System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving presentation contents from an employee; means for transmitting the received presentation contents to a generation-related AI and requesting analysis; means for receiving an analysis result from the generation-related AI and arranging and analyzing the analysis result; means for notifying a specialized team of the arranged and analyzed analysis result as a wall-hitting result; and means for generating the wall-hitting result as materials for a management meeting and distributing the materials to management executives.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 management meetings, important management policies must be decided within a limited time frame, and the content of employee presentations and the depth of executive comments vary widely, leading to potential misjudgments. This can lead to a lack of sufficient information for management to make optimal decisions and a lack of diverse perspectives. Furthermore, relying solely on human experience and knowledge can make it difficult to incorporate new perspectives and insights. [Means for solving the problem]

[0005] This invention provides a system that receives presentation content from employees, sends it to a generative AI for analysis, and then receives and organizes and analyzes the analysis results from the generative AI. Furthermore, the results of the organization and analysis are reported to a specialized team as a "brainstorming" session, and the brainstorming results are generated as materials for management meetings and distributed to executive officers, allowing management to obtain input from a more diverse range of perspectives when making management decisions. This reduces the number of decisions made within the limited timeframes that have traditionally been made and allows for the incorporation of new perspectives and insights. Furthermore, the system supports more accurate and efficient management decisions by formulating a final management policy based on the brainstorming results, recording that policy, and providing feedback for revising presentation materials by referencing the analysis results.

[0006] "Employee" refers to employees working for a company or organization, including those who are responsible for preparing presentation materials and providing them to management meetings.

[0007] "Presentation content" refers to the materials and presentations prepared by employees for management meetings, including company strategies and proposals, market analysis of new products, etc.

[0008] "Generative AI" refers to an artificial intelligence system that generates and analyzes text and data, and ChatGPT can be given as a specific example here.

[0009] "Analysis" refers to the process by which the generative AI receives the presentation content, analyzes it, and provides feedback.

[0010] A "specialized team" refers to a specific team that receives the results analyzed by generative AI and uses them as a reference for bouncing ideas off of the wall.

[0011] "Bubbling" refers to the process of freely exchanging opinions on a specific topic or proposal and digging deeper into its contents.

[0012] A "management meeting" refers to a meeting in which executives and management gather to decide important management policies and strategies in a company or organization.

[0013] "Chief executives" refers to officials who are responsible for making important decisions in a company or organization, and includes those who make comments and judgments at management meetings.

[0014] "Management policy" refers to a policy that indicates the direction and strategy that a company or organization should take in its future management activities.

[0015] "Feedback" refers to suggestions and advice to improve the content of a presentation based on the analysis results of generative AI.

[0016] "Server" refers to a computer system that receives and processes information over a network and provides that information to other terminals or systems. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] System Overview

[0039] This system works in conjunction with generative AI to analyze the content of presentations given by employees and feeds the results back to management meetings, allowing for input from multiple perspectives to be used to determine management policy. The system consists of the following main components:

[0040] Employee device: A device used to create and upload presentation content.

[0041] Server: A central processing unit that receives presentation content, sends it to the generative AI, receives the analysis results, and generates feedback results.

[0042] Generative AI: An artificial intelligence system that analyzes presentation content and provides insights.

[0043] Executive terminal: A device used to check presentation content and analysis results at management meetings and provide comments.

[0044] Management terminal: A device that receives all information to make final management decisions.

[0045] Program processing

[0046] Server Processing

[0047] The server first receives the presentation content from the employee. This content is uploaded from the employee's device and is immediately saved in a database after being received. The server then sends the saved presentation content to the generative AI. The generative AI analyzes the presentation content and returns the results to the server. The server then organizes and analyzes the analysis results, converting them into a format that is easy for humans to understand. This organized analysis result is then reported to the specialist team as a result of discussion.

[0048] Processing of employee terminals

[0049] The employee terminals include a function that allows them to create presentation materials and upload them to the server after completion. After uploading the presentation materials, employees can receive the results of the discussion generated by the server, review them, and revise the presentation materials as necessary.

[0050] Executive terminal processing

[0051] The executive terminals receive the results of the discussions distributed by the server and review them. During management meetings, executives can provide comments based on the presentation content and the generative AI's analysis results, contributing to management decisions.

[0052] Management terminal processing

[0053] The management terminal receives all information, including comments from executives and the results of the discussions, and is responsible for deciding the final management policy. The decided management policy is recorded on the server and notified to all employees.

[0054] Specific examples

[0055] Below is a concrete example of how this system works in practice.

[0056] 1. Employee A's actions: Employee A creates a presentation document on the market analysis of a new product and uploads it to the server from his / her terminal. The server receives the document and saves it in a database. The server then sends the saved document to the generative AI and requests its analysis.

[0057] 2. Generative AI Actions: The generative AI analyzes the received material and offers insights and suggestions for improvement. These suggestions are fed back to the server.

[0058] 3. Server actions: The server receives suggestions from the generative AI and organizes them into a format that is easy for humans to understand. The organized results are then sent to the expert team as a discussion result, and are then generated as materials for a management meeting.

[0059] 4. Actions of Executive B: Executive B receives the results of the discussion from the server before the management meeting and checks the contents. At the management meeting, he / she makes specific comments and proposals based on this material.

[0060] 5. President's Action: The president will consider all comments and the results of the discussion and decide on the market entry policy for the new product. The decision will be recorded on the server and notified to all employees.

[0061] The processing flow will be explained below.

[0062] Server Processing

[0063] Step 1: Receiving the presentation

[0064] The server receives the presentation content sent from the employee terminal.

[0065] The server listens for HTTP requests.

[0066] The received file is analyzed and stored in the database.

[0067] Step 2: Requesting analysis from generative AI

[0068] The server sends the saved presentation content to the generative AI.

[0069] The server retrieves the presentation content from the database.

[0070] The acquired data is converted into a format suitable for the generative AI API.

[0071] The converted data is sent to the generative AI and a response is awaited.

[0072] Step 3: Receive and organize the analysis results

[0073] The server receives the analysis results from the generative AI and organizes and analyzes them.

[0074] The server receives the response from ChatGPT in JSON format.

[0075] Analyze the received data and extract the key points.

[0076] The extracted key points are converted into a format that is easy for humans to understand.

[0077] Step 4: Notify the Specialist Team

[0078] The server notifies the expert team of the organized analysis results.

[0079] The server will send a notification email to the email address of the specialist team.

[0080] The notification email will include details such as the analysis results and feedback deadline.

[0081] Step 5: Generate and distribute materials on the results of the discussion

[0082] The server generates materials for management meetings based on the results of the expert team's discussions and distributes them to executives and management.

[0083] The server converts the results of the test into a report format.

[0084] Convert the report to PDF or presentation format and save it on the server.

[0085] The download link will be sent to each executive and management device.

[0086] Terminal (employee) processing

[0087] Step 1: Create your presentation materials

[0088] The terminal (employee) creates presentation materials.

[0089] The terminal (employee) creates documents using a word processor or presentation software.

[0090] Step 2: Upload your presentation materials

[0091] The terminal (employee) uploads the created documents to the server.

[0092] Employees access a dedicated web interface for uploading.

[0093] Select the file you created and click the Upload button.

[0094] Check the notification that the upload is complete.

[0095] Step 3: Receive and confirm the results

[0096] The terminal (employee) receives and checks the results of the test from the server.

[0097] Access the URL notified by the server and download the report of the test results.

[0098] Check the results of the discussion and revise the presentation materials as necessary.

[0099] Terminal (Executive) Processing

[0100] Step 1: Receive and confirm the results

[0101] The terminal (officer) receives and checks the test results distributed from the server.

[0102] Download the report of your test results via the link provided.

[0103] Review the contents of the report and prepare comments for the management meeting.

[0104] Step 2: Comments at the management meeting

[0105] The terminal (executive) makes comments at management meetings based on the content of employees' presentations and the analysis results of the generative AI.

[0106] By referring to the content of the presentation and the results of the discussion, specific feedback and suggestions will be provided.

[0107] Terminal (management) processing

[0108] Step 1: Review the results and presentation

[0109] The terminal (management) checks the executives' comments and the results of the discussion.

[0110] Use the provided link to retrieve all materials and review them before the meeting.

[0111] Prepare for decision-making, taking into account feedback from executives.

[0112] Step 2: Decide and record management policies

[0113] The terminal (management) decides on management policy based on the content of employees' presentations and the analysis results of the generative AI.

[0114] The final management policy will be formulated based on the discussions at the management meeting.

[0115] The decided management policy is recorded on the server and prepared for distribution as materials for the general meeting.

[0116] Example 1

[0117] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0118] Conventional systems for evaluating and providing feedback on presentation content have struggled to efficiently and objectively analyze presentations and provide rapid feedback based on the results. Delays in the analysis and feedback process are particularly problematic when a large number of presentation materials need to be analyzed in a short period of time. Other issues include converting the analysis results into a human-understandable format and ensuring transparency when formulating final management policies.

[0119] 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.

[0120] In this invention, the server includes means for receiving presentation content from employees, means for transmitting the received presentation content to a generative AI and requesting analysis, means for receiving analysis results from the generative AI and organizing and analyzing the analysis results, means for notifying the expert team of the organized and analyzed analysis results as a discussion result, means for generating the discussion results as materials for management meetings and distributing them to executive officers, means for employees to upload presentation materials and store the uploaded materials in a database, means for creating an API request to transmit the materials to the generative AI, and means for organizing the analysis results in a format that is easy for humans to understand. This improves the efficiency and objectivity of presentation analysis and feedback, enabling faster decision-making.

[0121] "Presentation content" refers to materials and documents prepared by employees to explain and report on various business matters.

[0122] "Generative AI" is an artificial intelligence system that performs advanced analysis and generation based on given information.

[0123] "Analysis results" refers to the insights and suggestions derived by the generative AI after analyzing the presentation content.

[0124] The "test results" are the feedback that the server organizes and analyzes based on the analysis results and presents to the specialist team.

[0125] A "specialized team" is a group of employees with high levels of expertise in a particular field or task.

[0126] "Materials for management meetings" are documents that compile the information and data necessary for management to make final decisions.

[0127] "Chief executive officer" refers to a person in a position with the authority to make important decisions in the management of a company.

[0128] A "database" is a system for systematically storing and managing data such as presentation content and analysis results.

[0129] An "API request" is a protocol that allows different software components to communicate with each other and utilize their functionality.

[0130] "Feedback" refers to advice and opinions to improve presentation materials based on the analysis results.

[0131] "Employee terminal" refers to a computer or digital device used by an employee for work purposes.

[0132] A "server" is the central processing unit for the entire system, and is a device that receives, stores, analyzes, and distributes data.

[0133] This invention is a system that efficiently and objectively analyzes the content of presentations created by employees and provides rapid feedback based on the results. This system is primarily composed of employee terminals, a server, a generative AI, executive terminals, and management terminals.

[0134] System Overview

[0135] The core of this system is the server, which receives and stores the presentation content and sends it to the generative AI. It also organizes the analysis results from the generative AI and generates materials for expert teams and management meetings as a result of discussions. The overall flow is as follows:

[0136] Hardware and software used

[0137] Employee devices: laptops and desktop computers used by employees for work (examples: Dell, HP)

[0138] Server: High-performance server (examples: AWS EC2, Microsoft Azure)

[0139] Generation AI: GPT-4 (OpenAI)

[0140] Program processing

[0141] Server Processing

[0142] The server receives the presentation content from employees. This content is uploaded from employee devices and is immediately saved in a database after being received. The server then sends the saved presentation content to the generative AI. The generative AI analyzes the presentation content and returns the results to the server. The server organizes the analysis results and converts them into a format that is easy for humans to understand. This organized analysis result is then reported to the specialist team as a result of discussions.

[0143] Processing employee terminals

[0144] The employee terminals include a function that allows employees to create presentation materials and upload them to the server after completion. Once the presentation materials are uploaded, the server receives them and stores them in a database. The server then sends the saved presentation content to the generative AI and requests it to be analyzed.

[0145] Generative AI processing

[0146] Generative AI analyzes the presentation content sent from the server, generates analytical results, and sends them back to the server. Generative AI provides insights into market analysis, competitive analysis, customer needs, and predicted trends, among other things.

[0147] Executive terminal processing

[0148] The executive terminals receive the results of the discussions distributed by the server and review them. During management meetings, executives can provide comments based on the presentation content and the generative AI's analysis results, contributing to management decisions.

[0149] Management terminal processing

[0150] The management terminal receives all information, including comments from executives and the results of the discussions, and is responsible for deciding the final management policy. The decided management policy is recorded on the server and notified to all employees.

[0151] Specific examples

[0152] A specific example of use is shown below.

[0153] 1. Employee A's actions: Employee A creates a presentation document on the market analysis of a new product and uploads it to the server from his / her terminal. The server receives the document and stores it in a database. The server then sends the saved document to the generative AI and requests its analysis.

[0154] 2. Generative AI Actions: The generative AI analyzes the received material and offers insights and suggestions for improvement. These suggestions are fed back to the server.

[0155] 3. Server actions: The server receives suggestions from the generative AI and organizes them into a format that is easy for humans to understand. The organized results are then reported to the expert team as a result of discussions, and are then generated as materials for management meetings.

[0156] 4. Actions of Executive B: Executive B receives the results of the discussion from the server before the management meeting and checks the contents. At the management meeting, he / she makes specific comments and proposals based on this material.

[0157] 5. President's Action: The president will consider all comments and the results of the discussion and decide on the market entry policy for the new product. The decision will be recorded on the server and notified to all employees.

[0158] Examples of prompt statements

[0159] Below is an example of a prompt sentence to input to the generative AI model.

[0160] Analyze a presentation about a new product market analysis and provide suggestions for improvement and insights. The main content of the presentation is as follows:

[0161] 1. Current market situation

[0162] 2. Competitive analysis

[0163] 3. Customer needs

[0164] 4. Predicted trends

[0165] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0166] Step 1: Create and upload presentation materials using employee devices

[0167] Specific explanation: An employee (the user) creates a presentation document related to his or her work. The document is based on a theme such as a market analysis of a new product or a competitive analysis. The software used is Microsoft PowerPoint or Google Slides. The completed document is sent to the server using the upload function on the employee's terminal.

[0168] Input and Output: The input is the presentation materials created by employees, and the output is the presentation materials uploaded to the server.

[0169] Specific operation: An employee creates a presentation in PowerPoint, clicks the "Upload" button to open a file selection dialog, selects the created presentation file, and uploads it to the server.

[0170] Step 2: The server receives the data and stores it in a database

[0171] Specific explanation: The server receives presentation materials uploaded from employee terminals. The received materials are saved in a temporary directory and then saved in a database.

[0172] Input and output: The input is the presentation materials uploaded from the employee terminals, and the output is the presentation materials saved in the database.

[0173] Specific operation: The server receives the HTTP request, saves the uploaded file in the specified temporary directory, and records the file metadata in the database after saving is complete.

[0174] Step 3: Send the data to the generative AI and receive the analysis results

[0175] Specifically, the server reads the presentation materials stored in the database and creates an API request to send them to the generative AI. The generative AI receives the request, analyzes the materials, and returns the analysis results to the server.

[0176] Input and output: The input is the presentation materials stored in the database, and the output is the analysis results from the generative AI.

[0177] Specific operation: The server reads the saved file and sends an HTTP POST request to the generative AI's API endpoint. The generative AI performs analysis and returns the results to the server as an HTTP response.

[0178] Step 4: Organizing the analysis results and generating the results by the server

[0179] Specifically, the server receives the analysis results sent back from the generative AI and organizes them into a format that is easy for humans to understand. These organized analysis results are then reported to the expert team as a result of the discussion.

[0180] Input and output: The input is the analysis result from the generative AI, and the output is the result of the discussion that is communicated to the expert team.

[0181] Specific operation: The analysis results are read from the database and formatted. The results are generated in HTML or PDF format and sent to the specialist team via email.

[0182] Step 5: Notify employees of the results of the discussion and confirm

[0183] Specific explanation: Employees check the results of the discussions sent by the server. They receive the results via email or the internal portal site, carefully examine the content, and revise the presentation materials as necessary.

[0184] Input and output: The input is the result of the discussion sent from the server, and the output is the revised presentation materials.

[0185] Specific actions: An employee opens the email and clicks on the attached link with the results of the discussion. The employee checks the results on the in-house portal site and uses the feedback to revise the presentation materials.

[0186] Step 6: Executives review and comment on the results

[0187] Specific explanation: The executive terminal receives the results of the discussion distributed from the server, checks the contents, and then compiles specific comments and suggestions into a document.

[0188] Input and Output: The input is the results of the discussion distributed by the server, and the output is a document containing comments and suggestions from the executives.

[0189] Specific operation: Executives log in to a dedicated portal site, download the results of the discussion, write comments based on the results, and upload them back to the server.

[0190] Step 7: Management decides on and notifies the final management policy

[0191] Specific explanation: The management terminal receives all information, including comments from executives and the results of discussions, and decides on the final management policy. The decided management policy is recorded on the server and notified to all employees.

[0192] Input and output: The input is the comments and feedback from executives, and the output is the final management policy.

[0193] What it does: Management reviews all feedback using meeting tablets or laptops, makes a final decision at the meeting, enters the decision into the server administration page, and sends an email notification to all employees.

[0194] (Application example 1)

[0195] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0196] Conventional autonomous vehicle operation management systems lack the ability to analyze operational data and provide real-time feedback, making it difficult to provide drivers and vehicle managers with sufficient operational assistance. Furthermore, executives were also unable to make effective management decisions based on operational data.

[0197] 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.

[0198] In this invention, the server includes means for receiving presentation content from employees, means for transmitting the received presentation content to a generative AI and requesting analysis, means for receiving analysis results from the generative AI and organizing and analyzing the analysis results, means for notifying the expert team of the organized and analyzed analysis results as a consultation result, means for analyzing operation data of autonomous vehicles based on the notified analysis results, means for providing feedback to drivers and vehicle managers in real time based on the analyzed operation data, and means for distributing the feedback results to executive officers. This enables analysis of operation data of autonomous vehicles and real-time feedback, enabling immediate responses to drivers and vehicle managers and accurate management decisions by executive officers.

[0199] "Employee" refers to a person who belongs to a company or organization and performs work.

[0200] "Presentation content" refers to written or digital materials that organize and present information or data related to a task or project.

[0201] "Generative AI" refers to artificial intelligence systems that analyze input data and generate insights and suggestions.

[0202] "Analysis results" refers to the information, including insights and suggestions, that generative AI provides after analyzing data.

[0203] "Bubble-thumping results" refers to evaluations and proposals made by a specialized team based on the analysis results of generative AI.

[0204] A "specialized team" refers to a group with high levels of expertise in a particular field.

[0205] "Chief executive" refers to a high-ranking manager who is responsible for determining the business strategies and policies of a company or organization.

[0206] "Autonomous vehicle" refers to a vehicle that has the ability to drive autonomously using software and sensors.

[0207] "Operation data" refers to various information about autonomous vehicles during operation, such as their speed, location, road conditions, and distance from other vehicles.

[0208] "Driver" refers to the person who drives the vehicle, but in the case of autonomous vehicles, it can also refer to the person who supervises or responds to emergencies.

[0209] "Vehicle manager" refers to a person whose role is to manage and supervise the operation status and maintenance of vehicles.

[0210] "Feedback" refers to improvement suggestions and evaluations provided based on the analysis results.

[0211] This invention is a system that uses generative AI to analyze presentation content created by employees and provides feedback based on the results. This system can be applied to managing operational data for autonomous vehicles and supporting management decisions.

[0212] System Overview

[0213] 1. Employee terminals:

[0214] Function: Employees create presentation materials and upload them to the server.

[0215] Hardware / Software: Personal computers, tablets (Windows, macOS, iOS, Android)

[0216] 2. Server:

[0217] Function: Receive presentation materials, send them to generative AI for analysis, and receive the results. Organize and analyze the analysis results and notify the expert team as a result of discussion. Also, analyze the operation data of autonomous vehicles based on the notified analysis results and provide feedback to drivers and vehicle managers.

[0218] Hardware: Server machine (Linux-based)

[0219] Software: Flask (web framework), requests (library for sending HTTP requests), JSON library

[0220] 3. Generative AI:

[0221] Function: Analyzes presentation content and operational data to provide suggestions for improvements and operational optimization.

[0222] Software: Natural language processing models (GPT-3, etc.)

[0223] 4. Specialized team terminal:

[0224] Function: Receives the results of the test from the server and evaluates or suggests additional steps as needed.

[0225] Hardware: Personal computers, tablets

[0226] Software: Text viewing and editing software (Microsoft Word, etc.)

[0227] 5. Traffic control terminal:

[0228] Function: Optimizes operations in real time based on autonomous vehicle operation data and analysis results, and provides feedback to drivers and fleet managers.

[0229] Hardware: Smartphones, tablets

[0230] Software: Dedicated fleet management application

[0231] 6. Executive Terminal:

[0232] Function: Receive feedback and discussion results from executives and decide on the final management policy.

[0233] Hardware: Personal computers, tablets

[0234] Software: Text viewing and editing software (Microsoft Word, etc.)

[0235] Program processing explanation

[0236] The server receives the presentation content from employees and stores it in a database. The received data is then sent to the generative AI for analysis. The generative AI analyzes the presentation content and operational data and feeds the results back to the server. Based on this analysis, the server provides real-time feedback to drivers and vehicle managers, and also organizes and analyzes the results as materials for management. The feedback includes optimal driving routes and recommendations for necessary maintenance.

[0237] For example, suppose an employee creates a presentation about the market launch of a new product and uploads it to a server. The server then sends the presentation to a generative AI and requests it to analyze it. The analysis results include a suggestion that "it is recommended to replace the brake pads at the next scheduled inspection," and this is immediately fed back to the driver via the fleet management terminal.

[0238] Example prompt sentence:

[0239] "Please provide us with optimal driving routes and maintenance suggestions based on the operational data of our autonomous vehicles."

[0240] This system will enable autonomous vehicles to operate more efficiently and safely.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] Employee terminals create presentation content and upload it to the server. The input is the presentation materials created by the employee, and the output is the presentation data saved on the server. Specifically, employees create materials using presentation software on their personal computers or tablets, and then send them using the server's upload function.

[0244] Step 2:

[0245] The server saves the presentation content received from the employee terminal in a database. The input is the presentation data sent from the employee terminal, and the output is the presentation content saved in the database. Specifically, the server's API receives the presentation data and saves it in the database in JSON format.

[0246] Step 3:

[0247] The server sends the saved presentation content to the generative AI and requests it to analyze it. The input is the presentation data saved in the database, and the output is the request sent to the generative AI. Specifically, the server generates an HTTP request and sends the data to the generative AI's API.

