System

Abstract

A system is provided.SOLUTION: A system, comprising: input means; means for packaging input data; means for transmitting the packaged data; means for receiving the transmitted data; means for verifying the received data; means for inputting the verified data into a generator; means for receiving a visualized diagram from the generator; and means for displaying the visualized diagram.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 conferences and online meetings, participants often communicate verbally, which can lead to misunderstandings due to mismatched perceptions. This misunderstanding can extend the meeting time, lead to rework, and ultimately reduce work efficiency. The present invention aims to solve these problems, promote efficient communication, and minimize misunderstandings in meetings. [Means for solving the problem]

[0005] The present invention provides a system including the following means: First, an input means receives text or sketches from a user; Next, the data is appropriately formatted through a means for packaging the input data; A means for transmitting packaged data transmits the data to a server, and the server receives the transmitted data; After reception, a means for verifying the data ensures the reliability and integrity of the content, and then the verified data is input to a generation device, and a visualized diagram is received from the generation device; Then, a means for displaying the visualized diagram displays the diagram to the user and conference participants in real time. This system can eliminate misunderstandings and improve the efficiency of conferences.

[0006] "Input means" refers to an interface that allows a user to input information such as text or sketches into the system.

[0007] "Means for packaging input data" refers to the process of converting the information entered by the user into an appropriate format (e.g., JSON) and compiling it into a format that can be sent.

[0008] "Means for transmitting packaged data" refers to the communication protocol or infrastructure for transmitting packaged information to an external system such as a server.

[0009] The "means for receiving transmitted data" refers to the process or mechanism by which the server receives information transmitted from the client side.

[0010] "Means for validating received data" refers to the process of checking received information for correct format, punctuation, and errors.

[0011] "Means for inputting verified data into the generating device" refers to the process for passing the data to the generating AI or visualization device after verification is complete.

[0012] The "means for receiving a visualized diagram from a generating device" is a process by which the system receives the visualization data (diagram) created by the generating device.

[0013] "Means for displaying a visualization" refers to an interface or display device that allows a user to view the visualization data received from the generating device.

[0014] An "online conferencing tool" is software or a service that allows multiple participants in remote locations to communicate through audio, video, chat, etc.

[0015] The "sketch function" is an interactive interface that allows users to draw simple diagrams and pictures.

[0016] "Generative AI" is an artificial intelligence model that automatically generates visualizations based on the data provided. [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] The present invention provides a system that instantly visualizes text and sketch data entered by a user, eliminating misunderstandings during meetings. This system is realized by a specific program.

[0039] System Overview

[0040] The system is configured by combining an input means, a means for packaging input data, a means for transmitting the packaged data, a means for receiving the transmitted data, a means for verifying the received data, a means for inputting the verified data into a generating device, a means for receiving a visualized diagram from the generating device, and a means for displaying the visualized diagram.

[0041] Explanation of program processing

[0042] 1. User: Providing Input

[0043] Users enter text through the interface of the online meeting tool, adding sketches as needed. For example, they can enter a project workflow such as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client." Once they've finished entering the information, they press the send button.

[0044] 2. Terminal: Sending data

[0045] The device packages the text and sketch data entered by the user into JSON format, and once packaged, sends the data to the server using an HTTP POST request.

[0046] 3. Server: Receiving and processing data

[0047] The server receives an HTTP POST request from the device and parses the JSON data included in the request body. It verifies that the received data is in the correct format and checks for any missing or incorrect data. It then inputs the verified data into the generation AI, which generates a visualized diagram based on the text and sketch data. This generated diagram is then returned to the server.

[0048] 4. Server: Sending results

[0049] The server constructs a response containing the generated diagram and its metadata, and sends it back to the terminal, including the diagram's binary data or its URL.

[0050] 5. Terminal: Displaying the results

[0051] The terminal receives the response from the server, parses the received data, and if visualization is successful, displays the diagram on the user interface.

[0052] 6. User: Image review and feedback

[0053] Users can review the displayed visualizations and share them with other meeting participants, gathering feedback and providing further corrections or additional input as needed.

[0054] Specific examples

[0055] As a concrete example, let's say a user wants to visualize the workflow of a new project. The user enters the following into an online conferencing tool: "Receive requirements from client -> Analyze requirements -> Forward to design team -> Start development -> Test -> Deliver to client." They also sketch boxes and arrows to indicate the process flow. When the user presses the send button, the device sends the input data in JSON format to the server. The server receives the data and passes it to the generation AI for visualization. The generated diagram is sent back from the server to the device and displayed in the user interface. The user provides a detailed explanation based on the displayed diagram, collecting feedback in the process.

[0056] In this way, the system of the present invention can minimize misunderstandings and significantly improve the efficiency of meetings.

[0057] The processing flow will be explained below.

[0058] Program processing flow and details of each step

[0059] Step 1: User enters text

[0060] Users enter their description in a text input field in the online meeting tool, and can optionally add a quick sketch to clarify the image or process they want to share during the meeting.

[0061] Step 2: User presses submit button

[0062] After completing the input, the user presses the send button to send the input data to the server, which passes the input information to the system.

[0063] Step 3: The device packages the data

[0064] The device takes the text and sketch data entered by the user and packages it into JSON format, which makes the data easier to handle on the server side.

[0065] Step 4: The device sends data to the server

[0066] The device sends an HTTP POST request containing the packaged JSON data to the server, which causes the data to reach the server.

[0067] Step 5: Server receives data

[0068] The server receives an HTTP POST request from a client and retrieves the JSON data contained in the request body, which is the trigger for the core processing of the system.

[0069] Step 6: Server verifies the data

[0070] The server validates the format and content of the data it receives, ensuring that the data is in the correct format and checking for any missing or incorrect data. After validation is complete, it proceeds to the next step.

[0071] Step 7: The server inputs data into the generated AI

[0072] The server inputs the verified data into the generative AI, which begins the process of generating a visualization based on the text and sketch data.

[0073] Step 8: The server receives the visualization

[0074] Once the AI ​​generates the visualization, it sends the diagram data back to the server, which receives the generated diagram and prepares it for further processing.

[0075] Step 9: Server constructs result response

[0076] The server constructs a response message based on the received visualization data, which includes the binary data of the diagram, the diagram URL, and the generated metadata.

[0077] Step 10: The server sends a response to the device

[0078] The server sends the constructed response to the device, which then makes the visualized results available.

[0079] Step 11: The device receives the response

[0080] The terminal receives the HTTP response from the server and obtains the data contained in the response body, which includes the data of the generated diagram.

[0081] Step 12: The terminal displays the diagram

[0082] The terminal analyzes the received visualization data and displays it on the user interface, preparing the user for visual explanations during the meeting.

[0083] Step 13: Users review and share diagrams

[0084] Users can view the visualized diagrams displayed on their devices and share them with other participants in the meeting, eliminating misunderstandings and realizing efficient communication.

[0085] In this way, the system goes through a series of steps to instantly visualize the user's input and increase the efficiency of the meeting.

[0086] Example 1

[0087] 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."

[0088] In modern online meetings, communication between participants can sometimes be skewed. In particular, when explaining complex project workflows and concepts, words and text alone are insufficient and visual materials are required. However, on-the-spot visualization and data sharing remain time-consuming and disruptive. Traditional methods lack a means to instantly generate visualizations from sketches and text, making efficient communication difficult.

[0089] 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.

[0090] In this invention, the server includes an input means, a means for packaging input data, a means for transmitting the packaged data, a means for receiving the transmitted data, a means for verifying the received data, a means for inputting the verified data to a generation device, a means for receiving a visualized diagram from the generation device, a means for displaying the visualized diagram, a visualization means based on a generative AI model for instantly visualizing text and sketch data provided by a user, and a means for analyzing the generated visualization data and displaying it on a user interface. This allows for the instant generation of visual materials during an online meeting, reducing misunderstandings among participants and enabling efficient communication.

[0091] "Input means" refers to a digital interface that allows users to input text and sketch data through the online meeting tool.

[0092] "Means for packaging input data" refers to the process of converting user-provided text or sketch data into an appropriate format, such as JSON format, and packaging it.

[0093] The "means for transmitting packaged data" is a process for transmitting packaged data to a server using a communication means such as an HTTP POST request.

[0094] The "means for receiving transmitted data" is a communication interface for receiving data transmitted from a terminal on the server side.

[0095] "Means for validating received data" refers to a data validation process to ensure that the received data is in the correct format and is free of any missing or erroneous data.

[0096] "Means for inputting verified data into a generating device" refers to the process of inputting verified data into a generative AI model.

[0097] The "means for receiving a visualization from a generating device" is a process for receiving visualization data output from a generative AI model.

[0098] The "means for displaying the visualized diagram" is a display device or software for displaying the generated visualization data on a user interface.

[0099] A "generative AI model" is an artificial intelligence model that instantly generates visualizations based on text and sketch data provided by the user.

[0100] A "visualization method" is a process that uses a generative AI model to generate a visual diagram based on input data.

[0101] This invention is a system that instantly visualizes text and sketch data entered by users during online meetings, improving meeting efficiency. This system is mainly realized using a server, terminals, and a generative AI model.

[0102] Users enter text through the interface of the online meeting tool and add sketch data as needed. This interface is designed to be intuitive and easy to use, making it easy for users to operate. For example, a user can enter a project workflow such as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client" and add a sketch using boxes and arrows.

[0103] The device packages the input text and sketch data into JSON format, which is a key-value object format that ensures data integrity and compatibility, and then sends the packaged data to the server using an HTTP POST request.

[0104] The server receives the HTTP POST request sent from the device. It parses the JSON data included in the request body and verifies that the received data is in the correct format. Specifically, it checks that all required fields are present and that the data is not corrupted. The verified data is then input into a generative AI model, which converts it into a prompt for visualization.

[0105] The generative AI model generates a visualized diagram based on a prompt. For example, the prompt might be, "Visualize the project workflow. Illustrate the process: Receive requirements from the client -> Analyze requirements -> Transfer to the design team -> Start development -> Test -> Deliver to the client." The generated diagram is then sent back to the server, which then constructs a response containing the diagram's binary data and URL and sends it to the device.

[0106] The device receives the response from the server, parses the data, and if the visualization is successful, displays the diagram on the user interface. This allows the user to review the visualized data and share it with other meeting participants. Additional feedback can be collected if necessary, allowing for further refinements.

[0107] As a concrete example, let's say a user wants to visualize the workflow of a new project. They enter "Receive requirements from client -> Analyze requirements -> Forward to design team -> Start development -> Test -> Deliver to client" into an online conferencing tool and sketch boxes and arrows to show the process flow. When they press the send button, the device sends this data in JSON format to the server. The server receives the data and passes it to the generation AI for visualization. The generated diagram is sent back from the server to the device and displayed in the user interface. The user provides a detailed explanation based on the displayed diagram, collecting feedback along the way.

[0108] In this way, the invention can minimize misunderstandings and significantly improve meeting efficiency.

[0109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0110] Step 1:

[0111] Users enter text and sketch data using the interface of the online meeting tool. For example, they can enter a project workflow such as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and add sketches using boxes and arrows. Once they're done, they press the send button.

[0112] Input: Text and sketch data

[0113] Output: Input data is passed to the terminal

[0114] Step 2:

[0115] The device receives the text and sketch data entered by the user, packages it into JSON format, and creates a key-value object with the text and sketch information. This data is then added to the body of an HTTP POST request and sent to the server.

[0116] Input: User-provided text and sketch data

[0117] Output: Data packaged in JSON format

[0118] Step 3:

[0119] The server receives the HTTP POST request sent from the terminal and parses the JSON data included in the request body. Specifically, it converts the JSON data from a string to an object format.

[0120] Input: JSON format data

[0121] Output: Parsed object data

[0122] Step 4:

[0123] The server validates that the parsed data is well-formed, ensuring that all required fields are present and that the data is not corrupted (for example, checking that text fields are not empty and that sketch data conforms to the image format).

[0124] Input: Parsed object data

[0125] Output: Validation result (success / failure) and validated data

[0126] Step 5:

[0127] The server inputs the verified data into the generative AI model. Specifically, it converts the text and sketch data into a prompt and sends it to the generative AI model. An example of a prompt would be, "Visualize the project workflow. Please illustrate the process: Receive requirements from the client -> Analyze the requirements -> Transfer to the design team -> Start development -> Test -> Deliver to the client."

[0128] Input: Validated Data

[0129] Output: The prompt passed to the generative AI model

[0130] Step 6:

[0131] The generative AI model generates a visualization based on the prompt and sends the results back to the server, where the generated diagram is presented in a visually easy-to-understand format.

[0132] Input: prompt statement

[0133] Output: Visualized diagram data

[0134] Step 7:

[0135] The server constructs a response containing the generated diagram and its metadata, and sends it to the terminal. The response includes the diagram's binary data or the diagram's URL.

[0136] Input: Visualized diagram data

[0137] Output: Response (binary data or URL)

[0138] Step 8:

[0139] The terminal receives the response from the server, parses the response body, verifies that the received data is in the correct format, and if visualization is successful, displays the diagram on the user interface. Specifically, it renders the diagram's binary data as an image.

[0140] Input: Response from the server

[0141] Output: Visualized data displayed in a user interface

[0142] Step 9:

[0143] The user can review the visualization data displayed on their device, evaluate whether the content is appropriate, share it with other meeting participants, and gather feedback as needed. Based on the feedback, they can input additional data and revisit the visualization.

[0144] Input: Visualization data displayed in the user interface

[0145] Output: User feedback and additional input data

[0146] (Application example 1)

[0147] 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."

[0148] The present invention aims to solve problems related to systems that visualize factory work processes and machine operation flows in real time, thereby eliminating misunderstandings that arise between managers and workers and improving work efficiency and accuracy.

[0149] 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.

[0150] In this invention, the server includes an input means, a means for packaging input data, a means for generating a prompt sentence using a generative AI model, a means for transmitting the packaged data, a means for verifying the received data, a means for inputting the verified data to the generation device, a means for receiving a visualized diagram from the generation device, and a means for displaying the visualized diagram. This makes it possible to visualize the factory work process in real time and reduce discrepancies in work instructions.

[0151] "Input means" refers to the means by which the system receives text and sketch data from the user.

[0152] "Means for packaging input data" refers to means for assembling received text or sketch data into an appropriate format.

[0153] A "generative AI model" is an artificial intelligence that generates visualized diagrams or images based on input data.

[0154] A "prompt" is a text command that instructs the generative AI model on how to visualize something.

[0155] The "means for transmitting packaged data" is a means for transmitting packaged data to a server.

[0156] "Means for verifying received data" refers to the means by which the server checks the format and content of the data received to determine whether it is correct.

[0157] The "means for inputting verified data into the generating device" refers to the means for passing verified data to the AI ​​model for visualization.

[0158] A "generator" is a device that generates a visualization based on verified data.

[0159] The "means for receiving a visualized diagram" is a means for receiving a generated diagram from a generating device.

[0160] The "means for displaying a visualized diagram" refers to a means for displaying the generated diagram on a user interface.

[0161] The present invention relates to a system for visualizing factory work processes and machine operation flows in real time, and is mainly composed of three main elements: a user, a terminal, and a server.

[0162] First, the user inputs the factory work process using text and sketch data via a terminal. For example, the user might input a work flow such as "take out part A -> connect to part B -> inspect the finished product -> pack the finished product," and then draw a diagram of each step using additional sketches. This input is received by the terminal's input means.

[0163] The device then packages the input text and sketch data, which is then sent to the server via an HTTP POST request via a generative AI model to generate a prompt.

[0164] The server receives the data sent from the device, analyzes and verifies it, and the verified data is input into the generative AI model, which generates a visualization based on the text and sketch data.

[0165] The generated diagram is received by the server and the results are sent back to the terminal, which displays the received diagram on the user interface, allowing the user to check the generated visualization data and gain a detailed understanding of the factory's work processes.

[0166] The main hardware and software used in this system are as follows: First, the terminal side inputs and packages data and sends HTTP requests. On the server side, a lightweight web framework called Flask is used to process requests, validate received data, and visualize it using a generative AI model. The system also handles the binary data of diagrams generated using the Pillow (PIL) library.

[0167] As a concrete example, consider a scenario in which a factory manager is designing the flow of a new line and giving instructions to new workers. For example, the manager types the following prompt:

[0168] Text: Take out part A -> Connect to part B -> Inspect the finished product -> Pack the finished product

[0169] Sketch: Draw simple work steps

[0170] Based on this prompt, the generative AI model analyzes the input data and automatically generates a visualized diagram, which is then sent back to the device from the server, allowing the manager to create new, detailed work instructions based on the diagram.

[0171] In this way, the system of the present invention makes it possible to visualize the factory work process in real time and reduce discrepancies in work instructions.

[0172] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0173] Step 1:

[0174] The user inputs text and sketch data related to the factory work process and machine operation flow. For example, the user inputs "take out part A -> connect to part B -> inspect the finished product -> pack the finished product" and sketches each step. This input data is sent to the terminal. The input data is text and sketch data, and is used in the next step.

[0175] Step 2:

[0176] The device packages the text and sketch data received from the user into an appropriate format (e.g., JSON), ensuring that each data item is correctly formatted and ready for use throughout the system. The input is text data and sketch data, and the output is the packaged data.

[0177] Step 3:

[0178] The device uses the packaged data to generate a prompt using a generative AI model. This prompt is a text command that instructs the generative AI model on how to visualize the data. Specifically, the device generates a prompt that combines text data and sketch data. The input is the packaged data, and the output is the prompt.

[0179] Step 4:

[0180] The terminal sends packaged data including the prompt text to the server via an HTTP POST request. The input is the packaged data including the prompt text, and the output is a request to the server.

[0181] Step 5:

[0182] The server receives the data sent by the device and analyzes it. Specifically, it parses the received data to verify that it is in the correct format and checks for any missing or incorrect data. The input is the data sent in the HTTP POST request, and the output is the validated data.

[0183] Step 6:

[0184] The server inputs the verified data into the generative AI model and generates a visualized diagram. The generative AI model performs calculations to create the visualization based on the prompt sentence and generates the diagram. The input is the verified data and the prompt sentence, and the output is the visualized diagram.

[0185] Step 7:

[0186] The server receives the generated visualized diagram and returns it to the terminal. Specifically, it constructs the binary data of the generated diagram or the URL of the diagram as a response and sends it to the terminal. The input is the generated diagram, and the output is the response to the terminal.

[0187] Step 8:

[0188] The terminal parses the visualized diagram received from the server and displays it on the user interface. Specifically, it interprets the received data and performs processing to display the diagram. The input is the response data from the server, and the output is the visualized diagram displayed to the user.

[0189] Step 9:

[0190] The user examines the displayed visualization data to understand the contents of the factory work instructions. If necessary, they can input additional text or sketches and repeat the process. The input is the diagram displayed to the user, and the output is the understood contents of the work instructions.

[0191] 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.

[0192] This invention is a system that instantly visualizes text and sketch data entered by users to resolve misunderstandings during meetings. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it aims to provide deeper understanding and effective communication. This system is realized by a specific program.

[0193] System Overview

[0194] The system consists of the following elements:

[0195] 1. Input means: Accepts text and sketches from the user.

