticipated question and answer data.

[1227] Regarding schedule adjustment, the server uses an automatic scheduling function to find the optimal meeting date and time based on the schedules of the participants and notifies all participants. For example, it determines that "September 10th from 2:00 to 3:00 PM is optimal" and notifies all participants.

[1228] Finally, the server uses automatically generated explanatory materials and anticipated question and answer data to facilitate real-time progress of the meeting. During the meeting, the server supports participants in Q&A sessions and other interactive activities.

[1229] As a concrete example, imagine a meeting to decide on a marketing strategy for a new product. The user enters the name and title of the president, and sets the purpose of the meeting as "deciding on a marketing strategy for a new product," for example. Then, the user sets the opinions of each participant and enters constraints on the progress of the meeting (e.g., the meeting must last no more than one hour). Based on these settings, the server generates explanatory materials and prepares anticipated questions and answers. At the same time, the server automatically adjusts the optimal date and time for the meeting, taking into account the schedules of the participants. Finally, once the meeting begins, the server provides real-time support for the progress and also handles questions and answers.

[1230] In this way, the system of the present invention allows for efficient and effective meeting preparation and conduct with minimal human intervention.

[1231] An example of a prompt sentence might be, "Please set up scenario information for a marketing strategy meeting for a new product. For example, please enter specific information about the characters, the purpose of the scenario, their positions and opinions, and instructions and constraints."

[1232] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1233] Step 1:

[1234] The user inputs the participant information. The user inputs the names, positions, and affiliations of the people participating in the conference into the terminal, and the terminal sends this information to the server. The server stores the received information in a database.

[1235] Input: Character's personal information (name, position, affiliation)

[1236] Data processing: The device organizes the input information and sends it to the server

[1237] Output: Character information is saved in the database

[1238] Specific operation: The user enters "Taro Tanaka, President, Sales Department," and the server records this information in the database.

[1239] Step 2:

[1240] The user sets up a meeting scenario. The user inputs the purpose and agenda of the meeting into the terminal, and the terminal sends this to the server. The server stores the scenario information in a database.

[1241] Input: Meeting purpose, agenda

[1242] Data processing: The device organizes the input information and sends it to the server

[1243] Output: Meeting scenario information is saved in the database

[1244] Specific operation: The user inputs "Deciding on marketing strategy for new product, budget consideration, market analysis," and the server records this information in the database.

[1245] Step 3:

[1246] The user sets the position and opinion of each character. The user inputs the corresponding information into the terminal, and the terminal sends it to the server. The server stores the position and opinion information in a database.

[1247] Input: Position and opinion of each character

[1248] Data processing: The device organizes the input information and sends it to the server

[1249] Output: The positions and opinions of the characters are stored in a database.

[1250] Specific operation: The user enters "Taro Tanaka, prioritizes market expansion" and "Jiro Suzuki, prioritizes cost reduction," and the server records the information in the database.

[1251] Step 4:

[1252] Users set instructions and constraints for the conference proceedings. The users input specific instructions and constraints into their terminals, which then transmit them to the server, which stores the received information in a database.

[1253] Input: Meeting instructions, constraints

[1254] Data processing: The device organizes the input information and sends it to the server

[1255] Output: Meeting instructions and constraints stored in a database

[1256] Specific operation: The user inputs "meeting time within 1 hour" and "presentation of materials within 5 minutes," and the server records this information in the database.

[1257] Step 5:

[1258] The server automatically generates explanatory materials using generative AI tools. Specifically, materials are reviewed with ChatPDF, slides are created with gamma, text is generated with Writesonic, and motion animations are added with Fliki.

[1259] Input: Scenario information, characters' positions and opinions, instructions and constraints

[1260] Data processing: Automatically generate materials using generative AI tools

[1261] Output: explanatory materials are generated and stored in a database

[1262] Specific Action: The server generates slides with the central theme of "New Product Features," supplements them with text, and adds action animations.

[1263] Step 6:

[1264] The server prepares anticipated questions and answers. The server retrieves anticipated questions and answers from the database and prepares them in advance.

[1265] Input: Past meeting data in the database

[1266] Data processing: The server extracts and prepares expected questions and answers

[1267] Output: Expected question and answer data is generated and saved in a database

[1268] What happens next: The server prepares questions and answers such as "What is the price of the new product?" and "What are your sales targets for the first year?"

[1269] Step 7:

[1270] The server coordinates the schedule. Based on the schedule information of the participants, the server uses an automatic scheduling function to determine the optimal meeting date and time and notifies all participants.