[0248] Step 4:

[0249] The generative AI analyzes the presentation content and returns the analysis results to the server. The input is the presentation data sent from the server, and the output is the analysis results. Specifically, the generative AI analyzes the data using a natural language processing model (e.g., GPT-3) and returns the results to the server in JSON format.

[0250] Step 5:

[0251] The server organizes and analyzes the analysis results from the generative AI and notifies the expert team of the results. The input is the analysis results returned from the generative AI, and the output is the results sent to the expert team. Specifically, the server reads the analysis results, extracts the necessary information, organizes them as the results, and notifies the expert team's terminal.

[0252] Step 6:

[0253] The expert team receives the test results from the server and checks and evaluates them. The input is the test results sent from the server, and the output is the expert team's evaluation and additional suggestions. Specifically, the expert team displays the test results on their terminal, and adds comments and suggestions for corrections as needed.

[0254] Step 7:

[0255] The server analyzes the autonomous vehicle's operational data based on the notified analysis results. The input is the analysis results of the generative AI and the autonomous vehicle's operational data, and the output is the analysis results of the operational data. Specifically, the server resends the operational data to the generative AI, which then performs a detailed analysis of the operational status.

[0256] Step 8:

[0257] The server provides real-time feedback to drivers and vehicle managers based on the analyzed operational data. The input is the analysis results of the operational data, and the output is feedback sent to the driver's or vehicle manager's device. Specifically, the server reads the generative AI's suggestions and sends operational optimization and maintenance suggestions in real time.

[0258] Step 9:

[0259] The server organizes and generates the analysis results as materials for management and distributes them to executive officers. The input is the analysis results and feedback from the expert team, and the output is the materials distributed to management. Specifically, the server integrates all feedback, generates presentation-style materials, and distributes them to the executive officers' terminals.

[0260] Step 10:

[0261] The executive officers decide on the final management policy based on the materials distributed from the server and record that policy. The input is the distributed materials, and the output is the recorded management policy. Specifically, the executive officers check the materials on their terminals, decide on the policy through discussion in a meeting, and record it on the server.

[0262] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0263] System Overview

[0264] This invention allows employees to analyze presentation content using a generative AI and an emotion engine, and feeds the results back to management meetings, providing input from multiple perspectives to help determine management policy. Furthermore, it recognizes and analyzes the emotions of employees and executives, and supports management decisions based on that data.

[0265] The system consists of the following main components:

[0266] Employee device: A device used to create and upload presentation content.

[0267] Server: A central processing unit that receives presentation content, sends it to the generative AI and emotion engine, receives the analysis results, and generates feedback.

[0268] Generative AI: An artificial intelligence system that analyzes presentation content and provides insights.

[0269] Emotion engine: A system that analyzes the emotions of employees and executives and reflects the results in feedback.

[0270] Executive terminal: A device used to check presentation content and analysis results at management meetings and provide comments.

[0271] Management terminal: A device that receives all information to make final management decisions.

[0272] Program processing

[0273] Server Processing

[0274] The server first receives the presentation content from the employee. After receiving the content uploaded from the employee's device, it saves it in a database. Next, the server sends the saved presentation content to the generative AI. The generative AI analyzes the presentation content and returns the results to the server. The server then organizes and analyzes the analysis results, converting them into a format that is easy for humans to understand. It then uses an emotion engine to analyze the emotions contained in the employee's presentation content and adds the results to the analysis content. The organized analysis results are notified to a specialist team and used as a discussion point.

[0275] Processing of employee terminals

[0276] The employee terminals include a function that allows them to create presentation materials and upload them to the server after completion. After uploading the presentation materials, employees can receive the results of the discussion generated by the server, review them, and revise the presentation materials as necessary.

[0277] Executive terminal processing

[0278] The executive terminal receives the results of the discussion distributed by the server and confirms their content. During management meetings, executives make comments based on the presentation content and the analysis results of the generative AI, and contribute to management decisions by taking into account the emotional data evaluated by the emotion engine.

[0279] Management terminal processing

[0280] The management terminal receives all information, including executives' comments and the results of the discussions, and is responsible for deciding the final management policy. The reactions of executives and management during meetings are also analyzed in real time by the emotion engine, and feedback is provided based on that emotional data. The decided management policy is recorded on the server and notified to all employees.

[0281] Specific examples

[0282] Below is a concrete example of how this system works in practice.

[0283] 1. Employee A's actions: Employee A creates a presentation document on the market analysis of a new product and uploads it to the server from his / her terminal. The server receives the document and stores it in a database. The server then sends the saved document to the generative AI and emotion engine for analysis.

[0284] 2. Behavior of generative AI and emotion engine: The generative AI analyzes the received materials and suggests insights and improvements. The emotion engine analyzes the emotions contained in the presentation content and feeds the results back to the server.

[0285] 3. Server actions: The server receives suggestions from the generative AI and emotion engine, organizes them into a format that is easy for humans to understand, and notifies the expert team of the organized results as a discussion result, and then generates materials for the management meeting.

[0286] 4. Actions of Executive B: Executive B receives the results of the discussion from the server before the management meeting and checks the contents. At the management meeting, he makes specific comments and proposals based on this material, and expresses his opinion while taking into consideration the emotional data.

[0287] 5. President's Actions: The president considers all comments and the results of the discussions and decides on the market entry policy for the new product. During the meeting, the emotion engine analyzes the reactions of executives and management in real time and takes that feedback into account when deciding on a policy. The decision is recorded on the server and notified to all employees.

[0288] The processing flow will be explained below.

[0289] Server Processing

[0290] Step 1: Receiving the presentation

[0291] The server receives the presentation content sent from the employee terminal.

[0292] The server listens for HTTP requests.

[0293] The received file is analyzed and stored in the database.

[0294] Step 2: Request analysis from generative AI and emotion engine

[0295] The server sends the saved presentation content to the generative AI and emotion engine.

[0296] The server retrieves the presentation content from the database.

[0297] The acquired data is converted into a format suitable for the generative AI API.

[0298] The converted data is sent to the generative AI and a response is awaited.

[0299] At the same time, the presentation content is sent to the emotion engine for emotion analysis.

[0300] Step 3: Receive and organize the analysis results

[0301] The server receives, organizes, and analyzes the analysis results from the generative AI and emotion engine.

[0302] The server receives the response from the generative AI in JSON format.

[0303] Analyze the received data and extract the key points.

[0304] It receives the emotion analysis results from the emotion engine and adds them to the analysis results of the generative AI.

[0305] The extracted key points and sentiment analysis results are converted into a format that is easy for humans to understand.

[0306] Step 4: Notify the Specialist Team

[0307] The server notifies the expert team of the organized analysis results.

[0308] The server will send a notification email to the email address of the specialist team.

[0309] The notification email will include details such as the analysis results and feedback deadline.

[0310] Step 5: Generate and distribute materials on the results of the discussion

[0311] The server generates materials for management meetings based on the results of the expert team's discussions and distributes them to executives and management.

[0312] The server converts the results of the test into a report format.

[0313] Convert the report to PDF or presentation format and save it on the server.

[0314] The download link will be sent to each executive and management device.

[0315] Terminal (employee) processing

[0316] Step 1: Create your presentation materials

[0317] The terminal (employee) creates presentation materials.

[0318] The terminal (employee) creates documents using a word processor or presentation software.

[0319] Step 2: Upload your presentation materials

[0320] The terminal (employee) uploads the created documents to the server.

[0321] Employees access a dedicated web interface for uploading.

[0322] Select the file you created and click the Upload button.

[0323] Check the notification that the upload is complete.

[0324] Step 3: Receive and confirm the results

[0325] The terminal (employee) receives and checks the results of the test from the server.

[0326] Access the URL notified by the server and download the report of the test results.

[0327] Check the results of the discussion and revise the presentation materials as necessary.

[0328] Terminal (Executive) Processing

[0329] Step 1: Receive and confirm the results

[0330] The terminal (officer) receives and checks the test results distributed from the server.

[0331] Download the report of your test results via the link provided.

[0332] Review the contents of the report and prepare comments for the management meeting.

[0333] Step 2: Comments at the management meeting

[0334] The terminal (executive) makes comments at management meetings based on the content of employees' presentations and the analysis results of the generative AI and emotion engine.

[0335] By referring to the content of the presentation and the results of the discussion, specific feedback and suggestions will be provided.

[0336] The results of the emotion engine are also taken into account to assess their relevance to the emotional responses of executives.

[0337] Terminal (management) processing

[0338] Step 1: Review the results and presentation

[0339] The terminal (management) checks the executives' comments and the results of the discussion.

[0340] Use the provided link to retrieve all materials and review them before the meeting.

[0341] Prepare for decision-making, taking into account feedback from executives.

[0342] Step 2: Decide and record management policies

[0343] The terminal (management) decides on management policy based on the content of employees' presentations, the analysis results of the generative AI, and the analysis results of the emotion engine.

[0344] The final management policy will be formulated based on the discussions at the management meeting.

[0345] During meetings, the emotion engine analyzes the reactions of executives and management in real time and takes that feedback into consideration when deciding on policy.

[0346] The decided management policy is recorded on the server and prepared for distribution as materials for the general meeting.

[0347] Example 2

[0348] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0349] Conventional management decision-making systems have difficulty analyzing the content of employee presentations and providing useful feedback, and even evaluating the emotions expressed in the presentations. As a result, it has been impossible to obtain input from multiple perspectives, making it difficult to make optimal management decisions. The present invention aims to solve these problems and support efficient and accurate management decisions.

[0350] 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.

[0351] In this invention, the server includes means for receiving presentation content from employees, means for transmitting the received presentation content to a generative artificial intelligence (AI) and requesting analysis, means for receiving analysis results from the AI ​​and organizing and analyzing the analysis results, means for analyzing emotions contained in the presentation and reflecting the results in feedback, and means for notifying the specialized team of the organized and analyzed analysis results as discussion results, generating the discussion results and emotion analysis results as materials for management meetings and distributing them to executive officers. This makes it possible to obtain input from multiple perspectives through the analysis of employees' presentation content and emotional evaluation, enabling more accurate management decisions.

[0352] An "employee" is someone who works for a company or organization.

[0353] "Presentation content" refers to materials and explanations that compile information and proposals related to the business of a company or organization.

[0354] "Receiving" is the act of receiving transmitted data.

[0355] "Generative AI" refers to artificial intelligence systems that mimic human-like intellectual activity and provide analysis and insights tailored to specific purposes.

[0356] "Analysis" is the act of examining data or information in detail to clarify its structure and content.

[0357] "Organization and analysis" means classifying and summarizing the received data and putting it into an easy-to-understand form.

[0358] "Bubble-talking results" refers to feedback based on the analysis results and suggestions obtained at the initial stage, used to consider future improvements and directions.

[0359] A "specialized team" is a group of people with specialized knowledge and skills in a particular field.

[0360] "Notification" is the act of communicating specific information to other people.

[0361] "Chief executives" are people in positions that make important management decisions in a company or organization.

[0362] "Distribution" is the act of sending materials or information to multiple people.

[0363] "Emotion analysis means" refers to systems and technologies that read emotions from people's conversations and writings, and analyze and evaluate them.

[0364] "Feedback" is the act of conveying evaluations and opinions about behavior or results.

[0365] A "final management policy" is an important policy that determines the organization's future direction and goals.

[0366] "Recording" is the act of saving events or data for future reference.

[0367] MODE FOR CARRYING OUT THE INVENTION

[0368] The present invention is a system that allows employees to analyze presentation content using a generative artificial intelligence (AI) in conjunction with an emotion analysis system, and then feeds the results back to a management meeting, thereby obtaining input from multiple perspectives and deciding on management policies. Specific embodiments for implementing this system are described below.

[0369] System Overview

[0370] The system consists of the following main components: employee terminals, a server, generative artificial intelligence, an emotion analysis system, executive terminals, and management terminals.

[0371] Hardware and software used

[0372] Employee devices: Devices used by employees to create and upload presentation materials. Examples include PCs and tablets.

[0373] Server: A central processing unit that receives presentation content and sends it to the generative AI and emotion analysis system. The server has a database and manages and analyzes information.

[0374] Generative AI: An AI system that analyzes presentation content and provides insights. An example is OpenAI's GPT-4.

[0375] Emotion analysis system: A system that analyzes the content of a presentation and evaluates the emotional tone. An example is the IBM Watson Tone Analyzer.

[0376] Executive terminal: A device used in management meetings to review presentation content and analysis results and provide comments. Examples include PCs and tablets.

[0377] Executive terminals: Devices that receive information to make final management decisions. These also include PCs and tablets.

[0378] Specific examples of processing

[0379] 1. Employee A's actions: Employee A uses his / her own employee terminal to create a presentation document on the market analysis of a new product. After completing the document, he / she uploads the document to the server from his / her employee terminal.

[0380] 2. Server processing: The server receives the uploaded presentation materials and stores them in a database. It then sends the stored materials to GPT-4, a generative AI, using the following prompt:

[0381] Analyze "New Product Market Analysis Presentation" and provide insights into what parts can be improved. Also, take into account the results of the sentiment analysis system and comment on the emotional tone and audience reaction.

[0382] 3. Generative AI analysis: Generative AI analyzes the received materials and generates insights and suggestions on what can be improved. For example, it might say, "Chapter 3 of your presentation lacks specificity, so adding more detailed data would be helpful."

[0383] 4. Analysis by the emotion analysis system: The emotion analysis system analyzes the emotions contained in the content of the presentation materials and generates results such as "70% positive emotions." These results are sent back to the server.

[0384] 5. Server result integration: The server receives the results from the generative AI and the emotion analysis system, organizes them into a format that is easy for humans to understand, and finally compiles them as "test results."

[0385] 6. Feedback notification: The server notifies the employee and executive terminals of the results of the discussion. Employees can review the feedback and revise their presentation materials. Executives can also use this information to make comments and suggestions at management meetings.

[0386] Management meeting and final decision

[0387] 1. Operation of executive terminals: Executives receive the results of the discussion from the server before the management meeting and check the details. They also use the executive terminals during the meeting to provide specific comments based on the content of the presentation and the analysis results of the generative AI.

[0388] 2. Operation of management terminals: The management team makes the final decision on the management policy. The reactions of executives and management during the meeting are analyzed in real time by the emotion analysis system, and this feedback is also taken into consideration. The final management policy is recorded on the server and notified to all employees.

[0389] As a result, the present invention can provide a system that evaluates the content of employees' presentations from multiple angles and supports efficient and accurate management decisions.

[0390] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0391] Step 1:

[0392] Employees prepare presentation materials.

[0393] Input: Data about the topic and content of the presentation

[0394] Specific operations: Employees use their own devices to create presentation materials and enter market analysis data and proposal details into each slide.

[0395] Output: Finished presentation file

[0396] Step 2:

[0397] The employee terminal uploads the presentation materials to the server.

[0398] Input: Completed presentation file

[0399] Specific operation: The employee performs the upload operation on the terminal and sends the presentation materials to the server.

[0400] Output: Presentation materials uploaded to the server

[0401] Step 3:

[0402] The server stores the received presentation materials in a database.

[0403] Input: Uploaded presentation materials

[0404] Specific operation: The server temporarily loads the presentation materials into memory and stores them in a database.

[0405] Output: Presentation materials stored in a database

[0406] Step 4:

[0407] The server transmits the presentation materials to the generative artificial intelligence.

[0408] Input: Presentation materials and prompts stored in the database

[0409] Specific operation: The server sends the presentation to a generative AI (e.g., GPT-3) via an API and requests it to analyze it. The prompt is "Please provide your insights on which parts of this presentation can be improved."

[0410] Output: Data sent to the generative artificial intelligence

[0411] Step 5:

[0412] Generative AI analyzes presentation materials and provides insights.

[0413] Input: Presentation materials and analysis requests

[0414] Specific operation: Generative AI analyzes the content of presentation materials and generates improvements and insights. For example, it generates feedback such as, "Chapter 3 is lacking in specificity, so more detailed data should be added."

[0415] Output: Analysis results returned from the generative AI

[0416] Step 6:

[0417] The server receives the results from the generative artificial intelligence and organizes the data.

[0418] Input: Analysis results from generative artificial intelligence

[0419] Specific operation: The server summarizes and organizes the analysis results received in a format that is easy for humans to understand, for example, by converting the analysis results into bullet points or diagrams.

[0420] Output: Organized analysis results

[0421] Step 7:

[0422] The server sends the organized analysis results to the emotion analysis system.

[0423] Input: Organized analysis results

[0424] Specific operation: The server sends the organized analysis results to an emotion analysis system (e.g., IBM Watson Tone Analyzer) and requests emotion analysis.

[0425] Output: Data sent to the sentiment analysis system

[0426] Step 8:

[0427] The emotion analysis system analyzes the emotions contained in presentation materials.

[0428] Input: Analysis results after receiving a request for emotion analysis

[0429] Specific operation: The emotion analysis system analyzes the content of the presentation materials and generates emotion data such as "70% positive tone."

[0430] Output: Emotion analysis results

[0431] Step 9:

[0432] The server receives the sentiment analysis results and generates the final feedback.

[0433] Input: Sentiment analysis results and previously organized analysis results

[0434] Specific operation: The server integrates the sentiment analysis results with the previously organized analysis results to generate an overall feedback document.

[0435] Output: Final feedback document

[0436] Step 10:

[0437] The server notifies the employee terminal and the executive terminal of the final feedback.

[0438] Input: Final feedback document

[0439] Specific operation: The server sends the final feedback document to the terminals of the employees and executives and notifies them.

[0440] Output: Employee and executive terminals that received the notification

[0441] Step 11:

[0442] The employee terminals then revise the presentation materials based on the feedback.

[0443] Input: Final feedback document

[0444] Specific action: The employee reviews the feedback and makes revisions to the presentation as needed, for example adding more detailed data to Chapter 3.

[0445] Output: Revised presentation

[0446] Step 12:

[0447] The executive terminals provide comments based on feedback at management meetings.

[0448] Input: Final feedback document and revised presentation

[0449] Specific actions: Executives will make comments and suggestions at the management meeting, referring to the final feedback and revised presentation materials.

[0450] Output: Comments from executives at management meetings

[0451] Step 13:

[0452] The management terminal decides the final management policy.

[0453] Input: Executive comments at management meetings and final feedback

[0454] Specific actions: Management decides on the final management policy based on the comments and feedback provided during the meeting.

[0455] Output: Decided management policy

[0456] Step 14:

[0457] The server notifies all employees of the decided management policy.

[0458] Input: Decided management policy

[0459] Specific operation: The server records the management policy decision and sends a notification to all employee terminals, for example, a message saying, "A decision has been made to enter the market for a new product."

[0460] Output: All employee terminals that received the notification

[0461] (Application example 2)

[0462] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0463] In modern factories, improving the efficiency and optimizing of manufacturing processes are important issues. However, in many factories, workers' explanations and presentations are based on intuition, and specific areas for improvement are often overlooked. In addition, opportunities for management and work teams to receive accurate feedback are limited, making rapid improvement a difficult challenge. Against this backdrop, there is a need for a system that can analyze the content of factory workers' explanations and provide immediate feedback on the results.

[0464] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the explanation of the manufacturing process from the factory worker, means for sending the received explanation to the generative AI and requesting analysis, means for receiving the analysis results from the generative AI and organizing and analyzing the analysis results, means for notifying the organized and analyzed analysis results to the work team as efficiency results, and means for generating the efficiency results as materials for a factory meeting and distributing them to management executives. This enables the explanation of the manufacturing process to be analyzed and the results to be quickly fed back.

[0465] "Manufacturing process" refers to the manufacturing process and procedures for products in the manufacturing industry.

[0466] "Explanatory content" refers to information and materials provided by factory workers about manufacturing processes and efficiency proposals.

[0467] "Receiving means" refers to the functions and devices that receive explanations from factory workers and input them into the system.

[0468] "Generative AI" refers to artificial intelligence systems that analyze human-created materials and information and provide insights and suggestions for improvement.

[0469] "Analysis means" refers to the functions and devices that request the generative AI to analyze the content of the explanation and receive the results.

[0470] "Organization and analysis means" refers to the functions and devices that compile the analysis results from generative AI and convert them into an easy-to-understand format.

[0471] "Efficiency results" refers to improvements and efficiency proposals for the manufacturing process proposed based on the analysis results of generative AI.

[0472] "Notification means" refers to the functions and devices used to report efficiency results to work teams and other stakeholders.

[0473] "Factory meeting materials" refers to materials that compile information on improvements to manufacturing processes and proposals for efficiency, and are used to discuss these at meetings.

[0474] "Management officers" refers to officials responsible for the operation and management of a factory.

[0475] "Feedback" refers to reactions to the analysis results and efficiency improvement proposals, and suggestions for improvements. In the present invention, feedback is provided to the worker so that the worker can improve the content of the explanation.

[0476] System Overview

[0477] This invention is a system to support the improvement of efficiency in manufacturing processes. Specifically, it uses generative AI and an emotion engine to analyze the explanations of manufacturing processes provided by factory workers, and provides immediate feedback on the results of the analysis to suggest improvements and improvements to improve efficiency.

[0478] Hardware and software used

[0479] This system uses the following hardware and software:

[0480] Smart glasses: For example, Google Glass or Vuzix Blade.

[0481] EmotionEngine: A software module for emotion analysis.

[0482] AIAnalysis: A generative AI module for analyzing narrative content and providing insights.

[0483] Python programming language: Used to implement programs.

[0484] System Operation

[0485] 1. Upload your presentation:

[0486] Factory workers upload a description of the manufacturing process to the application through smart glasses, either by recording it as voice or by typing it in text format.

[0487] 2. Analysis process:

[0488] The server sends the uploaded explanation to the generative AI module (AIAnalysis) for analysis. The generative AI analyzes the explanation and suggests improvements and efficiency suggestions. The explanation is also sent to the Emotion Engine module for emotional analysis.

[0489] 3. Feedback generation:

[0490] The server receives the results of generative AI and emotion analysis, organizes and analyzes them, and notifies the work team and management executives of the organized analysis results as "efficiency results."

[0491] 4. View Feedback:

[0492] Workers can view analysis results and feedback in real time through the smart glasses' display, allowing them to make any necessary improvements immediately.

[0493] Specific examples

[0494] Factory worker action: A worker creates a presentation to explain a "new manufacturing process" and uploads it using smart glasses.

[0495] How the generative AI and emotion engine work: Uploaded presentation materials are sent to AIAnalysis and EmotionEngine for analysis. AIAnalysis suggests areas for improvement, and EmotionEngine evaluates the emotions contained in the presentation content.

[0496] Server operation: The server receives these analysis results, organizes them into a format that is easy for humans to understand, and notifies the work team and management executives of the organized results as "efficiency results."

