system

Abstract

To provide a system.SOLUTION: A system includes: means for analyzing a meeting notice agenda of the meeting to be held using a natural language processing technique; means for acquiring related information including at least any of the past performance and supplier candidates of the agenda on the basis of the agenda analysis results; means for sharing the acquired related information by the participants of the meeting; means for analyzing the minutes of the meeting and extracting tasks; means for making a list of the extracted tasks; and means for reflecting the listed tasks on standardized documents.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In traditional meetings, the necessary information was extracted from the agenda and minutes, and then tasks were listed based on that information, and the information was reflected in standard materials, all of which was done manually. In addition, in preparation for meetings, information such as past performance, potential suppliers, and trends needed to be shared, but this was also done manually. These tasks were time-consuming and labor-intensive, and inefficient. [Means for solving the problem]

[0005] This invention provides a means for sharing information such as past performance and potential suppliers (taking into account business transaction records, etc.) in advance from the agenda of a meeting notification, and a means for creating a task list from the minutes of a meeting and automatically reflecting the information in standard documents. It also provides a means for sending documents to potential suppliers (automatically creating email text) and a means for automatically generating report documents from proposals. This allows for more efficient meeting preparation and follow-up. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The present invention is a system that includes a means for sharing information such as past performance and potential suppliers (taking into account business transaction records, etc.) in advance from the agenda of a meeting notice, and a means for creating a task list from the minutes and automatically reflecting the information in standard materials. Specifically, the system analyzes the agenda of the meeting notice, obtains information such as past performance and potential suppliers from a database, and shares this information with meeting participants in advance. The system also analyzes the minutes, automatically creates a task list, and reflects this in standard materials.

[0029] "Example 2"

[0030] The embodiment described in claim 2 further includes a means for sharing supplier candidates (reflecting past performance, sales performance, etc.), past satisfaction survey materials, and trends in advance. Specifically, information on supplier candidates is obtained from a database and shared with meeting participants in advance. Past satisfaction survey materials and trend information are also shared in the same way.

[0031] "Example 3"

[0032] The embodiment described in claim 3 further includes a means for sending materials to potential suppliers (automatic creation of email text) and a means for automatically generating report materials from proposals. Specifically, email text for sending materials to potential suppliers is automatically generated and sent. Also, the contents of proposals made at meetings are analyzed, and report materials are automatically generated based on the analysis.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: Analyze the agenda of the meeting notice. This analysis uses natural language processing technology to analyze the agenda text and extract the meeting topic and items to be discussed.

[0036] Step 2: Based on the extracted items, information such as past performance and potential suppliers is obtained from the database. This is done using the database's search function.

[0037] Step 3: Share the acquired information with the meeting participants in advance by email or via a website.

[0038] Step 4: Analyze the minutes and automatically create a list of tasks. This analysis uses natural language processing technology to analyze the text in the minutes and extract action items and decisions.

[0039] Step 5: The extracted tasks are reflected in standard documents. This is done using template technology.

[0040] "Example 2"

[0041] Step 1: Analyze the agenda of the meeting notice. This analysis uses natural language processing technology to analyze the agenda text and extract the meeting topic and items to be discussed.

[0042] Step 2: Based on the extracted items, information such as past performance and potential suppliers is obtained from the database. This is done using the database's search function.

[0043] Step 3: Share the acquired information with the meeting participants in advance by email or via a website.

[0044] Step 4: Share past satisfaction survey materials and trend information as well. This can be done via email or on the website.

[0045] "Example 3"

[0046] Step 1: Analyze the agenda of the meeting notice. This analysis uses natural language processing technology to analyze the agenda text and extract the meeting topic and items to be discussed.

[0047] Step 2: Based on the extracted items, information such as past performance and potential suppliers is obtained from the database. This is done using the database's search function.

[0048] Step 3: Share the acquired information with the meeting participants in advance by email or via a website.

[0049] Step 4: Automatically generate and send emails to potential suppliers using template technology.

[0050] Step 5: Analyze the proposals made at the meeting and automatically generate presentation materials based on them. This automatic generation is done using template technology.

[0051] Example 1

[0052] Next, a description will be given of Example 1 of Form 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."

[0053] In traditional meeting management, sharing information on past performance and potential suppliers based on the agenda of the meeting notice is done manually, which takes time and effort. In addition, the work of listing tasks from the minutes and reflecting them in standard documents is often done manually, which is inefficient. This means that a great deal of time and effort is required to prepare for and follow up on meetings, resulting in a decrease in work efficiency.

[0054] 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.

[0055] In this invention, the server includes means for sharing past performance and potential suppliers in advance from the agenda of the meeting notification, means for listing tasks from the minutes and automatically reflecting the information in standard materials, means for analyzing the agenda using natural language processing technology, means for acquiring related information from a database, means for sharing the acquired information with meeting participants, means for analyzing the minutes and extracting tasks, means for listing the extracted tasks, and means for reflecting the listed tasks in standard materials. This automates meeting preparation and follow-up, enabling improved work efficiency.

[0056] A "meeting notice" is a notice that includes information such as the date, time, location, participant list, and agenda of a meeting.

[0057] An "agenda" is a list of topics or subjects to be discussed at a meeting.

[0058] "Past performance" refers to information about the results and outcomes of past work and projects.

[0059] A "potential supplier" is a list of potential suppliers of goods or services.

[0060] Minutes are a document that records what was discussed and what decisions were made at a meeting.

[0061] A "task" is an operation or activity that must be performed to achieve a specific purpose.

[0062] A "standard document" is a document or report prepared according to a specific form or format.

[0063] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[0064] A "database" is a system for efficiently storing, managing, and retrieving data.

[0065] "Information sharing" means communicating and making available specific information among multiple people and systems.

[0066] "Listing" means organizing multiple items in a list format.

[0067] "Extraction" means taking out specific data or information from the whole.

[0068] This invention is a system that shares information such as past performance and potential suppliers in advance from the agenda of a meeting notice, creates a task list from the minutes of the meeting, and automatically reflects the information in standard documents. A specific embodiment of this system will be described below.

[0069] Meeting notification agenda analysis and information sharing

[0070] A user sends a meeting notice to the system. The meeting notice includes the meeting date, time, location, attendee list, and agenda. Meeting notices can be sent using any popular calendar application.

[0071] The server extracts the agenda from the received meeting notification and uses natural language processing technology to analyze the agenda content, using software such as Google® Cloud Natural Language API or IBM Watson® Natural Language Understanding.

[0072] Based on the analysis results, the server retrieves related information such as past performance and potential suppliers from a database. This database uses a database management system such as MySQL (registered trademark) or PostgreSQL.

[0073] The server shares the acquired information with the conference participants in advance using an email transmission system.

[0074] Examples:

[0075] A user sends a meeting notification.

[0076] The server analyzes the agenda of the meeting notice and extracts the topic "Selecting a supplier for a new product."

[0077] The server retrieves data on past supplier selection results from a database and generates a list of related supplier candidates.

[0078] The server will email this list to the conference participants.

[0079] Example prompts to input to a generative AI model:

[0080] Generate a list of potential suppliers and past performance data related to the topic "Supplier Selection for New Product."

[0081] Analysis of minutes and task list creation

[0082] After the meeting, the user sends the minutes to the system. The minutes include the contents discussed and decisions made in the meeting. A general document creation tool can be used to send the minutes.

[0083] The server then uses natural language processing technology to analyze the received minutes, again using software such as the aforementioned Google Cloud Natural Language API and IBM Watson Natural Language Understanding.

[0084] The server automatically creates a list of tasks from the minutes based on the analysis results, using, for example, the Python pandas library.

[0085] The server reflects the generated task list in a standard document, which is generated using, for example, Microsoft Office Excel or Google Sheets.

[0086] Examples:

[0087] A user sends the minutes after the meeting.

[0088] The server analyzes the minutes and extracts the task "Conduct research on selecting suppliers for new products."

[0089] The server adds this task to the task list and reflects it in the Excel file.

[0090] The server shares the generated Excel file with the conference participants.

[0091] Example prompts to input to a generative AI model:

[0092] Extract the task "Conduct research on selecting suppliers for new products" from the minutes and add it to your task list.

[0093] This system improves meeting efficiency by analyzing the agenda in meeting notifications, sharing relevant information in advance, and listing tasks from meeting minutes and reflecting them in standard materials.Specific hardware and software used include Google Cloud Natural Language API, IBM Watson Natural Language Understanding, MySQL, an email sending system, Python's pandas library, and MICROSOFT EXCEL.

[0094] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0095] Step 1:

[0096] A user sends a meeting notice to the system. The meeting notice includes the meeting date, time, location, attendee list, and agenda. The input is the meeting notice data, and the output is the meeting notice sent to the server. In concrete terms, the user uses a calendar application to create a meeting notice and send it to the system.

[0097] Step 2:

[0098] The server extracts the agenda from the received meeting notice. The input is the data of the meeting notice, and the output is the extracted agenda. Specifically, the server obtains the contents of the meeting notice in text format and parses the agenda part.

[0099] Step 3:

[0100] The server uses natural language processing technology to analyze the content of the agenda. The input is the extracted agenda, and the output is the analysis result. Specifically, the server sends the agenda to the Google Cloud Natural Language API and receives the analysis result.

[0101] Step 4:

[0102] Based on the analysis results, the server retrieves related information such as past performance and potential suppliers from the database. The input is the analysis results, and the output is the retrieved related information. Specifically, the server generates a query based on the analysis results and sends it to the MySQL database.

[0103] Step 5:

[0104] The server shares the acquired information with the conference participants in advance. The input is the acquired relevant information, and the output is the information sent to the conference participants. Specifically, the server uses the email sending system API to send the relevant information to the conference participants in email format.

[0105] Step 6:

[0106] After the meeting, the user sends the minutes to the system. The input is the minutes data, and the output is the minutes sent to the server. Specifically, the user creates the minutes using a document creation tool and sends them to the system.

[0107] Step 7:

[0108] The server again uses natural language processing technology to analyze the received minutes. The input is the minutes data, and the output is the analysis results. Specifically, the server sends the minutes to IBM Watson Natural Language Understanding and receives the analysis results.

[0109] Step 8:

[0110] The server automatically lists tasks from the minutes based on the analysis results. The input is the analysis results, and the output is the generated task list. Specifically, the server uses the Python pandas library to convert the analysis results into a data frame and generate the task list.

[0111] Step 9:

[0112] The server reflects the generated task list in the standard document. The input is the generated task list, and the output is the standard document. Specifically, the server uses Microsoft Excel to write the task list to an Excel file and create the standard document.

[0113] Step 10:

[0114] The server shares the created standard materials with the meeting participants. The input is the standard materials, and the output is the standard materials sent to the meeting participants. Specifically, the server uses the email sending system API to send the standard materials to the meeting participants.

[0115] (Application example 1)

[0116] Next, a description will be given of Application Example 1 of Embodiment 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."

[0117] With conventional meeting management systems, it was difficult to share information such as past performance and potential suppliers in advance from the agenda of the meeting notice, and it was not possible to list tasks from the minutes and automatically reflect them in standard documents. This resulted in problems such as reduced meeting efficiency and complicated task management. In particular, in factories, where rapid updates to production plans are required, these issues could lead to reduced production efficiency.

[0118] 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.

[0119] In this invention, the server includes: means for sharing in advance information such as past performance and potential suppliers (taking into account business transaction records, etc.) from the agenda of the meeting notice; means for listing tasks from the minutes and automatically reflecting the information in standard documents; means installed in a factory robot for analyzing the agenda of the meeting notice, retrieving past production performance and potential suppliers from a database, and sharing this information with meeting participants in advance; and means for analyzing the minutes, automatically listing tasks, and automatically reflecting this in the production plan. This improves meeting efficiency, facilitates task management, and enables rapid updates to production plans, especially in factories.

[0120] A "meeting notice" is a notice to inform participants of information such as the date, time, location, and agenda of a meeting.

[0121] An "agenda" is a list of items or topics to be discussed at a meeting.

[0122] "Past performance" refers to records of the results and outcomes of past work or projects.

[0123] A "potential supplier" is a potential supplier of goods or services.

[0124] "Business transaction performance" refers to records of past business activities and transactions.

[0125] Minutes are a document that records what was discussed and what decisions were made at a meeting.

[0126] A "task" refers to the work or task that must be performed to achieve a specific goal.

[0127] "Listing" refers to organizing items in a list format.

[0128] "Standardized materials" are documents or materials prepared according to a specific form or format.

[0129] A "factory robot" is a mechanical device used to perform automated tasks in a factory.

[0130] "Production records" are records of products and quantities that have been produced in the past at a factory or on a production line.

[0131] A "database" is a system for efficiently managing and searching data.

[0132] "Production plan" refers to the plan or schedule for the production of a product.

[0133] The following system is constructed as an embodiment of this invention. The system shares past performance and potential suppliers in advance from the agenda of a meeting notice, lists tasks from the minutes, and automatically reflects the information in standard documents. Furthermore, the system is installed on a factory robot and has the function of analyzing the agenda of a meeting notice, retrieving past production performance and potential suppliers from a database, and sharing this information with meeting participants in advance. It also includes a function to analyze the minutes, automatically list tasks, and automatically reflect this in production plans.

[0134] Hardware and software used

[0135] Hardware: Factory robots, servers, user terminals

[0136] Software: Python, SQLite, spaCy (natural language processing library)

[0137] System configuration

[0138] 1. Meeting notification analysis function:

[0139] The server receives the agenda of the meeting notification and parses the agenda text using spaCy, a natural language processing library.

[0140] Keywords (organization names, dates, product names, etc.) are extracted from the analysis results, and past production records and potential suppliers are obtained from the SQLite database.

[0141] The acquired information is sent to the user terminal and shared with the conference participants in advance.

[0142] 2. Meeting minutes analysis function:

[0143] The server receives the meeting minutes and again uses spaCy to parse the minutes text.

[0144] Tasks are extracted from the analysis results and listed.

[0145] The listed tasks are automatically reflected in standard documents and then in production plans.

[0146] Specific examples

[0147] Meeting Notice Example

[0148] Agenda for the next meeting: Review production results, consider potential suppliers

[0149] Sample minutes

[0150] Minutes: Task 1: Selecting a new supplier, Task 2: Reviewing the production line

[0151] Processing flow

[0152] 1. Receiving and parsing meeting notifications:

[0153] The server receives the agenda of the meeting notification and parses it using spaCy.

[0154] Keywords are extracted from the analysis results and related information is retrieved from the SQLite database.

[0155] The acquired information is sent to the user terminal and shared with the conference participants in advance.

[0156] 2. Receipt and Analysis of Transcripts:

[0157] The server receives the meeting minutes and parses them again using spaCy.

[0158] Tasks are extracted from the analysis results and listed.

[0159] The listed tasks are automatically reflected in standard documents and then in production plans.

[0160] In this way, meetings can be made more efficient, tasks can be managed more easily, and production plans can be updated more quickly, especially in factories.

[0161] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0162] Step 1:

[0163] The server receives the agenda of a meeting notice. As input, it is given the agenda text of the meeting notice. The server parses this text using spaCy, a natural language processing library, to extract keywords (organization names, dates, product names, etc.). As output, it receives a list of the extracted keywords.

[0164] Step 2:

[0165] The server searches the SQLite database for past production records and potential suppliers based on the keywords extracted in step 1. A list of keywords is given as input. The server executes a database query to retrieve relevant information on past production records and potential suppliers. The output is a list of the retrieved information.

[0166] Step 3:

[0167] The server sends the information acquired in step 2 to the user terminal. As input, a list of acquired information is given. The server sends this information to the user terminal and shares it with the conference participants in advance. As output, the information displayed on the user terminal is obtained.

[0168] Step 4:

[0169] The server receives the minutes of a meeting. As input, it receives the text of the minutes. The server parses this text again using spaCy to extract tasks. As output, it receives a list of extracted tasks.

[0170] Step 5:

[0171] The server lists the tasks extracted in step 4. The server receives the list of extracted tasks as input. The server organizes this into a list and automatically updates the standard documents. The server outputs the updated standard documents.

[0172] Step 6:

[0173] The server reflects the tasks listed in step 5 in the production plan. The list of tasks is given as input. The server updates the production plan database to reflect the tasks. The updated production plan is obtained as output.

[0174] In this way, the server performs a series of processes: analyzing the agenda and minutes of the meeting notice, acquiring and sharing related information, listing tasks, and reflecting them in the production plan.

[0175] Example 2

[0176] Next, a description will be given of Example 2 of Form 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."

[0177] Conventional conference systems lacked a means to efficiently share necessary information before a meeting, resulting in the significant time and effort required for meeting preparation. They also lacked a means to quickly summarize the content discussed during the meeting and extract key points. This reduced the efficiency and quality of meetings and hindered the rapid pace of decision-making.

[0178] 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.

[0179] In this invention, the server includes a means for sharing in advance information such as past performance and potential suppliers (taking into account business transaction performance, etc.) from the agenda of the meeting notice, a means for creating a task list from the minutes of the meeting and automatically reflecting the information in standardized materials, a means for acquiring information from a database, a means for sharing the acquired information with meeting participants, and a means for extracting a summary of the meeting materials and key points using a generative AI model. This makes it possible to efficiently share necessary information before the meeting and quickly summarize the contents discussed during the meeting and extract key points.

[0180] A "meeting notice agenda" is a document that details the purpose, agenda, participants, date, time, location, etc. of a meeting.

[0181] "Past performance" refers to data showing the results and performance of past transactions and business operations.

[0182] "Prospective Suppliers" is a list of suppliers with which we may do business in the future.

[0183] "Business transaction performance" is data showing the history of past business activities and transactions.

[0184] Minutes are a document that records what was discussed and what decisions were made during a meeting.

[0185] "Listing tasks" means organizing specific work items in a list format based on the minutes.

[0186] A "standard document" is a standard document that is created according to a specific format.

[0187] A "database" is a system for efficiently storing, managing, and retrieving data.

[0188] A "generative AI model" is an algorithm that uses artificial intelligence to generate text and analyze data.

[0189] A "summary" is a short summary of a longer piece of text or data.

[0190] "Key points" refer to particularly important parts or key points of information.

[0191] "Conference participants" refers to people attending a conference.

[0192] This invention is a system for improving the efficiency and quality of meetings, in which servers, terminals, and users work together to share information and use generative AI models to extract summaries and key points from meeting materials.

[0193] Hardware and software used

[0194] Hardware: Servers, devices (PCs, tablets, smartphones)

[0195] Software: Database management systems (e.g., MySQL), generative AI models (e.g., GPT-4®), web application frameworks (e.g., Django)

[0196] Program processing overview

[0197] Retrieving information from a database

[0198] The server uses a database management system (MySQL) to obtain information on potential suppliers. Specifically, it executes the following SQL query:

[0199] sql

[0200] SELECT FROM suppliers WHERE past_performance >= 80;

[0201] This query extracts potential suppliers with a past performance of 80% or more.

[0202] Acquiring satisfaction survey materials and trend information

[0203] The server also retrieves past satisfaction survey data and trend information from the database by executing the following SQL query:

[0204] sql

[0205] SELECT FROM satisfaction_surveys WHERE date >= '2022-01-01';

[0206] SELECT FROM trends WHERE category = 'market';

[0207] These queries extract the latest satisfaction survey and market trend information.

[0208] Information Sharing

[0209] The server shares the acquired information with the conference participants through a web application framework (Django). Specifically, it performs the following processes:

[0210] The server uses Django to generate web pages.

[0211] The server embeds the retrieved data into a web page.

[0212] The server sends the URL of the web page to the terminals (PCs, tablets, smartphones) of the conference participants.

[0213] Using generative AI models

[0214] The server uses a generative AI model (GPT-4) to summarize the meeting materials and extract key points. Specifically, the server inputs the following prompt sentences into the generative AI model:

[0215] "Please summarize the following potential supplier information: [potential supplier information]"

[0216] The generative AI model generates a summary based on this prompt.

[0217] Specific examples

[0218] For example, the server executes the following SQL query to obtain information about potential suppliers:

[0219] sql

[0220] SELECT FROM suppliers WHERE past_performance >= 80;

[0221] The acquired data is displayed on a web page using Django. The following prompt sentence is input to the generative AI model (GPT-4) to generate a summary of the conference materials.

[0222] "Please summarize the following potential suppliers: Supplier A has achieved a satisfaction rate of over 80% over the past five years. Supplier B has a strong sales record and has maintained a delivery on-time rate of over 90% over the past three years."

[0223] In this way, the system allows the server, terminals, and users to work together to share information efficiently, improving the quality of meetings.

[0224] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0225] Step 1:

[0226] The server connects to a database management system (MySQL) and retrieves information on potential suppliers. As input, it uses an SQL query to extract supplier candidates with a past performance of 80% or more. Specifically, it executes the following SQL query.

[0227] sql

[0228] SELECT FROM suppliers WHERE past_performance >= 80;

[0229] The result of this query is a list of potential suppliers, which the server stores in memory.

[0230] Step 2:

[0231] The server retrieves past satisfaction survey data and trend information from the database. As input, it uses SQL queries to extract the latest satisfaction survey data and market trend information. Specifically, it executes the following SQL queries:

[0232] sql

[0233] SELECT FROM satisfaction_surveys WHERE date >= '2022-01-01';

[0234] SELECT FROM trends WHERE category = 'market';

[0235] The results of these queries are the satisfaction survey data and trend information, which are then stored in memory by the server.

[0236] Step 3:

[0237] The server shares the acquired information with the conference participants through a web application framework (Django). The acquired data is used as input. Specifically, the following processes are performed:

[0238] The server generates web pages using Django templates.

[0239] The server embeds the acquired data into a template.

[0240] The server sends the URL of the generated web page to the conference participants by email.

[0241] As an output, a web page is generated that can be accessed by conference participants.

[0242] Step 4:

[0243] The server uses a generative AI model (GPT-4) to summarize the meeting materials and extract key points. As input, it embeds the acquired information about potential suppliers into a prompt. Specifically, it inputs the following prompt into the generative AI model:

[0244] "Please summarize the following potential supplier information: [potential supplier information]"

[0245] The generative AI model generates a summary based on the prompt. The output is a summarized version of the meeting material. The server displays the summary on a web page.

[0246] Step 5:

[0247] The user accesses the web page URL sent from the server and checks the meeting materials. The user prepares for the meeting based on the summarized information and key points. Specifically, the user accesses the web page using a PC, tablet, or smartphone and views the displayed information.

[0248] In this way, the system allows the server, terminals, and users to work together to share information efficiently, improving the quality of meetings.

[0249] (Application example 2)

[0250] Next, a description will be given of Application Example 2 of Form 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."

[0251] Conventional conference systems lacked a means to efficiently share information on potential suppliers, past satisfaction surveys, and trend information, resulting in problems such as time-consuming meeting preparation and decision-making. Furthermore, the lack of real-time information updates and the ability to provide data summaries in natural language made it difficult for users to quickly obtain the information they needed. This led to issues such as reduced operational efficiency at logistics centers and other on-site locations.

[0252] 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.

[0253] In this invention, the server includes a means for sharing information such as past performance and potential suppliers (taking into account business transaction records) in advance from the agenda of the meeting notification, a means for creating a task list from the minutes of the meeting and automatically updating the information in standard documents, a means for acquiring information on potential suppliers from a database and displaying it on a smart device, a means for displaying supplier evaluations based on the results of past satisfaction surveys, a means for acquiring and displaying the latest market trend information in real time, a means for summarizing data in natural language using a generative AI model and providing it to users, and a means for updating data in real time using a cloud service. This enables quick and efficient meeting preparation and decision-making, improving operational efficiency at logistics centers and other sites.

[0254] A "meeting notice agenda" is a document that lists details of a meeting, such as the purpose, agenda, participants, date and time.

[0255] "Past performance" refers to data showing the results and outcomes of past transactions and business operations.