[1271] Input: Character schedule information

[1272] Data processing: Automatic scheduling algorithm calculates optimal date and time

[1273] Output: The best meeting time is determined and notified

[1274] Specific operation: The server determines that "September 10th, between 2:00 and 3:00 PM is optimal" and notifies participants of that date and time.

[1275] Step 8:

[1276] The server manages the real-time progress of the meeting using automatically generated explanatory materials and anticipated question and answer data. When a participant asks a question during the meeting, the server responds using anticipated questions and answers prepared in advance.

[1277] Input: explanatory materials, expected question and answer data

[1278] Data processing: Real-time presentation of materials and management of questions and answers

[1279] Output: Meetings run smoothly

[1280] Specific operation: The server conducts a meeting in real time, and answers the question "What is the target market for the product?" with "The target markets are North America and Europe."

[1281] (Application example 1)

[1282] 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."

[1283] Preparing and conducting meetings in modern corporate activities is extremely complicated, requiring a great deal of effort and time. In particular, in factories and other workplaces, where executives and workers are busy, there is a need to improve the efficiency and quality of meetings. The frequent time spent by employees and managers engaged in workplace work preparing for meetings is a serious problem that reduces productivity. Furthermore, insufficient preparation for explanatory materials and Q&A sessions can reduce the effectiveness of meetings, delay decision-making, and lead to misunderstandings. This creates a need for work efficiency and real-time support.

[1284] 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.

[1285] In this invention, the server includes a means for inputting participant information, a means for inputting a meeting scenario, a means for inputting the participants' positions and opinions, a means for automatically generating explanatory materials based on the generated data, a means for preparing anticipated questions and answers for a Q&A session, a means for adjusting the meeting schedule using an automatic scheduling function, and a means for conducting the meeting in real time. This makes it possible to improve the efficiency and quality of meeting preparation and conduct. Particularly in factories, this can improve the quality of meetings while maintaining productivity.

[1286] "Participant information" refers to information such as the name, position, and department of each person participating in the conference.

[1287] A "meeting scenario" is a plan that includes the purpose and agenda of a meeting and serves as a guide for the progress of the meeting.

[1288] "Positions and opinions" refers to each participant's views and suggestions on the agenda of the meeting.

[1289] "Instructions and Restrictions" indicates specific instructions and restrictions regarding the progress of the meeting.

[1290] "Explanatory materials" are documents and presentation materials used to clearly explain the contents of a meeting.

[1291] "Anticipated questions and answers" are a set of questions and answers that are predicted to occur during the meeting, and are preparatory materials to guide participants smoothly.

[1292] "Automatic scheduling" is the process of automatically scheduling a meeting in an optimal way based on the schedules of the participants.

[1293] "Real-time progress" refers to the process of presenting explanatory materials and answering questions in real time as the meeting progresses.

[1294] A "generative AI model" is an algorithm or tool that uses artificial intelligence to automatically generate documents or presentation materials.

[1295] The "automatic scheduling function" is a function that automatically arranges the optimal meeting date and time taking into account the schedules of the participants.

[1296] The present invention is a system for improving the efficiency of meeting preparation and progress at a workplace such as a factory. Specifically, the system automates the input of participant information, setting of a meeting scenario, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, scheduling, and real-time progress of the meeting. An embodiment of the present invention is described below.

[1297] First, the user uses a terminal to input information about the people participating in the meeting, including their names, job titles, and departments. The terminal collects this information and sends it to the server, which then stores it in a database.

[1298] Next, the user sets up a meeting scenario, which is a plan that includes the purpose and agenda of the meeting. The user inputs this scenario information and sends it to the server via their terminal. The server receives the scenario information and stores it in a database.

[1299] Next, the user sets the position and opinion of each person. This information represents the participants' views and suggestions on the meeting agenda. The user enters this information into the terminal and sends it to the server. The server stores the received information in a database.

[1300] The user then sets the meeting instructions and constraints, including specific instructions and constraints for the meeting and the designation of the moderator. The user enters these instructions and constraints into the terminal and sends them to the server, which stores the received information in a database.

[1301] The server automatically generates explanatory materials in cooperation with various generative AI models. Specifically, ChatPDF reviews related materials, gamma creates slides, Writesonic generates text, and Fliki creates actions. The generated explanatory materials are stored on the server.

[1302] For the Q&A session, the server prepares anticipated questions and answers in advance. This anticipated question and answer data includes predicted questions and answers based on the participants' positions and opinions. The server creates this anticipated question and answer data and stores it in a database.