[0497] Real-time feedback: Workers can view analysis results through the smart glasses display and immediately implement improvements to the manufacturing process.

[0498] Prompt Sentence Examples

[0499] Analyze a presentation deck for a new manufacturing process. Evaluate areas for improvement, as well as the tone and emotion of the presentation.

[0500] This system will enable the efficiency of manufacturing processes within the factory, enabling work teams and management to quickly and accurately implement improvement proposals.

[0501] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0502] Step 1:

[0503] Upload a presentation

[0504] How it works: A worker uses smart glasses to upload a description of a manufacturing process to the application, either as a voice recording or text input.

[0505] Input: Description of the manufacturing process (audio or text data).

[0506] Output: The server has received the description.

[0507] Step 2:

[0508] Data reception by the server

[0509] How it works: The server receives the uploaded description and stores it in a database.

[0510] Input: The uploaded description.

[0511] Output: The description is saved in the database.

[0512] Step 3:

[0513] Analysis request to generative AI

[0514] Operation: The server sends the saved description to the generative AI module (AIAnalysis) and requests it to analyze it.

[0515] Input: Description stored in the database.

[0516] Output: The status of the analysis request.

[0517] Step 4:

[0518] Analysis of generative AI

[0519] How it works: Generative AI (AIAnalysis) analyzes the content of the explanation and suggests improvements and efficiency suggestions.

[0520] Input: The description sent by the server.

[0521] Output: Analysis results (improvements and efficiency suggestions).

[0522] Step 5:

[0523] Emotion analysis using an emotion engine

[0524] How it works: The server sends the description to the Emotion Engine and requests it to analyze the emotions contained in the content.

[0525] Input: The description sent by the server.

[0526] Output: Emotion analysis results.

[0527] Step 6:

[0528] Receiving analysis results and sentiment analysis results

[0529] How it works: The server receives the analysis results from the generative AI and the emotion analysis results from the emotion engine.

[0530] Input: Analysis results of the generative AI, emotion analysis results of the emotion engine.

[0531] Output: Analysis results and sentiment analysis results are aggregated on the server.

[0532] Step 7:

[0533] Generate feedback

[0534] Operation: The server organizes the analysis results and sentiment analysis results it receives, converts them into a format that is easy for humans to understand, and notifies them as "efficiency results."

[0535] Input: Analysis results, sentiment analysis results.

[0536] Output: Organized efficiency results.

[0537] Step 8:

[0538] Notification to work teams and management officers

[0539] How it works: The server notifies the work team and management of the efficiency results, providing real-time feedback through the smart glasses display.

[0540] Input: Organized efficiency results.

[0541] Output: The work team and management receive the efficiency results.

[0542] Prompt Sentence Examples

[0543] Analyze a presentation deck for a new manufacturing process. Evaluate areas for improvement, as well as the tone and emotion of the presentation.

[0544] These processing steps enable a series of steps to analyze the explanations of factory workers and provide immediate feedback, enabling work teams and management executives to quickly and accurately improve the manufacturing process.

[0545] 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.

[0546] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0547] 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.

[0548] [Second embodiment]

[0549] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0550] 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.

[0551] 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).

[0552] 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.

[0553] 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.

[0554] 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).

[0555] 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.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0561] System Overview

[0562] This system works in conjunction with generative AI to analyze the content of presentations given by employees and feeds the results back to management meetings, allowing for input from multiple perspectives to be used to determine management policy. The system consists of the following main components:

[0563] Employee device: A device used to create and upload presentation content.

[0564] Server: A central processing unit that receives presentation content, sends it to the generative AI, receives the analysis results, and generates feedback results.

[0565] Generative AI: An artificial intelligence system that analyzes presentation content and provides insights.

[0566] Executive terminal: A device used to check presentation content and analysis results at management meetings and provide comments.

[0567] Management terminal: A device that receives all information to make final management decisions.

[0568] Program processing

[0569] Server Processing

[0570] The server first receives the presentation content from the employee. This content is uploaded from the employee's device and is immediately saved in a database after being received. The server then sends the saved presentation content to the generative AI. The generative AI analyzes the presentation content and returns the results to the server. The server then organizes and analyzes the analysis results, converting them into a format that is easy for humans to understand. This organized analysis result is then reported to the specialist team as a result of discussion.

[0571] Processing of employee terminals

[0572] The employee terminals include a function that allows them to create presentation materials and upload them to the server after completion. After uploading the presentation materials, employees can receive the results of the discussion generated by the server, review them, and revise the presentation materials as necessary.

[0573] Executive terminal processing

[0574] The executive terminals receive the results of the discussions distributed by the server and review them. During management meetings, executives can provide comments based on the presentation content and the generative AI's analysis results, contributing to management decisions.

[0575] Management terminal processing

[0576] The management terminal receives all information, including comments from executives and the results of the discussions, and is responsible for deciding the final management policy. The decided management policy is recorded on the server and notified to all employees.

[0577] Specific examples

[0578] Below is a concrete example of how this system works in practice.

[0579] 1. Employee A's actions: Employee A creates a presentation document on the market analysis of a new product and uploads it to the server from his / her terminal. The server receives the document and saves it in a database. The server then sends the saved document to the generative AI and requests its analysis.

[0580] 2. Generative AI Actions: The generative AI analyzes the received material and offers insights and suggestions for improvement. These suggestions are fed back to the server.

[0581] 3. Server actions: The server receives suggestions from the generative AI and organizes them into a format that is easy for humans to understand. The organized results are then sent to the expert team as a discussion result, and are then generated as materials for a management meeting.

[0582] 4. Actions of Executive B: Executive B receives the results of the discussion from the server before the management meeting and checks the contents. At the management meeting, he / she makes specific comments and proposals based on this material.

[0583] 5. President's Action: The president will consider all comments and the results of the discussion and decide on the market entry policy for the new product. The decision will be recorded on the server and notified to all employees.

[0584] The processing flow will be explained below.

[0585] Server Processing

[0586] Step 1: Receiving the presentation

[0587] The server receives the presentation content sent from the employee terminal.

[0588] The server listens for HTTP requests.

[0589] The received file is analyzed and stored in the database.

[0590] Step 2: Requesting analysis from generative AI

[0591] The server sends the saved presentation content to the generative AI.

[0592] The server retrieves the presentation content from the database.

[0593] The acquired data is converted into a format suitable for the generative AI API.

[0594] The converted data is sent to the generative AI and a response is awaited.

[0595] Step 3: Receive and organize the analysis results

[0596] The server receives the analysis results from the generative AI and organizes and analyzes them.

[0597] The server receives the response from ChatGPT in JSON format.

[0598] Analyze the received data and extract the key points.

[0599] The extracted key points are converted into a format that is easy for humans to understand.

[0600] Step 4: Notify the Specialist Team

[0601] The server notifies the expert team of the organized analysis results.

[0602] The server will send a notification email to the email address of the specialist team.

[0603] The notification email will include details such as the analysis results and feedback deadline.

[0604] Step 5: Generate and distribute materials on the results of the discussion

[0605] The server generates materials for management meetings based on the results of the expert team's discussions and distributes them to executives and management.

[0606] The server converts the results of the test into a report format.

[0607] Convert the report to PDF or presentation format and save it on the server.

[0608] The download link will be sent to each executive and management device.

[0609] Terminal (employee) processing

[0610] Step 1: Create your presentation materials

[0611] The terminal (employee) creates presentation materials.

[0612] The terminal (employee) creates documents using a word processor or presentation software.

[0613] Step 2: Upload your presentation materials

[0614] The terminal (employee) uploads the created documents to the server.

[0615] Employees access a dedicated web interface for uploading.

[0616] Select the file you created and click the Upload button.

[0617] Check the notification that the upload is complete.

[0618] Step 3: Receive and confirm the results

[0619] The terminal (employee) receives and checks the results of the test from the server.

[0620] Access the URL notified by the server and download the report of the test results.

[0621] Check the results of the discussion and revise the presentation materials as necessary.

[0622] Terminal (Executive) Processing

[0623] Step 1: Receive and confirm the results

[0624] The terminal (officer) receives and checks the test results distributed from the server.

[0625] Download the report of your test results via the link provided.

[0626] Review the contents of the report and prepare comments for the management meeting.

[0627] Step 2: Comments at the management meeting

[0628] The terminal (executive) makes comments at management meetings based on the content of employees' presentations and the analysis results of the generative AI.

[0629] By referring to the content of the presentation and the results of the discussion, specific feedback and suggestions will be provided.

[0630] Terminal (management) processing

[0631] Step 1: Review the results and presentation

[0632] The terminal (management) checks the executives' comments and the results of the discussion.

[0633] Use the provided link to retrieve all materials and review them before the meeting.

[0634] Prepare for decision-making, taking into account feedback from executives.

[0635] Step 2: Decide and record management policies

[0636] The terminal (management) decides on management policy based on the content of employees' presentations and the analysis results of the generative AI.

[0637] The final management policy will be formulated based on the discussions at the management meeting.

[0638] The decided management policy is recorded on the server and prepared for distribution as materials for the general meeting.

[0639] Example 1

[0640] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0641] Conventional systems for evaluating and providing feedback on presentation content have struggled to efficiently and objectively analyze presentations and provide rapid feedback based on the results. Delays in the analysis and feedback process are particularly problematic when a large number of presentation materials need to be analyzed in a short period of time. Other issues include converting the analysis results into a human-understandable format and ensuring transparency when formulating final management policies.

[0642] 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.

[0643] In this invention, the server includes means for receiving presentation content from employees, means for transmitting the received presentation content to a generative AI and requesting analysis, means for receiving analysis results from the generative AI and organizing and analyzing the analysis results, means for notifying the expert team of the organized and analyzed analysis results as a discussion result, means for generating the discussion results as materials for management meetings and distributing them to executive officers, means for employees to upload presentation materials and store the uploaded materials in a database, means for creating an API request to transmit the materials to the generative AI, and means for organizing the analysis results in a format that is easy for humans to understand. This improves the efficiency and objectivity of presentation analysis and feedback, enabling faster decision-making.

[0644] "Presentation content" refers to materials and documents prepared by employees to explain and report on various business matters.

[0645] "Generative AI" is an artificial intelligence system that performs advanced analysis and generation based on given information.

[0646] "Analysis results" refers to the insights and suggestions derived by the generative AI after analyzing the presentation content.

[0647] The "test results" are the feedback that the server organizes and analyzes based on the analysis results and presents to the specialist team.

[0648] A "specialized team" is a group of employees with high levels of expertise in a particular field or task.

[0649] "Materials for management meetings" are documents that compile the information and data necessary for management to make final decisions.

[0650] "Chief executive officer" refers to a person in a position with the authority to make important decisions in the management of a company.

[0651] A "database" is a system for systematically storing and managing data such as presentation content and analysis results.

[0652] An "API request" is a protocol that allows different software components to communicate with each other and utilize their functionality.

[0653] "Feedback" refers to advice and opinions to improve presentation materials based on the analysis results.

[0654] "Employee terminal" refers to a computer or digital device used by an employee for work purposes.

[0655] A "server" is the central processing unit for the entire system, and is a device that receives, stores, analyzes, and distributes data.

[0656] This invention is a system that efficiently and objectively analyzes the content of presentations created by employees and provides rapid feedback based on the results. This system is primarily composed of employee terminals, a server, a generative AI, executive terminals, and management terminals.

[0657] System Overview

[0658] The core of this system is the server, which receives and stores the presentation content and sends it to the generative AI. It also organizes the analysis results from the generative AI and generates materials for expert teams and management meetings as a result of discussions. The overall flow is as follows:

[0659] Hardware and software used

[0660] Employee devices: laptops and desktop computers used by employees for work (examples: Dell, HP)

[0661] Server: High-performance server (examples: AWS EC2, Microsoft Azure)

[0662] Generation AI: GPT-4 (OpenAI)

[0663] Program processing

[0664] Server Processing

[0665] The server receives the presentation content from employees. This content is uploaded from employee devices and is immediately saved in a database after being received. The server then sends the saved presentation content to the generative AI. The generative AI analyzes the presentation content and returns the results to the server. The server organizes the analysis results and converts them into a format that is easy for humans to understand. This organized analysis result is then reported to the specialist team as a result of discussions.

[0666] Processing employee terminals

[0667] The employee terminals include a function that allows employees to create presentation materials and upload them to the server after completion. Once the presentation materials are uploaded, the server receives them and stores them in a database. The server then sends the saved presentation content to the generative AI and requests it to be analyzed.

[0668] Generative AI processing

[0669] Generative AI analyzes the presentation content sent from the server, generates analytical results, and sends them back to the server. Generative AI provides insights into market analysis, competitive analysis, customer needs, and predicted trends, among other things.

[0670] Executive terminal processing

[0671] The executive terminals receive the results of the discussions distributed by the server and review them. During management meetings, executives can provide comments based on the presentation content and the generative AI's analysis results, contributing to management decisions.

[0672] Management terminal processing

[0673] The management terminal receives all information, including comments from executives and the results of the discussions, and is responsible for deciding the final management policy. The decided management policy is recorded on the server and notified to all employees.

[0674] Specific examples

[0675] A specific example of use is shown below.

[0676] 1. Employee A's actions: Employee A creates a presentation document on the market analysis of a new product and uploads it to the server from his / her terminal. The server receives the document and stores it in a database. The server then sends the saved document to the generative AI and requests its analysis.

[0677] 2. Generative AI Actions: The generative AI analyzes the received material and offers insights and suggestions for improvement. These suggestions are fed back to the server.

[0678] 3. Server actions: The server receives suggestions from the generative AI and organizes them into a format that is easy for humans to understand. The organized results are then reported to the expert team as a result of discussions, and are then generated as materials for management meetings.

[0679] 4. Actions of Executive B: Executive B receives the results of the discussion from the server before the management meeting and checks the contents. At the management meeting, he / she makes specific comments and proposals based on this material.

[0680] 5. President's Action: The president will consider all comments and the results of the discussion and decide on the market entry policy for the new product. The decision will be recorded on the server and notified to all employees.

[0681] Examples of prompt statements

[0682] Below is an example of a prompt sentence to input to the generative AI model.

[0683] Analyze a presentation about a new product market analysis and provide suggestions for improvement and insights. The main content of the presentation is as follows:

[0684] 1. Current market situation

[0685] 2. Competitive analysis

[0686] 3. Customer needs

[0687] 4. Predicted trends

[0688] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0689] Step 1: Create and upload presentation materials using employee devices

[0690] Specific explanation: An employee (the user) creates a presentation document related to his or her work. The document is based on a theme such as a market analysis of a new product or a competitive analysis. The software used is Microsoft PowerPoint or Google Slides. The completed document is sent to the server using the upload function on the employee's terminal.

[0691] Input and Output: The input is the presentation materials created by employees, and the output is the presentation materials uploaded to the server.

[0692] Specific operation: An employee creates a presentation in PowerPoint, clicks the "Upload" button to open a file selection dialog, selects the created presentation file, and uploads it to the server.

[0693] Step 2: The server receives the data and stores it in a database

[0694] Specific explanation: The server receives presentation materials uploaded from employee terminals. The received materials are saved in a temporary directory and then saved in a database.

[0695] Input and output: The input is the presentation materials uploaded from the employee terminals, and the output is the presentation materials saved in the database.

[0696] Specific operation: The server receives the HTTP request, saves the uploaded file in the specified temporary directory, and records the file metadata in the database after saving is complete.

[0697] Step 3: Send the data to the generative AI and receive the analysis results

[0698] Specifically, the server reads the presentation materials stored in the database and creates an API request to send them to the generative AI. The generative AI receives the request, analyzes the materials, and returns the analysis results to the server.

[0699] Input and output: The input is the presentation materials stored in the database, and the output is the analysis results from the generative AI.

[0700] Specific operation: The server reads the saved file and sends an HTTP POST request to the generative AI's API endpoint. The generative AI performs analysis and returns the results to the server as an HTTP response.

[0701] Step 4: Organizing the analysis results and generating the results by the server

[0702] Specifically, the server receives the analysis results sent back from the generative AI and organizes them into a format that is easy for humans to understand. These organized analysis results are then reported to the expert team as a result of the discussion.

[0703] Input and output: The input is the analysis result from the generative AI, and the output is the result of the discussion that is communicated to the expert team.

[0704] Specific operation: The analysis results are read from the database and formatted. The results are generated in HTML or PDF format and sent to the specialist team via email.

[0705] Step 5: Notify employees of the results of the discussion and confirm

[0706] Specific explanation: Employees check the results of the discussions sent by the server. They receive the results via email or the internal portal site, carefully examine the content, and revise the presentation materials as necessary.

[0707] Input and output: The input is the result of the discussion sent from the server, and the output is the revised presentation materials.

[0708] Specific actions: An employee opens the email and clicks on the attached link with the results of the discussion. The employee checks the results on the in-house portal site and uses the feedback to revise the presentation materials.

[0709] Step 6: Executives review and comment on the results

[0710] Specific explanation: The executive terminal receives the results of the discussion distributed from the server, checks the contents, and then compiles specific comments and suggestions into a document.

[0711] Input and Output: The input is the results of the discussion distributed by the server, and the output is a document containing comments and suggestions from the executives.

[0712] Specific operation: Executives log in to a dedicated portal site, download the results of the discussion, write comments based on the results, and upload them back to the server.

[0713] Step 7: Management decides on and notifies the final management policy

[0714] Specific explanation: The management terminal receives all information, including comments from executives and the results of discussions, and decides on the final management policy. The decided management policy is recorded on the server and notified to all employees.

[0715] Input and output: The input is the comments and feedback from executives, and the output is the final management policy.

[0716] What it does: Management reviews all feedback using meeting tablets or laptops, makes a final decision at the meeting, enters the decision into the server administration page, and sends an email notification to all employees.

[0717] (Application example 1)

[0718] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0719] Conventional autonomous vehicle operation management systems lack the ability to analyze operational data and provide real-time feedback, making it difficult to provide drivers and vehicle managers with sufficient operational assistance. Furthermore, executives were also unable to make effective management decisions based on operational data.

[0720] 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.

[0721] In this invention, the server includes means for receiving presentation content from employees, means for transmitting the received presentation content to a generative AI and requesting analysis, means for receiving analysis results from the generative AI and organizing and analyzing the analysis results, means for notifying the expert team of the organized and analyzed analysis results as a consultation result, means for analyzing operation data of autonomous vehicles based on the notified analysis results, means for providing feedback to drivers and vehicle managers in real time based on the analyzed operation data, and means for distributing the feedback results to executive officers. This enables analysis of operation data of autonomous vehicles and real-time feedback, enabling immediate responses to drivers and vehicle managers and accurate management decisions by executive officers.

[0722] "Employee" refers to a person who belongs to a company or organization and performs work.

[0723] "Presentation content" refers to written or digital materials that organize and present information or data related to a task or project.

[0724] "Generative AI" refers to artificial intelligence systems that analyze input data and generate insights and suggestions.

[0725] "Analysis results" refers to the information, including insights and suggestions, that generative AI provides after analyzing data.

[0726] "Bubble-thumping results" refers to evaluations and proposals made by a specialized team based on the analysis results of generative AI.

[0727] A "specialized team" refers to a group with high levels of expertise in a particular field.

[0728] "Chief executive" refers to a high-ranking manager who is responsible for determining the business strategies and policies of a company or organization.

[0729] "Autonomous vehicle" refers to a vehicle that has the ability to drive autonomously using software and sensors.

[0730] "Operation data" refers to various information about autonomous vehicles during operation, such as their speed, location, road conditions, and distance from other vehicles.

[0731] "Driver" refers to the person who drives the vehicle, but in the case of autonomous vehicles, it can also refer to the person who supervises or responds to emergencies.

[0732] "Vehicle manager" refers to a person whose role is to manage and supervise the operation status and maintenance of vehicles.

[0733] "Feedback" refers to improvement suggestions and evaluations provided based on the analysis results.

[0734] This invention is a system that uses generative AI to analyze presentation content created by employees and provides feedback based on the results. This system can be applied to managing operational data for autonomous vehicles and supporting management decisions.

[0735] System Overview

[0736] 1. Employee terminals:

[0737] Function: Employees create presentation materials and upload them to the server.

[0738] Hardware / Software: Personal computers, tablets (Windows, macOS, iOS, Android)

[0739] 2. Server:

[0740] Function: Receive presentation materials, send them to generative AI for analysis, and receive the results. Organize and analyze the analysis results and notify the expert team as a result of discussion. Also, analyze the operation data of autonomous vehicles based on the notified analysis results and provide feedback to drivers and vehicle managers.

[0741] Hardware: Server machine (Linux-based)

[0742] Software: Flask (web framework), requests (library for sending HTTP requests), JSON library

[0743] 3. Generative AI:

[0744] Function: Analyzes presentation content and operational data to provide suggestions for improvements and operational optimization.

[0745] Software: Natural language processing models (GPT-3, etc.)

[0746] 4. Specialized team terminal:

[0747] Function: Receives the results of the test from the server and evaluates or suggests additional steps as needed.

[0748] Hardware: Personal computers, tablets

[0749] Software: Text viewing and editing software (Microsoft Word, etc.)

[0750] 5. Traffic control terminal:

[0751] Function: Optimizes operations in real time based on autonomous vehicle operation data and analysis results, and provides feedback to drivers and fleet managers.

[0752] Hardware: Smartphones, tablets

[0753] Software: Dedicated fleet management application

[0754] 6. Executive Terminal:

[0755] Function: Receive feedback and discussion results from executives and decide on the final management policy.

[0756] Hardware: Personal computers, tablets

[0757] Software: Text viewing and editing software (Microsoft Word, etc.)

[0758] Program processing explanation

[0759] The server receives the presentation content from employees and stores it in a database. The received data is then sent to the generative AI for analysis. The generative AI analyzes the presentation content and operational data and feeds the results back to the server. Based on this analysis, the server provides real-time feedback to drivers and vehicle managers, and also organizes and analyzes the results as materials for management. The feedback includes optimal driving routes and recommendations for necessary maintenance.

[0760] For example, suppose an employee creates a presentation about the market launch of a new product and uploads it to a server. The server then sends the presentation to a generative AI and requests it to analyze it. The analysis results include a suggestion that "it is recommended to replace the brake pads at the next scheduled inspection," and this is immediately fed back to the driver via the fleet management terminal.

[0761] Example prompt sentence:

[0762] "Please provide us with optimal driving routes and maintenance suggestions based on the operational data of our autonomous vehicles."

[0763] This system will enable autonomous vehicles to operate more efficiently and safely.

[0764] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0765] Step 1:

[0766] Employee terminals create presentation content and upload it to the server. The input is the presentation materials created by the employee, and the output is the presentation data saved on the server. Specifically, employees create materials using presentation software on their personal computers or tablets, and then send them using the server's upload function.