[0256] "Prospective Suppliers" is a list of companies and businesses that may provide goods or services.

[0257] "Business transaction performance" is data showing the history of past business activities and transactions.

[0258] A "minutes" is a document that records the contents of a meeting, decisions made, and statements made.

[0259] "Listing tasks" means organizing the work and tasks decided in the meeting in a list format.

[0260] A "standard document" is a document or report prepared according to a specific form or format.

[0261] A "database" is a system for efficiently managing and searching data.

[0262] "Smart devices" are electronic devices with advanced functions, such as smartphones and smart glasses.

[0263] A "satisfaction survey" is a questionnaire or survey used to assess customer or user satisfaction.

[0264] "Market trend information" is data that indicates current market trends and fashions.

[0265] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate and analyze data.

[0266] "Natural language summarization" means concisely summarizing data and information in language that is easy for humans to understand.

[0267] "Cloud services" are computer resources and services provided over the Internet.

[0268] "Real-time data updating" means keeping data up to date instantly.

[0269] The system for implementing the present invention uses the following hardware and software.

[0270] Hardware and software used

[0271] Hardware:

[0272] Smartphones (e.g., iPhone, Android devices)

[0273] Smart glasses (e.g., Google Glass (registered trademark), Microsoft HoloLens (registered trademark))

[0274] software:

[0275] Database management systems (e.g. MySQL, PostgreSQL)

[0276] Cloud services (e.g., AWS (registered trademark), Google Cloud)

[0277] Mobile app development frameworks (e.g., React Native, Flutter (registered trademark))

[0278] Generative AI models (e.g., OpenAI® GPT-4)

[0279] System configuration and operation

[0280] 1. Sharing information from the agenda in the meeting notice

[0281] The server retrieves information on past performance and potential suppliers from the agenda of the meeting notification and shares this information in advance. Using a database management system, detailed information including past transaction performance and evaluations is retrieved and displayed on the smart device.

[0282] 2. Creating a task list from meeting minutes

[0283] The server creates a list of tasks from the minutes and automatically updates standard documents, allowing for efficient management of the work and tasks decided in meetings.

[0284] 3. Satisfaction surveys and trend information sharing

[0285] The server displays supplier evaluations based on the results of past satisfaction surveys. It also obtains the latest market trend information in real time and displays it on smart devices. Cloud services are used to instantly keep the data up to date.

[0286] 4. Data Summarization with Generative AI Models

[0287] The server uses a generative AI model to summarize the data in natural language and provide it to the user, allowing them to quickly understand the information they need and make decisions.

[0288] Specific examples

[0289] A logistics center manager wears smart glasses and checks information on potential suppliers. Based on the results of past satisfaction surveys, the manager decides which supplier to do business with. Based on the latest market trend information, the manager makes future purchasing plans.

[0290] Prompt Sentence Examples

[0291] The data is summarized by inputting the following prompt sentences to the generative AI model.

[0292] Please summarize the potential supplier information using the following data: Data: {Potential Supplier Information}, {Past Satisfaction Survey Results}, {Latest Market Trend Information}

[0293] In this way, meeting preparations and decision-making can be carried out quickly and efficiently, improving operational efficiency at logistics centers and other locations.

[0294] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0295] Step 1:

[0296] The server retrieves information on past performance and potential suppliers from the meeting notification agenda. As input, it receives the meeting notification agenda and queries a database management system (e.g., MySQL, PostgreSQL) to retrieve detailed information, including past transaction performance and ratings. As output, the retrieved information is generated in JSON format.

[0297] Step 2:

[0298] The server sends the acquired information to the smart device. As input, it receives the JSON format information generated in step 1 and generates data to be displayed on the smartphone or smart glasses using a mobile app development framework (e.g., React Native, Flutter). As output, it generates information to be displayed on the smart device.

[0299] Step 3:

[0300] The server creates a list of tasks from the minutes and automatically reflects the information in standard documents. The text data of the minutes is given as input, and tasks are extracted and listed using natural language processing technology. A task list is generated as output and reflected in standard documents.

[0301] Step 4:

[0302] The server displays supplier evaluations based on the results of past satisfaction surveys. Past satisfaction survey data is given as input, and evaluation data is retrieved using a database management system. Supplier evaluation information is generated as output and displayed on the smart device.

[0303] Step 5:

[0304] The server obtains the latest market trend information in real time and displays it on the smart device. As input, market trend information obtained from the Internet is given, and data is updated in real time using cloud services (e.g., AWS, Google Cloud). As output, the latest market trend information is displayed on the smart device.

[0305] Step 6:

[0306] The server uses a generative AI model to summarize the data in natural language and provide it to the user. As input, information on potential suppliers, past satisfaction survey results, and the latest market trend information are given, and a generative AI model (e.g., OpenAI GPT-4) is used to generate a summary. As output, the summarized information is displayed on the smart device.

[0307] Step 7:

[0308] The server updates data in real time using a cloud service. As input, the latest data is provided and the database is updated using the cloud service. As output, the updated data is reflected on the smart device.

[0309] Example 3

[0310] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0311] With conventional meeting management systems, it was difficult to share past performance and potential suppliers in advance from the agenda of meeting notifications, and there was a lack of means to list tasks from minutes and automatically reflect information in standard documents. The system also lacked the functionality to automatically send materials to potential suppliers or generate report materials based on proposals made in meetings. This resulted in reduced work efficiency and increased manual work, making errors more likely.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0313] In this invention, the server includes a means for sharing past performance and potential suppliers in advance from the agenda of the meeting notification, a means for listing tasks from the minutes of the meeting and automatically reflecting the information in standard documents, a means for automatically generating email text to send to potential suppliers, a means for analyzing the content of proposals made in the meeting and automatically generating materials for escalation, and a means for automatically generating email text using a generative AI model and sending it using email sending software. This makes it possible to streamline meeting preparation and follow-up, reduce manual work, and improve the accuracy and efficiency of work.

[0314] A "meeting notice agenda" is a document that details the purpose, agenda, participants, date, time, location, etc. of a meeting.

[0315] "Past performance" refers to data and records that show the results and outcomes of past work or projects.

[0316] "Prospective Suppliers" are potential suppliers or vendors with whom we may do business in the future.

[0317] A "minutes" is a document that records the contents of a meeting, decisions made, and statements made.

[0318] "Listing tasks" means organizing specific work items that arise in meetings or work in a list format.

[0319] A "standard document" is a document or report that is prepared according to a specific form or format.

[0320] "Means for automatically generating email text" refers to technology or systems that automatically create email content based on specific input information.

[0321] "Analyzing the proposal content" means analyzing the information presented in a meeting or proposal and extracting key points and summaries.

[0322] "Presentation materials" are reports or proposals to be submitted to higher-level managers or decision makers.

[0323] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate text or data.

[0324] "Email sending software" refers to a program or application for sending email.

[0325] This invention is a meeting management system that shares past performance and potential suppliers in advance from the agenda of the meeting notice, lists tasks from the minutes, and automatically reflects the information in standard documents. It also includes functions to automatically generate email text to send to potential suppliers and to automatically generate report documents by analyzing the contents of proposals made in the meeting.

[0326] Hardware and software used

[0327] The system uses the following hardware and software:

[0328] Server: Responsible for data processing and running generative AI models.

[0329] Terminal: Provides an interface for users to enter information.

[0330] Generative AI models: Perform text generation and data analysis (e.g., OpenAI's GPT-4).

[0331] Email sending software: Sending automatically generated emails (e.g., email sending systems).

[0332] Data processing and calculation

[0333] Sending materials to potential suppliers

[0334] 1. The user enters information about a potential supplier into the terminal.

[0335] 2. The server uses the generative AI model to automatically generate email text to send to potential suppliers.

[0336] 3. The server uses email sending software to send the generated email text to the potential supplier.

[0337] Examples:

[0338] The user types, "Add Supplier A as a potential supplier for a new part."

[0339] The server uses the generative AI model to generate an email that reads, "Dear Supplier A, We are considering purchasing new parts and are interested in your products. Could you please send us detailed information?"

[0340] The server uses the mail sending system to send this mail to supplier A.

[0341] Analysis of proposals made in meetings and automatic generation of presentation materials

[0342] 1. The user inputs the content of the proposal for the meeting into the terminal.

[0343] 2. The server uses the generative AI model to analyze the suggestions.

[0344] 3. The server automatically generates the application documents and provides them to the user.

[0345] Examples:

[0346] A user enters the following as a "Proposal for launching a new product into the market": "We are planning to launch new product X into the market next month. As a marketing strategy, we will utilize social media advertising and influencer marketing."

[0347] The server uses the generated AI model to analyze the proposal and generate a proposal document titled "New product X market launch plan: scheduled for next month. Marketing strategy: social media advertising, influencer marketing."

[0348] The server provides the generated submission materials to the user.

[0349] Prompt Sentence Examples

[0350] Example prompt for generating emails to potential suppliers:

[0351] "Add Supplier A as a potential supplier for a new part. Please generate the email."

[0352] Sample prompts for analyzing proposals and generating presentation materials in meetings:

[0353] "Enter your proposal for launching a new product into the market. Generate the proposal."

[0354] In this way, the system utilizes the generative AI model based on the user's input to automatically generate email text and report documents, thereby efficiently supporting business operations. The flow of the specific processing in the third embodiment will be described with reference to FIG. 15.

[0355] Step 1:

[0356] The user inputs information about the potential supplier into the terminal.

[0357] Input: Name and contact information of the potential supplier (e.g., "Supplier A, contact@supplierA.com").

[0358] Specific operation: A user inputs information about a potential supplier through the system interface.

[0359] Step 2:

[0360] The server receives the input information about potential suppliers and sends prompt statements to the generative AI model.

[0361] Input: User-entered information about the potential supplier.

[0362] Data processing: The server generates a prompt and sends it to the generative AI model (e.g., "Add supplier A as a potential supplier for a new part. Please generate the email text.").

[0363] Specific operation: The server sends a prompt sentence to the generative AI model.

[0364] Step 3:

[0365] The server receives the email text returned by the generative AI model.

[0366] Input: The email text returned by the generative AI model.

[0367] Output: Generated email text (e.g., "Dear Supplier A, We are considering purchasing new parts and are interested in your products. Could you please send us detailed information?").

[0368] Specific operation: The server receives and verifies the email text returned by the generative AI model.

[0369] Step 4:

[0370] The server uses email sending software to send the generated email text to the potential supplier.

[0371] Input: Generated email text and potential supplier contact information.

[0372] Output: Email sent to potential supplier.

[0373] Specific operation: The server uses email sending software (e.g., email sending system) to send the generated email text to potential suppliers.

[0374] Step 5:

[0375] The user inputs the content of the proposal for the meeting into the terminal.

[0376] Input: What was proposed in the meeting (e.g., "We plan to launch new product X in the market next month. Our marketing strategy will involve social media advertising and influencer marketing.").

[0377] Specific operation: The user inputs the content of the proposal for the meeting through the system interface.

[0378] Step 6:

[0379] The server receives the input suggestions and sends prompts to the generative AI model.

[0380] Input: The suggestion entered by the user.

[0381] Data processing: The server generates a prompt and sends it to the generative AI model (e.g., "Please enter your proposal for launching a new product. Please generate a proposal document.").

[0382] Specific operation: The server sends a prompt sentence to the generative AI model.

[0383] Step 7:

[0384] The server receives the analysis results returned by the generative AI model.

[0385] Input: The analysis results returned by the generative AI model.

[0386] Output: Submission materials based on the analysis results (e.g., "New product X market launch plan: next month. Marketing strategy: social media advertising, influencer marketing").

[0387] Specific operation: The server receives and confirms the analysis results returned by the generative AI model.

[0388] Step 8:

[0389] The server automatically generates a report document based on the analysis results and provides it to the user.

[0390] Input: Analysis results from a generative AI model.

[0391] Output: Auto-generated appeal documents.

[0392] Specific operation: The server automatically generates a report document based on the analysis results and provides it to the user.

[0393] In this way, the system uses a generative AI model based on user input to automatically generate email text and report documents, efficiently supporting business operations.

[0394] (Application example 3)

[0395] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0396] In conventional logistics centers, sending materials to potential suppliers and preparing documents for submission based on proposals made at meetings were done manually, which was time-consuming and labor-intensive. Furthermore, these tasks were prone to human error, hindering efficient business operations. Furthermore, it was difficult to prepare documents for submission that accurately reflected proposals made at meetings, and there was a need for faster decision-making.

[0397] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0398] In this invention, the server includes: means for sharing past performance and potential suppliers in advance from the agenda of a meeting notification; means for listing tasks from minutes of a meeting and automatically reflecting the information in standard documents; means for sending documents to potential suppliers (automatic email generation); means for automatically generating report documents from proposals; means for generating email documents from prompts using a generative AI model; means for generating report documents from prompts using a generative AI model; means for automatically generating and sending email documents to send to potential suppliers as an application installed on a smartphone; and means for analyzing proposals made at a meeting and automatically generating report documents. This automates the sending of documents and the creation of report documents at a logistics center, improving work efficiency and accuracy.

[0399] A "meeting notice agenda" is a document that includes information about a meeting, such as the schedule, agenda, and participants.

[0400] "Past performance" refers to data showing the results and outcomes of past operations and transactions.

[0401] "Prospective suppliers" are candidates for suppliers with which we may do business in the future.

[0402] "Minutes" are documents that record the contents of a meeting and the decisions made.

[0403] A "task" is an operation or activity that must be performed to achieve a specific purpose.

[0404] A "standard document" is a document that is created according to a specific form or format.

[0405] "Automatic email creation" is the process of automatically generating the body of an email based on specific content.

[0406] A "proposal" refers to presenting a solution or improvement plan for a specific problem or issue.

[0407] "Presentation materials" are reports or proposals to be submitted to higher-level managers or decision makers.

[0408] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate text or data.

[0409] A "prompt" is an instruction or question that is input into a generative AI model.

[0410] A "smartphone" is a multi-functional mobile device that can connect to the Internet and use applications in addition to the functions of a mobile phone.

[0411] An "application" is a software program that provides a particular function or service.

[0412] "Sending documents" refers to the act of sending specific documents or data to another person.

[0413] "Analysis" is the process of examining data or information in detail to understand its meaning and structure.

[0414] The system for implementing this invention automates the sending of documents and the creation of report documents at a logistics center. Specifically, it is implemented as follows using a server, a smartphone, and a generative AI model.

[0415] Hardware and Software Configuration

[0416] Server: A central management system for storing and processing data. The server manages data such as meeting notice agendas, past performance, potential suppliers, and meeting minutes.

[0417] Smartphone: A mobile device used by the logistics center manager. Applications for sending documents and preparing report documents are installed on the smartphone.

[0418] Generative AI model: An algorithm that uses OpenAI's API to generate email text and report documents from prompts.

[0419] Data processing and calculation

[0420] 1. Share the agenda in the meeting notice:

[0421] The server extracts information on past performance and potential suppliers from the meeting notification agenda and sends it to the smartphone, allowing users to check the necessary information before the meeting.

[0422] 2. Creating a task list from the minutes:

[0423] After the meeting, the server analyzes the minutes and creates a list of tasks. The tasks are automatically reflected in standard documents and sent to smartphones.

[0424] 3. Automatically generate and send emails:

[0425] The smartphone application uses a generative AI model to automatically generate emails to send to potential suppliers, which are then sent directly from the smartphone.

[0426] 4. Automatic generation of submission materials:

[0427] The server analyzes the content of proposals made during meetings and automatically generates presentation materials using a generative AI model. The generated presentation materials are then sent to the user's smartphone, where they can be reviewed and revised.

[0428] Specific examples

[0429] Email generation prompt:

[0430] Generate email content to send to potential new suppliers.

[0431] Prompt for generating escalation documents:

[0432] Please prepare your appeal based on the following meeting notes:

[0433] Proposals made at the meeting: We discussed the introduction of a new logistics system.

[0434] In this way, the sending of documents and the preparation of report documents at the logistics center are automated, resulting in improved efficiency and accuracy of operations.

[0435] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0436] Step 1:

[0437] The server receives the agenda of the meeting notice and extracts information on past performance and potential suppliers.

[0438] Input: Meeting Notice Agenda

[0439] Data processing: Analyze past performance and information on potential suppliers from the agenda and extract the necessary data.

[0440] Output: Past performance and supplier candidate information

[0441] Step 2:

[0442] The server sends the extracted information on past performance and potential suppliers to the smartphone.

[0443] Input: Past performance and supplier candidate information

[0444] Data calculation: Converts the extracted information into a format for sending to a smartphone.

[0445] Output: Past performance and supplier candidate information sent to your smartphone

[0446] Step 3:

[0447] The user checks the information on past performance and potential suppliers received on their smartphone.

[0448] Input: Past performance and supplier candidate information sent to your smartphone

[0449] Data processing: The user checks the information and makes corrections or additions as necessary.

[0450] Output: Checked and corrected information

[0451] Step 4:

[0452] The server receives the minutes after the meeting and lists the tasks.

[0453] Input: Post-meeting minutes

[0454] Data processing: Analyze minutes and list tasks.

[0455] Output: List of tasks

[0456] Step 5:

[0457] The server automatically reflects the listed tasks in a standard document and sends it to the smartphone.

[0458] Input: Listed tasks

[0459] Data calculation: The task is reflected in standard documents and converted into a format for sending to a smartphone.

[0460] Output: Standardized documents sent to your smartphone

[0461] Step 6:

[0462] The smartphone application uses a generative AI model to automatically generate email text for sending materials to potential suppliers.

[0463] Input: Prompt text "Generate the content of an email to send to new potential suppliers."

[0464] Data calculation: Using a generative AI model, email text is generated from the prompt.

[0465] Output: Generated email text

[0466] Step 7:

[0467] The smartphone application sends the generated email text to potential suppliers.

[0468] Input: Generated email text

[0469] Data calculation: Convert the email text into a format for sending and send it.

[0470] Output: Email sent to potential suppliers

[0471] Step 8:

[0472] The server analyzes the proposals made at the meeting and automatically generates presentation materials using a generative AI model.

[0473] Input: Proposal made at the meeting

[0474] Data calculation: Using a generative AI model, a report document is generated from the proposal content.

[0475] Output: Generated petition documents

[0476] Step 9:

[0477] The server sends the generated statement materials to the smartphone.

[0478] Input: Generated petition documents

[0479] Data processing: Converts submitted documents into a format suitable for sending to a smartphone.

[0480] Output: Appeal documents sent to smartphone

[0481] Step 10:

[0482] The user checks the submitted documents received on their smartphone and makes corrections or additions as necessary.

[0483] Input: Appeal documents sent to smartphone

[0484] Data processing: The user checks the materials and makes corrections or additions as necessary.

[0485] Output: Confirmed and corrected petition documents

[0486] Furthermore, an emotion engine that estimates the user's emotion may be combined. The logic unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0487] "Example 1"

[0488] One embodiment of the present invention combines a system that shares information such as past performance and potential suppliers in advance from the agenda of a meeting notification, creates a task list from minutes, and automatically reflects the information in standard documents with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's emotions from their tone of voice, facial expressions, and word choice, and feeds the results back into the system. For example, if the engine detects anger or frustration from the tone of a speaker's voice during a meeting, it communicates this information to the system, which then reflects it in the minutes. This allows the system to grasp the atmosphere of the meeting and the emotional state of the participants, enabling more effective meeting management and follow-up afterwards.

[0489] "Example 2"

[0490] Another embodiment of the present invention is a system that combines an emotion engine with pre-shared information about potential suppliers, previously submitted satisfaction survey materials, and trends. In this system, the emotion engine analyzes user responses and adjusts the content of the shared satisfaction survey materials and trends based on the results. For example, if the system senses that a user is particularly interested in a particular trend, it adjusts the system to share more information related to that trend. This enables information sharing tailored to the user's interests and concerns, improving the effectiveness of meetings.

[0491] "Example 3"

[0492] Yet another embodiment of the present invention is a system that includes a means for sending documents to potential suppliers and a means for automatically generating report documents from proposals, and further combines an emotion engine. In this system, the emotion engine analyzes the user's emotions and automatically generates email text for sending the documents based on the results. For example, if the user feels favorably toward a particular potential supplier, an email text that reflects that emotion is generated. Furthermore, the user's emotions are taken into consideration when automatically generating report documents from proposals. This enables effective communication that reflects the user's emotions.

[0493] The processing flow of each embodiment will be described below.

[0494] "Example 1"

[0495] Step 1: Share past performance and potential suppliers in advance from the agenda in the meeting notice.

[0496] Step 2: The emotion engine analyzes the user's emotions based on their tone of voice, facial expressions, choice of words, etc.

[0497] Step 3: The analysis results are fed back into the system and the information is reflected in the minutes.

[0498] "Example 2"

[0499] Step 1: Share information about potential suppliers, past satisfaction surveys, and trends in advance. Step 2: The emotion engine analyzes user responses.

[0500] Step 3: Based on the analysis results, adjust the content of the satisfaction survey materials and trend sharing.

[0501] "Example 3"

[0502] Step 1: Prepare a system that includes a means of sending documents to potential suppliers and a means of automatically generating proposal documents from proposals.

[0503] Step 2: The emotion engine analyzes the user's emotions.

[0504] Step 3: Automatically generate email text to send documents based on the analysis results.

[0505] Step 4: User sentiment is also taken into consideration when automatically generating presentation materials from proposals.

[0506] Example 1

[0507] Next, a description will be given of Example 1 of Form 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."

[0508] In conventional meeting management systems, information sharing on past performance and potential suppliers based on the agenda of meeting notifications is done manually, resulting in inefficiency and occasional information leaks and delays. Furthermore, the process of creating a task list from minutes and incorporating it into standard materials is often done manually, which is time-consuming, labor-intensive, and prone to errors. Furthermore, there is no way to grasp the emotions of participants during a meeting, making it impossible to properly reflect the atmosphere of the meeting or the emotional state of participants. To solve these problems, the present invention aims to improve meeting efficiency and the accuracy of information sharing.

[0509] 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.

[0510] In this invention, the server includes a means for sharing information such as past performance and potential suppliers in advance from the agenda of the meeting notification, a means for listing tasks from the minutes and automatically reflecting the information in standard documents, and a means for analyzing user emotions and reflecting the results in the minutes, thereby improving the efficiency of meetings and the accuracy of information sharing.

[0511] A "meeting notice" is a notice that includes information such as the date, time, location, participants, and agenda of a meeting.

[0512] An "agenda" is a list of topics or topics to be discussed at a meeting.

[0513] "Past performance" refers to the records and results of past meetings and transactions.

[0514] A "potential supplier" is a potential supplier of goods or services.

[0515] "Minutes" are documents that record the contents of a meeting and the decisions made.

[0516] A "task" is an operation or activity that must be performed to achieve a specific purpose.

[0517] "Standard documents" are documents or tables that are created according to a specific format.

[0518] "User emotion" is information indicating the emotional state of the conference participants.

[0519] "Emotion analysis" is the process of identifying emotions from a user's tone of voice, facial expressions, etc.

[0520] "Information sharing" refers to the transmission of specific information between multiple people or systems.

[0521] "Automatic generation" refers to the system automatically creating documents or data without manual intervention.

[0522] This invention is a system that combines agenda analysis of meeting notices, task list creation of minutes, and user emotion analysis. A specific embodiment of this system will be described below.

[0523] Meeting notification agenda analysis and information sharing

[0524] When the server receives a meeting notification, it first analyzes its contents. This analysis is performed using natural language processing (NLP) technology. Specifically, it uses the Google Cloud Natural Language API. Based on the analysis results, related information such as past meeting records and potential suppliers is retrieved from a database (e.g., a relational database management system). The retrieved information is shared with the meeting participants in advance. This information can be shared via email or a dedicated meeting management application (e.g., an online meeting tool).

