System

Abstract

A system is provided.SOLUTION: A system comprising: inputting means for inputting agenda and participant information by a user; communicating means for transmitting the inputted agenda and participant information to a server; storing means for storing the inputted agenda and participant information in an internal database; generating means for generating a meeting agenda and time allocation based on the agenda and participant information by a generation and AI tool; and displaying means for transmitting and displaying the generated agenda and time allocation to terminals of users.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] Many organizations face the problem of meetings not proceeding efficiently and a lot of time being wasted. This can reduce the time that should be spent on core business, leading to a decline in productivity. Specifically, the main causes of this are unclear agendas and inappropriate time allocation. This invention aims to provide a system for making meetings proceed more efficiently and reducing wasted time, thereby realizing meaningful meetings. [Means for solving the problem]

[0005] This invention provides a system that includes an input means for a user to input agenda and participant information, a communication means for transmitting the input data to a server, a storage means for the server to process the data and store it in an internal database, a generation means for a generation AI tool to generate a meeting agenda and time allocation based on the agenda and participant information, and a display means for transmitting and displaying the generated agenda and time allocation to a user's terminal. The generation AI tool generates an efficient meeting agenda and time allocation using a pre-trained model. This system clarifies the purpose of the meeting, enables efficient time management, and reduces the total meeting time.

[0006] "User" refers to an individual or organizational member who operates the system and enters meeting agendas and participant information.

[0007] An "agenda" refers to a specific theme or topic to be discussed at a meeting.

[0008] "Participant information" refers to information about the names and roles of members participating in a conference.

[0009] "Input means" refers to a terminal or interface through which a user inputs information.

[0010] "Communication means" refers to the network and protocol used to send data from the user's terminal to the server.

[0011] "Server" refers to the computer system for processing input data and generating an agenda using generative AI tools.

[0012] "Storage" refers to the mechanism by which the Server stores received data in its internal database.

[0013] "Generative AI tools" refer to artificial intelligence tools that automatically generate efficient meeting agendas and time allocations based on pre-trained models.

[0014] "Generation method" refers to the process of generating meeting agendas and time allocations using generative AI tools.

[0015] "Display means" refers to a device or interface for displaying the generated agenda and time allocation on the user's terminal.

[0016] A "meeting agenda" refers to a schedule that shows the order and content of a meeting.

[0017] "Time allocation" refers to the meeting time allocated for each agenda item. [Brief explanation of the drawings]

[0018] [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 illustrating 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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention is a system in which a user inputs the meeting topic and participant information, and a generation AI tool automatically generates the meeting agenda and time allocation, allowing the meeting to proceed efficiently. Below, the program processing of this system is explained in natural language. Specific examples are also provided.

[0040] Program processing

[0041] 1. Data Entry

[0042] A user uses a terminal to input the meeting agenda and participant information. For example, a user inputs the agenda for the next meeting, "Designing new features" and "Test plan," and adds "Mr. A, Mr. B, and Mr. C" as participants.

[0043] 2. Data transmission

[0044] The terminal sends the entered data to the server using a transmission protocol such as HTTP POST request.

[0045] 3. Receipt and storage of information

[0046] The server receives the data sent from the terminal.

[0047] The received data is parsed to extract the agenda and participant information, which is then stored in an internal database.

[0048] 4. Agenda generation

[0049] The server retrieves the agenda and participant information from its internal database and passes it to the generative AI tool.

[0050] The generative AI tool generates an efficient meeting agenda and time allocation based on the received data. For example, the generative AI tool might generate an agenda such as "10:00-10:30: Design new features" or "10:30-11:00: Test planning."

[0051] 5. Sending and displaying results

[0052] The server transmits the generated agenda and time allocation to the user's terminal.

[0053] The terminal displays the received agenda and time allocation to the user, who then checks the displayed content and uses it to actually proceed with the meeting.

[0054] Specific examples

[0055] Consider the following example. For next week's project meeting, a user enters "Project progress review" and "Plan for next release" as agenda items on their device, and adds "Mr. X, Mr. Y, and Mr. Z" as participants. The device sends this information to the server, which receives and stores it. The server then calls a generation AI tool, which generates an efficient agenda based on the saved agenda and participant information. The generation AI tool generates an agenda with the following schedules: "9:00-9:30: Project progress review" and "9:30-10:00: Plan for next release," and passes it to the server. The server then sends the generated agenda to the user's device, which displays it. The user can use this information to conduct the meeting and hold efficient discussions.

[0056] In this way, this system uses AI to automatically generate meeting agendas and time allocations based on information entered by the user, helping to ensure efficient and meaningful meeting proceedings.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user enters the meeting agenda and participant information. Specifically, the user uses the input form on the device to enter the agenda items "Designing new features" and "Test plan" and the participant information of "Mr. A, Mr. B, and Mr. C."

[0060] Step 2:

[0061] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request.

[0062] Step 3:

[0063] The server receives the data packet sent from the terminal, where it parses the body of the HTTP request and extracts the agenda and participant information.

[0064] Step 4:

[0065] The server saves the extracted agenda and participant information to an internal database, storing this information in the appropriate tables and fields within the database.

[0066] Step 5:

[0067] The server calls the generation AI tool based on the saved agenda and participant information. At this time, the server passes the saved data to the generation AI tool as necessary parameters.

[0068] Step 6:

[0069] The generative AI tool generates an efficient meeting agenda and time allocation based on the topic and participant information it receives. For example, the generative AI tool uses past data and pre-trained models to generate an agenda such as "10:00-10:30: Design new features" or "10:30-11:00: Test planning."

[0070] Step 7:

[0071] The generative AI tool returns the generated agenda and time allocation to the server, where the returned data is converted into an appropriate format and temporarily stored.

[0072] Step 8:

[0073] The server packages the generated agenda and time allocation into a data packet and sends it to the user's terminal, where the data is returned using an HTTP response.

[0074] Step 9:

[0075] The terminal receives the data packet sent from the server, analyzes the data packet, and extracts the agenda and time allocation.

[0076] Step 10:

[0077] The device displays the extracted agenda and time allocation to the user. Specifically, information such as "10:00-10:30: Design new functions" and "10:30-11:00: Test planning" is displayed on the device screen.

[0078] Step 11:

[0079] The user conducts the meeting based on the displayed agenda and time allocation. The user and participants efficiently discuss each topic within the specified time.

[0080] This series of processing steps enables users to conduct meetings efficiently and reduce wasted time.

[0081] Example 1

[0082] 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."

[0083] In conventional meeting management systems, creating an agenda to efficiently conduct a meeting is a time-consuming and labor-intensive process. It is also difficult to allocate time optimally based on the agenda and participant information, resulting in a decrease in meeting efficiency. To solve this problem, a system is needed that can automatically generate an agenda based on input information and propose efficient time allocation.

[0084] 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.

[0085] In this invention, the server includes an input means for a user to input the agenda and participant information, a communication means for transmitting the input data to the server, a storage means for the server to analyze the data and store it in an internal database, a generation means for a generative AI model to generate a meeting agenda and time allocation based on the agenda and participant information, and a display means for transmitting the generated agenda and time allocation to the user's terminal and displaying it. This supports the progress of the meeting and enables the user to effectively manage the meeting by using the automatically generated efficient agenda and time allocation.

[0086] The "input means" is a means by which a user inputs the agenda and participant information of a meeting.

[0087] "Communication means" refers to a means for transmitting input data to a server.

[0088] The "storage means" is a means for the server to analyze the received data and store it in an internal database.

[0089] "Generation means" refers to the means by which the generative AI model generates a meeting agenda and time allocation based on the topic and participant information.

[0090] The "display means" is a means for transmitting the generated agenda and time allocation to the user's terminal and displaying them.

[0091] A "generative AI model" is an AI model that generates a meeting agenda and time allocation from given input data based on pre-trained data.

[0092] MODE FOR CARRYING OUT THE INVENTION

[0093] This invention is a system designed to efficiently and effectively conduct meetings, which automatically generates a meeting agenda and time allocation based on information entered by a user. The system includes input means, communication means, storage means, generation means, and display means.

[0094] First, the user uses the terminal to enter the meeting agenda and participant information. The terminal provides a dedicated HTML-based form, allowing the user to easily enter the agenda and participant information. For example, the user enters the agenda items "Designing new features" and "Test plan" and adds "Mr. A, Mr. B, and Mr. C" as participants.

[0095] The device uses JavaScript to convert input data into JSON format and sends it to the server via an HTTP POST request. The server endpoint is implemented using Node.js and the Express framework to receive the data.

[0096] The server analyzes the received data using a JSON parser to extract the agenda and participant information. The analysis results are stored in a MongoDB database, allowing for flexible data management.

[0097] The server then retrieves the stored data and passes it to a generative AI tool, which uses OpenAI's GPT-4 model to generate the meeting agenda and time allocation, using prompts such as:

[0098] "Please generate an agenda for the next meeting. The topics are 'Marketing strategy for new products' and 'Expanding sales channels'. The participants are 'Tanaka-san, Suzuki-san, Sato-san'."

[0099] The AI ​​tool generates an agenda based on the prompt and returns it to the server, which then sends the result in JSON format to the user's device.

[0100] The device parses the received agenda and displays it to the user using HTML and JavaScript. Specifically, the generated agenda items are displayed in a list format, and color-coded to visually indicate the time allocation of each item.

[0101] In this way, the system allows users to simply input the meeting topic and participant information, and the generative AI model automatically generates an efficient agenda and time allocation and provides it to the user, thereby streamlining the progress of meetings.

[0102] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0103] Step 1:

[0104] The user enters the meeting agenda and participant information. Specifically, the user accesses the input form on their device, enters the agenda items "Design of new features" and "Test plan," and registers "Mr. A, Mr. B, and Mr. C" as participants. Once the input is complete, the user presses the "Send" button. The agenda and participant information are obtained as input data.

[0105] Step 2:

[0106] The terminal converts the data entered by the user into JSON format and sends it to the server using an HTTP POST request. At this time, JavaScript code serializes the data using JSON.stringify and sends the data according to the transmission protocol. JSON format data is received as input and a request to the server is generated as output.

[0107] Step 3:

[0108] The server receives data sent from the terminal. The server uses Node.js and Express and parses the received data with a JSON parser. The received JSON data contains the agenda and participant information entered by the user. The received data is parsed and converted into a format for saving in MongoDB.

[0109] Step 4:

[0110] The server analyzes the received data, extracts the agenda and participant information, and saves it in a MongoDB database. The extracted data will have a format such as "Agenda: New feature design, test plan" and "Participants: A, B, C." Database operations are performed using the MongoDB driver, and if successful, a status indicating that the data has been saved is returned.

[0111] Step 5:

[0112] The server retrieves the saved agenda and participant information from its internal database and passes it to the AI ​​generation tool. At this time, the server executes a Python script to call the AI ​​generation tool's API. Specifically, it generates the following prompt:

[0113] "Generate an agenda for the next meeting. The topics are 'Design new features' and 'Test plan'. The participants are 'A, B, C'."

[0114] Step 6:

[0115] The generative AI tool generates a meeting agenda and time allocation based on the prompt text. At this time, the generative AI model GPT-4 analyzes the prompt text and outputs the optimal meeting agenda and time allocation. For example, the output may be an agenda in the format "10:00-10:30: Design new features" or "10:30-11:00: Test plan."

[0116] Step 7:

[0117] The server receives the generated agenda and time allocation from the AI ​​tool, formats it in JSON, and sends it to the user's device. The server returns JSON data containing the generated agenda as an HTTP response.

[0118] Step 8:

[0119] The device analyzes the data received from the server and displays it to the user. Using HTML and JavaScript, the generated agenda items are displayed on the screen in list format, and each time allocation is color-coded to make it visually easy to understand. The user can check this and use it to progress the meeting.

[0120] (Application example 1)

[0121] 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."

[0122] There is a need for a system that can efficiently collect meeting topic and participant information, quickly convert user voice input into text information, and automatically generate an efficient meeting agenda and time allocation using a generative AI tool. Conventional systems often require users to input information manually, which is time-consuming and hinders efficient meeting progress. Furthermore, in workplaces such as factories, there is a lack of means for workers to input meeting information without using their hands. Technology is needed to solve these problems and improve the productivity and efficiency of meetings.

[0123] 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.

[0124] In this invention, the server includes a conversion means that converts voice input into text information using voice recognition technology, a communication means that sends the input data to the server, a storage means that the server processes the data and stores it in an internal database, a generation means that uses a generative AI tool to generate a meeting agenda and time allocation based on the topic and participant information, and a display means that sends the generated agenda and time allocation to a user's terminal and displays it. This allows users to efficiently input meeting information through voice input, and the server can automatically generate a fast and accurate agenda using a generative AI model.

[0125] A "user" is a person who operates the system and is responsible for inputting the meeting agenda and participant information.

[0126] An "agenda" refers to the topics or issues to be addressed at a meeting.

[0127] "Participant Information" refers to the names, titles, and other relevant information of people attending a meeting.

[0128] "Input means" is a general term for devices and methods that allow users to input the agenda and participant information for a meeting.

[0129] "Voice recognition technology" refers to the technology that converts voice into text data.

[0130] "Conversion means" is a general term for mechanisms and methods for converting voice input into text information using voice recognition technology.

[0131] "Communication means" is a general term that includes the technology and protocols used to transmit input data to a server.

[0132] "Server" means a computer system for receiving, processing, and storing data and generating an agenda using a generation AI tool.

[0133] "Storage means" is a general term for mechanisms and methods for storing data received by the server in an internal database.

[0134] "Generation method" is a general term for the process of using a generative AI tool to generate a meeting agenda and time allocation based on the topic and participant information.

[0135] "Generative AI tools" refers to software or algorithms that use AI technology to automatically generate meeting agendas and time allocations.

[0136] "Display means" is a general term for mechanisms and methods for transmitting the generated agenda and time allocation to the user's terminal and displaying them.

[0137] "Terminal" refers to a device that allows a user to enter information and view a generated agenda.

[0138] "Text information" refers to text data converted using voice recognition technology.

[0139] "Internal database" refers to the database system used to store and manage data within a server.

[0140] "Time allocation" refers to the amount of time allocated to each agenda item in a meeting.

[0141] MODE FOR CARRYING OUT THE INVENTION

[0142] This invention relates to a system that automatically generates a meeting agenda and time allocation using speech recognition technology and generative AI tools. Hereinafter, the embodiments of the invention will be described in detail.

[0143] System Configuration

[0144] The system consists of the following components:

[0145] 1. User Device

[0146] A device for voice entry of meeting agenda and participant information.

[0147] It is equipped with a voice recognition microphone, display, and internet connection functions.

[0148] Examples of hardware used: industrial robots or smart speakers.

[0149] Examples of software used: Speech recognition engine (e.g. Google Cloud Speech-to-Text).

[0150] 2. Server

[0151] Voice input is converted into text information using voice recognition technology.

[0152] Receive the converted data and store it in a database.

[0153] Use generative AI tools (e.g., OpenAI GPT-4) to generate meeting agendas and time allocations.

[0154] The generated agenda is sent to the user's terminal.

[0155] Server configuration example: Linux server, HTTP server (such as Nginx or Apache), database (MySQL, PostgreSQL, etc.).

[0156] 3. Generative AI Tools

[0157] It runs within the server and generates an efficient meeting agenda and time allocation based on the inputted agenda and participant information.

[0158] Example of generative AI model used: GPT-4.

[0159] Specific processing steps

[0160] Data Entry and Conversion

[0161] Users input meeting agendas and participant information by voice. Voice recognition technology is used to convert the speech into text. This allows users to input information without using their hands, making it easy to operate even while working on-site in a factory or other facility.

[0162] Data transmission and storage

[0163] The user device sends the converted text information to the server, which receives the HTTP POST request, analyzes the data, and stores it in an internal database. This process ensures that the entered information is saved and used for subsequent processing.

[0164] Agenda generation

[0165] The server retrieves the agenda and participant information from the database and passes it to the generative AI tool. The generative AI tool automatically generates an efficient meeting agenda and time allocation based on the input data. For example, it might generate content such as "10:00-10:30: Design new features" and "10:30-11:00: Test plan."

[0166] Sending and displaying results

[0167] The server sends the generated agenda and time allocation to the user's device, which then notifies the user of the results by voice and displays them on the screen. This allows the user to immediately check the generated results and efficiently conduct the meeting.

[0168] Specific examples

[0169] Here's an example: A user speaks:

[0170] "The next meeting is about optimizing the production line and introducing new machinery, and the participants are Tanaka-san, Suzuki-san, and Sato-san."

[0171] The server converts this speech into text and passes it to a generative AI tool, which generates an agenda like this:

[0172] "14:00-14:30 Optimization of production line" "14:30-15:00 Introduction of new machinery"

[0173] The server sends this agenda to the user's terminal, which then notifies the results by voice and display.

[0174] Prompt Sentence Examples

[0175] An example prompt to pass to a generative AI tool might look something like this:

[0176] Meeting agenda:

[0177] Production line optimization

[0178] Introduction of new machinery

[0179] Participants:

[0180] Tanaka-san

[0181] Suzuki-san

[0182] Sato-san

[0183] Use this information to generate an efficient meeting agenda and time allocation for each topic.

[0184] In this manner, the system of the present invention can be implemented.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1: Data entry

[0187] The user inputs the meeting agenda and participant information by voice. The voice is input into the terminal via a voice recognition microphone. Specifically, the user says the following: "The agenda for the next meeting is a meeting regarding the optimization of the production line and the introduction of new machinery. The participants are Tanaka-san, Suzuki-san, and Sato-san."

[0188] Step 2: Audio conversion

[0189] The device uses voice recognition technology (such as Google Cloud Speech-to-Text) to convert voice input into text information. This voice recognition technology analyzes the input voice data and converts it into text data. The input is voice data and the output is text data. For example, the voice input "The agenda for the next meeting is a meeting regarding production line optimization and the introduction of new machinery. The participants are Tanaka, Suzuki, and Sato." is converted to "The agenda for the next meeting is production line optimization and the introduction of new machinery. The participants are Tanaka, Suzuki, and Sato."

[0190] Step 3: Send data

[0191] The terminal sends the converted text data to the server using an HTTP POST request. The data sent is in text format and includes the agenda and participant information. The input is the converted text data, and the output is the send request.

[0192] Step 4: Receiving and storing data

[0193] The server receives the HTTP POST request, analyzes the text data, and extracts the agenda and participant information. After extraction, the obtained data is stored in an internal database. The input is the text data sent from the terminal, and the output is the agenda and participant information stored in the database.

[0194] Step 5: Generate the agenda

[0195] The server retrieves the saved agenda and participant information from the database and passes it to a generative AI tool (e.g., GPT-4). The generative AI tool automatically generates an efficient meeting agenda and time allocation based on the retrieved data. The input is the saved agenda and participant information, and the output is the generated agenda and time allocation. For example, an agenda such as "14:00-14:30 Optimize the production line" or "14:30-15:00 Install new machinery" may be generated.

[0196] Step 6: Sending the results

[0197] The server sends the generated agenda and time allocation to the user's terminal. The input is the generated agenda and time allocation, and the output is a transmission request.

[0198] Step 7: View and notify results

[0199] The terminal displays the received agenda and time allocation to the user. The user terminal can also display the results on a screen and notify the user by voice. The input is the generated agenda and time allocation, and the output is the display and voice notification. For example, "Next meeting agenda: 14:00-14:30 Production line optimization, 14:30-15:00 Installation of new machine" is displayed and notified by voice.

[0200] 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.

[0201] This invention is a system in which a user inputs the meeting topic and participant information, and a generation AI tool automatically generates the meeting agenda and time allocation. By combining this with an emotion engine, the system recognizes the user's emotions and optimizes the progress of the meeting in real time. Below, the program processing of this system is explained in natural language. Specific examples are also provided.

[0202] Program processing

[0203] 1. Data Entry

[0204] A user uses a terminal to input the meeting agenda and participant information. For example, the user inputs agenda items such as "design of new features" and "test plan" and participant information such as "Mr. A, Mr. B, and Mr. C."

[0205] 2. Data transmission

[0206] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request.

[0207] 3. Receipt and storage of information

[0208] The server receives the data packet sent from the terminal, parses the HTTP request body, and extracts the agenda and participant information.