[0767] Step 2:

[0768] The server saves the presentation content received from the employee terminal in a database. The input is the presentation data sent from the employee terminal, and the output is the presentation content saved in the database. Specifically, the server's API receives the presentation data and saves it in the database in JSON format.

[0769] Step 3:

[0770] The server sends the saved presentation content to the generative AI and requests it to analyze it. The input is the presentation data saved in the database, and the output is the request sent to the generative AI. Specifically, the server generates an HTTP request and sends the data to the generative AI's API.

[0771] Step 4:

[0772] The generative AI analyzes the presentation content and returns the analysis results to the server. The input is the presentation data sent from the server, and the output is the analysis results. Specifically, the generative AI analyzes the data using a natural language processing model (e.g., GPT-3) and returns the results to the server in JSON format.

[0773] Step 5:

[0774] The server organizes and analyzes the analysis results from the generative AI and notifies the expert team of the results. The input is the analysis results returned from the generative AI, and the output is the results sent to the expert team. Specifically, the server reads the analysis results, extracts the necessary information, organizes them as the results, and notifies the expert team's terminal.

[0775] Step 6:

[0776] The expert team receives the test results from the server and checks and evaluates them. The input is the test results sent from the server, and the output is the expert team's evaluation and additional suggestions. Specifically, the expert team displays the test results on their terminal, and adds comments and suggestions for corrections as needed.

[0777] Step 7:

[0778] The server analyzes the autonomous vehicle's operational data based on the notified analysis results. The input is the analysis results of the generative AI and the autonomous vehicle's operational data, and the output is the analysis results of the operational data. Specifically, the server resends the operational data to the generative AI, which then performs a detailed analysis of the operational status.

[0779] Step 8:

[0780] The server provides real-time feedback to drivers and vehicle managers based on the analyzed operational data. The input is the analysis results of the operational data, and the output is feedback sent to the driver's or vehicle manager's device. Specifically, the server reads the generative AI's suggestions and sends operational optimization and maintenance suggestions in real time.

[0781] Step 9:

[0782] The server organizes and generates the analysis results as materials for management and distributes them to executive officers. The input is the analysis results and feedback from the expert team, and the output is the materials distributed to management. Specifically, the server integrates all feedback, generates presentation-style materials, and distributes them to the executive officers' terminals.

[0783] Step 10:

[0784] The executive officers decide on the final management policy based on the materials distributed from the server and record that policy. The input is the distributed materials, and the output is the recorded management policy. Specifically, the executive officers check the materials on their terminals, decide on the policy through discussion in a meeting, and record it on the server.

[0785] 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.

[0786] System Overview

[0787] This invention allows employees to analyze presentation content using a generative AI and an emotion engine, and feeds the results back to management meetings, providing input from multiple perspectives to help determine management policy. Furthermore, it recognizes and analyzes the emotions of employees and executives, and supports management decisions based on that data.

[0788] The system consists of the following main components:

[0789] Employee device: A device used to create and upload presentation content.

[0790] Server: A central processing unit that receives presentation content, sends it to the generative AI and emotion engine, receives the analysis results, and generates feedback.

[0791] Generative AI: An artificial intelligence system that analyzes presentation content and provides insights.

[0792] Emotion engine: A system that analyzes the emotions of employees and executives and reflects the results in feedback.

[0793] Executive terminal: A device used to check presentation content and analysis results at management meetings and provide comments.

[0794] Management terminal: A device that receives all information to make final management decisions.

[0795] Program processing

[0796] Server Processing

[0797] The server first receives the presentation content from the employee. After receiving the content uploaded from the employee's device, it saves it in a database. Next, the server sends the saved presentation content to the generative AI. The generative AI analyzes the presentation content and returns the results to the server. The server then organizes and analyzes the analysis results, converting them into a format that is easy for humans to understand. It then uses an emotion engine to analyze the emotions contained in the employee's presentation content and adds the results to the analysis content. The organized analysis results are notified to a specialist team and used as a discussion point.

[0798] Processing of employee terminals

[0799] The employee terminals include a function that allows them to create presentation materials and upload them to the server after completion. After uploading the presentation materials, employees can receive the results of the discussion generated by the server, review them, and revise the presentation materials as necessary.

[0800] Executive terminal processing

[0801] The executive terminal receives the results of the discussion distributed by the server and confirms their content. During management meetings, executives make comments based on the presentation content and the analysis results of the generative AI, and contribute to management decisions by taking into account the emotional data evaluated by the emotion engine.

[0802] Management terminal processing

[0803] The management terminal receives all information, including executives' comments and the results of the discussions, and is responsible for deciding the final management policy. The reactions of executives and management during meetings are also analyzed in real time by the emotion engine, and feedback is provided based on that emotional data. The decided management policy is recorded on the server and notified to all employees.

[0804] Specific examples

[0805] Below is a concrete example of how this system works in practice.

[0806] 1. Employee A's actions: Employee A creates a presentation document on the market analysis of a new product and uploads it to the server from his / her terminal. The server receives the document and stores it in a database. The server then sends the saved document to the generative AI and emotion engine for analysis.

[0807] 2. Behavior of generative AI and emotion engine: The generative AI analyzes the received materials and suggests insights and improvements. The emotion engine analyzes the emotions contained in the presentation content and feeds the results back to the server.

[0808] 3. Server actions: The server receives suggestions from the generative AI and emotion engine, organizes them into a format that is easy for humans to understand, and notifies the expert team of the organized results as a discussion result, and then generates materials for the management meeting.

[0809] 4. Actions of Executive B: Executive B receives the results of the discussion from the server before the management meeting and checks the contents. At the management meeting, he makes specific comments and proposals based on this material, and expresses his opinion while taking into consideration the emotional data.

[0810] 5. President's Actions: The president considers all comments and the results of the discussions and decides on the market entry policy for the new product. During the meeting, the emotion engine analyzes the reactions of executives and management in real time and takes that feedback into account when deciding on a policy. The decision is recorded on the server and notified to all employees.

[0811] The processing flow will be explained below.

[0812] Server Processing

[0813] Step 1: Receiving the presentation

[0814] The server receives the presentation content sent from the employee terminal.

[0815] The server listens for HTTP requests.

[0816] The received file is analyzed and stored in the database.

[0817] Step 2: Request analysis from generative AI and emotion engine

[0818] The server sends the saved presentation content to the generative AI and emotion engine.

[0819] The server retrieves the presentation content from the database.

[0820] The acquired data is converted into a format suitable for the generative AI API.

[0821] The converted data is sent to the generative AI and a response is awaited.

[0822] At the same time, the presentation content is sent to the emotion engine for emotion analysis.

[0823] Step 3: Receive and organize the analysis results

[0824] The server receives, organizes, and analyzes the analysis results from the generative AI and emotion engine.

[0825] The server receives the response from the generative AI in JSON format.

[0826] Analyze the received data and extract the key points.

[0827] It receives the emotion analysis results from the emotion engine and adds them to the analysis results of the generative AI.

[0828] The extracted key points and sentiment analysis results are converted into a format that is easy for humans to understand.

[0829] Step 4: Notify the Specialist Team

[0830] The server notifies the expert team of the organized analysis results.

[0831] The server will send a notification email to the email address of the specialist team.

[0832] The notification email will include details such as the analysis results and feedback deadline.

[0833] Step 5: Generate and distribute materials on the results of the discussion

[0834] The server generates materials for management meetings based on the results of the expert team's discussions and distributes them to executives and management.

[0835] The server converts the results of the test into a report format.

[0836] Convert the report to PDF or presentation format and save it on the server.

[0837] The download link will be sent to each executive and management device.

[0838] Terminal (employee) processing

[0839] Step 1: Create your presentation materials

[0840] The terminal (employee) creates presentation materials.

[0841] The terminal (employee) creates documents using a word processor or presentation software.

[0842] Step 2: Upload your presentation materials

[0843] The terminal (employee) uploads the created documents to the server.

[0844] Employees access a dedicated web interface for uploading.

[0845] Select the file you created and click the Upload button.

[0846] Check the notification that the upload is complete.

[0847] Step 3: Receive and confirm the results

[0848] The terminal (employee) receives and checks the results of the test from the server.

[0849] Access the URL notified by the server and download the report of the test results.

[0850] Check the results of the discussion and revise the presentation materials as necessary.

[0851] Terminal (Executive) Processing

[0852] Step 1: Receive and confirm the results

[0853] The terminal (officer) receives and checks the test results distributed from the server.

[0854] Download the report of your test results via the link provided.

[0855] Review the contents of the report and prepare comments for the management meeting.

[0856] Step 2: Comments at the management meeting

[0857] The terminal (executive) makes comments at management meetings based on the content of employees' presentations and the analysis results of the generative AI and emotion engine.

[0858] By referring to the content of the presentation and the results of the discussion, specific feedback and suggestions will be provided.

[0859] The results of the emotion engine are also taken into account to assess their relevance to the emotional responses of executives.

[0860] Terminal (management) processing

[0861] Step 1: Review the results and presentation

[0862] The terminal (management) checks the executives' comments and the results of the discussion.

[0863] Use the provided link to retrieve all materials and review them before the meeting.

[0864] Prepare for decision-making, taking into account feedback from executives.

[0865] Step 2: Decide and record management policies

[0866] The terminal (management) decides on management policy based on the content of employees' presentations, the analysis results of the generative AI, and the analysis results of the emotion engine.

[0867] The final management policy will be formulated based on the discussions at the management meeting.

[0868] During meetings, the emotion engine analyzes the reactions of executives and management in real time and takes that feedback into consideration when deciding on policy.

[0869] The decided management policy is recorded on the server and prepared for distribution as materials for the general meeting.

[0870] Example 2

[0871] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0872] Conventional management decision-making systems have difficulty analyzing the content of employee presentations and providing useful feedback, and even evaluating the emotions expressed in the presentations. As a result, it has been impossible to obtain input from multiple perspectives, making it difficult to make optimal management decisions. The present invention aims to solve these problems and support efficient and accurate management decisions.

[0873] 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.

[0874] In this invention, the server includes means for receiving presentation content from employees, means for transmitting the received presentation content to a generative artificial intelligence (AI) and requesting analysis, means for receiving analysis results from the AI ​​and organizing and analyzing the analysis results, means for analyzing emotions contained in the presentation and reflecting the results in feedback, and means for notifying the specialized team of the organized and analyzed analysis results as discussion results, generating the discussion results and emotion analysis results as materials for management meetings and distributing them to executive officers. This makes it possible to obtain input from multiple perspectives through the analysis of employees' presentation content and emotional evaluation, enabling more accurate management decisions.

[0875] An "employee" is someone who works for a company or organization.

[0876] "Presentation content" refers to materials and explanations that compile information and proposals related to the business of a company or organization.

[0877] "Receiving" is the act of receiving transmitted data.

[0878] "Generative AI" refers to artificial intelligence systems that mimic human-like intellectual activity and provide analysis and insights tailored to specific purposes.

[0879] "Analysis" is the act of examining data or information in detail to clarify its structure and content.

[0880] "Organization and analysis" means classifying and summarizing the received data and putting it into an easy-to-understand form.

[0881] "Bubble-talking results" refers to feedback based on the analysis results and suggestions obtained at the initial stage, used to consider future improvements and directions.

[0882] A "specialized team" is a group of people with specialized knowledge and skills in a particular field.

[0883] "Notification" is the act of communicating specific information to other people.

[0884] "Chief executives" are people in positions that make important management decisions in a company or organization.

[0885] "Distribution" is the act of sending materials or information to multiple people.

[0886] "Emotion analysis means" refers to systems and technologies that read emotions from people's conversations and writings, and analyze and evaluate them.

[0887] "Feedback" is the act of conveying evaluations and opinions about behavior or results.

[0888] A "final management policy" is an important policy that determines the organization's future direction and goals.

[0889] "Recording" is the act of saving events or data for future reference.

[0890] MODE FOR CARRYING OUT THE INVENTION

[0891] The present invention is a system that allows employees to analyze presentation content using a generative artificial intelligence (AI) in conjunction with an emotion analysis system, and then feeds the results back to a management meeting, thereby obtaining input from multiple perspectives and deciding on management policies. Specific embodiments for implementing this system are described below.

[0892] System Overview

[0893] The system consists of the following main components: employee terminals, a server, generative artificial intelligence, an emotion analysis system, executive terminals, and management terminals.

[0894] Hardware and software used

[0895] Employee devices: Devices used by employees to create and upload presentation materials. Examples include PCs and tablets.

[0896] Server: A central processing unit that receives presentation content and sends it to the generative AI and emotion analysis system. The server has a database and manages and analyzes information.

[0897] Generative AI: An AI system that analyzes presentation content and provides insights. An example is OpenAI's GPT-4.

[0898] Emotion analysis system: A system that analyzes the content of a presentation and evaluates the emotional tone. An example is the IBM Watson Tone Analyzer.

[0899] Executive terminal: A device used in management meetings to review presentation content and analysis results and provide comments. Examples include PCs and tablets.

[0900] Executive terminals: Devices that receive information to make final management decisions. These also include PCs and tablets.

[0901] Specific examples of processing

[0902] 1. Employee A's actions: Employee A uses his / her own employee terminal to create a presentation document on the market analysis of a new product. After completing the document, he / she uploads the document to the server from his / her employee terminal.

[0903] 2. Server processing: The server receives the uploaded presentation materials and stores them in a database. It then sends the stored materials to GPT-4, a generative AI, using the following prompt:

[0904] Analyze "New Product Market Analysis Presentation" and provide insights into what parts can be improved. Also, take into account the results of the sentiment analysis system and comment on the emotional tone and audience reaction.

[0905] 3. Generative AI analysis: Generative AI analyzes the received materials and generates insights and suggestions on what can be improved. For example, it might say, "Chapter 3 of your presentation lacks specificity, so adding more detailed data would be helpful."

[0906] 4. Analysis by the emotion analysis system: The emotion analysis system analyzes the emotions contained in the content of the presentation materials and generates results such as "70% positive emotions." These results are sent back to the server.

[0907] 5. Server result integration: The server receives the results from the generative AI and the emotion analysis system, organizes them into a format that is easy for humans to understand, and finally compiles them as "test results."

[0908] 6. Feedback notification: The server notifies the employee and executive terminals of the results of the discussion. Employees can review the feedback and revise their presentation materials. Executives can also use this information to make comments and suggestions at management meetings.

[0909] Management meeting and final decision

[0910] 1. Operation of executive terminals: Executives receive the results of the discussion from the server before the management meeting and check the details. They also use the executive terminals during the meeting to provide specific comments based on the content of the presentation and the analysis results of the generative AI.

[0911] 2. Operation of management terminals: The management team makes the final decision on the management policy. The reactions of executives and management during the meeting are analyzed in real time by the emotion analysis system, and this feedback is also taken into consideration. The final management policy is recorded on the server and notified to all employees.

[0912] As a result, the present invention can provide a system that evaluates the content of employees' presentations from multiple angles and supports efficient and accurate management decisions.

[0913] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0914] Step 1:

[0915] Employees prepare presentation materials.

[0916] Input: Data about the topic and content of the presentation

[0917] Specific operations: Employees use their own devices to create presentation materials and enter market analysis data and proposal details into each slide.

[0918] Output: Finished presentation file

[0919] Step 2:

[0920] The employee terminal uploads the presentation materials to the server.

[0921] Input: Completed presentation file

[0922] Specific operation: The employee performs the upload operation on the terminal and sends the presentation materials to the server.

[0923] Output: Presentation materials uploaded to the server

[0924] Step 3:

[0925] The server stores the received presentation materials in a database.

[0926] Input: Uploaded presentation materials

[0927] Specific operation: The server temporarily loads the presentation materials into memory and stores them in a database.

[0928] Output: Presentation materials stored in a database

[0929] Step 4:

[0930] The server transmits the presentation materials to the generative artificial intelligence.

[0931] Input: Presentation materials and prompts stored in the database

[0932] Specific operation: The server sends the presentation to a generative AI (e.g., GPT-3) via an API and requests it to analyze it. The prompt is "Please provide your insights on which parts of this presentation can be improved."

[0933] Output: Data sent to the generative artificial intelligence

[0934] Step 5:

[0935] Generative AI analyzes presentation materials and provides insights.

[0936] Input: Presentation materials and analysis requests

[0937] Specific operation: Generative AI analyzes the content of presentation materials and generates improvements and insights. For example, it generates feedback such as, "Chapter 3 is lacking in specificity, so more detailed data should be added."

[0938] Output: Analysis results returned from the generative AI

[0939] Step 6:

[0940] The server receives the results from the generative artificial intelligence and organizes the data.

[0941] Input: Analysis results from generative artificial intelligence

[0942] Specific operation: The server summarizes and organizes the analysis results received in a format that is easy for humans to understand, for example, by converting the analysis results into bullet points or diagrams.

[0943] Output: Organized analysis results

[0944] Step 7:

[0945] The server sends the organized analysis results to the emotion analysis system.

[0946] Input: Organized analysis results

[0947] Specific operation: The server sends the organized analysis results to an emotion analysis system (e.g., IBM Watson Tone Analyzer) and requests emotion analysis.

[0948] Output: Data sent to the sentiment analysis system

[0949] Step 8:

[0950] The emotion analysis system analyzes the emotions contained in presentation materials.

[0951] Input: Analysis results after receiving a request for emotion analysis

[0952] Specific operation: The emotion analysis system analyzes the content of the presentation materials and generates emotion data such as "70% positive tone."

[0953] Output: Emotion analysis results

[0954] Step 9:

[0955] The server receives the sentiment analysis results and generates the final feedback.

[0956] Input: Sentiment analysis results and previously organized analysis results

[0957] Specific operation: The server integrates the sentiment analysis results with the previously organized analysis results to generate an overall feedback document.

[0958] Output: Final feedback document

[0959] Step 10:

[0960] The server notifies the employee terminal and the executive terminal of the final feedback.

[0961] Input: Final feedback document

[0962] Specific operation: The server sends the final feedback document to the terminals of the employees and executives and notifies them.

[0963] Output: Employee and executive terminals that received the notification

[0964] Step 11:

[0965] The employee terminals then revise the presentation materials based on the feedback.

[0966] Input: Final feedback document

[0967] Specific action: The employee reviews the feedback and makes revisions to the presentation as needed, for example adding more detailed data to Chapter 3.

[0968] Output: Revised presentation

[0969] Step 12:

[0970] The executive terminals provide comments based on feedback at management meetings.

[0971] Input: Final feedback document and revised presentation

[0972] Specific actions: Executives will make comments and suggestions at the management meeting, referring to the final feedback and revised presentation materials.

[0973] Output: Comments from executives at management meetings

[0974] Step 13:

[0975] The management terminal decides the final management policy.

[0976] Input: Executive comments at management meetings and final feedback

[0977] Specific actions: Management decides on the final management policy based on the comments and feedback provided during the meeting.

[0978] Output: Decided management policy

[0979] Step 14:

[0980] The server notifies all employees of the decided management policy.

[0981] Input: Decided management policy

[0982] Specific operation: The server records the management policy decision and sends a notification to all employee terminals, for example, a message saying, "A decision has been made to enter the market for a new product."

[0983] Output: All employee terminals that received the notification

[0984] (Application example 2)

[0985] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0986] In modern factories, improving the efficiency and optimizing of manufacturing processes are important issues. However, in many factories, workers' explanations and presentations are based on intuition, and specific areas for improvement are often overlooked. In addition, opportunities for management and work teams to receive accurate feedback are limited, making rapid improvement a difficult challenge. Against this backdrop, there is a need for a system that can analyze the content of factory workers' explanations and provide immediate feedback on the results.

[0987] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the explanation of the manufacturing process from the factory worker, means for sending the received explanation to the generative AI and requesting analysis, means for receiving the analysis results from the generative AI and organizing and analyzing the analysis results, means for notifying the organized and analyzed analysis results to the work team as efficiency results, and means for generating the efficiency results as materials for a factory meeting and distributing them to management executives. This enables the explanation of the manufacturing process to be analyzed and the results to be quickly fed back.

[0988] "Manufacturing process" refers to the manufacturing process and procedures for products in the manufacturing industry.

[0989] "Explanatory content" refers to information and materials provided by factory workers about manufacturing processes and efficiency proposals.

[0990] "Receiving means" refers to the functions and devices that receive explanations from factory workers and input them into the system.

[0991] "Generative AI" refers to artificial intelligence systems that analyze human-created materials and information and provide insights and suggestions for improvement.

[0992] "Analysis means" refers to the functions and devices that request the generative AI to analyze the content of the explanation and receive the results.

[0993] "Organization and analysis means" refers to the functions and devices that compile the analysis results from generative AI and convert them into an easy-to-understand format.

[0994] "Efficiency results" refers to improvements and efficiency proposals for the manufacturing process proposed based on the analysis results of generative AI.

[0995] "Notification means" refers to the functions and devices used to report efficiency results to work teams and other stakeholders.

[0996] "Factory meeting materials" refers to materials that compile information on improvements to manufacturing processes and proposals for efficiency, and are used to discuss these at meetings.

[0997] "Management officers" refers to officials responsible for the operation and management of a factory.

[0998] "Feedback" refers to reactions to the analysis results and efficiency improvement proposals, and suggestions for improvements. In the present invention, feedback is provided to the worker so that the worker can improve the content of the explanation.

[0999] System Overview

[1000] This invention is a system to support the improvement of efficiency in manufacturing processes. Specifically, it uses generative AI and an emotion engine to analyze the explanations of manufacturing processes provided by factory workers, and provides immediate feedback on the results of the analysis to suggest improvements and improvements to improve efficiency.

[1001] Hardware and software used

[1002] This system uses the following hardware and software:

[1003] Smart glasses: For example, Google Glass or Vuzix Blade.

[1004] EmotionEngine: A software module for emotion analysis.

[1005] AIAnalysis: A generative AI module for analyzing narrative content and providing insights.

[1006] Python programming language: Used to implement programs.

[1007] System Operation

[1008] 1. Upload your presentation:

[1009] Factory workers upload a description of the manufacturing process to the application through smart glasses, either by recording it as voice or by typing it in text format.

[1010] 2. Analysis process:

[1011] The server sends the uploaded explanation to the generative AI module (AIAnalysis) for analysis. The generative AI analyzes the explanation and suggests improvements and efficiency suggestions. The explanation is also sent to the Emotion Engine module for emotional analysis.

[1012] 3. Feedback generation:

[1013] The server receives the results of generative AI and emotion analysis, organizes and analyzes them, and notifies the work team and management executives of the organized analysis results as "efficiency results."