[0525] Examples:

[0526] If the agenda of the meeting notice includes "Supplier selection for new product," the server retrieves the minutes of past supplier selection meetings and transaction records from the database and shares them with the participants.

[0527] Example prompt sentence:

[0528] Please obtain past conference and transaction records related to "Supplier selection for new products" and share them with participants.

[0529] Analysis of minutes and task list creation

[0530] After the meeting, the server receives the minutes and analyzes their contents. Natural language processing technology is used to extract tasks from the minutes. The extracted tasks are automatically listed and reflected in standardized documents (e.g., spreadsheet software).

[0531] Examples:

[0532] If the minutes include a statement such as "conduct market research before the next meeting," the server will list this task and add it to a spreadsheet.

[0533] Example prompt sentence:

[0534] Extract tasks from the minutes and list them in a standard document.

[0535] Emotional analysis of users using an emotion engine

[0536] Devices (e.g., participants' computers or mobile devices) capture users' tone of voice and facial expressions in real time during the meeting. This data is sent to an emotion engine (e.g., an emotion analysis API) to analyze the user's emotions. The analysis results are fed back to the server and reflected in the minutes.

[0537] Examples:

[0538] If anger is sensed from the tone of a speaker's voice during a meeting, that information will be reflected in the minutes as "Speaker A is angry."

[0539] Example prompt sentence:

[0540] Analyze the emotions of speakers during meetings from their tone of voice and reflect the results in the minutes.

[0541] summary

[0542] This system combines agenda analysis of meeting notifications, task list creation of meeting minutes, and user sentiment analysis to achieve more effective meeting management and follow-up. Specific technologies used include the Google Cloud Natural Language API, a relational database management system, an online meeting tool, and a sentiment analysis API.

[0543] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0544] Step 1:

[0545] Receive meeting notifications

[0546] The server receives meeting notifications. The input includes the agenda, date, time, location, and participant information. The output is to store the received meeting notification data. Specifically, the server retrieves meeting notifications from emails and conference management applications and stores them in a database.

[0547] Step 2:

[0548] Agenda Analysis

[0549] The server parses the agenda of the received meeting notification. The input contains the agenda of the meeting notification. The output contains the parsed topics and keywords. Specifically, the server uses the Google Cloud Natural Language API to parse the agenda content, classify it by topic, and extract related keywords.

[0550] Step 3:

[0551] Retrieving information from a database

[0552] The server retrieves related information, such as past conference records and potential suppliers, from a database based on the extracted keywords. The input includes the analyzed keywords. The output is the related information. Specifically, the server queries a relational database management system to retrieve information on past conference records and potential suppliers.

[0553] Step 4:

[0554] Information Sharing

[0555] The server shares the acquired information with the conference participants in advance. The acquired related information is included as input. The shared information is obtained as output. As a specific operation, the server sends the information to the conference participants using email or an online conference tool.

[0556] Step 5:

[0557] Receiving minutes

[0558] The server receives the minutes after the meeting ends. The minutes include the contents of the minutes as input. The received minutes data is saved as output. Specifically, the server retrieves the minutes from email or the meeting management application and saves them in a database.

[0559] Step 6:

[0560] Analysis of meeting minutes

[0561] The server parses the received minutes. The input contains the contents of the minutes. The output contains extracted tasks. Specifically, the server uses the Google Cloud Natural Language API to parse the contents of the minutes and extract tasks.

[0562] Step 7:

[0563] Generate a task list

[0564] The server lists the extracted tasks. The extracted tasks are included as input. The generated task list is obtained as output. Specifically, the server uses spreadsheet software to reflect the task list in a standard document.

[0565] Step 8:

[0566] Capturing Emotional Data

[0567] The device captures the user's voice tone and facial expressions in real time during a meeting. The input includes the user's voice tone and facial expression data. The output is captured emotion data. Specifically, the device uses a microphone and a camera to capture the user's voice tone and facial expression.

[0568] Step 9:

[0569] Emotion Analysis

[0570] The server uses an emotion engine to analyze the user's emotions from the captured data. The input includes the captured emotion data. The output is the analyzed emotion information. Specifically, the server uses an emotion analysis API to analyze the user's emotions.

[0571] Step 10:

[0572] Emotional information feedback

[0573] The server reflects the analysis results in the minutes. The input contains the analyzed emotional information. The output is minutes that reflect the emotional information. Specifically, the server adds emotional information such as "Speaker A is feeling angry" to the minutes.

[0574] (Application example 1)

[0575] Next, a description will be given of Application Example 1 of Embodiment 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."

[0576] Conventional meeting support systems require a great deal of time and effort to prepare for meetings and create minutes, and they also face the challenge of making it difficult to grasp the emotional state of participants during meetings. Furthermore, they lacked a means to efficiently share the contents of meetings and conduct follow-up afterward. This can lead to meetings being less effective and the quality of decision-making declining.

[0577] 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.

[0578] In this invention, the server includes a means for sharing information such as past performance and potential suppliers (taking into account business transaction records) in advance from the agenda of the meeting notification, a means for creating a task list from the minutes and automatically reflecting the information in standard documents, and a means for analyzing the user's tone of voice and facial expression to grasp their emotional state and reflect this in the minutes. This makes it possible to automate everything from meeting preparation to creating minutes and emotional analysis, enabling efficient meeting management and high-quality decision-making.

[0579] A "meeting notice agenda" is a document that includes information such as the purpose and agenda of a meeting, the participants, date, time, and location.

[0580] "Past performance" refers to data that shows the results and outcomes of past work and projects.

[0581] A "potential supplier" is a list of potential suppliers of a particular product or service.

[0582] "Business transaction performance" is data showing the history of past business activities and transactions.

[0583] "Means for pre-sharing" refers to a method or system for providing participants with necessary information before the meeting.

[0584] Minutes are a document that records what was discussed and what decisions were made during a meeting.

[0585] A "means for listing tasks" is a method or system for organizing tasks extracted from minutes in a list format.

[0586] A "standard document" is a document created according to a specific form or format.

[0587] "Means for automatically reflecting information" refers to a method or system for automatically incorporating specific data into a document or system.

[0588] "Tone of a user's voice" is a feature of speech that indicates the speaker's emotions and intentions.

[0589] "Means for analyzing facial expressions" refers to a method or system that uses a camera or sensor to analyze a user's facial expressions.

[0590] A "means for grasping emotional state" is a method or system for recognizing and understanding a user's emotions from their tone of voice and facial expressions.

[0591] The system for implementing this invention has the functions of sharing past performance and potential suppliers in advance from the agenda of the meeting notice, creating a task list from the minutes, and automatically reflecting the information in standard documents.It also includes the functions of analyzing the user's tone of voice and facial expression to understand their emotional state and reflect it in the minutes.

[0592] System configuration

[0593] The system consists of the following main components:

[0594] 1. Server: Includes a database, a speech recognition engine, a sentiment analysis engine, and a document generation engine.

[0595] 2. Terminal: The device used by the meeting participants (smartphone, tablet, PC, etc.).

[0596] 3. User: A participant in a meeting.

[0597] Hardware and software used

[0598] Speech Recognition Engine: Uses the speech_recognition library to convert meeting audio to text.

[0599] Emotion Analysis Engine: Uses the cv2 and emotion_recognition libraries to analyze the facial expressions of participants in a meeting to understand their emotional state.

[0600] Database: A database for storing information on past performance and potential suppliers.

[0601] Document generation engine: Use the DocumentGenerator class to automatically create meeting minutes and task lists.

[0602] Processing flow

[0603] 1. Meeting preparation: The server analyzes the agenda in the meeting notification and retrieves information on past performance and potential suppliers from the database. This information is shared in advance with the terminals of the meeting participants.

[0604] 2. Minute Creation: During the meeting, the server uses a speech recognition engine to convert the meeting speech into text and create minutes in real time.

[0605] 3. Emotion Analysis: The server uses an emotion analysis engine to analyze the tone of voice and facial expressions of participants during the meeting to understand their emotional state, which is reflected in the minutes.

[0606] 4. Task list generation: The server extracts tasks from the minutes and automatically generates a task list. The generated task list is reflected in standard materials and shared with meeting participants.

[0607] Specific examples

[0608] For example, based on the agenda item "improving the production line," the server can retrieve past production performance data and share it with meeting participants in advance. If participants express dissatisfaction during the meeting, that information can be reflected in the minutes to help with subsequent follow-up.

[0609] Prompt Sentence Examples

[0610] Based on the agenda of "production line improvement," obtain past production performance data and share it with meeting participants in advance. Also, analyze participants' tone of voice and facial expressions during the meeting to understand their emotional state and reflect this in the minutes.

[0611] In this way, the system can automatically handle everything from meeting preparation to minutes creation and sentiment analysis, enabling efficient meeting management and high-quality decision-making.

[0612] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0613] Step 1:

[0614] The server receives the agenda for the meeting notification. As input, it receives the agenda, which includes information such as the purpose and topic of the meeting, participants, date and time, and location. The server analyzes the agenda and retrieves information on past performance and potential suppliers from the database. As output, it generates the retrieved information on past performance and potential suppliers.

[0615] Step 2:

[0616] The server shares the acquired information on past performance and potential suppliers with the terminals of the conference participants in advance. It receives information on past performance and potential suppliers as input and sends the information to the terminals of the conference participants as output. Specific operations include distributing the information using email or a notification system.

[0617] Step 3:

[0618] During the meeting, the server uses a speech recognition engine to convert the meeting audio into text. It receives the meeting audio data as input and generates a text transcript as output. Specifically, it uses the speech_recognition library to analyze the audio data and convert it into text.

[0619] Step 4:

[0620] The server uses an emotion analysis engine to analyze the tone of voice and facial expressions of participants in a meeting to understand their emotional state. It receives audio and video data from the meeting as input and generates emotional state data as output. Specifically, it uses the cv2 and emotion_recognition libraries to analyze audio and video and recognize emotional states.

[0621] Step 5:

[0622] The server reflects the emotional state data in the minutes. As input, it receives text minutes and emotional state data, and as output, it generates minutes with added emotional information. Specifically, it adds a comment about the emotional state to the relevant part of the minutes.

[0623] Step 6:

[0624] The server extracts tasks from the minutes and automatically generates a task list. It receives the minutes in text format as input and generates a task list as output. Specifically, it analyzes the text of the minutes and extracts tasks based on keywords such as "task" and "action item."

[0625] Step 7:

[0626] The server reflects the generated task list in a standard document and shares it with the meeting participants. It receives the task list as input, generates standard documents as output, and sends them to the meeting participants' devices. Specifically, it uses the DocumentGenerator class to create standard documents and distributes them via email or a notification system.

[0627] Example 2

[0628] Next, a description will be given of Example 2 of Form 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."

[0629] Conventional conference systems lacked a means to efficiently share necessary information before the meeting, which reduced the effectiveness of the meeting. Furthermore, there was no way to analyze participants' reactions in real time during the meeting and adjust the information based on the results, making it difficult to provide information tailored to the participants' interests.

[0630] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for sharing in advance information such as past performance and potential suppliers (taking into account business transaction performance, etc.) from the agenda of the meeting notification, a means for creating a task list from the minutes of the meeting and automatically reflecting the information in standardized materials, a means for acquiring information on potential suppliers from a database and sharing it with the meeting participants in advance, a means for acquiring materials from past satisfaction surveys and trend information from the database and sharing it with the meeting participants in advance, and a means for analyzing user reactions using a sentiment analysis engine and adjusting the information to be shared based on the results. This makes it possible to efficiently share necessary information before the meeting and adjust the information during the meeting based on the participants' reactions.

[0631] A "meeting notice agenda" is a document that details the purpose, agenda, participants, date, time, location, etc. of a meeting.

[0632] "Past performance" is data showing the results and history of past transactions and projects.

[0633] "Prospective Suppliers" is a list of suppliers with which we may do business in the future.

[0634] "Business transaction performance" is data showing the history of past business activities and transactions.

[0635] Minutes are a document that records what was discussed and what decisions were made during a meeting.

[0636] "Listing tasks" means organizing specific work items in a list format based on the minutes.

[0637] A "standard document" is a document or report prepared according to a specific form or format.

[0638] A "database" is a system for efficiently managing, searching, and updating data.

[0639] A "satisfaction survey" is a questionnaire or survey used to assess customer or user satisfaction.

[0640] "Trend information" is data that shows current market trends and the latest industry information.

[0641] An "emotion analysis engine" is software that analyzes a user's emotions from facial expressions, tone of voice, etc.

[0642] "User reactions" refer to changes in interest, concern, and emotions shown by the user during the meeting.

[0643] "Adjusting information" means changing the content and amount of information provided based on the user's response.

[0644] The present invention is a system for improving the effectiveness of a conference, in which the elements of a server, a terminal, and a user work in cooperation with each other. A specific embodiment of this system will be described below.

[0645] Hardware and software used

[0646] Server: Database server, sentiment analysis engine

[0647] Device: PC or tablet of meeting participants

[0648] Software: Database management systems (e.g., MySQL), sentiment analysis software (e.g., IBM Watson)

[0649] System Overview

[0650] This system shares information on past performance and potential suppliers from the agenda in meeting notifications in advance, creates a list of tasks from the minutes, and automatically reflects the information in standard documents. It also retrieves information on potential suppliers, past satisfaction surveys, and trend information from a database and shares them with meeting participants in advance. It also uses a sentiment analysis engine to analyze user reactions and adjust the information to be shared based on the results.

[0651] Data Acquisition

[0652] The server retrieves information about potential suppliers from the database. Specifically, the server connects to the MySQL database and executes an SQL query to retrieve information about potential suppliers. For example, it executes the query SELECT FROM suppliers WHERE status='active';

[0653] Next, the server retrieves past satisfaction survey data from the database, again by executing an SQL query, such as SELECT FROM satisfaction_surveys WHERE date > '2022-01-01';

[0654] Additionally, the server retrieves the latest trend information from the database, for example by executing the query SELECT FROM trends ORDER BY date DESC LIMIT 10;

[0655] Information Sharing

[0656] The server sends the acquired information to the terminals of the conference participants. Specifically, the server converts the acquired information into JSON format and sends it to the terminals of the conference participants. For example, the server sends information about potential suppliers in the format {"suppliers": [{"name": "Supplier A", "status": "active"}, ...]}.

[0657] Past satisfaction survey materials should be sent in PDF format, for example, {"surveys": [{"title": "Survey 2022", "file": "survey_2022.pdf"}, ...]}.

[0658] Send the latest trend information in HTML format, for example, {"trends": [{"title": "Eco Products", "description": "Latest trends in eco-friendly products"}, ...]}.

[0659] Emotion analysis

[0660] During the meeting, the device collects the user's reactions. The device uses a camera and microphone to collect the user's facial expressions and tone of voice. The collected data is sent to the server in real time. For example, it is sent in the following format: {"user_reactions": [{"timestamp": "2023-10-01T10:00:00Z", "emotion": "interest"}, ...]}.

[0661] The server analyzes the user's reaction using a sentiment analysis engine, such as IBM Watson, and obtains the result {"emotion": "interest", "confidence": 0.95}.

[0662] Coordination of information

[0663] The server then adjusts the information it shares based on the results of the sentiment analysis. For example, if a user expresses interest in a particular trend, it retrieves additional information related to that trend. For example, it executes a query like SELECT FROM trends WHERE category='Eco Products';

[0664] The adjusted information is then sent back to the conference participants' devices, for example, in the format {"trends": [{"title": "Eco Products", "description": "Additional information on eco-friendly products"}, ...]}.

[0665] Examples of concrete examples and prompts

[0666] Examples:

[0667] If a conference attendee expresses interest in "new eco-product trends," the system will share additional up-to-date market data and success stories related to that trend.

[0668] Example prompt sentence:

[0669] "Please keep me up to date on new eco-product trends."

[0670] "Please share information that may be of interest to users based on past satisfaction survey results."

[0671] In this way, the system shares information tailored to the user's interests and concerns, improving the effectiveness of the meeting.

[0672] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0673] Step 1: Data Acquisition

[0674] Input: The server connects to the database and runs SQL queries to retrieve information about potential suppliers, past satisfaction surveys, and the latest trends.

[0675] What happens: The server connects to a MySQL database and executes the following SQL query:

[0676] Potential supplier information: SELECT FROM suppliers WHERE status='active';

[0677] Satisfaction survey materials: SELECT FROM satisfaction_surveys WHERE date > '2022-01-01';

[0678] Trend information: SELECT FROM trends ORDER BY date DESC LIMIT 10;

[0679] Output: The server stores the acquired data in its internal memory.

[0680] Step 2: Share information

[0681] Input: Based on the data obtained in step 1, the server prepares the information to be shared with the meeting participants.

[0682] Specific operation: The server converts the acquired data into JSON or PDF format and sends it to the terminals of the meeting participants.

[0683] Potential Supplier Information: {"suppliers": [{"name": "Supplier A", "status": "active"}, ...]}

[0684] Satisfaction survey materials: {"surveys": [{"title": "Survey 2022", "file": "survey_2022.pdf"}, ...]}

[0685] Trends: {"trends": [{"title": "Eco Products", "description": "Latest trends in eco-friendly products"}, ...]}

[0686] Output: Information is sent to and displayed on the conference participants' devices.

[0687] Step 3: Sentiment Analysis

[0688] Input: The device collects data using a camera and microphone to gather user responses during the meeting.

[0689] Specific operation: The device collects the user's facial expressions and tone of voice in real time and sends the data to the server.

[0690] Example of collected data: {"user_reactions": [{"timestamp": "2023-10-01T10:00:00Z", "emotion": "interest"}, ...]}

[0691] Output: User response data is sent to the server.

[0692] Step 4: Perform sentiment analysis

[0693] Input: The server runs the sentiment analysis engine based on the user response data received in step 3.

[0694] Specific operation: The server uses an emotion analysis engine (e.g., IBM Watson) to analyze the user's reaction data.

[0695] Example of analysis result: {"emotion": "interest", "confidence": 0.95}

[0696] Output: The server stores the analysis results in its internal memory.

[0697] Step 5: Adjust the information

[0698] Input: The server adjusts the information it shares based on the sentiment analysis results from step 4.

[0699] What happens: Based on the analysis results, the server executes SQL queries to retrieve additional information related to the trends the user has shown interest in.

[0700] Get additional information: SELECT FROM trends WHERE category='Eco Products';

[0701] Output: The server stores the additional information it has obtained in its internal memory.

[0702] Step 6: Re-share adjusted information

[0703] Input: Based on the information adjusted in step 5, the server again prepares the information to be shared with the conference participants.

[0704] Specific operation: The server converts the adjusted information into JSON or HTML format and sends it to the terminals of the meeting participants.

[0705] Adjusted information: {"trends": [{"title": "Eco Products", "description": "Additional information on eco-friendly products"}, ...]}

[0706] Output: The adjusted information is sent to the meeting participants' devices and displayed.

[0707] In this way, the system shares information tailored to the user's interests and concerns, improving the effectiveness of the meeting.

[0708] (Application example 2)

[0709] Next, a description will be given of Application Example 2 of Form 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."

[0710] Conventional conference systems lacked a means to efficiently share necessary information before the meeting, reducing the effectiveness of the meeting. Furthermore, there was no way to provide appropriate information based on the customer's interests and concerns, which hindered the quality of customer service. Furthermore, there was a lack of technology to analyze customer emotions and adjust the content of information shared, making it difficult to increase customer satisfaction.

[0711] 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.

[0712] In this invention, the server includes a means for sharing information such as past performance and potential suppliers (taking into account business transaction records) in advance from the agenda of the meeting notification, a means for creating a task list from the minutes and automatically reflecting the information in standard documents, a means for analyzing user reactions using a sentiment analysis engine and adjusting the content of the information to be shared based on the results, and a means for providing trend information and satisfaction survey materials based on customer interests and concerns. This makes it possible to improve the effectiveness of meetings, enhance the quality of customer service, and increase customer satisfaction.

[0713] A "meeting notice agenda" is a document that includes information such as the content, subject, participants, date and time of a meeting.

[0714] "Past performance" refers to data showing the results and outcomes of past transactions and business operations.

[0715] "Prospective Suppliers" is a list of suppliers and vendors with which you may do business in the future.

[0716] "Business transaction performance" is data showing the history of past business activities and transactions.

[0717] A "minutes" is a document that records the contents of a meeting, decisions made, statements made, etc.

[0718] "Listing tasks" means organizing the work and tasks decided in the meeting in a list format.

[0719] A "standard document" is a document or report prepared according to a specific form or format.

[0720] An "emotion analysis engine" is software that analyzes a user's facial expressions and behavior to identify their emotions.

[0721] "User reaction" refers to the user's facial expressions, actions, comments, and other reactions.

[0722] "Information sharing content" refers to the content of information shared during meetings and customer interactions.

[0723] "Customer interest" refers to the degree of interest or concern a customer has in a particular product or service.

[0724] "Trend information" is data that shows current market and industry trends and fads.

[0725] "Satisfaction survey materials" are documents containing data and survey results collected to assess customer satisfaction.

[0726] A system for implementing this invention includes means for sharing in advance past performance and potential suppliers (taking into account sales transaction performance, etc.) from the agenda of a meeting notification, means for listing tasks from minutes of a meeting and automatically reflecting the information in standard documents, means for analyzing user reactions using an emotion analysis engine and adjusting the content of information sharing based on the results, and means for providing trend information and satisfaction survey materials based on customer interests and concerns.

[0727] Hardware and software used

[0728] Hardware: Smart glasses (with camera)

[0729] Software: OpenCV (video analysis), EmotionEngine (emotion analysis), Database (information acquisition)

[0730] Data processing and calculation

[0731] Video acquisition

[0732] The smart glasses' camera captures real-time video footage, allowing it to capture customer expressions and behavior.

[0733] Emotion analysis

[0734] Using EmotionEngine, the company analyzes customer facial expressions from captured video to identify their interests. The emotion analysis engine is software that analyzes users' facial expressions and behavior to identify their emotions.

[0735] Information acquisition

[0736] Based on customer sentiment, trend information and past satisfaction survey data are retrieved from a database that stores past performance, potential suppliers, trend information, and satisfaction survey data.

[0737] Information provision

[0738] The acquired information is provided to staff to suggest the most suitable products and services to customers, thereby improving the quality of customer service and increasing customer satisfaction.

[0739] Specific examples

[0740] If a customer expresses interest in a new smartphone, the smart glasses will analyze that information and provide staff with information on past customer satisfaction surveys and the latest smartphone trends, allowing them to recommend the best smartphone for the customer.

[0741] Prompt Sentence Examples

[0742] Provide trending information about products that customers have shown interest in and past satisfaction surveys.

[0743] In this way, customer service can be more effectively provided in physical stores.

[0744] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0745] Step 1:

[0746] Real-time video footage is captured from the smart glasses camera.

[0747] Input: Smart glasses camera image

[0748] Output: Video data including customer facial expressions and behavior

[0749] How it works: The camera in the smart glasses captures the customer's face and movements and sends the video data to a processing system in real time.

[0750] Step 2:

[0751] Using EmotionEngine, the company analyzes customers' facial expressions from captured video footage to identify their interests and concerns.

[0752] Input: Video data

[0753] Output: Customer sentiment data (interests, concerns, etc.)

[0754] How it works: EmotionEngine analyzes video data and identifies emotions from the customer's facial expressions and behavior. For example, if a customer is smiling and looking at a product, it will determine that they are interested.

[0755] Step 3:

[0756] Based on customer sentiment, trend information and past satisfaction survey data are retrieved from the database.

[0757] Input: Customer sentiment data

[0758] Output: Trend information, satisfaction survey materials

[0759] Specific operation: The server accesses the database and searches for and retrieves trend information and satisfaction survey materials that correspond to the customer's emotional data. For example, if the customer is determined to be "interested," the server retrieves the latest related trend information and past satisfaction survey results.