[0209] The extracted agenda and participant information is stored in an internal database.

[0210] 4. Agenda generation

[0211] The server retrieves the agenda and participant information from its internal database and passes it to the generative AI tool.

[0212] Based on the data received, the generative AI tool generates an efficient meeting agenda and time allocation, such as "10:00-10:30: Design new features" and "10:30-11:00: Test planning."

[0213] 5. Sending and displaying results

[0214] The server sends the generated agenda and time allocation to the user's device. The data is sent using an HTTP response.

[0215] The terminal displays the received agenda and time allocation to the user. The terminal screen displays "10:00-10:30 New function design" and "10:30-11:00 Test plan."

[0216] 6. Enabling the Emotion Engine

[0217] The device activates an emotion engine while the meeting is in progress and recognizes emotions by analyzing the user's facial expressions and voice. For example, it uses a camera or microphone to detect the user's emotions in real time.

[0218] 7. Emotional Feedback

[0219] The emotion engine sends the emotion data it recognizes to the server. For example, if it recognizes that the user is tired, that information is sent to the server.

[0220] 8. Real-time agenda revision

[0221] Based on the emotion data received by the server, the generative AI tool modifies the current agenda and time allocation in real time, adjusting the time for each topic or postponing some topics depending on the emotion.

[0222] 9. Displaying the correction results

[0223] The server transmits the revised agenda and time allocation back to the user's terminal.

[0224] The terminal redisplays the revised agenda to the user, who then proceeds with the conference accordingly.

[0225] Specific examples

[0226] For example, for next week's project meeting, a user enters "Project Progress Review" and "Next Release Plan" as agenda items on their device and adds "X, Y, and Z" as participants. The device sends this information to the server, which receives and stores it. The server then calls the generation AI tool and generates an agenda based on the saved agenda and participant information. The generation AI tool generates an agenda with the following schedules: "9:00-9:30 Project Progress Review" and "9:30-10:00 Next Release Plan" and returns it to the server. The server then sends the generated agenda to the user's device, which displays it. During the meeting, the emotion engine analyzes the user's facial expressions and voice. If it determines that the user is tired, it sends that information to the server. The generation AI tool then modifies the agenda based on this emotional data, and the new agenda is displayed on the user's device. The user then proceeds with the meeting according to the modified agenda.

[0227] In this way, the system uses AI to automatically generate meeting agendas and time allocations based on information entered by users and data from the emotion engine, and then corrects them in real time, thereby supporting efficient and meaningful meeting conduct.

[0228] The processing flow will be explained below.

[0229] Step 1:

[0230] The user enters the meeting agenda and participant information. Specifically, the user uses an input form on the terminal to enter agenda items such as "Designing new features" and "Test plan," as well as participant information for "Mr. A, Mr. B, and Mr. C."

[0231] Step 2:

[0232] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request.

[0233] Step 3:

[0234] The server receives the data packet sent from the terminal, parses the HTTP request body, and extracts the agenda and participant information.

[0235] Step 4:

[0236] The server saves the extracted agenda and participant information to an internal database, storing this information in the appropriate tables and fields within the database.

[0237] Step 5:

[0238] The server retrieves the agenda and participant information from an internal database and passes it to the generative AI tool, which then converts it into the appropriate API calls and data formats before inputting it.

[0239] Step 6:

[0240] The generative AI tool generates an efficient meeting agenda and time allocation based on the received data. For example, the generative AI tool might generate an agenda such as "10:00-10:30: Design new features" or "10:30-11:00: Test planning."

[0241] Step 7:

[0242] The generative AI tool returns the generated agenda and time allocation to the server, which converts the returned data into an appropriate format and temporarily stores it on the server.

[0243] Step 8:

[0244] The server packages the generated agenda and time allocation into a data packet and sends it to the user's terminal, where the data is returned using an HTTP response.

[0245] Step 9:

[0246] The terminal receives the data packet sent from the server, analyzes the data packet, and extracts the agenda and time allocation.

[0247] Step 10:

[0248] The device displays the extracted agenda and time allocation to the user. Specifically, the device displays "10:00-10:30: Design new functions" and "10:30-11:00: Test planning" on the screen.

[0249] Step 11:

[0250] The user conducts the meeting based on the displayed agenda and time allocation. The user and participants efficiently discuss each topic within the specified time.

[0251] Step 12:

[0252] The device activates the emotion engine while the meeting is in progress. Specifically, it uses the device's camera and microphone to analyze the user's facial expressions and voice in real time.

[0253] Step 13:

[0254] The emotion engine analyzes the user's emotions and sends the recognized emotion data to the server. For example, if the user is recognized as being fatigued, that information is packetized as emotion data and sent to the server.

[0255] Step 14:

[0256] The server analyzes the received emotional data and provides feedback to the generative AI tool, which then uses it to adjust the meeting agenda and time allocation in real time.

[0257] Step 15:

[0258] Based on the feedback, the generative AI tool reevaluates the current agenda and time allocation and makes adjustments as necessary. For example, if the agenda item "Design new features" is taking too long, the tool will shorten the time and adjust the agenda to move on to the next agenda item, "Test planning," sooner.

[0259] Step 16:

[0260] The generative AI tool sends the revised agenda and time allocation back to the server.

[0261] Step 17:

[0262] The server resends the revised agenda and time allocation to the user's device. The data is sent using an HTTP response.

[0263] Step 18:

[0264] The terminal displays the received revised agenda and time allocation again to the user, who then proceeds with the conference according to the new agenda.

[0265] Through these steps, the system optimizes the progress of meetings in real time and realizes flexible time allocation according to the user's emotions, thereby improving meeting efficiency and reducing wasted time.

[0266] Example 2

[0267] 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."

[0268] In conventional conference systems, setting the agenda and allocating time for a meeting is done manually, making it difficult to run the meeting efficiently. Furthermore, there is no way to revise the agenda in real time during the meeting, taking into account the emotions and fatigue levels of participants, which can disrupt the smooth progress of the meeting and cause participants to lose concentration. The present invention aims to solve these problems and realize efficient and flexible meeting management.

[0269] 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.

[0270] In this invention, the server includes: input means for a user to input agenda and participant information; communication means for transmitting the input data to the server; storage means for the server to process the data and store it in an internal database; generation means for a generative AI model to generate a meeting agenda and time allocation based on the agenda and participant information; display means for transmitting the generated agenda and time allocation to the user's terminal and displaying it; recognition means for an emotion engine to recognize the user's emotions; feedback means for transmitting the recognized emotion data to the server; correction means for the server to correct the agenda and time allocation in real time based on the emotion data; and re-display means for transmitting the corrected agenda and time allocation to the user's terminal and displaying it again. This not only enables automatic generation of an efficient meeting agenda and time allocation based on information input by the user, but also enables recognition of the emotions of participants during the meeting and correction of the agenda in real time accordingly.

[0271] A "user" is a person or entity that utilizes the system to enter meeting agendas and participant information.

[0272] A "terminal" is an electronic device that a user uses to enter meeting agenda and participant information.

[0273] "Server" means a device that receives and processes data sent from a terminal, and stores and manages it in an internal database.

[0274] "Input means" refers to the device or software by which a user inputs meeting agenda and participant information into the system.

[0275] "Communication means" refers to a protocol or device for transmitting data input by an input means to a server.

[0276] "Storage means" is the function or process by which the server stores the data it receives in its internal database.

[0277] A "generative AI model" is an artificial intelligence model that automatically generates a meeting agenda and time allocation based on the topic and participant information.

[0278] A "generator" is a process or function that uses a generative AI model to generate a meeting agenda and time allocation.

[0279] The "display means" is software or a device for transmitting the generated agenda and time allocation to the user's terminal and displaying it.

[0280] The "emotion engine" is an analysis engine for recognizing the user's emotions in real time.

[0281] A "recognition means" is a process or function that uses an emotion engine to recognize a user's emotion.

[0282] The "feedback means" is a means for transmitting the recognized emotion data to the server.

[0283] "Modification means" refers to a process or function by which the server modifies the meeting agenda and time allocation in real time based on emotion data.

[0284] The "redisplay means" is software or a device for transmitting the revised agenda and time allocation to the user's terminal and displaying them again.

[0285] This invention is a system that automatically generates a meeting agenda and time allocation based on the meeting topic and participant information using a generative AI tool, and further combines it with an emotion engine to recognize the user's emotions and optimize the progress of the meeting in real time.

[0286] Specific Embodiments of the System

[0287] 1. Data Entry

[0288] Users use a device to input the meeting agenda and participant information. The device can be a PC, tablet, smartphone, or other electronic device. The input data is collected via the device's application.

[0289] 2. Data Transmission

[0290] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request. The communication method uses a communication protocol via the Internet.

[0291] 3. Receipt and storage of information

[0292] The server receives data packets sent from the devices, parses the HTTP POST requests using the Python Flask framework, and extracts the agenda and participant information. The extracted information is then stored in an internal database (e.g., an SQL database).

[0293] 4. Agenda generation

[0294] The server retrieves the agenda and participant information from its internal database and passes it to a generative AI model (e.g., OpenAI's GPT-3). The generative AI model generates an efficient meeting agenda and time allocation based on the provided data. The generated agenda is returned to the server.

[0295] 5. Sending and Displaying the Agenda

[0296] The server sends the generated agenda and time allocation to the user's device. The data is sent using an HTTP response. The device analyzes the received agenda and displays it to the user. For example, the device screen might display "10:00-10:30 Design new features" and "10:30-11:00 Test planning."

[0297] 6. Enabling the Emotion Engine

[0298] The device activates its emotion engine during the conference and uses the camera and microphone to analyze the user's facial expressions and voice to recognize emotions. The recognized emotion data is obtained via analysis software (e.g., EmotionAPI, FaceAPI).

[0299] 7. Emotional Feedback

[0300] The emotion engine sends the recognized emotion data to the server as an HTTP request.

[0301] 8. Real-time agenda revision

[0302] The server uses a generative AI model to modify the current agenda and time allocation in real time based on the received emotional data, and the modified agenda is returned to the server.

[0303] 9. Displaying the correction results

[0304] The server then sends the revised agenda and time allocation back to the user's terminal, which then displays the revised agenda again, allowing the user to proceed with the meeting accordingly.

[0305] Specific Examples

[0306] For example, a user enters "Project progress review" and "Planning the next release" as agenda items for next week's project meeting on their device, and adds "Mr. X, Mr. Y, and Mr. Z" as participants. The device sends this information to the server, which receives and stores it. The server then uses a generation AI tool to generate an agenda and returns it as "9:00-9:30: Project progress review" and "9:30-10:00: Planning the next release."

[0307] During a meeting, the emotion engine analyzes the user's facial expressions and voice, and if it determines that the user is tired, it sends that information to the server. The server then modifies the agenda based on this emotional data, and the new agenda is displayed on the user's device. The user then proceeds with the meeting according to the modified agenda.

[0308] Prompt Sentence Examples

[0309] Create an efficient meeting agenda based on "checking project progress" and "planning the next release."

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

[0311] Step 1:

[0312] Data Entry

[0313] The user inputs the meeting agenda and participant information into the terminal.

[0314] Input: The user enters the agenda, such as "Designing new features" or "Test plan," and participant information, such as "A, B, and C," into an input form.

[0315] What it does: A user enters information into form fields, such as text fields and drop-down menus, in a browser or application on their device.

[0316] Step 2:

[0317] Data transmission

[0318] The terminal converts the input data into data packets and transmits them to the server.

[0319] Input: The topic and participant information entered by the user in the previous step.

[0320] Data processing: The terminal serializes the input data into JSON format.

[0321] Output: Data packet in JSON format.

[0322] What happens: Front-end JavaScript converts the data to JSON format and sends it to the server as the body of an HTTP POST request.

[0323] Step 3:

[0324] Receiving and storing information

[0325] The server receives the data packets sent from the terminal and stores them in an internal database.

[0326] Input: A data packet in JSON format.

[0327] Data processing: The server parses the data packets and extracts the agenda and participant information.

[0328] Output: Agenda and participant information stored in a database.

[0329] Specific operation: The server receives the HTTP request using Flask, extracts the data using the request.get_json() method, and executes an INSERT query to the database using SQLAlchemy.

[0330] Step 4:

[0331] Agenda generation

[0332] The server passes the data to a generative AI model to generate the meeting agenda and time allocation.

[0333] Input: Agenda and participant information retrieved from the database.

[0334] Data processing: The acquired data is transformed into a prompt sentence and sent to the generative AI model.

[0335] Output: Generated meeting agenda and time allocation.

[0336] Specifically, the server retrieves the necessary information from the database using a SELECT query, then sends an API request based on that data to the generative AI model. The generative AI model (e.g., GPT-3) generates an agenda based on the prompt and responds in JSON format.

[0337] Step 5:

[0338] Sending and displaying results

[0339] The server transmits the generated agenda and time allocation to the user's terminal and displays them.

[0340] Input: Meeting agenda and time allocation returned by the generative AI model.

[0341] Data processing: Format the generated agenda as an HTTP response.

[0342] Output: The meeting agenda displayed on the user's device.

[0343] Specific behavior: The server returns the generated agenda as a JSON response, which the device parses, renders in HTML, and displays to the user.

[0344] Step 6:

[0345] Enabling the Emotion Engine

[0346] The device activates an emotion engine to recognize the user's emotions.

[0347] Input: User facial and voice data.

[0348] Data processing: Data captured by the camera and microphone is passed to emotion recognition algorithms.

[0349] Output: Recognized emotion data.

[0350] How it works: The device's application captures the user's facial expressions and voice in real time using the camera and microphone, and analyzes the data using libraries such as EmotionAPI and FacialRecognition.

[0351] Step 7:

[0352] Emotional feedback

[0353] The emotion engine transmits the recognized emotion data to the server.

[0354] Input: Recognized emotion data.

[0355] Data processing: Serialize emotion data into JSON format.

[0356] Output: Emotion data sent to the server.

[0357] Specific operation: The emotion engine analyzes the recognized emotions and sends the information in JSON format to the server as an HTTP POST request.

[0358] Step 8:

[0359] Real-time agenda modification

[0360] The server modifies the agenda and time allocation based on the emotional data.

[0361] Input: Recognized emotion data and existing meeting agenda.

[0362] Data processing: Create a new prompt sentence based on the emotion data and request it from the generative AI model.

[0363] Output: Revised meeting agenda and time allocation.

[0364] Specific operation: Based on the emotion data received by the server, a correction request is sent to the generative AI model, which then generates a new agenda.

[0365] Step 9:

[0366] Displaying the correction results

[0367] The server transmits the revised agenda and time allocation to the user's terminal again and displays them.

[0368] Input: Revised meeting agenda and time allocation.

[0369] Data processing: Format the modified agenda as an HTTP response.

[0370] Output: The meeting agenda redisplayed on the user's device.

[0371] Specific behavior: The server returns the modified agenda in JSON format, and the device parses it and redisplays it.

[0372] (Application example 2)

[0373] 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."

[0374] Efficiently managing different work tasks within a factory and adjusting their progress in real time is important in environments where multiple robots and workers work together. However, with conventional systems, it is difficult to optimize the task progress and the status of each robot in real time, which hinders efficient work management. In particular, it is necessary to solve these issues, as rapid response to factors such as remaining battery life and declining work efficiency is required.

[0375] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0376] In this invention, the server includes an input means for a user to input task and assigned robot information, a communication means for transmitting the input data to the server, a storage means for the server to process the data and store it in an internal database, a generation means for a generation AI tool to generate an efficient task schedule based on the task and assigned robot information, a display means for transmitting the generated task schedule to the robot's terminal and displaying it, and a correction means for an emotion engine to analyze the robot's status (remaining battery level and work efficiency) and correct the task schedule in real time. This makes it possible to efficiently manage work tasks in a factory and optimize progress in real time according to the robot's status.

[0377] A "user device" is an electronic device used by a user to input information and display generated data, including a smartphone, smart glasses, a head-mounted display, or a robot.

[0378] "Input means" refers to an interface that allows a user to input information about a meeting or task, and includes a keyboard, a touch screen, voice input, and the like.

[0379] "Communication means" refers to the method or protocol for transmitting input data to the server, including HTTP requests and other data communication methods.

[0380] "Storage means" refers to the mechanism by which the server stores the received data in an internal database, and includes a database engine and a file system.

[0381] "Generation means" refers to the function of the generation AI tool to automatically generate meeting and work agendas and time allocations based on input information using a pre-trained model.

[0382] "Display means" refers to a method for displaying the generated agenda or schedule to the user or robot, and includes a display, a screen, a projector, etc.

[0383] An "emotion engine" is a software component that analyzes the state of the user or robot (e.g., facial expressions, voice, remaining battery level, work efficiency, etc.) and makes optimal adjustments and corrections in real time based on that data.

[0384] "Modification means" refers to the function whereby the generative AI tool modifies the agenda and schedule in real time based on data and feedback from the emotion engine and sends it to the display means.

[0385] This invention is a system for improving the efficiency of robot work task management in factories. The system includes an input means through which a user inputs task and assigned robot information, a communication means for transmitting the input data to a server, and a storage means for the server to store the received data in an internal database. The system also includes a generation means for a generation AI tool to generate an efficient task schedule based on the task and assigned robot information, a display means for transmitting the generated task schedule to the robot's terminal and displaying it, and a correction means for an emotion engine to analyze the robot's state and correct the task schedule in real time.

[0386] Specifically, users input task and robot information using devices such as smartphones, tablets, and PCs. For example, they input work tasks such as "assembly of product X" and "quality inspection," as well as information about the robots in charge, such as "robot A" and "robot B." This data is then sent to the server via communication means.

[0387] The server processes the received data, parses the HTTP request body to extract task and robot information, and stores it in an internal database.The server then invokes a generative AI tool to generate an efficient task schedule based on the stored task and robot information.This process uses a pre-trained generative AI model.

[0388] The generated task schedule is sent from the server to the terminal of the robot in charge, and each robot recognizes and displays its own tasks. The display means uses a device such as a display or touch screen that allows the robot to check the tasks.

[0389] The emotion engine analyzes the robot's status (remaining battery level, work efficiency, etc.) in real time while the robot is working and feeds that information back to the correction means. The correction means corrects the schedule in real time based on the data provided by the emotion engine and sends it back to the robot's terminal for display.

[0390] For example, if Robot A is engaged in assembling Product X and its battery level drops, the emotion engine analyzes this condition and the corrective measures regenerate a new task schedule, where Robot B takes over the task of assembling Product X and Robot A transitions to a quality inspection task.

[0391] An example of a prompt is as follows:

[0392] "Robot A's battery level is low. Please regenerate the schedule."

[0393] Based on this prompt, the task schedule is regenerated and redisplayed on each robot's terminal.

[0394] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0395] Step 1:

[0396] The user uses a terminal to input information about the task and the robot in charge. Through the terminal interface, the user inputs work tasks such as "assembly of product X" or "quality inspection" and information about the robot in charge, such as "Robot A, Robot B." The input data is saved in JSON format.

[0397] Step 2:

[0398] The device converts the input data into a data packet and sends it to the server using an HTTP POST request. At this time, the device uses a communication protocol to securely send the data. The input JSON data is sent as the body of the HTTP request.

[0399] Step 3:

[0400] The server receives the data packet and parses the HTTP request body to extract task and robot information, which is then stored in an internal database. Specifically, it uses database queries to store the task and robot information in the appropriate tables.

[0401] Step 4:

[0402] The server retrieves the stored task and robot information from the internal database and passes it to the generative AI tool. The generative AI tool uses a pre-trained generative AI model to generate an optimal task schedule based on the input data. It analyzes the input task and assigned robot information and outputs an efficient schedule.

[0403] Step 5:

[0404] The server sends the generated schedule to the terminal of the robot in charge. The server formats the generated schedule as an HTTP response and sends it to each robot's terminal. The robot's terminal analyzes the received schedule and displays it on a device such as a display.

[0405] Step 6:

[0406] The emotion engine analyzes the robot's status while it is working. The robot's terminal sends data such as remaining battery power and work efficiency to the emotion engine in real time. The emotion engine analyzes this data and determines the robot's current status.