[1303] Regarding scheduling, the server automatically adjusts the optimal meeting date and time based on the schedule information of all participants. For example, it manages the schedule using the Google Calendar API and notifies all participants.

[1304] Finally, the server is responsible for the real-time progress of the meeting. Using automatically generated explanatory materials and anticipated question and answer data, the server supports the progress of the meeting in real time and supports the interactive activities of participants.

[1305] As a concrete example, consider setting up a "maintenance meeting for a new production line at a factory." When entering the characters, enter the names, positions, and departments of the participants. When setting the scenario, set the agenda with the goal of "optimizing the maintenance of the new production line." When setting the opinions, enter the views of each participant. When setting the constraints, enter constraints such as "meeting time must be within one hour." ChatPDF, gamma, Writesonic, and Fliki are used to generate explanatory materials.

[1306] Example prompt sentence:

[1307] "Enter the information of all members participating in a factory maintenance meeting. Set the meeting purpose and agenda, and record each participant's opinions and positions. Set instructions and constraints for the meeting, use AI tools to generate briefing materials and anticipated questions and answers, and use the auto-scheduling function to arrange the optimal meeting date and time."

[1308] In this way, the system of the present invention can support efficient and effective preparation and conduct of meetings even at work sites such as factories.

[1309] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1310] Step 1:

[1311] The user uses a terminal to input information about the people participating in the meeting, including their names, job titles, and departments. The terminal collects this information and sends it to the server, which then stores it in a database.

[1312] Input: Name, position, department

[1313] Output: Character information stored in the database

[1314] Step 2:

[1315] The user sets up a meeting scenario, which is a plan that includes the purpose and agenda of the meeting. The user inputs this scenario information and sends it to the server via their terminal. The server receives the scenario information and stores it in a database.

[1316] Input: Meeting purpose, agenda

[1317] Output: Scenario information stored in the database

[1318] Step 3:

[1319] The user sets the position and opinion of each person involved. This information indicates the participants' views and suggestions regarding the meeting agenda. The user enters this information into the terminal and sends it to the server. The server stores the received information in a database.

[1320] Input: Position and opinion of each character

[1321] Output: Positions and opinions stored in a database

[1322] Step 4:

[1323] The user sets the meeting instructions and constraints, including specific instructions and constraints for the meeting's progress and the designation of the moderator. The user enters these instructions and constraints into the terminal and sends them to the server, which stores the received information in a database.

[1324] Input: Meeting instructions, constraints, and facilitator designation

[1325] Output: Instructions and constraints stored in a database

[1326] Step 5:

[1327] The server works with various generative AI models to automatically generate explanatory materials. Specifically, ChatPDF reviews related materials, gamma creates slides, Writesonic generates text, and Fliki creates videos. The generated explanatory materials are stored on the server.

[1328] Input: Meeting information stored in the database

[1329] Output: Generated explanatory material

[1330] Step 6:

[1331] The server prepares anticipated questions and answers for the Q&A session. This anticipated question and answer data includes predicted questions and answers based on the positions and opinions of the participants. The server creates this anticipated question and answer data and stores it in a database.

[1332] Input: Participants' positions and opinions

[1333] Output: Expected question and answer data

[1334] Step 7:

[1335] The server automatically adjusts the optimal meeting date and time based on the schedule information of all participants. It manages the schedule using the Google Calendar API and notifies all participants.

[1336] Input: Participant schedule information

[1337] Output: Determining the best meeting time and date and notifying

[1338] Step 8:

[1339] The server is responsible for the real-time progress of the meeting, using automatically generated explanatory materials and anticipated question and answer data to support the progress of the meeting in real time and to support the interactive activities of participants.

[1340] Input: explanatory materials, expected question and answer data

[1341] Output: Real-time meeting progress and Q&A

[1342] This series of steps will enable users to effectively prepare for and conduct a meeting.

[1343] 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.

[1344] The present invention is a system that includes input of participant information, setting of meeting scenarios, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, scheduling, real-time progress of meetings, and an emotion engine that recognizes user emotions. This system automates the preparation and progress of meetings, minimizing human intervention and realizing work efficiency and optimal use of resources.

[1345] First, the user inputs the participant information. This includes entering personal information (such as name, position, and affiliation) of the people in positions of authority who will be participating in the meeting into the system. The terminal collects this information and sends it to the server. The server then stores the received information in a database.

[1346] Next, the user sets the meeting scenario (objective, agenda, etc.). The user inputs this scenario information and sends it to the server via the terminal. The server receives the scenario information and stores it in a database.