[0196] 2. A means of packaging input data: Package the received data into JSON format.

[0197] 3. A means for transmitting the packaged data: Send the data to the server.

[0198] 4. Means for receiving the transmitted data: The server receives the data.

[0199] 5. Means of verifying received data: Checking the authenticity and integrity of the data.

[0200] 6. Means of inputting verified data into the generation device: Pass the data to the generation AI.

[0201] 7. Means for receiving the visualized diagram from the generating device: The visualized diagram is received by the server.

[0202] 8. Means for displaying the visualized diagram: Display the diagram on the terminal.

[0203] 9. Emotion Engine: An additional element that recognizes user emotions. It detects and analyzes emotions from text and speech.

[0204] 10. Means for adjusting diagrams based on emotion results:,Dynamic adjustment of visualized diagrams based on emotion recognition results.

[0205] Explanation of program processing

[0206] 1. User: Providing Input

[0207] Users enter text through the interface of the online conferencing tool and add sketches as needed. The emotion engine also analyzes the user's voice and text data in real time to recognize emotions. For example, a user can enter the progress of a project as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and then use the sketch function to draw a diagram showing the process.

[0208] 2. Terminal: Sending data

[0209] The device combines the text and sketch data entered by the user, as well as the emotion data recognized by the emotion engine, into a JSON package, and then sends this packaged data to the server as an HTTP POST request.

[0210] 3. Server: Receiving and processing data

[0211] The server receives an HTTP POST request from the device and parses the JSON data included in the request body. It then validates the data format and content to ensure authenticity and completeness. The validated data is passed to the generation AI, which generates a visualized diagram based on the text and sketch data. It also dynamically adjusts the diagram based on emotional data. For example, it reflects relevant emotions about the progress of the project (such as joy, confusion, or impatience) in the diagram's color and annotations.

[0212] 4. Server: Sending results

[0213] A response containing the generated visualization diagram and associated emotion information is constructed and sent back to the device, including the binary data of the generated diagram and the diagram's URL.

[0214] 5. Terminal: Displaying the results

[0215] The terminal receives the response from the server and displays a visualized diagram on the user interface, which also reflects the results of the emotion engine, allowing users to share the project progress and their own emotions with meeting participants.

[0216] 6. User: Image review and feedback

[0217] Users can view the visualized diagrams and related emotions displayed on their devices and share them with other meeting participants. Meeting participants can also understand the user's emotions in real time, leading to deeper understanding and more effective discussions.

[0218] Specific examples

[0219] As a concrete example, let's say User A is holding a meeting to announce a marketing plan for a new product. User A enters the following text sequence: "Analyze target market -> Formulate marketing strategy -> Launch advertising campaign -> Collect sales data -> Optimize strategy," and adds emotional comments to the "target market" and "advertising campaign" sections. The emotion engine recognizes the anxieties and expectations User A feels as he or she enters the information, and reflects them in the visualized diagram. As a result, meeting participants can simultaneously perceive visual and emotional information, making the discussion more concrete and effective.

[0220] In this way, the system of the present invention can instantly visualize user input and provide integrated emotional information, thereby significantly improving the efficiency and quality of meetings.

[0221] The processing flow will be explained below.

[0222] Program processing flow and details of each step

[0223] Step 1: User enters text

[0224] The user enters a description into the text input field of the online meeting tool, for example, "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client."

[0225] Step 2: User adds sketch

[0226] Users can use the sketch feature to draw simple diagrams of process flows, which complements the parts that are difficult to convey with text alone.

[0227] Step 3: The emotion engine recognizes the user's emotion

[0228] When a user speaks, the emotion engine analyzes emotions from voice and text data in real time, recognizing emotions such as anxiety, anticipation, and joy.

[0229] Step 4: User presses submit button

[0230] After completing all input, the user presses the send button to trigger transmission of the input data to the server.

[0231] Step 5: The device packages the data

[0232] The device packages the text entered by the user, sketch data, and emotion data recognized by the emotion engine into JSON format, which allows all data to be converted into a format that is easy to centrally manage on the server side.

[0233] Step 6: The device sends the data to the server

[0234] The device sends JSON data packaged as an HTTP POST request to the server, which includes text, sketches, and emotion data.

[0235] Step 7: Server receives data

[0236] The server receives the HTTP POST request sent from the device and parses the JSON data included in the request body. The received data includes all input information.

[0237] Step 8: Server Validates Data

[0238] The server checks whether the received data is in the correct format and verifies that there are no missing or incorrect data. If the data is confirmed to be correct, it proceeds to the next step.

[0239] Step 9: The server inputs data into the generated AI

[0240] The server passes the verified text and sketch data to the generative AI, which then begins the process of generating a visualization based on this data.

[0241] Step 10: Generative AI generates the visualization

[0242] The generative AI generates visualized diagrams based on input text and sketch data, and these diagrams also reflect emotional data from the emotion engine.

[0243] Step 11: The server receives the visualization

[0244] The server receives the visualized diagram generated by the generation AI, and then constructs a response containing this diagram in the following steps:

[0245] Step 12: Server constructs result response

[0246] The server constructs the visualization, emotional information, and associated metadata (e.g., generation time, resolution) in the response.

[0247] Step 13: The server sends a response to the device

[0248] The server sends the constructed response back to the device, where the visualization results are available.

[0249] Step 14: The device receives the response

[0250] The device receives the HTTP response from the server and analyzes the visualized diagram and emotion data contained in the response body.

[0251] Step 15: The device displays the image and emotional information.

[0252] The device displays the analyzed visualized diagram and emotion data on the user interface, allowing users to visually present their own emotion information along with the diagram.

[0253] Step 16: User reviews and shares the diagram and emotional information

[0254] Users can view and share the displayed visualizations and emotional information with meeting participants, which leads to deeper understanding and more effective discussions among participants.

[0255] In this way, the system instantly visualizes text and sketch data and integrates and provides emotional information, minimizing misunderstandings during meetings and enabling efficient and in-depth communication.

[0256] Example 2

[0257] 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."

[0258] In conventional online meetings, participants are prone to differing interpretations and misunderstandings, making effective communication difficult. Furthermore, limited means for accurately conveying emotional nuances often reduce the depth and quality of discussions. The present invention aims to solve these problems, eliminate misunderstandings during meetings, and promote more effective communication.

[0259] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means, a means for packaging input data, a means for transmitting the packaged data, a means for receiving the transmitted data, a means for verifying the received data, a means for inputting the verified data to the generation device, a means for receiving a visualized diagram from the generation device, a means for displaying the visualized diagram, a means for analyzing emotions and packaging data including the analysis results, and a means for adjusting the visualized diagram based on the analysis results. This makes it possible to instantly visualize the user's input and integrate emotional information, thereby achieving deeper understanding and effective communication.

[0260] "Input means" refers to an interface device for receiving text or sketches from a user.

[0261] "Means for packaging input data" refers to a device or software that converts received text or sketch data into JSON format and packages it.

[0262] The "means for transmitting packaged data" refers to a communication device or software for transferring packaged data to a server.

[0263] The "means for receiving transmitted data" refers to a communication device or software that allows the server to receive data transferred from the terminal.

[0264] "Means for verifying received data" means a device or software for checking the authenticity and integrity of received data.

[0265] "Means for inputting verified data into the generating device" refers to a device or software for providing data whose reliability and integrity have been confirmed to the generating AI.

[0266] "Means for receiving a visualization from a generating device" means a device or software for receiving visualization data output from a generating AI.

[0267] The "means for displaying the visualized diagram" refers to a device or software for displaying the received visualization data on the user's terminal.

[0268] "Means for analyzing emotions and packaging data containing the analysis results" refers to a device or software that analyzes emotions from a user's text or voice and compiles the analysis results as data.

[0269] "Means for adjusting the visualization based on the analysis results" refers to a device or software for dynamically modifying the visualization data based on the results of the sentiment analysis.

[0270] The present invention provides a system that instantly visualizes text and sketch data entered by users in online meetings and analyzes their emotions to resolve misunderstandings among participants and promote effective communication. This system is realized using specific hardware and software.

[0271] First, users enter text and add sketches as needed through the online meeting tool's interface. The emotion engine then analyzes the user's voice and text data in real time to recognize their emotions. For example, a user can enter the project progress as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and then use the sketch function to draw a diagram showing the process.

[0272] Next, the device packages the user-entered text, sketch data, and analyzed emotion data in JSON format and sends it to the server as an HTTP POST request. Specific software used for this includes an HTTP library and a JSON library, for example.

[0273] The server receives the HTTP POST request sent from the device and analyzes the JSON data included in the request body. It then verifies the authenticity and completeness of the data and supplies the verified data to a generative AI model. This generative AI model generates a visualized diagram based on the text and sketch data, and dynamically adjusts the diagram based on the emotion data. Specific generative AI models that could be used include GPT-3 or DALL-E.

[0274] The visualization generated by the generative AI model and the associated emotional information are packaged as a response by the server and sent to the device. The device receives the response and displays the visualization on the user interface. This visualization reflects the data entered by the user and the results of the emotional analysis, allowing meeting participants to simultaneously understand the progress of the project and the user's emotions.

[0275] Users can view the visualized diagrams and related emotions and share feedback with other meeting participants. Meeting participants can understand the user's emotional information along with the diagrams in real time, leading to deeper understanding and more effective discussions.

[0276] Specific examples

[0277] As a concrete example, consider a meeting where User A is presenting a marketing plan for a new product. User A enters the following text sequence: "Analyze target market -> Formulate marketing strategy -> Launch advertising campaign -> Collect sales data -> Optimize strategy," and adds emotional comments to the "target market" and "advertising campaign" sections. The emotion engine recognizes the anxieties and expectations User A feels as he or she enters the information, and reflects them in the visualized diagram. As a result, meeting participants can simultaneously perceive visual and emotional information, making the discussion more concrete and effective.

[0278] Prompt Sentence Examples

[0279] "Describe a system for visualizing the progress of a new marketing plan and creating an emotive sketch highlighting key steps."

[0280] As described above, the system of the present invention can instantly visualize user input and provide integrated emotional information, thereby significantly improving the quality and efficiency of communication in online meetings.

[0281] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0282] Step 1: Provide user input

[0283] Users input text through the interface of the online conferencing tool and add sketches as needed. The emotion engine then analyzes the user's voice and text data in real time to recognize emotions. Specifically, the user inputs the following sequence: "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and then uses the sketch function to draw a diagram showing the process. This input data becomes the initial dataset for the program.

[0284] Step 2: Sending data by device

[0285] The device packages the user-provided text, sketch data, and parsed emotion data into JSON format. It then generates an HTTP POST request to send this packaged data to the server. Specifically, the device uses a JSON library to parse the data and an HTTP library to construct the request. The input is the user-provided text, sketch, and emotion data, and the output is the JSON data sent to the server.

[0286] Step 3: Server receives and verifies data

[0287] The server receives the HTTP POST request sent from the terminal. After receiving it, it parses the JSON data included in the request body and verifies the data format and content. Specifically, the server uses a parsing library to parse the JSON data and checks whether each item matches the specified format. The input is the JSON data from the terminal, and the output is the validated data.

[0288] Step 4: Input data into the server-generated AI model

[0289] The verified data is input from the server to a generative AI model. The generative AI model generates a visualization based on the input text and sketch data, and dynamically adjusts the visualization based on the emotion data. Specifically, the generative AI model uses a pre-trained algorithm to analyze the text and draw a sketch. The input is the verified data, and the output is the generated visualization.

[0290] Step 5: Receiving the visualization

[0291] The server receives the visualization generated by the generative AI model. This visualization also includes adjustments based on the user's emotional data. Specifically, the server receives the response from the generative AI model and parses the visualization data. The input is the visualization data from the generative AI model, and the output is the visualization data ready to be sent to the device.

[0292] Step 6: Server sends results

[0293] The server packages the received visualization and related emotion information as a response body and sends it back to the device. Specifically, the server constructs an HTTP response and encodes JSON data including the binary data and URL of the visualization. The input is the visualization data, and the output is the response data to the device.

[0294] Step 7: Viewing the results in the terminal

[0295] The terminal analyzes the response received from the server and displays a visualized diagram on the user interface. This diagram reflects the results of the emotion engine. Specifically, the terminal decodes the response data and updates the display using the GUI framework. The input is the response data from the server, and the output is the displayed visualized diagram.

[0296] Step 8: User review and feedback on images

[0297] The user checks the displayed visualization and related emotional information, and exchanges opinions with other meeting participants based on that information. Specifically, the user points out each element in the visualization and advances the discussion. The final input is the displayed visualization, and the output is the user's feedback and the progress of the meeting.

[0298] (Application example 2)

[0299] 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."

[0300] Modern brick-and-mortar stores require fast and appropriate customer service. However, it is difficult for store staff to instantly grasp customers' requests and emotions, which can result in inconsistent service quality. In particular, in busy stores, customers often have to wait for long periods of time, which can lead to a decline in customer satisfaction. With conventional technology, it has been difficult to grasp customers' emotions in real time while meeting their requests.

[0301] 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 a voice input means, a means for generating text from voice, a means for recognizing emotions, and a means for dynamically adjusting the generated visualized image. This allows store staff to instantly visualize and confirm customer requests and emotions, enabling more appropriate and prompt customer service. This system also improves customer satisfaction and standardizes service quality.

[0302] The "input means" is an interface for taking in information from the user.

[0303] "Means for packaging input data" is a function that compiles the imported information into a certain format (e.g., JSON format).

[0304] The "means for transmitting packaged data" is a function for transmitting packaged information to a server or other system.

[0305] The "means for receiving transmitted data" is an interface that receives information sent from other systems.

[0306] "Means for verifying received data" refers to a function that checks the authenticity and integrity of received information.

[0307] The "means for inputting verified data to the generating device" is a function for passing verified information to the generating device.

[0308] The "means for receiving a visualized diagram from a generating device" is an interface that receives a visualized diagram sent back from the generating device.

[0309] The "means for displaying the visualized diagram" is a display or a display device for showing the received visualized diagram to the user.

[0310] The "voice input means" is an interface for capturing the user's voice.

[0311] The "means for generating text from speech" is a function for converting captured speech data into text data.

[0312] "Means for recognizing emotions" refers to the function of analyzing and recognizing the user's emotions from the captured data.

[0313] The "means for dynamically adjusting the generated visualization" is a function that modifies and adjusts the visualization in real time based on the recognized emotion data.

[0314] The present invention is a system for providing prompt and appropriate customer service in brick-and-mortar stores, which integrates functions such as voice input, speech-to-text conversion, emotion recognition, visualization, and dynamic illustration adjustment.

[0315] System configuration

[0316] The system consists of the following elements:

[0317] 1. Voice input means: An interface for capturing the user's voice. Specifically, it uses a microphone built into smart glasses or smart devices.

[0318] 2. A means of generating text from speech: This is a function that converts captured speech into text data. Specifically, it uses the speech_recognition library.

[0319] 3. Means of Emotion Recognition: This is a function that analyzes and recognizes the user's emotions from text data and voice data. Specifically, it uses tools such as EmotionRecognizer.

[0320] 4. Dynamic adjustment of the generated visualization: This function corrects and adjusts the visualization in real time based on the recognized emotion data. The generated visualization is displayed on the smart device display in the store.

[0321] 5. Data transmission and reception means: This is the interface for sending packaged data to the server and receiving visualization data generated by the server. Data is sent and received using HTTP POST requests.

[0322] System action

[0323] 1. Voice input

[0324] The terminal uses the microphone of the smart glasses or smart device to capture the user's (customer's) voice. This voice data is converted into text data by the speech_recognition library.

[0325] 2. Emotion recognition

[0326] EmotionRecognizer is used to recognize emotions from text obtained from speech. For example, it recognizes the emotion "worried" from text such as "I want to try on this dress, but I'm worried about whether it will fit."

[0327] 3. Data Packaging and Transmission

[0328] The text and sentiment data are packaged in JSON format and sent to the server using an HTTP POST request, which receives it and verifies the authenticity and integrity of the data.

[0329] 4. Visualization Generation and Adjustment

[0330] The server generates a visualization based on the received data. The generated visualization is dynamically adjusted based on the emotion data. For example, if the emotion "worry" is recognized, the color of the area of ​​interest is changed to highlight it.

[0331] Specific examples

[0332] For example, if a customer says to a store associate, "I'd like to try on this dress, but I'm worried about the size," the system works as follows:

[0333] 1. Convert speech to text: "I'd like to try on this dress, but I'm worried about the size."

[0334] 2. Recognize the emotion "worry."

[0335] 3. Package the text and sentiment into JSON format and send it to the server.

[0336] 4. The server generates a visualization and adjusts it based on emotions such as "worry."

[0337] 5. The diagram is sent to a terminal and staff can check it using smart glasses or other devices.

[0338] As a result, staff can instantly understand customer needs and emotions, enabling them to respond more appropriately.

[0339] Prompt Sentence Examples

[0340] "I'd like to try on this dress, but I'm worried about whether it will fit."

[0341] summary

[0342] The system of this invention integrates voice input and emotion recognition to speed up and improve the accuracy of customer service in brick-and-mortar stores. This makes it possible to respond based on emotions, which was difficult with conventional methods, and is expected to improve customer satisfaction.

[0343] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0344] Step 1:

[0345] The user inputs voice through the microphone of the smart glasses or smart device, and the input voice data is captured by the device. Specifically, the process of capturing voice data in real time begins.

[0346] Step 2:

[0347] The device uses a speech recognition library (e.g., the speech_recognition library) to convert voice data to text data. It uses an acoustic model and a language model to convert voice data to text data. The input is voice data, and the output is the converted text data.

[0348] Step 3:

[0349] The device consumes the converted text data and uses EmotionRecognizer to recognize emotions from the text. Specifically, it analyzes the text data and determines the emotional nuances of words and phrases. The input is text data, and the output is recognized emotion data.

[0350] Step 4:

[0351] The device packages the text data and emotion data in JSON format. Specifically, it converts the text data and emotion data into a key-value format and combines them into a single data structure. The input is text data and emotion data, and the output is a JSON-formatted data package.

[0352] Step 5:

[0353] The device sends the data package to the server using an HTTP POST request. Specifically, the data package is included in the HTTP request body and sent to the specified URL. The input is the data package in JSON format, and the output is the transmission status to the server.

[0354] Step 6:

[0355] The server receives the HTTP POST request sent from the terminal and analyzes the JSON data included in the request body. Specifically, it parses the received data and checks its authenticity and integrity. The input is JSON data, and the output is the validated data.

[0356] Step 7:

[0357] The server inputs the verified data into the generative AI model to generate a visualized diagram. Specifically, text data and emotion data are provided to the AI ​​model as prompts, and a process is executed to generate appropriate visual information. The input is text data and emotion data, and the output is a visualized diagram.

[0358] Step 8:

[0359] The server dynamically adjusts the generated visualization using emotion data. Specifically, it applies changes to the color and shape of the visualization to visually express the nuances of emotions. The input is the visualization and emotion data, and the output is the adjusted visualization.