[0407] Step 7:

[0408] The server regenerates the schedule based on the data analyzed by the emotion engine. The server then obtains feedback data from the emotion engine and uses the generation AI tool again to appropriately revise the schedule. At this time, new data processing and calculations are performed.

[0409] Step 8:

[0410] The server sends the revised schedule to the robot terminal again. The revised schedule is sent as an HTTP response and the robot terminal redisplays it. This allows the robot to continue working according to the latest schedule.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] [Second embodiment]

[0415] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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."

[0427] This invention is a system in which a user inputs the meeting topic and participant information, and a generation AI tool automatically generates the meeting agenda and time allocation, allowing the meeting to proceed efficiently. Below, the program processing of this system is explained in natural language. Specific examples are also provided.

[0428] Program processing

[0429] 1. Data Entry

[0430] A user uses a terminal to input the meeting agenda and participant information. For example, a user inputs the agenda for the next meeting, "Designing new features" and "Test plan," and adds "Mr. A, Mr. B, and Mr. C" as participants.

[0431] 2. Data transmission

[0432] The terminal sends the entered data to the server using a transmission protocol such as HTTP POST request.

[0433] 3. Receipt and storage of information

[0434] The server receives the data sent from the terminal.

[0435] The received data is parsed to extract the agenda and participant information, which is then stored in an internal database.

[0436] 4. Agenda generation

[0437] The server retrieves the agenda and participant information from its internal database and passes it to the generative AI tool.

[0438] The generative AI tool generates an efficient meeting agenda and time allocation based on the received data. For example, the generative AI tool might generate an agenda such as "10:00-10:30: Design new features" or "10:30-11:00: Test planning."

[0439] 5. Sending and displaying results

[0440] The server transmits the generated agenda and time allocation to the user's terminal.

[0441] The terminal displays the received agenda and time allocation to the user, who then checks the displayed content and uses it to actually proceed with the meeting.

[0442] Specific examples

[0443] Consider the following example. For next week's project meeting, a user enters "Project progress review" and "Plan for next release" as agenda items on their device, and adds "Mr. X, Mr. Y, and Mr. Z" as participants. The device sends this information to the server, which receives and stores it. The server then calls a generation AI tool, which generates an efficient agenda based on the saved agenda and participant information. The generation AI tool generates an agenda with the following schedules: "9:00-9:30: Project progress review" and "9:30-10:00: Plan for next release," and passes it to the server. The server then sends the generated agenda to the user's device, which displays it. The user can use this information to conduct the meeting and hold efficient discussions.

[0444] In this way, this system uses AI to automatically generate meeting agendas and time allocations based on information entered by the user, helping to ensure efficient and meaningful meeting proceedings.

[0445] The processing flow will be explained below.

[0446] Step 1:

[0447] The user enters the meeting agenda and participant information. Specifically, the user uses the input form on the device to enter the agenda items "Designing new features" and "Test plan" and the participant information of "Mr. A, Mr. B, and Mr. C."

[0448] Step 2:

[0449] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request.

[0450] Step 3:

[0451] The server receives the data packet sent from the terminal, where it parses the body of the HTTP request and extracts the agenda and participant information.

[0452] Step 4:

[0453] The server saves the extracted agenda and participant information to an internal database, storing this information in the appropriate tables and fields within the database.

[0454] Step 5:

[0455] The server calls the generation AI tool based on the saved agenda and participant information. At this time, the server passes the saved data to the generation AI tool as necessary parameters.

[0456] Step 6:

[0457] The generative AI tool generates an efficient meeting agenda and time allocation based on the topic and participant information it receives. For example, the generative AI tool uses past data and pre-trained models to generate an agenda such as "10:00-10:30: Design new features" or "10:30-11:00: Test planning."

[0458] Step 7:

[0459] The generative AI tool returns the generated agenda and time allocation to the server, where the returned data is converted into an appropriate format and temporarily stored.

[0460] Step 8:

[0461] The server packages the generated agenda and time allocation into a data packet and sends it to the user's terminal, where the data is returned using an HTTP response.

[0462] Step 9:

[0463] The terminal receives the data packet sent from the server, analyzes the data packet, and extracts the agenda and time allocation.

[0464] Step 10:

[0465] The device displays the extracted agenda and time allocation to the user. Specifically, information such as "10:00-10:30: Design new functions" and "10:30-11:00: Test planning" is displayed on the device screen.

[0466] Step 11:

[0467] The user conducts the meeting based on the displayed agenda and time allocation. The user and participants efficiently discuss each topic within the specified time.

[0468] This series of processing steps enables users to conduct meetings efficiently and reduce wasted time.

[0469] Example 1

[0470] 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."

[0471] In conventional meeting management systems, creating an agenda to efficiently conduct a meeting is a time-consuming and labor-intensive process. It is also difficult to allocate time optimally based on the agenda and participant information, resulting in a decrease in meeting efficiency. To solve this problem, a system is needed that can automatically generate an agenda based on input information and propose efficient time allocation.

[0472] 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.

[0473] In this invention, the server includes an input means for a user to input the agenda and participant information, a communication means for transmitting the input data to the server, a storage means for the server to analyze the data and store it in an internal database, a generation means for a generative AI model to generate a meeting agenda and time allocation based on the agenda and participant information, and a display means for transmitting the generated agenda and time allocation to the user's terminal and displaying it. This supports the progress of the meeting and enables the user to effectively manage the meeting by using the automatically generated efficient agenda and time allocation.

[0474] The "input means" is a means by which a user inputs the agenda and participant information of a meeting.

[0475] "Communication means" refers to a means for transmitting input data to a server.

[0476] The "storage means" is a means for the server to analyze the received data and store it in an internal database.

[0477] "Generation means" refers to the means by which the generative AI model generates a meeting agenda and time allocation based on the topic and participant information.

[0478] The "display means" is a means for transmitting the generated agenda and time allocation to the user's terminal and displaying them.

[0479] A "generative AI model" is an AI model that generates a meeting agenda and time allocation from given input data based on pre-trained data.

[0480] MODE FOR CARRYING OUT THE INVENTION

[0481] This invention is a system designed to efficiently and effectively conduct meetings, which automatically generates a meeting agenda and time allocation based on information entered by a user. The system includes input means, communication means, storage means, generation means, and display means.

[0482] First, the user uses the terminal to enter the meeting agenda and participant information. The terminal provides a dedicated HTML-based form, allowing the user to easily enter the agenda and participant information. For example, the user enters the agenda items "Designing new features" and "Test plan" and adds "Mr. A, Mr. B, and Mr. C" as participants.

[0483] The device uses JavaScript to convert input data into JSON format and sends it to the server via an HTTP POST request. The server endpoint is implemented using Node.js and the Express framework to receive the data.

[0484] The server analyzes the received data using a JSON parser to extract the agenda and participant information. The analysis results are stored in a MongoDB database, allowing for flexible data management.

[0485] The server then retrieves the stored data and passes it to a generative AI tool, which uses OpenAI's GPT-4 model to generate the meeting agenda and time allocation, using prompts such as:

[0486] "Please generate an agenda for the next meeting. The topics are 'Marketing strategy for new products' and 'Expanding sales channels'. The participants are 'Tanaka-san, Suzuki-san, Sato-san'."

[0487] The AI ​​tool generates an agenda based on the prompt and returns it to the server, which then sends the result in JSON format to the user's device.

[0488] The device parses the received agenda and displays it to the user using HTML and JavaScript. Specifically, the generated agenda items are displayed in a list format, and color-coded to visually indicate the time allocation of each item.

[0489] In this way, the system allows users to simply input the meeting topic and participant information, and the generative AI model automatically generates an efficient agenda and time allocation and provides it to the user, thereby streamlining the progress of meetings.

[0490] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0491] Step 1:

[0492] The user enters the meeting agenda and participant information. Specifically, the user accesses the input form on their device, enters the agenda items "Design of new features" and "Test plan," and registers "Mr. A, Mr. B, and Mr. C" as participants. Once the input is complete, the user presses the "Send" button. The agenda and participant information are obtained as input data.

[0493] Step 2:

[0494] The terminal converts the data entered by the user into JSON format and sends it to the server using an HTTP POST request. At this time, JavaScript code serializes the data using JSON.stringify and sends the data according to the transmission protocol. JSON format data is received as input and a request to the server is generated as output.

[0495] Step 3:

[0496] The server receives data sent from the terminal. The server uses Node.js and Express and parses the received data with a JSON parser. The received JSON data contains the agenda and participant information entered by the user. The received data is parsed and converted into a format for saving in MongoDB.

[0497] Step 4:

[0498] The server analyzes the received data, extracts the agenda and participant information, and saves it in a MongoDB database. The extracted data will have a format such as "Agenda: New feature design, test plan" and "Participants: A, B, C." Database operations are performed using the MongoDB driver, and if successful, a status indicating that the data has been saved is returned.

[0499] Step 5:

[0500] The server retrieves the saved agenda and participant information from its internal database and passes it to the AI ​​generation tool. At this time, the server executes a Python script to call the AI ​​generation tool's API. Specifically, it generates the following prompt:

[0501] "Generate an agenda for the next meeting. The topics are 'Design new features' and 'Test plan'. The participants are 'A, B, C'."

[0502] Step 6:

[0503] The generative AI tool generates a meeting agenda and time allocation based on the prompt text. At this time, the generative AI model GPT-4 analyzes the prompt text and outputs the optimal meeting agenda and time allocation. For example, the output may be an agenda in the format "10:00-10:30: Design new features" or "10:30-11:00: Test plan."

[0504] Step 7:

[0505] The server receives the generated agenda and time allocation from the AI ​​tool, formats it in JSON, and sends it to the user's device. The server returns JSON data containing the generated agenda as an HTTP response.

[0506] Step 8:

[0507] The device analyzes the data received from the server and displays it to the user. Using HTML and JavaScript, the generated agenda items are displayed on the screen in list format, and each time allocation is color-coded to make it visually easy to understand. The user can check this and use it to progress the meeting.

[0508] (Application example 1)

[0509] 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."

[0510] There is a need for a system that can efficiently collect meeting topic and participant information, quickly convert user voice input into text information, and automatically generate an efficient meeting agenda and time allocation using a generative AI tool. Conventional systems often require users to input information manually, which is time-consuming and hinders efficient meeting progress. Furthermore, in workplaces such as factories, there is a lack of means for workers to input meeting information without using their hands. Technology is needed to solve these problems and improve the productivity and efficiency of meetings.

[0511] 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.

[0512] In this invention, the server includes a conversion means that converts voice input into text information using voice recognition technology, a communication means that sends the input data to the server, a storage means that the server processes the data and stores it in an internal database, a generation means that uses a generative AI tool to generate a meeting agenda and time allocation based on the topic and participant information, and a display means that sends the generated agenda and time allocation to a user's terminal and displays it. This allows users to efficiently input meeting information through voice input, and the server can automatically generate a fast and accurate agenda using a generative AI model.

[0513] A "user" is a person who operates the system and is responsible for inputting the meeting agenda and participant information.

[0514] An "agenda" refers to the topics or issues to be addressed at a meeting.

[0515] "Participant Information" refers to the names, titles, and other relevant information of people attending a meeting.

[0516] "Input means" is a general term for devices and methods that allow users to input the agenda and participant information for a meeting.

[0517] "Voice recognition technology" refers to the technology that converts voice into text data.

[0518] "Conversion means" is a general term for mechanisms and methods for converting voice input into text information using voice recognition technology.

[0519] "Communication means" is a general term that includes the technology and protocols used to transmit input data to a server.

[0520] "Server" means a computer system for receiving, processing, and storing data and generating an agenda using a generation AI tool.

[0521] "Storage means" is a general term for mechanisms and methods for storing data received by the server in an internal database.

[0522] "Generation method" is a general term for the process of using a generative AI tool to generate a meeting agenda and time allocation based on the topic and participant information.

[0523] "Generative AI tools" refers to software or algorithms that use AI technology to automatically generate meeting agendas and time allocations.

[0524] "Display means" is a general term for mechanisms and methods for transmitting the generated agenda and time allocation to the user's terminal and displaying them.

[0525] "Terminal" refers to a device that allows a user to enter information and view a generated agenda.

[0526] "Text information" refers to text data converted using voice recognition technology.

[0527] "Internal database" refers to the database system used to store and manage data within a server.

[0528] "Time allocation" refers to the amount of time allocated to each agenda item in a meeting.

[0529] MODE FOR CARRYING OUT THE INVENTION

[0530] This invention relates to a system that automatically generates a meeting agenda and time allocation using speech recognition technology and generative AI tools. Hereinafter, the embodiments of the invention will be described in detail.

[0531] System Configuration

[0532] The system consists of the following components:

[0533] 1. User Device

[0534] A device for voice entry of meeting agenda and participant information.

[0535] It is equipped with a voice recognition microphone, display, and internet connection functions.

[0536] Examples of hardware used: industrial robots or smart speakers.

[0537] Examples of software used: Speech recognition engine (e.g. Google Cloud Speech-to-Text).

[0538] 2. Server

[0539] Voice input is converted into text information using voice recognition technology.

[0540] Receive the converted data and store it in a database.

[0541] Use generative AI tools (e.g., OpenAI GPT-4) to generate meeting agendas and time allocations.

[0542] The generated agenda is sent to the user's terminal.

[0543] Server configuration example: Linux server, HTTP server (such as Nginx or Apache), database (MySQL, PostgreSQL, etc.).

[0544] 3. Generative AI Tools

[0545] It runs within the server and generates an efficient meeting agenda and time allocation based on the inputted agenda and participant information.

[0546] Example of generative AI model used: GPT-4.

[0547] Specific processing steps

[0548] Data Entry and Conversion

[0549] Users input meeting agendas and participant information by voice. Voice recognition technology is used to convert the speech into text. This allows users to input information without using their hands, making it easy to operate even while working on-site in a factory or other facility.

[0550] Data transmission and storage

[0551] The user device sends the converted text information to the server, which receives the HTTP POST request, analyzes the data, and stores it in an internal database. This process ensures that the entered information is saved and used for subsequent processing.

[0552] Agenda generation

[0553] The server retrieves the agenda and participant information from the database and passes it to the generative AI tool. The generative AI tool automatically generates an efficient meeting agenda and time allocation based on the input data. For example, it might generate content such as "10:00-10:30: Design new features" and "10:30-11:00: Test plan."

[0554] Sending and displaying results

[0555] The server sends the generated agenda and time allocation to the user's device, which then notifies the user of the results by voice and displays them on the screen. This allows the user to immediately check the generated results and efficiently conduct the meeting.

[0556] Specific examples

[0557] Here's an example: A user speaks:

[0558] "The next meeting is about optimizing the production line and introducing new machinery, and the participants are Tanaka-san, Suzuki-san, and Sato-san."

[0559] The server converts this speech into text and passes it to a generative AI tool, which generates an agenda like this:

[0560] "14:00-14:30 Optimization of production line" "14:30-15:00 Introduction of new machinery"

[0561] The server sends this agenda to the user's terminal, which then notifies the results by voice and display.

[0562] Prompt Sentence Examples

[0563] An example prompt to pass to a generative AI tool might look something like this:

[0564] Meeting agenda:

[0565] Production line optimization

[0566] Introduction of new machinery

[0567] Participants:

[0568] Tanaka-san

[0569] Suzuki-san

[0570] Sato-san

[0571] Use this information to generate an efficient meeting agenda and time allocation for each topic.

[0572] In this manner, the system of the present invention can be implemented.

[0573] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0574] Step 1: Data entry

[0575] The user inputs the meeting agenda and participant information by voice. The voice is input into the terminal via a voice recognition microphone. Specifically, the user says the following: "The agenda for the next meeting is a meeting regarding the optimization of the production line and the introduction of new machinery. The participants are Tanaka-san, Suzuki-san, and Sato-san."

[0576] Step 2: Audio conversion

[0577] The device uses voice recognition technology (such as Google Cloud Speech-to-Text) to convert voice input into text information. This voice recognition technology analyzes the input voice data and converts it into text data. The input is voice data and the output is text data. For example, the voice input "The agenda for the next meeting is a meeting regarding production line optimization and the introduction of new machinery. The participants are Tanaka, Suzuki, and Sato." is converted to "The agenda for the next meeting is production line optimization and the introduction of new machinery. The participants are Tanaka, Suzuki, and Sato."

[0578] Step 3: Send data

[0579] The terminal sends the converted text data to the server using an HTTP POST request. The data sent is in text format and includes the agenda and participant information. The input is the converted text data, and the output is the send request.

[0580] Step 4: Receiving and storing data

[0581] The server receives the HTTP POST request, analyzes the text data, and extracts the agenda and participant information. After extraction, the obtained data is stored in an internal database. The input is the text data sent from the terminal, and the output is the agenda and participant information stored in the database.

[0582] Step 5: Generate the agenda

[0583] The server retrieves the saved agenda and participant information from the database and passes it to a generative AI tool (e.g., GPT-4). The generative AI tool automatically generates an efficient meeting agenda and time allocation based on the retrieved data. The input is the saved agenda and participant information, and the output is the generated agenda and time allocation. For example, an agenda such as "14:00-14:30 Optimize the production line" or "14:30-15:00 Install new machinery" may be generated.

[0584] Step 6: Sending the results

[0585] The server sends the generated agenda and time allocation to the user's terminal. The input is the generated agenda and time allocation, and the output is a transmission request.

[0586] Step 7: View and notify results

[0587] The terminal displays the received agenda and time allocation to the user. The user terminal can also display the results on a screen and notify the user by voice. The input is the generated agenda and time allocation, and the output is the display and voice notification. For example, "Next meeting agenda: 14:00-14:30 Production line optimization, 14:30-15:00 Installation of new machine" is displayed and notified by voice.

[0588] 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.

[0589] This invention is a system in which a user inputs the meeting topic and participant information, and a generation AI tool automatically generates the meeting agenda and time allocation. By combining this with an emotion engine, the system recognizes the user's emotions and optimizes the progress of the meeting in real time. Below, the program processing of this system is explained in natural language. Specific examples are also provided.

[0590] Program processing

[0591] 1. Data Entry

[0592] A user uses a terminal to input the meeting agenda and participant information. For example, the user inputs agenda items such as "design of new features" and "test plan" and participant information such as "Mr. A, Mr. B, and Mr. C."

[0593] 2. Data transmission

[0594] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request.

[0595] 3. Receipt and storage of information

[0596] The server receives the data packet sent from the terminal, parses the HTTP request body, and extracts the agenda and participant information.

[0597] The extracted agenda and participant information is stored in an internal database.

[0598] 4. Agenda generation

[0599] The server retrieves the agenda and participant information from its internal database and passes it to the generative AI tool.

[0600] Based on the data received, the generative AI tool generates an efficient meeting agenda and time allocation, such as "10:00-10:30: Design new features" and "10:30-11:00: Test planning."

[0601] 5. Sending and displaying results

[0602] The server sends the generated agenda and time allocation to the user's device. The data is sent using an HTTP response.

[0603] The terminal displays the received agenda and time allocation to the user. The terminal screen displays "10:00-10:30 New function design" and "10:30-11:00 Test plan."

[0604] 6. Enabling the Emotion Engine

[0605] The device activates an emotion engine while the meeting is in progress and recognizes emotions by analyzing the user's facial expressions and voice. For example, it uses a camera or microphone to detect the user's emotions in real time.

[0606] 7. Emotional Feedback

[0607] The emotion engine sends the emotion data it recognizes to the server. For example, if it recognizes that the user is tired, that information is sent to the server.

[0608] 8. Real-time agenda revision

[0609] Based on the emotion data received by the server, the generative AI tool modifies the current agenda and time allocation in real time, adjusting the time for each topic or postponing some topics depending on the emotion.

[0610] 9. Displaying the correction results

[0611] The server transmits the revised agenda and time allocation back to the user's terminal.

[0612] The terminal redisplays the revised agenda to the user, who then proceeds with the conference accordingly.

[0613] Specific examples

[0614] For example, for next week's project meeting, a user enters "Project Progress Review" and "Next Release Plan" as agenda items on their device and adds "X, Y, and Z" as participants. The device sends this information to the server, which receives and stores it. The server then calls the generation AI tool and generates an agenda based on the saved agenda and participant information. The generation AI tool generates an agenda with the following schedules: "9:00-9:30 Project Progress Review" and "9:30-10:00 Next Release Plan" and returns it to the server. The server then sends the generated agenda to the user's device, which displays it. During the meeting, the emotion engine analyzes the user's facial expressions and voice. If it determines that the user is tired, it sends that information to the server. The generation AI tool then modifies the agenda based on this emotional data, and the new agenda is displayed on the user's device. The user then proceeds with the meeting according to the modified agenda.