[1014] 4. View Feedback:

[1015] Workers can view analysis results and feedback in real time through the smart glasses' display, allowing them to make any necessary improvements immediately.

[1016] Specific examples

[1017] Factory worker action: A worker creates a presentation to explain a "new manufacturing process" and uploads it using smart glasses.

[1018] How the generative AI and emotion engine work: Uploaded presentation materials are sent to AIAnalysis and EmotionEngine for analysis. AIAnalysis suggests areas for improvement, and EmotionEngine evaluates the emotions contained in the presentation content.

[1019] Server operation: The server receives these analysis results, organizes them into a format that is easy for humans to understand, and notifies the work team and management executives of the organized results as "efficiency results."

[1020] Real-time feedback: Workers can view analysis results through the smart glasses display and immediately implement improvements to the manufacturing process.

[1021] Prompt Sentence Examples

[1022] Analyze a presentation deck for a new manufacturing process. Evaluate areas for improvement, as well as the tone and emotion of the presentation.

[1023] This system will enable the efficiency of manufacturing processes within the factory, enabling work teams and management to quickly and accurately implement improvement proposals.

[1024] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1025] Step 1:

[1026] Upload a presentation

[1027] How it works: A worker uses smart glasses to upload a description of a manufacturing process to the application, either as a voice recording or text input.

[1028] Input: Description of the manufacturing process (audio or text data).

[1029] Output: The server has received the description.

[1030] Step 2:

[1031] Data reception by the server

[1032] How it works: The server receives the uploaded description and stores it in a database.

[1033] Input: The uploaded description.

[1034] Output: The description is saved in the database.

[1035] Step 3:

[1036] Analysis request to generative AI

[1037] Operation: The server sends the saved description to the generative AI module (AIAnalysis) and requests it to analyze it.

[1038] Input: Description stored in the database.

[1039] Output: The status of the analysis request.

[1040] Step 4:

[1041] Analysis of generative AI

[1042] How it works: Generative AI (AIAnalysis) analyzes the content of the explanation and suggests improvements and efficiency suggestions.

[1043] Input: The description sent by the server.

[1044] Output: Analysis results (improvements and efficiency suggestions).

[1045] Step 5:

[1046] Emotion analysis using an emotion engine

[1047] How it works: The server sends the description to the Emotion Engine and requests it to analyze the emotions contained in the content.

[1048] Input: The description sent by the server.

[1049] Output: Emotion analysis results.

[1050] Step 6:

[1051] Receiving analysis results and sentiment analysis results

[1052] How it works: The server receives the analysis results from the generative AI and the emotion analysis results from the emotion engine.

[1053] Input: Analysis results of the generative AI, emotion analysis results of the emotion engine.

[1054] Output: Analysis results and sentiment analysis results are aggregated on the server.

[1055] Step 7:

[1056] Generate feedback

[1057] Operation: The server organizes the analysis results and sentiment analysis results it receives, converts them into a format that is easy for humans to understand, and notifies them as "efficiency results."

[1058] Input: Analysis results, sentiment analysis results.

[1059] Output: Organized efficiency results.

[1060] Step 8:

[1061] Notification to work teams and management officers

[1062] How it works: The server notifies the work team and management of the efficiency results, providing real-time feedback through the smart glasses display.

[1063] Input: Organized efficiency results.

[1064] Output: The work team and management receive the efficiency results.

[1065] Prompt Sentence Examples

[1066] Analyze a presentation deck for a new manufacturing process. Evaluate areas for improvement, as well as the tone and emotion of the presentation.

[1067] These processing steps enable a series of steps to analyze the explanations of factory workers and provide immediate feedback, enabling work teams and management executives to quickly and accurately improve the manufacturing process.

[1068] 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.

[1069] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1070] 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.

[1071] [Third embodiment]

[1072] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1073] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1074] 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).

[1075] 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.

[1076] 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.

[1077] 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).

[1078] 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.

[1079] 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.

[1080] 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.

[1081] 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.

[1082] 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.

[1083] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1084] System Overview

[1085] This system works in conjunction with generative AI to analyze the content of presentations given by employees and feeds the results back to management meetings, allowing for input from multiple perspectives to be used to determine management policy. The system consists of the following main components:

[1086] Employee device: A device used to create and upload presentation content.

[1087] Server: A central processing unit that receives presentation content, sends it to the generative AI, receives the analysis results, and generates feedback results.

[1088] Generative AI: An artificial intelligence system that analyzes presentation content and provides insights.

[1089] Executive terminal: A device used to check presentation content and analysis results at management meetings and provide comments.

[1090] Management terminal: A device that receives all information to make final management decisions.

[1091] Program processing

[1092] Server Processing

[1093] The server first receives the presentation content from the employee. This content is uploaded from the employee's device and is immediately saved in a database after being received. The server then sends the saved presentation content to the generative AI. The generative AI analyzes the presentation content and returns the results to the server. The server then organizes and analyzes the analysis results, converting them into a format that is easy for humans to understand. This organized analysis result is then reported to the specialist team as a result of discussion.

[1094] Processing of employee terminals

[1095] The employee terminals include a function that allows them to create presentation materials and upload them to the server after completion. After uploading the presentation materials, employees can receive the results of the discussion generated by the server, review them, and revise the presentation materials as necessary.

[1096] Executive terminal processing

[1097] The executive terminals receive the results of the discussions distributed by the server and review them. During management meetings, executives can provide comments based on the presentation content and the generative AI's analysis results, contributing to management decisions.

[1098] Management terminal processing

[1099] The management terminal receives all information, including comments from executives and the results of the discussions, and is responsible for deciding the final management policy. The decided management policy is recorded on the server and notified to all employees.

[1100] Specific examples

[1101] Below is a concrete example of how this system works in practice.

[1102] 1. Employee A's actions: Employee A creates a presentation document on the market analysis of a new product and uploads it to the server from his / her terminal. The server receives the document and saves it in a database. The server then sends the saved document to the generative AI and requests its analysis.

[1103] 2. Generative AI Actions: The generative AI analyzes the received material and offers insights and suggestions for improvement. These suggestions are fed back to the server.

[1104] 3. Server actions: The server receives suggestions from the generative AI and organizes them into a format that is easy for humans to understand. The organized results are then sent to the expert team as a discussion result, and are then generated as materials for a management meeting.

[1105] 4. Actions of Executive B: Executive B receives the results of the discussion from the server before the management meeting and checks the contents. At the management meeting, he / she makes specific comments and proposals based on this material.

[1106] 5. President's Action: The president will consider all comments and the results of the discussion and decide on the market entry policy for the new product. The decision will be recorded on the server and notified to all employees.

[1107] The processing flow will be explained below.

[1108] Server Processing

[1109] Step 1: Receiving the presentation

[1110] The server receives the presentation content sent from the employee terminal.

[1111] The server listens for HTTP requests.

[1112] The received file is analyzed and stored in the database.

[1113] Step 2: Requesting analysis from generative AI

[1114] The server sends the saved presentation content to the generative AI.

[1115] The server retrieves the presentation content from the database.

[1116] The acquired data is converted into a format suitable for the generative AI API.

[1117] The converted data is sent to the generative AI and a response is awaited.

[1118] Step 3: Receive and organize the analysis results

[1119] The server receives the analysis results from the generative AI and organizes and analyzes them.

[1120] The server receives the response from ChatGPT in JSON format.

[1121] Analyze the received data and extract the key points.

[1122] The extracted key points are converted into a format that is easy for humans to understand.

[1123] Step 4: Notify the Specialist Team

[1124] The server notifies the expert team of the organized analysis results.

[1125] The server will send a notification email to the email address of the specialist team.

[1126] The notification email will include details such as the analysis results and feedback deadline.

[1127] Step 5: Generate and distribute materials on the results of the discussion

[1128] The server generates materials for management meetings based on the results of the expert team's discussions and distributes them to executives and management.

[1129] The server converts the results of the test into a report format.

[1130] Convert the report to PDF or presentation format and save it on the server.

[1131] The download link will be sent to each executive and management device.

[1132] Terminal (employee) processing

[1133] Step 1: Create your presentation materials

[1134] The terminal (employee) creates presentation materials.

[1135] The terminal (employee) creates documents using a word processor or presentation software.

[1136] Step 2: Upload your presentation materials

[1137] The terminal (employee) uploads the created documents to the server.

[1138] Employees access a dedicated web interface for uploading.

[1139] Select the file you created and click the Upload button.

[1140] Check the notification that the upload is complete.

[1141] Step 3: Receive and confirm the results

[1142] The terminal (employee) receives and checks the results of the test from the server.

[1143] Access the URL notified by the server and download the report of the test results.

[1144] Check the results of the discussion and revise the presentation materials as necessary.

[1145] Terminal (Executive) Processing

[1146] Step 1: Receive and confirm the results

[1147] The terminal (officer) receives and checks the test results distributed from the server.

[1148] Download the report of your test results via the link provided.

[1149] Review the contents of the report and prepare comments for the management meeting.

[1150] Step 2: Comments at the management meeting

[1151] The terminal (executive) makes comments at management meetings based on the content of employees' presentations and the analysis results of the generative AI.

[1152] By referring to the content of the presentation and the results of the discussion, specific feedback and suggestions will be provided.

[1153] Terminal (management) processing

[1154] Step 1: Review the results and presentation

[1155] The terminal (management) checks the executives' comments and the results of the discussion.

[1156] Use the provided link to retrieve all materials and review them before the meeting.

[1157] Prepare for decision-making, taking into account feedback from executives.

[1158] Step 2: Decide and record management policies

[1159] The terminal (management) decides on management policy based on the content of employees' presentations and the analysis results of the generative AI.

[1160] The final management policy will be formulated based on the discussions at the management meeting.

[1161] The decided management policy is recorded on the server and prepared for distribution as materials for the general meeting.

[1162] Example 1

[1163] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1164] Conventional systems for evaluating and providing feedback on presentation content have struggled to efficiently and objectively analyze presentations and provide rapid feedback based on the results. Delays in the analysis and feedback process are particularly problematic when a large number of presentation materials need to be analyzed in a short period of time. Other issues include converting the analysis results into a human-understandable format and ensuring transparency when formulating final management policies.

[1165] 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.

[1166] In this invention, the server includes means for receiving presentation content from employees, means for transmitting the received presentation content to a generative AI and requesting analysis, means for receiving analysis results from the generative AI and organizing and analyzing the analysis results, means for notifying the expert team of the organized and analyzed analysis results as a discussion result, means for generating the discussion results as materials for management meetings and distributing them to executive officers, means for employees to upload presentation materials and store the uploaded materials in a database, means for creating an API request to transmit the materials to the generative AI, and means for organizing the analysis results in a format that is easy for humans to understand. This improves the efficiency and objectivity of presentation analysis and feedback, enabling faster decision-making.

[1167] "Presentation content" refers to materials and documents prepared by employees to explain and report on various business matters.

[1168] "Generative AI" is an artificial intelligence system that performs advanced analysis and generation based on given information.

[1169] "Analysis results" refers to the insights and suggestions derived by the generative AI after analyzing the presentation content.

[1170] The "test results" are the feedback that the server organizes and analyzes based on the analysis results and presents to the specialist team.

[1171] A "specialized team" is a group of employees with high levels of expertise in a particular field or task.

[1172] "Materials for management meetings" are documents that compile the information and data necessary for management to make final decisions.

[1173] "Chief executive officer" refers to a person in a position with the authority to make important decisions in the management of a company.

[1174] A "database" is a system for systematically storing and managing data such as presentation content and analysis results.

[1175] An "API request" is a protocol that allows different software components to communicate with each other and utilize their functionality.

[1176] "Feedback" refers to advice and opinions to improve presentation materials based on the analysis results.

[1177] "Employee terminal" refers to a computer or digital device used by an employee for work purposes.

[1178] A "server" is the central processing unit for the entire system, and is a device that receives, stores, analyzes, and distributes data.

[1179] This invention is a system that efficiently and objectively analyzes the content of presentations created by employees and provides rapid feedback based on the results. This system is primarily composed of employee terminals, a server, a generative AI, executive terminals, and management terminals.

[1180] System Overview

[1181] The core of this system is the server, which receives and stores the presentation content and sends it to the generative AI. It also organizes the analysis results from the generative AI and generates materials for expert teams and management meetings as a result of discussions. The overall flow is as follows:

[1182] Hardware and software used

[1183] Employee devices: laptops and desktop computers used by employees for work (examples: Dell, HP)

[1184] Server: High-performance server (examples: AWS EC2, Microsoft Azure)

[1185] Generation AI: GPT-4 (OpenAI)

[1186] Program processing

[1187] Server Processing

[1188] The server receives the presentation content from employees. This content is uploaded from employee devices and is immediately saved in a database after being received. The server then sends the saved presentation content to the generative AI. The generative AI analyzes the presentation content and returns the results to the server. The server organizes the analysis results and converts them into a format that is easy for humans to understand. This organized analysis result is then reported to the specialist team as a result of discussions.

[1189] Processing employee terminals

[1190] The employee terminals include a function that allows employees to create presentation materials and upload them to the server after completion. Once the presentation materials are uploaded, the server receives them and stores them in a database. The server then sends the saved presentation content to the generative AI and requests it to be analyzed.

[1191] Generative AI processing

[1192] Generative AI analyzes the presentation content sent from the server, generates analytical results, and sends them back to the server. Generative AI provides insights into market analysis, competitive analysis, customer needs, and predicted trends, among other things.

[1193] Executive terminal processing

[1194] The executive terminals receive the results of the discussions distributed by the server and review them. During management meetings, executives can provide comments based on the presentation content and the generative AI's analysis results, contributing to management decisions.

[1195] Management terminal processing

[1196] The management terminal receives all information, including comments from executives and the results of the discussions, and is responsible for deciding the final management policy. The decided management policy is recorded on the server and notified to all employees.

[1197] Specific examples

[1198] A specific example of use is shown below.

[1199] 1. Employee A's actions: Employee A creates a presentation document on the market analysis of a new product and uploads it to the server from his / her terminal. The server receives the document and stores it in a database. The server then sends the saved document to the generative AI and requests its analysis.

[1200] 2. Generative AI Actions: The generative AI analyzes the received material and offers insights and suggestions for improvement. These suggestions are fed back to the server.

[1201] 3. Server actions: The server receives suggestions from the generative AI and organizes them into a format that is easy for humans to understand. The organized results are then reported to the expert team as a result of discussions, and are then generated as materials for management meetings.

[1202] 4. Actions of Executive B: Executive B receives the results of the discussion from the server before the management meeting and checks the contents. At the management meeting, he / she makes specific comments and proposals based on this material.

[1203] 5. President's Action: The president will consider all comments and the results of the discussion and decide on the market entry policy for the new product. The decision will be recorded on the server and notified to all employees.

[1204] Examples of prompt statements

[1205] Below is an example of a prompt sentence to input to the generative AI model.

[1206] Analyze a presentation about a new product market analysis and provide suggestions for improvement and insights. The main content of the presentation is as follows:

[1207] 1. Current market situation

[1208] 2. Competitive analysis

[1209] 3. Customer needs

[1210] 4. Predicted trends

[1211] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1212] Step 1: Create and upload presentation materials using employee devices

[1213] Specific explanation: An employee (the user) creates a presentation document related to his or her work. The document is based on a theme such as a market analysis of a new product or a competitive analysis. The software used is Microsoft PowerPoint or Google Slides. The completed document is sent to the server using the upload function on the employee's terminal.

[1214] Input and Output: The input is the presentation materials created by employees, and the output is the presentation materials uploaded to the server.

[1215] Specific operation: An employee creates a presentation in PowerPoint, clicks the "Upload" button to open a file selection dialog, selects the created presentation file, and uploads it to the server.

[1216] Step 2: The server receives the data and stores it in a database

[1217] Specific explanation: The server receives presentation materials uploaded from employee terminals. The received materials are saved in a temporary directory and then saved in a database.

[1218] Input and output: The input is the presentation materials uploaded from the employee terminals, and the output is the presentation materials saved in the database.

[1219] Specific operation: The server receives the HTTP request, saves the uploaded file in the specified temporary directory, and records the file metadata in the database after saving is complete.

[1220] Step 3: Send the data to the generative AI and receive the analysis results

[1221] Specifically, the server reads the presentation materials stored in the database and creates an API request to send them to the generative AI. The generative AI receives the request, analyzes the materials, and returns the analysis results to the server.

[1222] Input and output: The input is the presentation materials stored in the database, and the output is the analysis results from the generative AI.

[1223] Specific operation: The server reads the saved file and sends an HTTP POST request to the generative AI's API endpoint. The generative AI performs analysis and returns the results to the server as an HTTP response.

[1224] Step 4: Organizing the analysis results and generating the results by the server

[1225] Specifically, the server receives the analysis results sent back from the generative AI and organizes them into a format that is easy for humans to understand. These organized analysis results are then reported to the expert team as a result of the discussion.

[1226] Input and output: The input is the analysis result from the generative AI, and the output is the result of the discussion that is communicated to the expert team.

[1227] Specific operation: The analysis results are read from the database and formatted. The results are generated in HTML or PDF format and sent to the specialist team via email.

[1228] Step 5: Notify employees of the results of the discussion and confirm

[1229] Specific explanation: Employees check the results of the discussions sent by the server. They receive the results via email or the internal portal site, carefully examine the content, and revise the presentation materials as necessary.

[1230] Input and output: The input is the result of the discussion sent from the server, and the output is the revised presentation materials.

[1231] Specific actions: An employee opens the email and clicks on the attached link with the results of the discussion. The employee checks the results on the in-house portal site and uses the feedback to revise the presentation materials.

[1232] Step 6: Executives review and comment on the results

[1233] Specific explanation: The executive terminal receives the results of the discussion distributed from the server, checks the contents, and then compiles specific comments and suggestions into a document.

[1234] Input and Output: The input is the results of the discussion distributed by the server, and the output is a document containing comments and suggestions from the executives.

[1235] Specific operation: Executives log in to a dedicated portal site, download the results of the discussion, write comments based on the results, and upload them back to the server.

[1236] Step 7: Management decides on and notifies the final management policy

[1237] Specific explanation: The management terminal receives all information, including comments from executives and the results of discussions, and decides on the final management policy. The decided management policy is recorded on the server and notified to all employees.

[1238] Input and output: The input is the comments and feedback from executives, and the output is the final management policy.

[1239] What it does: Management reviews all feedback using meeting tablets or laptops, makes a final decision at the meeting, enters the decision into the server administration page, and sends an email notification to all employees.

[1240] (Application example 1)

[1241] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1242] Conventional autonomous vehicle operation management systems lack the ability to analyze operational data and provide real-time feedback, making it difficult to provide drivers and vehicle managers with sufficient operational assistance. Furthermore, executives were also unable to make effective management decisions based on operational data.

[1243] 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.

[1244] In this invention, the server includes means for receiving presentation content from employees, means for transmitting the received presentation content to a generative AI and requesting analysis, means for receiving analysis results from the generative AI and organizing and analyzing the analysis results, means for notifying the expert team of the organized and analyzed analysis results as a consultation result, means for analyzing operation data of autonomous vehicles based on the notified analysis results, means for providing feedback to drivers and vehicle managers in real time based on the analyzed operation data, and means for distributing the feedback results to executive officers. This enables analysis of operation data of autonomous vehicles and real-time feedback, enabling immediate responses to drivers and vehicle managers and accurate management decisions by executive officers.

[1245] "Employee" refers to a person who belongs to a company or organization and performs work.

[1246] "Presentation content" refers to written or digital materials that organize and present information or data related to a task or project.

[1247] "Generative AI" refers to artificial intelligence systems that analyze input data and generate insights and suggestions.

[1248] "Analysis results" refers to the information, including insights and suggestions, that generative AI provides after analyzing data.

[1249] "Bubble-thumping results" refers to evaluations and proposals made by a specialized team based on the analysis results of generative AI.

[1250] A "specialized team" refers to a group with high levels of expertise in a particular field.

[1251] "Chief executive" refers to a high-ranking manager who is responsible for determining the business strategies and policies of a company or organization.

[1252] "Autonomous vehicle" refers to a vehicle that has the ability to drive autonomously using software and sensors.

[1253] "Operation data" refers to various information about autonomous vehicles during operation, such as their speed, location, road conditions, and distance from other vehicles.

[1254] "Driver" refers to the person who drives the vehicle, but in the case of autonomous vehicles, it can also refer to the person who supervises or responds to emergencies.

[1255] "Vehicle manager" refers to a person whose role is to manage and supervise the operation status and maintenance of vehicles.

[1256] "Feedback" refers to improvement suggestions and evaluations provided based on the analysis results.

[1257] This invention is a system that uses generative AI to analyze presentation content created by employees and provides feedback based on the results. This system can be applied to managing operational data for autonomous vehicles and supporting management decisions.

[1258] System Overview

[1259] 1. Employee terminals:

[1260] Function: Employees create presentation materials and upload them to the server.

[1261] Hardware / Software: Personal computers, tablets (Windows, macOS, iOS, Android)

[1262] 2. Server:

[1263] Function: Receive presentation materials, send them to generative AI for analysis, and receive the results. Organize and analyze the analysis results and notify the expert team as a result of discussion. Also, analyze the operation data of autonomous vehicles based on the notified analysis results and provide feedback to drivers and vehicle managers.

[1264] Hardware: Server machine (Linux-based)

[1265] Software: Flask (web framework), requests (library for sending HTTP requests), JSON library

[1266] 3. Generative AI:

[1267] Function: Analyzes presentation content and operational data to provide suggestions for improvements and operational optimization.

[1268] Software: Natural language processing models (GPT-3, etc.)

[1269] 4. Specialized team terminal:

[1270] Function: Receives the results of the test from the server and evaluates or suggests additional steps as needed.

[1271] Hardware: Personal computers, tablets

[1272] Software: Text viewing and editing software (Microsoft Word, etc.)

[1273] 5. Traffic control terminal:

[1274] Function: Optimizes operations in real time based on autonomous vehicle operation data and analysis results, and provides feedback to drivers and fleet managers.

[1275] Hardware: Smartphones, tablets

[1276] Software: Dedicated fleet management application

[1277] 6. Executive Terminal:

[1278] Function: Receive feedback and discussion results from executives and decide on the final management policy.

[1279] Hardware: Personal computers, tablets

[1280] Software: Text viewing and editing software (Microsoft Word, etc.)

[1281] Program processing explanation

[1282] The server receives the presentation content from employees and stores it in a database. The received data is then sent to the generative AI for analysis. The generative AI analyzes the presentation content and operational data and feeds the results back to the server. Based on this analysis, the server provides real-time feedback to drivers and vehicle managers, and also organizes and analyzes the results as materials for management. The feedback includes optimal driving routes and recommendations for necessary maintenance.