[0360] Step 9:

[0361] The server sends the adjusted visualized diagram to the terminal as an HTTP response. Specifically, the visualized diagram is included in the response body as binary data or a URL and sent as an HTTP response. The input is the adjusted visualized diagram, and the output is the transmission status to the terminal.

[0362] Step 10:

[0363] The terminal receives the response from the server and displays the visualized diagram on the user interface. Specifically, the terminal displays the received visualized diagram on a display or a head-mounted display for the user to see. The input is the adjusted visualized diagram, and the output is the display on the user interface.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] [Second embodiment]

[0368] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0369] 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.

[0370] 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).

[0371] 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.

[0372] 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.

[0373] 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).

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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."

[0380] The present invention provides a system that instantly visualizes text and sketch data entered by a user, eliminating misunderstandings during meetings. This system is realized by a specific program.

[0381] System Overview

[0382] The system is configured by combining an input means, a means for packaging input data, a means for transmitting the packaged data, a means for receiving the transmitted data, a means for verifying the received data, a means for inputting the verified data into a generating device, a means for receiving a visualized diagram from the generating device, and a means for displaying the visualized diagram.

[0383] Explanation of program processing

[0384] 1. User: Providing Input

[0385] Users enter text through the interface of the online meeting tool, adding sketches as needed. For example, they can enter a project workflow such as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client." Once they've finished entering the information, they press the send button.

[0386] 2. Terminal: Sending data

[0387] The device packages the text and sketch data entered by the user into JSON format, and once packaged, sends the data to the server using an HTTP POST request.

[0388] 3. Server: Receiving and processing data

[0389] The server receives an HTTP POST request from the device and parses the JSON data included in the request body. It verifies that the received data is in the correct format and checks for any missing or incorrect data. It then inputs the verified data into the generation AI, which generates a visualized diagram based on the text and sketch data. This generated diagram is then returned to the server.

[0390] 4. Server: Sending results

[0391] The server constructs a response containing the generated diagram and its metadata, and sends it back to the terminal, including the diagram's binary data or its URL.

[0392] 5. Terminal: Displaying the results

[0393] The terminal receives the response from the server, parses the received data, and if visualization is successful, displays the diagram on the user interface.

[0394] 6. User: Image review and feedback

[0395] Users can review the displayed visualizations and share them with other meeting participants, gathering feedback and providing further corrections or additional input as needed.

[0396] Specific examples

[0397] As a concrete example, let's say a user wants to visualize the workflow of a new project. The user enters the following into an online conferencing tool: "Receive requirements from client -> Analyze requirements -> Forward to design team -> Start development -> Test -> Deliver to client." They also sketch boxes and arrows to indicate the process flow. When the user presses the send button, the device sends the input data in JSON format to the server. The server receives the data and passes it to the generation AI for visualization. The generated diagram is sent back from the server to the device and displayed in the user interface. The user provides a detailed explanation based on the displayed diagram, collecting feedback in the process.

[0398] In this way, the system of the present invention can minimize misunderstandings and significantly improve the efficiency of meetings.

[0399] The processing flow will be explained below.

[0400] Program processing flow and details of each step

[0401] Step 1: User enters text

[0402] Users enter their description in a text input field in the online meeting tool, and can optionally add a quick sketch to clarify the image or process they want to share during the meeting.

[0403] Step 2: User presses submit button

[0404] After completing the input, the user presses the send button to send the input data to the server, which passes the input information to the system.

[0405] Step 3: The device packages the data

[0406] The device takes the text and sketch data entered by the user and packages it into JSON format, which makes the data easier to handle on the server side.

[0407] Step 4: The device sends data to the server

[0408] The device sends an HTTP POST request containing the packaged JSON data to the server, which causes the data to reach the server.

[0409] Step 5: Server receives data

[0410] The server receives an HTTP POST request from a client and retrieves the JSON data contained in the request body, which is the trigger for the core processing of the system.

[0411] Step 6: Server verifies the data

[0412] The server validates the format and content of the data it receives, ensuring that the data is in the correct format and checking for any missing or incorrect data. After validation is complete, it proceeds to the next step.

[0413] Step 7: The server inputs data into the generated AI

[0414] The server inputs the verified data into the generative AI, which begins the process of generating a visualization based on the text and sketch data.

[0415] Step 8: The server receives the visualization

[0416] Once the AI ​​generates the visualization, it sends the diagram data back to the server, which receives the generated diagram and prepares it for further processing.

[0417] Step 9: Server constructs result response

[0418] The server constructs a response message based on the received visualization data, which includes the binary data of the diagram, the diagram URL, and the generated metadata.

[0419] Step 10: The server sends a response to the device

[0420] The server sends the constructed response to the device, which then makes the visualized results available.

[0421] Step 11: The device receives the response

[0422] The terminal receives the HTTP response from the server and obtains the data contained in the response body, which includes the data of the generated diagram.

[0423] Step 12: The terminal displays the diagram

[0424] The terminal analyzes the received visualization data and displays it on the user interface, preparing the user for visual explanations during the meeting.

[0425] Step 13: Users review and share diagrams

[0426] Users can view the visualized diagrams displayed on their devices and share them with other participants in the meeting, eliminating misunderstandings and realizing efficient communication.

[0427] In this way, the system goes through a series of steps to instantly visualize the user's input and increase the efficiency of the meeting.

[0428] Example 1

[0429] 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."

[0430] In modern online meetings, communication between participants can sometimes be skewed. In particular, when explaining complex project workflows and concepts, words and text alone are insufficient and visual materials are required. However, on-the-spot visualization and data sharing remain time-consuming and disruptive. Traditional methods lack a means to instantly generate visualizations from sketches and text, making efficient communication difficult.

[0431] 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.

[0432] In this invention, the server includes an input means, a means for packaging input data, a means for transmitting the packaged data, a means for receiving the transmitted data, a means for verifying the received data, a means for inputting the verified data to a generation device, a means for receiving a visualized diagram from the generation device, a means for displaying the visualized diagram, a visualization means based on a generative AI model for instantly visualizing text and sketch data provided by a user, and a means for analyzing the generated visualization data and displaying it on a user interface. This allows for the instant generation of visual materials during an online meeting, reducing misunderstandings among participants and enabling efficient communication.

[0433] "Input means" refers to a digital interface that allows users to input text and sketch data through the online meeting tool.

[0434] "Means for packaging input data" refers to the process of converting user-provided text or sketch data into an appropriate format, such as JSON format, and packaging it.

[0435] The "means for transmitting packaged data" is a process for transmitting packaged data to a server using a communication means such as an HTTP POST request.

[0436] The "means for receiving transmitted data" is a communication interface for receiving data transmitted from a terminal on the server side.

[0437] "Means for validating received data" refers to a data validation process to ensure that the received data is in the correct format and is free of any missing or erroneous data.

[0438] "Means for inputting verified data into a generating device" refers to the process of inputting verified data into a generative AI model.

[0439] The "means for receiving a visualization from a generating device" is a process for receiving visualization data output from a generative AI model.

[0440] The "means for displaying the visualized diagram" is a display device or software for displaying the generated visualization data on a user interface.

[0441] A "generative AI model" is an artificial intelligence model that instantly generates visualizations based on text and sketch data provided by the user.

[0442] A "visualization method" is a process that uses a generative AI model to generate a visual diagram based on input data.

[0443] This invention is a system that instantly visualizes text and sketch data entered by users during online meetings, improving meeting efficiency. This system is mainly realized using a server, terminals, and a generative AI model.

[0444] Users enter text through the interface of the online meeting tool and add sketch data as needed. This interface is designed to be intuitive and easy to use, making it easy for users to operate. For example, a user can enter a project workflow such as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client" and add a sketch using boxes and arrows.

[0445] The device packages the input text and sketch data into JSON format, which is a key-value object format that ensures data integrity and compatibility, and then sends the packaged data to the server using an HTTP POST request.

[0446] The server receives the HTTP POST request sent from the device. It parses the JSON data included in the request body and verifies that the received data is in the correct format. Specifically, it checks that all required fields are present and that the data is not corrupted. The verified data is then input into a generative AI model, which converts it into a prompt for visualization.

[0447] The generative AI model generates a visualized diagram based on a prompt. For example, the prompt might be, "Visualize the project workflow. Illustrate the process: Receive requirements from the client -> Analyze requirements -> Transfer to the design team -> Start development -> Test -> Deliver to the client." The generated diagram is then sent back to the server, which then constructs a response containing the diagram's binary data and URL and sends it to the device.

[0448] The device receives the response from the server, parses the data, and if the visualization is successful, displays the diagram on the user interface. This allows the user to review the visualized data and share it with other meeting participants. Additional feedback can be collected if necessary, allowing for further refinements.

[0449] As a concrete example, let's say a user wants to visualize the workflow of a new project. They enter "Receive requirements from client -> Analyze requirements -> Forward to design team -> Start development -> Test -> Deliver to client" into an online conferencing tool and sketch boxes and arrows to show the process flow. When they press the send button, the device sends this data in JSON format to the server. The server receives the data and passes it to the generation AI for visualization. The generated diagram is sent back from the server to the device and displayed in the user interface. The user provides a detailed explanation based on the displayed diagram, collecting feedback along the way.

[0450] In this way, the invention can minimize misunderstandings and significantly improve meeting efficiency.

[0451] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0452] Step 1:

[0453] Users enter text and sketch data using the interface of the online meeting tool. For example, they can enter a project workflow such as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and add sketches using boxes and arrows. Once they're done, they press the send button.

[0454] Input: Text and sketch data

[0455] Output: Input data is passed to the terminal

[0456] Step 2:

[0457] The device receives the text and sketch data entered by the user, packages it into JSON format, and creates a key-value object with the text and sketch information. This data is then added to the body of an HTTP POST request and sent to the server.

[0458] Input: User-provided text and sketch data

[0459] Output: Data packaged in JSON format

[0460] Step 3:

[0461] The server receives the HTTP POST request sent from the terminal and parses the JSON data included in the request body. Specifically, it converts the JSON data from a string to an object format.

[0462] Input: JSON format data

[0463] Output: Parsed object data

[0464] Step 4:

[0465] The server validates that the parsed data is well-formed, ensuring that all required fields are present and that the data is not corrupted (for example, checking that text fields are not empty and that sketch data conforms to the image format).

[0466] Input: Parsed object data

[0467] Output: Validation result (success / failure) and validated data

[0468] Step 5:

[0469] The server inputs the verified data into the generative AI model. Specifically, it converts the text and sketch data into a prompt and sends it to the generative AI model. An example of a prompt would be, "Visualize the project workflow. Please illustrate the process: Receive requirements from the client -> Analyze the requirements -> Transfer to the design team -> Start development -> Test -> Deliver to the client."

[0470] Input: Validated Data

[0471] Output: The prompt passed to the generative AI model

[0472] Step 6:

[0473] The generative AI model generates a visualization based on the prompt and sends the results back to the server, where the generated diagram is presented in a visually easy-to-understand format.

[0474] Input: prompt statement

[0475] Output: Visualized diagram data

[0476] Step 7:

[0477] The server constructs a response containing the generated diagram and its metadata, and sends it to the terminal. The response includes the diagram's binary data or the diagram's URL.

[0478] Input: Visualized diagram data

[0479] Output: Response (binary data or URL)

[0480] Step 8:

[0481] The terminal receives the response from the server, parses the response body, verifies that the received data is in the correct format, and if visualization is successful, displays the diagram on the user interface. Specifically, it renders the diagram's binary data as an image.

[0482] Input: Response from the server

[0483] Output: Visualized data displayed in a user interface

[0484] Step 9:

[0485] The user can review the visualization data displayed on their device, evaluate whether the content is appropriate, share it with other meeting participants, and gather feedback as needed. Based on the feedback, they can input additional data and revisit the visualization.

[0486] Input: Visualization data displayed in the user interface

[0487] Output: User feedback and additional input data

[0488] (Application example 1)

[0489] 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."

[0490] The present invention aims to solve problems related to systems that visualize factory work processes and machine operation flows in real time, thereby eliminating misunderstandings that arise between managers and workers and improving work efficiency and accuracy.

[0491] 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.

[0492] In this invention, the server includes an input means, a means for packaging input data, a means for generating a prompt sentence using a generative AI model, a means for transmitting the packaged data, a means for verifying the received data, a means for inputting the verified data to the generation device, a means for receiving a visualized diagram from the generation device, and a means for displaying the visualized diagram. This makes it possible to visualize the factory work process in real time and reduce discrepancies in work instructions.

[0493] "Input means" refers to the means by which the system receives text and sketch data from the user.

[0494] "Means for packaging input data" refers to means for assembling received text or sketch data into an appropriate format.

[0495] A "generative AI model" is an artificial intelligence that generates visualized diagrams or images based on input data.

[0496] A "prompt" is a text command that instructs the generative AI model on how to visualize something.

[0497] The "means for transmitting packaged data" is a means for transmitting packaged data to a server.

[0498] "Means for verifying received data" refers to the means by which the server checks the format and content of the data received to determine whether it is correct.

[0499] The "means for inputting verified data into the generating device" refers to the means for passing verified data to the AI ​​model for visualization.

[0500] A "generator" is a device that generates a visualization based on verified data.

[0501] The "means for receiving a visualized diagram" is a means for receiving a generated diagram from a generating device.

[0502] The "means for displaying a visualized diagram" refers to a means for displaying the generated diagram on a user interface.

[0503] The present invention relates to a system for visualizing factory work processes and machine operation flows in real time, and is mainly composed of three main elements: a user, a terminal, and a server.

[0504] First, the user inputs the factory work process using text and sketch data via a terminal. For example, the user might input a work flow such as "take out part A -> connect to part B -> inspect the finished product -> pack the finished product," and then draw a diagram of each step using additional sketches. This input is received by the terminal's input means.

[0505] The device then packages the input text and sketch data, which is then sent to the server via an HTTP POST request via a generative AI model to generate a prompt.

[0506] The server receives the data sent from the device, analyzes and verifies it, and the verified data is input into the generative AI model, which generates a visualization based on the text and sketch data.

[0507] The generated diagram is received by the server and the results are sent back to the terminal, which displays the received diagram on the user interface, allowing the user to check the generated visualization data and gain a detailed understanding of the factory's work processes.

[0508] The main hardware and software used in this system are as follows: First, the terminal side inputs and packages data and sends HTTP requests. On the server side, a lightweight web framework called Flask is used to process requests, validate received data, and visualize it using a generative AI model. The system also handles the binary data of diagrams generated using the Pillow (PIL) library.

[0509] As a concrete example, consider a scenario in which a factory manager is designing the flow of a new line and giving instructions to new workers. For example, the manager types the following prompt:

[0510] Text: Take out part A -> Connect to part B -> Inspect the finished product -> Pack the finished product

[0511] Sketch: Draw simple work steps

[0512] Based on this prompt, the generative AI model analyzes the input data and automatically generates a visualized diagram, which is then sent back to the device from the server, allowing the manager to create new, detailed work instructions based on the diagram.

[0513] In this way, the system of the present invention makes it possible to visualize the factory work process in real time and reduce discrepancies in work instructions.

[0514] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0515] Step 1:

[0516] The user inputs text and sketch data related to the factory work process and machine operation flow. For example, the user inputs "take out part A -> connect to part B -> inspect the finished product -> pack the finished product" and sketches each step. This input data is sent to the terminal. The input data is text and sketch data, and is used in the next step.

[0517] Step 2:

[0518] The device packages the text and sketch data received from the user into an appropriate format (e.g., JSON), ensuring that each data item is correctly formatted and ready for use throughout the system. The input is text data and sketch data, and the output is the packaged data.

[0519] Step 3:

[0520] The device uses the packaged data to generate a prompt using a generative AI model. This prompt is a text command that instructs the generative AI model on how to visualize the data. Specifically, the device generates a prompt that combines text data and sketch data. The input is the packaged data, and the output is the prompt.

[0521] Step 4:

[0522] The terminal sends packaged data including the prompt text to the server via an HTTP POST request. The input is the packaged data including the prompt text, and the output is a request to the server.

[0523] Step 5:

[0524] The server receives the data sent by the device and analyzes it. Specifically, it parses the received data to verify that it is in the correct format and checks for any missing or incorrect data. The input is the data sent in the HTTP POST request, and the output is the validated data.

[0525] Step 6:

[0526] The server inputs the verified data into the generative AI model and generates a visualized diagram. The generative AI model performs calculations to create the visualization based on the prompt sentence and generates the diagram. The input is the verified data and the prompt sentence, and the output is the visualized diagram.

[0527] Step 7:

[0528] The server receives the generated visualized diagram and returns it to the terminal. Specifically, it constructs the binary data of the generated diagram or the URL of the diagram as a response and sends it to the terminal. The input is the generated diagram, and the output is the response to the terminal.

[0529] Step 8:

[0530] The terminal parses the visualized diagram received from the server and displays it on the user interface. Specifically, it interprets the received data and performs processing to display the diagram. The input is the response data from the server, and the output is the visualized diagram displayed to the user.

[0531] Step 9:

[0532] The user examines the displayed visualization data to understand the contents of the factory work instructions. If necessary, they can input additional text or sketches and repeat the process. The input is the diagram displayed to the user, and the output is the understood contents of the work instructions.

[0533] 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.

[0534] This invention is a system that instantly visualizes text and sketch data entered by users to resolve misunderstandings during meetings. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it aims to provide deeper understanding and effective communication. This system is realized by a specific program.

[0535] System Overview

[0536] The system consists of the following elements:

[0537] 1. Input means: Accepts text and sketches from the user.

[0538] 2. A means of packaging input data: Package the received data into JSON format.

[0539] 3. A means for transmitting the packaged data: Send the data to the server.

[0540] 4. Means for receiving the transmitted data: The server receives the data.

[0541] 5. Means of verifying received data: Checking the authenticity and integrity of the data.

[0542] 6. Means of inputting verified data into the generation device: Pass the data to the generation AI.

[0543] 7. Means for receiving the visualized diagram from the generating device: The visualized diagram is received by the server.

[0544] 8. Means for displaying the visualized diagram: Display the diagram on the terminal.

[0545] 9. Emotion Engine: An additional element that recognizes user emotions. It detects and analyzes emotions from text and speech.

[0546] 10. Means for adjusting diagrams based on emotion results:,Dynamic adjustment of visualized diagrams based on emotion recognition results.

[0547] Explanation of program processing

[0548] 1. User: Providing Input

[0549] Users enter text through the interface of the online conferencing tool and add sketches as needed. The emotion engine also analyzes the user's voice and text data in real time to recognize emotions. For example, a user can enter the progress of a project as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and then use the sketch function to draw a diagram showing the process.

[0550] 2. Terminal: Sending data

[0551] The device combines the text and sketch data entered by the user, as well as the emotion data recognized by the emotion engine, into a JSON package, and then sends this packaged data to the server as an HTTP POST request.

[0552] 3. Server: Receiving and processing data

[0553] The server receives an HTTP POST request from the device and parses the JSON data included in the request body. It then validates the data format and content to ensure authenticity and completeness. The validated data is passed to the generation AI, which generates a visualized diagram based on the text and sketch data. It also dynamically adjusts the diagram based on emotional data. For example, it reflects relevant emotions about the progress of the project (such as joy, confusion, or impatience) in the diagram's color and annotations.