[1347] The user then sets the position and opinion of each person involved. This information reflects the views and opinions of each participant regarding the content of the meeting. The user inputs this information and sends it to the server via their device. The server then stores the received information in a database.

[1348] Next, the user sets instructions and constraints for the progress of the meeting, including specific conditions and constraints for the progress of the meeting, and the designation of the moderator. The user inputs this information and sends it to the server via their terminal. The server stores the received instructions and constraints in a database.

[1349] The server works with various AI tools to generate the explanatory materials. Specifically, ChatPDF reviews the relevant materials, gamma creates slides, Writesonic generates text, and Fliki creates actions. The generated explanatory materials are stored on the server.

[1350] For Q&A, the server prepares anticipated questions and answers in advance. This retrieves anticipated questions and their answers from a database and prepares for responses in real time. The server creates and stores this anticipated question and answer data.

[1351] The server uses an automatic scheduling function to adjust the schedule, arranging the optimal meeting date and time based on the schedule information of the participants and notifying all participants.

[1352] Furthermore, an emotion engine is combined to recognize the user's emotions. The server uses the emotion engine to analyze emotions from the user's voice and facial expressions in real time. The analyzed emotion data can be used to dynamically adjust the progress of the meeting and Q&A. For example, if a user shows anxiety during a meeting, the server can temporarily slow down the progress of the meeting and provide more detailed explanations.

[1353] Finally, the meeting proceeds in real time. The server starts the meeting based on the adjusted meeting schedule and displays the generated explanatory materials. Furthermore, based on the user's emotions recognized by the emotion engine, the server dynamically changes its response measures to ensure smooth meeting operation. It also conducts Q&A based on anticipated questions and answers, providing appropriate responses.

[1354] For example, consider a meeting to decide on a marketing strategy for a new product. The user inputs the personal information of the executives and sets the purpose of the meeting as "deciding on a marketing strategy for a new product." They then set the opinions of the participants and input constraints on the progress of the meeting (e.g., the meeting time must be within one hour). Based on these settings, the server generates explanatory materials and prepares anticipated questions and answers. At the same time, the emotion engine recognizes the user's emotions and provides appropriate feedback. Finally, once the meeting begins, the server will proceed with the meeting in real time and also handle questions and answers.

[1355] In this way, the system of the present invention, by incorporating emotion recognition technology, achieves more advanced conference management and provides interactive conferences that take into consideration the emotions of participants.

[1356] The processing flow will be explained below.

[1357] Step 1:

[1358] The user enters the character information. This involves entering the personal information of the person in a position of authority (such as name, position, affiliation, etc.) into a web form and pressing the submit button.

[1359] Step 2:

[1360] The terminal transmits the character information input by the user to the server, and the transmitted information reaches the server via the network.

[1361] Step 3:

[1362] The server stores the received character information in a database, which allows the information to be retained for later use.

[1363] Step 4:

[1364] The user enters the meeting scenario information, including details such as the purpose of the meeting and the agenda. The user completes the input and presses the send button.

[1365] Step 5:

[1366] The terminal transmits the scenario information input by the user to the server, and the transmitted information reaches the server via the network.

[1367] Step 6:

[1368] The server stores the received scenario information in a database, thereby storing the information necessary for the progress of the conference.

[1369] Step 7:

[1370] The user inputs the position and opinion of each character, and then presses the send button.

[1371] Step 8:

[1372] The terminal transmits the positions and opinions input by the user to the server, and the transmitted information reaches the server via the network.

[1373] Step 9:

[1374] The server stores the received positions and opinions in a database, thereby maintaining participants' views and opinions on the meeting agenda.

[1375] Step 10:

[1376] The user inputs instructions and restrictions for the progress of the conference, and then presses the send button.

[1377] Step 11:

[1378] The terminal transmits the instructions and constraints input by the user to the server, and the transmitted information reaches the server via the network.

[1379] Step 12:

[1380] The server stores the received instructions and constraints in a database, thereby maintaining the conditions for the progress of the conference.

[1381] Step 13:

[1382] The server works with various generative AI tools to prepare explanatory materials: ChatPDF reviews related documents, Gamma creates slides, Writesonic generates text, and Fliki creates actions.

[1383] Step 14:

[1384] The server stores the generated explanatory materials in a database, thereby maintaining the materials used in the conference.

[1385] Step 15:

[1386] The server generates data on anticipated questions and answers. The server retrieves information necessary to prepare anticipated questions and answers from the database and creates the anticipated questions and answers.