[0760] Step 4:

[0761] The acquired information is provided to staff to suggest the most suitable products and services to customers.

[0762] Input: Trend information, satisfaction survey materials

[0763] Output: Information provided to staff, proposals to customers

[0764] Specific operation: Trend information and satisfaction survey data acquired by the server are displayed on the smart glasses' display, and staff members use this information to suggest optimal products and services to customers. For example, the latest smartphone trend information is displayed, and staff members introduce the smartphone to the customer.

[0765] Step 5:

[0766] Re-analyze customer reactions and adjust the information shared as necessary.

[0767] Input: New customer facial and behavior data

[0768] Output: Updated information shared

[0769] How it works: The smart glasses' camera captures the customer's facial expressions and behavior again, and the Emotion Engine analyzes the data. Based on the analysis results, the server updates the information sharing content and provides new information to staff. For example, if the customer shows further interest, it can provide additional trend information or related product information.

[0770] Example 3

[0771] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0772] With conventional meeting systems, it was difficult to share past performance and potential suppliers in advance from the meeting notification agenda, and there was a lack of means to list tasks from minutes and automatically reflect information in standard documents. Furthermore, the functionality for automatically sending documents to potential suppliers or automatically generating report documents from proposal content was also insufficient. Furthermore, effective information transmission was difficult because communication could not take user emotions into consideration. To solve these issues, a system with more advanced automation and that takes user emotions into consideration was needed.

[0773] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0774] In this invention, the server includes means for sharing past performance and potential suppliers in advance from the agenda of a meeting notification, means for listing tasks from minutes of the meeting and automatically reflecting the information in standard documents, means for automatically generating email text to send to potential suppliers, means for analyzing proposal content and automatically generating submission documents, means for analyzing user emotions and generating email text reflecting the results, and means for generating submission documents taking user emotions into consideration. This enables efficient meetings and effective information sharing, and realizes communication that takes user emotions into consideration.

[0775] A "meeting notice agenda" is a document containing detailed information such as the purpose, subject matter, participants, date, time, and location of a meeting.

[0776] "Past performance" refers to data that shows the results and outcomes of past work and projects.

[0777] "Prospective Suppliers" is a list of suppliers and vendors with which you may do business in the future.

[0778] Minutes are a document that records what was discussed and what decisions were made during a meeting.

[0779] "Listing tasks" means organizing specific work items extracted from the minutes in a list format.

[0780] "Standard documents" are documents created based on a specific format or template.

[0781] "Email text for sending materials" refers to the body of an email created for the purpose of sending specific materials.

[0782] A "proposal" is a detailed description of a new idea or plan presented in a meeting or presentation.

[0783] "Presentation documents" are reports or proposals submitted to higher-level managers or decision makers.

[0784] "User emotions" refer to the psychological state and feelings of an individual using a system.

[0785] An "emotion engine" is software that analyzes a user's emotions and responds appropriately based on the results.

[0786] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate text or data.

[0787] This invention is a system that shares past performance and potential suppliers in advance from the agenda of a meeting notification, lists tasks from minutes, and automatically reflects the information in standard documents. It also automatically generates emails to send to potential suppliers and analyzes proposal content to automatically generate proposal documents. It also includes a function to analyze user sentiment and generate emails and proposal documents that reflect the results.

[0788] Hardware and software used

[0789] Server: Database management system, generative AI model (e.g. GPT-4), emotion engine (e.g. Affectiva), speech recognition software (e.g. Google Speech-to-Text)

[0790] Terminal: User interface, voice recording function

[0791] User: Accesses and operates the system

[0792] Program processing explanation

[0793] Sending materials to potential suppliers

[0794] 1. A user logs into the system and selects potential suppliers.

[0795] 2. The device sends the user's selection information to the server.

[0796] 3. The server retrieves the materials to be sent to the selected supplier candidates from the database and updates them with the latest information.

[0797] 4. The server inputs the prompt text into the generative AI model and generates an email text for sending the materials.

[0798] Example prompt: "Generate an email to send a catalog of new products to potential suppliers."

[0799] 5. The server sends the generated email to the potential supplier's email address.

[0800] Examples:

[0801] The user selects "Supplier Candidate A."

[0802] The server retrieves "New Product Catalog.pdf" from the database and updates it with the latest information.

[0803] The server instructs the AI ​​model to generate an email that reads, "We have attached a catalog of our new products. Please take a look."

[0804] The server sends this email to the email address of "Supplier Candidate A."

[0805] Automatic generation of presentation materials from proposals

[0806] 1. A user makes a proposal in a meeting.

[0807] 2. The device records the audio of the meeting and sends it to the server.

[0808] 3. The server uses speech recognition software to convert the suggestions into text.

[0809] 4. The server inputs the prompt text into the generative AI model and generates a report document based on the proposal.

[0810] Example prompt: "Generate a presentation based on the new product introduction proposed in the meeting."

[0811] 5. The server stores the generated petition in the database and notifies the user.

[0812] Examples:

[0813] A user makes a "new product introduction proposal" at a meeting.

[0814] The device records the audio of the meeting and sends it to the server.

[0815] The server uses speech recognition software to convert the text to "Benefits and market analysis of new product introduction."

[0816] The server instructs the AI ​​model to generate a proposal document titled "Benefits and Market Analysis of New Product Introduction."

[0817] The server stores the submitted information in a database and notifies the user.

[0818] Use of emotion engine

[0819] 1. A user expresses feelings toward a potential supplier (e.g., positive feelings).

[0820] 2. The device collects the user's emotional data and sends it to the server.

[0821] 3. The server uses the emotion engine to analyze the user's emotions.

[0822] 4. Based on the analysis results, the server uses a generative AI model to generate email text that reflects emotions.

[0823] Example prompt: "Generate an email to send to potential suppliers with whom the user has positive feelings."

[0824] 5. The server analyzes the proposal and has the AI ​​model generate a report that takes into account the user's feelings.

[0825] Example prompt: "Generate escalation materials taking into account the user's positive sentiment."

[0826] Examples:

[0827] The user expresses favorable feelings toward "Supplier Candidate B."

[0828] The device collects the user's emotional data and sends it to the server.

[0829] The server uses an emotion engine to analyze the user's positive emotions.

[0830] The server instructs the AI ​​model to generate an email that reflects the sentiment, such as, "Thank you for your continued support. We have attached a catalog of our new products, so please take a look."

[0831] The server analyzes the proposal and generates "reporting materials that take into account the user's positive feelings."

[0832] In this way, the system analyzes the user's emotions and automatically generates email texts and report materials that reflect the analysis, thereby achieving effective communication. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[0833] Sending materials to potential suppliers

[0834] Step 1:

[0835] A user logs into the system and selects potential suppliers.

[0836] Input: User login information, supplier candidate selection information

[0837] Output: Information on selected potential suppliers

[0838] Specific operation: The user logs in to the system and selects "Supplier Candidate A" through the interface.

[0839] Step 2:

[0840] The terminal transmits the user's selection information to the server.

[0841] Input: Information on potential suppliers selected by the user

[0842] Output: Information of potential suppliers sent to the server

[0843] Specific operation: The terminal sends information about the selected "Supplier Candidate A" to the server.

[0844] Step 3:

[0845] The server retrieves the materials to be sent to the selected potential suppliers from the database and updates them with the latest information.

[0846] Input: Material information in the database, information on selected potential suppliers

[0847] Output: Updated documentation

[0848] Specific operation: The server retrieves "New Product Catalog.pdf" from the database and updates it with the latest information.

[0849] Step 4:

[0850] The server inputs a prompt into the generative AI model and generates an email message for sending the documents.

[0851] Input: prompt, generative AI model

[0852] Output: Auto-generated email text

[0853] Specific operation: The server inputs the prompt "Please generate an email message to send the new product catalog to potential suppliers" to the generation AI model, and generates the email message "We have attached the new product catalog. Please check it."

[0854] Step 5:

[0855] The server sends the generated email text to the email address of the potential supplier.

[0856] Input: Auto-generated email text, email address of potential supplier

[0857] Output: Email sent

[0858] Specific operation: The server sends the generated email text to the email address of "Supplier candidate A."

[0859] Automatic generation of presentation materials from proposals

[0860] Step 1:

[0861] A user makes a suggestion in a meeting.

[0862] Input: Proposal

[0863] Output: Proposals made at the meeting

[0864] Specific operation: A user makes a "proposal for introducing a new product" in a meeting.

[0865] Step 2:

[0866] The device records the audio of the meeting and sends it to the server.

[0867] Input: Meeting audio data

[0868] Output: Audio data sent to the server

[0869] Specific operation: The device records the audio of the meeting and sends it to the server.

[0870] Step 3:

[0871] The server uses speech recognition software to convert the suggestions into text.

[0872] Input: Voice data, voice recognition software

[0873] Output: Suggestions converted to text

[0874] What happens: The server uses speech recognition software to convert the text to "Benefits and market analysis of new product introduction."

[0875] Step 4:

[0876] The server inputs a prompt into the generative AI model and generates a report document based on the proposed content.

[0877] Input: prompt, generative AI model, and converted suggestions

[0878] Output: Auto-generated petition

[0879] Specific operation: The server inputs the prompt "Please generate a presentation document based on the new product introduction proposed in the meeting" to the generating AI model, and generates a presentation document titled "Benefits and market analysis of new product introduction."

[0880] Step 5:

[0881] The server stores the generated report data in a database and notifies the user.

[0882] Input: Auto-generated petition

[0883] Output: Escalation documents stored in database, notification to user

[0884] Specific operation: The server saves the generated application documents in the database and notifies the user.

[0885] Use of emotion engine

[0886] Step 1:

[0887] A user expresses a sentiment toward a potential supplier (e.g., positive sentiment).

[0888] Input: User sentiment

[0889] Output: Emotion data

[0890] Specific action: The user expresses positive feelings toward "Supplier Candidate B."

[0891] Step 2:

[0892] The device collects the user's emotional data and sends it to the server.

[0893] Input: User emotion data

[0894] Output: Emotion data sent to the server

[0895] Specific operation: The device collects the user's emotional data and sends it to the server.

[0896] Step 3:

[0897] The server uses an emotion engine to analyze the user's emotions.

[0898] Input: Emotion data, Emotion engine

[0899] Output: Parsed emotion results

[0900] Specific operation: The server uses the emotion engine to analyze the user's positive emotions.

[0901] Step 4:

[0902] Based on the analysis results, the server uses a generative AI model to generate email text that reflects emotions.

[0903] Input: Analyzed sentiment results, prompt, generative AI model

[0904] Output: Emotionally-driven email text

[0905] Specific operation: The server inputs the prompt text "Generate an email to send to potential suppliers about whom the user has favorable feelings" into the generation AI model, and generates an email text that reflects the user's feelings, such as "Thank you for your continued support. We have attached a catalog of our new products, so please take a look."

[0906] Step 5:

[0907] The server analyzes the proposal and has the AI ​​model generate a report that takes the user's feelings into account.

[0908] Input: Proposal content, analyzed sentiment results, prompt, generative AI model

[0909] Output: Emotionally-informed escalation materials

[0910] Specific operation: The server inputs the prompt sentence "Please generate the petition documents taking into consideration the user's positive feelings" into the generation AI model, and generates "petition documents taking into consideration the user's positive feelings."

[0911] (Application example 3)

[0912] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0913] With conventional conference systems, it was difficult to share meeting agendas, past performance, and information on potential suppliers in advance, which led to problems with reduced meeting efficiency. Additionally, there was a lack of a way to automatically create a task list from minutes and reflect the information in standard documents, which required a lot of manual work, taking time and effort. Furthermore, when sending documents to potential suppliers or automatically generating documents for escalation from proposals, the system was unable to take user emotions into account, making effective communication difficult.

[0914] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0915] In this invention, the server includes means for sharing information such as past performance and potential suppliers in advance from the agenda of a meeting notification, means for listing tasks from minutes and automatically reflecting the information in standard documents, means for sending documents to potential suppliers (automatic email creation), means for automatically generating report documents from proposals, means for analyzing user emotions using an emotion engine and generating email documents reflecting the results, and means for generating report documents taking user emotions into consideration using the emotion engine. This improves meeting efficiency, reduces manual work, and enables effective communication that reflects user emotions.

[0916] A "meeting notice agenda" is a document that contains information such as the purpose, subject, participants, date, time, and location of a meeting.

[0917] "Past performance" refers to data that shows the results and outcomes of past work and projects.

[0918] "Prospective Suppliers" refers to suppliers and vendors with whom we may do business in the future.

[0919] A "minutes" is a document that records the contents of a meeting, decisions made, statements made, etc.

[0920] "Listing tasks" refers to organizing and displaying specific tasks or work in a list format.

[0921] "Standard documents" refer to documents or reports prepared according to a specific format.

[0922] "Sending documents" refers to the act of sending specific documents or data to another person.

[0923] "Automatic email generation" is the process of automatically generating email content based on specific conditions and data.

[0924] "Proposal" refers to the act of presenting a solution or idea to a specific problem or issue.

[0925] "Presentation documents" refer to reports and proposals submitted to higher-level managers or organizations.

[0926] An "emotion engine" is software or algorithm that analyzes a user's emotions and executes specific actions based on the results.

[0927] "User emotion" refers to a user's psychological reaction or feelings to a particular situation or piece of information.

[0928] "Effective communication" refers to communication in which information is conveyed accurately and the recipient is able to understand and respond appropriately to that information.

[0929] A system for carrying out this invention includes a server, a user terminal, and an emotion engine. The server includes means for sharing in advance information such as past performance and potential suppliers from the agenda of a meeting notification, means for listing tasks from minutes of a meeting and automatically reflecting the information in standard documents, means for sending documents to potential suppliers (automatically creating email text), means for automatically generating report documents from proposals, means for analyzing user emotions using the emotion engine and generating email text that reflects the results, and means for generating report documents that take user emotions into consideration using the emotion engine.

[0930] System configuration

[0931] 1. Server:

[0932] The server analyzes the agenda of the meeting notification and shares information on past performance and potential suppliers in advance.

[0933] The server analyzes the minutes, lists tasks, and automatically reflects the information in standard documents.

[0934] The server automatically generates and transmits an email message for sending materials to potential suppliers.

[0935] The server analyzes the proposal and automatically generates the materials for submission.

[0936] The server uses an emotion engine to analyze the user's emotions and generates email text that reflects the results.

[0937] The server uses an emotion engine to generate submission materials that take the user's emotions into account.

[0938] 2. User Device:

[0939] The user terminal inputs the agenda and minutes of the meeting notice and transmits them to the server.

[0940] The user terminal receives and displays the documents and email messages sent from the server.

[0941] The user terminal collects the user's emotion data and transmits it to the server.

[0942] 3. Emotion Engine:

[0943] The emotion engine analyzes the user's emotion data to determine their current emotional state.

[0944] The emotion engine sends the analysis results to the server and reflects them in the generation of email text and report documents.

[0945] Hardware and software used

[0946] Hardware:

[0947] Server: Server equipment equipped with a high-performance processor and large memory capacity.

[0948] User device: A device such as a computer, smartphone, or tablet.

[0949] software:

[0950] Emotion engine: Software for analyzing user emotions.

[0951] Proposal analysis software: Software for analyzing proposal content and generating submission materials.

[0952] Email auto-generation software: Software for automatically generating emails to potential suppliers.

[0953] Specific examples

[0954] For example, when a user sends a meeting notification agenda to the server, the server analyzes past performance and information on potential suppliers and shares it in advance. When the meeting ends, the user sends the minutes to the server, which then lists tasks and automatically reflects the information in standard documents. Furthermore, the server automatically generates email text to send to potential suppliers, analyzing the user's emotions using an emotion engine and generating email text that reflects the results. The emotion engine also takes the user's emotions into account when analyzing proposal content and automatically generating submission documents.

[0955] Prompt Sentence Examples

[0956] "Analyze the user's emotional data and generate emails to potential suppliers. If the user's emotional state is positive, reflect that emotional state in the email. Also, analyze the proposal content and generate proposal documents. If the user's emotional state is positive, reflect that emotional state in the proposal documents."

[0957] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0958] Step 1:

[0959] A user inputs an agenda for a meeting notice into a terminal and transmits it to a server.

[0960] Input: Meeting Notice Agenda

[0961] Output: Agenda data sent to the server

[0962] Specific operation: The user inputs the agenda for the meeting notification into the input form on the terminal and presses the send button. The terminal then sends the agenda data to the server.

[0963] Step 2:

[0964] The server analyzes the agenda data and shares information on past performance and potential suppliers in advance.

[0965] Input: Agenda data

[0966] Output: Past performance and supplier candidate information

[0967] Specific operation: The server analyzes the agenda data, retrieves related past performance and information on potential suppliers from the database, and sends it to the user's terminal.

[0968] Step 3:

[0969] A user holds a conference, inputs the minutes into a terminal, and transmits them to a server.

[0970] Input: Minutes data

[0971] Output: Meeting minutes data sent to the server

[0972] Specific operation: After the meeting ends, the user enters the minutes into the input form on the terminal and presses the send button. The terminal then sends the minutes data to the server.

[0973] Step 4:

[0974] The server analyzes the minutes data, lists tasks, and automatically reflects the information in standard documents.

[0975] Input: Minutes data

[0976] Output: Task list and boilerplate

[0977] Specific operation: The server analyzes the minutes data, extracts and lists tasks, and automatically updates the information according to the standard document format.

[0978] Step 5:

[0979] The server automatically generates and sends an email message to send materials to potential suppliers.

[0980] Input: information on potential suppliers, document data

[0981] Output: Auto-generated email text, sent email

[0982] Specific operation: The server automatically generates email messages based on the supplier candidate's information and document data, and sends the generated email messages to the supplier candidate.

[0983] Step 6:

[0984] The server analyzes the proposal and automatically generates the presentation materials.

[0985] Input: Proposal content data

[0986] Output: Automatically generated petition documents

[0987] Specific operation: The server analyzes the proposal content data and automatically reflects the information according to the format of the submission document.

[0988] Step 7:

[0989] The server uses an emotion engine to analyze the user's emotions and generates email text that reflects the results.

[0990] Input: User emotion data

[0991] Output: Emotionally-driven email text

[0992] Specific operation: The server uses an emotion engine to analyze the user's emotion data and generate email text that reflects the emotion.

[0993] Step 8:

[0994] The server uses an emotion engine to generate submission materials that take the user's emotions into consideration.

[0995] Input: User emotion data, suggestion content data

[0996] Output: Emotional report

[0997] Specific operation: The server uses an emotion engine to analyze the user's emotion data, combines it with the proposal content data, and generates a proposal document that reflects the emotion.

[0998] 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.

[0999] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[1000] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1001] 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.

[1002] [Second embodiment]

[1003] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[1004] 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.

[1005] 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).

[1006] 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.

[1007] 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.

[1008] 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).

[1009] 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.

[1010] 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.

[1011] 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.

[1012] 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.

[1013] 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.

[1014] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1015] "Example 1"

[1016] The present invention is a system that includes a means for sharing information such as past performance and potential suppliers (taking into account business transaction records, etc.) in advance from the agenda of a meeting notice, and a means for creating a task list from the minutes and automatically reflecting the information in standard materials. Specifically, the system analyzes the agenda of the meeting notice, obtains information such as past performance and potential suppliers from a database, and shares this information with meeting participants in advance. The system also analyzes the minutes, automatically creates a task list, and reflects this in standard materials.

[1017] "Example 2"

[1018] The embodiment described in claim 2 further includes a means for sharing supplier candidates (reflecting past performance, sales performance, etc.), past satisfaction survey materials, and trends in advance. Specifically, information on supplier candidates is obtained from a database and shared with meeting participants in advance. Past satisfaction survey materials and trend information are also shared in the same way.

[1019] "Example 3"

[1020] The embodiment described in claim 3 further includes a means for sending materials to potential suppliers (automatic creation of email text) and a means for automatically generating report materials from proposals. Specifically, email text for sending materials to potential suppliers is automatically generated and sent. Also, the contents of proposals made at meetings are analyzed, and report materials are automatically generated based on the analysis.

[1021] The processing flow of each embodiment will be described below.

[1022] "Example 1"

[1023] Step 1: Analyze the agenda of the meeting notice. This analysis uses natural language processing technology to analyze the agenda text and extract the meeting topic and items to be discussed.

[1024] Step 2: Based on the extracted items, information such as past performance and potential suppliers is obtained from the database. This is done using the database's search function.

[1025] Step 3: Share the acquired information with the meeting participants in advance by email or via a website.

[1026] Step 4: Analyze the minutes and automatically create a list of tasks. This analysis uses natural language processing technology to analyze the text in the minutes and extract action items and decisions.

[1027] Step 5: The extracted tasks are reflected in standard documents. This is done using template technology.

[1028] "Example 2"

[1029] Step 1: Analyze the agenda of the meeting notice. This analysis uses natural language processing technology to analyze the agenda text and extract the meeting topic and items to be discussed.

[1030] Step 2: Based on the extracted items, information such as past performance and potential suppliers is obtained from the database. This is done using the database's search function.

[1031] Step 3: Share the acquired information with the meeting participants in advance by email or via a website.

[1032] Step 4: Share past satisfaction survey materials and trend information as well. This can be done via email or on the website.

[1033] "Example 3"

[1034] Step 1: Analyze the agenda of the meeting notice. This analysis uses natural language processing technology to analyze the agenda text and extract the meeting topic and items to be discussed.

[1035] Step 2: Based on the extracted items, information such as past performance and potential suppliers is obtained from the database. This is done using the database's search function.

[1036] Step 3: Share the acquired information with the meeting participants in advance by email or via a website.

[1037] Step 4: Automatically generate and send emails to potential suppliers using template technology.

[1038] Step 5: Analyze the proposals made at the meeting and automatically generate presentation materials based on them. This automatic generation is done using template technology.

[1039] Example 1

[1040] Next, a description will be given of Example 1 of Form 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."

[1041] In traditional meeting management, sharing information on past performance and potential suppliers based on the agenda of the meeting notice is done manually, which takes time and effort. In addition, the work of listing tasks from the minutes and reflecting them in standard documents is often done manually, which is inefficient. This means that a great deal of time and effort is required to prepare for and follow up on meetings, resulting in a decrease in work efficiency.

[1042] 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.

[1043] In this invention, the server includes means for sharing past performance and potential suppliers in advance from the agenda of the meeting notification, means for listing tasks from the minutes and automatically reflecting the information in standard materials, means for analyzing the agenda using natural language processing technology, means for acquiring related information from a database, means for sharing the acquired information with meeting participants, means for analyzing the minutes and extracting tasks, means for listing the extracted tasks, and means for reflecting the listed tasks in standard materials. This automates meeting preparation and follow-up, enabling improved work efficiency.

[1044] A "meeting notice" is a notice that includes information such as the date, time, location, participant list, and agenda of a meeting.

[1045] An "agenda" is a list of topics or subjects to be discussed at a meeting.

[1046] "Past performance" refers to information about the results and outcomes of past work and projects.

[1047] A "potential supplier" is a list of potential suppliers of goods or services.

[1048] Minutes are a document that records what was discussed and what decisions were made at a meeting.

[1049] A "task" is an operation or activity that must be performed to achieve a specific purpose.

[1050] A "standard document" is a document or report prepared according to a specific form or format.

[1051] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[1052] A "database" is a system for efficiently storing, managing, and retrieving data.

[1053] "Information sharing" means communicating and making available specific information among multiple people and systems.

[1054] "Listing" means organizing multiple items in a list format.

[1055] "Extraction" means taking out specific data or information from the whole.

[1056] This invention is a system that shares information such as past performance and potential suppliers in advance from the agenda of a meeting notice, creates a task list from the minutes of the meeting, and automatically reflects the information in standard documents. A specific embodiment of this system will be described below.