[0554] 4. Server: Sending results

[0555] A response containing the generated visualization diagram and associated emotion information is constructed and sent back to the device, including the binary data of the generated diagram and the diagram's URL.

[0556] 5. Terminal: Displaying the results

[0557] The terminal receives the response from the server and displays a visualized diagram on the user interface, which also reflects the results of the emotion engine, allowing users to share the project progress and their own emotions with meeting participants.

[0558] 6. User: Image review and feedback

[0559] Users can view the visualized diagrams and related emotions displayed on their devices and share them with other meeting participants. Meeting participants can also understand the user's emotions in real time, leading to deeper understanding and more effective discussions.

[0560] Specific examples

[0561] As a concrete example, let's say User A is holding a meeting to announce a marketing plan for a new product. User A enters the following text sequence: "Analyze target market -> Formulate marketing strategy -> Launch advertising campaign -> Collect sales data -> Optimize strategy," and adds emotional comments to the "target market" and "advertising campaign" sections. The emotion engine recognizes the anxieties and expectations User A feels as he or she enters the information, and reflects them in the visualized diagram. As a result, meeting participants can simultaneously perceive visual and emotional information, making the discussion more concrete and effective.

[0562] In this way, the system of the present invention can instantly visualize user input and provide integrated emotional information, thereby significantly improving the efficiency and quality of meetings.

[0563] The processing flow will be explained below.

[0564] Program processing flow and details of each step

[0565] Step 1: User enters text

[0566] The user enters a description into the text input field of the online meeting tool, for example, "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client."

[0567] Step 2: User adds sketch

[0568] Users can use the sketch feature to draw simple diagrams of process flows, which complements the parts that are difficult to convey with text alone.

[0569] Step 3: The emotion engine recognizes the user's emotion

[0570] When a user speaks, the emotion engine analyzes emotions from voice and text data in real time, recognizing emotions such as anxiety, anticipation, and joy.

[0571] Step 4: User presses submit button

[0572] After completing all input, the user presses the send button to trigger transmission of the input data to the server.

[0573] Step 5: The device packages the data

[0574] The device packages the text entered by the user, sketch data, and emotion data recognized by the emotion engine into JSON format, which allows all data to be converted into a format that is easy to centrally manage on the server side.

[0575] Step 6: The device sends the data to the server

[0576] The device sends JSON data packaged as an HTTP POST request to the server, which includes text, sketches, and emotion data.

[0577] Step 7: Server receives data

[0578] The server receives the HTTP POST request sent from the device and parses the JSON data included in the request body. The received data includes all input information.

[0579] Step 8: Server Validates Data

[0580] The server checks whether the received data is in the correct format and verifies that there are no missing or incorrect data. If the data is confirmed to be correct, it proceeds to the next step.

[0581] Step 9: The server inputs data into the generated AI

[0582] The server passes the verified text and sketch data to the generative AI, which then begins the process of generating a visualization based on this data.

[0583] Step 10: Generative AI generates the visualization

[0584] The generative AI generates visualized diagrams based on input text and sketch data, and these diagrams also reflect emotional data from the emotion engine.

[0585] Step 11: The server receives the visualization

[0586] The server receives the visualized diagram generated by the generation AI, and then constructs a response containing this diagram in the following steps:

[0587] Step 12: Server constructs result response

[0588] The server constructs the visualization, emotional information, and associated metadata (e.g., generation time, resolution) in the response.

[0589] Step 13: The server sends a response to the device

[0590] The server sends the constructed response back to the device, where the visualization results are available.

[0591] Step 14: The device receives the response

[0592] The device receives the HTTP response from the server and analyzes the visualized diagram and emotion data contained in the response body.

[0593] Step 15: The device displays the image and emotional information.

[0594] The device displays the analyzed visualized diagram and emotion data on the user interface, allowing users to visually present their own emotion information along with the diagram.

[0595] Step 16: User reviews and shares the diagram and emotional information

[0596] Users can view and share the displayed visualizations and emotional information with meeting participants, which leads to deeper understanding and more effective discussions among participants.

[0597] In this way, the system instantly visualizes text and sketch data and integrates and provides emotional information, minimizing misunderstandings during meetings and enabling efficient and in-depth communication.

[0598] Example 2

[0599] 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."

[0600] In conventional online meetings, participants are prone to differing interpretations and misunderstandings, making effective communication difficult. Furthermore, limited means for accurately conveying emotional nuances often reduce the depth and quality of discussions. The present invention aims to solve these problems, eliminate misunderstandings during meetings, and promote more effective communication.

[0601] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means, a means for packaging input data, a means for transmitting the packaged data, a means for receiving the transmitted data, a means for verifying the received data, a means for inputting the verified data to the generation device, a means for receiving a visualized diagram from the generation device, a means for displaying the visualized diagram, a means for analyzing emotions and packaging data including the analysis results, and a means for adjusting the visualized diagram based on the analysis results. This makes it possible to instantly visualize the user's input and integrate emotional information, thereby achieving deeper understanding and effective communication.

[0602] "Input means" refers to an interface device for receiving text or sketches from a user.

[0603] "Means for packaging input data" refers to a device or software that converts received text or sketch data into JSON format and packages it.

[0604] The "means for transmitting packaged data" refers to a communication device or software for transferring packaged data to a server.

[0605] The "means for receiving transmitted data" refers to a communication device or software that allows the server to receive data transferred from the terminal.

[0606] "Means for verifying received data" means a device or software for checking the authenticity and integrity of received data.

[0607] "Means for inputting verified data into the generating device" refers to a device or software for providing data whose reliability and integrity have been confirmed to the generating AI.

[0608] "Means for receiving a visualization from a generating device" means a device or software for receiving visualization data output from a generating AI.

[0609] The "means for displaying the visualized diagram" refers to a device or software for displaying the received visualization data on the user's terminal.

[0610] "Means for analyzing emotions and packaging data containing the analysis results" refers to a device or software that analyzes emotions from a user's text or voice and compiles the analysis results as data.

[0611] "Means for adjusting the visualization based on the analysis results" refers to a device or software for dynamically modifying the visualization data based on the results of the sentiment analysis.

[0612] The present invention provides a system that instantly visualizes text and sketch data entered by users in online meetings and analyzes their emotions to resolve misunderstandings among participants and promote effective communication. This system is realized using specific hardware and software.

[0613] First, users enter text and add sketches as needed through the online meeting tool's interface. The emotion engine then analyzes the user's voice and text data in real time to recognize their emotions. For example, a user can enter the project progress as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and then use the sketch function to draw a diagram showing the process.

[0614] Next, the device packages the user-entered text, sketch data, and analyzed emotion data in JSON format and sends it to the server as an HTTP POST request. Specific software used for this includes an HTTP library and a JSON library, for example.

[0615] The server receives the HTTP POST request sent from the device and analyzes the JSON data included in the request body. It then verifies the authenticity and completeness of the data and supplies the verified data to a generative AI model. This generative AI model generates a visualized diagram based on the text and sketch data, and dynamically adjusts the diagram based on the emotion data. Specific generative AI models that could be used include GPT-3 or DALL-E.

[0616] The visualization generated by the generative AI model and the associated emotional information are packaged as a response by the server and sent to the device. The device receives the response and displays the visualization on the user interface. This visualization reflects the data entered by the user and the results of the emotional analysis, allowing meeting participants to simultaneously understand the progress of the project and the user's emotions.

[0617] Users can view the visualized diagrams and related emotions and share feedback with other meeting participants. Meeting participants can understand the user's emotional information along with the diagrams in real time, leading to deeper understanding and more effective discussions.

[0618] Specific examples

[0619] As a concrete example, consider a meeting where User A is presenting a marketing plan for a new product. User A enters the following text sequence: "Analyze target market -> Formulate marketing strategy -> Launch advertising campaign -> Collect sales data -> Optimize strategy," and adds emotional comments to the "target market" and "advertising campaign" sections. The emotion engine recognizes the anxieties and expectations User A feels as he or she enters the information, and reflects them in the visualized diagram. As a result, meeting participants can simultaneously perceive visual and emotional information, making the discussion more concrete and effective.

[0620] Prompt Sentence Examples

[0621] "Describe a system for visualizing the progress of a new marketing plan and creating an emotive sketch highlighting key steps."

[0622] As described above, the system of the present invention can instantly visualize user input and provide integrated emotional information, thereby significantly improving the quality and efficiency of communication in online meetings.

[0623] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0624] Step 1: Provide user input

[0625] Users input text through the interface of the online conferencing tool and add sketches as needed. The emotion engine then analyzes the user's voice and text data in real time to recognize emotions. Specifically, the user inputs the following sequence: "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and then uses the sketch function to draw a diagram showing the process. This input data becomes the initial dataset for the program.

[0626] Step 2: Sending data by device

[0627] The device packages the user-provided text, sketch data, and parsed emotion data into JSON format. It then generates an HTTP POST request to send this packaged data to the server. Specifically, the device uses a JSON library to parse the data and an HTTP library to construct the request. The input is the user-provided text, sketch, and emotion data, and the output is the JSON data sent to the server.

[0628] Step 3: Server receives and verifies data

[0629] The server receives the HTTP POST request sent from the terminal. After receiving it, it parses the JSON data included in the request body and verifies the data format and content. Specifically, the server uses a parsing library to parse the JSON data and checks whether each item matches the specified format. The input is the JSON data from the terminal, and the output is the validated data.

[0630] Step 4: Input data into the server-generated AI model

[0631] The verified data is input from the server to a generative AI model. The generative AI model generates a visualization based on the input text and sketch data, and dynamically adjusts the visualization based on the emotion data. Specifically, the generative AI model uses a pre-trained algorithm to analyze the text and draw a sketch. The input is the verified data, and the output is the generated visualization.

[0632] Step 5: Receiving the visualization

[0633] The server receives the visualization generated by the generative AI model. This visualization also includes adjustments based on the user's emotional data. Specifically, the server receives the response from the generative AI model and parses the visualization data. The input is the visualization data from the generative AI model, and the output is the visualization data ready to be sent to the device.

[0634] Step 6: Server sends results

[0635] The server packages the received visualization and related emotion information as a response body and sends it back to the device. Specifically, the server constructs an HTTP response and encodes JSON data including the binary data and URL of the visualization. The input is the visualization data, and the output is the response data to the device.

[0636] Step 7: Viewing the results in the terminal

[0637] The terminal analyzes the response received from the server and displays a visualized diagram on the user interface. This diagram reflects the results of the emotion engine. Specifically, the terminal decodes the response data and updates the display using the GUI framework. The input is the response data from the server, and the output is the displayed visualized diagram.

[0638] Step 8: User review and feedback on images

[0639] The user checks the displayed visualization and related emotional information, and exchanges opinions with other meeting participants based on that information. Specifically, the user points out each element in the visualization and advances the discussion. The final input is the displayed visualization, and the output is the user's feedback and the progress of the meeting.

[0640] (Application example 2)

[0641] 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."

[0642] Modern brick-and-mortar stores require fast and appropriate customer service. However, it is difficult for store staff to instantly grasp customers' requests and emotions, which can result in inconsistent service quality. In particular, in busy stores, customers often have to wait for long periods of time, which can lead to a decline in customer satisfaction. With conventional technology, it has been difficult to grasp customers' emotions in real time while meeting their requests.

[0643] 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 a voice input means, a means for generating text from voice, a means for recognizing emotions, and a means for dynamically adjusting the generated visualized image. This allows store staff to instantly visualize and confirm customer requests and emotions, enabling more appropriate and prompt customer service. This system also improves customer satisfaction and standardizes service quality.

[0644] The "input means" is an interface for taking in information from the user.

[0645] "Means for packaging input data" is a function that compiles the imported information into a certain format (e.g., JSON format).

[0646] The "means for transmitting packaged data" is a function for transmitting packaged information to a server or other system.

[0647] The "means for receiving transmitted data" is an interface that receives information sent from other systems.

[0648] "Means for verifying received data" refers to a function that checks the authenticity and integrity of received information.

[0649] The "means for inputting verified data to the generating device" is a function for passing verified information to the generating device.

[0650] The "means for receiving a visualized diagram from a generating device" is an interface that receives a visualized diagram sent back from the generating device.

[0651] The "means for displaying the visualized diagram" is a display or a display device for showing the received visualized diagram to the user.

[0652] The "voice input means" is an interface for capturing the user's voice.

[0653] The "means for generating text from speech" is a function for converting captured speech data into text data.

[0654] "Means for recognizing emotions" refers to the function of analyzing and recognizing the user's emotions from the captured data.

[0655] The "means for dynamically adjusting the generated visualization" is a function that modifies and adjusts the visualization in real time based on the recognized emotion data.

[0656] The present invention is a system for providing prompt and appropriate customer service in brick-and-mortar stores, which integrates functions such as voice input, speech-to-text conversion, emotion recognition, visualization, and dynamic illustration adjustment.

[0657] System configuration

[0658] The system consists of the following elements:

[0659] 1. Voice input means: An interface for capturing the user's voice. Specifically, it uses a microphone built into smart glasses or smart devices.

[0660] 2. A means of generating text from speech: This is a function that converts captured speech into text data. Specifically, it uses the speech_recognition library.

[0661] 3. Means of Emotion Recognition: This is a function that analyzes and recognizes the user's emotions from text data and voice data. Specifically, it uses tools such as EmotionRecognizer.

[0662] 4. Dynamic adjustment of the generated visualization: This function corrects and adjusts the visualization in real time based on the recognized emotion data. The generated visualization is displayed on the smart device display in the store.

[0663] 5. Data transmission and reception means: This is the interface for sending packaged data to the server and receiving visualization data generated by the server. Data is sent and received using HTTP POST requests.

[0664] System action

[0665] 1. Voice input

[0666] The terminal uses the microphone of the smart glasses or smart device to capture the user's (customer's) voice. This voice data is converted into text data by the speech_recognition library.

[0667] 2. Emotion recognition

[0668] EmotionRecognizer is used to recognize emotions from text obtained from speech. For example, it recognizes the emotion "worried" from text such as "I want to try on this dress, but I'm worried about whether it will fit."

[0669] 3. Data Packaging and Transmission

[0670] The text and sentiment data are packaged in JSON format and sent to the server using an HTTP POST request, which receives it and verifies the authenticity and integrity of the data.

[0671] 4. Visualization Generation and Adjustment

[0672] The server generates a visualization based on the received data. The generated visualization is dynamically adjusted based on the emotion data. For example, if the emotion "worry" is recognized, the color of the area of ​​interest is changed to highlight it.

[0673] Specific examples

[0674] For example, if a customer says to a store associate, "I'd like to try on this dress, but I'm worried about the size," the system works as follows:

[0675] 1. Convert speech to text: "I'd like to try on this dress, but I'm worried about the size."

[0676] 2. Recognize the emotion "worry."

[0677] 3. Package the text and sentiment into JSON format and send it to the server.

[0678] 4. The server generates a visualization and adjusts it based on emotions such as "worry."

[0679] 5. The diagram is sent to a terminal and staff can check it using smart glasses or other devices.

[0680] As a result, staff can instantly understand customer needs and emotions, enabling them to respond more appropriately.

[0681] Prompt Sentence Examples

[0682] "I'd like to try on this dress, but I'm worried about whether it will fit."

[0683] summary

[0684] The system of this invention integrates voice input and emotion recognition to speed up and improve the accuracy of customer service in brick-and-mortar stores. This makes it possible to respond based on emotions, which was difficult with conventional methods, and is expected to improve customer satisfaction.

[0685] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0686] Step 1:

[0687] The user inputs voice through the microphone of the smart glasses or smart device, and the input voice data is captured by the device. Specifically, the process of capturing voice data in real time begins.

[0688] Step 2:

[0689] The device uses a speech recognition library (e.g., the speech_recognition library) to convert voice data to text data. It uses an acoustic model and a language model to convert voice data to text data. The input is voice data, and the output is the converted text data.

[0690] Step 3:

[0691] The device consumes the converted text data and uses EmotionRecognizer to recognize emotions from the text. Specifically, it analyzes the text data and determines the emotional nuances of words and phrases. The input is text data, and the output is recognized emotion data.

[0692] Step 4:

[0693] The device packages the text data and emotion data in JSON format. Specifically, it converts the text data and emotion data into a key-value format and combines them into a single data structure. The input is text data and emotion data, and the output is a JSON-formatted data package.

[0694] Step 5:

[0695] The device sends the data package to the server using an HTTP POST request. Specifically, the data package is included in the HTTP request body and sent to the specified URL. The input is the data package in JSON format, and the output is the transmission status to the server.

[0696] Step 6:

[0697] The server receives the HTTP POST request sent from the terminal and analyzes the JSON data included in the request body. Specifically, it parses the received data and checks its authenticity and integrity. The input is JSON data, and the output is the validated data.

[0698] Step 7:

[0699] The server inputs the verified data into the generative AI model to generate a visualized diagram. Specifically, text data and emotion data are provided to the AI ​​model as prompts, and a process is executed to generate appropriate visual information. The input is text data and emotion data, and the output is a visualized diagram.

[0700] Step 8:

[0701] The server dynamically adjusts the generated visualization using emotion data. Specifically, it applies changes to the color and shape of the visualization to visually express the nuances of emotions. The input is the visualization and emotion data, and the output is the adjusted visualization.

[0702] Step 9:

[0703] The server sends the adjusted visualized diagram to the terminal as an HTTP response. Specifically, the visualized diagram is included in the response body as binary data or a URL and sent as an HTTP response. The input is the adjusted visualized diagram, and the output is the transmission status to the terminal.

[0704] Step 10:

[0705] The terminal receives the response from the server and displays the visualized diagram on the user interface. Specifically, the terminal displays the received visualized diagram on a display or a head-mounted display for the user to see. The input is the adjusted visualized diagram, and the output is the display on the user interface.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] [Third embodiment]

[0710] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0711] 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.

[0712] 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).

[0713] 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.

[0714] 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.

[0715] 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).

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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."

[0722] The present invention provides a system that instantly visualizes text and sketch data entered by a user, eliminating misunderstandings during meetings. This system is realized by a specific program.

[0723] System Overview

[0724] The system is configured by combining an input means, a means for packaging input data, a means for transmitting the packaged data, a means for receiving the transmitted data, a means for verifying the received data, a means for inputting the verified data into a generating device, a means for receiving a visualized diagram from the generating device, and a means for displaying the visualized diagram.

[0725] Explanation of program processing

[0726] 1. User: Providing Input

[0727] Users enter text through the interface of the online meeting tool, adding sketches as needed. For example, they can enter a project workflow such as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client." Once they've finished entering the information, they press the send button.

[0728] 2. Terminal: Sending data

[0729] The device packages the text and sketch data entered by the user into JSON format, and once packaged, sends the data to the server using an HTTP POST request.

[0730] 3. Server: Receiving and processing data

[0731] The server receives an HTTP POST request from the device and parses the JSON data included in the request body. It verifies that the received data is in the correct format and checks for any missing or incorrect data. It then inputs the verified data into the generation AI, which generates a visualized diagram based on the text and sketch data. This generated diagram is then returned to the server.