[1283] For example, suppose an employee creates a presentation about the market launch of a new product and uploads it to a server. The server then sends the presentation to a generative AI and requests it to analyze it. The analysis results include a suggestion that "it is recommended to replace the brake pads at the next scheduled inspection," and this is immediately fed back to the driver via the fleet management terminal.

[1284] Example prompt sentence:

[1285] "Please provide us with optimal driving routes and maintenance suggestions based on the operational data of our autonomous vehicles."

[1286] This system will enable autonomous vehicles to operate more efficiently and safely.

[1287] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1288] Step 1:

[1289] Employee terminals create presentation content and upload it to the server. The input is the presentation materials created by the employee, and the output is the presentation data saved on the server. Specifically, employees create materials using presentation software on their personal computers or tablets, and then send them using the server's upload function.

[1290] Step 2:

[1291] The server saves the presentation content received from the employee terminal in a database. The input is the presentation data sent from the employee terminal, and the output is the presentation content saved in the database. Specifically, the server's API receives the presentation data and saves it in the database in JSON format.

[1292] Step 3:

[1293] The server sends the saved presentation content to the generative AI and requests it to analyze it. The input is the presentation data saved in the database, and the output is the request sent to the generative AI. Specifically, the server generates an HTTP request and sends the data to the generative AI's API.

[1294] Step 4:

[1295] The generative AI analyzes the presentation content and returns the analysis results to the server. The input is the presentation data sent from the server, and the output is the analysis results. Specifically, the generative AI analyzes the data using a natural language processing model (e.g., GPT-3) and returns the results to the server in JSON format.

[1296] Step 5:

[1297] The server organizes and analyzes the analysis results from the generative AI and notifies the expert team of the results. The input is the analysis results returned from the generative AI, and the output is the results sent to the expert team. Specifically, the server reads the analysis results, extracts the necessary information, organizes them as the results, and notifies the expert team's terminal.

[1298] Step 6:

[1299] The expert team receives the test results from the server and checks and evaluates them. The input is the test results sent from the server, and the output is the expert team's evaluation and additional suggestions. Specifically, the expert team displays the test results on their terminal, and adds comments and suggestions for corrections as needed.

[1300] Step 7:

[1301] The server analyzes the autonomous vehicle's operational data based on the notified analysis results. The input is the analysis results of the generative AI and the autonomous vehicle's operational data, and the output is the analysis results of the operational data. Specifically, the server resends the operational data to the generative AI, which then performs a detailed analysis of the operational status.

[1302] Step 8:

[1303] The server provides real-time feedback to drivers and vehicle managers based on the analyzed operational data. The input is the analysis results of the operational data, and the output is feedback sent to the driver's or vehicle manager's device. Specifically, the server reads the generative AI's suggestions and sends operational optimization and maintenance suggestions in real time.

[1304] Step 9:

[1305] The server organizes and generates the analysis results as materials for management and distributes them to executive officers. The input is the analysis results and feedback from the expert team, and the output is the materials distributed to management. Specifically, the server integrates all feedback, generates presentation-style materials, and distributes them to the executive officers' terminals.

[1306] Step 10:

[1307] The executive officers decide on the final management policy based on the materials distributed from the server and record that policy. The input is the distributed materials, and the output is the recorded management policy. Specifically, the executive officers check the materials on their terminals, decide on the policy through discussion in a meeting, and record it on the server.

[1308] 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.

[1309] System Overview

[1310] This invention allows employees to analyze presentation content using a generative AI and an emotion engine, and feeds the results back to management meetings, providing input from multiple perspectives to help determine management policy. Furthermore, it recognizes and analyzes the emotions of employees and executives, and supports management decisions based on that data.

[1311] The system consists of the following main components:

[1312] Employee device: A device used to create and upload presentation content.

[1313] Server: A central processing unit that receives presentation content, sends it to the generative AI and emotion engine, receives the analysis results, and generates feedback.

[1314] Generative AI: An artificial intelligence system that analyzes presentation content and provides insights.

[1315] Emotion engine: A system that analyzes the emotions of employees and executives and reflects the results in feedback.

[1316] Executive terminal: A device used to check presentation content and analysis results at management meetings and provide comments.

[1317] Management terminal: A device that receives all information to make final management decisions.

[1318] Program processing

[1319] Server Processing

[1320] The server first receives the presentation content from the employee. After receiving the content uploaded from the employee's device, it saves it in a database. Next, the server sends the saved presentation content to the generative AI. The generative AI analyzes the presentation content and returns the results to the server. The server then organizes and analyzes the analysis results, converting them into a format that is easy for humans to understand. It then uses an emotion engine to analyze the emotions contained in the employee's presentation content and adds the results to the analysis content. The organized analysis results are notified to a specialist team and used as a discussion point.

[1321] Processing of employee terminals

[1322] The employee terminals include a function that allows them to create presentation materials and upload them to the server after completion. After uploading the presentation materials, employees can receive the results of the discussion generated by the server, review them, and revise the presentation materials as necessary.

[1323] Executive terminal processing

[1324] The executive terminal receives the results of the discussion distributed by the server and confirms their content. During management meetings, executives make comments based on the presentation content and the analysis results of the generative AI, and contribute to management decisions by taking into account the emotional data evaluated by the emotion engine.

[1325] Management terminal processing

[1326] The management terminal receives all information, including executives' comments and the results of the discussions, and is responsible for deciding the final management policy. The reactions of executives and management during meetings are also analyzed in real time by the emotion engine, and feedback is provided based on that emotional data. The decided management policy is recorded on the server and notified to all employees.

[1327] Specific examples

[1328] Below is a concrete example of how this system works in practice.

[1329] 1. Employee A's actions: Employee A creates a presentation document on the market analysis of a new product and uploads it to the server from his / her terminal. The server receives the document and stores it in a database. The server then sends the saved document to the generative AI and emotion engine for analysis.

[1330] 2. Behavior of generative AI and emotion engine: The generative AI analyzes the received materials and suggests insights and improvements. The emotion engine analyzes the emotions contained in the presentation content and feeds the results back to the server.

[1331] 3. Server actions: The server receives suggestions from the generative AI and emotion engine, organizes them into a format that is easy for humans to understand, and notifies the expert team of the organized results as a discussion result, and then generates materials for the management meeting.

[1332] 4. Actions of Executive B: Executive B receives the results of the discussion from the server before the management meeting and checks the contents. At the management meeting, he makes specific comments and proposals based on this material, and expresses his opinion while taking into consideration the emotional data.

[1333] 5. President's Actions: The president considers all comments and the results of the discussions and decides on the market entry policy for the new product. During the meeting, the emotion engine analyzes the reactions of executives and management in real time and takes that feedback into account when deciding on a policy. The decision is recorded on the server and notified to all employees.

[1334] The processing flow will be explained below.

[1335] Server Processing

[1336] Step 1: Receiving the presentation

[1337] The server receives the presentation content sent from the employee terminal.

[1338] The server listens for HTTP requests.

[1339] The received file is analyzed and stored in the database.

[1340] Step 2: Request analysis from generative AI and emotion engine

[1341] The server sends the saved presentation content to the generative AI and emotion engine.

[1342] The server retrieves the presentation content from the database.

[1343] The acquired data is converted into a format suitable for the generative AI API.

[1344] The converted data is sent to the generative AI and a response is awaited.

[1345] At the same time, the presentation content is sent to the emotion engine for emotion analysis.

[1346] Step 3: Receive and organize the analysis results

[1347] The server receives, organizes, and analyzes the analysis results from the generative AI and emotion engine.

[1348] The server receives the response from the generative AI in JSON format.

[1349] Analyze the received data and extract the key points.

[1350] It receives the emotion analysis results from the emotion engine and adds them to the analysis results of the generative AI.

[1351] The extracted key points and sentiment analysis results are converted into a format that is easy for humans to understand.

[1352] Step 4: Notify the Specialist Team

[1353] The server notifies the expert team of the organized analysis results.

[1354] The server will send a notification email to the email address of the specialist team.

[1355] The notification email will include details such as the analysis results and feedback deadline.

[1356] Step 5: Generate and distribute materials on the results of the discussion

[1357] The server generates materials for management meetings based on the results of the expert team's discussions and distributes them to executives and management.

[1358] The server converts the results of the test into a report format.

[1359] Convert the report to PDF or presentation format and save it on the server.

[1360] The download link will be sent to each executive and management device.

[1361] Terminal (employee) processing

[1362] Step 1: Create your presentation materials

[1363] The terminal (employee) creates presentation materials.

[1364] The terminal (employee) creates documents using a word processor or presentation software.

[1365] Step 2: Upload your presentation materials

[1366] The terminal (employee) uploads the created documents to the server.

[1367] Employees access a dedicated web interface for uploading.

[1368] Select the file you created and click the Upload button.

[1369] Check the notification that the upload is complete.

[1370] Step 3: Receive and confirm the results

[1371] The terminal (employee) receives and checks the results of the test from the server.

[1372] Access the URL notified by the server and download the report of the test results.

[1373] Check the results of the discussion and revise the presentation materials as necessary.

[1374] Terminal (Executive) Processing

[1375] Step 1: Receive and confirm the results

[1376] The terminal (officer) receives and checks the test results distributed from the server.

[1377] Download the report of your test results via the link provided.

[1378] Review the contents of the report and prepare comments for the management meeting.

[1379] Step 2: Comments at the management meeting

[1380] The terminal (executive) makes comments at management meetings based on the content of employees' presentations and the analysis results of the generative AI and emotion engine.

[1381] By referring to the content of the presentation and the results of the discussion, specific feedback and suggestions will be provided.

[1382] The results of the emotion engine are also taken into account to assess their relevance to the emotional responses of executives.

[1383] Terminal (management) processing

[1384] Step 1: Review the results and presentation

[1385] The terminal (management) checks the executives' comments and the results of the discussion.

[1386] Use the provided link to retrieve all materials and review them before the meeting.

[1387] Prepare for decision-making, taking into account feedback from executives.

[1388] Step 2: Decide and record management policies

[1389] The terminal (management) decides on management policy based on the content of employees' presentations, the analysis results of the generative AI, and the analysis results of the emotion engine.

[1390] The final management policy will be formulated based on the discussions at the management meeting.

[1391] During meetings, the emotion engine analyzes the reactions of executives and management in real time and takes that feedback into consideration when deciding on policy.

[1392] The decided management policy is recorded on the server and prepared for distribution as materials for the general meeting.

[1393] Example 2

[1394] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1395] Conventional management decision-making systems have difficulty analyzing the content of employee presentations and providing useful feedback, and even evaluating the emotions expressed in the presentations. As a result, it has been impossible to obtain input from multiple perspectives, making it difficult to make optimal management decisions. The present invention aims to solve these problems and support efficient and accurate management decisions.

[1396] 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.

[1397] In this invention, the server includes means for receiving presentation content from employees, means for transmitting the received presentation content to a generative artificial intelligence (AI) and requesting analysis, means for receiving analysis results from the AI ​​and organizing and analyzing the analysis results, means for analyzing emotions contained in the presentation and reflecting the results in feedback, and means for notifying the specialized team of the organized and analyzed analysis results as discussion results, generating the discussion results and emotion analysis results as materials for management meetings and distributing them to executive officers. This makes it possible to obtain input from multiple perspectives through the analysis of employees' presentation content and emotional evaluation, enabling more accurate management decisions.

[1398] An "employee" is someone who works for a company or organization.

[1399] "Presentation content" refers to materials and explanations that compile information and proposals related to the business of a company or organization.

[1400] "Receiving" is the act of receiving transmitted data.

[1401] "Generative AI" refers to artificial intelligence systems that mimic human-like intellectual activity and provide analysis and insights tailored to specific purposes.

[1402] "Analysis" is the act of examining data or information in detail to clarify its structure and content.

[1403] "Organization and analysis" means classifying and summarizing the received data and putting it into an easy-to-understand form.

[1404] "Bubble-talking results" refers to feedback based on the analysis results and suggestions obtained at the initial stage, used to consider future improvements and directions.

[1405] A "specialized team" is a group of people with specialized knowledge and skills in a particular field.

[1406] "Notification" is the act of communicating specific information to other people.

[1407] "Chief executives" are people in positions that make important management decisions in a company or organization.

[1408] "Distribution" is the act of sending materials or information to multiple people.

[1409] "Emotion analysis means" refers to systems and technologies that read emotions from people's conversations and writings, and analyze and evaluate them.

[1410] "Feedback" is the act of conveying evaluations and opinions about behavior or results.

[1411] A "final management policy" is an important policy that determines the organization's future direction and goals.

[1412] "Recording" is the act of saving events or data for future reference.

[1413] MODE FOR CARRYING OUT THE INVENTION

[1414] The present invention is a system that allows employees to analyze presentation content using a generative artificial intelligence (AI) in conjunction with an emotion analysis system, and then feeds the results back to a management meeting, thereby obtaining input from multiple perspectives and deciding on management policies. Specific embodiments for implementing this system are described below.

[1415] System Overview

[1416] The system consists of the following main components: employee terminals, a server, generative artificial intelligence, an emotion analysis system, executive terminals, and management terminals.

[1417] Hardware and software used

[1418] Employee devices: Devices used by employees to create and upload presentation materials. Examples include PCs and tablets.

[1419] Server: A central processing unit that receives presentation content and sends it to the generative AI and emotion analysis system. The server has a database and manages and analyzes information.

[1420] Generative AI: An AI system that analyzes presentation content and provides insights. An example is OpenAI's GPT-4.

[1421] Emotion analysis system: A system that analyzes the content of a presentation and evaluates the emotional tone. An example is the IBM Watson Tone Analyzer.

[1422] Executive terminal: A device used in management meetings to review presentation content and analysis results and provide comments. Examples include PCs and tablets.

[1423] Executive terminals: Devices that receive information to make final management decisions. These also include PCs and tablets.

[1424] Specific examples of processing

[1425] 1. Employee A's actions: Employee A uses his / her own employee terminal to create a presentation document on the market analysis of a new product. After completing the document, he / she uploads the document to the server from his / her employee terminal.

[1426] 2. Server processing: The server receives the uploaded presentation materials and stores them in a database. It then sends the stored materials to GPT-4, a generative AI, using the following prompt:

[1427] Analyze "New Product Market Analysis Presentation" and provide insights into what parts can be improved. Also, take into account the results of the sentiment analysis system and comment on the emotional tone and audience reaction.

[1428] 3. Generative AI analysis: Generative AI analyzes the received materials and generates insights and suggestions on what can be improved. For example, it might say, "Chapter 3 of your presentation lacks specificity, so adding more detailed data would be helpful."

[1429] 4. Analysis by the emotion analysis system: The emotion analysis system analyzes the emotions contained in the content of the presentation materials and generates results such as "70% positive emotions." These results are sent back to the server.

[1430] 5. Server result integration: The server receives the results from the generative AI and the emotion analysis system, organizes them into a format that is easy for humans to understand, and finally compiles them as "test results."

[1431] 6. Feedback notification: The server notifies the employee and executive terminals of the results of the discussion. Employees can review the feedback and revise their presentation materials. Executives can also use this information to make comments and suggestions at management meetings.

[1432] Management meeting and final decision

[1433] 1. Operation of executive terminals: Executives receive the results of the discussion from the server before the management meeting and check the details. They also use the executive terminals during the meeting to provide specific comments based on the content of the presentation and the analysis results of the generative AI.

[1434] 2. Operation of management terminals: The management team makes the final decision on the management policy. The reactions of executives and management during the meeting are analyzed in real time by the emotion analysis system, and this feedback is also taken into consideration. The final management policy is recorded on the server and notified to all employees.

[1435] As a result, the present invention can provide a system that evaluates the content of employees' presentations from multiple angles and supports efficient and accurate management decisions.

[1436] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1437] Step 1:

[1438] Employees prepare presentation materials.

[1439] Input: Data about the topic and content of the presentation

[1440] Specific operations: Employees use their own devices to create presentation materials and enter market analysis data and proposal details into each slide.

[1441] Output: Finished presentation file

[1442] Step 2:

[1443] The employee terminal uploads the presentation materials to the server.

[1444] Input: Completed presentation file

[1445] Specific operation: The employee performs the upload operation on the terminal and sends the presentation materials to the server.

[1446] Output: Presentation materials uploaded to the server

[1447] Step 3:

[1448] The server stores the received presentation materials in a database.

[1449] Input: Uploaded presentation materials

[1450] Specific operation: The server temporarily loads the presentation materials into memory and stores them in a database.

[1451] Output: Presentation materials stored in a database

[1452] Step 4:

[1453] The server transmits the presentation materials to the generative artificial intelligence.

[1454] Input: Presentation materials and prompts stored in the database

[1455] Specific operation: The server sends the presentation to a generative AI (e.g., GPT-3) via an API and requests it to analyze it. The prompt is "Please provide your insights on which parts of this presentation can be improved."

[1456] Output: Data sent to the generative artificial intelligence

[1457] Step 5:

[1458] Generative AI analyzes presentation materials and provides insights.

[1459] Input: Presentation materials and analysis requests

[1460] Specific operation: Generative AI analyzes the content of presentation materials and generates improvements and insights. For example, it generates feedback such as, "Chapter 3 is lacking in specificity, so more detailed data should be added."

[1461] Output: Analysis results returned from the generative AI

[1462] Step 6:

[1463] The server receives the results from the generative artificial intelligence and organizes the data.

[1464] Input: Analysis results from generative artificial intelligence

[1465] Specific operation: The server summarizes and organizes the analysis results received in a format that is easy for humans to understand, for example, by converting the analysis results into bullet points or diagrams.

[1466] Output: Organized analysis results

[1467] Step 7:

[1468] The server sends the organized analysis results to the emotion analysis system.

[1469] Input: Organized analysis results

[1470] Specific operation: The server sends the organized analysis results to an emotion analysis system (e.g., IBM Watson Tone Analyzer) and requests emotion analysis.

[1471] Output: Data sent to the sentiment analysis system

[1472] Step 8:

[1473] The emotion analysis system analyzes the emotions contained in presentation materials.

[1474] Input: Analysis results after receiving a request for emotion analysis

[1475] Specific operation: The emotion analysis system analyzes the content of the presentation materials and generates emotion data such as "70% positive tone."

[1476] Output: Emotion analysis results

[1477] Step 9:

[1478] The server receives the sentiment analysis results and generates the final feedback.

[1479] Input: Sentiment analysis results and previously organized analysis results

[1480] Specific operation: The server integrates the sentiment analysis results with the previously organized analysis results to generate an overall feedback document.

[1481] Output: Final feedback document

[1482] Step 10:

[1483] The server notifies the employee terminal and the executive terminal of the final feedback.

[1484] Input: Final feedback document

[1485] Specific operation: The server sends the final feedback document to the terminals of the employees and executives and notifies them.

[1486] Output: Employee and executive terminals that received the notification

[1487] Step 11:

[1488] The employee terminals then revise the presentation materials based on the feedback.

[1489] Input: Final feedback document

[1490] Specific action: The employee reviews the feedback and makes revisions to the presentation as needed, for example adding more detailed data to Chapter 3.

[1491] Output: Revised presentation

[1492] Step 12:

[1493] The executive terminals provide comments based on feedback at management meetings.

[1494] Input: Final feedback document and revised presentation

[1495] Specific actions: Executives will make comments and suggestions at the management meeting, referring to the final feedback and revised presentation materials.

[1496] Output: Comments from executives at management meetings

[1497] Step 13:

[1498] The management terminal decides the final management policy.

[1499] Input: Executive comments at management meetings and final feedback

[1500] Specific actions: Management decides on the final management policy based on the comments and feedback provided during the meeting.

[1501] Output: Decided management policy

[1502] Step 14:

[1503] The server notifies all employees of the decided management policy.

[1504] Input: Decided management policy

[1505] Specific operation: The server records the management policy decision and sends a notification to all employee terminals, for example, a message saying, "A decision has been made to enter the market for a new product."

[1506] Output: All employee terminals that received the notification

[1507] (Application example 2)

[1508] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1509] In modern factories, improving the efficiency and optimizing of manufacturing processes are important issues. However, in many factories, workers' explanations and presentations are based on intuition, and specific areas for improvement are often overlooked. In addition, opportunities for management and work teams to receive accurate feedback are limited, making rapid improvement a difficult challenge. Against this backdrop, there is a need for a system that can analyze the content of factory workers' explanations and provide immediate feedback on the results.

[1510] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the explanation of the manufacturing process from the factory worker, means for sending the received explanation to the generative AI and requesting analysis, means for receiving the analysis results from the generative AI and organizing and analyzing the analysis results, means for notifying the organized and analyzed analysis results to the work team as efficiency results, and means for generating the efficiency results as materials for a factory meeting and distributing them to management executives. This enables the explanation of the manufacturing process to be analyzed and the results to be quickly fed back.

[1511] "Manufacturing process" refers to the manufacturing process and procedures for products in the manufacturing industry.

[1512] "Explanatory content" refers to information and materials provided by factory workers about manufacturing processes and efficiency proposals.

[1513] "Receiving means" refers to the functions and devices that receive explanations from factory workers and input them into the system.

[1514] "Generative AI" refers to artificial intelligence systems that analyze human-created materials and information and provide insights and suggestions for improvement.

[1515] "Analysis means" refers to the functions and devices that request the generative AI to analyze the content of the explanation and receive the results.

[1516] "Organization and analysis means" refers to the functions and devices that compile the analysis results from generative AI and convert them into an easy-to-understand format.

[1517] "Efficiency results" refers to improvements and efficiency proposals for the manufacturing process proposed based on the analysis results of generative AI.

[1518] "Notification means" refers to the functions and devices used to report efficiency results to work teams and other stakeholders.

[1519] "Factory meeting materials" refers to materials that compile information on improvements to manufacturing processes and proposals for efficiency, and are used to discuss these at meetings.

[1520] "Management officers" refers to officials responsible for the operation and management of a factory.

[1521] "Feedback" refers to reactions to the analysis results and efficiency improvement proposals, and suggestions for improvements. In the present invention, feedback is provided to the worker so that the worker can improve the content of the explanation.

[1522] System Overview

[1523] This invention is a system to support the improvement of efficiency in manufacturing processes. Specifically, it uses generative AI and an emotion engine to analyze the explanations of manufacturing processes provided by factory workers, and provides immediate feedback on the results of the analysis to suggest improvements and improvements to improve efficiency.

[1524] Hardware and software used

[1525] This system uses the following hardware and software:

[1526] Smart glasses: For example, Google Glass or Vuzix Blade.

[1527] EmotionEngine: A software module for emotion analysis.

[1528] AIAnalysis: A generative AI module for analyzing narrative content and providing insights.

[1529] Python programming language: Used to implement programs.