[0615] In this way, the system uses AI to automatically generate meeting agendas and time allocations based on information entered by users and data from the emotion engine, and then corrects them in real time, thereby supporting efficient and meaningful meeting conduct.

[0616] The processing flow will be explained below.

[0617] Step 1:

[0618] The user enters the meeting agenda and participant information. Specifically, the user uses an input form on the terminal to enter agenda items such as "Designing new features" and "Test plan," as well as participant information for "Mr. A, Mr. B, and Mr. C."

[0619] Step 2:

[0620] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request.

[0621] Step 3:

[0622] The server receives the data packet sent from the terminal, parses the HTTP request body, and extracts the agenda and participant information.

[0623] Step 4:

[0624] The server saves the extracted agenda and participant information to an internal database, storing this information in the appropriate tables and fields within the database.

[0625] Step 5:

[0626] The server retrieves the agenda and participant information from an internal database and passes it to the generative AI tool, which then converts it into the appropriate API calls and data formats before inputting it.

[0627] Step 6:

[0628] The generative AI tool generates an efficient meeting agenda and time allocation based on the received data. For example, the generative AI tool might generate an agenda such as "10:00-10:30: Design new features" or "10:30-11:00: Test planning."

[0629] Step 7:

[0630] The generative AI tool returns the generated agenda and time allocation to the server, which converts the returned data into an appropriate format and temporarily stores it on the server.

[0631] Step 8:

[0632] The server packages the generated agenda and time allocation into a data packet and sends it to the user's terminal, where the data is returned using an HTTP response.

[0633] Step 9:

[0634] The terminal receives the data packet sent from the server, analyzes the data packet, and extracts the agenda and time allocation.

[0635] Step 10:

[0636] The device displays the extracted agenda and time allocation to the user. Specifically, the device displays "10:00-10:30: Design new functions" and "10:30-11:00: Test planning" on the screen.

[0637] Step 11:

[0638] The user conducts the meeting based on the displayed agenda and time allocation. The user and participants efficiently discuss each topic within the specified time.

[0639] Step 12:

[0640] The device activates the emotion engine while the meeting is in progress. Specifically, it uses the device's camera and microphone to analyze the user's facial expressions and voice in real time.

[0641] Step 13:

[0642] The emotion engine analyzes the user's emotions and sends the recognized emotion data to the server. For example, if the user is recognized as being fatigued, that information is packetized as emotion data and sent to the server.

[0643] Step 14:

[0644] The server analyzes the received emotional data and provides feedback to the generative AI tool, which then uses it to adjust the meeting agenda and time allocation in real time.

[0645] Step 15:

[0646] Based on the feedback, the generative AI tool reevaluates the current agenda and time allocation and makes adjustments as necessary. For example, if the agenda item "Design new features" is taking too long, the tool will shorten the time and adjust the agenda to move on to the next agenda item, "Test planning," sooner.

[0647] Step 16:

[0648] The generative AI tool sends the revised agenda and time allocation back to the server.

[0649] Step 17:

[0650] The server resends the revised agenda and time allocation to the user's device. The data is sent using an HTTP response.

[0651] Step 18:

[0652] The terminal displays the received revised agenda and time allocation again to the user, who then proceeds with the conference according to the new agenda.

[0653] Through these steps, the system optimizes the progress of meetings in real time and realizes flexible time allocation according to the user's emotions, thereby improving meeting efficiency and reducing wasted time.

[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] In conventional conference systems, setting the agenda and allocating time for a meeting is done manually, making it difficult to run the meeting efficiently. Furthermore, there is no way to revise the agenda in real time during the meeting, taking into account the emotions and fatigue levels of participants, which can disrupt the smooth progress of the meeting and cause participants to lose concentration. The present invention aims to solve these problems and realize efficient and flexible meeting management.

[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: input means for a user to input agenda and participant information; communication means for transmitting the input data to the server; storage means for the server to process the data and store it in an internal database; generation means for a generative AI model to generate a meeting agenda and time allocation based on the agenda and participant information; display means for transmitting the generated agenda and time allocation to the user's terminal and displaying it; recognition means for an emotion engine to recognize the user's emotions; feedback means for transmitting the recognized emotion data to the server; correction means for the server to correct the agenda and time allocation in real time based on the emotion data; and re-display means for transmitting the corrected agenda and time allocation to the user's terminal and displaying it again. This not only enables automatic generation of an efficient meeting agenda and time allocation based on information input by the user, but also enables recognition of the emotions of participants during the meeting and correction of the agenda in real time accordingly.

[0659] A "user" is a person or entity that utilizes the system to enter meeting agendas and participant information.

[0660] A "terminal" is an electronic device that a user uses to enter meeting agenda and participant information.

[0661] "Server" means a device that receives and processes data sent from a terminal, and stores and manages it in an internal database.

[0662] "Input means" refers to the device or software by which a user inputs meeting agenda and participant information into the system.

[0663] "Communication means" refers to a protocol or device for transmitting data input by an input means to a server.

[0664] "Storage means" is the function or process by which the server stores the data it receives in its internal database.

[0665] A "generative AI model" is an artificial intelligence model that automatically generates a meeting agenda and time allocation based on the topic and participant information.

[0666] A "generator" is a process or function that uses a generative AI model to generate a meeting agenda and time allocation.

[0667] The "display means" is software or a device for transmitting the generated agenda and time allocation to the user's terminal and displaying it.

[0668] The "emotion engine" is an analysis engine for recognizing the user's emotions in real time.

[0669] A "recognition means" is a process or function that uses an emotion engine to recognize a user's emotion.

[0670] The "feedback means" is a means for transmitting the recognized emotion data to the server.

[0671] "Modification means" refers to a process or function by which the server modifies the meeting agenda and time allocation in real time based on emotion data.

[0672] The "redisplay means" is software or a device for transmitting the revised agenda and time allocation to the user's terminal and displaying them again.

[0673] This invention is a system that automatically generates a meeting agenda and time allocation based on the meeting topic and participant information using a generative AI tool, and further combines it with an emotion engine to recognize the user's emotions and optimize the progress of the meeting in real time.

[0674] Specific Embodiments of the System

[0675] 1. Data Entry

[0676] Users use a device to input the meeting agenda and participant information. The device can be a PC, tablet, smartphone, or other electronic device. The input data is collected via the device's application.

[0677] 2. Data Transmission

[0678] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request. The communication method uses a communication protocol via the Internet.

[0679] 3. Receipt and storage of information

[0680] The server receives data packets sent from the devices, parses the HTTP POST requests using the Python Flask framework, and extracts the agenda and participant information. The extracted information is then stored in an internal database (e.g., an SQL database).

[0681] 4. Agenda generation

[0682] The server retrieves the agenda and participant information from its internal database and passes it to a generative AI model (e.g., OpenAI's GPT-3). The generative AI model generates an efficient meeting agenda and time allocation based on the provided data. The generated agenda is returned to the server.

[0683] 5. Sending and Displaying the Agenda

[0684] The server sends the generated agenda and time allocation to the user's device. The data is sent using an HTTP response. The device analyzes the received agenda and displays it to the user. For example, the device screen might display "10:00-10:30 Design new features" and "10:30-11:00 Test planning."

[0685] 6. Enabling the Emotion Engine

[0686] The device activates its emotion engine during the conference and uses the camera and microphone to analyze the user's facial expressions and voice to recognize emotions. The recognized emotion data is obtained via analysis software (e.g., EmotionAPI, FaceAPI).

[0687] 7. Emotional Feedback

[0688] The emotion engine sends the recognized emotion data to the server as an HTTP request.

[0689] 8. Real-time agenda revision

[0690] The server uses a generative AI model to modify the current agenda and time allocation in real time based on the received emotional data, and the modified agenda is returned to the server.

[0691] 9. Displaying the correction results

[0692] The server then sends the revised agenda and time allocation back to the user's terminal, which then displays the revised agenda again, allowing the user to proceed with the meeting accordingly.

[0693] Specific Examples

[0694] For example, a user enters "Project progress review" and "Planning the next release" as agenda items for next week's project meeting on their device, and adds "Mr. X, Mr. Y, and Mr. Z" as participants. The device sends this information to the server, which receives and stores it. The server then uses a generation AI tool to generate an agenda and returns it as "9:00-9:30: Project progress review" and "9:30-10:00: Planning the next release."

[0695] During a meeting, the emotion engine analyzes the user's facial expressions and voice, and if it determines that the user is tired, it sends that information to the server. The server then modifies the agenda based on this emotional data, and the new agenda is displayed on the user's device. The user then proceeds with the meeting according to the modified agenda.

[0696] Prompt Sentence Examples

[0697] Create an efficient meeting agenda based on "checking project progress" and "planning the next release."

[0698] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0699] Step 1:

[0700] Data Entry

[0701] The user inputs the meeting agenda and participant information into the terminal.

[0702] Input: The user enters the agenda, such as "Designing new features" or "Test plan," and participant information, such as "A, B, and C," into an input form.

[0703] What it does: A user enters information into form fields, such as text fields and drop-down menus, in a browser or application on their device.

[0704] Step 2:

[0705] Data transmission

[0706] The terminal converts the input data into data packets and transmits them to the server.

[0707] Input: The topic and participant information entered by the user in the previous step.

[0708] Data processing: The terminal serializes the input data into JSON format.

[0709] Output: Data packet in JSON format.

[0710] What happens: Front-end JavaScript converts the data to JSON format and sends it to the server as the body of an HTTP POST request.

[0711] Step 3:

[0712] Receiving and storing information

[0713] The server receives the data packets sent from the terminal and stores them in an internal database.

[0714] Input: A data packet in JSON format.

[0715] Data processing: The server parses the data packets and extracts the agenda and participant information.

[0716] Output: Agenda and participant information stored in a database.

[0717] Specific operation: The server receives the HTTP request using Flask, extracts the data using the request.get_json() method, and executes an INSERT query to the database using SQLAlchemy.

[0718] Step 4:

[0719] Agenda generation

[0720] The server passes the data to a generative AI model to generate the meeting agenda and time allocation.

[0721] Input: Agenda and participant information retrieved from the database.

[0722] Data processing: The acquired data is transformed into a prompt sentence and sent to the generative AI model.

[0723] Output: Generated meeting agenda and time allocation.

[0724] Specifically, the server retrieves the necessary information from the database using a SELECT query, then sends an API request based on that data to the generative AI model. The generative AI model (e.g., GPT-3) generates an agenda based on the prompt and responds in JSON format.

[0725] Step 5:

[0726] Sending and displaying results

[0727] The server transmits the generated agenda and time allocation to the user's terminal and displays them.

[0728] Input: Meeting agenda and time allocation returned by the generative AI model.

[0729] Data processing: Format the generated agenda as an HTTP response.

[0730] Output: The meeting agenda displayed on the user's device.

[0731] Specific behavior: The server returns the generated agenda as a JSON response, which the device parses, renders in HTML, and displays to the user.

[0732] Step 6:

[0733] Enabling the Emotion Engine

[0734] The device activates an emotion engine to recognize the user's emotions.

[0735] Input: User facial and voice data.

[0736] Data processing: Data captured by the camera and microphone is passed to emotion recognition algorithms.

[0737] Output: Recognized emotion data.

[0738] How it works: The device's application captures the user's facial expressions and voice in real time using the camera and microphone, and analyzes the data using libraries such as EmotionAPI and FacialRecognition.

[0739] Step 7:

[0740] Emotional feedback

[0741] The emotion engine transmits the recognized emotion data to the server.

[0742] Input: Recognized emotion data.

[0743] Data processing: Serialize emotion data into JSON format.

[0744] Output: Emotion data sent to the server.

[0745] Specific operation: The emotion engine analyzes the recognized emotions and sends the information in JSON format to the server as an HTTP POST request.

[0746] Step 8:

[0747] Real-time agenda modification

[0748] The server modifies the agenda and time allocation based on the emotional data.

[0749] Input: Recognized emotion data and existing meeting agenda.

[0750] Data processing: Create a new prompt sentence based on the emotion data and request it from the generative AI model.

[0751] Output: Revised meeting agenda and time allocation.

[0752] Specific operation: Based on the emotion data received by the server, a correction request is sent to the generative AI model, which then generates a new agenda.

[0753] Step 9:

[0754] Displaying the correction results

[0755] The server transmits the revised agenda and time allocation to the user's terminal again and displays them.

[0756] Input: Revised meeting agenda and time allocation.

[0757] Data processing: Format the modified agenda as an HTTP response.

[0758] Output: The meeting agenda redisplayed on the user's device.

[0759] Specific behavior: The server returns the modified agenda in JSON format, and the device parses it and redisplays it.

[0760] (Application example 2)

[0761] 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."

[0762] Efficiently managing different work tasks within a factory and adjusting their progress in real time is important in environments where multiple robots and workers work together. However, with conventional systems, it is difficult to optimize the task progress and the status of each robot in real time, which hinders efficient work management. In particular, it is necessary to solve these issues, as rapid response to factors such as remaining battery life and declining work efficiency is required.

[0763] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0764] In this invention, the server includes an input means for a user to input task and assigned robot information, a communication means for transmitting the input data to the server, a storage means for the server to process the data and store it in an internal database, a generation means for a generation AI tool to generate an efficient task schedule based on the task and assigned robot information, a display means for transmitting the generated task schedule to the robot's terminal and displaying it, and a correction means for an emotion engine to analyze the robot's status (remaining battery level and work efficiency) and correct the task schedule in real time. This makes it possible to efficiently manage work tasks in a factory and optimize progress in real time according to the robot's status.

[0765] A "user device" is an electronic device used by a user to input information and display generated data, including a smartphone, smart glasses, a head-mounted display, or a robot.

[0766] "Input means" refers to an interface that allows a user to input information about a meeting or task, and includes a keyboard, a touch screen, voice input, and the like.

[0767] "Communication means" refers to the method or protocol for transmitting input data to the server, including HTTP requests and other data communication methods.

[0768] "Storage means" refers to the mechanism by which the server stores the received data in an internal database, and includes a database engine and a file system.

[0769] "Generation means" refers to the function of the generation AI tool to automatically generate meeting and work agendas and time allocations based on input information using a pre-trained model.

[0770] "Display means" refers to a method for displaying the generated agenda or schedule to the user or robot, and includes a display, a screen, a projector, etc.

[0771] An "emotion engine" is a software component that analyzes the state of the user or robot (e.g., facial expressions, voice, remaining battery level, work efficiency, etc.) and makes optimal adjustments and corrections in real time based on that data.

[0772] "Modification means" refers to the function whereby the generative AI tool modifies the agenda and schedule in real time based on data and feedback from the emotion engine and sends it to the display means.

[0773] This invention is a system for improving the efficiency of robot work task management in factories. The system includes an input means through which a user inputs task and assigned robot information, a communication means for transmitting the input data to a server, and a storage means for the server to store the received data in an internal database. The system also includes a generation means for a generation AI tool to generate an efficient task schedule based on the task and assigned robot information, a display means for transmitting the generated task schedule to the robot's terminal and displaying it, and a correction means for an emotion engine to analyze the robot's state and correct the task schedule in real time.

[0774] Specifically, users input task and robot information using devices such as smartphones, tablets, and PCs. For example, they input work tasks such as "assembly of product X" and "quality inspection," as well as information about the robots in charge, such as "robot A" and "robot B." This data is then sent to the server via communication means.

[0775] The server processes the received data, parses the HTTP request body to extract task and robot information, and stores it in an internal database.The server then invokes a generative AI tool to generate an efficient task schedule based on the stored task and robot information.This process uses a pre-trained generative AI model.

[0776] The generated task schedule is sent from the server to the terminal of the robot in charge, and each robot recognizes and displays its own tasks. The display means uses a device such as a display or touch screen that allows the robot to check the tasks.

[0777] The emotion engine analyzes the robot's status (remaining battery level, work efficiency, etc.) in real time while the robot is working and feeds that information back to the correction means. The correction means corrects the schedule in real time based on the data provided by the emotion engine and sends it back to the robot's terminal for display.

[0778] For example, if Robot A is engaged in assembling Product X and its battery level drops, the emotion engine analyzes this condition and the corrective measures regenerate a new task schedule, where Robot B takes over the task of assembling Product X and Robot A transitions to a quality inspection task.

[0779] An example of a prompt is as follows:

[0780] "Robot A's battery level is low. Please regenerate the schedule."

[0781] Based on this prompt, the task schedule is regenerated and redisplayed on each robot's terminal.

[0782] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0783] Step 1:

[0784] The user uses a terminal to input information about the task and the robot in charge. Through the terminal interface, the user inputs work tasks such as "assembly of product X" or "quality inspection" and information about the robot in charge, such as "Robot A, Robot B." The input data is saved in JSON format.

[0785] Step 2:

[0786] The device converts the input data into a data packet and sends it to the server using an HTTP POST request. At this time, the device uses a communication protocol to securely send the data. The input JSON data is sent as the body of the HTTP request.

[0787] Step 3:

[0788] The server receives the data packet and parses the HTTP request body to extract task and robot information, which is then stored in an internal database. Specifically, it uses database queries to store the task and robot information in the appropriate tables.

[0789] Step 4:

[0790] The server retrieves the stored task and robot information from the internal database and passes it to the generative AI tool. The generative AI tool uses a pre-trained generative AI model to generate an optimal task schedule based on the input data. It analyzes the input task and assigned robot information and outputs an efficient schedule.

[0791] Step 5:

[0792] The server sends the generated schedule to the terminal of the robot in charge. The server formats the generated schedule as an HTTP response and sends it to each robot's terminal. The robot's terminal analyzes the received schedule and displays it on a device such as a display.

[0793] Step 6:

[0794] The emotion engine analyzes the robot's status while it is working. The robot's terminal sends data such as remaining battery power and work efficiency to the emotion engine in real time. The emotion engine analyzes this data and determines the robot's current status.

[0795] Step 7:

[0796] The server regenerates the schedule based on the data analyzed by the emotion engine. The server then obtains feedback data from the emotion engine and uses the generation AI tool again to appropriately revise the schedule. At this time, new data processing and calculations are performed.

[0797] Step 8:

[0798] The server sends the revised schedule to the robot terminal again. The revised schedule is sent as an HTTP response and the robot terminal redisplays it. This allows the robot to continue working according to the latest schedule.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] [Third embodiment]

[0803] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0804] 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.

[0805] 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).

[0806] 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.

[0807] 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.

[0808] 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).

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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."

[0815] This invention is a system in which a user inputs the meeting topic and participant information, and a generation AI tool automatically generates the meeting agenda and time allocation, allowing the meeting to proceed efficiently. Below, the program processing of this system is explained in natural language. Specific examples are also provided.

[0816] Program processing

[0817] 1. Data Entry

[0818] A user uses a terminal to input the meeting agenda and participant information. For example, a user inputs the agenda for the next meeting, "Designing new features" and "Test plan," and adds "Mr. A, Mr. B, and Mr. C" as participants.

[0819] 2. Data transmission

[0820] The terminal sends the entered data to the server using a transmission protocol such as HTTP POST request.

[0821] 3. Receipt and storage of information

[0822] The server receives the data sent from the terminal.

[0823] The received data is parsed to extract the agenda and participant information, which is then stored in an internal database.

[0824] 4. Agenda generation

[0825] The server retrieves the agenda and participant information from its internal database and passes it to the generative AI tool.

[0826] The generative AI tool generates an efficient meeting agenda and time allocation based on the received data. For example, the generative AI tool might generate an agenda such as "10:00-10:30: Design new features" or "10:30-11:00: Test planning."

[0827] 5. Sending and displaying results

[0828] The server transmits the generated agenda and time allocation to the user's terminal.

[0829] The terminal displays the received agenda and time allocation to the user, who then checks the displayed content and uses it to actually proceed with the meeting.

