System

Abstract

A system is provided.SOLUTION: The system includes a means for analyzing the extracted natural language data, a means for converting the business process into a graphic on the basis of the analysis result, and a means for sharing the generated graphic with the user and the client.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's world, where teleworking and free address systems are becoming more common, there are increasing opportunities for work-related instructions and requests to be given solely via email, chat, or voice. This often leads to anxiety and difficulty in accurately understanding the instructions and requests received. Furthermore, new graduates and young employees who lack the skills and experience to visualize work content face the problem of being unable to do so efficiently. In such situations, miscommunication and oversight of work are more likely to occur. The present invention aims to provide a means to solve these problems. [Means for solving the problem]

[0005] The present invention provides a system that analyzes extracted natural language data, visualizes a business process based on the analysis results, and shares the generated diagram with users and clients. This system includes the following means: a means for analyzing extracted natural language data, a means for visualizing a business process based on the analysis results, and a means for sharing the generated diagram. The system also includes a means for inputting the extracted natural language data as speech and converting it into text. Furthermore, by including a means for using natural language processing technology when analyzing the extracted natural language data, more accurate analysis and visualization can be achieved, improving business efficiency and promoting understanding.

[0006] "Extracted natural language data" refers to data expressed in natural language that is input by a user and that the system analyzes.

[0007] "Means of analyzing" refers to the process by which the system receives extracted natural language data, examines and evaluates its content, and identifies and extracts intent, actions, and relevant elements.

[0008] "Graphical representation" refers to the process of automatically generating diagrams such as flow charts and structure diagrams to visually represent business processes and flows based on analyzed natural language data.

[0009] "Means of sharing" refers to communication and display means for providing the generated figures to the user and requester, and for enabling confirmation and correction.

[0010] "Means for inputting as voice" refers to a process in which a user provides instructions or requests to the system as voice input, and the voice data is converted into text data.

[0011] "Means using natural language processing technology" refers to algorithms and models for analyzing extracted natural language data using technology that enables computers to understand and analyze human natural language. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] The present invention provides a system that analyzes extracted natural language data, visualizes business processes based on the analysis results, and shares the generated diagrams with users and clients. An embodiment of this system will be described in detail.

[0034] Server Roles

[0035] The server plays a central role in the system. First, it receives natural language data entered by the user from the terminal. Natural language data can be entered in the form of voice or text, but in the case of voice, it must first be converted into text data using voice recognition technology.

[0036] The server is equipped with an analysis module that incorporates natural language processing (NLP) technology. This analysis module analyzes the input natural language data and extracts important information, including the purpose of the instruction or request, the required action, and the elements involved (people, processes, etc.).

[0037] Next, the server uses the analyzed data to generate flow diagrams and structure diagrams using a module that visualizes business processes. This module learns from past business frameworks and visualized data, and automatically creates appropriate diagrams.

[0038] The generated diagram is saved as a diagram file and then shared with the user and requester. The server sends this diagram file via email or a file sharing service, making it easy to check the configuration.

[0039] Device Role

[0040] The terminal used by the user provides an interface for inputting instructions or requests. Input methods include text input, and instructions can be input through a text box or a chat interface, for example.

[0041] There is also a voice input option, where the user can use the microphone to input commands by voice, and the device will use voice recognition technology to convert this speech into text and send it to the server.

[0042] User Roles

[0043] The user's role begins by inputting instructions and requests via the terminal. The user then receives and checks the diagram generated by the server. The user checks whether there are any misunderstandings or missing information in the generated diagram, and requests corrections as necessary.

[0044] As a specific example, consider the case where a user inputs text into a terminal to instruct the start of a new project. The input text data is sent to a server, which uses NLP technology to analyze the content of the instruction. Based on the analysis results, the server generates a flow diagram for starting the project. The generated flow diagram is shared with the requester via email, who then confirms its contents. The confirmed diagram can support the user's work progress and prevent misunderstandings.

[0045] The main feature of this system is that the process of analyzing and visualizing natural language data is automated. This allows even new graduates and junior employees to smoothly understand and proceed with their work. This invention contributes to the efficiency of business communication and the promotion of accurate understanding.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The user inputs instructions or requests into the terminal. Input can be selected as either text input via a text box or voice input using a microphone.

[0049] Step 2:

[0050] In the case of voice input, the device captures the voice and converts it into text data using voice recognition technology, which is then used in subsequent processing steps.

[0051] Step 3:

[0052] The terminal sends the entered text data to the server, which receives the text data and starts the analysis process.

[0053] Step 4:

[0054] The server analyzes the received text data using natural language processing (NLP) technology. This analysis includes grammatical analysis, word recognition, and semantic analysis. Specifically, it extracts the purpose of the instruction or request, the required actions, and related elements (people, processes, etc.).

[0055] Step 5:

[0056] The server creates a diagram of the business process based on the analysis results. The server references past business frameworks and existing diagram data to automatically generate appropriate flow diagrams and structure diagrams. This diagram definition includes the relationships between related elements and the order of actions.

[0057] Step 6:

[0058] The generated flow diagrams and structure diagrams are saved as files by the server in common image formats (e.g. PNG, JPEG) or vector data formats (e.g. SVG).

[0059] Step 7:

[0060] The server shares the generated shape with the user and the requester. Sharing can be done via email or using a file sharing service. The generated shape file is attached to the email and sent to the requester.

[0061] Step 8:

[0062] The user and requester review the diagram and check the accuracy of the content. They provide corrections and additional feedback as needed. If there are any problems with the diagram content, the user can re-enter instructions or requests for a revised version, and the server will regenerate it.

[0063] Step 9:

[0064] The final confirmed diagram is used as a tool to support the user in progressing with their work. The generated diagram is used as a means to help users understand the business process and improve work efficiency.

[0065] Example 1

[0066] 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."

[0067] Modern business processes contain a large amount of information, and efficiently analyzing and visualizing it is important for smooth communication between teams and individuals. However, current systems often require manual analysis and visualization of natural language data, which is time-consuming and prone to human error. This invention provides a system that eliminates these inefficiencies and enables accurate and rapid visualization and sharing of business processes.

[0068] 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.

[0069] In this invention, the server includes: a means for a user to input natural language data from a terminal; a means for transmitting the input natural language data to the server; a means for the server to analyze the natural language data using natural language processing technology; a means for diagramming a business process based on the analysis results; and a means for sharing the generated diagram with the user and the requester. This makes it possible to automatically analyze the natural language data input by the user, generate an appropriate business process diagram, and quickly share it.

[0070] "Natural language data" refers to information expressed by humans using normal language, such as text or speech.

[0071] "Terminal" refers to an electronic device through which a user inputs natural language data.

[0072] "Server" refers to a central computer system that receives input natural language data and performs analysis and visualization.

[0073] "Natural language processing technology" refers to information processing technology for analyzing human language and understanding meaning and intent.