[0732] 4. Server: Sending results

[0733] The server constructs a response containing the generated diagram and its metadata, and sends it back to the terminal, including the diagram's binary data or its URL.

[0734] 5. Terminal: Displaying the results

[0735] The terminal receives the response from the server, parses the received data, and if visualization is successful, displays the diagram on the user interface.

[0736] 6. User: Image review and feedback

[0737] Users can review the displayed visualizations and share them with other meeting participants, gathering feedback and providing further corrections or additional input as needed.

[0738] Specific examples

[0739] As a concrete example, let's say a user wants to visualize the workflow of a new project. The user enters the following into an online conferencing tool: "Receive requirements from client -> Analyze requirements -> Forward to design team -> Start development -> Test -> Deliver to client." They also sketch boxes and arrows to indicate the process flow. When the user presses the send button, the device sends the input data in JSON format to the server. The server receives the data and passes it to the generation AI for visualization. The generated diagram is sent back from the server to the device and displayed in the user interface. The user provides a detailed explanation based on the displayed diagram, collecting feedback in the process.

[0740] In this way, the system of the present invention can minimize misunderstandings and significantly improve the efficiency of meetings.

[0741] The processing flow will be explained below.

[0742] Program processing flow and details of each step

[0743] Step 1: User enters text

[0744] Users enter their description in a text input field in the online meeting tool, and can optionally add a quick sketch to clarify the image or process they want to share during the meeting.

[0745] Step 2: User presses submit button

[0746] After completing the input, the user presses the send button to send the input data to the server, which passes the input information to the system.

[0747] Step 3: The device packages the data

[0748] The device takes the text and sketch data entered by the user and packages it into JSON format, which makes the data easier to handle on the server side.

[0749] Step 4: The device sends data to the server

[0750] The device sends an HTTP POST request containing the packaged JSON data to the server, which causes the data to reach the server.

[0751] Step 5: Server receives data

[0752] The server receives an HTTP POST request from a client and retrieves the JSON data contained in the request body, which is the trigger for the core processing of the system.

[0753] Step 6: Server verifies the data

[0754] The server validates the format and content of the data it receives, ensuring that the data is in the correct format and checking for any missing or incorrect data. After validation is complete, it proceeds to the next step.

[0755] Step 7: The server inputs data into the generated AI

[0756] The server inputs the verified data into the generative AI, which begins the process of generating a visualization based on the text and sketch data.

[0757] Step 8: The server receives the visualization

[0758] Once the AI ​​generates the visualization, it sends the diagram data back to the server, which receives the generated diagram and prepares it for further processing.

[0759] Step 9: Server constructs result response

[0760] The server constructs a response message based on the received visualization data, which includes the binary data of the diagram, the diagram URL, and the generated metadata.

[0761] Step 10: The server sends a response to the device

[0762] The server sends the constructed response to the device, which then makes the visualized results available.

[0763] Step 11: The device receives the response

[0764] The terminal receives the HTTP response from the server and obtains the data contained in the response body, which includes the data of the generated diagram.

[0765] Step 12: The terminal displays the diagram

[0766] The terminal analyzes the received visualization data and displays it on the user interface, preparing the user for visual explanations during the meeting.

[0767] Step 13: Users review and share diagrams

[0768] Users can view the visualized diagrams displayed on their devices and share them with other participants in the meeting, eliminating misunderstandings and realizing efficient communication.

[0769] In this way, the system goes through a series of steps to instantly visualize the user's input and increase the efficiency of the meeting.

[0770] Example 1

[0771] 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."

[0772] In modern online meetings, communication between participants can sometimes be skewed. In particular, when explaining complex project workflows and concepts, words and text alone are insufficient and visual materials are required. However, on-the-spot visualization and data sharing remain time-consuming and disruptive. Traditional methods lack a means to instantly generate visualizations from sketches and text, making efficient communication difficult.

[0773] 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.

[0774] In this invention, the server includes an input means, a means for packaging input data, a means for transmitting the packaged data, a means for receiving the transmitted data, a means for verifying the received data, a means for inputting the verified data to a generation device, a means for receiving a visualized diagram from the generation device, a means for displaying the visualized diagram, a visualization means based on a generative AI model for instantly visualizing text and sketch data provided by a user, and a means for analyzing the generated visualization data and displaying it on a user interface. This allows for the instant generation of visual materials during an online meeting, reducing misunderstandings among participants and enabling efficient communication.

[0775] "Input means" refers to a digital interface that allows users to input text and sketch data through the online meeting tool.

[0776] "Means for packaging input data" refers to the process of converting user-provided text or sketch data into an appropriate format, such as JSON format, and packaging it.

[0777] The "means for transmitting packaged data" is a process for transmitting packaged data to a server using a communication means such as an HTTP POST request.

[0778] The "means for receiving transmitted data" is a communication interface for receiving data transmitted from a terminal on the server side.

[0779] "Means for validating received data" refers to a data validation process to ensure that the received data is in the correct format and is free of any missing or erroneous data.

[0780] "Means for inputting verified data into a generating device" refers to the process of inputting verified data into a generative AI model.

[0781] The "means for receiving a visualization from a generating device" is a process for receiving visualization data output from a generative AI model.

[0782] The "means for displaying the visualized diagram" is a display device or software for displaying the generated visualization data on a user interface.

[0783] A "generative AI model" is an artificial intelligence model that instantly generates visualizations based on text and sketch data provided by the user.

[0784] A "visualization method" is a process that uses a generative AI model to generate a visual diagram based on input data.

[0785] This invention is a system that instantly visualizes text and sketch data entered by users during online meetings, improving meeting efficiency. This system is mainly realized using a server, terminals, and a generative AI model.

[0786] Users enter text through the interface of the online meeting tool and add sketch data as needed. This interface is designed to be intuitive and easy to use, making it easy for users to operate. For example, a user can enter a project workflow such as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client" and add a sketch using boxes and arrows.

[0787] The device packages the input text and sketch data into JSON format, which is a key-value object format that ensures data integrity and compatibility, and then sends the packaged data to the server using an HTTP POST request.

[0788] The server receives the HTTP POST request sent from the device. It parses the JSON data included in the request body and verifies that the received data is in the correct format. Specifically, it checks that all required fields are present and that the data is not corrupted. The verified data is then input into a generative AI model, which converts it into a prompt for visualization.

[0789] The generative AI model generates a visualized diagram based on a prompt. For example, the prompt might be, "Visualize the project workflow. Illustrate the process: Receive requirements from the client -> Analyze requirements -> Transfer to the design team -> Start development -> Test -> Deliver to the client." The generated diagram is then sent back to the server, which then constructs a response containing the diagram's binary data and URL and sends it to the device.

[0790] The device receives the response from the server, parses the data, and if the visualization is successful, displays the diagram on the user interface. This allows the user to review the visualized data and share it with other meeting participants. Additional feedback can be collected if necessary, allowing for further refinements.

[0791] As a concrete example, let's say a user wants to visualize the workflow of a new project. They enter "Receive requirements from client -> Analyze requirements -> Forward to design team -> Start development -> Test -> Deliver to client" into an online conferencing tool and sketch boxes and arrows to show the process flow. When they press the send button, the device sends this data in JSON format to the server. The server receives the data and passes it to the generation AI for visualization. The generated diagram is sent back from the server to the device and displayed in the user interface. The user provides a detailed explanation based on the displayed diagram, collecting feedback along the way.

[0792] In this way, the invention can minimize misunderstandings and significantly improve meeting efficiency.

[0793] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0794] Step 1:

[0795] Users enter text and sketch data using the interface of the online meeting tool. For example, they can enter a project workflow such as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and add sketches using boxes and arrows. Once they're done, they press the send button.

[0796] Input: Text and sketch data

[0797] Output: Input data is passed to the terminal

[0798] Step 2:

[0799] The device receives the text and sketch data entered by the user, packages it into JSON format, and creates a key-value object with the text and sketch information. This data is then added to the body of an HTTP POST request and sent to the server.

[0800] Input: User-provided text and sketch data

[0801] Output: Data packaged in JSON format

[0802] Step 3:

[0803] The server receives the HTTP POST request sent from the terminal and parses the JSON data included in the request body. Specifically, it converts the JSON data from a string to an object format.

[0804] Input: JSON format data

[0805] Output: Parsed object data

[0806] Step 4:

[0807] The server validates that the parsed data is well-formed, ensuring that all required fields are present and that the data is not corrupted (for example, checking that text fields are not empty and that sketch data conforms to the image format).

[0808] Input: Parsed object data

[0809] Output: Validation result (success / failure) and validated data

[0810] Step 5:

[0811] The server inputs the verified data into the generative AI model. Specifically, it converts the text and sketch data into a prompt and sends it to the generative AI model. An example of a prompt would be, "Visualize the project workflow. Please illustrate the process: Receive requirements from the client -> Analyze the requirements -> Transfer to the design team -> Start development -> Test -> Deliver to the client."

[0812] Input: Validated Data

[0813] Output: The prompt passed to the generative AI model

[0814] Step 6:

[0815] The generative AI model generates a visualization based on the prompt and sends the results back to the server, where the generated diagram is presented in a visually easy-to-understand format.

[0816] Input: prompt statement

[0817] Output: Visualized diagram data

[0818] Step 7:

[0819] The server constructs a response containing the generated diagram and its metadata, and sends it to the terminal. The response includes the diagram's binary data or the diagram's URL.

[0820] Input: Visualized diagram data

[0821] Output: Response (binary data or URL)

[0822] Step 8:

[0823] The terminal receives the response from the server, parses the response body, verifies that the received data is in the correct format, and if visualization is successful, displays the diagram on the user interface. Specifically, it renders the diagram's binary data as an image.

[0824] Input: Response from the server

[0825] Output: Visualized data displayed in a user interface

[0826] Step 9:

[0827] The user can review the visualization data displayed on their device, evaluate whether the content is appropriate, share it with other meeting participants, and gather feedback as needed. Based on the feedback, they can input additional data and revisit the visualization.

[0828] Input: Visualization data displayed in the user interface

[0829] Output: User feedback and additional input data

[0830] (Application example 1)

[0831] 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."

[0832] The present invention aims to solve problems related to systems that visualize factory work processes and machine operation flows in real time, thereby eliminating misunderstandings that arise between managers and workers and improving work efficiency and accuracy.

[0833] 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.

[0834] In this invention, the server includes an input means, a means for packaging input data, a means for generating a prompt sentence using a generative AI model, a means for transmitting the packaged data, a means for verifying the received data, a means for inputting the verified data to the generation device, a means for receiving a visualized diagram from the generation device, and a means for displaying the visualized diagram. This makes it possible to visualize the factory work process in real time and reduce discrepancies in work instructions.

[0835] "Input means" refers to the means by which the system receives text and sketch data from the user.

[0836] "Means for packaging input data" refers to means for assembling received text or sketch data into an appropriate format.

[0837] A "generative AI model" is an artificial intelligence that generates visualized diagrams or images based on input data.

[0838] A "prompt" is a text command that instructs the generative AI model on how to visualize something.

[0839] The "means for transmitting packaged data" is a means for transmitting packaged data to a server.

[0840] "Means for verifying received data" refers to the means by which the server checks the format and content of the data received to determine whether it is correct.

[0841] The "means for inputting verified data into the generating device" refers to the means for passing verified data to the AI ​​model for visualization.

[0842] A "generator" is a device that generates a visualization based on verified data.

[0843] The "means for receiving a visualized diagram" is a means for receiving a generated diagram from a generating device.

[0844] The "means for displaying a visualized diagram" refers to a means for displaying the generated diagram on a user interface.

[0845] The present invention relates to a system for visualizing factory work processes and machine operation flows in real time, and is mainly composed of three main elements: a user, a terminal, and a server.

[0846] First, the user inputs the factory work process using text and sketch data via a terminal. For example, the user might input a work flow such as "take out part A -> connect to part B -> inspect the finished product -> pack the finished product," and then draw a diagram of each step using additional sketches. This input is received by the terminal's input means.

[0847] The device then packages the input text and sketch data, which is then sent to the server via an HTTP POST request via a generative AI model to generate a prompt.

[0848] The server receives the data sent from the device, analyzes and verifies it, and the verified data is input into the generative AI model, which generates a visualization based on the text and sketch data.

[0849] The generated diagram is received by the server and the results are sent back to the terminal, which displays the received diagram on the user interface, allowing the user to check the generated visualization data and gain a detailed understanding of the factory's work processes.

[0850] The main hardware and software used in this system are as follows: First, the terminal side inputs and packages data and sends HTTP requests. On the server side, a lightweight web framework called Flask is used to process requests, validate received data, and visualize it using a generative AI model. The system also handles the binary data of diagrams generated using the Pillow (PIL) library.

[0851] As a concrete example, consider a scenario in which a factory manager is designing the flow of a new line and giving instructions to new workers. For example, the manager types the following prompt:

[0852] Text: Take out part A -> Connect to part B -> Inspect the finished product -> Pack the finished product

[0853] Sketch: Draw simple work steps

[0854] Based on this prompt, the generative AI model analyzes the input data and automatically generates a visualized diagram, which is then sent back to the device from the server, allowing the manager to create new, detailed work instructions based on the diagram.

[0855] In this way, the system of the present invention makes it possible to visualize the factory work process in real time and reduce discrepancies in work instructions.

[0856] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0857] Step 1:

[0858] The user inputs text and sketch data related to the factory work process and machine operation flow. For example, the user inputs "take out part A -> connect to part B -> inspect the finished product -> pack the finished product" and sketches each step. This input data is sent to the terminal. The input data is text and sketch data, and is used in the next step.

[0859] Step 2:

[0860] The device packages the text and sketch data received from the user into an appropriate format (e.g., JSON), ensuring that each data item is correctly formatted and ready for use throughout the system. The input is text data and sketch data, and the output is the packaged data.

[0861] Step 3:

[0862] The device uses the packaged data to generate a prompt using a generative AI model. This prompt is a text command that instructs the generative AI model on how to visualize the data. Specifically, the device generates a prompt that combines text data and sketch data. The input is the packaged data, and the output is the prompt.

[0863] Step 4:

[0864] The terminal sends packaged data including the prompt text to the server via an HTTP POST request. The input is the packaged data including the prompt text, and the output is a request to the server.

[0865] Step 5:

[0866] The server receives the data sent by the device and analyzes it. Specifically, it parses the received data to verify that it is in the correct format and checks for any missing or incorrect data. The input is the data sent in the HTTP POST request, and the output is the validated data.

[0867] Step 6:

[0868] The server inputs the verified data into the generative AI model and generates a visualized diagram. The generative AI model performs calculations to create the visualization based on the prompt sentence and generates the diagram. The input is the verified data and the prompt sentence, and the output is the visualized diagram.

[0869] Step 7:

[0870] The server receives the generated visualized diagram and returns it to the terminal. Specifically, it constructs the binary data of the generated diagram or the URL of the diagram as a response and sends it to the terminal. The input is the generated diagram, and the output is the response to the terminal.

[0871] Step 8:

[0872] The terminal parses the visualized diagram received from the server and displays it on the user interface. Specifically, it interprets the received data and performs processing to display the diagram. The input is the response data from the server, and the output is the visualized diagram displayed to the user.

[0873] Step 9:

[0874] The user examines the displayed visualization data to understand the contents of the factory work instructions. If necessary, they can input additional text or sketches and repeat the process. The input is the diagram displayed to the user, and the output is the understood contents of the work instructions.

[0875] 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.

[0876] This invention is a system that instantly visualizes text and sketch data entered by users to resolve misunderstandings during meetings. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it aims to provide deeper understanding and effective communication. This system is realized by a specific program.

[0877] System Overview

[0878] The system consists of the following elements:

[0879] 1. Input means: Accepts text and sketches from the user.

[0880] 2. A means of packaging input data: Package the received data into JSON format.

[0881] 3. A means for transmitting the packaged data: Send the data to the server.

[0882] 4. Means for receiving the transmitted data: The server receives the data.

[0883] 5. Means of verifying received data: Checking the authenticity and integrity of the data.

[0884] 6. Means of inputting verified data into the generation device: Pass the data to the generation AI.

[0885] 7. Means for receiving the visualized diagram from the generating device: The visualized diagram is received by the server.

[0886] 8. Means for displaying the visualized diagram: Display the diagram on the terminal.

[0887] 9. Emotion Engine: An additional element that recognizes user emotions. It detects and analyzes emotions from text and speech.

[0888] 10. Means for adjusting diagrams based on emotion results:,Dynamic adjustment of visualized diagrams based on emotion recognition results.

[0889] Explanation of program processing

[0890] 1. User: Providing Input

[0891] Users enter text through the interface of the online conferencing tool and add sketches as needed. The emotion engine also analyzes the user's voice and text data in real time to recognize emotions. For example, a user can enter the progress of a project as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and then use the sketch function to draw a diagram showing the process.

[0892] 2. Terminal: Sending data

[0893] The device combines the text and sketch data entered by the user, as well as the emotion data recognized by the emotion engine, into a JSON package, and then sends this packaged data to the server as an HTTP POST request.

[0894] 3. Server: Receiving and processing data

[0895] The server receives an HTTP POST request from the device and parses the JSON data included in the request body. It then validates the data format and content to ensure authenticity and completeness. The validated data is passed to the generation AI, which generates a visualized diagram based on the text and sketch data. It also dynamically adjusts the diagram based on emotional data. For example, it reflects relevant emotions about the progress of the project (such as joy, confusion, or impatience) in the diagram's color and annotations.

[0896] 4. Server: Sending results

[0897] A response containing the generated visualization diagram and associated emotion information is constructed and sent back to the device, including the binary data of the generated diagram and the diagram's URL.

[0898] 5. Terminal: Displaying the results

[0899] The terminal receives the response from the server and displays a visualized diagram on the user interface, which also reflects the results of the emotion engine, allowing users to share the project progress and their own emotions with meeting participants.

[0900] 6. User: Image review and feedback

[0901] Users can view the visualized diagrams and related emotions displayed on their devices and share them with other meeting participants. Meeting participants can also understand the user's emotions in real time, leading to deeper understanding and more effective discussions.

[0902] Specific examples

[0903] As a concrete example, let's say User A is holding a meeting to announce a marketing plan for a new product. User A enters the following text sequence: "Analyze target market -> Formulate marketing strategy -> Launch advertising campaign -> Collect sales data -> Optimize strategy," and adds emotional comments to the "target market" and "advertising campaign" sections. The emotion engine recognizes the anxieties and expectations User A feels as he or she enters the information, and reflects them in the visualized diagram. As a result, meeting participants can simultaneously perceive visual and emotional information, making the discussion more concrete and effective.

[0904] In this way, the system of the present invention can instantly visualize user input and provide integrated emotional information, thereby significantly improving the efficiency and quality of meetings.

[0905] The processing flow will be explained below.

[0906] Program processing flow and details of each step

[0907] Step 1: User enters text

[0908] The user enters a description into the text input field of the online meeting tool, for example, "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client."

[0909] Step 2: User adds sketch

[0910] Users can use the sketch feature to draw simple diagrams of process flows, which complements the parts that are difficult to convey with text alone.

[0911] Step 3: The emotion engine recognizes the user's emotion

[0912] When a user speaks, the emotion engine analyzes emotions from voice and text data in real time, recognizing emotions such as anxiety, anticipation, and joy.