[1387] Step 16:

[1388] The server stores the generated anticipated question and answer data in a database, thereby storing data in preparation for the question and answer session.

[1389] Step 17:

[1390] The server uses an automatic scheduling function to adjust the meeting date and time, calculating the optimal meeting date and time based on the schedules of the participants, and then determines the schedule.

[1391] Step 18:

[1392] The server notifies all participants of the adjusted meeting schedule, allowing them to check the meeting details.

[1393] Step 19:

[1394] The server uses an emotion engine to analyze the user's emotions in real time, and obtains the user's emotional data through voice analysis and facial expression analysis.

[1395] Step 20:

[1396] The server dynamically adjusts the progress of the meeting based on the emotion data. For example, if the user shows anxiety, the server may slow down the progress of the meeting and add detailed explanations.

[1397] Step 21:

[1398] The server runs the meeting in real time, displays explanatory materials when the meeting starts, and if necessary, holds a Q&A session based on anticipated questions and answers. The server provides appropriate responses based on emotions recognized by the emotion engine.

[1399] Example 2

[1400] 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."

[1401] Conventional meeting preparation and proceeding systems require a lot of manual human effort to set up participant information and meeting scenarios, create explanatory materials, prepare Q&A sessions, coordinate schedules, and proceed with the meeting, resulting in issues with efficiency and accuracy. It is also difficult to recognize participants' emotions during a meeting and respond appropriately, which can sometimes prevent the meeting from proceeding smoothly. It is necessary to solve these issues in order to improve meeting efficiency and participant satisfaction.

[1402] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1403] In this invention, the server includes means for inputting participant information, means for inputting a meeting scenario, means for inputting the positions and opinions of the participants, means for inputting instructions and constraints for the progress of the meeting, means for creating explanatory materials based on data generated using a generative AI model, means for preparing anticipated questions and answers for a question and answer session, means for automatically adjusting the meeting schedule, means for recognizing user emotions in real time using an emotion analysis engine, means for dynamically adjusting the progress of the meeting based on the emotion data, and means for progressing the meeting in real time. This automates the preparation and progress of the meeting, minimizes human intervention, and enables smooth meeting management and efficient resource utilization.

[1404] "Participant information" is personal information such as the names, positions, and affiliations of individuals participating in the conference.

[1405] A "meeting scenario" is detailed planning information necessary for the progress of a meeting, such as the purpose and agenda of the meeting.

[1406] "Positions and opinions" is information that represents the views and opinions of each person participating in the meeting.

[1407] "Instructions and constraints" refers to information including specific conditions and constraints regarding the progress of the meeting and the designation of the facilitator.

[1408] A "generative AI model" is an artificial intelligence technology that automatically performs tasks such as natural language processing and image recognition based on large amounts of data.

[1409] "Explanatory materials" are materials created to support the content of a conference, and may include slides, documents, and actions.

[1410] "Anticipated Questions and Answers" is a list of anticipated questions and their answers prepared in advance.

[1411] "Automatically adjusting the schedule" means automatically determining the optimal date and time for a meeting based on the schedule information of the people participating in the meeting.

[1412] An "emotion analysis engine" is a system that analyzes emotions in real time based on a user's voice and facial expressions.

[1413] "Emotion data" is data about the user's emotional state obtained by the emotion analysis engine.

[1414] "Real-time progress" refers to responding immediately, providing information, and adjusting the flow of the meeting while the meeting is in progress.

[1415] This invention is a system that automates the preparation and progress of meetings, including the input of participant information, setting of the meeting scenario, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, scheduling, real-time recognition of emotions, and progress of the meeting. This system minimizes human intervention, realizes work efficiency, and optimal use of resources.

[1416] First, the user inputs the participant information through the terminal. This includes personal information (such as name, position, and affiliation) of the executives participating in the meeting. The terminal then sends this information to the server, which then stores the received information in a database.

[1417] Next, the user sets the meeting scenario (objectives, agenda, etc.). The scenario information entered through the terminal is sent to the server and stored in the database. When the user sets the position and opinion of each character, they input the views and opinions of each participant regarding the meeting content. This information is also sent to the server through the terminal and stored in the database.

[1418] The user then sets instructions and constraints for the conference, including specific conditions and constraints for the conference and the designation of a moderator. The input information is sent from the terminal to the server and stored in a database.

[1419] The server uses a generative AI model to generate explanatory materials. Specifically, it uses a natural language processing engine to review related materials, presentation software to create slides, a text generation engine to generate text, and an animation generation tool to create animations. The generated explanatory materials are stored on the server.