[0074] "Analysis" refers to the process of extracting and understanding important information from natural language data.

[0075] A "business process" refers to a series of activities and procedures for carrying out a specific business task.

[0076] "Graphing" refers to the presentation of analytical results in the form of graphs or figures that are visually easy to understand.

[0077] "Sharing" refers to the act of sharing the generated graphics and information with other users or requesters.

[0078] "Speech recognition technology" refers to technology for converting voice data into text.

[0079] "Business framework data" refers to data that records past business procedures and activities.

[0080] A "shape generation library" refers to a collection of software for generating shapes programmatically.

[0081] A "flow diagram" is a diagram that visually shows a series of steps or processes using arrows and symbols.

[0082] The present invention provides a system for analyzing natural language data, graphically representing a business process, and sharing the generated graphical representation with users and clients. Specific embodiments of the system are described below.

[0083] Server Roles

[0084] The server plays a central role in the system. First, it receives the natural language data entered by the user from the device. If the natural language data is voice data, it is converted into text data using voice recognition technology such as the Google Cloud Speech-to-Text API.

[0085] The server is equipped with an analysis module that uses natural language processing (NLP) techniques such as BERT and the Transformers library. This analysis module analyzes the input natural language data and extracts important information, such as the purpose of the instruction or request, the required action, and the elements involved (people, processes, etc.).

[0086] Next, the server generates flow diagrams and structure diagrams based on the analyzed data using a diagram generation library such as Graphviz. This module learns from past business framework data and automatically creates appropriate diagrams.

[0087] The generated diagram is saved as an image file (e.g., PNG format) and then shared with the user and requester. The server sends this diagram file via email, Google Drive, or Dropbox to make it easier to check the configuration.

[0088] Device Role

[0089] The device used by the user provides an interface for inputting instructions or requests. In the case of text input, instructions are entered through a text box or a chat interface, for example, a web form or a messaging app interface.

[0090] It also offers a voice input option: the user can use the microphone to input commands by voice, and the device will convert this speech into text using speech recognition technology (e.g., Amazon Transcribe) and send it to the server.

[0091] User Roles

[0092] The user's role begins by inputting instructions and requests via the terminal. Then, the user receives the diagram generated by the server and checks its contents. The user checks whether there are any misunderstandings or missing information in the generated diagram, and requests corrections as necessary.

[0093] Specific examples

[0094] For example, consider the case where a user inputs a command to start a new project into a terminal as text. The user inputs "Please start a new project" into the text box. This text data is sent to the server.

[0095] The server uses NLP technology to analyze the instructions and generate a "Start a New Project" flow chart based on the analysis results. The generated flow chart is shared with the requester via email. The requester can review the content and provide feedback if necessary.

[0096] An example of a prompt for a generative AI model is:

[0097] "Generate a flow diagram showing the steps to take when starting a new project. Analyze the following instructions: Project name: AI system development, Start date: October 1, 2023, First task: Hold a requirements definition meeting."

[0098] The above is a specific embodiment of the present invention. Through this system, the process of analyzing and visualizing natural language data is automated, making it possible to improve the efficiency of business communication and promote accurate understanding.

[0099] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0100] Step 1:

[0101] Input of natural language data via terminal

[0102] The user inputs natural language data from the device. Input methods include text input and voice input. For example, the user types "Start a new project" into a text box. In the case of voice input, the user speaks the same content into the microphone. The device converts the voice data into text data using voice recognition technology (for example, Google Cloud Speech-to-Text API).

[0103] Input: Natural language data: "Start a new project"

[0104] Output: Natural language data in text format

[0105] Step 2:

[0106] Sending data from the device to the server

[0107] The device sends the generated text data to the server. The HTTPS protocol is used for communication to ensure data security. For example, the device sends the text data "Start a new project" to the server via HTTPS.

[0108] Input: Natural language data in text format

[0109] Output: Natural language data sent to the server

[0110] Step 3:

[0111] Data analysis by server

[0112] The server passes the received text data to a Natural Language Processing (NLP) module, which uses BERT and Transformers libraries to analyze the data and extract key information. For example, it can analyze a request like "Start a new project" to extract the goal, required actions, and related elements.

[0113] Input: Natural language data received by the server

[0114] Output: Analyzed key information (objectives, actions, stakeholders, etc.)

[0115] Step 4:

[0116] Server-based business process visualization

[0117] The server uses a diagram generation library such as Graphviz to visualize the business process based on the analyzed data. For example, it generates a flow diagram for "Starting a new project" based on the analysis results. The flow diagram includes specific steps such as "Hold a requirements definition meeting."

[0118] Input: Parsed data (critical information)

[0119] Output: Business process graphic file (e.g. PNG format)

[0120] Step 5:

[0121] Saving and sharing generated shape files

[0122] The server saves the generated graphic files and shares them with the user or requester via email, Google Drive, or Dropbox. For example, the generated flow diagram is saved as a PNG file and sent to the requester's email address.

[0123] Input: Business process shape file

[0124] Output: Shared geometry files (sent to users and requesters)

[0125] Step 6:

[0126] User shape confirmation and feedback

[0127] The user checks the received graphic file and provides feedback as necessary. If there is a misunderstanding or missing information, they can request corrections again. For example, if the user checks the received flow diagram and determines that there is an error in the details of the "Requirements Definition Meeting," they will send an email with feedback to that effect.

[0128] Input: Received shape file

[0129] Output: Feedback provided (requests for corrections if necessary)

[0130] (Application example 1)

[0131] 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."

[0132] Currently, many factories are automating their business processes, but there is no system that can understand instructions in natural language, accurately visualize the business process as a flow diagram, and control robots based on that diagram. There is also a need to quickly and accurately share the generated diagrams and instructions with users and clients. The objective of this invention is to solve this technical deficiency.

[0133] 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.

[0134] In this invention, the server includes means for analyzing the extracted natural language data, means for diagramming the business process based on the analysis results, means for sharing the generated diagram with the user and the client, and means for controlling the robot based on the generated diagram, thereby enabling accurate understanding of instructions in natural language, automatic generation of a process diagram, and automatic control of the robot based on the diagram.

[0135] "Extracted natural language data" refers to instructions or requests entered by a user in voice or text format.

[0136] The "analysis means" refers to natural language processing technology used to interpret input natural language data and understand its content.

[0137] "Means for visualizing business processes" refers to technology that generates flow diagrams and structure diagrams that visually represent business flows and processes based on analyzed data.

[0138] "Generated diagrams" are flow diagrams or structure diagrams that are automatically created based on analyzed natural language data.

[0139] "Means for sharing with users and requesters" refers to the technology and functions for sending the generated figures to users and requesters via email or file sharing services.

[0140] "Means for controlling robots" refers to the technology and functions that give specific operational instructions to robots in the factory based on the information in the generated flow diagrams and structural diagrams.