[1530] System Operation

[1531] 1. Upload your presentation:

[1532] Factory workers upload a description of the manufacturing process to the application through smart glasses, either by recording it as voice or by typing it in text format.

[1533] 2. Analysis process:

[1534] The server sends the uploaded explanation to the generative AI module (AIAnalysis) for analysis. The generative AI analyzes the explanation and suggests improvements and efficiency suggestions. The explanation is also sent to the Emotion Engine module for emotional analysis.

[1535] 3. Feedback generation:

[1536] The server receives the results of generative AI and emotion analysis, organizes and analyzes them, and notifies the work team and management executives of the organized analysis results as "efficiency results."

[1537] 4. View Feedback:

[1538] Workers can view analysis results and feedback in real time through the smart glasses' display, allowing them to make any necessary improvements immediately.

[1539] Specific examples

[1540] Factory worker action: A worker creates a presentation to explain a "new manufacturing process" and uploads it using smart glasses.

[1541] How the generative AI and emotion engine work: Uploaded presentation materials are sent to AIAnalysis and EmotionEngine for analysis. AIAnalysis suggests areas for improvement, and EmotionEngine evaluates the emotions contained in the presentation content.

[1542] Server operation: The server receives these analysis results, organizes them into a format that is easy for humans to understand, and notifies the work team and management executives of the organized results as "efficiency results."

[1543] Real-time feedback: Workers can view analysis results through the smart glasses display and immediately implement improvements to the manufacturing process.

[1544] Prompt Sentence Examples

[1545] Analyze a presentation deck for a new manufacturing process. Evaluate areas for improvement, as well as the tone and emotion of the presentation.

[1546] This system will enable the efficiency of manufacturing processes within the factory, enabling work teams and management to quickly and accurately implement improvement proposals.

[1547] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1548] Step 1:

[1549] Upload a presentation

[1550] How it works: A worker uses smart glasses to upload a description of a manufacturing process to the application, either as a voice recording or text input.

[1551] Input: Description of the manufacturing process (audio or text data).

[1552] Output: The server has received the description.

[1553] Step 2:

[1554] Data reception by the server

[1555] How it works: The server receives the uploaded description and stores it in a database.

[1556] Input: The uploaded description.

[1557] Output: The description is saved in the database.

[1558] Step 3:

[1559] Analysis request to generative AI

[1560] Operation: The server sends the saved description to the generative AI module (AIAnalysis) and requests it to analyze it.

[1561] Input: Description stored in the database.

[1562] Output: The status of the analysis request.

[1563] Step 4:

[1564] Analysis of generative AI

[1565] How it works: Generative AI (AIAnalysis) analyzes the content of the explanation and suggests improvements and efficiency suggestions.

[1566] Input: The description sent by the server.

[1567] Output: Analysis results (improvements and efficiency suggestions).

[1568] Step 5:

[1569] Emotion analysis using an emotion engine

[1570] How it works: The server sends the description to the Emotion Engine and requests it to analyze the emotions contained in the content.

[1571] Input: The description sent by the server.

[1572] Output: Emotion analysis results.

[1573] Step 6:

[1574] Receiving analysis results and sentiment analysis results

[1575] How it works: The server receives the analysis results from the generative AI and the emotion analysis results from the emotion engine.

[1576] Input: Analysis results of the generative AI, emotion analysis results of the emotion engine.

[1577] Output: Analysis results and sentiment analysis results are aggregated on the server.

[1578] Step 7:

[1579] Generate feedback

[1580] Operation: The server organizes the analysis results and sentiment analysis results it receives, converts them into a format that is easy for humans to understand, and notifies them as "efficiency results."

[1581] Input: Analysis results, sentiment analysis results.

[1582] Output: Organized efficiency results.

[1583] Step 8:

[1584] Notification to work teams and management officers

[1585] How it works: The server notifies the work team and management of the efficiency results, providing real-time feedback through the smart glasses display.

[1586] Input: Organized efficiency results.

[1587] Output: The work team and management receive the efficiency results.

[1588] Prompt Sentence Examples

[1589] Analyze a presentation deck for a new manufacturing process. Evaluate areas for improvement, as well as the tone and emotion of the presentation.

[1590] These processing steps enable a series of steps to analyze the explanations of factory workers and provide immediate feedback, enabling work teams and management executives to quickly and accurately improve the manufacturing process.

[1591] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1592] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1593] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1594] [Fourth embodiment]

[1595] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1596] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1597] 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).

[1598] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1599] 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.

[1600] 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).

[1601] 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.

[1602] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1603] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1604] 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.

[1605] 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.

[1606] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1607] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1608] System Overview

[1609] This system works in conjunction with generative AI to analyze the content of presentations given by employees and feeds the results back to management meetings, allowing for input from multiple perspectives to be used to determine management policy. The system consists of the following main components:

[1610] Employee device: A device used to create and upload presentation content.

[1611] Server: A central processing unit that receives presentation content, sends it to the generative AI, receives the analysis results, and generates feedback results.

[1612] Generative AI: An artificial intelligence system that analyzes presentation content and provides insights.

[1613] Executive terminal: A device used to check presentation content and analysis results at management meetings and provide comments.

[1614] Management terminal: A device that receives all information to make final management decisions.

[1615] Program processing

[1616] Server Processing

[1617] The server first receives the presentation content from the employee. This content is uploaded from the employee's device and is immediately saved in a database after being received. The server then sends the saved presentation content to the generative AI. The generative AI analyzes the presentation content and returns the results to the server. The server then organizes and analyzes the analysis results, converting them into a format that is easy for humans to understand. This organized analysis result is then reported to the specialist team as a result of discussion.

[1618] Processing of employee terminals

[1619] The employee terminals include a function that allows them to create presentation materials and upload them to the server after completion. After uploading the presentation materials, employees can receive the results of the discussion generated by the server, review them, and revise the presentation materials as necessary.

[1620] Executive terminal processing

[1621] The executive terminals receive the results of the discussions distributed by the server and review them. During management meetings, executives can provide comments based on the presentation content and the generative AI's analysis results, contributing to management decisions.

[1622] Management terminal processing

[1623] The management terminal receives all information, including comments from executives and the results of the discussions, and is responsible for deciding the final management policy. The decided management policy is recorded on the server and notified to all employees.

[1624] Specific examples

[1625] Below is a concrete example of how this system works in practice.

[1626] 1. Employee A's actions: Employee A creates a presentation document on the market analysis of a new product and uploads it to the server from his / her terminal. The server receives the document and saves it in a database. The server then sends the saved document to the generative AI and requests its analysis.

[1627] 2. Generative AI Actions: The generative AI analyzes the received material and offers insights and suggestions for improvement. These suggestions are fed back to the server.

[1628] 3. Server actions: The server receives suggestions from the generative AI and organizes them into a format that is easy for humans to understand. The organized results are then sent to the expert team as a discussion result, and are then generated as materials for a management meeting.

[1629] 4. Actions of Executive B: Executive B receives the results of the discussion from the server before the management meeting and checks the contents. At the management meeting, he / she makes specific comments and proposals based on this material.

[1630] 5. President's Action: The president will consider all comments and the results of the discussion and decide on the market entry policy for the new product. The decision will be recorded on the server and notified to all employees.

[1631] The processing flow will be explained below.

[1632] Server Processing

[1633] Step 1: Receiving the presentation

[1634] The server receives the presentation content sent from the employee terminal.

[1635] The server listens for HTTP requests.

[1636] The received file is analyzed and stored in the database.

[1637] Step 2: Requesting analysis from generative AI

[1638] The server sends the saved presentation content to the generative AI.

[1639] The server retrieves the presentation content from the database.

[1640] The acquired data is converted into a format suitable for the generative AI API.

[1641] The converted data is sent to the generative AI and a response is awaited.

[1642] Step 3: Receive and organize the analysis results

[1643] The server receives the analysis results from the generative AI and organizes and analyzes them.

[1644] The server receives the response from ChatGPT in JSON format.

[1645] Analyze the received data and extract the key points.

[1646] The extracted key points are converted into a format that is easy for humans to understand.

[1647] Step 4: Notify the Specialist Team

[1648] The server notifies the expert team of the organized analysis results.

[1649] The server will send a notification email to the email address of the specialist team.

[1650] The notification email will include details such as the analysis results and feedback deadline.

[1651] Step 5: Generate and distribute materials on the results of the discussion

[1652] The server generates materials for management meetings based on the results of the expert team's discussions and distributes them to executives and management.

[1653] The server converts the results of the test into a report format.

[1654] Convert the report to PDF or presentation format and save it on the server.

[1655] The download link will be sent to each executive and management device.

[1656] Terminal (employee) processing

[1657] Step 1: Create your presentation materials

[1658] The terminal (employee) creates presentation materials.

[1659] The terminal (employee) creates documents using a word processor or presentation software.

[1660] Step 2: Upload your presentation materials

[1661] The terminal (employee) uploads the created documents to the server.

[1662] Employees access a dedicated web interface for uploading.

[1663] Select the file you created and click the Upload button.

[1664] Check the notification that the upload is complete.

[1665] Step 3: Receive and confirm the results

[1666] The terminal (employee) receives and checks the results of the test from the server.

[1667] Access the URL notified by the server and download the report of the test results.

[1668] Check the results of the discussion and revise the presentation materials as necessary.

[1669] Terminal (Executive) Processing

[1670] Step 1: Receive and confirm the results

[1671] The terminal (officer) receives and checks the test results distributed from the server.

[1672] Download the report of your test results via the link provided.

[1673] Review the contents of the report and prepare comments for the management meeting.

[1674] Step 2: Comments at the management meeting

[1675] The terminal (executive) makes comments at management meetings based on the content of employees' presentations and the analysis results of the generative AI.

[1676] By referring to the content of the presentation and the results of the discussion, specific feedback and suggestions will be provided.

[1677] Terminal (management) processing

[1678] Step 1: Review the results and presentation

[1679] The terminal (management) checks the executives' comments and the results of the discussion.

[1680] Use the provided link to retrieve all materials and review them before the meeting.

[1681] Prepare for decision-making, taking into account feedback from executives.

[1682] Step 2: Decide and record management policies

[1683] The terminal (management) decides on management policy based on the content of employees' presentations and the analysis results of the generative AI.

[1684] The final management policy will be formulated based on the discussions at the management meeting.

[1685] The decided management policy is recorded on the server and prepared for distribution as materials for the general meeting.

[1686] Example 1

[1687] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1688] Conventional systems for evaluating and providing feedback on presentation content have struggled to efficiently and objectively analyze presentations and provide rapid feedback based on the results. Delays in the analysis and feedback process are particularly problematic when a large number of presentation materials need to be analyzed in a short period of time. Other issues include converting the analysis results into a human-understandable format and ensuring transparency when formulating final management policies.

[1689] 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.

[1690] In this invention, the server includes means for receiving presentation content from employees, means for transmitting the received presentation content to a generative AI and requesting analysis, means for receiving analysis results from the generative AI and organizing and analyzing the analysis results, means for notifying the expert team of the organized and analyzed analysis results as a discussion result, means for generating the discussion results as materials for management meetings and distributing them to executive officers, means for employees to upload presentation materials and store the uploaded materials in a database, means for creating an API request to transmit the materials to the generative AI, and means for organizing the analysis results in a format that is easy for humans to understand. This improves the efficiency and objectivity of presentation analysis and feedback, enabling faster decision-making.

[1691] "Presentation content" refers to materials and documents prepared by employees to explain and report on various business matters.

[1692] "Generative AI" is an artificial intelligence system that performs advanced analysis and generation based on given information.

[1693] "Analysis results" refers to the insights and suggestions derived by the generative AI after analyzing the presentation content.

[1694] The "test results" are the feedback that the server organizes and analyzes based on the analysis results and presents to the specialist team.

[1695] A "specialized team" is a group of employees with high levels of expertise in a particular field or task.

[1696] "Materials for management meetings" are documents that compile the information and data necessary for management to make final decisions.

[1697] "Chief executive officer" refers to a person in a position with the authority to make important decisions in the management of a company.

[1698] A "database" is a system for systematically storing and managing data such as presentation content and analysis results.

[1699] An "API request" is a protocol that allows different software components to communicate with each other and utilize their functionality.

[1700] "Feedback" refers to advice and opinions to improve presentation materials based on the analysis results.

[1701] "Employee terminal" refers to a computer or digital device used by an employee for work purposes.

[1702] A "server" is the central processing unit for the entire system, and is a device that receives, stores, analyzes, and distributes data.

[1703] This invention is a system that efficiently and objectively analyzes the content of presentations created by employees and provides rapid feedback based on the results. This system is primarily composed of employee terminals, a server, a generative AI, executive terminals, and management terminals.

[1704] System Overview

[1705] The core of this system is the server, which receives and stores the presentation content and sends it to the generative AI. It also organizes the analysis results from the generative AI and generates materials for expert teams and management meetings as a result of discussions. The overall flow is as follows:

[1706] Hardware and software used

[1707] Employee devices: laptops and desktop computers used by employees for work (examples: Dell, HP)

[1708] Server: High-performance server (examples: AWS EC2, Microsoft Azure)

[1709] Generation AI: GPT-4 (OpenAI)

[1710] Program processing

[1711] Server Processing

[1712] The server receives the presentation content from employees. This content is uploaded from employee devices and is immediately saved in a database after being received. The server then sends the saved presentation content to the generative AI. The generative AI analyzes the presentation content and returns the results to the server. The server organizes the analysis results and converts them into a format that is easy for humans to understand. This organized analysis result is then reported to the specialist team as a result of discussions.

[1713] Processing employee terminals

[1714] The employee terminals include a function that allows employees to create presentation materials and upload them to the server after completion. Once the presentation materials are uploaded, the server receives them and stores them in a database. The server then sends the saved presentation content to the generative AI and requests it to be analyzed.

[1715] Generative AI processing

[1716] Generative AI analyzes the presentation content sent from the server, generates analytical results, and sends them back to the server. Generative AI provides insights into market analysis, competitive analysis, customer needs, and predicted trends, among other things.

[1717] Executive terminal processing

[1718] The executive terminals receive the results of the discussions distributed by the server and review them. During management meetings, executives can provide comments based on the presentation content and the generative AI's analysis results, contributing to management decisions.

[1719] Management terminal processing

[1720] The management terminal receives all information, including comments from executives and the results of the discussions, and is responsible for deciding the final management policy. The decided management policy is recorded on the server and notified to all employees.

[1721] Specific examples

[1722] A specific example of use is shown below.

[1723] 1. Employee A's actions: Employee A creates a presentation document on the market analysis of a new product and uploads it to the server from his / her terminal. The server receives the document and stores it in a database. The server then sends the saved document to the generative AI and requests its analysis.

[1724] 2. Generative AI Actions: The generative AI analyzes the received material and offers insights and suggestions for improvement. These suggestions are fed back to the server.

[1725] 3. Server actions: The server receives suggestions from the generative AI and organizes them into a format that is easy for humans to understand. The organized results are then reported to the expert team as a result of discussions, and are then generated as materials for management meetings.

[1726] 4. Actions of Executive B: Executive B receives the results of the discussion from the server before the management meeting and checks the contents. At the management meeting, he / she makes specific comments and proposals based on this material.

[1727] 5. President's Action: The president will consider all comments and the results of the discussion and decide on the market entry policy for the new product. The decision will be recorded on the server and notified to all employees.

[1728] Examples of prompt statements

[1729] Below is an example of a prompt sentence to input to the generative AI model.

[1730] Analyze a presentation about a new product market analysis and provide suggestions for improvement and insights. The main content of the presentation is as follows:

[1731] 1. Current market situation

[1732] 2. Competitive analysis

[1733] 3. Customer needs

[1734] 4. Predicted trends

[1735] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1736] Step 1: Create and upload presentation materials using employee devices

[1737] Specific explanation: An employee (the user) creates a presentation document related to his or her work. The document is based on a theme such as a market analysis of a new product or a competitive analysis. The software used is Microsoft PowerPoint or Google Slides. The completed document is sent to the server using the upload function on the employee's terminal.

[1738] Input and Output: The input is the presentation materials created by employees, and the output is the presentation materials uploaded to the server.

[1739] Specific operation: An employee creates a presentation in PowerPoint, clicks the "Upload" button to open a file selection dialog, selects the created presentation file, and uploads it to the server.

[1740] Step 2: The server receives the data and stores it in a database

[1741] Specific explanation: The server receives presentation materials uploaded from employee terminals. The received materials are saved in a temporary directory and then saved in a database.

[1742] Input and output: The input is the presentation materials uploaded from the employee terminals, and the output is the presentation materials saved in the database.

[1743] Specific operation: The server receives the HTTP request, saves the uploaded file in the specified temporary directory, and records the file metadata in the database after saving is complete.

[1744] Step 3: Send the data to the generative AI and receive the analysis results

[1745] Specifically, the server reads the presentation materials stored in the database and creates an API request to send them to the generative AI. The generative AI receives the request, analyzes the materials, and returns the analysis results to the server.

[1746] Input and output: The input is the presentation materials stored in the database, and the output is the analysis results from the generative AI.

[1747] Specific operation: The server reads the saved file and sends an HTTP POST request to the generative AI's API endpoint. The generative AI performs analysis and returns the results to the server as an HTTP response.

[1748] Step 4: Organizing the analysis results and generating the results by the server

[1749] Specifically, the server receives the analysis results sent back from the generative AI and organizes them into a format that is easy for humans to understand. These organized analysis results are then reported to the expert team as a result of the discussion.

[1750] Input and output: The input is the analysis result from the generative AI, and the output is the result of the discussion that is communicated to the expert team.

[1751] Specific operation: The analysis results are read from the database and formatted. The results are generated in HTML or PDF format and sent to the specialist team via email.

[1752] Step 5: Notify employees of the results of the discussion and confirm

[1753] Specific explanation: Employees check the results of the discussions sent by the server. They receive the results via email or the internal portal site, carefully examine the content, and revise the presentation materials as necessary.

[1754] Input and output: The input is the result of the discussion sent from the server, and the output is the revised presentation materials.

[1755] Specific actions: An employee opens the email and clicks on the attached link with the results of the discussion. The employee checks the results on the in-house portal site and uses the feedback to revise the presentation materials.

[1756] Step 6: Executives review and comment on the results

[1757] Specific explanation: The executive terminal receives the results of the discussion distributed from the server, checks the contents, and then compiles specific comments and suggestions into a document.

[1758] Input and Output: The input is the results of the discussion distributed by the server, and the output is a document containing comments and suggestions from the executives.

[1759] Specific operation: Executives log in to a dedicated portal site, download the results of the discussion, write comments based on the results, and upload them back to the server.

[1760] Step 7: Management decides on and notifies the final management policy

[1761] Specific explanation: The management terminal receives all information, including comments from executives and the results of discussions, and decides on the final management policy. The decided management policy is recorded on the server and notified to all employees.

[1762] Input and output: The input is the comments and feedback from executives, and the output is the final management policy.

[1763] What it does: Management reviews all feedback using meeting tablets or laptops, makes a final decision at the meeting, enters the decision into the server administration page, and sends an email notification to all employees.

[1764] (Application example 1)

[1765] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1766] Conventional autonomous vehicle operation management systems lack the ability to analyze operational data and provide real-time feedback, making it difficult to provide drivers and vehicle managers with sufficient operational assistance. Furthermore, executives were also unable to make effective management decisions based on operational data.

[1767] 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.

[1768] In this invention, the server includes means for receiving presentation content from employees, means for transmitting the received presentation content to a generative AI and requesting analysis, means for receiving analysis results from the generative AI and organizing and analyzing the analysis results, means for notifying the expert team of the organized and analyzed analysis results as a consultation result, means for analyzing operation data of autonomous vehicles based on the notified analysis results, means for providing feedback to drivers and vehicle managers in real time based on the analyzed operation data, and means for distributing the feedback results to executive officers. This enables analysis of operation data of autonomous vehicles and real-time feedback, enabling immediate responses to drivers and vehicle managers and accurate management decisions by executive officers.

[1769] "Employee" refers to a person who belongs to a company or organization and performs work.

[1770] "Presentation content" refers to written or digital materials that organize and present information or data related to a task or project.

[1771] "Generative AI" refers to artificial intelligence systems that analyze input data and generate insights and suggestions.

[1772] "Analysis results" refers to the information, including insights and suggestions, that generative AI provides after analyzing data.

[1773] "Bubble-thumping results" refers to evaluations and proposals made by a specialized team based on the analysis results of generative AI.

[1774] A "specialized team" refers to a group with high levels of expertise in a particular field.

[1775] "Chief executive" refers to a high-ranking manager who is responsible for determining the business strategies and policies of a company or organization.

[1776] "Autonomous vehicle" refers to a vehicle that has the ability to drive autonomously using software and sensors.

[1777] "Operation data" refers to various information about autonomous vehicles during operation, such as their speed, location, road conditions, and distance from other vehicles.

[1778] "Driver" refers to the person who drives the vehicle, but in the case of autonomous vehicles, it can also refer to the person who supervises or responds to emergencies.

[1779] "Vehicle manager" refers to a person whose role is to manage and supervise the operation status and maintenance of vehicles.

[1780] "Feedback" refers to improvement suggestions and evaluations provided based on the analysis results.

[1781] This invention is a system that uses generative AI to analyze presentation content created by employees and provides feedback based on the results. This system can be applied to managing operational data for autonomous vehicles and supporting management decisions.

[1782] System Overview

[1783] 1. Employee terminals:

[1784] Function: Employees create presentation materials and upload them to the server.

[1785] Hardware / Software: Personal computers, tablets (Windows, macOS, iOS, Android)

[1786] 2. Server:

[1787] Function: Receive presentation materials, send them to generative AI for analysis, and receive the results. Organize and analyze the analysis results and notify the expert team as a result of discussion. Also, analyze the operation data of autonomous vehicles based on the notified analysis results and provide feedback to drivers and vehicle managers.

[1788] Hardware: Server machine (Linux-based)

[1789] Software: Flask (web framework), requests (library for sending HTTP requests), JSON library

[1790] 3. Generative AI:

[1791] Function: Analyzes presentation content and operational data to provide suggestions for improvements and operational optimization.

[1792] Software: Natural language processing models (GPT-3, etc.)

[1793] 4. Specialized team terminal:

[1794] Function: Receives the results of the test from the server and evaluates or suggests additional steps as needed.

[1795] Hardware: Personal computers, tablets

[1796] Software: Text viewing and editing software (Microsoft Word, etc.)

[1797] 5. Traffic control terminal:

[1798] Function: Optimizes operations in real time based on autonomous vehicle operation data and analysis results, and provides feedback to drivers and fleet managers.

[1799] Hardware: Smartphones, tablets

[1800] Software: Dedicated fleet management application

[1801] 6. Executive Terminal:

[1802] Function: Receive feedback and discussion results from executives and decide on the final management policy.