[0830] Specific examples

[0831] Consider the following example. For next week's project meeting, a user enters "Project progress review" and "Plan for next release" as agenda items on their device, and adds "Mr. X, Mr. Y, and Mr. Z" as participants. The device sends this information to the server, which receives and stores it. The server then calls a generation AI tool, which generates an efficient agenda based on the saved agenda and participant information. The generation AI tool generates an agenda with the following schedules: "9:00-9:30: Project progress review" and "9:30-10:00: Plan for next release," and passes it to the server. The server then sends the generated agenda to the user's device, which displays it. The user can use this information to conduct the meeting and hold efficient discussions.

[0832] In this way, this system uses AI to automatically generate meeting agendas and time allocations based on information entered by the user, helping to ensure efficient and meaningful meeting proceedings.

[0833] The processing flow will be explained below.

[0834] Step 1:

[0835] The user enters the meeting agenda and participant information. Specifically, the user uses the input form on the device to enter the agenda items "Designing new features" and "Test plan" and the participant information of "Mr. A, Mr. B, and Mr. C."

[0836] Step 2:

[0837] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request.

[0838] Step 3:

[0839] The server receives the data packet sent from the terminal, where it parses the body of the HTTP request and extracts the agenda and participant information.

[0840] Step 4:

[0841] The server saves the extracted agenda and participant information to an internal database, storing this information in the appropriate tables and fields within the database.

[0842] Step 5:

[0843] The server calls the generation AI tool based on the saved agenda and participant information. At this time, the server passes the saved data to the generation AI tool as necessary parameters.

[0844] Step 6:

[0845] The generative AI tool generates an efficient meeting agenda and time allocation based on the topic and participant information it receives. For example, the generative AI tool uses past data and pre-trained models to generate an agenda such as "10:00-10:30: Design new features" or "10:30-11:00: Test planning."

[0846] Step 7:

[0847] The generative AI tool returns the generated agenda and time allocation to the server, where the returned data is converted into an appropriate format and temporarily stored.

[0848] Step 8:

[0849] The server packages the generated agenda and time allocation into a data packet and sends it to the user's terminal, where the data is returned using an HTTP response.

[0850] Step 9:

[0851] The terminal receives the data packet sent from the server, analyzes the data packet, and extracts the agenda and time allocation.

[0852] Step 10:

[0853] The device displays the extracted agenda and time allocation to the user. Specifically, information such as "10:00-10:30: Design new functions" and "10:30-11:00: Test planning" is displayed on the device screen.

[0854] Step 11:

[0855] The user conducts the meeting based on the displayed agenda and time allocation. The user and participants efficiently discuss each topic within the specified time.

[0856] This series of processing steps enables users to conduct meetings efficiently and reduce wasted time.

[0857] Example 1

[0858] 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."

[0859] In conventional meeting management systems, creating an agenda to efficiently conduct a meeting is a time-consuming and labor-intensive process. It is also difficult to allocate time optimally based on the agenda and participant information, resulting in a decrease in meeting efficiency. To solve this problem, a system is needed that can automatically generate an agenda based on input information and propose efficient time allocation.

[0860] 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.

[0861] In this invention, the server includes an input means for a user to input the agenda and participant information, a communication means for transmitting the input data to the server, a storage means for the server to analyze the data and store it in an internal database, a generation means for a generative AI model to generate a meeting agenda and time allocation based on the agenda and participant information, and a display means for transmitting the generated agenda and time allocation to the user's terminal and displaying it. This supports the progress of the meeting and enables the user to effectively manage the meeting by using the automatically generated efficient agenda and time allocation.

[0862] The "input means" is a means by which a user inputs the agenda and participant information of a meeting.

[0863] "Communication means" refers to a means for transmitting input data to a server.

[0864] The "storage means" is a means for the server to analyze the received data and store it in an internal database.

[0865] "Generation means" refers to the means by which the generative AI model generates a meeting agenda and time allocation based on the topic and participant information.

[0866] The "display means" is a means for transmitting the generated agenda and time allocation to the user's terminal and displaying them.

[0867] A "generative AI model" is an AI model that generates a meeting agenda and time allocation from given input data based on pre-trained data.

[0868] MODE FOR CARRYING OUT THE INVENTION

[0869] This invention is a system designed to efficiently and effectively conduct meetings, which automatically generates a meeting agenda and time allocation based on information entered by a user. The system includes input means, communication means, storage means, generation means, and display means.

[0870] First, the user uses the terminal to enter the meeting agenda and participant information. The terminal provides a dedicated HTML-based form, allowing the user to easily enter the agenda and participant information. For example, the user enters the agenda items "Designing new features" and "Test plan" and adds "Mr. A, Mr. B, and Mr. C" as participants.

[0871] The device uses JavaScript to convert input data into JSON format and sends it to the server via an HTTP POST request. The server endpoint is implemented using Node.js and the Express framework to receive the data.

[0872] The server analyzes the received data using a JSON parser to extract the agenda and participant information. The analysis results are stored in a MongoDB database, allowing for flexible data management.

[0873] The server then retrieves the stored data and passes it to a generative AI tool, which uses OpenAI's GPT-4 model to generate the meeting agenda and time allocation, using prompts such as:

[0874] "Please generate an agenda for the next meeting. The topics are 'Marketing strategy for new products' and 'Expanding sales channels'. The participants are 'Tanaka-san, Suzuki-san, Sato-san'."

[0875] The AI ​​tool generates an agenda based on the prompt and returns it to the server, which then sends the result in JSON format to the user's device.

[0876] The device parses the received agenda and displays it to the user using HTML and JavaScript. Specifically, the generated agenda items are displayed in a list format, and color-coded to visually indicate the time allocation of each item.

[0877] In this way, the system allows users to simply input the meeting topic and participant information, and the generative AI model automatically generates an efficient agenda and time allocation and provides it to the user, thereby streamlining the progress of meetings.

[0878] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0879] Step 1:

[0880] The user enters the meeting agenda and participant information. Specifically, the user accesses the input form on their device, enters the agenda items "Design of new features" and "Test plan," and registers "Mr. A, Mr. B, and Mr. C" as participants. Once the input is complete, the user presses the "Send" button. The agenda and participant information are obtained as input data.

[0881] Step 2:

[0882] The terminal converts the data entered by the user into JSON format and sends it to the server using an HTTP POST request. At this time, JavaScript code serializes the data using JSON.stringify and sends the data according to the transmission protocol. JSON format data is received as input and a request to the server is generated as output.

[0883] Step 3:

[0884] The server receives data sent from the terminal. The server uses Node.js and Express and parses the received data with a JSON parser. The received JSON data contains the agenda and participant information entered by the user. The received data is parsed and converted into a format for saving in MongoDB.

[0885] Step 4:

[0886] The server analyzes the received data, extracts the agenda and participant information, and saves it in a MongoDB database. The extracted data will have a format such as "Agenda: New feature design, test plan" and "Participants: A, B, C." Database operations are performed using the MongoDB driver, and if successful, a status indicating that the data has been saved is returned.

[0887] Step 5:

[0888] The server retrieves the saved agenda and participant information from its internal database and passes it to the AI ​​generation tool. At this time, the server executes a Python script to call the AI ​​generation tool's API. Specifically, it generates the following prompt:

[0889] "Generate an agenda for the next meeting. The topics are 'Design new features' and 'Test plan'. The participants are 'A, B, C'."

[0890] Step 6:

[0891] The generative AI tool generates a meeting agenda and time allocation based on the prompt text. At this time, the generative AI model GPT-4 analyzes the prompt text and outputs the optimal meeting agenda and time allocation. For example, the output may be an agenda in the format "10:00-10:30: Design new features" or "10:30-11:00: Test plan."

[0892] Step 7:

[0893] The server receives the generated agenda and time allocation from the AI ​​tool, formats it in JSON, and sends it to the user's device. The server returns JSON data containing the generated agenda as an HTTP response.

[0894] Step 8:

[0895] The device analyzes the data received from the server and displays it to the user. Using HTML and JavaScript, the generated agenda items are displayed on the screen in list format, and each time allocation is color-coded to make it visually easy to understand. The user can check this and use it to progress the meeting.

[0896] (Application example 1)

[0897] 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."

[0898] There is a need for a system that can efficiently collect meeting topic and participant information, quickly convert user voice input into text information, and automatically generate an efficient meeting agenda and time allocation using a generative AI tool. Conventional systems often require users to input information manually, which is time-consuming and hinders efficient meeting progress. Furthermore, in workplaces such as factories, there is a lack of means for workers to input meeting information without using their hands. Technology is needed to solve these problems and improve the productivity and efficiency of meetings.

[0899] 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.

[0900] In this invention, the server includes a conversion means that converts voice input into text information using voice recognition technology, a communication means that sends the input data to the server, a storage means that the server processes the data and stores it in an internal database, a generation means that uses a generative AI tool to generate a meeting agenda and time allocation based on the topic and participant information, and a display means that sends the generated agenda and time allocation to a user's terminal and displays it. This allows users to efficiently input meeting information through voice input, and the server can automatically generate a fast and accurate agenda using a generative AI model.

[0901] A "user" is a person who operates the system and is responsible for inputting the meeting agenda and participant information.

[0902] An "agenda" refers to the topics or issues to be addressed at a meeting.

[0903] "Participant Information" refers to the names, titles, and other relevant information of people attending a meeting.

[0904] "Input means" is a general term for devices and methods that allow users to input the agenda and participant information for a meeting.

[0905] "Voice recognition technology" refers to the technology that converts voice into text data.

[0906] "Conversion means" is a general term for mechanisms and methods for converting voice input into text information using voice recognition technology.

[0907] "Communication means" is a general term that includes the technology and protocols used to transmit input data to a server.

[0908] "Server" means a computer system for receiving, processing, and storing data and generating an agenda using a generation AI tool.

[0909] "Storage means" is a general term for mechanisms and methods for storing data received by the server in an internal database.

[0910] "Generation method" is a general term for the process of using a generative AI tool to generate a meeting agenda and time allocation based on the topic and participant information.

[0911] "Generative AI tools" refers to software or algorithms that use AI technology to automatically generate meeting agendas and time allocations.

[0912] "Display means" is a general term for mechanisms and methods for transmitting the generated agenda and time allocation to the user's terminal and displaying them.

[0913] "Terminal" refers to a device that allows a user to enter information and view a generated agenda.

[0914] "Text information" refers to text data converted using voice recognition technology.

[0915] "Internal database" refers to the database system used to store and manage data within a server.

[0916] "Time allocation" refers to the amount of time allocated to each agenda item in a meeting.

[0917] MODE FOR CARRYING OUT THE INVENTION

[0918] This invention relates to a system that automatically generates a meeting agenda and time allocation using speech recognition technology and generative AI tools. Hereinafter, the embodiments of the invention will be described in detail.

[0919] System Configuration

[0920] The system consists of the following components:

[0921] 1. User Device

[0922] A device for voice entry of meeting agenda and participant information.

[0923] It is equipped with a voice recognition microphone, display, and internet connection functions.

[0924] Examples of hardware used: industrial robots or smart speakers.

[0925] Examples of software used: Speech recognition engine (e.g. Google Cloud Speech-to-Text).

[0926] 2. Server

[0927] Voice input is converted into text information using voice recognition technology.

[0928] Receive the converted data and store it in a database.

[0929] Use generative AI tools (e.g., OpenAI GPT-4) to generate meeting agendas and time allocations.

[0930] The generated agenda is sent to the user's terminal.

[0931] Server configuration example: Linux server, HTTP server (such as Nginx or Apache), database (MySQL, PostgreSQL, etc.).

[0932] 3. Generative AI Tools

[0933] It runs within the server and generates an efficient meeting agenda and time allocation based on the inputted agenda and participant information.

[0934] Example of generative AI model used: GPT-4.

[0935] Specific processing steps

[0936] Data Entry and Conversion

[0937] Users input meeting agendas and participant information by voice. Voice recognition technology is used to convert the speech into text. This allows users to input information without using their hands, making it easy to operate even while working on-site in a factory or other facility.

[0938] Data transmission and storage

[0939] The user device sends the converted text information to the server, which receives the HTTP POST request, analyzes the data, and stores it in an internal database. This process ensures that the entered information is saved and used for subsequent processing.

[0940] Agenda generation

[0941] The server retrieves the agenda and participant information from the database and passes it to the generative AI tool. The generative AI tool automatically generates an efficient meeting agenda and time allocation based on the input data. For example, it might generate content such as "10:00-10:30: Design new features" and "10:30-11:00: Test plan."

[0942] Sending and displaying results

[0943] The server sends the generated agenda and time allocation to the user's device, which then notifies the user of the results by voice and displays them on the screen. This allows the user to immediately check the generated results and efficiently conduct the meeting.

[0944] Specific examples

[0945] Here's an example: A user speaks:

[0946] "The next meeting is about optimizing the production line and introducing new machinery, and the participants are Tanaka-san, Suzuki-san, and Sato-san."

[0947] The server converts this speech into text and passes it to a generative AI tool, which generates an agenda like this:

[0948] "14:00-14:30 Optimization of production line" "14:30-15:00 Introduction of new machinery"

[0949] The server sends this agenda to the user's terminal, which then notifies the results by voice and display.

[0950] Prompt Sentence Examples

[0951] An example prompt to pass to a generative AI tool might look something like this:

[0952] Meeting agenda:

[0953] Production line optimization

[0954] Introduction of new machinery

[0955] Participants:

[0956] Tanaka-san

[0957] Suzuki-san

[0958] Sato-san

[0959] Use this information to generate an efficient meeting agenda and time allocation for each topic.

[0960] In this manner, the system of the present invention can be implemented.

[0961] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0962] Step 1: Data entry

[0963] The user inputs the meeting agenda and participant information by voice. The voice is input into the terminal via a voice recognition microphone. Specifically, the user says the following: "The agenda for the next meeting is a meeting regarding the optimization of the production line and the introduction of new machinery. The participants are Tanaka-san, Suzuki-san, and Sato-san."

[0964] Step 2: Audio conversion

[0965] The device uses voice recognition technology (such as Google Cloud Speech-to-Text) to convert voice input into text information. This voice recognition technology analyzes the input voice data and converts it into text data. The input is voice data and the output is text data. For example, the voice input "The agenda for the next meeting is a meeting regarding production line optimization and the introduction of new machinery. The participants are Tanaka, Suzuki, and Sato." is converted to "The agenda for the next meeting is production line optimization and the introduction of new machinery. The participants are Tanaka, Suzuki, and Sato."

[0966] Step 3: Send data

[0967] The terminal sends the converted text data to the server using an HTTP POST request. The data sent is in text format and includes the agenda and participant information. The input is the converted text data, and the output is the send request.

[0968] Step 4: Receiving and storing data

[0969] The server receives the HTTP POST request, analyzes the text data, and extracts the agenda and participant information. After extraction, the obtained data is stored in an internal database. The input is the text data sent from the terminal, and the output is the agenda and participant information stored in the database.

[0970] Step 5: Generate the agenda

[0971] The server retrieves the saved agenda and participant information from the database and passes it to a generative AI tool (e.g., GPT-4). The generative AI tool automatically generates an efficient meeting agenda and time allocation based on the retrieved data. The input is the saved agenda and participant information, and the output is the generated agenda and time allocation. For example, an agenda such as "14:00-14:30 Optimize the production line" or "14:30-15:00 Install new machinery" may be generated.

[0972] Step 6: Sending the results

[0973] The server sends the generated agenda and time allocation to the user's terminal. The input is the generated agenda and time allocation, and the output is a transmission request.

[0974] Step 7: View and notify results

[0975] The terminal displays the received agenda and time allocation to the user. The user terminal can also display the results on a screen and notify the user by voice. The input is the generated agenda and time allocation, and the output is the display and voice notification. For example, "Next meeting agenda: 14:00-14:30 Production line optimization, 14:30-15:00 Installation of new machine" is displayed and notified by voice.

[0976] 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.

[0977] This invention is a system in which a user inputs the meeting topic and participant information, and a generation AI tool automatically generates the meeting agenda and time allocation. By combining this with an emotion engine, the system recognizes the user's emotions and optimizes the progress of the meeting in real time. Below, the program processing of this system is explained in natural language. Specific examples are also provided.

[0978] Program processing

[0979] 1. Data Entry

[0980] A user uses a terminal to input the meeting agenda and participant information. For example, the user inputs agenda items such as "design of new features" and "test plan" and participant information such as "Mr. A, Mr. B, and Mr. C."

[0981] 2. Data transmission

[0982] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request.

[0983] 3. Receipt and storage of information

[0984] The server receives the data packet sent from the terminal, parses the HTTP request body, and extracts the agenda and participant information.

[0985] The extracted agenda and participant information is stored in an internal database.

[0986] 4. Agenda generation

[0987] The server retrieves the agenda and participant information from its internal database and passes it to the generative AI tool.

[0988] Based on the data received, the generative AI tool generates an efficient meeting agenda and time allocation, such as "10:00-10:30: Design new features" and "10:30-11:00: Test planning."

[0989] 5. Sending and displaying results

[0990] The server sends the generated agenda and time allocation to the user's device. The data is sent using an HTTP response.

[0991] The terminal displays the received agenda and time allocation to the user. The terminal screen displays "10:00-10:30 New function design" and "10:30-11:00 Test plan."

[0992] 6. Enabling the Emotion Engine

[0993] The device activates an emotion engine while the meeting is in progress and recognizes emotions by analyzing the user's facial expressions and voice. For example, it uses a camera or microphone to detect the user's emotions in real time.

[0994] 7. Emotional Feedback

[0995] The emotion engine sends the emotion data it recognizes to the server. For example, if it recognizes that the user is tired, that information is sent to the server.

[0996] 8. Real-time agenda revision

[0997] Based on the emotion data received by the server, the generative AI tool modifies the current agenda and time allocation in real time, adjusting the time for each topic or postponing some topics depending on the emotion.

[0998] 9. Displaying the correction results

[0999] The server transmits the revised agenda and time allocation back to the user's terminal.

[1000] The terminal redisplays the revised agenda to the user, who then proceeds with the conference accordingly.

[1001] Specific examples

[1002] For example, for next week's project meeting, a user enters "Project Progress Review" and "Next Release Plan" as agenda items on their device and adds "X, Y, and Z" as participants. The device sends this information to the server, which receives and stores it. The server then calls the generation AI tool and generates an agenda based on the saved agenda and participant information. The generation AI tool generates an agenda with the following schedules: "9:00-9:30 Project Progress Review" and "9:30-10:00 Next Release Plan" and returns it to the server. The server then sends the generated agenda to the user's device, which displays it. During the meeting, the emotion engine analyzes the user's facial expressions and voice. If it determines that the user is tired, it sends that information to the server. The generation AI tool then modifies the agenda based on this emotional data, and the new agenda is displayed on the user's device. The user then proceeds with the meeting according to the modified agenda.

[1003] In this way, the system uses AI to automatically generate meeting agendas and time allocations based on information entered by users and data from the emotion engine, and then corrects them in real time, thereby supporting efficient and meaningful meeting conduct.

[1004] The processing flow will be explained below.

[1005] Step 1:

[1006] The user enters the meeting agenda and participant information. Specifically, the user uses an input form on the terminal to enter agenda items such as "Designing new features" and "Test plan," as well as participant information for "Mr. A, Mr. B, and Mr. C."

[1007] Step 2:

[1008] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request.

[1009] Step 3:

[1010] The server receives the data packet sent from the terminal, parses the HTTP request body, and extracts the agenda and participant information.

[1011] Step 4:

[1012] The server saves the extracted agenda and participant information to an internal database, storing this information in the appropriate tables and fields within the database.

[1013] Step 5:

[1014] The server retrieves the agenda and participant information from an internal database and passes it to the generative AI tool, which then converts it into the appropriate API calls and data formats before inputting it.

[1015] Step 6:

[1016] The generative AI tool generates an efficient meeting agenda and time allocation based on the received data. For example, the generative AI tool might generate an agenda such as "10:00-10:30: Design new features" or "10:30-11:00: Test planning."

[1017] Step 7:

[1018] The generative AI tool returns the generated agenda and time allocation to the server, which converts the returned data into an appropriate format and temporarily stores it on the server.

[1019] Step 8:

[1020] The server packages the generated agenda and time allocation into a data packet and sends it to the user's terminal, where the data is returned using an HTTP response.