[0141] The present invention is a system that analyzes extracted natural language data, visualizes business processes, and shares the generated diagrams with users and clients. An embodiment of this system will be described in detail below.

[0142] Server Roles

[0143] The server plays a central role in the system. First, the server receives natural language data input by the user from their device. The natural language data is input in the form of voice or text, and in the case of voice, it is converted into text data using voice recognition technology. For voice recognition, common voice recognition technology or APIs are used.

[0144] The server then uses an analysis module that includes natural language processing (NLP) technology to analyze the input natural language data and extract key information, such as the purpose of the instruction or request, the required action, and the elements involved (people, processes, etc.). The NLP technology used can be the Google Cloud Natural Language API.

[0145] Based on the analyzed data, a module that visualizes business processes is used to generate flow diagrams and structure diagrams. This module learns from past business frameworks and visualized data, and automatically creates appropriate diagrams. Visualization tools such as Graphviz are used for visualization.

[0146] The generated diagram is saved as a diagram file and then shared with the user and the requester. The server sends this diagram file via email or file sharing service, making it easier to check the configuration. It also includes a means to control the robot based on the generated diagram. ROS (Robot Operating System) can be used to control the robot.

[0147] Device Role

[0148] The device used by the user provides an interface for inputting instructions and requests. Input methods include text input, for example, inputting instructions through a text box or a chat interface. Voice input is also available. When the user inputs instructions by voice using a microphone, the device uses voice recognition technology to convert this voice into text and send it to the server.

[0149] User Roles

[0150] The user's role begins by inputting instructions and requests via a terminal. The user then receives and checks the diagram generated by the server. The robot begins to operate based on the generated diagram, and the business process progresses. The user checks the diagram for misunderstandings or missing information, and requests corrections as necessary.

[0151] As a specific example, when a new production line is added in a factory, instructions are input by voice, such as "Please start the inspection process on production line 1. Next, please pack the finished products." The voice data is converted into text and, after NLP analysis, a business flow diagram for the inspection process and packaging process is generated. The factory manager reviews the generated flow diagram and requests any necessary revisions. The following are examples of prompt sentences that could be considered:

[0152] "Please parse the following natural language instructions and generate a workflow diagram.

[0153] Instructions: Start the inspection process on production line 1. Then, package the finished products.

[0154] Generate a flow diagram using Graphviz based on the analysis results of the generative AI.

[0155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0156] Step 1:

[0157] A user uses a device to input natural language data. This data can be input in the form of voice or text. For voice input, the user uses a microphone to input instructions, and the device converts the speech to text using speech recognition technology such as Google Speech-to-Text. For text input, the user inputs instructions through a chat interface or text box.

[0158] Input: Natural language audio or text data

[0159] Output: Text data

[0160] Step 2:

[0161] The device sends text data to the server, which then analyzes it using the Google Cloud Natural Language API. The API analyzes the instruction and extracts important information (the purpose of the instruction, the required action, and the relevant elements). The server receives the analysis results and continues processing.

[0162] Input: Text data

[0163] Output: Analysis results (extracted important information)

[0164] Step 3:

[0165] The server visualizes the business process based on the analysis results. Here, a visualization tool such as Graphviz is used to automatically generate a flow diagram and structure diagram of the business process. The server saves the generated flow diagram and structure diagram and proceeds to the next step.

[0166] Input: Analysis results

[0167] Output: Flow diagram or structure diagram

[0168] Step 4:

[0169] The server shares the generated graphic files with the user and requester. To do this, the server sends the graphic files via email or a file sharing system. The recipients check the graphic files and provide feedback if necessary.

[0170] Input: Flow diagram or structure diagram

[0171] Output: Sharing shape files

[0172] Step 5:

[0173] The server executes the means to control the robot based on the generated diagram, and sends specific operation instructions to the robot using ROS (Robot Operating System). The server controls the robot so that it automatically executes the business process according to each step of the generated flow diagram.

[0174] Input: Flow diagram or structure diagram

[0175] Output: Control instructions to the robot

[0176] Through the above steps, a system is realized in which a user inputs instructions in natural language, a business process is visualized based on the instructions, and a robot automatically operates based on the visualized instructions.

[0177] 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.

[0178] The present invention combines a system that analyzes extracted natural language data, visualizes business processes based on the analysis results, and shares the generated diagrams with users and clients, with an emotion engine that recognizes user emotions. An embodiment of this system will be described in detail.

[0179] Server Roles

[0180] The server plays a central role in the system. First, it receives natural language data entered by the user from the terminal. Natural language data can be entered in the form of voice or text, but in the case of voice, it must first be converted into text data using voice recognition technology.

[0181] The server is equipped with an analysis module that incorporates natural language processing (NLP) technology. This analysis module analyzes the input natural language data and extracts important information, including the purpose of the instruction or request, the required action, and related elements (people, processes, etc.).

[0182] Next, the server uses the analysis results to generate flow diagrams and structure diagrams using a module that visualizes business processes. This module learns from past business frameworks and visualized data, and automatically creates appropriate diagrams.

[0183] The generated diagram is saved as a diagram file and then shared with the user and requester. The server sends this diagram file via email or a file sharing service, making it easy to check the configuration.

[0184] Furthermore, the present invention incorporates an emotion engine that extracts emotions from the user's natural language data and incorporates them as part of the analysis. This emotion data is used to influence the visualization of business processes, for example, to generate diagrams that reduce stress or increase motivation.

[0185] Device Role

[0186] The terminal used by the user provides an interface for inputting instructions or requests. Input methods include text input, and instructions can be input through a text box or a chat interface, for example.

[0187] There is also a voice input option, where the user can use the microphone to input commands by voice, and the device will use voice recognition technology to convert this speech into text and send it to the server.

[0188] User Roles

[0189] The user's role begins by inputting instructions and requests via the terminal. The user then receives and checks the diagram generated by the server. The user checks whether there are any misunderstandings or missing information in the generated diagram, and requests corrections as necessary.

[0190] Furthermore, the system also checks the analysis results generated by the emotion engine. For example, if it determines that the user's input indicates stress, it will modify the diagram based on that information to make the work process go more smoothly.

[0191] As a specific example, consider the case where a user inputs a command to start a new project into a terminal. The input text data is sent to the server, which analyzes the command using NLP technology and an emotion engine. Based on the analysis results, the server generates a flow diagram for starting the project. The generated flow diagram is shared with the requester via email, who then confirms its contents. The confirmed diagram can support the user's work progress and prevent misunderstandings. Furthermore, emotion data is used to adjust the business process so that it takes the user's emotions into consideration.

[0192] The main features of this system are that the process of analyzing natural language data and visualizing it is automated, and that emotion recognition and the results are reflected in the visualization. This allows even new graduates and junior employees to smoothly understand and proceed with their work. This invention contributes to the efficiency of business communication and the promotion of accurate understanding.