[1420] For Q&A sessions, the server prepares anticipated questions and answers in advance. This retrieves anticipated questions and their answers from a database and prepares for the Q&A session. The server creates and saves this anticipated question and answer data.

[1421] The server uses an automatic scheduling function to adjust the schedule, arranging the optimal meeting date and time based on the schedule information of the participants and notifying all participants.

[1422] The emotion analysis engine recognizes emotions by analyzing the voice and facial expression data input by the user. The server uses the emotion analysis engine and uses the analyzed data to dynamically adjust the progress of the meeting and Q&A in real time. For example, if a user shows signs of anxiety during a meeting, the server may temporarily slow down the meeting and provide more detailed explanations.

[1423] Finally, the server starts the meeting based on the adjusted schedule and displays the generated explanatory materials. Furthermore, the server dynamically changes its response based on the user's emotions recognized by the emotion analysis engine. This allows the meeting to proceed smoothly and questions and answers to be answered appropriately.

[1424] As a concrete example, consider a meeting to decide on a marketing strategy for a new product. The user inputs the personal information of the person in charge and sets the purpose of the meeting as "deciding on a marketing strategy for a new product." They then set their positions and opinions and input constraints on the progress of the meeting (e.g., the meeting time must be within one hour). The server generates explanatory materials based on these settings and prepares anticipated questions and answers. At the same time, an emotion analysis engine recognizes the user's emotions and provides appropriate feedback. Finally, once the meeting begins, the server proceeds with the meeting in real time and also handles questions and answers. This system enables highly efficient, interactive meetings that take emotions into consideration.

[1425] An example prompt is:

[1426] "I want to schedule a marketing strategy meeting for a new product. First, let's create a meeting scenario by entering the personal information of the executives."

[1427] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1428] Step 1:

[1429] The user inputs character information. Specifically, the user enters their name, position, and affiliation into the system's input form and clicks the "Submit" button. The device collects the input information and sends it to the server. The server stores the received information in a database. The input is the character's personal information, and the output is the information recorded in the database.

[1430] Step 2:

[1431] The user sets up a meeting scenario. The user inputs the purpose and agenda of the meeting and clicks the "Save Scenario" button. The terminal sends the scenario information to the server. The server saves the scenario information in a database. The input is the scenario information, and the output is the scenario information recorded in the database.

[1432] Step 3:

[1433] The user sets the position and opinion of each character. The user inputs the views and opinions of each character and clicks the "Save Opinion" button. The device sends the information to the server. The server stores the received positions and opinions in a database. The input is the positions and opinions, and the output is the positions and opinions recorded in the database.

[1434] Step 4:

[1435] The user sets instructions and constraints for the progress of the conference. The user sets the conditions and constraints for the progress of the conference and clicks the "Save Constraints" button. The terminal sends the information to the server. The server saves the information in a database. The input is instructions and constraints, and the output is the instructions and constraints recorded in the database.

[1436] Step 5:

[1437] The server generates explanatory materials using a generative AI model. It uses a natural language processing engine to review related materials and presentation software to create slides. It also uses a text generation engine to generate text and an animation generation tool to create animations. The generated explanatory materials are stored on the server. The input is data such as the meeting scenario, positions and opinions, instructions and constraints, and the output is the generated explanatory materials.

[1438] Step 6:

[1439] The server prepares expected questions and answers. The server extracts relevant questions and answers from the database and creates a list of expected questions and answers. This list is used to prepare for Q&A sessions. The input is past meeting data and related documents, and the output is a list of expected questions and answers.

[1440] Step 7:

[1441] The server adjusts the schedule. Using its automatic scheduling function, the server calculates the optimal meeting date and time based on the schedule information of the participants and sends a notification email to all participants. The input is the schedule information of the participants, and the output is the adjusted meeting date and time and the notification email.

[1442] Step 8:

[1443] The server uses an emotion analysis engine to recognize the user's emotions in real time. It acquires voice and facial expression data from the user and analyzes it using the emotion analysis engine. The acquired emotional data is reflected in the progress of the conference. The input is the user's voice and facial expression data, and the output is the analyzed emotional data.

[1444] Step 9:

[1445] The server runs the meeting in real time. It starts the meeting based on the adjusted meeting schedule and displays explanatory materials. It dynamically adjusts the meeting progress based on emotional data obtained from the emotion analysis engine and also conducts appropriate Q&A. The inputs are the adjusted meeting schedule, generated explanatory materials, analyzed emotional data, and a list of anticipated questions and answers. The output is a smoothly progressing meeting and appropriate Q&A provided in real time.