[1021] Step 9:

[1022] The terminal receives the data packet sent from the server, analyzes the data packet, and extracts the agenda and time allocation.

[1023] Step 10:

[1024] The device displays the extracted agenda and time allocation to the user. Specifically, the device displays "10:00-10:30: Design new functions" and "10:30-11:00: Test planning" on the screen.

[1025] Step 11:

[1026] The user conducts the meeting based on the displayed agenda and time allocation. The user and participants efficiently discuss each topic within the specified time.

[1027] Step 12:

[1028] The device activates the emotion engine while the meeting is in progress. Specifically, it uses the device's camera and microphone to analyze the user's facial expressions and voice in real time.

[1029] Step 13:

[1030] The emotion engine analyzes the user's emotions and sends the recognized emotion data to the server. For example, if the user is recognized as being fatigued, that information is packetized as emotion data and sent to the server.

[1031] Step 14:

[1032] The server analyzes the received emotional data and provides feedback to the generative AI tool, which then uses it to adjust the meeting agenda and time allocation in real time.

[1033] Step 15:

[1034] Based on the feedback, the generative AI tool reevaluates the current agenda and time allocation and makes adjustments as necessary. For example, if the agenda item "Design new features" is taking too long, the tool will shorten the time and adjust the agenda to move on to the next agenda item, "Test planning," sooner.

[1035] Step 16:

[1036] The generative AI tool sends the revised agenda and time allocation back to the server.

[1037] Step 17:

[1038] The server resends the revised agenda and time allocation to the user's device. The data is sent using an HTTP response.

[1039] Step 18:

[1040] The terminal displays the received revised agenda and time allocation again to the user, who then proceeds with the conference according to the new agenda.

[1041] Through these steps, the system optimizes the progress of meetings in real time and realizes flexible time allocation according to the user's emotions, thereby improving meeting efficiency and reducing wasted time.

[1042] Example 2

[1043] 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."

[1044] In conventional conference systems, setting the agenda and allocating time for a meeting is done manually, making it difficult to run the meeting efficiently. Furthermore, there is no way to revise the agenda in real time during the meeting, taking into account the emotions and fatigue levels of participants, which can disrupt the smooth progress of the meeting and cause participants to lose concentration. The present invention aims to solve these problems and realize efficient and flexible meeting management.

[1045] 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.

[1046] In this invention, the server includes: input means for a user to input agenda and participant information; communication means for transmitting the input data to the server; storage means for the server to process the data and store it in an internal database; generation means for a generative AI model to generate a meeting agenda and time allocation based on the agenda and participant information; display means for transmitting the generated agenda and time allocation to the user's terminal and displaying it; recognition means for an emotion engine to recognize the user's emotions; feedback means for transmitting the recognized emotion data to the server; correction means for the server to correct the agenda and time allocation in real time based on the emotion data; and re-display means for transmitting the corrected agenda and time allocation to the user's terminal and displaying it again. This not only enables automatic generation of an efficient meeting agenda and time allocation based on information input by the user, but also enables recognition of the emotions of participants during the meeting and correction of the agenda in real time accordingly.

[1047] A "user" is a person or entity that utilizes the system to enter meeting agendas and participant information.

[1048] A "terminal" is an electronic device that a user uses to enter meeting agenda and participant information.

[1049] "Server" means a device that receives and processes data sent from a terminal, and stores and manages it in an internal database.

[1050] "Input means" refers to the device or software by which a user inputs meeting agenda and participant information into the system.

[1051] "Communication means" refers to a protocol or device for transmitting data input by an input means to a server.

[1052] "Storage means" is the function or process by which the server stores the data it receives in its internal database.

[1053] A "generative AI model" is an artificial intelligence model that automatically generates a meeting agenda and time allocation based on the topic and participant information.

[1054] A "generator" is a process or function that uses a generative AI model to generate a meeting agenda and time allocation.

[1055] The "display means" is software or a device for transmitting the generated agenda and time allocation to the user's terminal and displaying it.

[1056] The "emotion engine" is an analysis engine for recognizing the user's emotions in real time.

[1057] A "recognition means" is a process or function that uses an emotion engine to recognize a user's emotion.

[1058] The "feedback means" is a means for transmitting the recognized emotion data to the server.

[1059] "Modification means" refers to a process or function by which the server modifies the meeting agenda and time allocation in real time based on emotion data.

[1060] The "redisplay means" is software or a device for transmitting the revised agenda and time allocation to the user's terminal and displaying them again.

[1061] This invention is a system that automatically generates a meeting agenda and time allocation based on the meeting topic and participant information using a generative AI tool, and further combines it with an emotion engine to recognize the user's emotions and optimize the progress of the meeting in real time.

[1062] Specific Embodiments of the System

[1063] 1. Data Entry

[1064] Users use a device to input the meeting agenda and participant information. The device can be a PC, tablet, smartphone, or other electronic device. The input data is collected via the device's application.

[1065] 2. Data Transmission

[1066] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request. The communication method uses a communication protocol via the Internet.

[1067] 3. Receipt and storage of information

[1068] The server receives data packets sent from the devices, parses the HTTP POST requests using the Python Flask framework, and extracts the agenda and participant information. The extracted information is then stored in an internal database (e.g., an SQL database).

[1069] 4. Agenda generation

[1070] The server retrieves the agenda and participant information from its internal database and passes it to a generative AI model (e.g., OpenAI's GPT-3). The generative AI model generates an efficient meeting agenda and time allocation based on the provided data. The generated agenda is returned to the server.

[1071] 5. Sending and Displaying the Agenda

[1072] The server sends the generated agenda and time allocation to the user's device. The data is sent using an HTTP response. The device analyzes the received agenda and displays it to the user. For example, the device screen might display "10:00-10:30 Design new features" and "10:30-11:00 Test planning."

[1073] 6. Enabling the Emotion Engine

[1074] The device activates its emotion engine during the conference and uses the camera and microphone to analyze the user's facial expressions and voice to recognize emotions. The recognized emotion data is obtained via analysis software (e.g., EmotionAPI, FaceAPI).

[1075] 7. Emotional Feedback

[1076] The emotion engine sends the recognized emotion data to the server as an HTTP request.

[1077] 8. Real-time agenda revision

[1078] The server uses a generative AI model to modify the current agenda and time allocation in real time based on the received emotional data, and the modified agenda is returned to the server.

[1079] 9. Displaying the correction results

[1080] The server then sends the revised agenda and time allocation back to the user's terminal, which then displays the revised agenda again, allowing the user to proceed with the meeting accordingly.

[1081] Specific Examples

[1082] For example, a user enters "Project progress review" and "Planning the next release" as agenda items for next week's project meeting on their device, and adds "Mr. X, Mr. Y, and Mr. Z" as participants. The device sends this information to the server, which receives and stores it. The server then uses a generation AI tool to generate an agenda and returns it as "9:00-9:30: Project progress review" and "9:30-10:00: Planning the next release."

[1083] During a meeting, the emotion engine analyzes the user's facial expressions and voice, and if it determines that the user is tired, it sends that information to the server. The server then modifies the agenda based on this emotional data, and the new agenda is displayed on the user's device. The user then proceeds with the meeting according to the modified agenda.

[1084] Prompt Sentence Examples

[1085] Create an efficient meeting agenda based on "checking project progress" and "planning the next release."

[1086] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1087] Step 1:

[1088] Data Entry

[1089] The user inputs the meeting agenda and participant information into the terminal.

[1090] Input: The user enters the agenda, such as "Designing new features" or "Test plan," and participant information, such as "A, B, and C," into an input form.

[1091] What it does: A user enters information into form fields, such as text fields and drop-down menus, in a browser or application on their device.

[1092] Step 2:

[1093] Data transmission

[1094] The terminal converts the input data into data packets and transmits them to the server.

[1095] Input: The topic and participant information entered by the user in the previous step.

[1096] Data processing: The terminal serializes the input data into JSON format.

[1097] Output: Data packet in JSON format.

[1098] What happens: Front-end JavaScript converts the data to JSON format and sends it to the server as the body of an HTTP POST request.

[1099] Step 3:

[1100] Receiving and storing information

[1101] The server receives the data packets sent from the terminal and stores them in an internal database.

[1102] Input: A data packet in JSON format.

[1103] Data processing: The server parses the data packets and extracts the agenda and participant information.

[1104] Output: Agenda and participant information stored in a database.

[1105] Specific operation: The server receives the HTTP request using Flask, extracts the data using the request.get_json() method, and executes an INSERT query to the database using SQLAlchemy.

[1106] Step 4:

[1107] Agenda generation

[1108] The server passes the data to a generative AI model to generate the meeting agenda and time allocation.

[1109] Input: Agenda and participant information retrieved from the database.

[1110] Data processing: The acquired data is transformed into a prompt sentence and sent to the generative AI model.

[1111] Output: Generated meeting agenda and time allocation.

[1112] Specifically, the server retrieves the necessary information from the database using a SELECT query, then sends an API request based on that data to the generative AI model. The generative AI model (e.g., GPT-3) generates an agenda based on the prompt and responds in JSON format.

[1113] Step 5:

[1114] Sending and displaying results

[1115] The server transmits the generated agenda and time allocation to the user's terminal and displays them.

[1116] Input: Meeting agenda and time allocation returned by the generative AI model.

[1117] Data processing: Format the generated agenda as an HTTP response.

[1118] Output: The meeting agenda displayed on the user's device.

[1119] Specific behavior: The server returns the generated agenda as a JSON response, which the device parses, renders in HTML, and displays to the user.

[1120] Step 6:

[1121] Enabling the Emotion Engine

[1122] The device activates an emotion engine to recognize the user's emotions.

[1123] Input: User facial and voice data.

[1124] Data processing: Data captured by the camera and microphone is passed to emotion recognition algorithms.

[1125] Output: Recognized emotion data.

[1126] How it works: The device's application captures the user's facial expressions and voice in real time using the camera and microphone, and analyzes the data using libraries such as EmotionAPI and FacialRecognition.

[1127] Step 7:

[1128] Emotional feedback

[1129] The emotion engine transmits the recognized emotion data to the server.

[1130] Input: Recognized emotion data.

[1131] Data processing: Serialize emotion data into JSON format.

[1132] Output: Emotion data sent to the server.

[1133] Specific operation: The emotion engine analyzes the recognized emotions and sends the information in JSON format to the server as an HTTP POST request.

[1134] Step 8:

[1135] Real-time agenda modification

[1136] The server modifies the agenda and time allocation based on the emotional data.

[1137] Input: Recognized emotion data and existing meeting agenda.

[1138] Data processing: Create a new prompt sentence based on the emotion data and request it from the generative AI model.

[1139] Output: Revised meeting agenda and time allocation.

[1140] Specific operation: Based on the emotion data received by the server, a correction request is sent to the generative AI model, which then generates a new agenda.

[1141] Step 9:

[1142] Displaying the correction results

[1143] The server transmits the revised agenda and time allocation to the user's terminal again and displays them.

[1144] Input: Revised meeting agenda and time allocation.

[1145] Data processing: Format the modified agenda as an HTTP response.

[1146] Output: The meeting agenda redisplayed on the user's device.

[1147] Specific behavior: The server returns the modified agenda in JSON format, and the device parses it and redisplays it.

[1148] (Application example 2)

[1149] 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."

[1150] Efficiently managing different work tasks within a factory and adjusting their progress in real time is important in environments where multiple robots and workers work together. However, with conventional systems, it is difficult to optimize the task progress and the status of each robot in real time, which hinders efficient work management. In particular, it is necessary to solve these issues, as rapid response to factors such as remaining battery life and declining work efficiency is required.

[1151] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1152] In this invention, the server includes an input means for a user to input task and assigned robot information, a communication means for transmitting the input data to the server, a storage means for the server to process the data and store it in an internal database, a generation means for a generation AI tool to generate an efficient task schedule based on the task and assigned robot information, a display means for transmitting the generated task schedule to the robot's terminal and displaying it, and a correction means for an emotion engine to analyze the robot's status (remaining battery level and work efficiency) and correct the task schedule in real time. This makes it possible to efficiently manage work tasks in a factory and optimize progress in real time according to the robot's status.

[1153] A "user device" is an electronic device used by a user to input information and display generated data, including a smartphone, smart glasses, a head-mounted display, or a robot.

[1154] "Input means" refers to an interface that allows a user to input information about a meeting or task, and includes a keyboard, a touch screen, voice input, and the like.

[1155] "Communication means" refers to the method or protocol for transmitting input data to the server, including HTTP requests and other data communication methods.

[1156] "Storage means" refers to the mechanism by which the server stores the received data in an internal database, and includes a database engine and a file system.

[1157] "Generation means" refers to the function of the generation AI tool to automatically generate meeting and work agendas and time allocations based on input information using a pre-trained model.

[1158] "Display means" refers to a method for displaying the generated agenda or schedule to the user or robot, and includes a display, a screen, a projector, etc.

[1159] An "emotion engine" is a software component that analyzes the state of the user or robot (e.g., facial expressions, voice, remaining battery level, work efficiency, etc.) and makes optimal adjustments and corrections in real time based on that data.

[1160] "Modification means" refers to the function whereby the generative AI tool modifies the agenda and schedule in real time based on data and feedback from the emotion engine and sends it to the display means.

[1161] This invention is a system for improving the efficiency of robot work task management in factories. The system includes an input means through which a user inputs task and assigned robot information, a communication means for transmitting the input data to a server, and a storage means for the server to store the received data in an internal database. The system also includes a generation means for a generation AI tool to generate an efficient task schedule based on the task and assigned robot information, a display means for transmitting the generated task schedule to the robot's terminal and displaying it, and a correction means for an emotion engine to analyze the robot's state and correct the task schedule in real time.

[1162] Specifically, users input task and robot information using devices such as smartphones, tablets, and PCs. For example, they input work tasks such as "assembly of product X" and "quality inspection," as well as information about the robots in charge, such as "robot A" and "robot B." This data is then sent to the server via communication means.

[1163] The server processes the received data, parses the HTTP request body to extract task and robot information, and stores it in an internal database.The server then invokes a generative AI tool to generate an efficient task schedule based on the stored task and robot information.This process uses a pre-trained generative AI model.

[1164] The generated task schedule is sent from the server to the terminal of the robot in charge, and each robot recognizes and displays its own tasks. The display means uses a device such as a display or touch screen that allows the robot to check the tasks.

[1165] The emotion engine analyzes the robot's status (remaining battery level, work efficiency, etc.) in real time while the robot is working and feeds that information back to the correction means. The correction means corrects the schedule in real time based on the data provided by the emotion engine and sends it back to the robot's terminal for display.

[1166] For example, if Robot A is engaged in assembling Product X and its battery level drops, the emotion engine analyzes this condition and the corrective measures regenerate a new task schedule, where Robot B takes over the task of assembling Product X and Robot A transitions to a quality inspection task.

[1167] An example of a prompt is as follows:

[1168] "Robot A's battery level is low. Please regenerate the schedule."

[1169] Based on this prompt, the task schedule is regenerated and redisplayed on each robot's terminal.

[1170] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1171] Step 1:

[1172] The user uses a terminal to input information about the task and the robot in charge. Through the terminal interface, the user inputs work tasks such as "assembly of product X" or "quality inspection" and information about the robot in charge, such as "Robot A, Robot B." The input data is saved in JSON format.

[1173] Step 2:

[1174] The device converts the input data into a data packet and sends it to the server using an HTTP POST request. At this time, the device uses a communication protocol to securely send the data. The input JSON data is sent as the body of the HTTP request.

[1175] Step 3:

[1176] The server receives the data packet and parses the HTTP request body to extract task and robot information, which is then stored in an internal database. Specifically, it uses database queries to store the task and robot information in the appropriate tables.

[1177] Step 4:

[1178] The server retrieves the stored task and robot information from the internal database and passes it to the generative AI tool. The generative AI tool uses a pre-trained generative AI model to generate an optimal task schedule based on the input data. It analyzes the input task and assigned robot information and outputs an efficient schedule.

[1179] Step 5:

[1180] The server sends the generated schedule to the terminal of the robot in charge. The server formats the generated schedule as an HTTP response and sends it to each robot's terminal. The robot's terminal analyzes the received schedule and displays it on a device such as a display.

[1181] Step 6:

[1182] The emotion engine analyzes the robot's status while it is working. The robot's terminal sends data such as remaining battery power and work efficiency to the emotion engine in real time. The emotion engine analyzes this data and determines the robot's current status.

[1183] Step 7:

[1184] The server regenerates the schedule based on the data analyzed by the emotion engine. The server then obtains feedback data from the emotion engine and uses the generation AI tool again to appropriately revise the schedule. At this time, new data processing and calculations are performed.

[1185] Step 8:

[1186] The server sends the revised schedule to the robot terminal again. The revised schedule is sent as an HTTP response and the robot terminal redisplays it. This allows the robot to continue working according to the latest schedule.

[1187] 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.

[1188] 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.

[1189] 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.

[1190] [Fourth embodiment]

[1191] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1192] 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.

[1193] 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).

[1194] 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.

[1195] 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.

[1196] 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).

[1197] 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.

[1198] 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.

[1199] 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.

[1200] 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.

[1201] 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.

[1202] 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.

[1203] 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."

[1204] This invention is a system in which a user inputs the meeting topic and participant information, and a generation AI tool automatically generates the meeting agenda and time allocation, allowing the meeting to proceed efficiently. Below, the program processing of this system is explained in natural language. Specific examples are also provided.

[1205] Program processing

[1206] 1. Data Entry

[1207] A user uses a terminal to input the meeting agenda and participant information. For example, a user inputs the agenda for the next meeting, "Designing new features" and "Test plan," and adds "Mr. A, Mr. B, and Mr. C" as participants.

[1208] 2. Data transmission

[1209] The terminal sends the entered data to the server using a transmission protocol such as HTTP POST request.

[1210] 3. Receipt and storage of information

[1211] The server receives the data sent from the terminal.

[1212] The received data is parsed to extract the agenda and participant information, which is then stored in an internal database.

[1213] 4. Agenda generation

[1214] The server retrieves the agenda and participant information from its internal database and passes it to the generative AI tool.

[1215] The generative AI tool generates an efficient meeting agenda and time allocation based on the received data. For example, the generative AI tool might generate an agenda such as "10:00-10:30: Design new features" or "10:30-11:00: Test planning."

[1216] 5. Sending and displaying results

[1217] The server transmits the generated agenda and time allocation to the user's terminal.

[1218] The terminal displays the received agenda and time allocation to the user, who then checks the displayed content and uses it to actually proceed with the meeting.

[1219] Specific examples

[1220] Consider the following example. For next week's project meeting, a user enters "Project progress review" and "Plan for next release" as agenda items on their device, and adds "Mr. X, Mr. Y, and Mr. Z" as participants. The device sends this information to the server, which receives and stores it. The server then calls a generation AI tool, which generates an efficient agenda based on the saved agenda and participant information. The generation AI tool generates an agenda with the following schedules: "9:00-9:30: Project progress review" and "9:30-10:00: Plan for next release," and passes it to the server. The server then sends the generated agenda to the user's device, which displays it. The user can use this information to conduct the meeting and hold efficient discussions.

[1221] In this way, this system uses AI to automatically generate meeting agendas and time allocations based on information entered by the user, helping to ensure efficient and meaningful meeting proceedings.

[1222] The processing flow will be explained below.

[1223] Step 1:

[1224] The user enters the meeting agenda and participant information. Specifically, the user uses the input form on the device to enter the agenda items "Designing new features" and "Test plan" and the participant information of "Mr. A, Mr. B, and Mr. C."

[1225] Step 2:

[1226] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request.

[1227] Step 3:

[1228] The server receives the data packet sent from the terminal, where it parses the body of the HTTP request and extracts the agenda and participant information.

[1229] Step 4:

[1230] The server saves the extracted agenda and participant information to an internal database, storing this information in the appropriate tables and fields within the database.

[1231] Step 5:

[1232] The server calls the generation AI tool based on the saved agenda and participant information. At this time, the server passes the saved data to the generation AI tool as necessary parameters.

[1233] Step 6:

[1234] The generative AI tool generates an efficient meeting agenda and time allocation based on the topic and participant information it receives. For example, the generative AI tool uses past data and pre-trained models to generate an agenda such as "10:00-10:30: Design new features" or "10:30-11:00: Test planning."