[0193] The processing flow will be explained below.

[0194] Step 1:

[0195] The user inputs instructions or requests into the terminal. Input can be selected as either text input via a text box or voice input using a microphone.

[0196] Step 2:

[0197] In the case of voice input, the device captures the voice and converts it into text data using voice recognition technology, which is then used in subsequent processing steps.

[0198] Step 3:

[0199] The terminal sends the entered text data to the server, which receives the text data and starts the analysis process.

[0200] Step 4:

[0201] The server analyzes the received text data using natural language processing (NLP) technology. This analysis includes grammatical analysis, word recognition, and semantic analysis. Specifically, it extracts the purpose of the instruction or request, the required actions, and related elements (people, processes, etc.).

[0202] Step 5:

[0203] The server performs emotion analysis on the extracted data using an emotion engine, which recognizes the user's emotional state (e.g., stress, satisfaction, excitement, etc.) and incorporates this information into the subsequent visualization process.

[0204] Step 6:

[0205] The server visualizes business processes based on the analysis results and emotional data. The server references past business frameworks and existing graphic data to automatically generate appropriate flow diagrams and structure diagrams. These diagrams include the relationships between related elements and the order of actions. Furthermore, the diagrams are adjusted according to the user's emotional state, incorporating elements that reduce stress and increase motivation.

[0206] Step 7:

[0207] The generated flow diagrams and structure diagrams are saved as files by the server in common image formats (e.g. PNG, JPEG) or vector data formats (e.g. SVG).

[0208] Step 8:

[0209] The server shares the generated shape with the user and the requester. Sharing can be done via email or using a file sharing service. The generated shape file is attached to the email and sent to the requester.

[0210] Step 9:

[0211] The user and requester review the diagram and check the accuracy of the content. They provide corrections and additional feedback as needed. If there are any problems with the diagram content, the user can re-enter instructions or requests for a revised version, and the server will regenerate it.

[0212] Step 10:

[0213] The final confirmed diagram is used as a tool to support the user's work progress. The generated diagram helps users to understand the work process more easily and is used as a means to improve work efficiency. In addition, the diagram created using emotion data takes into account the user's emotional state, contributing to an improvement of the work environment.

[0214] Example 2

[0215] 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."

[0216] Conventional business process management systems lack the ability to efficiently analyze natural language data and automatically visualize it. Furthermore, they do not optimize business processes by taking into account user emotions. This has led to problems such as reduced work efficiency, stress, and increased misunderstandings.

[0217] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for analyzing extracted natural language data, a means for diagramming a business process based on the analysis result, a means for sharing the generated diagram with the user and the requester, a means for recognizing the user's emotion, and a means for adjusting the diagram of the business process based on the emotion recognition. This enables efficient analysis of natural language data and automatic diagramming of the business process taking emotion into consideration.

[0218] "Extracted natural language data" refers to human language data input in speech or text format.

[0219] The "means for analyzing" is the part of the system that includes natural language processing technology to identify important information from natural language data (such as the purpose of the instruction, the action required, and the people and processes involved).

[0220] "Means for visualizing business processes" refers to an automatically generated module that visually represents business steps and flows based on analyzed information.

[0221] "Generated shapes" refers to flow diagrams and structure diagrams that visually represent business processes.

[0222] "Means for sharing with users and requesters" refers to a function for sending the generated graphic files via email or file sharing services.

[0223] "Means for recognizing emotions" refers to a sentiment analysis engine for extracting emotional information from the user's natural language data.

[0224] "Means for adjusting the business process diagram based on emotion recognition" refers to an adjustment function for adapting the business process to the user's emotions using the extracted emotion data.

[0225] The present invention combines a system that analyzes extracted natural language data, visualizes business processes, and shares the generated diagrams with users and clients, with an emotion engine that recognizes user emotions. This system can be implemented as follows.

[0226] First, the server, which plays a central role in this system, receives natural language data input by the user from their device. Natural language data can be input in the form of voice or text, but if it is voice, it must be converted into text data using voice recognition technology. This conversion is achieved using voice recognition services such as Google Cloud Speech-to-Text and IBM Watson Speech to Text.

[0227] The server is then equipped with an analysis module that includes natural language processing (NLP) technology. This module uses Python NLP libraries such as spaCy and NLTK to analyze the input natural language data and extract important information, such as the purpose of the instruction, required actions, and related people and processes.

[0228] The server then runs a module that visualizes the business process based on the analysis results. This module uses a deep learning model to learn from past business data and business frameworks, and generates appropriate flow diagrams and structure diagrams. The visualization can be done using tools such as Diagram or Lucidchart API.

[0229] The generated shape is saved as a shape file and then shared with the user and the requester. The server sends this shape file via email or a file sharing service (e.g., Google Drive, Dropbox, etc.), making it easy to check the contents.

[0230] Furthermore, the present invention incorporates an emotion engine that extracts emotions from the user's natural language data using Microsoft Azure's Text Analytics for sentiment analysis, IBM Watson Tone Analyzer, etc. The extracted emotion data is reflected in the visualization of business processes and is used to generate, for example, flows that reduce stress or diagrams that increase motivation.

[0231] As a specific example, consider the case where a user inputs text into a terminal to instruct the start of a new project. For example, if the user inputs "Please set up a testing process for new software," the terminal sends this data to the server. The server uses NLP technology and an emotion engine to analyze the instruction and generate a flow diagram for starting the project. The generated flow diagram is shared with the requester via email, who then confirms its contents. This diagram supports the user's work progress and prevents misunderstandings. Emotional data is also used to adjust the business process to take the user's emotions into consideration.

[0232] A concrete example of a prompt is:

[0233] Set up a testing process for new software.

[0234] Examples include:

[0235] This system is capable of efficiently analyzing natural language data and automatically diagramming business processes that take emotions into account, achieving both improved business efficiency and consideration for user emotions.

[0236] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0237] The flow of this system's program processing

[0238] Step 1:

[0239] User

[0240] The user inputs instructions and requests through the terminal, either via text input (using a text box or chat interface) or voice input (using a microphone).

[0241] Input: User instructions or requests (natural language data)

[0242] Output: Text or audio data on the device

[0243] Specific behavior:

[0244] The user inputs a text into the terminal saying, "I want you to create a promotion plan for a new product."

[0245] Step 2:

[0246] Terminal

[0247] When the device receives voice data, it converts it into text data using voice recognition technology, using voice recognition services such as Google Cloud Speech-to-Text and IBM Watson Speech to Text.

[0248] Input: User's voice data

[0249] Output: Text data

[0250] Specific behavior:

[0251] The user types "Please automatically generate the agenda for the next meeting" by voice, and the device converts this into text data.

[0252] Step 3:

[0253] Terminal

[0254] The device sends the acquired text data to the server, which then uses it as input data for analysis.