[0913] Step 4: User presses submit button

[0914] After completing all input, the user presses the send button to trigger transmission of the input data to the server.

[0915] Step 5: The device packages the data

[0916] The device packages the text entered by the user, sketch data, and emotion data recognized by the emotion engine into JSON format, which allows all data to be converted into a format that is easy to centrally manage on the server side.

[0917] Step 6: The device sends the data to the server

[0918] The device sends JSON data packaged as an HTTP POST request to the server, which includes text, sketches, and emotion data.

[0919] Step 7: Server receives data

[0920] The server receives the HTTP POST request sent from the device and parses the JSON data included in the request body. The received data includes all input information.

[0921] Step 8: Server Validates Data

[0922] The server checks whether the received data is in the correct format and verifies that there are no missing or incorrect data. If the data is confirmed to be correct, it proceeds to the next step.

[0923] Step 9: The server inputs data into the generated AI

[0924] The server passes the verified text and sketch data to the generative AI, which then begins the process of generating a visualization based on this data.

[0925] Step 10: Generative AI generates the visualization

[0926] The generative AI generates visualized diagrams based on input text and sketch data, and these diagrams also reflect emotional data from the emotion engine.

[0927] Step 11: The server receives the visualization

[0928] The server receives the visualized diagram generated by the generation AI, and then constructs a response containing this diagram in the following steps:

[0929] Step 12: Server constructs result response

[0930] The server constructs the visualization, emotional information, and associated metadata (e.g., generation time, resolution) in the response.

[0931] Step 13: The server sends a response to the device

[0932] The server sends the constructed response back to the device, where the visualization results are available.

[0933] Step 14: The device receives the response

[0934] The device receives the HTTP response from the server and analyzes the visualized diagram and emotion data contained in the response body.

[0935] Step 15: The device displays the image and emotional information.

[0936] The device displays the analyzed visualized diagram and emotion data on the user interface, allowing users to visually present their own emotion information along with the diagram.

[0937] Step 16: User reviews and shares the diagram and emotional information

[0938] Users can view and share the displayed visualizations and emotional information with meeting participants, which leads to deeper understanding and more effective discussions among participants.

[0939] In this way, the system instantly visualizes text and sketch data and integrates and provides emotional information, minimizing misunderstandings during meetings and enabling efficient and in-depth communication.

[0940] Example 2

[0941] 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."

[0942] In conventional online meetings, participants are prone to differing interpretations and misunderstandings, making effective communication difficult. Furthermore, limited means for accurately conveying emotional nuances often reduce the depth and quality of discussions. The present invention aims to solve these problems, eliminate misunderstandings during meetings, and promote more effective communication.

[0943] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means, a means for packaging input data, a means for transmitting the packaged data, a means for receiving the transmitted data, a means for verifying the received data, a means for inputting the verified data to the generation device, a means for receiving a visualized diagram from the generation device, a means for displaying the visualized diagram, a means for analyzing emotions and packaging data including the analysis results, and a means for adjusting the visualized diagram based on the analysis results. This makes it possible to instantly visualize the user's input and integrate emotional information, thereby achieving deeper understanding and effective communication.

[0944] "Input means" refers to an interface device for receiving text or sketches from a user.

[0945] "Means for packaging input data" refers to a device or software that converts received text or sketch data into JSON format and packages it.

[0946] The "means for transmitting packaged data" refers to a communication device or software for transferring packaged data to a server.

[0947] The "means for receiving transmitted data" refers to a communication device or software that allows the server to receive data transferred from the terminal.

[0948] "Means for verifying received data" means a device or software for checking the authenticity and integrity of received data.

[0949] "Means for inputting verified data into the generating device" refers to a device or software for providing data whose reliability and integrity have been confirmed to the generating AI.

[0950] "Means for receiving a visualization from a generating device" means a device or software for receiving visualization data output from a generating AI.

[0951] The "means for displaying the visualized diagram" refers to a device or software for displaying the received visualization data on the user's terminal.

[0952] "Means for analyzing emotions and packaging data containing the analysis results" refers to a device or software that analyzes emotions from a user's text or voice and compiles the analysis results as data.

[0953] "Means for adjusting the visualization based on the analysis results" refers to a device or software for dynamically modifying the visualization data based on the results of the sentiment analysis.

[0954] The present invention provides a system that instantly visualizes text and sketch data entered by users in online meetings and analyzes their emotions to resolve misunderstandings among participants and promote effective communication. This system is realized using specific hardware and software.

[0955] First, users enter text and add sketches as needed through the online meeting tool's interface. The emotion engine then analyzes the user's voice and text data in real time to recognize their emotions. For example, a user can enter the project progress as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and then use the sketch function to draw a diagram showing the process.

[0956] Next, the device packages the user-entered text, sketch data, and analyzed emotion data in JSON format and sends it to the server as an HTTP POST request. Specific software used for this includes an HTTP library and a JSON library, for example.

[0957] The server receives the HTTP POST request sent from the device and analyzes the JSON data included in the request body. It then verifies the authenticity and completeness of the data and supplies the verified data to a generative AI model. This generative AI model generates a visualized diagram based on the text and sketch data, and dynamically adjusts the diagram based on the emotion data. Specific generative AI models that could be used include GPT-3 or DALL-E.

[0958] The visualization generated by the generative AI model and the associated emotional information are packaged as a response by the server and sent to the device. The device receives the response and displays the visualization on the user interface. This visualization reflects the data entered by the user and the results of the emotional analysis, allowing meeting participants to simultaneously understand the progress of the project and the user's emotions.

[0959] Users can view the visualized diagrams and related emotions and share feedback with other meeting participants. Meeting participants can understand the user's emotional information along with the diagrams in real time, leading to deeper understanding and more effective discussions.

[0960] Specific examples

[0961] As a concrete example, consider a meeting where User A is presenting a marketing plan for a new product. User A enters the following text sequence: "Analyze target market -> Formulate marketing strategy -> Launch advertising campaign -> Collect sales data -> Optimize strategy," and adds emotional comments to the "target market" and "advertising campaign" sections. The emotion engine recognizes the anxieties and expectations User A feels as he or she enters the information, and reflects them in the visualized diagram. As a result, meeting participants can simultaneously perceive visual and emotional information, making the discussion more concrete and effective.

[0962] Prompt Sentence Examples

[0963] "Describe a system for visualizing the progress of a new marketing plan and creating an emotive sketch highlighting key steps."

[0964] As described above, the system of the present invention can instantly visualize user input and provide integrated emotional information, thereby significantly improving the quality and efficiency of communication in online meetings.

[0965] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0966] Step 1: Provide user input

[0967] Users input text through the interface of the online conferencing tool and add sketches as needed. The emotion engine then analyzes the user's voice and text data in real time to recognize emotions. Specifically, the user inputs the following sequence: "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and then uses the sketch function to draw a diagram showing the process. This input data becomes the initial dataset for the program.

[0968] Step 2: Sending data by device

[0969] The device packages the user-provided text, sketch data, and parsed emotion data into JSON format. It then generates an HTTP POST request to send this packaged data to the server. Specifically, the device uses a JSON library to parse the data and an HTTP library to construct the request. The input is the user-provided text, sketch, and emotion data, and the output is the JSON data sent to the server.

[0970] Step 3: Server receives and verifies data

[0971] The server receives the HTTP POST request sent from the terminal. After receiving it, it parses the JSON data included in the request body and verifies the data format and content. Specifically, the server uses a parsing library to parse the JSON data and checks whether each item matches the specified format. The input is the JSON data from the terminal, and the output is the validated data.

[0972] Step 4: Input data into the server-generated AI model

[0973] The verified data is input from the server to a generative AI model. The generative AI model generates a visualization based on the input text and sketch data, and dynamically adjusts the visualization based on the emotion data. Specifically, the generative AI model uses a pre-trained algorithm to analyze the text and draw a sketch. The input is the verified data, and the output is the generated visualization.

[0974] Step 5: Receiving the visualization

[0975] The server receives the visualization generated by the generative AI model. This visualization also includes adjustments based on the user's emotional data. Specifically, the server receives the response from the generative AI model and parses the visualization data. The input is the visualization data from the generative AI model, and the output is the visualization data ready to be sent to the device.

[0976] Step 6: Server sends results

[0977] The server packages the received visualization and related emotion information as a response body and sends it back to the device. Specifically, the server constructs an HTTP response and encodes JSON data including the binary data and URL of the visualization. The input is the visualization data, and the output is the response data to the device.

[0978] Step 7: Viewing the results in the terminal

[0979] The terminal analyzes the response received from the server and displays a visualized diagram on the user interface. This diagram reflects the results of the emotion engine. Specifically, the terminal decodes the response data and updates the display using the GUI framework. The input is the response data from the server, and the output is the displayed visualized diagram.

[0980] Step 8: User review and feedback on images

[0981] The user checks the displayed visualization and related emotional information, and exchanges opinions with other meeting participants based on that information. Specifically, the user points out each element in the visualization and advances the discussion. The final input is the displayed visualization, and the output is the user's feedback and the progress of the meeting.

[0982] (Application example 2)

[0983] 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."

[0984] Modern brick-and-mortar stores require fast and appropriate customer service. However, it is difficult for store staff to instantly grasp customers' requests and emotions, which can result in inconsistent service quality. In particular, in busy stores, customers often have to wait for long periods of time, which can lead to a decline in customer satisfaction. With conventional technology, it has been difficult to grasp customers' emotions in real time while meeting their requests.

[0985] 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 a voice input means, a means for generating text from voice, a means for recognizing emotions, and a means for dynamically adjusting the generated visualized image. This allows store staff to instantly visualize and confirm customer requests and emotions, enabling more appropriate and prompt customer service. This system also improves customer satisfaction and standardizes service quality.

[0986] The "input means" is an interface for taking in information from the user.

[0987] "Means for packaging input data" is a function that compiles the imported information into a certain format (e.g., JSON format).

[0988] The "means for transmitting packaged data" is a function for transmitting packaged information to a server or other system.

[0989] The "means for receiving transmitted data" is an interface that receives information sent from other systems.

[0990] "Means for verifying received data" refers to a function that checks the authenticity and integrity of received information.

[0991] The "means for inputting verified data to the generating device" is a function for passing verified information to the generating device.

[0992] The "means for receiving a visualized diagram from a generating device" is an interface that receives a visualized diagram sent back from the generating device.

[0993] The "means for displaying the visualized diagram" is a display or a display device for showing the received visualized diagram to the user.

[0994] The "voice input means" is an interface for capturing the user's voice.

[0995] The "means for generating text from speech" is a function for converting captured speech data into text data.

[0996] "Means for recognizing emotions" refers to the function of analyzing and recognizing the user's emotions from the captured data.

[0997] The "means for dynamically adjusting the generated visualization" is a function that modifies and adjusts the visualization in real time based on the recognized emotion data.

[0998] The present invention is a system for providing prompt and appropriate customer service in brick-and-mortar stores, which integrates functions such as voice input, speech-to-text conversion, emotion recognition, visualization, and dynamic illustration adjustment.

[0999] System configuration

[1000] The system consists of the following elements:

[1001] 1. Voice input means: An interface for capturing the user's voice. Specifically, it uses a microphone built into smart glasses or smart devices.

[1002] 2. A means of generating text from speech: This is a function that converts captured speech into text data. Specifically, it uses the speech_recognition library.

[1003] 3. Means of Emotion Recognition: This is a function that analyzes and recognizes the user's emotions from text data and voice data. Specifically, it uses tools such as EmotionRecognizer.

[1004] 4. Dynamic adjustment of the generated visualization: This function corrects and adjusts the visualization in real time based on the recognized emotion data. The generated visualization is displayed on the smart device display in the store.

[1005] 5. Data transmission and reception means: This is the interface for sending packaged data to the server and receiving visualization data generated by the server. Data is sent and received using HTTP POST requests.

[1006] System action

[1007] 1. Voice input

[1008] The terminal uses the microphone of the smart glasses or smart device to capture the user's (customer's) voice. This voice data is converted into text data by the speech_recognition library.

[1009] 2. Emotion recognition

[1010] EmotionRecognizer is used to recognize emotions from text obtained from speech. For example, it recognizes the emotion "worried" from text such as "I want to try on this dress, but I'm worried about whether it will fit."

[1011] 3. Data Packaging and Transmission

[1012] The text and sentiment data are packaged in JSON format and sent to the server using an HTTP POST request, which receives it and verifies the authenticity and integrity of the data.

[1013] 4. Visualization Generation and Adjustment

[1014] The server generates a visualization based on the received data. The generated visualization is dynamically adjusted based on the emotion data. For example, if the emotion "worry" is recognized, the color of the area of ​​interest is changed to highlight it.

[1015] Specific examples

[1016] For example, if a customer says to a store associate, "I'd like to try on this dress, but I'm worried about the size," the system works as follows:

[1017] 1. Convert speech to text: "I'd like to try on this dress, but I'm worried about the size."

[1018] 2. Recognize the emotion "worry."

[1019] 3. Package the text and sentiment into JSON format and send it to the server.

[1020] 4. The server generates a visualization and adjusts it based on emotions such as "worry."

[1021] 5. The diagram is sent to a terminal and staff can check it using smart glasses or other devices.

[1022] As a result, staff can instantly understand customer needs and emotions, enabling them to respond more appropriately.

[1023] Prompt Sentence Examples

[1024] "I'd like to try on this dress, but I'm worried about whether it will fit."

[1025] summary

[1026] The system of this invention integrates voice input and emotion recognition to speed up and improve the accuracy of customer service in brick-and-mortar stores. This makes it possible to respond based on emotions, which was difficult with conventional methods, and is expected to improve customer satisfaction.

[1027] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1028] Step 1:

[1029] The user inputs voice through the microphone of the smart glasses or smart device, and the input voice data is captured by the device. Specifically, the process of capturing voice data in real time begins.

[1030] Step 2:

[1031] The device uses a speech recognition library (e.g., the speech_recognition library) to convert voice data to text data. It uses an acoustic model and a language model to convert voice data to text data. The input is voice data, and the output is the converted text data.

[1032] Step 3:

[1033] The device consumes the converted text data and uses EmotionRecognizer to recognize emotions from the text. Specifically, it analyzes the text data and determines the emotional nuances of words and phrases. The input is text data, and the output is recognized emotion data.

[1034] Step 4:

[1035] The device packages the text data and emotion data in JSON format. Specifically, it converts the text data and emotion data into a key-value format and combines them into a single data structure. The input is text data and emotion data, and the output is a JSON-formatted data package.

[1036] Step 5:

[1037] The device sends the data package to the server using an HTTP POST request. Specifically, the data package is included in the HTTP request body and sent to the specified URL. The input is the data package in JSON format, and the output is the transmission status to the server.

[1038] Step 6:

[1039] The server receives the HTTP POST request sent from the terminal and analyzes the JSON data included in the request body. Specifically, it parses the received data and checks its authenticity and integrity. The input is JSON data, and the output is the validated data.

[1040] Step 7:

[1041] The server inputs the verified data into the generative AI model to generate a visualized diagram. Specifically, text data and emotion data are provided to the AI ​​model as prompts, and a process is executed to generate appropriate visual information. The input is text data and emotion data, and the output is a visualized diagram.

[1042] Step 8:

[1043] The server dynamically adjusts the generated visualization using emotion data. Specifically, it applies changes to the color and shape of the visualization to visually express the nuances of emotions. The input is the visualization and emotion data, and the output is the adjusted visualization.

[1044] Step 9:

[1045] The server sends the adjusted visualized diagram to the terminal as an HTTP response. Specifically, the visualized diagram is included in the response body as binary data or a URL and sent as an HTTP response. The input is the adjusted visualized diagram, and the output is the transmission status to the terminal.

[1046] Step 10:

[1047] The terminal receives the response from the server and displays the visualized diagram on the user interface. Specifically, the terminal displays the received visualized diagram on a display or a head-mounted display for the user to see. The input is the adjusted visualized diagram, and the output is the display on the user interface.

[1048] 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.

[1049] 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.

[1050] 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.

[1051] [Fourth embodiment]

[1052] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1053] 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.

[1054] 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).

[1055] 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.

[1056] 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.

[1057] 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).

[1058] 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.

[1059] 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.

[1060] 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.

[1061] 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.

[1062] 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.

[1063] 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.

[1064] 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."

[1065] The present invention provides a system that instantly visualizes text and sketch data entered by a user, eliminating misunderstandings during meetings. This system is realized by a specific program.

[1066] System Overview

[1067] The system is configured by combining an input means, a means for packaging input data, a means for transmitting the packaged data, a means for receiving the transmitted data, a means for verifying the received data, a means for inputting the verified data into a generating device, a means for receiving a visualized diagram from the generating device, and a means for displaying the visualized diagram.

[1068] Explanation of program processing

[1069] 1. User: Providing Input

[1070] Users enter text through the interface of the online meeting tool, adding sketches as needed. For example, they can enter a project workflow such as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client." Once they've finished entering the information, they press the send button.

[1071] 2. Terminal: Sending data

[1072] The device packages the text and sketch data entered by the user into JSON format, and once packaged, sends the data to the server using an HTTP POST request.

[1073] 3. Server: Receiving and processing data

[1074] The server receives an HTTP POST request from the device and parses the JSON data included in the request body. It verifies that the received data is in the correct format and checks for any missing or incorrect data. It then inputs the verified data into the generation AI, which generates a visualized diagram based on the text and sketch data. This generated diagram is then returned to the server.

[1075] 4. Server: Sending results

[1076] The server constructs a response containing the generated diagram and its metadata, and sends it back to the terminal, including the diagram's binary data or its URL.

[1077] 5. Terminal: Displaying the results

[1078] The terminal receives the response from the server, parses the received data, and if visualization is successful, displays the diagram on the user interface.

[1079] 6. User: Image review and feedback

[1080] Users can review the displayed visualizations and share them with other meeting participants, gathering feedback and providing further corrections or additional input as needed.

[1081] Specific examples

[1082] As a concrete example, let's say a user wants to visualize the workflow of a new project. The user enters the following into an online conferencing tool: "Receive requirements from client -> Analyze requirements -> Forward to design team -> Start development -> Test -> Deliver to client." They also sketch boxes and arrows to indicate the process flow. When the user presses the send button, the device sends the input data in JSON format to the server. The server receives the data and passes it to the generation AI for visualization. The generated diagram is sent back from the server to the device and displayed in the user interface. The user provides a detailed explanation based on the displayed diagram, collecting feedback in the process.

[1083] In this way, the system of the present invention can minimize misunderstandings and significantly improve the efficiency of meetings.

[1084] The processing flow will be explained below.

[1085] Program processing flow and details of each step

[1086] Step 1: User enters text

[1087] Users enter their description in a text input field in the online meeting tool, and can optionally add a quick sketch to clarify the image or process they want to share during the meeting.

[1088] Step 2: User presses submit button

[1089] After completing the input, the user presses the send button to send the input data to the server, which passes the input information to the system.

[1090] Step 3: The device packages the data

[1091] The device takes the text and sketch data entered by the user and packages it into JSON format, which makes the data easier to handle on the server side.