[1057] Meeting notification agenda analysis and information sharing

[1058] A user sends a meeting notice to the system. The meeting notice includes the meeting date, time, location, attendee list, and agenda. Meeting notices can be sent using any popular calendar application.

[1059] The server extracts the agenda from the received meeting notification and uses natural language processing technology to analyze the agenda content, using software such as Google Cloud Natural Language API or IBM Watson Natural Language Understanding.

[1060] Based on the analysis results, the server retrieves related information such as past performance and potential suppliers from a database, which uses a database management system such as MySQL or PostgreSQL.

[1061] The server shares the acquired information with the conference participants in advance using an email transmission system.

[1062] Examples:

[1063] A user sends a meeting notification.

[1064] The server analyzes the agenda of the meeting notice and extracts the topic "Selecting a supplier for a new product."

[1065] The server retrieves data on past supplier selection results from a database and generates a list of related supplier candidates.

[1066] The server will email this list to the conference participants.

[1067] Example prompts to input to a generative AI model:

[1068] Generate a list of potential suppliers and past performance data related to the topic "Supplier Selection for New Product."

[1069] Analysis of minutes and task list creation

[1070] After the meeting, the user sends the minutes to the system. The minutes include the contents discussed and decisions made in the meeting. A general document creation tool can be used to send the minutes.

[1071] The server then uses natural language processing technology to analyze the received minutes, again using software such as the aforementioned Google Cloud Natural Language API and IBM Watson Natural Language Understanding.

[1072] The server automatically creates a list of tasks from the minutes based on the analysis results, using, for example, the Python pandas library.

[1073] The server reflects the generated task list in a standard document, which can be generated using Microsoft Office Excel or Google Sheets, for example.

[1074] Examples:

[1075] A user sends the minutes after the meeting.

[1076] The server analyzes the minutes and extracts the task "Conduct research on selecting suppliers for new products."

[1077] The server adds this task to the task list and reflects it in the Excel file.

[1078] The server shares the generated Excel file with the conference participants.

[1079] Example prompts to input to a generative AI model:

[1080] Extract the task "Conduct research on selecting suppliers for new products" from the minutes and add it to your task list.

[1081] This system improves meeting efficiency by analyzing the agenda in meeting notifications, sharing relevant information in advance, and listing tasks from meeting minutes and reflecting them in standard materials.Specific hardware and software used include Google Cloud Natural Language API, IBM Watson Natural Language Understanding, MySQL, an email sending system, the Python pandas library, and Microsoft Excel.

[1082] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1083] Step 1:

[1084] A user sends a meeting notice to the system. The meeting notice includes the meeting date, time, location, attendee list, and agenda. The input is the meeting notice data, and the output is the meeting notice sent to the server. In concrete terms, the user uses a calendar application to create a meeting notice and send it to the system.

[1085] Step 2:

[1086] The server extracts the agenda from the received meeting notice. The input is the data of the meeting notice, and the output is the extracted agenda. Specifically, the server obtains the contents of the meeting notice in text format and parses the agenda part.

[1087] Step 3:

[1088] The server uses natural language processing technology to analyze the content of the agenda. The input is the extracted agenda, and the output is the analysis result. Specifically, the server sends the agenda to the Google Cloud Natural Language API and receives the analysis result.

[1089] Step 4:

[1090] Based on the analysis results, the server retrieves related information such as past performance and potential suppliers from the database. The input is the analysis results, and the output is the retrieved related information. Specifically, the server generates a query based on the analysis results and sends it to the MySQL database.

[1091] Step 5:

[1092] The server shares the acquired information with the conference participants in advance. The input is the acquired relevant information, and the output is the information sent to the conference participants. Specifically, the server uses the email sending system API to send the relevant information to the conference participants in email format.

[1093] Step 6:

[1094] After the meeting, the user sends the minutes to the system. The input is the minutes data, and the output is the minutes sent to the server. Specifically, the user creates the minutes using a document creation tool and sends them to the system.

[1095] Step 7:

[1096] The server again uses natural language processing technology to analyze the received minutes. The input is the minutes data, and the output is the analysis results. Specifically, the server sends the minutes to IBM Watson Natural Language Understanding and receives the analysis results.

[1097] Step 8:

[1098] The server automatically lists tasks from the minutes based on the analysis results. The input is the analysis results, and the output is the generated task list. Specifically, the server uses the Python pandas library to convert the analysis results into a data frame and generate the task list.

[1099] Step 9:

[1100] The server reflects the generated task list in the standard document. The input is the generated task list, and the output is the standard document. Specifically, the server uses Microsoft Excel to write the task list to an Excel file and create the standard document.

[1101] Step 10:

[1102] The server shares the created standard materials with the meeting participants. The input is the standard materials, and the output is the standard materials sent to the meeting participants. Specifically, the server uses the email sending system API to send the standard materials to the meeting participants.

[1103] (Application example 1)

[1104] Next, a description will be given of Application Example 1 of Form 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."

[1105] With conventional meeting management systems, it was difficult to share information such as past performance and potential suppliers in advance from the agenda of the meeting notice, and it was not possible to list tasks from the minutes and automatically reflect them in standard documents. This resulted in problems such as reduced meeting efficiency and complicated task management. In particular, in factories, where rapid updates to production plans are required, these issues could lead to reduced production efficiency.

[1106] 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.

[1107] In this invention, the server includes: means for sharing in advance information such as past performance and potential suppliers (taking into account business transaction records, etc.) from the agenda of the meeting notice; means for listing tasks from the minutes and automatically reflecting the information in standard documents; means installed in a factory robot for analyzing the agenda of the meeting notice, retrieving past production performance and potential suppliers from a database, and sharing this information with meeting participants in advance; and means for analyzing the minutes, automatically listing tasks, and automatically reflecting this in the production plan. This improves meeting efficiency, facilitates task management, and enables rapid updates to production plans, especially in factories.

[1108] A "meeting notice" is a notice to inform participants of information such as the date, time, location, and agenda of a meeting.

[1109] An "agenda" is a list of items or topics to be discussed at a meeting.

[1110] "Past performance" refers to records of the results and outcomes of past work or projects.

[1111] A "potential supplier" is a potential supplier of goods or services.

[1112] "Business transaction performance" refers to records of past business activities and transactions.

[1113] Minutes are a document that records what was discussed and what decisions were made at a meeting.

[1114] A "task" refers to the work or task that must be performed to achieve a specific goal.

[1115] "Listing" refers to organizing items in a list format.

[1116] "Standardized materials" are documents or materials prepared according to a specific form or format.

[1117] A "factory robot" is a mechanical device used to perform automated tasks in a factory.

[1118] "Production records" are records of products and quantities that have been produced in the past at a factory or on a production line.

[1119] A "database" is a system for efficiently managing and searching data.

[1120] "Production plan" refers to the plan or schedule for the production of a product.

[1121] The following system is constructed as an embodiment of this invention. The system shares past performance and potential suppliers in advance from the agenda of a meeting notice, lists tasks from the minutes, and automatically reflects the information in standard documents. Furthermore, the system is installed on a factory robot and has the function of analyzing the agenda of a meeting notice, retrieving past production performance and potential suppliers from a database, and sharing this information with meeting participants in advance. It also includes a function to analyze the minutes, automatically list tasks, and automatically reflect this in production plans.

[1122] Hardware and software used

[1123] Hardware: Factory robots, servers, user terminals

[1124] Software: Python, SQLite, spaCy (natural language processing library)

[1125] System configuration

[1126] 1. Meeting notification analysis function:

[1127] The server receives the agenda of the meeting notification and parses the agenda text using spaCy, a natural language processing library.

[1128] Keywords (organization names, dates, product names, etc.) are extracted from the analysis results, and past production records and potential suppliers are obtained from the SQLite database.

[1129] The acquired information is sent to the user terminal and shared with the conference participants in advance.

[1130] 2. Meeting minutes analysis function:

[1131] The server receives the meeting minutes and again uses spaCy to parse the minutes text.

[1132] Tasks are extracted from the analysis results and listed.

[1133] The listed tasks are automatically reflected in standard documents and then in production plans.

[1134] Specific examples

[1135] Meeting Notice Example

[1136] Agenda for the next meeting: Review production results, consider potential suppliers

[1137] Sample minutes

[1138] Minutes: Task 1: Selecting a new supplier, Task 2: Reviewing the production line

[1139] Processing flow

[1140] 1. Receiving and parsing meeting notifications:

[1141] The server receives the agenda of the meeting notification and parses it using spaCy.

[1142] Keywords are extracted from the analysis results and related information is retrieved from the SQLite database.

[1143] The acquired information is sent to the user terminal and shared with the conference participants in advance.

[1144] 2. Receiving and analyzing transcripts:

[1145] The server receives the meeting minutes and parses them again using spaCy.

[1146] Tasks are extracted from the analysis results and listed.

[1147] The listed tasks are automatically reflected in standard documents and then in production plans.

[1148] In this way, meetings can be made more efficient, tasks can be managed more easily, and production plans can be updated more quickly, especially in factories.

[1149] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1150] Step 1:

[1151] The server receives the agenda of a meeting notice. As input, it is given the agenda text of the meeting notice. The server parses this text using spaCy, a natural language processing library, to extract keywords (organization names, dates, product names, etc.). As output, it receives a list of the extracted keywords.

[1152] Step 2:

[1153] The server searches the SQLite database for past production records and potential suppliers based on the keywords extracted in step 1. A list of keywords is given as input. The server executes a database query to retrieve relevant information on past production records and potential suppliers. The output is a list of the retrieved information.

[1154] Step 3:

[1155] The server sends the information acquired in step 2 to the user terminal. As input, a list of acquired information is given. The server sends this information to the user terminal and shares it with the conference participants in advance. As output, the information displayed on the user terminal is obtained.

[1156] Step 4:

[1157] The server receives the minutes of a meeting. As input, it receives the text of the minutes. The server parses this text again using spaCy to extract tasks. As output, it receives a list of extracted tasks.

[1158] Step 5:

[1159] The server lists the tasks extracted in step 4. The server receives the list of extracted tasks as input. The server organizes this into a list and automatically updates the standard documents. The server outputs the updated standard documents.

[1160] Step 6:

[1161] The server reflects the tasks listed in step 5 in the production plan. The list of tasks is given as input. The server updates the production plan database to reflect the tasks. The updated production plan is obtained as output.

[1162] In this way, the server performs a series of processes: analyzing the agenda and minutes of the meeting notice, acquiring and sharing related information, listing tasks, and reflecting them in the production plan.

[1163] Example 2

[1164] Next, a description will be given of Example 2 of Form 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."

[1165] Conventional conference systems lacked a means to efficiently share necessary information before a meeting, resulting in the significant time and effort required for meeting preparation. They also lacked a means to quickly summarize the content discussed during the meeting and extract key points. This reduced the efficiency and quality of meetings and hindered the rapid pace of decision-making.

[1166] 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.

[1167] In this invention, the server includes a means for sharing in advance information such as past performance and potential suppliers (taking into account business transaction performance, etc.) from the agenda of the meeting notice, a means for creating a task list from the minutes of the meeting and automatically reflecting the information in standardized materials, a means for acquiring information from a database, a means for sharing the acquired information with meeting participants, and a means for extracting a summary of the meeting materials and key points using a generative AI model. This makes it possible to efficiently share necessary information before the meeting and quickly summarize the contents discussed during the meeting and extract key points.

[1168] A "meeting notice agenda" is a document that details the purpose, agenda, participants, date, time, location, etc. of a meeting.

[1169] "Past performance" refers to data showing the results and performance of past transactions and business operations.

[1170] "Prospective Suppliers" is a list of suppliers with which we may do business in the future.

[1171] "Business transaction performance" is data showing the history of past business activities and transactions.

[1172] Minutes are a document that records what was discussed and what decisions were made during a meeting.

[1173] "Listing tasks" means organizing specific work items in a list format based on the minutes.

[1174] A "standard document" is a standard document that is created according to a specific format.

[1175] A "database" is a system for efficiently storing, managing, and retrieving data.

[1176] A "generative AI model" is an algorithm that uses artificial intelligence to generate text and analyze data.

[1177] A "summary" is a short summary of a longer piece of text or data.

[1178] "Key points" refer to particularly important parts or key points of information.

[1179] "Conference participants" refers to people attending a conference.

[1180] This invention is a system for improving the efficiency and quality of meetings, in which servers, terminals, and users work together to share information and use generative AI models to extract summaries and key points from meeting materials.

[1181] Hardware and software used

[1182] Hardware: Servers, devices (PCs, tablets, smartphones)

[1183] Software: Database management systems (e.g., MySQL), generative AI models (e.g., GPT-4), web application frameworks (e.g., Django)

[1184] Program processing overview

[1185] Retrieving information from a database

[1186] The server uses a database management system (MySQL) to obtain information on potential suppliers. Specifically, it executes the following SQL query:

[1187] sql

[1188] SELECT FROM suppliers WHERE past_performance >= 80;

[1189] This query extracts potential suppliers with a past performance of 80% or more.

[1190] Acquiring satisfaction survey materials and trend information

[1191] The server also retrieves past satisfaction survey data and trend information from the database by executing the following SQL query:

[1192] sql

[1193] SELECT FROM satisfaction_surveys WHERE date >= '2022-01-01';

[1194] SELECT FROM trends WHERE category = 'market';

[1195] These queries extract the latest satisfaction survey and market trend information.

[1196] Information Sharing

[1197] The server shares the acquired information with the conference participants through a web application framework (Django). Specifically, it performs the following processes:

[1198] The server uses Django to generate web pages.

[1199] The server embeds the retrieved data into a web page.

[1200] The server sends the URL of the web page to the terminals (PCs, tablets, smartphones) of the conference participants.

[1201] Using generative AI models

[1202] The server uses a generative AI model (GPT-4) to summarize the meeting materials and extract key points. Specifically, the server inputs the following prompt sentences into the generative AI model:

[1203] "Please summarize the following potential supplier information: [potential supplier information]"

[1204] The generative AI model generates a summary based on this prompt.

[1205] Specific examples

[1206] For example, the server executes the following SQL query to obtain information about potential suppliers:

[1207] sql

[1208] SELECT FROM suppliers WHERE past_performance >= 80;

[1209] The acquired data is displayed on a web page using Django. The following prompt sentence is input to the generative AI model (GPT-4) to generate a summary of the conference materials.

[1210] "Please summarize the following potential suppliers: Supplier A has achieved a satisfaction rate of over 80% over the past five years. Supplier B has a strong sales record and has maintained a delivery on-time rate of over 90% over the past three years."

[1211] In this way, the system allows the server, terminals, and users to work together to share information efficiently, improving the quality of meetings.

[1212] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1213] Step 1:

[1214] The server connects to a database management system (MySQL) and retrieves information on potential suppliers. As input, it uses an SQL query to extract supplier candidates with a past performance of 80% or more. Specifically, it executes the following SQL query.

[1215] sql

[1216] SELECT FROM suppliers WHERE past_performance >= 80;

[1217] The result of this query is a list of potential suppliers, which the server stores in memory.

[1218] Step 2:

[1219] The server retrieves past satisfaction survey data and trend information from the database. As input, it uses SQL queries to extract the latest satisfaction survey data and market trend information. Specifically, it executes the following SQL queries:

[1220] sql

[1221] SELECT FROM satisfaction_surveys WHERE date >= '2022-01-01';

[1222] SELECT FROM trends WHERE category = 'market';

[1223] The results of these queries are the satisfaction survey data and trend information, which are then stored in memory by the server.

[1224] Step 3:

[1225] The server shares the acquired information with the conference participants through a web application framework (Django). The acquired data is used as input. Specifically, the following processes are performed:

[1226] The server generates web pages using Django templates.

[1227] The server embeds the acquired data into a template.

[1228] The server sends the URL of the generated web page to the conference participants by email.

[1229] As an output, a web page is generated that can be accessed by conference participants.

[1230] Step 4:

[1231] The server uses a generative AI model (GPT-4) to summarize the meeting materials and extract key points. As input, it embeds the acquired information about potential suppliers into a prompt. Specifically, it inputs the following prompt into the generative AI model:

[1232] "Please summarize the following potential supplier information: [potential supplier information]"

[1233] The generative AI model generates a summary based on the prompt. The output is a summarized version of the meeting material. The server displays the summary on a web page.

[1234] Step 5:

[1235] The user accesses the web page URL sent from the server and checks the meeting materials. The user prepares for the meeting based on the summarized information and key points. Specifically, the user accesses the web page using a PC, tablet, or smartphone and views the displayed information.

[1236] In this way, the system allows the server, terminals, and users to work together to share information efficiently, improving the quality of meetings.

[1237] (Application example 2)

[1238] Next, a description will be given of Application Example 2 of Form 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."

[1239] Conventional conference systems lacked a means to efficiently share information on potential suppliers, past satisfaction surveys, and trend information, resulting in problems such as time-consuming meeting preparation and decision-making. Furthermore, the lack of real-time information updates and the ability to provide data summaries in natural language made it difficult for users to quickly obtain the information they needed. This led to issues such as reduced operational efficiency at logistics centers and other on-site locations.

[1240] 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.

[1241] In this invention, the server includes a means for sharing information such as past performance and potential suppliers (taking into account business transaction records) in advance from the agenda of the meeting notification, a means for creating a task list from the minutes of the meeting and automatically updating the information in standard documents, a means for acquiring information on potential suppliers from a database and displaying it on a smart device, a means for displaying supplier evaluations based on the results of past satisfaction surveys, a means for acquiring and displaying the latest market trend information in real time, a means for summarizing data in natural language using a generative AI model and providing it to users, and a means for updating data in real time using a cloud service. This enables quick and efficient meeting preparation and decision-making, improving operational efficiency at logistics centers and other sites.

[1242] A "meeting notice agenda" is a document that lists details of a meeting, such as the purpose, agenda, participants, date and time.

[1243] "Past performance" refers to data showing the results and outcomes of past transactions and business operations.

[1244] "Prospective Suppliers" is a list of companies and businesses that may provide goods or services.

[1245] "Business transaction performance" is data showing the history of past business activities and transactions.

[1246] A "minutes" is a document that records the contents of a meeting, decisions made, and statements made.

[1247] "Listing tasks" means organizing the work and tasks decided in the meeting in a list format.

[1248] A "standard document" is a document or report prepared according to a specific form or format.

[1249] A "database" is a system for efficiently managing and searching data.

[1250] "Smart devices" are electronic devices with advanced functions, such as smartphones and smart glasses.

[1251] A "satisfaction survey" is a questionnaire or survey used to assess customer or user satisfaction.

[1252] "Market trend information" is data that indicates current market trends and fashions.

[1253] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate and analyze data.

[1254] "Natural language summarization" means concisely summarizing data and information in language that is easy for humans to understand.

[1255] "Cloud services" are computer resources and services provided over the Internet.

[1256] "Real-time data updating" means keeping data up to date instantly.

[1257] The system for implementing the present invention uses the following hardware and software.

[1258] Hardware and software used

[1259] Hardware:

[1260] Smartphones (e.g. iPhone, Android devices)

[1261] Smart glasses (e.g. Google Glass, Microsoft HoloLens)

[1262] software:

[1263] Database management systems (e.g. MySQL, PostgreSQL)

[1264] Cloud services (e.g. AWS, Google Cloud)

[1265] Mobile app development frameworks (e.g., React Native, Flutter)

[1266] Generative AI models (e.g., OpenAI GPT-4)

[1267] System configuration and operation

[1268] 1. Sharing information from the agenda in the meeting notice

[1269] The server retrieves information on past performance and potential suppliers from the agenda of the meeting notification and shares this information in advance. Using a database management system, detailed information including past transaction performance and evaluations is retrieved and displayed on the smart device.

[1270] 2. Creating a task list from meeting minutes

[1271] The server creates a list of tasks from the minutes and automatically updates standard documents, allowing for efficient management of the work and tasks decided in meetings.

[1272] 3. Satisfaction surveys and trend information sharing

[1273] The server displays supplier evaluations based on the results of past satisfaction surveys. It also obtains the latest market trend information in real time and displays it on smart devices. Cloud services are used to instantly keep the data up to date.

[1274] 4. Data Summarization with Generative AI Models

[1275] The server uses a generative AI model to summarize the data in natural language and provide it to the user, allowing them to quickly understand the information they need and make decisions.

[1276] Specific examples

[1277] A logistics center manager wears smart glasses and checks information on potential suppliers. Based on the results of past satisfaction surveys, the manager decides which supplier to do business with. Based on the latest market trend information, the manager makes future purchasing plans.

[1278] Prompt Sentence Examples

[1279] The data is summarized by inputting the following prompt sentences to the generative AI model.

[1280] Please summarize the potential supplier information using the following data: Data: {Potential Supplier Information}, {Past Satisfaction Survey Results}, {Latest Market Trend Information}

[1281] In this way, meeting preparations and decision-making can be carried out quickly and efficiently, improving operational efficiency at logistics centers and other locations.

[1282] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1283] Step 1:

[1284] The server retrieves information on past performance and potential suppliers from the meeting notification agenda. As input, it receives the meeting notification agenda and queries a database management system (e.g., MySQL, PostgreSQL) to retrieve detailed information, including past transaction performance and ratings. As output, the retrieved information is generated in JSON format.

[1285] Step 2:

[1286] The server sends the acquired information to the smart device. As input, it receives the JSON format information generated in step 1 and generates data to be displayed on the smartphone or smart glasses using a mobile app development framework (e.g., React Native, Flutter). As output, it generates information to be displayed on the smart device.

[1287] Step 3:

[1288] The server creates a list of tasks from the minutes and automatically reflects the information in standard documents. The text data of the minutes is given as input, and tasks are extracted and listed using natural language processing technology. A task list is generated as output and reflected in standard documents.

[1289] Step 4:

[1290] The server displays supplier evaluations based on the results of past satisfaction surveys. Past satisfaction survey data is given as input, and evaluation data is retrieved using a database management system. Supplier evaluation information is generated as output and displayed on the smart device.

[1291] Step 5:

[1292] The server obtains the latest market trend information in real time and displays it on the smart device. As input, market trend information obtained from the Internet is given, and data is updated in real time using cloud services (e.g., AWS, Google Cloud). As output, the latest market trend information is displayed on the smart device.

[1293] Step 6:

[1294] The server uses a generative AI model to summarize the data in natural language and provide it to the user. As input, information on potential suppliers, past satisfaction survey results, and the latest market trend information are given, and a generative AI model (e.g., OpenAI GPT-4) is used to generate a summary. As output, the summarized information is displayed on the smart device.

[1295] Step 7:

[1296] The server updates data in real time using a cloud service. As input, the latest data is provided and the database is updated using the cloud service. As output, the updated data is reflected on the smart device.

[1297] Example 3

[1298] Next, a description will be given of Example 3 of Form Example 3. 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."

[1299] With conventional meeting management systems, it was difficult to share past performance and potential suppliers in advance from the agenda of meeting notifications, and there was a lack of means to list tasks from minutes and automatically reflect information in standard documents. The system also lacked the functionality to automatically send materials to potential suppliers or generate report materials based on proposals made in meetings. This resulted in reduced work efficiency and increased manual work, making errors more likely.

[1300] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1301] In this invention, the server includes a means for sharing past performance and potential suppliers in advance from the agenda of the meeting notification, a means for listing tasks from the minutes of the meeting and automatically reflecting the information in standard documents, a means for automatically generating email text to send to potential suppliers, a means for analyzing the content of proposals made in the meeting and automatically generating materials for escalation, and a means for automatically generating email text using a generative AI model and sending it using email sending software. This makes it possible to streamline meeting preparation and follow-up, reduce manual work, and improve the accuracy and efficiency of work.

[1302] A "meeting notice agenda" is a document that details the purpose, agenda, participants, date, time, location, etc. of a meeting.

[1303] "Past performance" refers to data and records that show the results and outcomes of past work or projects.