[0255] Input: Text data

[0256] Output: Text data sent to the server

[0257] Specific behavior:

[0258] The terminal transmits text data saying "Please prepare a campaign to launch a new product" to the server.

[0259] Step 4:

[0260] server

[0261] The server passes the received text data to an NLP analysis module that extracts important information using the Python libraries spaCy and NLTK.

[0262] Input: Text data

[0263] Output: Analysis results (purpose of instruction, required actions, related elements)

[0264] Specific behavior:

[0265] The server analyzes the instruction "Prepare a campaign to launch a new product" and extracts keywords such as "new product," "launch," "campaign," and "preparation."

[0266] Step 5:

[0267] server

[0268] The server runs a module that visualizes business processes based on the analysis results, generating flow diagrams and structure diagrams using Diagram and Lucidchart APIs.

[0269] Input: Analysis results

[0270] Output: Graphic files (flow diagrams and structure diagrams)

[0271] Specific behavior:

[0272] The server generates a flow diagram for "preparing for a new product launch campaign" and creates figures showing steps (e.g., procuring products, creating advertising materials, planning a marketing strategy, etc.).

[0273] Step 6:

[0274] server

[0275] The server stores the generated graphic files and then shares them with users and requesters via email or file sharing services (e.g., Google Drive, Dropbox).

[0276] Input: Shape file

[0277] Output: Shared link or shared file

[0278] Specific behavior:

[0279] The server uploads the generated shape file to Google Drive and sends the shared link to the user via email.

[0280] Step 7:

[0281] server

[0282] The server uses a sentiment engine to extract sentiment from the user's natural language data, such as Microsoft Azure's Text Analytics for sentiment analysis or IBM Watson Tone Analyzer.

[0283] Input: Natural language data

[0284] Output: Emotional information

[0285] Specific behavior:

[0286] The server detects high stress levels from the user's input and outputs emotional data such as "Stress: High."

[0287] Step 8:

[0288] server

[0289] The server adjusts the business process diagram based on the emotion recognition, reflecting the extracted emotion data and providing a business process that is adapted to the user's emotions.

[0290] Input: Emotion information, generated shapes

[0291] Output: adjusted geometry file

[0292] Specific behavior:

[0293] To reduce the high stress on the server, a new flow diagram is generated in which a low-priority task is inserted instead of the normal business flow.

[0294] (Application example 2)

[0295] 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."

[0296] Modern business processes require systems that can automatically analyze and visualize instructions and reports in natural language. However, conventional systems are unable to consider the user's emotions, making it difficult to appropriately alleviate stress and tension. Furthermore, there are limited means for visualizing the generated diagrams and business process flows in real time and providing them to users. This can increase the workload of workers during work and potentially reduce productivity.

[0297] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0298] In this invention, the server includes means for analyzing extracted natural language data, means for diagramming a business process based on the analysis results, means for sharing the generated diagram with the user and the client, means including an emotion engine for recognizing the user's emotions, means for optimizing the business process based on the user's input content and emotion data, and means for displaying the generated diagram and advice within the user's field of view. This not only automatically analyzes and diagrams instructions and reports in natural language, but also enables process optimization that takes the user's emotions into consideration, thereby reducing worker stress and improving the efficiency of work execution.

[0299] "Means for analyzing extracted natural language data" refers to technology that analyzes natural language data input in voice or text format, understands the content of instructions or reports, and extracts important information.

[0300] "Means of visualizing business processes based on analysis results" refers to a technology that automatically generates diagrams such as flowcharts and structural diagrams that visually represent the flow and structure of business processes in an easy-to-understand manner based on the extracted information.

[0301] "Means for sharing the generated graphics with the user and the requester" refers to a technology for sending the generated graphics file to the user and the requester via email or a file sharing service, so that they can easily check it.

[0302] "Means including an emotion engine that recognizes user emotions" refers to technology that identifies emotions from natural language data entered by a user and incorporates them into analysis. For example, it is technology that determines whether a user is feeling stressed.

[0303] "Means for optimizing business processes based on user input and emotional data" refers to technology that adjusts the flow and structure of business processes based on user instructions and emotional data, allowing work to proceed efficiently while reducing the stress and tension felt by users.

[0304] "Means for displaying generated diagrams and advice in the user's field of vision" refers to technology that uses a device such as smart glasses to display generated business process diagrams and advice for the user on the screen in real time.

[0305] The present invention is a system that analyzes natural language data, visualizes the user's work process, and recognizes the user's emotions to optimize the work, using a "security support smart glasses app" applied to security services as an example. The specific configuration and processing procedure of this system are described below.

[0306] Server Roles

[0307] The server plays a central role in the system. The main processes performed by the server are shown below.

[0308] 1. Speech Recognition:

[0309] When a user gives instructions or reports by voice, the voice data is sent to the server through the microphone in the smart glasses.

[0310] The server uses the speech_recognition library to convert the audio data into text data.

[0311] 2. Natural Language Processing:

[0312] The converted text data is then analyzed using spaCy's Japanese model, which extracts the instructions and relevant important information.

[0313] 3. Business process visualization:

[0314] Based on the extracted information, we generate a flowchart of the business process using networkx and matplotlib, which helps users to quickly understand the business process.

[0315] 4. Emotion recognition:

[0316] The server uses the emotion_recognition library to identify emotions from the natural language data entered by the user and incorporate them into the analysis.

[0317] 5. Business process optimization:

[0318] The server optimizes business processes based on the user's instructions and emotional data. For example, if the user is feeling stressed, it generates processes and advice to alleviate the stress.

[0319] 6. Data Sharing:

[0320] The generated diagrams and advice are displayed to the user in real time through the smart glasses display.

[0321] Device Role

[0322] The device (smart glasses) provides an interface with the user. Specifically, it plays the following roles:

[0323] Input acceptance:

[0324] It is equipped with a microphone that allows users to give voice instructions and reports, and transmits voice data to the server.

[0325] display:

[0326] The generated figures and advice sent from the server are displayed on the display of the smart glasses and provided to the user.

[0327] User Roles

[0328] The user needs to operate the system.

[0329] 1. Voice input:

[0330] The user gives voice instructions and reports to the smart glasses, for example, "There was a strange noise in the south corridor on the third floor. We will begin investigating."

[0331] 2. Check and respond:

[0332] The user can view the generated business process flowchart on the smart glasses display and take appropriate action.

[0333] Specific use cases

[0334] A security guard on patrol at a commercial facility at night might say, "I heard a strange noise in the south corridor on the third floor. I will begin investigating." This voice data is sent to a server via smart glasses and analyzed using natural language processing technology. Based on the analysis results, a flowchart of the business process is generated and displayed in real time on the smart glasses' display. Furthermore, an emotion engine detects the user's stress and displays appropriate advice.

[0335] Specific prompt examples:

[0336] "We heard a strange noise in the south corridor on the third floor. We are beginning an investigation."