[1235] Step 7:

[1236] The generative AI tool returns the generated agenda and time allocation to the server, where the returned data is converted into an appropriate format and temporarily stored.

[1237] Step 8:

[1238] The server packages the generated agenda and time allocation into a data packet and sends it to the user's terminal, where the data is returned using an HTTP response.

[1239] Step 9:

[1240] The terminal receives the data packet sent from the server, analyzes the data packet, and extracts the agenda and time allocation.

[1241] Step 10:

[1242] The device displays the extracted agenda and time allocation to the user. Specifically, information such as "10:00-10:30: Design new functions" and "10:30-11:00: Test planning" is displayed on the device screen.

[1243] Step 11:

[1244] The user conducts the meeting based on the displayed agenda and time allocation. The user and participants efficiently discuss each topic within the specified time.

[1245] This series of processing steps enables users to conduct meetings efficiently and reduce wasted time.

[1246] Example 1

[1247] 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."

[1248] In conventional meeting management systems, creating an agenda to efficiently conduct a meeting is a time-consuming and labor-intensive process. It is also difficult to allocate time optimally based on the agenda and participant information, resulting in a decrease in meeting efficiency. To solve this problem, a system is needed that can automatically generate an agenda based on input information and propose efficient time allocation.

[1249] 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.

[1250] In this invention, the server includes an input means for a user to input the agenda and participant information, a communication means for transmitting the input data to the server, a storage means for the server to analyze the data and store it in an internal database, a generation means for a generative AI model to generate a meeting agenda and time allocation based on the agenda and participant information, and a display means for transmitting the generated agenda and time allocation to the user's terminal and displaying it. This supports the progress of the meeting and enables the user to effectively manage the meeting by using the automatically generated efficient agenda and time allocation.

[1251] The "input means" is a means by which a user inputs the agenda and participant information of a meeting.

[1252] "Communication means" refers to a means for transmitting input data to a server.

[1253] The "storage means" is a means for the server to analyze the received data and store it in an internal database.

[1254] "Generation means" refers to the means by which the generative AI model generates a meeting agenda and time allocation based on the topic and participant information.

[1255] The "display means" is a means for transmitting the generated agenda and time allocation to the user's terminal and displaying them.

[1256] A "generative AI model" is an AI model that generates a meeting agenda and time allocation from given input data based on pre-trained data.

[1257] MODE FOR CARRYING OUT THE INVENTION

[1258] This invention is a system designed to efficiently and effectively conduct meetings, which automatically generates a meeting agenda and time allocation based on information entered by a user. The system includes input means, communication means, storage means, generation means, and display means.

[1259] First, the user uses the terminal to enter the meeting agenda and participant information. The terminal provides a dedicated HTML-based form, allowing the user to easily enter the agenda and participant information. For example, the user enters the agenda items "Designing new features" and "Test plan" and adds "Mr. A, Mr. B, and Mr. C" as participants.

[1260] The device uses JavaScript to convert input data into JSON format and sends it to the server via an HTTP POST request. The server endpoint is implemented using Node.js and the Express framework to receive the data.

[1261] The server analyzes the received data using a JSON parser to extract the agenda and participant information. The analysis results are stored in a MongoDB database, allowing for flexible data management.

[1262] The server then retrieves the stored data and passes it to a generative AI tool, which uses OpenAI's GPT-4 model to generate the meeting agenda and time allocation, using prompts such as:

[1263] "Please generate an agenda for the next meeting. The topics are 'Marketing strategy for new products' and 'Expanding sales channels'. The participants are 'Tanaka-san, Suzuki-san, Sato-san'."

[1264] The AI ​​tool generates an agenda based on the prompt and returns it to the server, which then sends the result in JSON format to the user's device.

[1265] The device parses the received agenda and displays it to the user using HTML and JavaScript. Specifically, the generated agenda items are displayed in a list format, and color-coded to visually indicate the time allocation of each item.

[1266] In this way, the system allows users to simply input the meeting topic and participant information, and the generative AI model automatically generates an efficient agenda and time allocation and provides it to the user, thereby streamlining the progress of meetings.

[1267] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1268] Step 1:

[1269] The user enters the meeting agenda and participant information. Specifically, the user accesses the input form on their device, enters the agenda items "Design of new features" and "Test plan," and registers "Mr. A, Mr. B, and Mr. C" as participants. Once the input is complete, the user presses the "Send" button. The agenda and participant information are obtained as input data.

[1270] Step 2:

[1271] The terminal converts the data entered by the user into JSON format and sends it to the server using an HTTP POST request. At this time, JavaScript code serializes the data using JSON.stringify and sends the data according to the transmission protocol. JSON format data is received as input and a request to the server is generated as output.

[1272] Step 3:

[1273] The server receives data sent from the terminal. The server uses Node.js and Express and parses the received data with a JSON parser. The received JSON data contains the agenda and participant information entered by the user. The received data is parsed and converted into a format for saving in MongoDB.

[1274] Step 4:

[1275] The server analyzes the received data, extracts the agenda and participant information, and saves it in a MongoDB database. The extracted data will have a format such as "Agenda: New feature design, test plan" and "Participants: A, B, C." Database operations are performed using the MongoDB driver, and if successful, a status indicating that the data has been saved is returned.

[1276] Step 5:

[1277] The server retrieves the saved agenda and participant information from its internal database and passes it to the AI ​​generation tool. At this time, the server executes a Python script to call the AI ​​generation tool's API. Specifically, it generates the following prompt:

[1278] "Generate an agenda for the next meeting. The topics are 'Design new features' and 'Test plan'. The participants are 'A, B, C'."

[1279] Step 6:

[1280] The generative AI tool generates a meeting agenda and time allocation based on the prompt text. At this time, the generative AI model GPT-4 analyzes the prompt text and outputs the optimal meeting agenda and time allocation. For example, the output may be an agenda in the format "10:00-10:30: Design new features" or "10:30-11:00: Test plan."

[1281] Step 7:

[1282] The server receives the generated agenda and time allocation from the AI ​​tool, formats it in JSON, and sends it to the user's device. The server returns JSON data containing the generated agenda as an HTTP response.

[1283] Step 8:

[1284] The device analyzes the data received from the server and displays it to the user. Using HTML and JavaScript, the generated agenda items are displayed on the screen in list format, and each time allocation is color-coded to make it visually easy to understand. The user can check this and use it to progress the meeting.

[1285] (Application example 1)

[1286] 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."

[1287] There is a need for a system that can efficiently collect meeting topic and participant information, quickly convert user voice input into text information, and automatically generate an efficient meeting agenda and time allocation using a generative AI tool. Conventional systems often require users to input information manually, which is time-consuming and hinders efficient meeting progress. Furthermore, in workplaces such as factories, there is a lack of means for workers to input meeting information without using their hands. Technology is needed to solve these problems and improve the productivity and efficiency of meetings.

[1288] 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.

[1289] In this invention, the server includes a conversion means that converts voice input into text information using voice recognition technology, a communication means that sends the input data to the server, a storage means that the server processes the data and stores it in an internal database, a generation means that uses a generative AI tool to generate a meeting agenda and time allocation based on the topic and participant information, and a display means that sends the generated agenda and time allocation to a user's terminal and displays it. This allows users to efficiently input meeting information through voice input, and the server can automatically generate a fast and accurate agenda using a generative AI model.

[1290] A "user" is a person who operates the system and is responsible for inputting the meeting agenda and participant information.

[1291] An "agenda" refers to the topics or issues to be addressed at a meeting.

[1292] "Participant Information" refers to the names, titles, and other relevant information of people attending a meeting.

[1293] "Input means" is a general term for devices and methods that allow users to input the agenda and participant information for a meeting.

[1294] "Voice recognition technology" refers to the technology that converts voice into text data.

[1295] "Conversion means" is a general term for mechanisms and methods for converting voice input into text information using voice recognition technology.

[1296] "Communication means" is a general term that includes the technology and protocols used to transmit input data to a server.

[1297] "Server" means a computer system for receiving, processing, and storing data and generating an agenda using a generation AI tool.

[1298] "Storage means" is a general term for mechanisms and methods for storing data received by the server in an internal database.

[1299] "Generation method" is a general term for the process of using a generative AI tool to generate a meeting agenda and time allocation based on the topic and participant information.

[1300] "Generative AI tools" refers to software or algorithms that use AI technology to automatically generate meeting agendas and time allocations.

[1301] "Display means" is a general term for mechanisms and methods for transmitting the generated agenda and time allocation to the user's terminal and displaying them.

[1302] "Terminal" refers to a device that allows a user to enter information and view a generated agenda.

[1303] "Text information" refers to text data converted using voice recognition technology.

[1304] "Internal database" refers to the database system used to store and manage data within a server.

[1305] "Time allocation" refers to the amount of time allocated to each agenda item in a meeting.

[1306] MODE FOR CARRYING OUT THE INVENTION

[1307] This invention relates to a system that automatically generates a meeting agenda and time allocation using speech recognition technology and generative AI tools. Hereinafter, the embodiments of the invention will be described in detail.

[1308] System Configuration

[1309] The system consists of the following components:

[1310] 1. User Device

[1311] A device for voice entry of meeting agenda and participant information.

[1312] It is equipped with a voice recognition microphone, display, and internet connection functions.

[1313] Examples of hardware used: industrial robots or smart speakers.

[1314] Examples of software used: Speech recognition engine (e.g. Google Cloud Speech-to-Text).

[1315] 2. Server

[1316] Voice input is converted into text information using voice recognition technology.

[1317] Receive the converted data and store it in a database.

[1318] Use generative AI tools (e.g., OpenAI GPT-4) to generate meeting agendas and time allocations.

[1319] The generated agenda is sent to the user's terminal.

[1320] Server configuration example: Linux server, HTTP server (such as Nginx or Apache), database (MySQL, PostgreSQL, etc.).

[1321] 3. Generative AI Tools

[1322] It runs within the server and generates an efficient meeting agenda and time allocation based on the inputted agenda and participant information.

[1323] Example of generative AI model used: GPT-4.

[1324] Specific processing steps

[1325] Data Entry and Conversion

[1326] Users input meeting agendas and participant information by voice. Voice recognition technology is used to convert the speech into text. This allows users to input information without using their hands, making it easy to operate even while working on-site in a factory or other facility.

[1327] Data transmission and storage

[1328] The user device sends the converted text information to the server, which receives the HTTP POST request, analyzes the data, and stores it in an internal database. This process ensures that the entered information is saved and used for subsequent processing.

[1329] Agenda generation

[1330] The server retrieves the agenda and participant information from the database and passes it to the generative AI tool. The generative AI tool automatically generates an efficient meeting agenda and time allocation based on the input data. For example, it might generate content such as "10:00-10:30: Design new features" and "10:30-11:00: Test plan."

[1331] Sending and displaying results

[1332] The server sends the generated agenda and time allocation to the user's device, which then notifies the user of the results by voice and displays them on the screen. This allows the user to immediately check the generated results and efficiently conduct the meeting.

[1333] Specific examples

[1334] Here's an example: A user speaks:

[1335] "The next meeting is about optimizing the production line and introducing new machinery, and the participants are Tanaka-san, Suzuki-san, and Sato-san."

[1336] The server converts this speech into text and passes it to a generative AI tool, which generates an agenda like this:

[1337] "14:00-14:30 Optimization of production line" "14:30-15:00 Introduction of new machinery"

[1338] The server sends this agenda to the user's terminal, which then notifies the results by voice and display.

[1339] Prompt Sentence Examples

[1340] An example prompt to pass to a generative AI tool might look something like this:

[1341] Meeting agenda:

[1342] Production line optimization

[1343] Introduction of new machinery

[1344] Participants:

[1345] Tanaka-san

[1346] Suzuki-san

[1347] Sato-san

[1348] Use this information to generate an efficient meeting agenda and time allocation for each topic.

[1349] In this manner, the system of the present invention can be implemented.

[1350] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1351] Step 1: Data entry

[1352] The user inputs the meeting agenda and participant information by voice. The voice is input into the terminal via a voice recognition microphone. Specifically, the user says the following: "The agenda for the next meeting is a meeting regarding the optimization of the production line and the introduction of new machinery. The participants are Tanaka-san, Suzuki-san, and Sato-san."

[1353] Step 2: Audio conversion

[1354] The device uses voice recognition technology (such as Google Cloud Speech-to-Text) to convert voice input into text information. This voice recognition technology analyzes the input voice data and converts it into text data. The input is voice data and the output is text data. For example, the voice input "The agenda for the next meeting is a meeting regarding production line optimization and the introduction of new machinery. The participants are Tanaka, Suzuki, and Sato." is converted to "The agenda for the next meeting is production line optimization and the introduction of new machinery. The participants are Tanaka, Suzuki, and Sato."

[1355] Step 3: Send data

[1356] The terminal sends the converted text data to the server using an HTTP POST request. The data sent is in text format and includes the agenda and participant information. The input is the converted text data, and the output is the send request.

[1357] Step 4: Receiving and storing data

[1358] The server receives the HTTP POST request, analyzes the text data, and extracts the agenda and participant information. After extraction, the obtained data is stored in an internal database. The input is the text data sent from the terminal, and the output is the agenda and participant information stored in the database.

[1359] Step 5: Generate the agenda

[1360] The server retrieves the saved agenda and participant information from the database and passes it to a generative AI tool (e.g., GPT-4). The generative AI tool automatically generates an efficient meeting agenda and time allocation based on the retrieved data. The input is the saved agenda and participant information, and the output is the generated agenda and time allocation. For example, an agenda such as "14:00-14:30 Optimize the production line" or "14:30-15:00 Install new machinery" may be generated.

[1361] Step 6: Sending the results

[1362] The server sends the generated agenda and time allocation to the user's terminal. The input is the generated agenda and time allocation, and the output is a transmission request.

[1363] Step 7: View and notify results

[1364] The terminal displays the received agenda and time allocation to the user. The user terminal can also display the results on a screen and notify the user by voice. The input is the generated agenda and time allocation, and the output is the display and voice notification. For example, "Next meeting agenda: 14:00-14:30 Production line optimization, 14:30-15:00 Installation of new machine" is displayed and notified by voice.

[1365] 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.

[1366] This invention is a system in which a user inputs the meeting topic and participant information, and a generation AI tool automatically generates the meeting agenda and time allocation. By combining this with an emotion engine, the system recognizes the user's emotions and optimizes the progress of the meeting in real time. Below, the program processing of this system is explained in natural language. Specific examples are also provided.

[1367] Program processing

[1368] 1. Data Entry

[1369] A user uses a terminal to input the meeting agenda and participant information. For example, the user inputs agenda items such as "design of new features" and "test plan" and participant information such as "Mr. A, Mr. B, and Mr. C."

[1370] 2. Data transmission

[1371] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request.

[1372] 3. Receipt and storage of information

[1373] The server receives the data packet sent from the terminal, parses the HTTP request body, and extracts the agenda and participant information.

[1374] The extracted agenda and participant information is stored in an internal database.

[1375] 4. Agenda generation

[1376] The server retrieves the agenda and participant information from its internal database and passes it to the generative AI tool.

[1377] Based on the data received, the generative AI tool generates an efficient meeting agenda and time allocation, such as "10:00-10:30: Design new features" and "10:30-11:00: Test planning."

[1378] 5. Sending and displaying results

[1379] The server sends the generated agenda and time allocation to the user's device. The data is sent using an HTTP response.

[1380] The terminal displays the received agenda and time allocation to the user. The terminal screen displays "10:00-10:30 New function design" and "10:30-11:00 Test plan."

[1381] 6. Enabling the Emotion Engine

[1382] The device activates an emotion engine while the meeting is in progress and recognizes emotions by analyzing the user's facial expressions and voice. For example, it uses a camera or microphone to detect the user's emotions in real time.

[1383] 7. Emotional Feedback

[1384] The emotion engine sends the emotion data it recognizes to the server. For example, if it recognizes that the user is tired, that information is sent to the server.

[1385] 8. Real-time agenda revision

[1386] Based on the emotion data received by the server, the generative AI tool modifies the current agenda and time allocation in real time, adjusting the time for each topic or postponing some topics depending on the emotion.

[1387] 9. Displaying the correction results

[1388] The server transmits the revised agenda and time allocation back to the user's terminal.

[1389] The terminal redisplays the revised agenda to the user, who then proceeds with the conference accordingly.

[1390] Specific examples

[1391] For example, for next week's project meeting, a user enters "Project Progress Review" and "Next Release Plan" as agenda items on their device and adds "X, Y, and Z" as participants. The device sends this information to the server, which receives and stores it. The server then calls the generation AI tool and generates an agenda based on the saved agenda and participant information. The generation AI tool generates an agenda with the following schedules: "9:00-9:30 Project Progress Review" and "9:30-10:00 Next Release Plan" and returns it to the server. The server then sends the generated agenda to the user's device, which displays it. During the meeting, the emotion engine analyzes the user's facial expressions and voice. If it determines that the user is tired, it sends that information to the server. The generation AI tool then modifies the agenda based on this emotional data, and the new agenda is displayed on the user's device. The user then proceeds with the meeting according to the modified agenda.

[1392] In this way, the system uses AI to automatically generate meeting agendas and time allocations based on information entered by users and data from the emotion engine, and then corrects them in real time, thereby supporting efficient and meaningful meeting conduct.

[1393] The processing flow will be explained below.

[1394] Step 1:

[1395] The user enters the meeting agenda and participant information. Specifically, the user uses an input form on the terminal to enter agenda items such as "Designing new features" and "Test plan," as well as participant information for "Mr. A, Mr. B, and Mr. C."

[1396] Step 2:

[1397] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request.

[1398] Step 3:

[1399] The server receives the data packet sent from the terminal, parses the HTTP request body, and extracts the agenda and participant information.

[1400] Step 4:

[1401] The server saves the extracted agenda and participant information to an internal database, storing this information in the appropriate tables and fields within the database.

[1402] Step 5:

[1403] The server retrieves the agenda and participant information from an internal database and passes it to the generative AI tool, which then converts it into the appropriate API calls and data formats before inputting it.

[1404] Step 6:

[1405] The generative AI tool generates an efficient meeting agenda and time allocation based on the received data. For example, the generative AI tool might generate an agenda such as "10:00-10:30: Design new features" or "10:30-11:00: Test planning."

[1406] Step 7:

[1407] The generative AI tool returns the generated agenda and time allocation to the server, which converts the returned data into an appropriate format and temporarily stores it on the server.

[1408] Step 8:

[1409] The server packages the generated agenda and time allocation into a data packet and sends it to the user's terminal, where the data is returned using an HTTP response.

[1410] Step 9:

[1411] The terminal receives the data packet sent from the server, analyzes the data packet, and extracts the agenda and time allocation.

[1412] Step 10:

[1413] The device displays the extracted agenda and time allocation to the user. Specifically, the device displays "10:00-10:30: Design new functions" and "10:30-11:00: Test planning" on the screen.

[1414] Step 11:

[1415] The user conducts the meeting based on the displayed agenda and time allocation. The user and participants efficiently discuss each topic within the specified time.

[1416] Step 12:

[1417] The device activates the emotion engine while the meeting is in progress. Specifically, it uses the device's camera and microphone to analyze the user's facial expressions and voice in real time.

[1418] Step 13:

[1419] The emotion engine analyzes the user's emotions and sends the recognized emotion data to the server. For example, if the user is recognized as being fatigued, that information is packetized as emotion data and sent to the server.

[1420] Step 14:

[1421] The server analyzes the received emotional data and provides feedback to the generative AI tool, which then uses it to adjust the meeting agenda and time allocation in real time.

[1422] Step 15:

[1423] Based on the feedback, the generative AI tool reevaluates the current agenda and time allocation and makes adjustments as necessary. For example, if the agenda item "Design new features" is taking too long, the tool will shorten the time and adjust the agenda to move on to the next agenda item, "Test planning," sooner.

[1424] Step 16:

[1425] The generative AI tool sends the revised agenda and time allocation back to the server.

[1426] Step 17:

[1427] The server resends the revised agenda and time allocation to the user's device. The data is sent using an HTTP response.