[1304] "Prospective Suppliers" are potential suppliers or vendors with whom we may do business in the future.

[1305] A "minutes" is a document that records the contents of a meeting, decisions made, and statements made.

[1306] "Listing tasks" means organizing specific work items that arise in meetings or work in a list format.

[1307] A "standard document" is a document or report that is prepared according to a specific form or format.

[1308] "Means for automatically generating email text" refers to technology or systems that automatically create email content based on specific input information.

[1309] "Analyzing the proposal content" means analyzing the information presented in a meeting or proposal and extracting key points and summaries.

[1310] "Presentation materials" are reports or proposals to be submitted to higher-level managers or decision makers.

[1311] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate text or data.

[1312] "Email sending software" refers to a program or application for sending email.

[1313] This invention is a meeting management system that shares past performance and potential suppliers in advance from the agenda of the meeting notice, lists tasks from the minutes, and automatically reflects the information in standard documents. It also includes functions to automatically generate email text to send to potential suppliers and to automatically generate report documents by analyzing the contents of proposals made in the meeting.

[1314] Hardware and software used

[1315] The system uses the following hardware and software:

[1316] Server: Responsible for data processing and running generative AI models.

[1317] Terminal: Provides an interface for users to enter information.

[1318] Generative AI models: Perform text generation and data analysis (e.g., OpenAI's GPT-4).

[1319] Email sending software: Sending automatically generated emails (e.g., email sending systems).

[1320] Data processing and calculation

[1321] Sending materials to potential suppliers

[1322] 1. The user enters information about a potential supplier into the terminal.

[1323] 2. The server uses the generative AI model to automatically generate email text to send to potential suppliers.

[1324] 3. The server uses email sending software to send the generated email text to the potential supplier.

[1325] Examples:

[1326] The user types, "Add Supplier A as a potential supplier for a new part."

[1327] The server uses the generative AI model to generate an email that reads, "Dear Supplier A, We are considering purchasing new parts and are interested in your products. Could you please send us detailed information?"

[1328] The server uses the mail sending system to send this mail to supplier A.

[1329] Analysis of proposals made in meetings and automatic generation of presentation materials

[1330] 1. The user inputs the content of the proposal for the meeting into the terminal.

[1331] 2. The server uses the generative AI model to analyze the suggestions.

[1332] 3. The server automatically generates the application documents and provides them to the user.

[1333] Examples:

[1334] A user enters the following as a "Proposal for launching a new product into the market": "We are planning to launch new product X into the market next month. As a marketing strategy, we will utilize social media advertising and influencer marketing."

[1335] The server uses the generated AI model to analyze the proposal and generate a proposal document titled "New product X market launch plan: scheduled for next month. Marketing strategy: social media advertising, influencer marketing."

[1336] The server provides the generated submission materials to the user.

[1337] Prompt Sentence Examples

[1338] Example prompt for generating emails to potential suppliers:

[1339] "Add Supplier A as a potential supplier for a new part. Please generate the email."

[1340] Sample prompts for analyzing proposals and generating presentation materials in meetings:

[1341] "Enter your proposal for launching a new product into the market. Generate the proposal."

[1342] In this way, the system utilizes the generative AI model based on the user's input to automatically generate email text and report documents, thereby efficiently supporting business operations. The flow of the specific processing in the third embodiment will be described with reference to FIG. 15.

[1343] Step 1:

[1344] The user inputs information about the potential supplier into the terminal.

[1345] Input: Name and contact information of the potential supplier (e.g., "Supplier A, contact@supplierA.com").

[1346] Specific operation: A user inputs information about a potential supplier through the system interface.

[1347] Step 2:

[1348] The server receives the input information about potential suppliers and sends prompt statements to the generative AI model.

[1349] Input: User-entered information about the potential supplier.

[1350] Data processing: The server generates a prompt and sends it to the generative AI model (e.g., "Add supplier A as a potential supplier for a new part. Please generate the email text.").

[1351] Specific operation: The server sends a prompt sentence to the generative AI model.

[1352] Step 3:

[1353] The server receives the email text returned by the generative AI model.

[1354] Input: The email text returned by the generative AI model.

[1355] Output: Generated email text (e.g., "Dear Supplier A, We are considering purchasing new parts and are interested in your products. Could you please send us detailed information?").

[1356] Specific operation: The server receives and verifies the email text returned by the generative AI model.

[1357] Step 4:

[1358] The server uses email sending software to send the generated email text to the potential supplier.

[1359] Input: Generated email text and potential supplier contact information.

[1360] Output: Email sent to potential supplier.

[1361] Specific operation: The server uses email sending software (e.g., email sending system) to send the generated email text to potential suppliers.

[1362] Step 5:

[1363] The user inputs the content of the proposal for the meeting into the terminal.

[1364] Input: What was proposed in the meeting (e.g., "We plan to launch new product X in the market next month. Our marketing strategy will involve social media advertising and influencer marketing.").

[1365] Specific operation: The user inputs the content of the proposal for the meeting through the system interface.

[1366] Step 6:

[1367] The server receives the input suggestions and sends prompts to the generative AI model.

[1368] Input: The suggestion entered by the user.

[1369] Data processing: The server generates a prompt and sends it to the generative AI model (e.g., "Please enter your proposal for launching a new product. Please generate a proposal document.").

[1370] Specific operation: The server sends a prompt sentence to the generative AI model.

[1371] Step 7:

[1372] The server receives the analysis results returned by the generative AI model.

[1373] Input: The analysis results returned by the generative AI model.

[1374] Output: Submission materials based on the analysis results (e.g., "New product X market launch plan: next month. Marketing strategy: social media advertising, influencer marketing").

[1375] Specific operation: The server receives and confirms the analysis results returned by the generative AI model.

[1376] Step 8:

[1377] The server automatically generates a report document based on the analysis results and provides it to the user.

[1378] Input: Analysis results from a generative AI model.

[1379] Output: Auto-generated appeal documents.

[1380] Specific operation: The server automatically generates a report document based on the analysis results and provides it to the user.

[1381] In this way, the system uses a generative AI model based on user input to automatically generate email text and report documents, efficiently supporting business operations.

[1382] (Application example 3)

[1383] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1384] In conventional logistics centers, sending materials to potential suppliers and preparing documents for submission based on proposals made at meetings were done manually, which was time-consuming and labor-intensive. Furthermore, these tasks were prone to human error, hindering efficient business operations. Furthermore, it was difficult to prepare documents for submission that accurately reflected proposals made at meetings, and there was a need for faster decision-making.

[1385] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1386] In this invention, the server includes: means for sharing past performance and potential suppliers in advance from the agenda of a meeting notification; means for listing tasks from minutes of a meeting and automatically reflecting the information in standard documents; means for sending documents to potential suppliers (automatic email generation); means for automatically generating report documents from proposals; means for generating email documents from prompts using a generative AI model; means for generating report documents from prompts using a generative AI model; means for automatically generating and sending email documents to send to potential suppliers as an application installed on a smartphone; and means for analyzing proposals made at a meeting and automatically generating report documents. This automates the sending of documents and the creation of report documents at a logistics center, improving work efficiency and accuracy.

[1387] A "meeting notice agenda" is a document that includes information about a meeting, such as the schedule, agenda, and participants.

[1388] "Past performance" refers to data showing the results and outcomes of past operations and transactions.

[1389] "Prospective suppliers" are candidates for suppliers with which we may do business in the future.

[1390] "Minutes" are documents that record the contents of a meeting and the decisions made.

[1391] A "task" is an operation or activity that must be performed to achieve a specific purpose.

[1392] A "standard document" is a document that is created according to a specific form or format.

[1393] "Automatic email creation" is the process of automatically generating the body of an email based on specific content.

[1394] A "proposal" refers to presenting a solution or improvement plan for a specific problem or issue.

[1395] "Presentation materials" are reports or proposals to be submitted to higher-level managers or decision makers.

[1396] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate text or data.

[1397] A "prompt" is an instruction or question that is input into a generative AI model.

[1398] A "smartphone" is a multi-functional mobile device that can connect to the Internet and use applications in addition to the functions of a mobile phone.

[1399] An "application" is a software program that provides a particular function or service.

[1400] "Sending documents" refers to the act of sending specific documents or data to another person.

[1401] "Analysis" is the process of examining data or information in detail to understand its meaning and structure.

[1402] The system for implementing this invention automates the sending of documents and the creation of report documents at a logistics center. Specifically, it is implemented as follows using a server, a smartphone, and a generative AI model.

[1403] Hardware and Software Configuration

[1404] Server: A central management system for storing and processing data. The server manages data such as meeting notice agendas, past performance, potential suppliers, and meeting minutes.

[1405] Smartphone: A mobile device used by the logistics center manager. Applications for sending documents and preparing report documents are installed on the smartphone.

[1406] Generative AI model: An algorithm that uses OpenAI's API to generate email text and report documents from prompts.

[1407] Data processing and calculation

[1408] 1. Share the agenda in the meeting notice:

[1409] The server extracts information on past performance and potential suppliers from the meeting notification agenda and sends it to the smartphone, allowing users to check the necessary information before the meeting.

[1410] 2. Creating a task list from the minutes:

[1411] After the meeting, the server analyzes the minutes and creates a list of tasks. The tasks are automatically reflected in standard documents and sent to smartphones.

[1412] 3. Automatically generate and send emails:

[1413] The smartphone application uses a generative AI model to automatically generate emails to send to potential suppliers, which are then sent directly from the smartphone.

[1414] 4. Automatic generation of submission materials:

[1415] The server analyzes the content of proposals made during meetings and automatically generates presentation materials using a generative AI model. The generated presentation materials are then sent to the user's smartphone, where they can be reviewed and revised.

[1416] Specific examples

[1417] Email generation prompt:

[1418] Generate email content to send to potential new suppliers.

[1419] Prompt for generating escalation documents:

[1420] Please prepare your appeal based on the following meeting notes:

[1421] Proposals made at the meeting: We discussed the introduction of a new logistics system.

[1422] In this way, the sending of documents and the preparation of report documents at the logistics center are automated, resulting in improved efficiency and accuracy of operations.

[1423] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1424] Step 1:

[1425] The server receives the agenda of the meeting notice and extracts information on past performance and potential suppliers.

[1426] Input: Meeting Notice Agenda

[1427] Data processing: Analyze past performance and information on potential suppliers from the agenda and extract the necessary data.

[1428] Output: Past performance and supplier candidate information

[1429] Step 2:

[1430] The server sends the extracted information on past performance and potential suppliers to the smartphone.

[1431] Input: Past performance and supplier candidate information

[1432] Data calculation: Converts the extracted information into a format for sending to a smartphone.

[1433] Output: Past performance and supplier candidate information sent to your smartphone

[1434] Step 3:

[1435] The user checks the information on past performance and potential suppliers received on their smartphone.

[1436] Input: Past performance and supplier candidate information sent to your smartphone

[1437] Data processing: The user checks the information and makes corrections or additions as necessary.

[1438] Output: Checked and corrected information

[1439] Step 4:

[1440] The server receives the minutes after the meeting and lists the tasks.

[1441] Input: Post-meeting minutes

[1442] Data processing: Analyze minutes and list tasks.

[1443] Output: List of tasks

[1444] Step 5:

[1445] The server automatically reflects the listed tasks in a standard document and sends it to the smartphone.

[1446] Input: Listed tasks

[1447] Data calculation: The task is reflected in standard documents and converted into a format for sending to a smartphone.

[1448] Output: Standardized documents sent to your smartphone

[1449] Step 6:

[1450] The smartphone application uses a generative AI model to automatically generate email text for sending materials to potential suppliers.

[1451] Input: Prompt text "Generate the content of an email to send to new potential suppliers."

[1452] Data calculation: Using a generative AI model, email text is generated from the prompt.

[1453] Output: Generated email text

[1454] Step 7:

[1455] The smartphone application sends the generated email text to potential suppliers.

[1456] Input: Generated email text

[1457] Data calculation: Convert the email text into a format for sending and send it.

[1458] Output: Email sent to potential suppliers

[1459] Step 8:

[1460] The server analyzes the proposals made at the meeting and automatically generates presentation materials using a generative AI model.

[1461] Input: Proposal made at the meeting

[1462] Data calculation: Using a generative AI model, a report document is generated from the proposal content.

[1463] Output: Generated petition

[1464] Step 9:

[1465] The server sends the generated statement materials to the smartphone.

[1466] Input: Generated petition documents

[1467] Data processing: Converts submitted documents into a format suitable for sending to a smartphone.

[1468] Output: Appeal documents sent to smartphone

[1469] Step 10:

[1470] The user checks the submitted documents received on their smartphone and makes corrections or additions as necessary.

[1471] Input: Appeal documents sent to smartphone

[1472] Data processing: The user checks the materials and makes corrections or additions as necessary.

[1473] Output: Confirmed and corrected petition documents

[1474] 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.

[1475] "Example 1"

[1476] One embodiment of the present invention combines a system that shares information such as past performance and potential suppliers in advance from the agenda of a meeting notification, creates a task list from minutes, and automatically reflects the information in standard documents with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's emotions from their tone of voice, facial expressions, and word choice, and feeds the results back into the system. For example, if the engine detects anger or frustration from the tone of a speaker's voice during a meeting, it communicates this information to the system, which then reflects it in the minutes. This allows the system to grasp the atmosphere of the meeting and the emotional state of the participants, enabling more effective meeting management and follow-up afterwards.

[1477] "Example 2"

[1478] Another embodiment of the present invention is a system that combines an emotion engine with pre-shared information about potential suppliers, previously submitted satisfaction survey materials, and trends. In this system, the emotion engine analyzes user responses and adjusts the content of the shared satisfaction survey materials and trends based on the results. For example, if the system senses that a user is particularly interested in a particular trend, it adjusts the system to share more information related to that trend. This enables information sharing tailored to the user's interests and concerns, improving the effectiveness of meetings.

[1479] "Example 3"

[1480] Yet another embodiment of the present invention is a system that includes a means for sending documents to potential suppliers and a means for automatically generating report documents from proposals, and further combines an emotion engine. In this system, the emotion engine analyzes the user's emotions and automatically generates email text for sending the documents based on the results. For example, if the user feels favorably toward a particular potential supplier, an email text that reflects that emotion is generated. Furthermore, the user's emotions are taken into consideration when automatically generating report documents from proposals. This enables effective communication that reflects the user's emotions.

[1481] The processing flow of each embodiment will be described below.

[1482] "Example 1"

[1483] Step 1: Share past performance and potential suppliers in advance from the agenda in the meeting notice.

[1484] Step 2: The emotion engine analyzes the user's emotions based on their tone of voice, facial expressions, choice of words, etc.

[1485] Step 3: The analysis results are fed back into the system and the information is reflected in the minutes.

[1486] "Example 2"

[1487] Step 1: Share information about potential suppliers, past satisfaction surveys, and trends in advance. Step 2: The emotion engine analyzes user responses.

[1488] Step 3: Based on the analysis results, adjust the content of the satisfaction survey materials and trend sharing.

[1489] "Example 3"

[1490] Step 1: Prepare a system that includes a means of sending documents to potential suppliers and a means of automatically generating proposal documents from proposals.

[1491] Step 2: The emotion engine analyzes the user's emotions.

[1492] Step 3: Automatically generate email text to send documents based on the analysis results.

[1493] Step 4: User sentiment is also taken into consideration when automatically generating presentation materials from proposals.

[1494] Example 1

[1495] Next, a description will be given of Example 1 of Form 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."

[1496] In conventional meeting management systems, information sharing on past performance and potential suppliers based on the agenda of meeting notifications is done manually, resulting in inefficiency and occasional information leaks and delays. Furthermore, the process of creating a task list from minutes and incorporating it into standard materials is often done manually, which is time-consuming, labor-intensive, and prone to errors. Furthermore, there is no way to grasp the emotions of participants during a meeting, making it impossible to properly reflect the atmosphere of the meeting or the emotional state of participants. To solve these problems, the present invention aims to improve meeting efficiency and the accuracy of information sharing.

[1497] 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.

[1498] In this invention, the server includes a means for sharing information such as past performance and potential suppliers in advance from the agenda of the meeting notification, a means for listing tasks from the minutes and automatically reflecting the information in standard documents, and a means for analyzing user emotions and reflecting the results in the minutes, thereby improving the efficiency of meetings and the accuracy of information sharing.

[1499] A "meeting notice" is a notice that includes information such as the date, time, location, participants, and agenda of a meeting.

[1500] An "agenda" is a list of topics or topics to be discussed at a meeting.

[1501] "Past performance" refers to the records and results of past meetings and transactions.

[1502] A "potential supplier" is a potential supplier of goods or services.

[1503] "Minutes" are documents that record the contents of a meeting and the decisions made.

[1504] A "task" is an operation or activity that must be performed to achieve a specific purpose.

[1505] "Standard documents" are documents or tables that are created according to a specific format.

[1506] "User emotion" is information indicating the emotional state of the conference participants.

[1507] "Emotion analysis" is the process of identifying emotions from a user's tone of voice, facial expressions, etc.

[1508] "Information sharing" refers to the transmission of specific information between multiple people or systems.

[1509] "Automatic generation" refers to the system automatically creating documents or data without manual intervention.

[1510] This invention is a system that combines agenda analysis of meeting notices, task list creation of minutes, and user emotion analysis. A specific embodiment of this system will be described below.

[1511] Meeting notification agenda analysis and information sharing

[1512] When the server receives a meeting notification, it first analyzes its contents. This analysis is performed using natural language processing (NLP) technology. Specifically, it uses the Google Cloud Natural Language API. Based on the analysis results, related information such as past meeting records and potential suppliers is retrieved from a database (e.g., a relational database management system). The retrieved information is shared with the meeting participants in advance. This information can be shared via email or a dedicated meeting management application (e.g., an online meeting tool).

[1513] Examples:

[1514] If the agenda of the meeting notice includes "Supplier selection for new product," the server retrieves the minutes of past supplier selection meetings and transaction records from the database and shares them with the participants.

[1515] Example prompt sentence:

[1516] Please obtain past conference and transaction records related to "Supplier selection for new products" and share them with participants.

[1517] Analysis of minutes and task list creation

[1518] After the meeting, the server receives the minutes and analyzes their contents. Natural language processing technology is used to extract tasks from the minutes. The extracted tasks are automatically listed and reflected in standardized documents (e.g., spreadsheet software).

[1519] Examples:

[1520] If the minutes include a statement such as "conduct market research before the next meeting," the server will list this task and add it to a spreadsheet.

[1521] Example prompt sentence:

[1522] Extract tasks from the minutes and list them in a standard document.

[1523] Emotional analysis of users using an emotion engine

[1524] Devices (e.g., participants' computers or mobile devices) capture users' tone of voice and facial expressions in real time during the meeting. This data is sent to an emotion engine (e.g., an emotion analysis API) to analyze the user's emotions. The analysis results are fed back to the server and reflected in the minutes.

[1525] Examples:

[1526] If anger is sensed from the tone of a speaker's voice during a meeting, that information will be reflected in the minutes as "Speaker A is angry."

[1527] Example prompt sentence:

[1528] Analyze the emotions of the speakers during the meeting from their tone of voice and reflect the results in the minutes.

[1529] summary

[1530] This system combines agenda analysis of meeting notifications, task list creation of meeting minutes, and user sentiment analysis to achieve more effective meeting management and follow-up. Specific technologies used include the Google Cloud Natural Language API, a relational database management system, an online meeting tool, and a sentiment analysis API.

[1531] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1532] Step 1:

[1533] Receive meeting notifications

[1534] The server receives meeting notifications. The input includes the agenda, date, time, location, and participant information. The output is to store the received meeting notification data. Specifically, the server retrieves meeting notifications from emails and conference management applications and stores them in a database.

[1535] Step 2:

[1536] Agenda Analysis

[1537] The server parses the agenda of the received meeting notification. The input contains the agenda of the meeting notification. The output contains the parsed topics and keywords. Specifically, the server uses the Google Cloud Natural Language API to parse the agenda content, classify it by topic, and extract related keywords.

[1538] Step 3:

[1539] Retrieving information from a database

[1540] The server retrieves related information, such as past conference records and potential suppliers, from a database based on the extracted keywords. The input includes the analyzed keywords. The output is the related information. Specifically, the server queries a relational database management system to retrieve information on past conference records and potential suppliers.

[1541] Step 4:

[1542] Information Sharing

[1543] The server shares the acquired information with the conference participants in advance. The acquired related information is included as input. The shared information is obtained as output. As a specific operation, the server sends the information to the conference participants using email or an online conference tool.

[1544] Step 5:

[1545] Receiving minutes

[1546] The server receives the minutes after the meeting ends. The minutes include the contents of the minutes as input. The received minutes data is saved as output. Specifically, the server retrieves the minutes from email or the meeting management application and saves them in a database.

[1547] Step 6:

[1548] Analysis of meeting minutes

[1549] The server parses the received minutes. The input contains the contents of the minutes. The output contains extracted tasks. Specifically, the server uses the Google Cloud Natural Language API to parse the contents of the minutes and extract tasks.

[1550] Step 7:

[1551] Generate a task list

[1552] The server lists the extracted tasks. The extracted tasks are included as input. The generated task list is obtained as output. Specifically, the server uses spreadsheet software to reflect the task list in a standard document.

[1553] Step 8:

[1554] Capturing Emotional Data

[1555] The device captures the user's voice tone and facial expressions in real time during a meeting. The input includes the user's voice tone and facial expression data. The output is captured emotion data. Specifically, the device uses a microphone and a camera to capture the user's voice tone and facial expression.

[1556] Step 9:

[1557] Emotion Analysis

[1558] The server uses an emotion engine to analyze the user's emotions from the captured data. The input includes the captured emotion data. The output is the analyzed emotion information. Specifically, the server uses an emotion analysis API to analyze the user's emotions.

[1559] Step 10:

[1560] Emotional information feedback

[1561] The server reflects the analysis results in the minutes. The input contains the analyzed emotional information. The output is minutes that reflect the emotional information. Specifically, the server adds emotional information such as "Speaker A is feeling angry" to the minutes.

[1562] (Application example 1)

[1563] Next, a description will be given of Application Example 1 of Form 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."

[1564] Conventional meeting support systems require a great deal of time and effort to prepare for meetings and create minutes, and they also face the challenge of making it difficult to grasp the emotional state of participants during meetings. Furthermore, they lacked a means to efficiently share the contents of meetings and conduct follow-up afterward. This can lead to meetings being less effective and the quality of decision-making declining.

[1565] 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.

[1566] In this invention, the server includes a means for sharing information such as past performance and potential suppliers (taking into account business transaction records) in advance from the agenda of the meeting notification, a means for creating a task list from the minutes and automatically reflecting the information in standard documents, and a means for analyzing the user's tone of voice and facial expression to grasp their emotional state and reflect this in the minutes. This makes it possible to automate everything from meeting preparation to creating minutes and emotional analysis, enabling efficient meeting management and high-quality decision-making.

[1567] A "meeting notice agenda" is a document that includes information such as the purpose and agenda of a meeting, the participants, date, time, and location.

[1568] "Past performance" refers to data that shows the results and outcomes of past work and projects.

[1569] A "potential supplier" is a list of potential suppliers of a particular product or service.

[1570] "Business transaction performance" is data showing the history of past business activities and transactions.

[1571] "Means for pre-sharing" refers to a method or system for providing participants with necessary information before the meeting.

[1572] Minutes are a document that records what was discussed and what decisions were made during a meeting.

[1573] A "means for listing tasks" is a method or system for organizing tasks extracted from minutes in a list format.

[1574] A "standard document" is a document created according to a specific form or format.

[1575] "Means for automatically reflecting information" refers to a method or system for automatically incorporating specific data into a document or system.

[1576] "Tone of a user's voice" is a feature of speech that indicates the speaker's emotions and intentions.