[1092] Step 4: The device sends data to the server

[1093] The device sends an HTTP POST request containing the packaged JSON data to the server, which causes the data to reach the server.

[1094] Step 5: Server receives data

[1095] The server receives an HTTP POST request from a client and retrieves the JSON data contained in the request body, which is the trigger for the core processing of the system.

[1096] Step 6: Server verifies the data

[1097] The server validates the format and content of the data it receives, ensuring that the data is in the correct format and checking for any missing or incorrect data. After validation is complete, it proceeds to the next step.

[1098] Step 7: The server inputs data into the generated AI

[1099] The server inputs the verified data into the generative AI, which begins the process of generating a visualization based on the text and sketch data.

[1100] Step 8: The server receives the visualization

[1101] Once the AI ​​generates the visualization, it sends the diagram data back to the server, which receives the generated diagram and prepares it for further processing.

[1102] Step 9: Server constructs result response

[1103] The server constructs a response message based on the received visualization data, which includes the binary data of the diagram, the diagram URL, and the generated metadata.

[1104] Step 10: The server sends a response to the device

[1105] The server sends the constructed response to the device, which then makes the visualized results available.

[1106] Step 11: The device receives the response

[1107] The terminal receives the HTTP response from the server and obtains the data contained in the response body, which includes the data of the generated diagram.

[1108] Step 12: The terminal displays the diagram

[1109] The terminal analyzes the received visualization data and displays it on the user interface, preparing the user for visual explanations during the meeting.

[1110] Step 13: Users review and share diagrams

[1111] Users can view the visualized diagrams displayed on their devices and share them with other participants in the meeting, eliminating misunderstandings and realizing efficient communication.

[1112] In this way, the system goes through a series of steps to instantly visualize the user's input and increase the efficiency of the meeting.

[1113] Example 1

[1114] 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."

[1115] In modern online meetings, communication between participants can sometimes be skewed. In particular, when explaining complex project workflows and concepts, words and text alone are insufficient and visual materials are required. However, on-the-spot visualization and data sharing remain time-consuming and disruptive. Traditional methods lack a means to instantly generate visualizations from sketches and text, making efficient communication difficult.

[1116] 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.

[1117] In this invention, the server includes an input means, a means for packaging input data, a means for transmitting the packaged data, a means for receiving the transmitted data, a means for verifying the received data, a means for inputting the verified data to a generation device, a means for receiving a visualized diagram from the generation device, a means for displaying the visualized diagram, a visualization means based on a generative AI model for instantly visualizing text and sketch data provided by a user, and a means for analyzing the generated visualization data and displaying it on a user interface. This allows for the instant generation of visual materials during an online meeting, reducing misunderstandings among participants and enabling efficient communication.

[1118] "Input means" refers to a digital interface that allows users to input text and sketch data through the online meeting tool.

[1119] "Means for packaging input data" refers to the process of converting user-provided text or sketch data into an appropriate format, such as JSON format, and packaging it.

[1120] The "means for transmitting packaged data" is a process for transmitting packaged data to a server using a communication means such as an HTTP POST request.

[1121] The "means for receiving transmitted data" is a communication interface for receiving data transmitted from a terminal on the server side.

[1122] "Means for validating received data" refers to a data validation process to ensure that the received data is in the correct format and is free of any missing or erroneous data.

[1123] "Means for inputting verified data into a generating device" refers to the process of inputting verified data into a generative AI model.

[1124] The "means for receiving a visualization from a generating device" is a process for receiving visualization data output from a generative AI model.

[1125] The "means for displaying the visualized diagram" is a display device or software for displaying the generated visualization data on a user interface.

[1126] A "generative AI model" is an artificial intelligence model that instantly generates visualizations based on text and sketch data provided by the user.

[1127] A "visualization method" is a process that uses a generative AI model to generate a visual diagram based on input data.

[1128] This invention is a system that instantly visualizes text and sketch data entered by users during online meetings, improving meeting efficiency. This system is mainly realized using a server, terminals, and a generative AI model.

[1129] Users enter text through the interface of the online meeting tool and add sketch data as needed. This interface is designed to be intuitive and easy to use, making it easy for users to operate. For example, a user can enter a project workflow such as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client" and add a sketch using boxes and arrows.

[1130] The device packages the input text and sketch data into JSON format, which is a key-value object format that ensures data integrity and compatibility, and then sends the packaged data to the server using an HTTP POST request.

[1131] The server receives the HTTP POST request sent from the device. It parses the JSON data included in the request body and verifies that the received data is in the correct format. Specifically, it checks that all required fields are present and that the data is not corrupted. The verified data is then input into a generative AI model, which converts it into a prompt for visualization.

[1132] The generative AI model generates a visualized diagram based on a prompt. For example, the prompt might be, "Visualize the project workflow. Illustrate the process: Receive requirements from the client -> Analyze requirements -> Transfer to the design team -> Start development -> Test -> Deliver to the client." The generated diagram is then sent back to the server, which then constructs a response containing the diagram's binary data and URL and sends it to the device.

[1133] The device receives the response from the server, parses the data, and if the visualization is successful, displays the diagram on the user interface. This allows the user to review the visualized data and share it with other meeting participants. Additional feedback can be collected if necessary, allowing for further refinements.

[1134] As a concrete example, let's say a user wants to visualize the workflow of a new project. They enter "Receive requirements from client -> Analyze requirements -> Forward to design team -> Start development -> Test -> Deliver to client" into an online conferencing tool and sketch boxes and arrows to show the process flow. When they press the send button, the device sends this data in JSON format to the server. The server receives the data and passes it to the generation AI for visualization. The generated diagram is sent back from the server to the device and displayed in the user interface. The user provides a detailed explanation based on the displayed diagram, collecting feedback along the way.

[1135] In this way, the invention can minimize misunderstandings and significantly improve meeting efficiency.

[1136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1137] Step 1:

[1138] Users enter text and sketch data using the interface of the online meeting tool. For example, they can enter a project workflow such as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and add sketches using boxes and arrows. Once they're done, they press the send button.

[1139] Input: Text and sketch data

[1140] Output: Input data is passed to the terminal

[1141] Step 2:

[1142] The device receives the text and sketch data entered by the user, packages it into JSON format, and creates a key-value object with the text and sketch information. This data is then added to the body of an HTTP POST request and sent to the server.

[1143] Input: User-provided text and sketch data

[1144] Output: Data packaged in JSON format

[1145] Step 3:

[1146] The server receives the HTTP POST request sent from the terminal and parses the JSON data included in the request body. Specifically, it converts the JSON data from a string to an object format.

[1147] Input: JSON format data

[1148] Output: Parsed object data

[1149] Step 4:

[1150] The server validates that the parsed data is well-formed, ensuring that all required fields are present and that the data is not corrupted (for example, checking that text fields are not empty and that sketch data conforms to the image format).

[1151] Input: Parsed object data

[1152] Output: Validation result (success / failure) and validated data

[1153] Step 5:

[1154] The server inputs the verified data into the generative AI model. Specifically, it converts the text and sketch data into a prompt and sends it to the generative AI model. An example of a prompt would be, "Visualize the project workflow. Please illustrate the process: Receive requirements from the client -> Analyze the requirements -> Transfer to the design team -> Start development -> Test -> Deliver to the client."

[1155] Input: Validated Data

[1156] Output: The prompt passed to the generative AI model

[1157] Step 6:

[1158] The generative AI model generates a visualization based on the prompt and sends the results back to the server, where the generated diagram is presented in a visually easy-to-understand format.

[1159] Input: prompt statement

[1160] Output: Visualized diagram data

[1161] Step 7:

[1162] The server constructs a response containing the generated diagram and its metadata, and sends it to the terminal. The response includes the diagram's binary data or the diagram's URL.

[1163] Input: Visualized diagram data

[1164] Output: Response (binary data or URL)

[1165] Step 8:

[1166] The terminal receives the response from the server, parses the response body, verifies that the received data is in the correct format, and if visualization is successful, displays the diagram on the user interface. Specifically, it renders the diagram's binary data as an image.

[1167] Input: Response from the server

[1168] Output: Visualized data displayed in a user interface

[1169] Step 9:

[1170] The user can review the visualization data displayed on their device, evaluate whether the content is appropriate, share it with other meeting participants, and gather feedback as needed. Based on the feedback, they can input additional data and revisit the visualization.

[1171] Input: Visualization data displayed in the user interface

[1172] Output: User feedback and additional input data

[1173] (Application example 1)

[1174] 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."

[1175] The present invention aims to solve problems related to systems that visualize factory work processes and machine operation flows in real time, thereby eliminating misunderstandings that arise between managers and workers and improving work efficiency and accuracy.

[1176] 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.

[1177] In this invention, the server includes an input means, a means for packaging input data, a means for generating a prompt sentence using a generative AI model, a means for transmitting the packaged data, a means for verifying the received data, a means for inputting the verified data to the generation device, a means for receiving a visualized diagram from the generation device, and a means for displaying the visualized diagram. This makes it possible to visualize the factory work process in real time and reduce discrepancies in work instructions.

[1178] "Input means" refers to the means by which the system receives text and sketch data from the user.

[1179] "Means for packaging input data" refers to means for assembling received text or sketch data into an appropriate format.

[1180] A "generative AI model" is an artificial intelligence that generates visualized diagrams or images based on input data.

[1181] A "prompt" is a text command that instructs the generative AI model on how to visualize something.

[1182] The "means for transmitting packaged data" is a means for transmitting packaged data to a server.

[1183] "Means for verifying received data" refers to the means by which the server checks the format and content of the data received to determine whether it is correct.

[1184] The "means for inputting verified data into the generating device" refers to the means for passing verified data to the AI ​​model for visualization.

[1185] A "generator" is a device that generates a visualization based on verified data.

[1186] The "means for receiving a visualized diagram" is a means for receiving a generated diagram from a generating device.

[1187] The "means for displaying a visualized diagram" refers to a means for displaying the generated diagram on a user interface.

[1188] The present invention relates to a system for visualizing factory work processes and machine operation flows in real time, and is mainly composed of three main elements: a user, a terminal, and a server.

[1189] First, the user inputs the factory work process using text and sketch data via a terminal. For example, the user might input a work flow such as "take out part A -> connect to part B -> inspect the finished product -> pack the finished product," and then draw a diagram of each step using additional sketches. This input is received by the terminal's input means.

[1190] The device then packages the input text and sketch data, which is then sent to the server via an HTTP POST request via a generative AI model to generate a prompt.

[1191] The server receives the data sent from the device, analyzes and verifies it, and the verified data is input into the generative AI model, which generates a visualization based on the text and sketch data.

[1192] The generated diagram is received by the server and the results are sent back to the terminal, which displays the received diagram on the user interface, allowing the user to check the generated visualization data and gain a detailed understanding of the factory's work processes.

[1193] The main hardware and software used in this system are as follows: First, the terminal side inputs and packages data and sends HTTP requests. On the server side, a lightweight web framework called Flask is used to process requests, validate received data, and visualize it using a generative AI model. The system also handles the binary data of diagrams generated using the Pillow (PIL) library.

[1194] As a concrete example, consider a scenario in which a factory manager is designing the flow of a new line and giving instructions to new workers. For example, the manager types the following prompt:

[1195] Text: Take out part A -> Connect to part B -> Inspect the finished product -> Pack the finished product

[1196] Sketch: Draw simple work steps

[1197] Based on this prompt, the generative AI model analyzes the input data and automatically generates a visualized diagram, which is then sent back to the device from the server, allowing the manager to create new, detailed work instructions based on the diagram.

[1198] In this way, the system of the present invention makes it possible to visualize the factory work process in real time and reduce discrepancies in work instructions.

[1199] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1200] Step 1:

[1201] The user inputs text and sketch data related to the factory work process and machine operation flow. For example, the user inputs "take out part A -> connect to part B -> inspect the finished product -> pack the finished product" and sketches each step. This input data is sent to the terminal. The input data is text and sketch data, and is used in the next step.

[1202] Step 2:

[1203] The device packages the text and sketch data received from the user into an appropriate format (e.g., JSON), ensuring that each data item is correctly formatted and ready for use throughout the system. The input is text data and sketch data, and the output is the packaged data.

[1204] Step 3:

[1205] The device uses the packaged data to generate a prompt using a generative AI model. This prompt is a text command that instructs the generative AI model on how to visualize the data. Specifically, the device generates a prompt that combines text data and sketch data. The input is the packaged data, and the output is the prompt.

[1206] Step 4:

[1207] The terminal sends packaged data including the prompt text to the server via an HTTP POST request. The input is the packaged data including the prompt text, and the output is a request to the server.

[1208] Step 5:

[1209] The server receives the data sent by the device and analyzes it. Specifically, it parses the received data to verify that it is in the correct format and checks for any missing or incorrect data. The input is the data sent in the HTTP POST request, and the output is the validated data.

[1210] Step 6:

[1211] The server inputs the verified data into the generative AI model and generates a visualized diagram. The generative AI model performs calculations to create the visualization based on the prompt sentence and generates the diagram. The input is the verified data and the prompt sentence, and the output is the visualized diagram.

[1212] Step 7:

[1213] The server receives the generated visualized diagram and returns it to the terminal. Specifically, it constructs the binary data of the generated diagram or the URL of the diagram as a response and sends it to the terminal. The input is the generated diagram, and the output is the response to the terminal.

[1214] Step 8:

[1215] The terminal parses the visualized diagram received from the server and displays it on the user interface. Specifically, it interprets the received data and performs processing to display the diagram. The input is the response data from the server, and the output is the visualized diagram displayed to the user.

[1216] Step 9:

[1217] The user examines the displayed visualization data to understand the contents of the factory work instructions. If necessary, they can input additional text or sketches and repeat the process. The input is the diagram displayed to the user, and the output is the understood contents of the work instructions.

[1218] 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.

[1219] This invention is a system that instantly visualizes text and sketch data entered by users to resolve misunderstandings during meetings. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it aims to provide deeper understanding and effective communication. This system is realized by a specific program.

[1220] System Overview

[1221] The system consists of the following elements:

[1222] 1. Input means: Accepts text and sketches from the user.

[1223] 2. A means of packaging input data: Package the received data into JSON format.

[1224] 3. A means for transmitting the packaged data: Send the data to the server.

[1225] 4. Means for receiving the transmitted data: The server receives the data.

[1226] 5. Means of verifying received data: Checking the authenticity and integrity of the data.

[1227] 6. Means of inputting verified data into the generation device: Pass the data to the generation AI.

[1228] 7. Means for receiving the visualized diagram from the generating device: The visualized diagram is received by the server.

[1229] 8. Means for displaying the visualized diagram: Display the diagram on the terminal.

[1230] 9. Emotion Engine: An additional element that recognizes user emotions. It detects and analyzes emotions from text and speech.

[1231] 10. Means for adjusting diagrams based on emotion results:,Dynamic adjustment of visualized diagrams based on emotion recognition results.

[1232] Explanation of program processing

[1233] 1. User: Providing Input

[1234] Users enter text through the interface of the online conferencing tool and add sketches as needed. The emotion engine also analyzes the user's voice and text data in real time to recognize emotions. For example, a user can enter the progress of a project as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and then use the sketch function to draw a diagram showing the process.

[1235] 2. Terminal: Sending data

[1236] The device combines the text and sketch data entered by the user, as well as the emotion data recognized by the emotion engine, into a JSON package, and then sends this packaged data to the server as an HTTP POST request.

[1237] 3. Server: Receiving and processing data

[1238] The server receives an HTTP POST request from the device and parses the JSON data included in the request body. It then validates the data format and content to ensure authenticity and completeness. The validated data is passed to the generation AI, which generates a visualized diagram based on the text and sketch data. It also dynamically adjusts the diagram based on emotional data. For example, it reflects relevant emotions about the progress of the project (such as joy, confusion, or impatience) in the diagram's color and annotations.

[1239] 4. Server: Sending results

[1240] A response containing the generated visualization diagram and associated emotion information is constructed and sent back to the device, including the binary data of the generated diagram and the diagram's URL.

[1241] 5. Terminal: Displaying the results

[1242] The terminal receives the response from the server and displays a visualized diagram on the user interface, which also reflects the results of the emotion engine, allowing users to share the project progress and their own emotions with meeting participants.

[1243] 6. User: Image review and feedback

[1244] Users can view the visualized diagrams and related emotions displayed on their devices and share them with other meeting participants. Meeting participants can also understand the user's emotions in real time, leading to deeper understanding and more effective discussions.

[1245] Specific examples

[1246] As a concrete example, let's say User A is holding a meeting to announce a marketing plan for a new product. User A enters the following text sequence: "Analyze target market -> Formulate marketing strategy -> Launch advertising campaign -> Collect sales data -> Optimize strategy," and adds emotional comments to the "target market" and "advertising campaign" sections. The emotion engine recognizes the anxieties and expectations User A feels as he or she enters the information, and reflects them in the visualized diagram. As a result, meeting participants can simultaneously perceive visual and emotional information, making the discussion more concrete and effective.

[1247] In this way, the system of the present invention can instantly visualize user input and provide integrated emotional information, thereby significantly improving the efficiency and quality of meetings.

[1248] The processing flow will be explained below.

[1249] Program processing flow and details of each step

[1250] Step 1: User enters text

[1251] The user enters a description into the text input field of the online meeting tool, for example, "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client."

[1252] Step 2: User adds sketch

[1253] Users can use the sketch feature to draw simple diagrams of process flows, which complements the parts that are difficult to convey with text alone.

[1254] Step 3: The emotion engine recognizes the user's emotion

[1255] When a user speaks, the emotion engine analyzes emotions from voice and text data in real time, recognizing emotions such as anxiety, anticipation, and joy.

[1256] Step 4: User presses submit button

[1257] After completing all input, the user presses the send button to trigger transmission of the input data to the server.

[1258] Step 5: The device packages the data

[1259] The device packages the text entered by the user, sketch data, and emotion data recognized by the emotion engine into JSON format, which allows all data to be converted into a format that is easy to centrally manage on the server side.

[1260] Step 6: The device sends the data to the server

[1261] The device sends JSON data packaged as an HTTP POST request to the server, which includes text, sketches, and emotion data.

[1262] Step 7: Server receives data

[1263] The server receives the HTTP POST request sent from the device and parses the JSON data included in the request body. The received data includes all input information.

[1264] Step 8: Server Validates Data

[1265] The server checks whether the received data is in the correct format and verifies that there are no missing or incorrect data. If the data is confirmed to be correct, it proceeds to the next step.

[1266] Step 9: The server inputs data into the generated AI

[1267] The server passes the verified text and sketch data to the generative AI, which then begins the process of generating a visualization based on this data.

[1268] Step 10: Generative AI generates the visualization

[1269] The generative AI generates visualized diagrams based on input text and sketch data, and these diagrams also reflect emotional data from the emotion engine.

[1270] Step 11: The server receives the visualization

[1271] The server receives the visualized diagram generated by the generation AI, and then constructs a response containing this diagram in the following steps:

[1272] Step 12: Server constructs result response

[1273] The server constructs the visualization, emotional information, and associated metadata (e.g., generation time, resolution) in the response.