[1428] Step 18:

[1429] The terminal displays the received revised agenda and time allocation again to the user, who then proceeds with the conference according to the new agenda.

[1430] Through these steps, the system optimizes the progress of meetings in real time and realizes flexible time allocation according to the user's emotions, thereby improving meeting efficiency and reducing wasted time.

[1431] Example 2

[1432] 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."

[1433] In conventional conference systems, setting the agenda and allocating time for a meeting is done manually, making it difficult to run the meeting efficiently. Furthermore, there is no way to revise the agenda in real time during the meeting, taking into account the emotions and fatigue levels of participants, which can disrupt the smooth progress of the meeting and cause participants to lose concentration. The present invention aims to solve these problems and realize efficient and flexible meeting management.

[1434] 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.

[1435] In this invention, the server includes: input means for a user to input agenda and participant information; communication means for transmitting the input data to the server; storage means for the server to process the data and store it in an internal database; generation means for a generative AI model to generate a meeting agenda and time allocation based on the agenda and participant information; display means for transmitting the generated agenda and time allocation to the user's terminal and displaying it; recognition means for an emotion engine to recognize the user's emotions; feedback means for transmitting the recognized emotion data to the server; correction means for the server to correct the agenda and time allocation in real time based on the emotion data; and re-display means for transmitting the corrected agenda and time allocation to the user's terminal and displaying it again. This not only enables automatic generation of an efficient meeting agenda and time allocation based on information input by the user, but also enables recognition of the emotions of participants during the meeting and correction of the agenda in real time accordingly.

[1436] A "user" is a person or entity that utilizes the system to enter meeting agendas and participant information.

[1437] A "terminal" is an electronic device that a user uses to enter meeting agenda and participant information.

[1438] "Server" means a device that receives and processes data sent from a terminal, and stores and manages it in an internal database.

[1439] "Input means" refers to the device or software by which a user inputs meeting agenda and participant information into the system.

[1440] "Communication means" refers to a protocol or device for transmitting data input by an input means to a server.

[1441] "Storage means" is the function or process by which the server stores the data it receives in its internal database.

[1442] A "generative AI model" is an artificial intelligence model that automatically generates a meeting agenda and time allocation based on the topic and participant information.

[1443] A "generator" is a process or function that uses a generative AI model to generate a meeting agenda and time allocation.

[1444] The "display means" is software or a device for transmitting the generated agenda and time allocation to the user's terminal and displaying it.

[1445] The "emotion engine" is an analysis engine for recognizing the user's emotions in real time.

[1446] A "recognition means" is a process or function that uses an emotion engine to recognize a user's emotion.

[1447] The "feedback means" is a means for transmitting the recognized emotion data to the server.

[1448] "Modification means" refers to a process or function by which the server modifies the meeting agenda and time allocation in real time based on emotion data.

[1449] The "redisplay means" is software or a device for transmitting the revised agenda and time allocation to the user's terminal and displaying them again.

[1450] This invention is a system that automatically generates a meeting agenda and time allocation based on the meeting topic and participant information using a generative AI tool, and further combines it with an emotion engine to recognize the user's emotions and optimize the progress of the meeting in real time.

[1451] Specific Embodiments of the System

[1452] 1. Data Entry

[1453] Users use a device to input the meeting agenda and participant information. The device can be a PC, tablet, smartphone, or other electronic device. The input data is collected via the device's application.

[1454] 2. Data Transmission

[1455] The terminal converts the input data into data packets and sends them to the server using a communication protocol such as an HTTP POST request. The communication method uses a communication protocol via the Internet.

[1456] 3. Receipt and storage of information

[1457] The server receives data packets sent from the devices, parses the HTTP POST requests using the Python Flask framework, and extracts the agenda and participant information. The extracted information is then stored in an internal database (e.g., an SQL database).

[1458] 4. Agenda generation

[1459] The server retrieves the agenda and participant information from its internal database and passes it to a generative AI model (e.g., OpenAI's GPT-3). The generative AI model generates an efficient meeting agenda and time allocation based on the provided data. The generated agenda is returned to the server.

[1460] 5. Sending and Displaying the Agenda

[1461] The server sends the generated agenda and time allocation to the user's device. The data is sent using an HTTP response. The device analyzes the received agenda and displays it to the user. For example, the device screen might display "10:00-10:30 Design new features" and "10:30-11:00 Test planning."

[1462] 6. Enabling the Emotion Engine

[1463] The device activates its emotion engine during the conference and uses the camera and microphone to analyze the user's facial expressions and voice to recognize emotions. The recognized emotion data is obtained via analysis software (e.g., EmotionAPI, FaceAPI).

[1464] 7. Emotional Feedback

[1465] The emotion engine sends the recognized emotion data to the server as an HTTP request.

[1466] 8. Real-time agenda revision

[1467] The server uses a generative AI model to modify the current agenda and time allocation in real time based on the received emotional data, and the modified agenda is returned to the server.

[1468] 9. Displaying the correction results

[1469] The server then sends the revised agenda and time allocation back to the user's terminal, which then displays the revised agenda again, allowing the user to proceed with the meeting accordingly.

[1470] Specific Examples

[1471] For example, a user enters "Project progress review" and "Planning the next release" as agenda items for next week's project meeting on their device, and adds "Mr. X, Mr. Y, and Mr. Z" as participants. The device sends this information to the server, which receives and stores it. The server then uses a generation AI tool to generate an agenda and returns it as "9:00-9:30: Project progress review" and "9:30-10:00: Planning the next release."

[1472] During a meeting, the emotion engine analyzes the user's facial expressions and voice, and if it determines that the user is tired, it sends that information to the server. The server then modifies the agenda based on this emotional data, and the new agenda is displayed on the user's device. The user then proceeds with the meeting according to the modified agenda.

[1473] Prompt Sentence Examples

[1474] Create an efficient meeting agenda based on "checking project progress" and "planning the next release."

[1475] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1476] Step 1:

[1477] Data Entry

[1478] The user inputs the meeting agenda and participant information into the terminal.

[1479] Input: The user enters the agenda, such as "Designing new features" or "Test plan," and participant information, such as "A, B, and C," into an input form.

[1480] What it does: A user enters information into form fields, such as text fields and drop-down menus, in a browser or application on their device.

[1481] Step 2:

[1482] Data transmission

[1483] The terminal converts the input data into data packets and transmits them to the server.

[1484] Input: The topic and participant information entered by the user in the previous step.

[1485] Data processing: The terminal serializes the input data into JSON format.

[1486] Output: Data packet in JSON format.

[1487] What happens: Front-end JavaScript converts the data to JSON format and sends it to the server as the body of an HTTP POST request.

[1488] Step 3:

[1489] Receiving and storing information

[1490] The server receives the data packets sent from the terminal and stores them in an internal database.

[1491] Input: A data packet in JSON format.

[1492] Data processing: The server parses the data packets and extracts the agenda and participant information.

[1493] Output: Agenda and participant information stored in a database.

[1494] Specific operation: The server receives the HTTP request using Flask, extracts the data using the request.get_json() method, and executes an INSERT query to the database using SQLAlchemy.

[1495] Step 4:

[1496] Agenda generation

[1497] The server passes the data to a generative AI model to generate the meeting agenda and time allocation.

[1498] Input: Agenda and participant information retrieved from the database.

[1499] Data processing: The acquired data is transformed into a prompt sentence and sent to the generative AI model.

[1500] Output: Generated meeting agenda and time allocation.

[1501] Specifically, the server retrieves the necessary information from the database using a SELECT query, then sends an API request based on that data to the generative AI model. The generative AI model (e.g., GPT-3) generates an agenda based on the prompt and responds in JSON format.

[1502] Step 5:

[1503] Sending and displaying results

[1504] The server transmits the generated agenda and time allocation to the user's terminal and displays them.

[1505] Input: Meeting agenda and time allocation returned by the generative AI model.

[1506] Data processing: Format the generated agenda as an HTTP response.

[1507] Output: The meeting agenda displayed on the user's device.

[1508] Specific behavior: The server returns the generated agenda as a JSON response, which the device parses, renders in HTML, and displays to the user.

[1509] Step 6:

[1510] Enabling the Emotion Engine

[1511] The device activates an emotion engine to recognize the user's emotions.

[1512] Input: User facial and voice data.

[1513] Data processing: Data captured by the camera and microphone is passed to emotion recognition algorithms.

[1514] Output: Recognized emotion data.

[1515] How it works: The device's application captures the user's facial expressions and voice in real time using the camera and microphone, and analyzes the data using libraries such as EmotionAPI and FacialRecognition.

[1516] Step 7:

[1517] Emotional feedback

[1518] The emotion engine transmits the recognized emotion data to the server.

[1519] Input: Recognized emotion data.

[1520] Data processing: Serialize emotion data into JSON format.

[1521] Output: Emotion data sent to the server.

[1522] Specific operation: The emotion engine analyzes the recognized emotions and sends the information in JSON format to the server as an HTTP POST request.

[1523] Step 8:

[1524] Real-time agenda modification

[1525] The server modifies the agenda and time allocation based on the emotional data.

[1526] Input: Recognized emotion data and existing meeting agenda.

[1527] Data processing: Create a new prompt sentence based on the emotion data and request it from the generative AI model.

[1528] Output: Revised meeting agenda and time allocation.

[1529] Specific operation: Based on the emotion data received by the server, a correction request is sent to the generative AI model, which then generates a new agenda.

[1530] Step 9:

[1531] Displaying the correction results

[1532] The server transmits the revised agenda and time allocation to the user's terminal again and displays them.

[1533] Input: Revised meeting agenda and time allocation.

[1534] Data processing: Format the modified agenda as an HTTP response.

[1535] Output: The meeting agenda redisplayed on the user's device.

[1536] Specific behavior: The server returns the modified agenda in JSON format, and the device parses it and redisplays it.

[1537] (Application example 2)

[1538] 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."

[1539] Efficiently managing different work tasks within a factory and adjusting their progress in real time is important in environments where multiple robots and workers work together. However, with conventional systems, it is difficult to optimize the task progress and the status of each robot in real time, which hinders efficient work management. In particular, it is necessary to solve these issues, as rapid response to factors such as remaining battery life and declining work efficiency is required.

[1540] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1541] In this invention, the server includes an input means for a user to input task and assigned robot information, a communication means for transmitting the input data to the server, a storage means for the server to process the data and store it in an internal database, a generation means for a generation AI tool to generate an efficient task schedule based on the task and assigned robot information, a display means for transmitting the generated task schedule to the robot's terminal and displaying it, and a correction means for an emotion engine to analyze the robot's status (remaining battery level and work efficiency) and correct the task schedule in real time. This makes it possible to efficiently manage work tasks in a factory and optimize progress in real time according to the robot's status.

[1542] A "user device" is an electronic device used by a user to input information and display generated data, including a smartphone, smart glasses, a head-mounted display, or a robot.

[1543] "Input means" refers to an interface that allows a user to input information about a meeting or task, and includes a keyboard, a touch screen, voice input, and the like.

[1544] "Communication means" refers to the method or protocol for transmitting input data to the server, including HTTP requests and other data communication methods.

[1545] "Storage means" refers to the mechanism by which the server stores the received data in an internal database, and includes a database engine and a file system.

[1546] "Generation means" refers to the function of the generation AI tool to automatically generate meeting and work agendas and time allocations based on input information using a pre-trained model.

[1547] "Display means" refers to a method for displaying the generated agenda or schedule to the user or robot, and includes a display, a screen, a projector, etc.

[1548] An "emotion engine" is a software component that analyzes the state of the user or robot (e.g., facial expressions, voice, remaining battery level, work efficiency, etc.) and makes optimal adjustments and corrections in real time based on that data.

[1549] "Modification means" refers to the function whereby the generative AI tool modifies the agenda and schedule in real time based on data and feedback from the emotion engine and sends it to the display means.

[1550] This invention is a system for improving the efficiency of robot work task management in factories. The system includes an input means through which a user inputs task and assigned robot information, a communication means for transmitting the input data to a server, and a storage means for the server to store the received data in an internal database. The system also includes a generation means for a generation AI tool to generate an efficient task schedule based on the task and assigned robot information, a display means for transmitting the generated task schedule to the robot's terminal and displaying it, and a correction means for an emotion engine to analyze the robot's state and correct the task schedule in real time.

[1551] Specifically, users input task and robot information using devices such as smartphones, tablets, and PCs. For example, they input work tasks such as "assembly of product X" and "quality inspection," as well as information about the robots in charge, such as "robot A" and "robot B." This data is then sent to the server via communication means.

[1552] The server processes the received data, parses the HTTP request body to extract task and robot information, and stores it in an internal database.The server then invokes a generative AI tool to generate an efficient task schedule based on the stored task and robot information.This process uses a pre-trained generative AI model.

[1553] The generated task schedule is sent from the server to the terminal of the robot in charge, and each robot recognizes and displays its own tasks. The display means uses a device such as a display or touch screen that allows the robot to check the tasks.

[1554] The emotion engine analyzes the robot's status (remaining battery level, work efficiency, etc.) in real time while the robot is working and feeds that information back to the correction means. The correction means corrects the schedule in real time based on the data provided by the emotion engine and sends it back to the robot's terminal for display.

[1555] For example, if Robot A is engaged in assembling Product X and its battery level drops, the emotion engine analyzes this condition and the corrective measures regenerate a new task schedule, where Robot B takes over the task of assembling Product X and Robot A transitions to a quality inspection task.

[1556] An example of a prompt is as follows:

[1557] "Robot A's battery level is low. Please regenerate the schedule."

[1558] Based on this prompt, the task schedule is regenerated and redisplayed on each robot's terminal.

[1559] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1560] Step 1:

[1561] The user uses a terminal to input information about the task and the robot in charge. Through the terminal interface, the user inputs work tasks such as "assembly of product X" or "quality inspection" and information about the robot in charge, such as "Robot A, Robot B." The input data is saved in JSON format.

[1562] Step 2:

[1563] The device converts the input data into a data packet and sends it to the server using an HTTP POST request. At this time, the device uses a communication protocol to securely send the data. The input JSON data is sent as the body of the HTTP request.

[1564] Step 3:

[1565] The server receives the data packet and parses the HTTP request body to extract task and robot information, which is then stored in an internal database. Specifically, it uses database queries to store the task and robot information in the appropriate tables.

[1566] Step 4:

[1567] The server retrieves the stored task and robot information from the internal database and passes it to the generative AI tool. The generative AI tool uses a pre-trained generative AI model to generate an optimal task schedule based on the input data. It analyzes the input task and assigned robot information and outputs an efficient schedule.

[1568] Step 5:

[1569] The server sends the generated schedule to the terminal of the robot in charge. The server formats the generated schedule as an HTTP response and sends it to each robot's terminal. The robot's terminal analyzes the received schedule and displays it on a device such as a display.

[1570] Step 6:

[1571] The emotion engine analyzes the robot's status while it is working. The robot's terminal sends data such as remaining battery power and work efficiency to the emotion engine in real time. The emotion engine analyzes this data and determines the robot's current status.

[1572] Step 7:

[1573] The server regenerates the schedule based on the data analyzed by the emotion engine. The server then obtains feedback data from the emotion engine and uses the generation AI tool again to appropriately revise the schedule. At this time, new data processing and calculations are performed.

[1574] Step 8:

[1575] The server sends the revised schedule to the robot terminal again. The revised schedule is sent as an HTTP response and the robot terminal redisplays it. This allows the robot to continue working according to the latest schedule.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] 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.

[1582] 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).

[1583] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1584] 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."

[1585] 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.

[1586] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1587] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1588] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1589] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1590] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1591] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1592] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1593] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1594] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1595] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1596] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1597] The following is further disclosed regarding the above embodiment.

[1598] (Claim 1)

[1599] an input means for a user to input an agenda and participant information;

[1600] a communication means for transmitting the input data to a server;

[1601] A storage means whereby the server processes the data and stores it in an internal database;

[1602] A generation means for generating a meeting agenda and time allocation based on the topic and participant information by a generation AI tool;

[1603] a display means for transmitting the generated agenda and time allocation to a user's terminal and displaying the same;

[1604] A system including:

[1605] (Claim 2)

[1606] 2. The system according to claim 1, wherein the input means is a user terminal.

[1607] (Claim 3)

[1608] 10. The system of claim 1, wherein the generative AI tool uses a pre-trained model to generate a meeting agenda and time allocation.

[1609] "Example 1"

[1610] (Claim 1)

[1611] an input means for a user to input an agenda and participant information;

[1612] a communication means for transmitting the input data to a server;

[1613] A storage means for the server to analyze the data and store it in an internal database;

[1614] A generating means for generating a meeting agenda and time allocation based on the topic and participant information by a generating AI model;

[1615] a display means for transmitting the generated agenda and time allocation to a user's terminal and displaying the same;

[1616] A system including:

[1617] (Claim 2)

[1618] 2. The system according to claim 1, wherein the input means is a user terminal.

[1619] (Claim 3)

[1620] 2. The system of claim 1, wherein the generative AI model uses a pre-trained model to generate a meeting agenda and time allocation.

[1621] "Application Example 1"

[1622] (Claim 1)

[1623] an input means for a user to input the agenda and participant information for the next meeting;

[1624] a conversion means for converting voice input into text information using voice recognition technology;

[1625] a communication means for transmitting the input data to a server;

[1626] A storage means whereby the server processes the data and stores it in an internal database;

[1627] A generation means for generating a meeting agenda and time allocation based on the topic and participant information by a generation AI tool;

[1628] a display means for transmitting the generated agenda and time allocation to a user's terminal and displaying the same;

[1629] A system including:

[1630] (Claim 2)

[1631] 2. The system of claim 1, wherein the input means is a device that accepts voice input.

[1632] (Claim 3)

[1633] 10. The system of claim 1, wherein the generative AI tool uses a pre-trained model to generate a meeting agenda and time allocation.

[1634] "Example 2: Combining Emotion Engines"

[1635] (Claim 1)

[1636] an input means for a user to input an agenda and participant information;

[1637] a communication means for transmitting the input data to a server;

[1638] A storage means whereby the server processes the data and stores it in an internal database;

[1639] A generating means for generating a meeting agenda and time allocation based on the topic and participant information by a generating AI model;

[1640] a display means for transmitting the generated agenda and time allocation to a user's terminal and displaying the same;

[1641] a recognition means for the emotion engine to recognize the emotion of the user;

[1642] a feedback means for transmitting the recognized emotion data to a server;

[1643] A means for the server to correct the agenda and time allocation in real time based on the emotion data;

[1644] a re-display means for transmitting the revised agenda and time allocation to the user's terminal and displaying the same;

[1645] A system including:

[1646] (Claim 2)

[1647] 2. The system according to claim 1, wherein the input means is a user terminal.

[1648] (Claim 3)

[1649] 2. The system of claim 1, wherein the generative AI model uses a pre-trained model to generate a meeting agenda and time allocation.

[1650] "Application example 2 when combining emotion engines"

[1651] (Claim 1)

[1652] an input means for a user to input an agenda and participant information;

[1653] a communication means for transmitting the input data to a server;

[1654] A storage means whereby the server processes the data and stores it in an internal database;

[1655] A generation means for generating a meeting agenda and time allocation based on the topic and participant information by a generation AI tool;

[1656] a display means for transmitting the generated agenda and time allocation to a user's terminal and displaying the same;

[1657] The emotion engine analyzes data and provides real-time corrections to optimize meeting progress.

[1658] A system including:

[1659] (Claim 2)

[1660] 2. The system of claim 1, wherein the user's terminal is a smartphone, smart glasses, a head-mounted display, or a robot.

[1661] (Claim 3)

[1662] 10. The system of claim 1, wherein the generative AI tool uses a pre-trained model to generate and modify meeting agendas and time allocations. [Explanation of symbols]

[1663] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for a user to input an agenda and participant information; a communication means for transmitting the input data to a server; A storage means whereby the server processes the data and stores it in an internal database; A generation means for generating a meeting agenda and time allocation based on the topic and participant information by a generation AI tool; a display means for transmitting the generated agenda and time allocation to a user's terminal and displaying the same; A system including:

2. 2. The system according to claim 1, wherein the input means is a user terminal.

3. The system of claim 1 , wherein the generating AI tool uses a pre-trained model to generate a meeting agenda and time allocation.

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