[1803] Hardware: Personal computers, tablets

[1804] Software: Text viewing and editing software (Microsoft Word, etc.)

[1805] Program processing explanation

[1806] The server receives the presentation content from employees and stores it in a database. The received data is then sent to the generative AI for analysis. The generative AI analyzes the presentation content and operational data and feeds the results back to the server. Based on this analysis, the server provides real-time feedback to drivers and vehicle managers, and also organizes and analyzes the results as materials for management. The feedback includes optimal driving routes and recommendations for necessary maintenance.

[1807] For example, suppose an employee creates a presentation about the market launch of a new product and uploads it to a server. The server then sends the presentation to a generative AI and requests it to analyze it. The analysis results include a suggestion that "it is recommended to replace the brake pads at the next scheduled inspection," and this is immediately fed back to the driver via the fleet management terminal.

[1808] Example prompt sentence:

[1809] "Please provide us with optimal driving routes and maintenance suggestions based on the operational data of our autonomous vehicles."

[1810] This system will enable autonomous vehicles to operate more efficiently and safely.

[1811] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1812] Step 1:

[1813] Employee terminals create presentation content and upload it to the server. The input is the presentation materials created by the employee, and the output is the presentation data saved on the server. Specifically, employees create materials using presentation software on their personal computers or tablets, and then send them using the server's upload function.

[1814] Step 2:

[1815] The server saves the presentation content received from the employee terminal in a database. The input is the presentation data sent from the employee terminal, and the output is the presentation content saved in the database. Specifically, the server's API receives the presentation data and saves it in the database in JSON format.

[1816] Step 3:

[1817] The server sends the saved presentation content to the generative AI and requests it to analyze it. The input is the presentation data saved in the database, and the output is the request sent to the generative AI. Specifically, the server generates an HTTP request and sends the data to the generative AI's API.

[1818] Step 4:

[1819] The generative AI analyzes the presentation content and returns the analysis results to the server. The input is the presentation data sent from the server, and the output is the analysis results. Specifically, the generative AI analyzes the data using a natural language processing model (e.g., GPT-3) and returns the results to the server in JSON format.

[1820] Step 5:

[1821] The server organizes and analyzes the analysis results from the generative AI and notifies the expert team of the results. The input is the analysis results returned from the generative AI, and the output is the results sent to the expert team. Specifically, the server reads the analysis results, extracts the necessary information, organizes them as the results, and notifies the expert team's terminal.

[1822] Step 6:

[1823] The expert team receives the test results from the server and checks and evaluates them. The input is the test results sent from the server, and the output is the expert team's evaluation and additional suggestions. Specifically, the expert team displays the test results on their terminal, and adds comments and suggestions for corrections as needed.

[1824] Step 7:

[1825] The server analyzes the autonomous vehicle's operational data based on the notified analysis results. The input is the analysis results of the generative AI and the autonomous vehicle's operational data, and the output is the analysis results of the operational data. Specifically, the server resends the operational data to the generative AI, which then performs a detailed analysis of the operational status.

[1826] Step 8:

[1827] The server provides real-time feedback to drivers and vehicle managers based on the analyzed operational data. The input is the analysis results of the operational data, and the output is feedback sent to the driver's or vehicle manager's device. Specifically, the server reads the generative AI's suggestions and sends operational optimization and maintenance suggestions in real time.

[1828] Step 9:

[1829] The server organizes and generates the analysis results as materials for management and distributes them to executive officers. The input is the analysis results and feedback from the expert team, and the output is the materials distributed to management. Specifically, the server integrates all feedback, generates presentation-style materials, and distributes them to the executive officers' terminals.

[1830] Step 10:

[1831] The executive officers decide on the final management policy based on the materials distributed from the server and record that policy. The input is the distributed materials, and the output is the recorded management policy. Specifically, the executive officers check the materials on their terminals, decide on the policy through discussion in a meeting, and record it on the server.

[1832] 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.

[1833] System Overview

[1834] This invention allows employees to analyze presentation content using a generative AI and an emotion engine, and feeds the results back to management meetings, providing input from multiple perspectives to help determine management policy. Furthermore, it recognizes and analyzes the emotions of employees and executives, and supports management decisions based on that data.

[1835] The system consists of the following main components:

[1836] Employee device: A device used to create and upload presentation content.

[1837] Server: A central processing unit that receives presentation content, sends it to the generative AI and emotion engine, receives the analysis results, and generates feedback.

[1838] Generative AI: An artificial intelligence system that analyzes presentation content and provides insights.

[1839] Emotion engine: A system that analyzes the emotions of employees and executives and reflects the results in feedback.

[1840] Executive terminal: A device used to check presentation content and analysis results at management meetings and provide comments.

[1841] Management terminal: A device that receives all information to make final management decisions.

[1842] Program processing

[1843] Server Processing

[1844] The server first receives the presentation content from the employee. After receiving the content uploaded from the employee's device, it saves it in a database. Next, the server sends the saved presentation content to the generative AI. The generative AI analyzes the presentation content and returns the results to the server. The server then organizes and analyzes the analysis results, converting them into a format that is easy for humans to understand. It then uses an emotion engine to analyze the emotions contained in the employee's presentation content and adds the results to the analysis content. The organized analysis results are notified to a specialist team and used as a discussion point.

[1845] Processing of employee terminals

[1846] The employee terminals include a function that allows them to create presentation materials and upload them to the server after completion. After uploading the presentation materials, employees can receive the results of the discussion generated by the server, review them, and revise the presentation materials as necessary.

[1847] Executive terminal processing

[1848] The executive terminal receives the results of the discussion distributed by the server and confirms their content. During management meetings, executives make comments based on the presentation content and the analysis results of the generative AI, and contribute to management decisions by taking into account the emotional data evaluated by the emotion engine.

[1849] Management terminal processing

[1850] The management terminal receives all information, including executives' comments and the results of the discussions, and is responsible for deciding the final management policy. The reactions of executives and management during meetings are also analyzed in real time by the emotion engine, and feedback is provided based on that emotional data. The decided management policy is recorded on the server and notified to all employees.

[1851] Specific examples

[1852] Below is a concrete example of how this system works in practice.

[1853] 1. Employee A's actions: Employee A creates a presentation document on the market analysis of a new product and uploads it to the server from his / her terminal. The server receives the document and stores it in a database. The server then sends the saved document to the generative AI and emotion engine for analysis.

[1854] 2. Behavior of generative AI and emotion engine: The generative AI analyzes the received materials and suggests insights and improvements. The emotion engine analyzes the emotions contained in the presentation content and feeds the results back to the server.

[1855] 3. Server actions: The server receives suggestions from the generative AI and emotion engine, organizes them into a format that is easy for humans to understand, and notifies the expert team of the organized results as a discussion result, and then generates materials for the management meeting.

[1856] 4. Actions of Executive B: Executive B receives the results of the discussion from the server before the management meeting and checks the contents. At the management meeting, he makes specific comments and proposals based on this material, and expresses his opinion while taking into consideration the emotional data.

[1857] 5. President's Actions: The president considers all comments and the results of the discussions and decides on the market entry policy for the new product. During the meeting, the emotion engine analyzes the reactions of executives and management in real time and takes that feedback into account when deciding on a policy. The decision is recorded on the server and notified to all employees.

[1858] The processing flow will be explained below.

[1859] Server Processing

[1860] Step 1: Receiving the presentation

[1861] The server receives the presentation content sent from the employee terminal.

[1862] The server listens for HTTP requests.

[1863] The received file is analyzed and stored in the database.

[1864] Step 2: Request analysis from generative AI and emotion engine

[1865] The server sends the saved presentation content to the generative AI and emotion engine.

[1866] The server retrieves the presentation content from the database.

[1867] The acquired data is converted into a format suitable for the generative AI API.

[1868] The converted data is sent to the generative AI and a response is awaited.

[1869] At the same time, the presentation content is sent to the emotion engine for emotion analysis.

[1870] Step 3: Receive and organize the analysis results

[1871] The server receives, organizes, and analyzes the analysis results from the generative AI and emotion engine.

[1872] The server receives the response from the generative AI in JSON format.

[1873] Analyze the received data and extract the key points.

[1874] It receives the emotion analysis results from the emotion engine and adds them to the analysis results of the generative AI.

[1875] The extracted key points and sentiment analysis results are converted into a format that is easy for humans to understand.

[1876] Step 4: Notify the Specialist Team

[1877] The server notifies the expert team of the organized analysis results.

[1878] The server will send a notification email to the email address of the specialist team.

[1879] The notification email will include details such as the analysis results and feedback deadline.

[1880] Step 5: Generate and distribute materials on the results of the discussion

[1881] The server generates materials for management meetings based on the results of the expert team's discussions and distributes them to executives and management.

[1882] The server converts the results of the test into a report format.

[1883] Convert the report to PDF or presentation format and save it on the server.

[1884] The download link will be sent to each executive and management device.

[1885] Terminal (employee) processing

[1886] Step 1: Create your presentation materials

[1887] The terminal (employee) creates presentation materials.

[1888] The terminal (employee) creates documents using a word processor or presentation software.

[1889] Step 2: Upload your presentation materials

[1890] The terminal (employee) uploads the created documents to the server.

[1891] Employees access a dedicated web interface for uploading.

[1892] Select the file you created and click the Upload button.

[1893] Check the notification that the upload is complete.

[1894] Step 3: Receive and confirm the results

[1895] The terminal (employee) receives and checks the results of the test from the server.

[1896] Access the URL notified by the server and download the report of the test results.

[1897] Check the results of the discussion and revise the presentation materials as necessary.

[1898] Terminal (Executive) Processing

[1899] Step 1: Receive and confirm the results

[1900] The terminal (officer) receives and checks the test results distributed from the server.

[1901] Download the report of your test results via the link provided.

[1902] Review the contents of the report and prepare comments for the management meeting.

[1903] Step 2: Comments at the management meeting

[1904] The terminal (executive) makes comments at management meetings based on the content of employees' presentations and the analysis results of the generative AI and emotion engine.

[1905] By referring to the content of the presentation and the results of the discussion, specific feedback and suggestions will be provided.

[1906] The results of the emotion engine are also taken into account to assess their relevance to the emotional responses of executives.

[1907] Terminal (management) processing

[1908] Step 1: Review the results and presentation

[1909] The terminal (management) checks the executives' comments and the results of the discussion.

[1910] Use the provided link to retrieve all materials and review them before the meeting.

[1911] Prepare for decision-making, taking into account feedback from executives.

[1912] Step 2: Decide and record management policies

[1913] The terminal (management) decides on management policy based on the content of employees' presentations, the analysis results of the generative AI, and the analysis results of the emotion engine.

[1914] The final management policy will be formulated based on the discussions at the management meeting.

[1915] During meetings, the emotion engine analyzes the reactions of executives and management in real time and takes that feedback into consideration when deciding on policy.

[1916] The decided management policy is recorded on the server and prepared for distribution as materials for the general meeting.

[1917] Example 2

[1918] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1919] Conventional management decision-making systems have difficulty analyzing the content of employee presentations and providing useful feedback, and even evaluating the emotions expressed in the presentations. As a result, it has been impossible to obtain input from multiple perspectives, making it difficult to make optimal management decisions. The present invention aims to solve these problems and support efficient and accurate management decisions.

[1920] 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.

[1921] In this invention, the server includes means for receiving presentation content from employees, means for transmitting the received presentation content to a generative artificial intelligence (AI) and requesting analysis, means for receiving analysis results from the AI ​​and organizing and analyzing the analysis results, means for analyzing emotions contained in the presentation and reflecting the results in feedback, and means for notifying the specialized team of the organized and analyzed analysis results as discussion results, generating the discussion results and emotion analysis results as materials for management meetings and distributing them to executive officers. This makes it possible to obtain input from multiple perspectives through the analysis of employees' presentation content and emotional evaluation, enabling more accurate management decisions.

[1922] An "employee" is someone who works for a company or organization.

[1923] "Presentation content" refers to materials and explanations that compile information and proposals related to the business of a company or organization.

[1924] "Receiving" is the act of receiving transmitted data.

[1925] "Generative AI" refers to artificial intelligence systems that mimic human-like intellectual activity and provide analysis and insights tailored to specific purposes.

[1926] "Analysis" is the act of examining data or information in detail to clarify its structure and content.

[1927] "Organization and analysis" means classifying and summarizing the received data and putting it into an easy-to-understand form.

[1928] "Bubble-talking results" refers to feedback based on the analysis results and suggestions obtained at the initial stage, used to consider future improvements and directions.

[1929] A "specialized team" is a group of people with specialized knowledge and skills in a particular field.

[1930] "Notification" is the act of communicating specific information to other people.

[1931] "Chief executives" are people in positions that make important management decisions in a company or organization.

[1932] "Distribution" is the act of sending materials or information to multiple people.

[1933] "Emotion analysis means" refers to systems and technologies that read emotions from people's conversations and writings, and analyze and evaluate them.

[1934] "Feedback" is the act of conveying evaluations and opinions about behavior or results.

[1935] A "final management policy" is an important policy that determines the organization's future direction and goals.

[1936] "Recording" is the act of saving events or data for future reference.

[1937] MODE FOR CARRYING OUT THE INVENTION

[1938] The present invention is a system that allows employees to analyze presentation content using a generative artificial intelligence (AI) in conjunction with an emotion analysis system, and then feeds the results back to a management meeting, thereby obtaining input from multiple perspectives and deciding on management policies. Specific embodiments for implementing this system are described below.

[1939] System Overview

[1940] The system consists of the following main components: employee terminals, a server, generative artificial intelligence, an emotion analysis system, executive terminals, and management terminals.

[1941] Hardware and software used

[1942] Employee devices: Devices used by employees to create and upload presentation materials. Examples include PCs and tablets.

[1943] Server: A central processing unit that receives presentation content and sends it to the generative AI and emotion analysis system. The server has a database and manages and analyzes information.

[1944] Generative AI: An AI system that analyzes presentation content and provides insights. An example is OpenAI's GPT-4.

[1945] Emotion analysis system: A system that analyzes the content of a presentation and evaluates the emotional tone. An example is the IBM Watson Tone Analyzer.

[1946] Executive terminal: A device used in management meetings to review presentation content and analysis results and provide comments. Examples include PCs and tablets.

[1947] Executive terminals: Devices that receive information to make final management decisions. These also include PCs and tablets.

[1948] Specific examples of processing

[1949] 1. Employee A's actions: Employee A uses his / her own employee terminal to create a presentation document on the market analysis of a new product. After completing the document, he / she uploads the document to the server from his / her employee terminal.

[1950] 2. Server processing: The server receives the uploaded presentation materials and stores them in a database. It then sends the stored materials to GPT-4, a generative AI, using the following prompt:

[1951] Analyze "New Product Market Analysis Presentation" and provide insights into what parts can be improved. Also, take into account the results of the sentiment analysis system and comment on the emotional tone and audience reaction.

[1952] 3. Generative AI analysis: Generative AI analyzes the received materials and generates insights and suggestions on what can be improved. For example, it might say, "Chapter 3 of your presentation lacks specificity, so adding more detailed data would be helpful."

[1953] 4. Analysis by the emotion analysis system: The emotion analysis system analyzes the emotions contained in the content of the presentation materials and generates results such as "70% positive emotions." These results are sent back to the server.

[1954] 5. Server result integration: The server receives the results from the generative AI and the emotion analysis system, organizes them into a format that is easy for humans to understand, and finally compiles them as "test results."

[1955] 6. Feedback notification: The server notifies the employee and executive terminals of the results of the discussion. Employees can review the feedback and revise their presentation materials. Executives can also use this information to make comments and suggestions at management meetings.

[1956] Management meeting and final decision

[1957] 1. Operation of executive terminals: Executives receive the results of the discussion from the server before the management meeting and check the details. They also use the executive terminals during the meeting to provide specific comments based on the content of the presentation and the analysis results of the generative AI.

[1958] 2. Operation of management terminals: The management team makes the final decision on the management policy. The reactions of executives and management during the meeting are analyzed in real time by the emotion analysis system, and this feedback is also taken into consideration. The final management policy is recorded on the server and notified to all employees.

[1959] As a result, the present invention can provide a system that evaluates the content of employees' presentations from multiple angles and supports efficient and accurate management decisions.

[1960] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1961] Step 1:

[1962] Employees prepare presentation materials.

[1963] Input: Data about the topic and content of the presentation

[1964] Specific operations: Employees use their own devices to create presentation materials and enter market analysis data and proposal details into each slide.

[1965] Output: Finished presentation file

[1966] Step 2:

[1967] The employee terminal uploads the presentation materials to the server.

[1968] Input: Completed presentation file

[1969] Specific operation: The employee performs the upload operation on the terminal and sends the presentation materials to the server.

[1970] Output: Presentation materials uploaded to the server

[1971] Step 3:

[1972] The server stores the received presentation materials in a database.

[1973] Input: Uploaded presentation materials

[1974] Specific operation: The server temporarily loads the presentation materials into memory and stores them in a database.

[1975] Output: Presentation materials stored in a database

[1976] Step 4:

[1977] The server transmits the presentation materials to the generative artificial intelligence.

[1978] Input: Presentation materials and prompts stored in the database

[1979] Specific operation: The server sends the presentation to a generative AI (e.g., GPT-3) via an API and requests it to analyze it. The prompt is "Please provide your insights on which parts of this presentation can be improved."

[1980] Output: Data sent to the generative artificial intelligence

[1981] Step 5:

[1982] Generative AI analyzes presentation materials and provides insights.

[1983] Input: Presentation materials and analysis requests

[1984] Specific operation: Generative AI analyzes the content of presentation materials and generates improvements and insights. For example, it generates feedback such as, "Chapter 3 is lacking in specificity, so more detailed data should be added."

[1985] Output: Analysis results returned from the generative AI

[1986] Step 6:

[1987] The server receives the results from the generative artificial intelligence and organizes the data.

[1988] Input: Analysis results from generative artificial intelligence

[1989] Specific operation: The server summarizes and organizes the analysis results received in a format that is easy for humans to understand, for example, by converting the analysis results into bullet points or diagrams.

[1990] Output: Organized analysis results

[1991] Step 7:

[1992] The server sends the organized analysis results to the emotion analysis system.

[1993] Input: Organized analysis results

[1994] Specific operation: The server sends the organized analysis results to an emotion analysis system (e.g., IBM Watson Tone Analyzer) and requests emotion analysis.

[1995] Output: Data sent to the sentiment analysis system

[1996] Step 8:

[1997] The emotion analysis system analyzes the emotions contained in presentation materials.

[1998] Input: Analysis results after receiving a request for emotion analysis

[1999] Specific operation: The emotion analysis system analyzes the content of the presentation materials and generates emotion data such as "70% positive tone."

[2000] Output: Emotion analysis results

[2001] Step 9:

[2002] The server receives the sentiment analysis results and generates the final feedback.

[2003] Input: Sentiment analysis results and previously organized analysis results

[2004] Specific operation: The server integrates the sentiment analysis results with the previously organized analysis results to generate an overall feedback document.

[2005] Output: Final feedback document

[2006] Step 10:

[2007] The server notifies the employee terminal and the executive terminal of the final feedback.

[2008] Input: Final feedback document

[2009] Specific operation: The server sends the final feedback document to the terminals of the employees and executives and notifies them.

[2010] Output: Employee and executive terminals that received the notification

[2011] Step 11:

[2012] The employee terminals then revise the presentation materials based on the feedback.

[2013] Input: Final feedback document

[2014] Specific action: The employee reviews the feedback and makes revisions to the presentation as needed, for example adding more detailed data to Chapter 3.

[2015] Output: Revised presentation

[2016] Step 12:

[2017] The executive terminals provide comments based on feedback at management meetings.

[2018] Input: Final feedback document and revised presentation

[2019] Specific actions: Executives will make comments and suggestions at the management meeting, referring to the final feedback and revised presentation materials.

[2020] Output: Comments from executives at management meetings

[2021] Step 13:

[2022] The management terminal decides the final management policy.

[2023] Input: Executive comments at management meetings and final feedback

[2024] Specific actions: Management decides on the final management policy based on the comments and feedback provided during the meeting.

[2025] Output: Decided management policy

[2026] Step 14:

[2027] The server notifies all employees of the decided management policy.

[2028] Input: Decided management policy

[2029] Specific operation: The server records the management policy decision and sends a notification to all employee terminals, for example, a message saying, "A decision has been made to enter the market for a new product."

[2030] Output: All employee terminals that received the notification

[2031] (Application example 2)

[2032] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2033] In modern factories, improving the efficiency and optimizing of manufacturing processes are important issues. However, in many factories, workers' explanations and presentations are based on intuition, and specific areas for improvement are often overlooked. In addition, opportunities for management and work teams to receive accurate feedback are limited, making rapid improvement a difficult challenge. Against this backdrop, there is a need for a system that can analyze the content of factory workers' explanations and provide immediate feedback on the results.

[2034] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the explanation of the manufacturing process from the factory worker, means for sending the received explanation to the generative AI and requesting analysis, means for receiving the analysis results from the generative AI and organizing and analyzing the analysis results, means for notifying the organized and analyzed analysis results to the work team as efficiency results, and means for generating the efficiency results as materials for a factory meeting and distributing them to management executives. This enables the explanation of the manufacturing process to be analyzed and the results to be quickly fed back.

[2035] "Manufacturing process" refers to the manufacturing process and procedures for products in the manufacturing industry.

[2036] "Explanatory content" refers to information and materials provided by factory workers about manufacturing processes and efficiency proposals.

[2037] "Receiving means" refers to the functions and devices that receive explanations from factory workers and input them into the system.

[2038] "Generative AI" refers to artificial intelligence systems that analyze human-created materials and information and provide insights and suggestions for improvement.

[2039] "Analysis means" refers to the functions and devices that request the generative AI to analyze the content of the explanation and receive the results.

[2040] "Organization and analysis means" refers to the functions and devices that compile the analysis results from generative AI and convert them into an easy-to-understand format.

[2041] "Efficiency results" refers to improvements and efficiency proposals for the manufacturing process proposed based on the analysis results of generative AI. ...

Claims

1. A means of receiving presentation content from employees; A means to send the received presentation content to a generative AI and request analysis, A means to receive the analysis results from the generative AI and organize and analyze them; The means to notify the expert team of the results of the analysis that has been organized and analyzed, and A means to generate the results of the discussion as materials for management meetings and distribute them to executive officers; A system including:

2. 2. The system according to claim 1, further comprising means for formulating a final management policy based on the results of the discussion and recording the policy.

3. The system according to claim 1 , further comprising means for referring to the analysis results and providing feedback for correcting the presentation materials.

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