[1274] Step 13: The server sends a response to the device

[1275] The server sends the constructed response back to the device, where the visualization results are available.

[1276] Step 14: The device receives the response

[1277] The device receives the HTTP response from the server and analyzes the visualized diagram and emotion data contained in the response body.

[1278] Step 15: The device displays the image and emotional information.

[1279] The device displays the analyzed visualized diagram and emotion data on the user interface, allowing users to visually present their own emotion information along with the diagram.

[1280] Step 16: User reviews and shares the diagram and emotional information

[1281] Users can view and share the displayed visualizations and emotional information with meeting participants, which leads to deeper understanding and more effective discussions among participants.

[1282] In this way, the system instantly visualizes text and sketch data and integrates and provides emotional information, minimizing misunderstandings during meetings and enabling efficient and in-depth communication.

[1283] Example 2

[1284] 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."

[1285] In conventional online meetings, participants are prone to differing interpretations and misunderstandings, making effective communication difficult. Furthermore, limited means for accurately conveying emotional nuances often reduce the depth and quality of discussions. The present invention aims to solve these problems, eliminate misunderstandings during meetings, and promote more effective communication.

[1286] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means, a means for packaging input data, a means for transmitting the packaged data, a means for receiving the transmitted data, a means for verifying the received data, a means for inputting the verified data to the generation device, a means for receiving a visualized diagram from the generation device, a means for displaying the visualized diagram, a means for analyzing emotions and packaging data including the analysis results, and a means for adjusting the visualized diagram based on the analysis results. This makes it possible to instantly visualize the user's input and integrate emotional information, thereby achieving deeper understanding and effective communication.

[1287] "Input means" refers to an interface device for receiving text or sketches from a user.

[1288] "Means for packaging input data" refers to a device or software that converts received text or sketch data into JSON format and packages it.

[1289] The "means for transmitting packaged data" refers to a communication device or software for transferring packaged data to a server.

[1290] The "means for receiving transmitted data" refers to a communication device or software that allows the server to receive data transferred from the terminal.

[1291] "Means for verifying received data" means a device or software for checking the authenticity and integrity of received data.

[1292] "Means for inputting verified data into the generating device" refers to a device or software for providing data whose reliability and integrity have been confirmed to the generating AI.

[1293] "Means for receiving a visualization from a generating device" means a device or software for receiving visualization data output from a generating AI.

[1294] The "means for displaying the visualized diagram" refers to a device or software for displaying the received visualization data on the user's terminal.

[1295] "Means for analyzing emotions and packaging data containing the analysis results" refers to a device or software that analyzes emotions from a user's text or voice and compiles the analysis results as data.

[1296] "Means for adjusting the visualization based on the analysis results" refers to a device or software for dynamically modifying the visualization data based on the results of the sentiment analysis.

[1297] The present invention provides a system that instantly visualizes text and sketch data entered by users in online meetings and analyzes their emotions to resolve misunderstandings among participants and promote effective communication. This system is realized using specific hardware and software.

[1298] First, users enter text and add sketches as needed through the online meeting tool's interface. The emotion engine then analyzes the user's voice and text data in real time to recognize their emotions. For example, a user can enter the project progress as "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and then use the sketch function to draw a diagram showing the process.

[1299] Next, the device packages the user-entered text, sketch data, and analyzed emotion data in JSON format and sends it to the server as an HTTP POST request. Specific software used for this includes an HTTP library and a JSON library, for example.

[1300] The server receives the HTTP POST request sent from the device and analyzes the JSON data included in the request body. It then verifies the authenticity and completeness of the data and supplies the verified data to a generative AI model. This generative AI model generates a visualized diagram based on the text and sketch data, and dynamically adjusts the diagram based on the emotion data. Specific generative AI models that could be used include GPT-3 or DALL-E.

[1301] The visualization generated by the generative AI model and the associated emotional information are packaged as a response by the server and sent to the device. The device receives the response and displays the visualization on the user interface. This visualization reflects the data entered by the user and the results of the emotional analysis, allowing meeting participants to simultaneously understand the progress of the project and the user's emotions.

[1302] Users can view the visualized diagrams and related emotions and share feedback with other meeting participants. Meeting participants can understand the user's emotional information along with the diagrams in real time, leading to deeper understanding and more effective discussions.

[1303] Specific examples

[1304] As a concrete example, consider a meeting where User A is presenting a marketing plan for a new product. User A enters the following text sequence: "Analyze target market -> Formulate marketing strategy -> Launch advertising campaign -> Collect sales data -> Optimize strategy," and adds emotional comments to the "target market" and "advertising campaign" sections. The emotion engine recognizes the anxieties and expectations User A feels as he or she enters the information, and reflects them in the visualized diagram. As a result, meeting participants can simultaneously perceive visual and emotional information, making the discussion more concrete and effective.

[1305] Prompt Sentence Examples

[1306] "Describe a system for visualizing the progress of a new marketing plan and creating an emotive sketch highlighting key steps."

[1307] As described above, the system of the present invention can instantly visualize user input and provide integrated emotional information, thereby significantly improving the quality and efficiency of communication in online meetings.

[1308] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1309] Step 1: Provide user input

[1310] Users input text through the interface of the online conferencing tool and add sketches as needed. The emotion engine then analyzes the user's voice and text data in real time to recognize emotions. Specifically, the user inputs the following sequence: "Receive requirements from client -> Analyze requirements -> Transfer to design team -> Start development -> Test -> Deliver to client," and then uses the sketch function to draw a diagram showing the process. This input data becomes the initial dataset for the program.

[1311] Step 2: Sending data by device

[1312] The device packages the user-provided text, sketch data, and parsed emotion data into JSON format. It then generates an HTTP POST request to send this packaged data to the server. Specifically, the device uses a JSON library to parse the data and an HTTP library to construct the request. The input is the user-provided text, sketch, and emotion data, and the output is the JSON data sent to the server.

[1313] Step 3: Server receives and verifies data

[1314] The server receives the HTTP POST request sent from the terminal. After receiving it, it parses the JSON data included in the request body and verifies the data format and content. Specifically, the server uses a parsing library to parse the JSON data and checks whether each item matches the specified format. The input is the JSON data from the terminal, and the output is the validated data.

[1315] Step 4: Input data into the server-generated AI model

[1316] The verified data is input from the server to a generative AI model. The generative AI model generates a visualization based on the input text and sketch data, and dynamically adjusts the visualization based on the emotion data. Specifically, the generative AI model uses a pre-trained algorithm to analyze the text and draw a sketch. The input is the verified data, and the output is the generated visualization.

[1317] Step 5: Receiving the visualization

[1318] The server receives the visualization generated by the generative AI model. This visualization also includes adjustments based on the user's emotional data. Specifically, the server receives the response from the generative AI model and parses the visualization data. The input is the visualization data from the generative AI model, and the output is the visualization data ready to be sent to the device.

[1319] Step 6: Server sends results

[1320] The server packages the received visualization and related emotion information as a response body and sends it back to the device. Specifically, the server constructs an HTTP response and encodes JSON data including the binary data and URL of the visualization. The input is the visualization data, and the output is the response data to the device.

[1321] Step 7: Viewing the results in the terminal

[1322] The terminal analyzes the response received from the server and displays a visualized diagram on the user interface. This diagram reflects the results of the emotion engine. Specifically, the terminal decodes the response data and updates the display using the GUI framework. The input is the response data from the server, and the output is the displayed visualized diagram.

[1323] Step 8: User review and feedback on images

[1324] The user checks the displayed visualization and related emotional information, and exchanges opinions with other meeting participants based on that information. Specifically, the user points out each element in the visualization and advances the discussion. The final input is the displayed visualization, and the output is the user's feedback and the progress of the meeting.

[1325] (Application example 2)

[1326] 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."

[1327] Modern brick-and-mortar stores require fast and appropriate customer service. However, it is difficult for store staff to instantly grasp customers' requests and emotions, which can result in inconsistent service quality. In particular, in busy stores, customers often have to wait for long periods of time, which can lead to a decline in customer satisfaction. With conventional technology, it has been difficult to grasp customers' emotions in real time while meeting their requests.

[1328] 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 a voice input means, a means for generating text from voice, a means for recognizing emotions, and a means for dynamically adjusting the generated visualized image. This allows store staff to instantly visualize and confirm customer requests and emotions, enabling more appropriate and prompt customer service. This system also improves customer satisfaction and standardizes service quality.

[1329] The "input means" is an interface for taking in information from the user.

[1330] "Means for packaging input data" is a function that compiles the imported information into a certain format (e.g., JSON format).

[1331] The "means for transmitting packaged data" is a function for transmitting packaged information to a server or other system.

[1332] The "means for receiving transmitted data" is an interface that receives information sent from other systems.

[1333] "Means for verifying received data" refers to a function that checks the authenticity and integrity of received information.

[1334] The "means for inputting verified data to the generating device" is a function for passing verified information to the generating device.

[1335] The "means for receiving a visualized diagram from a generating device" is an interface that receives a visualized diagram sent back from the generating device.

[1336] The "means for displaying the visualized diagram" is a display or a display device for showing the received visualized diagram to the user.

[1337] The "voice input means" is an interface for capturing the user's voice.

[1338] The "means for generating text from speech" is a function for converting captured speech data into text data.

[1339] "Means for recognizing emotions" refers to the function of analyzing and recognizing the user's emotions from the captured data.

[1340] The "means for dynamically adjusting the generated visualization" is a function that modifies and adjusts the visualization in real time based on the recognized emotion data.

[1341] The present invention is a system for providing prompt and appropriate customer service in brick-and-mortar stores, which integrates functions such as voice input, speech-to-text conversion, emotion recognition, visualization, and dynamic illustration adjustment.

[1342] System configuration

[1343] The system consists of the following elements:

[1344] 1. Voice input means: An interface for capturing the user's voice. Specifically, it uses a microphone built into smart glasses or smart devices.

[1345] 2. A means of generating text from speech: This is a function that converts captured speech into text data. Specifically, it uses the speech_recognition library.

[1346] 3. Means of Emotion Recognition: This is a function that analyzes and recognizes the user's emotions from text data and voice data. Specifically, it uses tools such as EmotionRecognizer.

[1347] 4. Dynamic adjustment of the generated visualization: This function corrects and adjusts the visualization in real time based on the recognized emotion data. The generated visualization is displayed on the smart device display in the store.

[1348] 5. Data transmission and reception means: This is the interface for sending packaged data to the server and receiving visualization data generated by the server. Data is sent and received using HTTP POST requests.

[1349] System action

[1350] 1. Voice input

[1351] The terminal uses the microphone of the smart glasses or smart device to capture the user's (customer's) voice. This voice data is converted into text data by the speech_recognition library.

[1352] 2. Emotion recognition

[1353] EmotionRecognizer is used to recognize emotions from text obtained from speech. For example, it recognizes the emotion "worried" from text such as "I want to try on this dress, but I'm worried about whether it will fit."

[1354] 3. Data Packaging and Transmission

[1355] The text and sentiment data are packaged in JSON format and sent to the server using an HTTP POST request, which receives it and verifies the authenticity and integrity of the data.

[1356] 4. Visualization Generation and Adjustment

[1357] The server generates a visualization based on the received data. The generated visualization is dynamically adjusted based on the emotion data. For example, if the emotion "worry" is recognized, the color of the area of ​​interest is changed to highlight it.

[1358] Specific examples

[1359] For example, if a customer says to a store associate, "I'd like to try on this dress, but I'm worried about the size," the system works as follows:

[1360] 1. Convert speech to text: "I'd like to try on this dress, but I'm worried about the size."

[1361] 2. Recognize the emotion "worry."

[1362] 3. Package the text and sentiment into JSON format and send it to the server.

[1363] 4. The server generates a visualization and adjusts it based on emotions such as "worry."

[1364] 5. The diagram is sent to a terminal and staff can check it using smart glasses or other devices.

[1365] As a result, staff can instantly understand customer needs and emotions, enabling them to respond more appropriately.

[1366] Prompt Sentence Examples

[1367] "I'd like to try on this dress, but I'm worried about whether it will fit."

[1368] summary

[1369] The system of this invention integrates voice input and emotion recognition to speed up and improve the accuracy of customer service in brick-and-mortar stores. This makes it possible to respond based on emotions, which was difficult with conventional methods, and is expected to improve customer satisfaction.

[1370] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1371] Step 1:

[1372] The user inputs voice through the microphone of the smart glasses or smart device, and the input voice data is captured by the device. Specifically, the process of capturing voice data in real time begins.

[1373] Step 2:

[1374] The device uses a speech recognition library (e.g., the speech_recognition library) to convert voice data to text data. It uses an acoustic model and a language model to convert voice data to text data. The input is voice data, and the output is the converted text data.

[1375] Step 3:

[1376] The device consumes the converted text data and uses EmotionRecognizer to recognize emotions from the text. Specifically, it analyzes the text data and determines the emotional nuances of words and phrases. The input is text data, and the output is recognized emotion data.

[1377] Step 4:

[1378] The device packages the text data and emotion data in JSON format. Specifically, it converts the text data and emotion data into a key-value format and combines them into a single data structure. The input is text data and emotion data, and the output is a JSON-formatted data package.

[1379] Step 5:

[1380] The device sends the data package to the server using an HTTP POST request. Specifically, the data package is included in the HTTP request body and sent to the specified URL. The input is the data package in JSON format, and the output is the transmission status to the server.

[1381] Step 6:

[1382] The server receives the HTTP POST request sent from the terminal and analyzes the JSON data included in the request body. Specifically, it parses the received data and checks its authenticity and integrity. The input is JSON data, and the output is the validated data.

[1383] Step 7:

[1384] The server inputs the verified data into the generative AI model to generate a visualized diagram. Specifically, text data and emotion data are provided to the AI ​​model as prompts, and a process is executed to generate appropriate visual information. The input is text data and emotion data, and the output is a visualized diagram.

[1385] Step 8:

[1386] The server dynamically adjusts the generated visualization using emotion data. Specifically, it applies changes to the color and shape of the visualization to visually express the nuances of emotions. The input is the visualization and emotion data, and the output is the adjusted visualization.

[1387] Step 9:

[1388] The server sends the adjusted visualized diagram to the terminal as an HTTP response. Specifically, the visualized diagram is included in the response body as binary data or a URL and sent as an HTTP response. The input is the adjusted visualized diagram, and the output is the transmission status to the terminal.

[1389] Step 10:

[1390] The terminal receives the response from the server and displays the visualized diagram on the user interface. Specifically, the terminal displays the received visualized diagram on a display or a head-mounted display for the user to see. The input is the adjusted visualized diagram, and the output is the display on the user interface.

[1391] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1392] 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.

[1393] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1394] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1395] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1396] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1397] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1398] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1399] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1400] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1401] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1402] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1403] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1404] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1405] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1406] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1407] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1408] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1409] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1410] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1411] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1412] The following is further disclosed regarding the above embodiment.

[1413] (Claim 1)

[1414] An input means;

[1415] means for packaging input data;

[1416] means for transmitting the packaged data;

[1417] means for receiving the transmitted data;

[1418] means for verifying received data;

[1419] means for inputting verified data into a generating device;

[1420] means for receiving a visualization from the generating device;

[1421] a means for displaying the visualization;

[1422] A system including:

[1423] (Claim 2)

[1424] 10. The system of claim 1, wherein the system receives text input from the user through an interface integrated with the online conferencing tool.

[1425] (Claim 3)

[1426] The system of claim 1, wherein the system receives and visualizes a drawing drawn by a user using a sketch function.

[1427] "Example 1"

[1428] (Claim 1)

[1429] An input means;

[1430] means for packaging input data;

[1431] means for transmitting the packaged data;

[1432] means for receiving the transmitted data;

[1433] means for verifying received data;

[1434] means for inputting verified data into a generating device;

[1435] means for receiving a visualization from the generating device;

[1436] a means for displaying the visualization;

[1437] a visualization means based on a generative AI model for instantly visualizing user-provided text and sketch data;

[1438] means for analyzing the generated visualization data and displaying it in a user interface;

[1439] A system including:

[1440] (Claim 2)

[1441] 10. The system of claim 1, wherein the system receives text input from the user through an interface integrated with the online conferencing tool.

[1442] (Claim 3)

[1443] The system of claim 1, wherein the system receives and visualizes a drawing drawn by a user using a sketch function.

[1444] "Application Example 1"

[1445] (Claim 1)

[1446] An input means;

[1447] means for packaging input data;

[1448] a means for generating a prompt sentence using a generative AI model;

[1449] means for transmitting the packaged data;

[1450] means for receiving the transmitted data;

[1451] means for verifying received data;

[1452] means for inputting verified data into a generating device;

[1453] means for receiving a visualization from the generating device;

[1454] a means for displaying the visualization;

[1455] A system including:

[1456] (Claim 2)

[1457] 10. The system of claim 1, which visualizes factory work processes in real time and reduces discrepancies in work instructions.

[1458] (Claim 3)

[1459] 10. The system of claim 1, wherein the system automatically generates visualization data based on text and sketch data entered by a factory manager.

[1460] "Example 2: Combining Emotion Engines"

[1461] (Claim 1)

[1462] An input means;

[1463] means for packaging input data;

[1464] means for transmitting the packaged data;

[1465] means for receiving the transmitted data;

[1466] means for verifying received data;

[1467] means for inputting verified data into a generating device;

[1468] means for receiving a visualization from the generating device;

[1469] a means for displaying the visualization;

[1470] means for analyzing emotions and packaging data containing the analysis results;

[1471] a means of adjusting the visualization based on the analysis results;

[1472] A system including:

[1473] (Claim 2)

[1474] 10. The system of claim 1, wherein the system receives text input from the user through an interface integrated with the online conferencing tool.

[1475] (Claim 3)

[1476] The system of claim 1, wherein the system receives and visualizes a drawing drawn by a user using a sketch function.

[1477] "Application example 2 when combining emotion engines"

[1478] (Claim 1)

[1479] An input means;

[1480] means for packaging input data;

[1481] means for transmitting the packaged data;

[1482] means for receiving the transmitted data;

[1483] means for verifying received data;

[1484] means for inputting verified data into a generating device;

[1485] means for receiving a visualization from the generating device;

[1486] a means for displaying the visualization;

[1487] A voice input means;

[1488] a means for generating text from speech;

[1489] A means of recognizing emotions;

[1490] means for dynamically adjusting the generated visualization;

[1491] A system including:

[1492] (Claim 2)

[1493] 10. The system of claim 1, wherein the system is installed on a smart device and receives voice input and provides real-time visualization during customer interactions in a store.

[1494] (Claim 3)

[1495] 10. The system of claim 1, further comprising: adjusting the visualization based on the emotion recognition results to reflect the customer's needs and emotions. [Explanation of symbols]

[1496] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. An input means; means for packaging input data; means for transmitting the packaged data; means for receiving the transmitted data; means for verifying received data; means for inputting verified data into a generating device; means for receiving a visualization from the generating device; a means for displaying the visualization; A system including:

2. 10. The system of claim 1, wherein the system receives text input from the user through an interface integrated with the online conferencing tool.

3. The system of claim 1 , wherein the system receives and visualizes a drawing drawn by a user using a sketch function.

Images