[1446] (Application example 2)

[1447] 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."

[1448] Conventional conferencing systems require a lot of human intervention and are not fully automated, which means that preparing and running a meeting requires a lot of time and effort. Furthermore, when it comes to factory production management and task management, data collection and analysis are cumbersome, making efficient management difficult. Furthermore, it is difficult to conduct an interactive meeting that takes into account the emotions of the meeting participants, which can easily lead to misunderstandings and mistakes due to poor communication.

[1449] 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 inputting participant information, means for inputting a meeting scenario, means for inputting the positions and opinions of the participants, means for inputting instructions and constraints for the progress of the meeting, means for creating explanatory materials based on the generated data, means for preparing anticipated questions and answers for a question-and-answer session, means for automatically adjusting the meeting schedule, emotion analysis means for recognizing the user's emotions, means for conducting the meeting in real time, and means for collecting and analyzing data for production management and task management. This not only automates the preparation and progress of the meeting, but also improves the efficiency of production management and task management in factories, and further enables smooth meeting management that takes into account the emotions of the participants by using an emotion recognition function.

[1450] The "means for inputting participant information" refers to a device or system for inputting and managing information (such as name, position, affiliation, etc.) about each person participating in the conference.

[1451] The "means for inputting a meeting scenario" refers to a device or system for inputting and setting scenario information necessary for the progress of a meeting, such as the purpose and agenda of the meeting.

[1452] The "means for inputting the positions and opinions of participants" is a device or system for inputting and managing the views and opinions of conference participants.

[1453] The "means for inputting instructions and constraints for the progress of the meeting" refers to a device or system for inputting specific conditions and constraints for the progress of the meeting and the designation of the facilitator.

[1454] The "means for creating explanatory materials based on the generated data" refers to a device or system for automatically creating explanatory materials, slides, etc. based on the collected and generated data.

[1455] The "means for preparing anticipated questions and answers for a question and answer session" refers to a device or system for preparing anticipated questions and answers in advance and storing them in a database.

[1456] The "means for automatically adjusting the schedule of a meeting" is a device or system for automatically adjusting the optimal date of a meeting based on the schedule information of the meeting participants.

[1457] The "emotion analysis means for recognizing the user's emotions" is a device or system for analyzing the emotions of the user from their voice and facial expressions and providing appropriate feedback based on the analysis.

[1458] A "means for conducting a meeting in real time" is a device or system for conducting and managing a meeting in real time based on a pre-prepared scenario, materials, participants' opinions, and sentiment analysis data.

[1459] "Means for collecting and analyzing data for production management and task management" refers to a device or system that collects and analyzes data on production lines and tasks in a factory to perform efficient production management and task management.

[1460] This invention provides a system that combines the input of participant information, setting of meeting scenarios, setting of positions and opinions, setting of instructions and constraints, generation of explanatory materials, preparation of anticipated questions and answers, schedule adjustment, real-time progress of meetings, and emotion recognition.The purpose is to apply this system to factory robots to improve the efficiency of production management and task management.

[1461] First, users enter information about each person participating in the meeting (such as name, position, and affiliation). This is done using a device such as a smartphone or tablet. The device collects this information and sends it to the server, which then stores the received information in a database.

[1462] Next, the user sets the meeting scenario (objective, agenda, etc.). The user inputs this scenario information and sends it from the terminal to the server. The server receives the scenario information and stores it in a database.

[1463] Furthermore, the user can set the position and opinion of each person, which reflects the participants' views and opinions on the content of the meeting. The information is sent from the terminal to the server, which stores it in a database.

[1464] Users also set specific conditions and constraints for the progress of the meeting, including the selection of a moderator and the duration of the meeting. This information is also sent from the device to the server, which stores it in a database.

[1465] The generation of explanatory materials is performed on the server using a generative AI model (e.g., gamma, Writesonic, Fliki, ChatPDF), and the server stores the generated materials in a database.

[1466] For Q&A, the server prepares anticipated questions and answers in advance by retrieving frequently asked questions and their answers from a database and saving them.

[1467] The server uses an automatic scheduling algorithm to schedule meetings, and it determines the optimal meeting date and time based on the participants' schedule information and notifies them.

[1468] The server uses an emotion analysis engine with TensorFlow for emotion recognition, which is a technology that analyzes emotions from the user's voice and facial expressions in real time.

[1469] Finally, the meeting proceeds in real time. The server displays the generated materials, adjusts the progress of the meeting based on the sentiment analysis data, and also conducts Q&A based on anticipated questions and answers.