[1577] "Means for analyzing facial expressions" refers to a method or system that uses a camera or sensor to analyze a user's facial expressions.

[1578] A "means for grasping emotional state" is a method or system for recognizing and understanding a user's emotions from their tone of voice and facial expressions.

[1579] The system for implementing this invention has the functions of sharing past performance and potential suppliers in advance from the agenda of the meeting notice, creating a task list from the minutes, and automatically reflecting the information in standard documents.It also includes the functions of analyzing the user's tone of voice and facial expression to understand their emotional state and reflect it in the minutes.

[1580] System configuration

[1581] The system consists of the following main components:

[1582] 1. Server: Includes a database, a speech recognition engine, a sentiment analysis engine, and a document generation engine.

[1583] 2. Terminal: The device used by the meeting participants (smartphone, tablet, PC, etc.).

[1584] 3. User: A participant in a meeting.

[1585] Hardware and software used

[1586] Speech Recognition Engine: Uses the speech_recognition library to convert meeting audio to text.

[1587] Emotion Analysis Engine: Uses the cv2 and emotion_recognition libraries to analyze the facial expressions of participants in a meeting to understand their emotional state.

[1588] Database: A database for storing information on past performance and potential suppliers.

[1589] Document generation engine: Use the DocumentGenerator class to automatically create meeting minutes and task lists.

[1590] Processing flow

[1591] 1. Meeting preparation: The server analyzes the agenda in the meeting notification and retrieves information on past performance and potential suppliers from the database. This information is shared in advance with the terminals of the meeting participants.

[1592] 2. Minute Creation: During the meeting, the server uses a speech recognition engine to convert the meeting speech into text and create minutes in real time.

[1593] 3. Emotion Analysis: The server uses an emotion analysis engine to analyze the tone of voice and facial expressions of participants during the meeting to understand their emotional state, which is reflected in the minutes.

[1594] 4. Task list generation: The server extracts tasks from the minutes and automatically generates a task list. The generated task list is reflected in standard materials and shared with meeting participants.

[1595] Specific examples

[1596] For example, based on the agenda item "improving the production line," the server can retrieve past production performance data and share it with meeting participants in advance. If participants express dissatisfaction during the meeting, that information can be reflected in the minutes to help with subsequent follow-up.

[1597] Prompt Sentence Examples

[1598] Based on the agenda of "production line improvement," obtain past production performance data and share it with meeting participants in advance. Also, analyze participants' tone of voice and facial expressions during the meeting to understand their emotional state and reflect this in the minutes.

[1599] In this way, the system can automatically handle everything from meeting preparation to minutes creation and sentiment analysis, enabling efficient meeting management and high-quality decision-making.

[1600] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1601] Step 1:

[1602] The server receives the agenda for the meeting notification. As input, it receives the agenda, which includes information such as the purpose and topic of the meeting, participants, date and time, and location. The server analyzes the agenda and retrieves information on past performance and potential suppliers from the database. As output, it generates the retrieved information on past performance and potential suppliers.

[1603] Step 2:

[1604] The server shares the acquired information on past performance and potential suppliers with the terminals of the conference participants in advance. It receives information on past performance and potential suppliers as input and sends the information to the terminals of the conference participants as output. Specific operations include distributing the information using email or a notification system.

[1605] Step 3:

[1606] During the meeting, the server uses a speech recognition engine to convert the meeting audio into text. It receives the meeting audio data as input and generates a text transcript as output. Specifically, it uses the speech_recognition library to analyze the audio data and convert it into text.

[1607] Step 4:

[1608] The server uses an emotion analysis engine to analyze the tone of voice and facial expressions of participants in a meeting to understand their emotional state. It receives audio and video data from the meeting as input and generates emotional state data as output. Specifically, it uses the cv2 and emotion_recognition libraries to analyze audio and video and recognize emotional states.

[1609] Step 5:

[1610] The server reflects the emotional state data in the minutes. As input, it receives text minutes and emotional state data, and as output, it generates minutes with added emotional information. Specifically, it adds a comment about the emotional state to the relevant part of the minutes.

[1611] Step 6:

[1612] The server extracts tasks from the minutes and automatically generates a task list. It receives the minutes in text format as input and generates a task list as output. Specifically, it analyzes the text of the minutes and extracts tasks based on keywords such as "task" and "action item."

[1613] Step 7:

[1614] The server reflects the generated task list in a standard document and shares it with the meeting participants. It receives the task list as input, generates standard documents as output, and sends them to the meeting participants' devices. Specifically, it uses the DocumentGenerator class to create standard documents and distributes them via email or a notification system.

[1615] Example 2

[1616] Next, a description will be given of Example 2 of Form 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."

[1617] Conventional conference systems lacked a means to efficiently share necessary information before the meeting, which reduced the effectiveness of the meeting. Furthermore, there was no way to analyze participants' reactions in real time during the meeting and adjust the information based on the results, making it difficult to provide information tailored to the participants' interests.

[1618] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for sharing in advance information such as past performance and potential suppliers (taking into account business transaction performance, etc.) from the agenda of the meeting notification, a means for creating a task list from the minutes of the meeting and automatically reflecting the information in standardized materials, a means for acquiring information on potential suppliers from a database and sharing it with the meeting participants in advance, a means for acquiring materials from past satisfaction surveys and trend information from the database and sharing it with the meeting participants in advance, and a means for analyzing user reactions using a sentiment analysis engine and adjusting the information to be shared based on the results. This makes it possible to efficiently share necessary information before the meeting and adjust the information during the meeting based on the participants' reactions.

[1619] A "meeting notice agenda" is a document that details the purpose, agenda, participants, date, time, location, etc. of a meeting.

[1620] "Past performance" is data showing the results and history of past transactions and projects.

[1621] "Prospective Suppliers" is a list of suppliers with which we may do business in the future.

[1622] "Business transaction performance" is data showing the history of past business activities and transactions.

[1623] Minutes are a document that records what was discussed and what decisions were made during a meeting.

[1624] "Listing tasks" means organizing specific work items in a list format based on the minutes.

[1625] A "standard document" is a document or report prepared according to a specific form or format.

[1626] A "database" is a system for efficiently managing, searching, and updating data.

[1627] A "satisfaction survey" is a questionnaire or survey used to assess customer or user satisfaction.

[1628] "Trend information" is data that shows current market trends and the latest industry information.

[1629] An "emotion analysis engine" is software that analyzes a user's emotions from facial expressions, tone of voice, etc.

[1630] "User reactions" refer to changes in interest, concern, and emotions shown by the user during the meeting.

[1631] "Adjusting information" means changing the content and amount of information provided based on the user's response.

[1632] The present invention is a system for improving the effectiveness of a conference, in which the elements of a server, a terminal, and a user work in cooperation with each other. A specific embodiment of this system will be described below.

[1633] Hardware and software used

[1634] Server: Database server, sentiment analysis engine

[1635] Device: Meeting participants' PCs and tablets

[1636] Software: Database management systems (e.g., MySQL), sentiment analysis software (e.g., IBM Watson)

[1637] System Overview

[1638] This system shares information on past performance and potential suppliers from the agenda in meeting notifications in advance, creates a list of tasks from the minutes, and automatically reflects the information in standard documents. It also retrieves information on potential suppliers, past satisfaction surveys, and trend information from a database and shares them with meeting participants in advance. It also uses a sentiment analysis engine to analyze user reactions and adjust the information to be shared based on the results.

[1639] Data Acquisition

[1640] The server retrieves information about potential suppliers from the database. Specifically, the server connects to the MySQL database and executes an SQL query to retrieve information about potential suppliers. For example, it executes the query SELECT FROM suppliers WHERE status='active';

[1641] Next, the server retrieves past satisfaction survey data from the database, again by executing an SQL query, such as SELECT FROM satisfaction_surveys WHERE date > '2022-01-01';

[1642] Additionally, the server retrieves the latest trend information from the database, for example by executing the query SELECT FROM trends ORDER BY date DESC LIMIT 10;

[1643] Information Sharing

[1644] The server sends the acquired information to the terminals of the conference participants. Specifically, the server converts the acquired information into JSON format and sends it to the terminals of the conference participants. For example, the server sends information about potential suppliers in the format {"suppliers": [{"name": "Supplier A", "status": "active"}, ...]}.

[1645] Past satisfaction survey materials should be sent in PDF format, for example, {"surveys": [{"title": "Survey 2022", "file": "survey_2022.pdf"}, ...]}.

[1646] Send the latest trend information in HTML format, for example, {"trends": [{"title": "Eco Products", "description": "Latest trends in eco-friendly products"}, ...]}.

[1647] Emotion analysis

[1648] During the meeting, the device collects the user's reactions. The device uses a camera and microphone to collect the user's facial expressions and tone of voice. The collected data is sent to the server in real time. For example, it is sent in the following format: {"user_reactions": [{"timestamp": "2023-10-01T10:00:00Z", "emotion": "interest"}, ...]}.

[1649] The server analyzes the user's reaction using a sentiment analysis engine, such as IBM Watson, and obtains the result {"emotion": "interest", "confidence": 0.95}.

[1650] Coordination of information

[1651] The server then adjusts the information it shares based on the results of the sentiment analysis. For example, if a user expresses interest in a particular trend, it retrieves additional information related to that trend. For example, it executes a query like SELECT FROM trends WHERE category='Eco Products';

[1652] The adjusted information is then sent back to the conference participants' devices, for example, in the following format: {"trends": [{"title": "Eco Products", "description": "Additional information on eco-friendly products"}, ...]}

[1653] Examples of concrete examples and prompts

[1654] Examples:

[1655] If a conference attendee expresses interest in "new eco-product trends," the system will share additional up-to-date market data and success stories related to that trend.

[1656] Example prompt sentence:

[1657] "Please keep me up to date on new eco-product trends."

[1658] "Please share information that may be of interest to users based on past satisfaction survey results."

[1659] In this way, the system shares information tailored to the user's interests and concerns, improving the effectiveness of the meeting.

[1660] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1661] Step 1: Data Acquisition

[1662] Input: The server connects to the database and runs SQL queries to retrieve information about potential suppliers, past satisfaction surveys, and the latest trends.

[1663] What happens: The server connects to a MySQL database and executes the following SQL query:

[1664] Potential supplier information: SELECT FROM suppliers WHERE status='active';

[1665] Satisfaction survey materials: SELECT FROM satisfaction_surveys WHERE date > '2022-01-01';

[1666] Trend information: SELECT FROM trends ORDER BY date DESC LIMIT 10;

[1667] Output: The server stores the acquired data in its internal memory.

[1668] Step 2: Share information

[1669] Input: Based on the data obtained in step 1, the server prepares the information to be shared with the meeting participants.

[1670] Specific operation: The server converts the acquired data into JSON or PDF format and sends it to the terminals of the meeting participants.

[1671] Potential Supplier Information: {"suppliers": [{"name": "Supplier A", "status": "active"}, ...]}

[1672] Satisfaction survey materials: {"surveys": [{"title": "Survey 2022", "file": "survey_2022.pdf"}, ...]}

[1673] Trends: {"trends": [{"title": "Eco Products", "description": "Latest trends in eco-friendly products"}, ...]}

[1674] Output: Information is sent to and displayed on the conference participants' devices.

[1675] Step 3: Sentiment Analysis

[1676] Input: The device collects data using a camera and microphone to gather user responses during the meeting.

[1677] Specific operation: The device collects the user's facial expressions and tone of voice in real time and sends the data to the server.

[1678] Example of collected data: {"user_reactions": [{"timestamp": "2023-10-01T10:00:00Z", "emotion": "interest"}, ...]}

[1679] Output: User response data is sent to the server.

[1680] Step 4: Perform sentiment analysis

[1681] Input: The server runs the sentiment analysis engine based on the user response data received in step 3.

[1682] Specific operation: The server uses an emotion analysis engine (e.g., IBM Watson) to analyze the user's reaction data.

[1683] Example of analysis result: {"emotion": "interest", "confidence": 0.95}

[1684] Output: The server stores the analysis results in its internal memory.

[1685] Step 5: Adjust the information

[1686] Input: The server adjusts the information it shares based on the sentiment analysis results from step 4.

[1687] What happens: Based on the analysis results, the server executes SQL queries to retrieve additional information related to the trends the user has shown interest in.

[1688] Get additional information: SELECT FROM trends WHERE category='Eco Products';

[1689] Output: The server stores the additional information it has obtained in its internal memory.

[1690] Step 6: Re-share adjusted information

[1691] Input: Based on the information adjusted in step 5, the server again prepares the information to be shared with the conference participants.

[1692] Specific operation: The server converts the adjusted information into JSON or HTML format and sends it to the terminals of the meeting participants.

[1693] Adjusted information: {"trends": [{"title": "Eco Products", "description": "Additional information on eco-friendly products"}, ...]}

[1694] Output: The adjusted information is sent to the meeting participants' devices and displayed.

[1695] In this way, the system shares information tailored to the user's interests and concerns, improving the effectiveness of the meeting.

[1696] (Application example 2)

[1697] Next, a description will be given of Application Example 2 of Form 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."

[1698] Conventional conference systems lacked a means to efficiently share necessary information before the meeting, reducing the effectiveness of the meeting. Furthermore, there was no way to provide appropriate information based on the customer's interests and concerns, which hindered the quality of customer service. Furthermore, there was a lack of technology to analyze customer emotions and adjust the content of information shared, making it difficult to increase customer satisfaction.

[1699] 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.

[1700] In this invention, the server includes a means for sharing information such as past performance and potential suppliers (taking into account business transaction records) in advance from the agenda of the meeting notification, a means for creating a task list from the minutes and automatically reflecting the information in standard documents, a means for analyzing user reactions using a sentiment analysis engine and adjusting the content of the information to be shared based on the results, and a means for providing trend information and satisfaction survey materials based on customer interests and concerns. This makes it possible to improve the effectiveness of meetings, enhance the quality of customer service, and increase customer satisfaction.

[1701] A "meeting notice agenda" is a document that includes information such as the content, subject, participants, date and time of a meeting.

[1702] "Past performance" refers to data showing the results and outcomes of past transactions and business operations.

[1703] "Prospective Suppliers" is a list of suppliers and vendors with which you may do business in the future.

[1704] "Business transaction performance" is data showing the history of past business activities and transactions.

[1705] A "minutes" is a document that records the contents of a meeting, decisions made, statements made, etc.

[1706] "Listing tasks" means organizing the work and tasks decided in the meeting in a list format.

[1707] A "standard document" is a document or report prepared according to a specific form or format.

[1708] An "emotion analysis engine" is software that analyzes a user's facial expressions and behavior to identify their emotions.

[1709] "User reaction" refers to the user's facial expressions, actions, comments, and other reactions.

[1710] "Information sharing content" refers to the content of information shared during meetings and customer interactions.

[1711] "Customer interest" refers to the degree of interest or concern a customer has in a particular product or service.

[1712] "Trend information" is data that shows current market and industry trends and fads.

[1713] "Satisfaction survey materials" are documents containing data and survey results collected to assess customer satisfaction.

[1714] A system for implementing this invention includes means for sharing in advance past performance and potential suppliers (taking into account sales transaction performance, etc.) from the agenda of a meeting notification, means for listing tasks from minutes of a meeting and automatically reflecting the information in standard documents, means for analyzing user reactions using an emotion analysis engine and adjusting the content of information sharing based on the results, and means for providing trend information and satisfaction survey materials based on customer interests and concerns.

[1715] Hardware and software used

[1716] Hardware: Smart glasses (with camera)

[1717] Software: OpenCV (video analysis), EmotionEngine (emotion analysis), Database (information acquisition)

[1718] Data processing and calculation

[1719] Video acquisition

[1720] The smart glasses' camera captures real-time video footage, allowing it to capture customer expressions and behavior.

[1721] Emotion analysis

[1722] Using EmotionEngine, the company analyzes customer facial expressions from captured video to identify their interests. The emotion analysis engine is software that analyzes users' facial expressions and behavior to identify their emotions.

[1723] Information acquisition

[1724] Based on customer sentiment, trend information and past satisfaction survey data are retrieved from a database that stores past performance, potential suppliers, trend information, and satisfaction survey data.

[1725] Information provision

[1726] The acquired information is provided to staff to suggest the most suitable products and services to customers, thereby improving the quality of customer service and increasing customer satisfaction.

[1727] Specific examples

[1728] If a customer expresses interest in a new smartphone, the smart glasses will analyze that information and provide staff with information on past customer satisfaction surveys and the latest smartphone trends, allowing them to recommend the best smartphone for the customer.

[1729] Prompt Sentence Examples

[1730] Provide trending information about products that customers have shown interest in and past satisfaction surveys.

[1731] In this way, customer service can be more effectively provided in physical stores.

[1732] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1733] Step 1:

[1734] Real-time video footage is captured from the smart glasses camera.

[1735] Input: Smart glasses camera image

[1736] Output: Video data including customer facial expressions and behavior

[1737] How it works: The camera in the smart glasses captures the customer's face and movements and sends the video data to a processing system in real time.

[1738] Step 2:

[1739] Using EmotionEngine, the company analyzes customers' facial expressions from captured video footage to identify their interests and concerns.

[1740] Input: Video data

[1741] Output: Customer sentiment data (interests, concerns, etc.)

[1742] How it works: EmotionEngine analyzes video data and identifies emotions from the customer's facial expressions and behavior. For example, if a customer is smiling and looking at a product, it will determine that they are interested.

[1743] Step 3:

[1744] Based on customer sentiment, trend information and past satisfaction survey data are retrieved from the database.

[1745] Input: Customer sentiment data

[1746] Output: Trend information, satisfaction survey materials

[1747] Specific operation: The server accesses the database and searches for and retrieves trend information and satisfaction survey materials that correspond to the customer's emotional data. For example, if the customer is determined to be "interested," the server retrieves the latest related trend information and past satisfaction survey results.

[1748] Step 4:

[1749] The acquired information is provided to staff to suggest the most suitable products and services to customers.

[1750] Input: Trend information, satisfaction survey materials

[1751] Output: Information provided to staff, proposals to customers

[1752] Specific operation: Trend information and satisfaction survey data obtained by the server are displayed on the smart glasses' display, and staff members use that information to suggest optimal products and services to customers. For example, the latest smartphone trend information is displayed, and staff members introduce the smartphone to the customer.

[1753] Step 5:

[1754] Re-analyze customer reactions and adjust the information shared as necessary.

[1755] Input: New customer facial and behavior data

[1756] Output: Updated information shared

[1757] How it works: The smart glasses' camera captures the customer's facial expressions and behavior again, and the Emotion Engine analyzes the data. Based on the analysis results, the server updates the information sharing content and provides new information to staff. For example, if the customer shows further interest, it can provide additional trend information or related product information.

[1758] Example 3

[1759] Next, a description will be given of Example 3 of Form Example 3. 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."

[1760] With conventional meeting systems, it was difficult to share past performance and potential suppliers in advance from the meeting notification agenda, and there was a lack of means to list tasks from minutes and automatically reflect information in standard documents. Furthermore, the functionality for automatically sending documents to potential suppliers or automatically generating report documents from proposal content was also insufficient. Furthermore, effective information transmission was difficult because communication could not take user emotions into consideration. To solve these issues, a system with more advanced automation and that takes user emotions into consideration was needed.

[1761] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1762] In this invention, the server includes means for sharing past performance and potential suppliers in advance from the agenda of a meeting notification, means for listing tasks from minutes of the meeting and automatically reflecting the information in standard documents, means for automatically generating email text to send to potential suppliers, means for analyzing proposal content and automatically generating submission documents, means for analyzing user emotions and generating email text reflecting the results, and means for generating submission documents taking user emotions into consideration. This enables efficient meetings and effective information sharing, and realizes communication that takes user emotions into consideration.

[1763] A "meeting notice agenda" is a document containing detailed information such as the purpose, subject matter, participants, date, time, and location of a meeting.

[1764] "Past performance" refers to data that shows the results and outcomes of past work and projects.

[1765] "Prospective Suppliers" is a list of suppliers and vendors with which you may do business in the future.

[1766] Minutes are a document that records what was discussed and what decisions were made during a meeting.

[1767] "Listing tasks" means organizing specific work items extracted from the minutes in a list format.

[1768] "Standard documents" are documents created based on a specific format or template.

[1769] "Email text for sending materials" refers to the body of an email created for the purpose of sending specific materials.

[1770] A "proposal" is a detailed description of a new idea or plan presented in a meeting or presentation.

[1771] "Presentation documents" are reports or proposals submitted to higher-level managers or decision makers.

[1772] "User emotions" refer to the psychological state and feelings of an individual using a system.

[1773] An "emotion engine" is software that analyzes a user's emotions and responds appropriately based on the results.

[1774] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate text or data.

[1775] This invention is a system that shares past performance and potential suppliers in advance from the agenda of a meeting notification, lists tasks from minutes, and automatically reflects the information in standard documents. It also automatically generates emails to send to potential suppliers and analyzes proposal content to automatically generate proposal documents. It also includes a function to analyze user sentiment and generate emails and proposal documents that reflect the results.

[1776] Hardware and software used

[1777] Server: Database management system, generative AI model (e.g. GPT-4), emotion engine (e.g. Affectiva), speech recognition software (e.g. Google Speech-to-Text)

[1778] Terminal: User interface, voice recording function

[1779] User: Accesses and operates the system

[1780] Program processing explanation

[1781] Sending materials to potential suppliers

[1782] 1. A user logs into the system and selects potential suppliers.

[1783] 2. The device sends the user's selection information to the server.

[1784] 3. The server retrieves the materials to be sent to the selected supplier candidates from the database and updates them with the latest information.

[1785] 4. The server inputs the prompt text into the generative AI model and generates an email text for sending the materials.

[1786] Example prompt: "Generate an email to send a catalog of new products to potential suppliers."

[1787] 5. The server sends the generated email to the potential supplier's email address.

[1788] Examples:

[1789] The user selects "Supplier Candidate A."

[1790] The server retrieves "New Product Catalog.pdf" from the database and updates it with the latest information.

[1791] The server instructs the AI ​​model to generate an email that reads, "We have attached a catalog of our new products. Please take a look."

[1792] The server sends this email to the email address of "Supplier Candidate A."

[1793] Automatic generation of presentation materials from proposals

[1794] 1. A user makes a proposal in a meeting.

[1795] 2. The device records the audio of the meeting and sends it to the server.

[1796] 3. The server uses speech recognition software to convert the suggestions into text.

[1797] 4. The server inputs the prompt text into the generative AI model and generates a report document based on the proposal.

[1798] Example prompt: "Generate a presentation based on the new product introduction proposed in the meeting."

[1799] 5. The server stores the generated petition in the database and notifies the user.

[1800] Examples:

[1801] A user makes a "new product introduction proposal" at a meeting.

[1802] The device records the audio of the meeting and sends it to the server.

[1803] The server uses speech recognition software to convert the text to "Benefits and market analysis of new product introduction."

[1804] The server instructs the AI ​​model to generate a proposal document titled "Benefits and Market Analysis of New Product Introduction."

[1805] The server stores the submitted information in a database and notifies the user.

[1806] Use of emotion engine

[1807] 1. A user expresses feelings toward a potential supplier (e.g., positive feelings).

[1808] 2. The device collects the user's emotional data and sends it to the server.

[1809] 3. The server uses the emotion engine to analyze the user's emotions.

[1810] 4. Based on the analysis results, the server uses a generative AI model to generate email text that reflects emotions.

[1811] Example prompt: "Generate an email to send to potential suppliers with whom the user has positive feelings."

[1812] 5. The server analyzes the proposal and has the AI ​​model generate a report that takes into account the user's feelings.

[1813] Example prompt: "Generate escalation materials taking into account the user's positive sentiment."

[1814] Examples:

[1815] The user expresses favorable feelings toward "Supplier Candidate B."

[1816] The device collects the user's emotional data and sends it to the server.

[1817] The server uses an emotion engine to analyze the user's positive emotions.