[0337] This system allows users to visually check business processes and respond appropriately, and also makes it easier to respond to changes in emotions.

[0338] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0339] Step 1:

[0340] The user provides voice input via the smart glasses.

[0341] Input: User voice command: "We've heard a strange noise in the south hallway on the third floor. We're beginning an investigation."

[0342] How it works: The microphone captures the user's voice and sends it to the device as audio data.

[0343] Output: Audio data is collected on the device.

[0344] Step 2:

[0345] The device sends the collected voice data to the server.

[0346] Input: Audio data collected on the device

[0347] Operation: The device sends voice data to a server via the Internet.

[0348] Output: The audio data arrives at the server.

[0349] Step 3:

[0350] The server performs voice recognition and converts the voice data into text data.

[0351] Input: Audio data sent to the server

[0352] How it works: The server uses the speech_recognition library to convert the audio data into text data.

[0353] Output: The audio data is converted to text data that reads, "A strange noise was heard in the south corridor on the third floor. An investigation will begin."

[0354] Step 4:

[0355] The server performs natural language processing to extract important information from the text data.

[0356] Input: Text data obtained by speech recognition

[0357] How it works: The server uses spaCy's Japanese model to analyze the text data and extract instructions (location of the abnormal noise and start of investigation).

[0358] Output: Analysis result data will be generated, including the location of the noise ("South Corridor on the 3rd floor") and the action "Begin investigation."

[0359] Step 5:

[0360] The server visualizes the business process.

[0361] Input: Analysis result data

[0362] How it works: The server uses networkx and matplotlib to generate a flowchart of a business process. For example, it creates a graph structure containing the nodes "abnormal noise detected" and "investigation started" and draws a visual flowchart.

[0363] Output: An image file of the business process flowchart is generated.

[0364] Step 6:

[0365] The server performs analysis to recognize the user's emotions.

[0366] Input: Text data

[0367] How it works: The server uses the emotion_recognition library to detect the user's emotions (e.g., stress level) from text data.

[0368] Output: The detected emotion data is generated.

[0369] Step 7:

[0370] The server optimizes business processes based on emotion data.

[0371] Input: Analysis result data and emotion data

[0372] Action: If the emotion data indicates high stress, the server adds relaxation advice (e.g., take a deep breath) to the business process. This advice is also included in the flowchart.

[0373] Output: An image file of the optimized business process flowchart is generated.

[0374] Step 8:

[0375] The terminal displays the generated graphics and advice in the user's field of view.

[0376] Input: Image file of optimized business process flowchart

[0377] Operation: The server sends an image file of the generated flowchart to the terminal, which displays the flowchart and advice on the display of the smart glasses.

[0378] Output: A business process flowchart and relaxation advice are displayed in the user's field of vision.

[0379] This allows users to visually check business processes and respond appropriately, and creates a system that can easily respond to changes in emotions.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] [Second embodiment]

[0384] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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."

[0396] The present invention provides a system that analyzes extracted natural language data, visualizes business processes based on the analysis results, and shares the generated diagrams with users and clients. An embodiment of this system will be described in detail.

[0397] Server Roles

[0398] The server plays a central role in the system. First, it receives natural language data entered by the user from the terminal. Natural language data can be entered in the form of voice or text, but in the case of voice, it must first be converted into text data using voice recognition technology.

[0399] The server is equipped with an analysis module that incorporates natural language processing (NLP) technology. This analysis module analyzes the input natural language data and extracts important information, including the purpose of the instruction or request, the required action, and the elements involved (people, processes, etc.).

[0400] Next, the server uses the analyzed data to generate flow diagrams and structure diagrams using a module that visualizes business processes. This module learns from past business frameworks and visualized data, and automatically creates appropriate diagrams.

[0401] The generated diagram is saved as a diagram file and then shared with the user and requester. The server sends this diagram file via email or a file sharing service, making it easy to check the configuration.

[0402] Device Role

[0403] The terminal used by the user provides an interface for inputting instructions or requests. Input methods include text input, and instructions can be input through a text box or a chat interface, for example.

[0404] There is also a voice input option, where the user can use the microphone to input commands by voice, and the device will use voice recognition technology to convert this speech into text and send it to the server.

[0405] User Roles

[0406] The user's role begins by inputting instructions and requests via the terminal. The user then receives and checks the diagram generated by the server. The user checks whether there are any misunderstandings or missing information in the generated diagram, and requests corrections as necessary.

[0407] As a specific example, consider the case where a user inputs text into a terminal to instruct the start of a new project. The input text data is sent to a server, which uses NLP technology to analyze the content of the instruction. Based on the analysis results, the server generates a flow diagram for starting the project. The generated flow diagram is shared with the requester via email, who then confirms its contents. The confirmed diagram can support the user's work progress and prevent misunderstandings.

[0408] The main feature of this system is that the process of analyzing and visualizing natural language data is automated. This allows even new graduates and junior employees to smoothly understand and proceed with their work. This invention contributes to the efficiency of business communication and the promotion of accurate understanding.

[0409] The processing flow will be explained below.

[0410] Step 1:

[0411] The user inputs instructions or requests into the terminal. Input can be selected as either text input via a text box or voice input using a microphone.

[0412] Step 2:

[0413] In the case of voice input, the device captures the voice and converts it into text data using voice recognition technology, which is then used in subsequent processing steps.

[0414] Step 3:

[0415] The terminal sends the entered text data to the server, which receives the text data and starts the analysis process.

[0416] Step 4:

[0417] The server analyzes the received text data using natural language processing (NLP) technology. This analysis includes grammatical analysis, word recognition, and semantic analysis. Specifically, it extracts the purpose of the instruction or request, the required actions, and related elements (people, processes, etc.).

[0418] Step 5:

[0419] The server creates a diagram of the business process based on the analysis results. The server references past business frameworks and existing diagram data to automatically generate appropriate flow diagrams and structure diagrams. This diagram definition includes the relationships between related elements and the order of actions.

[0420] Step 6:

[0421] The generated flow diagrams and structure diagrams are saved as files by the server in common image formats (e.g. PNG, JPEG) or vector data formats (e.g. SVG).

[0422] Step 7:

[0423] The server shares the generated shape with the user and the requester. Sharing can be done via email or using a file sharing service. The generated shape file is attached to the email and sent to the requester.

[0424] Step 8:

[0425] The user and requester review the diagram and check the accuracy of the content. They provide corrections and additional feedback as needed. If there are any problems with the diagram content, the user can re-enter instructions or requests for a revised version, and the server will regenerate it.

[0426] Step 9:

[0427] The final confirmed diagram is used as a tool to support the user in progressing with their work. The generated diagram is used as a means to help users understand the business process and improve work efficiency.

[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] Modern business processes contain a large amount of information, and efficiently analyzing and visualizing it is important for smooth communication between teams and individuals. However, current systems often require manual analysis and visualization of natural language data, which is time-consuming and prone to human error. This invention provides a system that eliminates these inefficiencies and enables accurate and rapid visualization and sharing of business processes.