[1470] When applied to factory robots, the robots collect data for production management and task management and analyze it on a server, thereby improving work efficiency within the factory.

[1471] As a concrete example, consider a meeting to introduce a new manufacturing line. The user inputs information about the manager and engineers and sets the purpose and content of the meeting. They then input the opinions of the participants and set the constraints of the meeting. A generative AI tool creates explanatory materials and prepares anticipated questions and answers. The server automatically adjusts the schedule and notifies everyone. An emotion analysis engine analyzes the voices and facial expressions of the participants and adjusts the progress of the meeting.

[1472] An example of a prompt is as follows:

[1473] Meeting objective: Introduction of a new production line

[1474] Characters:

[1475] Name: Administrator

[1476] Position: Production Department

[1477] Name: Technician

[1478] Position: Engineering Department

[1479] agenda:

[1480] 1. Issues with the current line

[1481] 2. Advantages and disadvantages of introducing a new line

[1482] 3. Implementation Schedule

[1483] 4. Cost-effectiveness

[1484] Expected Q&A:

[1485] Q: What is the cost of installing a new line?

[1486] A: The implementation cost is approximately 100 million yen, and the expected payback period is two years.

[1487] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1488] Step 1:

[1489] Users use devices such as smartphones or tablets to input information about each person participating in the meeting (such as name, position, and affiliation). This input data is sent from the device to a server. The server stores the received information in a database. The input data is personal information, and the output data is the personal information stored in the database.

[1490] Step 2:

[1491] The user inputs information to set up the meeting scenario (purpose, agenda, etc.) into the terminal and sends it to the server. The server receives the scenario information and stores it in a database. The input data is the scenario information, and the output data is the scenario information stored in the database.

[1492] Step 3:

[1493] The user inputs the position and opinion of each character. The information entered by the user is sent from the terminal to the server, which stores it in a database. The input data is the position and opinion information, and the output data is the position and opinion information stored in the database.

[1494] Step 4:

[1495] Users input instructions and constraints for the progress of the conference and send them to the server via their terminals. The server receives these instructions and constraints and stores them in a database. The input data is the instructions and constraints information, and the output data is the instructions and constraints information stored in the database.

[1496] Step 5:

[1497] The server automatically creates explanatory materials using a generation AI tool (gamma, Writesonic, Fliki, ChatPDF) based on the various input information. The server saves the generated materials in a database. The input data is the various input information, and the output data is the generated explanatory materials.

[1498] Step 6:

[1499] The server prepares expected questions and answers for the Q&A session. It retrieves frequently asked questions and their answers from the database, prepares them in advance, and saves them. The input data is the format of frequently asked questions and answers, and the output data is the expected questions and answers saved in the database.

[1500] Step 7:

[1501] The server adjusts the meeting schedule. It collects the schedule information of the participants and calculates the optimal meeting schedule using an automatic scheduling algorithm. The calculated schedule information is notified to all participants. The input data is the schedule information of the participants, and the output data is the adjusted meeting schedule.

[1502] Step 8:

[1503] The server uses an emotion analysis engine based on TensorFlow to analyze emotions from the user's voice and facial expressions in real time. The analysis results are stored in a database and used to adjust the progress of the meeting. The input data is voice and facial expression data, and the output data is analyzed emotional data.

[1504] Step 9:

[1505] The server conducts the meeting in real time, displays the generated explanatory materials, adjusts the progress based on emotion analysis data, and conducts Q&A based on anticipated questions and answers. The input data is the various generated materials and analysis data, and the output data is the meeting information conducted in real time.

[1506] Step 10:

[1507] Factory robots collect data for production management and task management. The server analyzes this data and helps improve work efficiency. The input data is production management and task management data, and the output data is analyzed management information.

[1508] 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.

[1509] 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.

[1510] 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.

[1511] 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.

[1512] 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.

[1513] 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.

[1514] 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).

[1515] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, motorcycles, and other devices, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1516] 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."

[1517] 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.

[1518] 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 metho...

Claims

1. A means for inputting character information; a means for inputting a meeting scenario; A means of inputting the characters' positions and opinions; a means for inputting instructions and constraints for the proceedings of the meeting; A means for creating explanatory materials based on the generated data; A means of preparing anticipated questions and answers for the Q&A session; A means of automatically scheduling meetings; A way to conduct meetings in real time, A system including:

2. The system of claim 1 further comprising means for storing the generated explanatory material.

3. The system according to claim 1 , further comprising means for storing the expected question and answer data.

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