[1818] The server instructs the AI ​​model to generate an email that reflects the sentiment, such as, "Thank you for your continued support. We have attached a catalog of our new products, so please take a look."

[1819] The server analyzes the proposal and generates "reporting materials that take into account the user's positive feelings."

[1820] In this way, the system analyzes the user's emotions and automatically generates email texts and report materials that reflect the analysis, thereby achieving effective communication. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1821] Sending materials to potential suppliers

[1822] Step 1:

[1823] A user logs into the system and selects potential suppliers.

[1824] Input: User login information, supplier candidate selection information

[1825] Output: Information on selected potential suppliers

[1826] Specific operation: The user logs in to the system and selects "Supplier Candidate A" through the interface.

[1827] Step 2:

[1828] The terminal transmits the user's selection information to the server.

[1829] Input: Information on potential suppliers selected by the user

[1830] Output: Information of potential suppliers sent to the server

[1831] Specific operation: The terminal sends information about the selected "Supplier Candidate A" to the server.

[1832] Step 3:

[1833] The server retrieves the materials to be sent to the selected potential suppliers from the database and updates them with the latest information.

[1834] Input: Material information in the database, information on selected potential suppliers

[1835] Output: Updated documentation

[1836] Specific operation: The server retrieves "New Product Catalog.pdf" from the database and updates it with the latest information.

[1837] Step 4:

[1838] The server inputs a prompt into the generative AI model and generates an email message for sending the documents.

[1839] Input: prompt, generative AI model

[1840] Output: Auto-generated email text

[1841] Specific operation: The server inputs the prompt "Please generate an email message to send the new product catalog to potential suppliers" to the generation AI model, and generates the email message "We have attached the new product catalog. Please check it."

[1842] Step 5:

[1843] The server sends the generated email text to the email address of the potential supplier.

[1844] Input: Auto-generated email text, email address of potential supplier

[1845] Output: Email sent

[1846] Specific operation: The server sends the generated email text to the email address of "Supplier candidate A."

[1847] Automatic generation of presentation materials from proposals

[1848] Step 1:

[1849] A user makes a suggestion in a meeting.

[1850] Input: Proposal

[1851] Output: Proposals made at the meeting

[1852] Specific operation: A user makes a "proposal for introducing a new product" in a meeting.

[1853] Step 2:

[1854] The device records the audio of the meeting and sends it to the server.

[1855] Input: Meeting audio data

[1856] Output: Audio data sent to the server

[1857] Specific operation: The device records the audio of the meeting and sends it to the server.

[1858] Step 3:

[1859] The server uses speech recognition software to convert the suggestions into text.

[1860] Input: Voice data, voice recognition software

[1861] Output: Suggestions converted to text

[1862] What happens: The server uses speech recognition software to convert the text to "New product introduction benefits and market analysis."

[1863] Step 4:

[1864] The server inputs a prompt into the generative AI model and generates a report document based on the proposed content.

[1865] Input: prompt, generative AI model, and converted suggestions

[1866] Output: Auto-generated petition

[1867] Specific operation: The server inputs the prompt "Please generate a presentation document based on the new product introduction proposed in the meeting" to the generating AI model, and generates a presentation document titled "Benefits and market analysis of new product introduction."

[1868] Step 5:

[1869] The server stores the generated report data in a database and notifies the user.

[1870] Input: Auto-generated petition

[1871] Output: Escalation documents stored in database, notification to user

[1872] Specific operation: The server saves the generated application documents in the database and notifies the user.

[1873] Use of emotion engine

[1874] Step 1:

[1875] A user expresses a sentiment toward a potential supplier (e.g., positive sentiment).

[1876] Input: User sentiment

[1877] Output: Emotion data

[1878] Specific action: The user expresses positive feelings toward "Supplier Candidate B."

[1879] Step 2:

[1880] The device collects the user's emotional data and sends it to the server.

[1881] Input: User emotion data

[1882] Output: Emotion data sent to the server

[1883] Specific operation: The device collects the user's emotional data and sends it to the server.

[1884] Step 3:

[1885] The server uses an emotion engine to analyze the user's emotions.

[1886] Input: Emotion data, Emotion engine

[1887] Output: Parsed emotion results

[1888] Specific operation: The server uses the emotion engine to analyze the user's positive emotions.

[1889] Step 4:

[1890] Based on the analysis results, the server uses a generative AI model to generate email text that reflects emotions.

[1891] Input: Analyzed sentiment results, prompt, generative AI model

[1892] Output: Emotionally-driven email text

[1893] Specific operation: The server inputs the prompt text "Generate an email to send to potential suppliers about whom the user has favorable feelings" into the generation AI model, and generates an email text that reflects the user's feelings, such as "Thank you for your continued support. We have attached a catalog of our new products, so please take a look."

[1894] Step 5:

[1895] The server analyzes the proposal and has the AI ​​model generate a report that takes the user's feelings into account.

[1896] Input: Proposal content, analyzed sentiment results, prompt, generative AI model

[1897] Output: Emotionally-informed escalation materials

[1898] Specific operation: The server inputs the prompt sentence "Please generate the petition documents taking into consideration the user's positive feelings" into the generation AI model, and generates "petition documents taking into consideration the user's positive feelings."

[1899] (Application example 3)

[1900] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1901] With conventional conference systems, it was difficult to share meeting agendas, past performance, and information on potential suppliers in advance, which led to problems with reduced meeting efficiency. Additionally, there was a lack of a way to automatically create a task list from minutes and reflect the information in standard documents, which required a lot of manual work, taking time and effort. Furthermore, when sending documents to potential suppliers or automatically generating documents for escalation from proposals, the system was unable to take user emotions into account, making effective communication difficult.

[1902] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1903] In this invention, the server includes means for sharing information such as past performance and potential suppliers in advance from the agenda of a meeting notification, means for listing tasks from minutes and automatically reflecting the information in standard documents, means for sending documents to potential suppliers (automatic email creation), means for automatically generating report documents from proposals, means for analyzing user emotions using an emotion engine and generating email documents reflecting the results, and means for generating report documents taking user emotions into consideration using the emotion engine. This improves meeting efficiency, reduces manual work, and enables effective communication that reflects user emotions.

[1904] A "meeting notice agenda" is a document that contains information such as the purpose, subject, participants, date, time, and location of a meeting.

[1905] "Past performance" refers to data that shows the results and outcomes of past work and projects.

[1906] "Prospective Suppliers" refers to suppliers and vendors with whom we may do business in the future.

[1907] A "minutes" is a document that records the contents of a meeting, decisions made, statements made, etc.

[1908] "Listing tasks" refers to organizing and displaying specific tasks or work in a list format.

[1909] "Standard documents" refer to documents or reports prepared according to a specific format.

[1910] "Sending documents" refers to the act of sending specific documents or data to another person.

[1911] "Automatic email generation" is the process of automatically generating email content based on specific conditions and data.

[1912] "Proposal" refers to the act of presenting a solution or idea to a specific problem or issue.

[1913] "Presentation documents" refer to reports and proposals submitted to higher-level managers or organizations.

[1914] An "emotion engine" is software or algorithm that analyzes a user's emotions and executes specific actions based on the results.

[1915] "User emotion" refers to a user's psychological reaction or feelings to a particular situation or piece of information.

[1916] "Effective communication" refers to communication in which information is conveyed accurately and the recipient is able to understand and respond appropriately to that information.

[1917] A system for carrying out this invention includes a server, a user terminal, and an emotion engine. The server includes means for sharing in advance information such as past performance and potential suppliers from the agenda of a meeting notification, means for listing tasks from minutes of a meeting and automatically reflecting the information in standard documents, means for sending documents to potential suppliers (automatically creating email text), means for automatically generating report documents from proposals, means for analyzing user emotions using the emotion engine and generating email text that reflects the results, and means for generating report documents that take user emotions into consideration using the emotion engine.

[1918] System configuration

[1919] 1. Server:

[1920] The server analyzes the agenda of the meeting notification and shares information on past performance and potential suppliers in advance.

[1921] The server analyzes the minutes, lists tasks, and automatically reflects the information in standard documents.

[1922] The server automatically generates and transmits an email message for sending materials to potential suppliers.

[1923] The server analyzes the proposal and automatically generates the materials for submission.

[1924] The server uses an emotion engine to analyze the user's emotions and generates email text that reflects the results.

[1925] The server uses an emotion engine to generate submission materials that take the user's emotions into account.

[1926] 2. User Device:

[1927] The user terminal inputs the agenda and minutes of the meeting notice and transmits them to the server.

[1928] The user terminal receives and displays the documents and email messages sent from the server.

[1929] The user terminal collects the user's emotion data and transmits it to the server.

[1930] 3. Emotion Engine:

[1931] The emotion engine analyzes the user's emotion data to determine their current emotional state.

[1932] The emotion engine sends the analysis results to the server and reflects them in the generation of email text and report documents.

[1933] Hardware and software used

[1934] Hardware:

[1935] Server: Server equipment equipped with a high-performance processor and large memory capacity.

[1936] User device: A device such as a computer, smartphone, or tablet.

[1937] software:

[1938] Emotion engine: Software for analyzing user emotions.

[1939] Proposal analysis software: Software for analyzing proposal content and generating submission materials.

[1940] Email auto-generation software: Software for automatically generating emails to potential suppliers.

[1941] Specific examples

[1942] For example, when a user sends a meeting notification agenda to the server, the server analyzes past performance and information on potential suppliers and shares it in advance. When the meeting ends, the user sends the minutes to the server, which then lists tasks and automatically reflects the information in standard documents. Furthermore, the server automatically generates email text to send to potential suppliers, analyzing the user's emotions using an emotion engine and generating email text that reflects the results. The emotion engine also takes the user's emotions into account when analyzing proposal content and automatically generating submission documents.

[1943] Prompt Sentence Examples

[1944] "Analyze the user's emotional data and generate emails to potential suppliers. If the user's emotional state is positive, reflect that emotional state in the email. Also, analyze the proposal content and generate proposal documents. If the user's emotional state is positive, reflect that emotional state in the proposal documents."

[1945] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1946] Step 1:

[1947] A user inputs an agenda for a meeting notice into a terminal and transmits it to a server.

[1948] Input: Meeting Notice Agenda

[1949] Output: Agenda data sent to the server

[1950] Specific operation: The user inputs the agenda for the meeting notification into the input form on the terminal and presses the send button. The terminal then sends the agenda data to the server.

[1951] Step 2:

[1952] The server analyzes the agenda data and shares information on past performance and potential suppliers in advance.

[1953] Input: Agenda data

[1954] Output: Past performance and supplier candidate information

[1955] Specific operation: The server analyzes the agenda data, retrieves related past performance and information on potential suppliers from the database, and sends it to the user's terminal.

[1956] Step 3:

[1957] A user holds a conference, inputs the minutes into a terminal, and transmits them to a server.

[1958] Input: Minutes data

[1959] Output: Meeting minutes data sent to the server

[1960] Specific operation: After the meeting ends, the user enters the minutes into the input form on the terminal and presses the send button. The terminal then sends the minutes data to the server.

[1961] Step 4:

[1962] The server analyzes the minutes data, lists tasks, and automatically reflects the information in standard documents.

[1963] Input: Minutes data

[1964] Output: Task list and boilerplate

[1965] Specific operation: The server analyzes the minutes data, extracts and lists tasks, and automatically updates the information according to the standard document format.

[1966] Step 5:

[1967] The server automatically generates and sends an email message to send materials to potential suppliers.

[1968] Input: information on potential suppliers, document data

[1969] Output: Auto-generated email text, sent email

[1970] Specific operation: The server automatically generates email messages based on the supplier candidate's information and document data, and sends the generated email messages to the supplier candidate.

[1971] Step 6:

[1972] The server analyzes the proposal and automatically generates the presentation materials.

[1973] Input: Proposal content data

[1974] Output: Automatically generated petition documents

[1975] Specific operation: The server analyzes the proposal content data and automatically reflects the information according to the format of the submission document.

[1976] Step 7:

[1977] The server uses an emotion engine to analyze the user's emotions and generates email text that reflects the results.

[1978] Input: User emotion data

[1979] Output: Emotionally-driven email text

[1980] Specific operation: The server uses an emotion engine to analyze the user's emotion data and generate email text that reflects the emotion.

[1981] Step 8:

[1982] The server uses an emotion engine to generate submission materials that take the user's emotions into consideration.

[1983] Input: User emotion data, suggestion content data

[1984] Output: Emotional report

[1985] Specific operation: The server uses an emotion engine to analyze the user's emotion data, combines it with the proposal content data, and generates a proposal document that reflects the emotion.

[1986] 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.

[1987] 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> ) 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.

[1988] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1989] 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.

[1990] [Third embodiment]

[1991] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1992] 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.

[1993] 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).

[1994] 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.

[1995] 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.

[1996] 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).

[1997] 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.

[1998] 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.

[1999] 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.

[2000] The storage 32 stores a data generation model 58 and an emotion identification model 59.

[2001] The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2002] 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.

[2003] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[2004] "Example 1"

[2005] The present invention is a system that includes a means for sharing information such as past performance and potential suppliers (taking into account business transaction records, etc.) in advance from the agenda of a meeting notice, and a means for creating a task list from the minutes and automatically reflecting the information in standard materials. Specifically, the system analyzes the agenda of the meeting notice, obtains information such as past performance and potential suppliers from a database, and shares this information with meeting participants in advance. The system also analyzes the minutes, automatically creates a task list, and reflects this in standard materials.

[2006] "Example 2"

[2007] The embodiment described in claim 2 further includes a means for sharing supplier candidates (reflecting past performance, sales performance, etc.), past satisfaction survey materials, and trends in advance. Specifically, information on supplier candidates is obtained from a database and shared with meeting participants in advance. Past satisfaction survey materials and trend information are also shared in the same way.

[2008] "Example 3"

[2009] The embodiment described in claim 3 further includes a means for sending materials to potential suppliers (automatic creation of email text) and a means for automatically generating report materials from proposals. Specifically, email text for sending materials to potential suppliers is automatically generated and sent. Also, the contents of proposals made at meetings are analyzed, and report materials are automatically generated based on the analysis.

[2010] The processing flow of each embodiment will be described below.

[2011] "Example 1"

[2012] Step 1: Analyze the agenda of the meeting notice. This analysis uses natural language processing technology to analyze the agenda text and extract the meeting topic and items to be discussed.

[2013] Step 2: Based on the extracted items, information such as past performance and potential suppliers is obtained from the database. This is done using the database's search function.

[2014] Step 3: Share the acquired information with the meeting participants in advance by email or via a website.

[2015] Step 4: Analyze the minutes and automatically create a list of tasks. This analysis uses natural language processing technology to analyze the text in the minutes and extract action items and decisions.

[2016] Step 5: The extracted tasks are reflected in standard documents. This is done using template technology.

[2017] "Example 2"

[2018] Step 1: Analyze the agenda of the meeting notice. This analysis uses natural language processing technology to analyze the agenda text and extract the meeting topic and items to be discussed.

[2019] Step 2: Based on the extracted items, information such as past performance and potential suppliers is obtained from the database. This is done using the database's search function.

[2020] Step 3: Share the acquired information with the meeting participants in advance by email or via a website.

[2021] Step 4: Share past satisfaction survey materials and trend information as well. This can be done via email or on the website.

[2022] "Example 3"

[2023] Step 1: Analyze the agenda of the meeting notice. This analysis uses natural language processing technology to analyze the agenda text and extract the meeting topic and items to be discussed.

[2024] Step 2: Based on the extracted items, information such as past performance and potential suppliers is obtained from the database. This is done using the database's search function.

[2025] Step 3: Share the acquired information with the meeting participants in advance by email or via a website.

[2026] Step 4: Automatically generate and send emails to potential suppliers using template technology.

[2027] Step 5: Analyze the proposals made at the meeting and automatically generate presentation materials based on them. This automatic generation is done using template technology.

[2028] Example 1

[2029] Next, a description will be given of Example 1 of Form 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."

[2030] In traditional meeting management, sharing information on past performance and potential suppliers based on the agenda of the meeting notice is done manually, which takes time and effort. In addition, the work of listing tasks from the minutes and reflecting them in standard documents is often done manually, which is inefficient. This means that a great deal of time and effort is required to prepare for and follow up on meetings, resulting in a decrease in work efficiency.

[2031] 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.

[2032] In this invention, the server includes means for sharing past performance and potential suppliers in advance from the agenda of the meeting notification, means for listing tasks from the minutes and automatically reflecting the information in standard materials, means for analyzing the agenda using natural language processing technology, means for acquiring related information from a database, means for sharing the acquired information with meeting participants, means for analyzing the minutes and extracting tasks, means for listing the extracted tasks, and means for reflecting the listed tasks in standard materials. This automates meeting preparation and follow-up, enabling improved work efficiency.

[2033] A "meeting notice" is a notice that includes information such as the date, time, location, participant list, and agenda of a meeting.

[2034] An "agenda" is a list of topics or subjects to be discussed at a meeting.

[2035] "Past performance" refers to information about the results and outcomes of past work and projects.

[2036] A "potential supplier" is a list of potential suppliers of goods or services.

[2037] Minutes are a document that records what was discussed and what decisions were made at a meeting.

[2038] A "task" is an operation or activity that must be performed to achieve a specific purpose.

[2039] A "standard document" is a document or report prepared according to a specific form or format.

[2040] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[2041] A "database" is a system for efficiently storing, managing, and retrieving data.

[2042] "Information sharing" means communicating and making available specific information among multiple people and systems.

[2043] "Listing" means organizing multiple items in a list format.

[2044] "Extraction" means taking out specific data or information from the whole.

[2045] This invention is a system that shares information such as past performance and potential suppliers in advance from the agenda of a meeting notice, creates a task list from the minutes of the meeting, and automatically reflects the information in standard documents. A specific embodiment of this system will be described below.

[2046] Meeting notification agenda analysis and information sharing

[2047] A user sends a meeting notice to the system. The meeting notice includes the meeting date, time, location, attendee list, and agenda. Meeting notices can be sent using any popular calendar application.

[2048] The server extracts the agenda from the received meeting notification and uses natural language processing technology to analyze the agenda content, using software such as Google Cloud Natural Language API or IBM Watson Natural Language Understanding.

[2049] Based on the analysis results, the server retrieves related information such as past performance and potential suppliers from a database, which uses a database management system such as MySQL or PostgreSQL.

[2050] The server shares the acquired information with the conference participants in advance using an email transmission system.

[2051] Examples:

[2052] A user sends a meeting notification.

[2053] The server analyzes the agenda of the meeting notice and extracts the topic "Selecting a supplier for a new product."

[2054] The server retrieves data on past supplier selection results from a database and generates a list of related supplier candidates.

[2055] The server will email this list to the conference participants.

[2056] Example prompts to input to a generative AI model:

[2057] Generate a list of potential suppliers and past performance data related to the topic "Supplier Selection for New Product."

[2058] Analysis of minutes and task list creation

[2059] After the meeting, the user sends the minutes to the system. The minutes include the contents discussed and decisions made in the meeting. A general document creation tool can be used to send the minutes.

[2060] The server then uses natural language processing technology to analyze the received minutes, again using software such as the aforementioned Google Cloud Natural Language API and IBM Watson Natural Language Understanding.

[2061] The server automatically creates a list of tasks from the minutes based on the analysis results, using, for example, the Python pandas library.

[2062] The server reflects the generated task list in a standard document, which can be generated using Microsoft Office Excel or Google Sheets, for example.

[2063] Examples:

[2064] A user sends the minutes after the meeting.

[2065] The server analyzes the minutes and extracts the task "Conduct research on selecting suppliers for new products."

[2066] The server adds this task to the task list and reflects it in the Excel file.

[2067] The server shares the generated Excel file with the conference participants.

[2068] Example prompts to input to a generative AI model:

[2069] Extract the task "Conduct research on selecting suppliers for new products" from the minutes and add it to your task list.

[2070] This system improves meeting efficiency by analyzing the agenda in meeting notifications, sharing relevant information in advance, and listing tasks from meeting minutes and reflecting them in standard materials.Specific hardware and software used include Google Cloud Natural Language API, IBM Watson Natural Language Understanding, MySQL, an email sending system, the Python pandas library, and Microsoft Excel.

[2071] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2072] Step 1:

[2073] A user sends a meeting notice to the system. The meeting notice includes the meeting date, time, location, attendee list, and agenda. The input is the meeting notice data, and the output is the meeting notice sent to the server. In concrete terms, the user uses a calendar application to create a meeting notice and send it to the system.

[2074] Step 2:

[2075] The server extracts the agenda from the received meeting notice. The input is the data of the meeting notice, and the output is the extracted agenda. Specifically, the server obtains the contents of the meeting notice in text format and parses the agenda part.

[2076] Step 3:

[2077] The server uses natural language processing technology to analyze the content of the agenda. The input is the extracted agenda, and the output is the analysis result. Specifically, the server sends the agenda to the Google Cloud Natural Language API and receives the analysis result.

[2078] Step 4:

[2079] Based on the analysis results, the server retrieves related information such as past performance and potential suppliers from the database. The input is the analysis results, and the output is the retrieved related information. Specifically, the server generates a query based on the analysis results and sends it to the MySQL database.

[2080] Step 5:

[2081] The server shares the acquired information with the conference participants in advance. The input is the acquired relevant information, and the output is the information sent to the conference participants. Specifically, the server uses the email sending system API to send the relevant information to the conference participants in email format.

[2082] Step 6:

[2083] After the meeting, the user sends the minutes to the system. The input is the minutes data, and the output is the minutes sent to the server. Specifically, the user creates the minutes using a document creation tool and sends them to the system.

[2084] Step 7:

[2085] The server again uses natural language processing technology to analyze the received minutes. The input is the minutes data, and the output is the analysis results. Specifically, the server sends the minutes to IBM Watson Natural Language Understanding and receives the analysis results.

[2086] Step 8:

[2087] The server automatically lists tasks from the minutes based on the analysis results. The input is the analysis results, and the output is the generated task list. Specifically, the server uses the Python pandas library to convert the analysis results into a data frame and generate the task list.

[2088] Step 9:

[2089] The server reflects the generated task list in the standard document. The input is the generated task list, and the output is the standard document. Specifically, the server uses Microsoft Excel to write the task list to an Excel file and create the standard document.

[2090] Step 10:

[2091] The server shares the created standard materials with the meeting participants. The input is the standard materials, and the output is the standard materials sent to the meeting participants. Specifically, the server uses the email sending system API to send the standard materials to the meeting participants.

[2092] (Application example 1)

[2093] Next, a description will be given of Application Example 1 of Form 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."

[2094] With conventional meeting management systems, it was difficult to share information such as past performance and potential suppliers in advance from the agenda of the meeting notice, and it was not possible to list tasks from the minutes and automatically reflect them in standard documents. This resulted in problems such as reduced meeting efficiency and complicated task management. In particular, in factories, where rapid updates to production plans are required, these issues could lead to reduced p...

Claims

[Claim 1] A means for extracting keywords including product names by analyzing the text of an agenda included in a meeting notice for a meeting using natural language processing technology; means for executing a query based on the keyword to retrieve related information including past performance and potential suppliers from a database; means for sharing the acquired related information with participants of the conference; a means for generating minutes of the meeting by converting the speech of the meeting into text using a speech recognition engine; a means for understanding the emotions of the user by analyzing the tone of voice, facial expression, or choice of words of the user during the meeting using an emotion engine, and reflecting the emotions of the user in the minutes; means for analyzing the minutes and extracting tasks; means for listing the extracted tasks; A means for reflecting the listed tasks in a standard document; a means for automatically generating the email text by creating a prompt sentence that instructs the user to generate an email text for sending materials to the supplier candidate for whom the user has a favorable feeling, based on the user's feelings, and inputting the prompt sentence into a generation AI model; A system including:

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