[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: a means for a user to input natural language data from a terminal; a means for transmitting the input natural language data to the server; a means for the server to analyze the natural language data using natural language processing technology; a means for diagramming a business process based on the analysis results; and a means for sharing the generated diagram with the user and the requester. This makes it possible to automatically analyze the natural language data input by the user, generate an appropriate business process diagram, and quickly share it.

[0433] "Natural language data" refers to information expressed by humans using normal language, such as text or speech.

[0434] "Terminal" refers to an electronic device through which a user inputs natural language data.

[0435] "Server" refers to a central computer system that receives input natural language data and performs analysis and visualization.

[0436] "Natural language processing technology" refers to information processing technology for analyzing human language and understanding meaning and intent.

[0437] "Analysis" refers to the process of extracting and understanding important information from natural language data.

[0438] A "business process" refers to a series of activities and procedures for carrying out a specific business task.

[0439] "Graphing" refers to the presentation of analytical results in the form of graphs or figures that are visually easy to understand.

[0440] "Sharing" refers to the act of sharing the generated graphics and information with other users or requesters.

[0441] "Speech recognition technology" refers to technology for converting voice data into text.

[0442] "Business framework data" refers to data that records past business procedures and activities.

[0443] A "shape generation library" refers to a collection of software for generating shapes programmatically.

[0444] A "flow diagram" is a diagram that visually shows a series of steps or processes using arrows and symbols.

[0445] The present invention provides a system for analyzing natural language data, graphically representing a business process, and sharing the generated graphical representation with users and clients. Specific embodiments of the system are described below.

[0446] Server Roles

[0447] The server plays a central role in the system. First, it receives the natural language data entered by the user from the device. If the natural language data is voice data, it is converted into text data using voice recognition technology such as the Google Cloud Speech-to-Text API.

[0448] The server is equipped with an analysis module that uses natural language processing (NLP) techniques such as BERT and the Transformers library. This analysis module analyzes the input natural language data and extracts important information, such as the purpose of the instruction or request, the required action, and the elements involved (people, processes, etc.).

[0449] Next, the server generates flow diagrams and structure diagrams based on the analyzed data using a diagram generation library such as Graphviz. This module learns from past business framework data and automatically creates appropriate diagrams.

[0450] The generated diagram is saved as an image file (e.g., PNG format) and then shared with the user and requester. The server sends this diagram file via email, Google Drive, or Dropbox to make it easier to check the configuration.

[0451] Device Role

[0452] The device used by the user provides an interface for inputting instructions or requests. In the case of text input, instructions are entered through a text box or a chat interface, for example, a web form or a messaging app interface.

[0453] It also offers a voice input option: the user can use the microphone to input commands by voice, and the device will convert this speech into text using speech recognition technology (e.g., Amazon Transcribe) and send it to the server.

[0454] User Roles

[0455] The user's role begins by inputting instructions and requests via the terminal. Then, the user receives the diagram generated by the server and checks its contents. The user checks whether there are any misunderstandings or missing information in the generated diagram, and requests corrections as necessary.

[0456] Specific examples

[0457] For example, consider the case where a user inputs a command to start a new project into a terminal as text. The user inputs "Please start a new project" into the text box. This text data is sent to the server.

[0458] The server uses NLP technology to analyze the instructions and generate a "Start a New Project" flow chart based on the analysis results. The generated flow chart is shared with the requester via email. The requester can review the content and provide feedback if necessary.

[0459] An example of a prompt for a generative AI model is:

[0460] "Generate a flow diagram showing the steps to take when starting a new project. Analyze the following instructions: Project name: AI system development, Start date: October 1, 2023, First task: Hold a requirements definition meeting."

[0461] The above is a specific embodiment of the present invention. Through this system, the process of analyzing and visualizing natural language data is automated, making it possible to improve the efficiency of business communication and promote accurate understanding.

[0462] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0463] Step 1:

[0464] Input of natural language data via terminal

[0465] The user inputs natural language data from the device. Input methods include text input and voice input. For example, the user types "Start a new project" into a text box. In the case of voice input, the user speaks the same content into the microphone. The device converts the voice data into text data using voice recognition technology (for example, Google Cloud Speech-to-Text API).

[0466] Input: Natural language data: "Start a new project"

[0467] Output: Natural language data in text format

[0468] Step 2:

[0469] Sending data from the device to the server

[0470] The device sends the generated text data to the server. The HTTPS protocol is used for communication to ensure data security. For example, the device sends the text data "Start a new project" to the server via HTTPS.

[0471] Input: Natural language data in text format

[0472] Output: Natural language data sent to the server

[0473] Step 3:

[0474] Data analysis by server

[0475] The server passes the received text data to a Natural Language Processing (NLP) module, which uses BERT and Transformers libraries to analyze the data and extract key information. For example, it can analyze a request like "Start a new project" to extract the goal, required actions, and related elements.

[0476] Input: Natural language data received by the server

[0477] Output: Analyzed key information (objectives, actions, stakeholders, etc.)

[0478] Step 4:

[0479] Server-based business process visualization

[0480] The server uses a diagram generation library such as Graphviz to visualize the business process based on the analyzed data. For example, it generates a flow diagram for "Starting a new project" based on the analysis results. The flow diagram includes specific steps such as "Hold a requirements definition meeting."

[0481] Input: Parsed data (critical information)

[0482] Output: Business process graphic file (e.g. PNG format)

[0483] Step 5:

[0484] Saving and sharing generated shape files

[0485] The server saves the generated graphic files and shares them with the user or requester via email, Google Drive, or Dropbox. For example, the generated flow diagram is saved as a PNG file and sent to the requester's email address.

[0486] Input: Business process shape file

[0487] Output: Shared geometry files (sent to users and requesters)

[0488] Step 6:

[0489] User shape confirmation and feedback

[0490] The user checks the received graphic file and provides feedback as necessary. If there is a misunderstanding or missing information, they can request corrections again. For example, if the user checks the received flow diagram and determines that there is an error in the details of the "Requirements Definition Meeting," they will send an email with feedback to that effect.

[0491] Input: Received shape file

[0492] Output: Feedback provided (requests for corrections if necessary)

[0493] (Application example 1)

[0494] 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."

[0495] Currently, many factories are automating their business processes, but there is no system that can understand instructions in natural language, accurately visualize the business process as a flow diagram, and control robots based on that diagram. There is also a need to quickly and accurately share the generated diagrams and instructions with users and clients. The objective of this invention is to solve this technical deficiency.

[0496] 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.

[0497] In this invention, the server includes means for analyzing the extracted natural language data, means for diagramming the business process based on the analysis results, means for sharing the generated diagram with the user and the client, and means for controlling the robot based on the generated diagram, thereby enabling accurate understanding of instructions in natural language, automatic generation of a process diagram, and automatic control of the robot based on the diagram.

[0498] "Extracted natural language data" refers to instructions or requests entered by a user in voice or text format.

[0499] The "analysis means" refers to natural language processing technology used to interpret input natural language data and understand its content.

[0500] "Means for visualizing business processes" refers to technology that generates flow diagrams and structure diagrams that visually represent business flows and processes based on analyzed data.

[0501] "Generated diagrams" are flow diagrams or structure diagrams that are automatically created based on analyzed natural language data.

[0502] "Means for sharing with users and requesters" refers to the technology and functions for sending the generated figures to users and requesters via email or file sharing services.

[0503] "Means for controlling robots" refers to the technology and functions that give specific operational instructions to robots in the factory based on the information in the generated flow diagrams and structural diagrams.

[0504] The present invention is a system that analyzes extracted natural language data, visualizes business processes, and shares the generated diagrams with users and clients. An embodiment of this system will be described in detail below.

[0505] Server Roles

[0506] The server plays a central role in the system. First, the server receives natural language data input by the user from their device. The natural language data is input in the form of voice or text, and in the case of voice, it is converted into text data using voice recognition technology. For voice recognition, common voice recognition technology or APIs are used.

[0507] The server then uses an analysis module that includes natural language processing (NLP) technology to analyze the input natural language data and extract key information, such as the purpose of the instruction or request, the required action, and the elements involved (people, processes, etc.). The NLP technology used can be the Google Cloud Natural Language API.

[0508] Based on the analyzed data, a module that visualizes business processes is used to generate flow diagrams and structure diagrams. This module learns from past business frameworks and visualized data, and automatically creates appropriate diagrams. Visualization tools such as Graphviz are used for visualization.

[0509] The generated diagram is saved as a diagram file and then shared with the user and the requester. The server sends this diagram file via email or file sharing service, making it easier to check the configuration. It also includes a means to control the robot based on the generated diagram. ROS (Robot Operating System) can be used to control the robot.

[0510] Device Role

[0511] The device used by the user provides an interface for inputting instructions and requests. Input methods include text input, for example, inputting instructions through a text box or a chat interface. Voice input is also available. When the user inputs instructions by voice using a microphone, the device uses voice recognition technology to convert this voice into text and send it to the server.

[0512] User Roles

[0513] The user's role begins by inputting instructions and requests via a terminal. The user then receives and checks the diagram generated by the server. The robot begins to operate based on the generated diagram, and the business process progresses. The user checks the diagram for misunderstandings or missing information, and requests corrections as necessary.

[0514] As a specific example, when a new production line is added in a factory, instructions are input by voice, such as "Please start the inspection process on production line 1. Next, please pack the finished products." The voice data is converted into text and, after NLP analysis, a business flow diagram for the inspection process and packaging process is generated. The factory manager reviews the generated flow diagram and requests any necessary revisions. The following are examples of prompt sentences that could be considered:

[0515] "Please parse the following natural language instructions and generate a workflow diagram.

[0516] Instructions: Start the inspection process on production line 1. Then, package the finished products.

[0517] Generate a flow diagram using Graphviz based on the analysis results of the generative AI.

[0518] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0519] Step 1:

[0520] A user uses a device to input natural language data. This data can be input in the form of voice or text. For voice input, the user uses a microphone to input instructions, and the device converts the speech to text using speech recognition technology such as Google Speech-to-Text. For text input, the user inputs instructions through a chat interface or text box.

[0521] Input: Natural language audio or text data

[0522] Output: Text data

[0523] Step 2:

[0524] The device sends text data to the server, which then analyzes it using the Google Cloud Natural Language API. The API analyzes the instruction and extracts important information (the purpose of the instruction, the required action, and the relevant elements). The server receives the analysis results and continues processing.

[0525] Input: Text data

[0526] Output: Analysis results (extracted important information)

[0527] Step 3:

[0528] The server visualizes the business process based on the analysis results. Here, a visualization tool such as Graphviz is used to automatically generate a flow diagram and structure diagram of the business process. The server saves the generated flow diagram and structure diagram and proceeds to the next step.

[0529] Input: Analysis results

[0530] Output: Flow diagram or structure diagram

[0531] Step 4:

[0532] The server shares the generated graphic files with the user and requester. To do this, the server sends the graphic files via email or a file sharing system. The recipients check the graphic files and provide feedback if necessary.

[0533] Input: Flow diagram or structure diagram

[0534] Output: Sharing shape files

[0535] Step 5:

[0536] The server executes the means to control the robot based on the generated diagram, and sends specific operation instructions to the robot using ROS (Robot Operating System). The server controls the robot so that it automatically executes the business process according to each step of the generated flow diagram.

[0537] Input: Flow diagram or structure diagram

[0538] Output: Control instructions to the robot

[0539] Through the above steps, a system is realized in which a user inputs instructions in natural language, a business process is visualized based on the instructions, and a robot automatically operates based on the visualized instructions.

[0540] 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.

[0541] The present invention combines a system that analyzes extracted natural language data, visualizes business processes based on the analysis results, and shares the generated diagrams with users and clients, with an emotion engine that recognizes user emotions. An embodiment of this system will be described in detail.

[0542] Server Roles

[0543] The server plays a central role in the system. First, it receives natural language data entered by the user from the terminal. Natural language data can be entered in the form of voice or text, but in the case of voice, it must first be converted into text data using voice recognition technology.

[0544] The server is equipped with an analysis module that incorporates natural language processing (NLP) technology. This analysis module analyzes the input natural language data and extracts important information, including the purpose of the instruction or request, the required action, and related elements (people, processes, etc.).

[0545] Next, the server uses the analysis results to generate flow diagrams and structure diagrams using a module that visualizes business processes. This module learns from past business frameworks and visualized data, and automatically creates appropriate diagrams.

[0546] The generated diagram is saved as a diagram file and then shared with the user and requester. The server sends this diagram file via email or a file sharing service, making it easy to check the configuration.

[0547] Furthermore, the present invention incorporates an emotion engine that extracts emotions from the user's natural language data and incorporates them as part of the analysis. This emotion data is used to influence the visualization of business processes, for example, to generate diagrams that reduce stress or increase motivation.

[0548] Device Role

[0549] The terminal used by the user provides an interface for inputting instructions or requests. Input methods include text input, and instructions can be input through a text box or a chat interface, for example.

[0550] There is also a voice input option, where the user can use the microphone to input commands by voice, and the device will use voice recognition technology to convert this speech into text and send it to the server.

[0551] User Roles

[0552] The user's role begins by inputting instructions and requests via the terminal. The user then receives and checks the diagram generated by the server. The user checks whether there are any misunderstandings or missing information in the generated diagram, and requests corrections as necessary.

[0553] Furthermore, the system also checks the analysis results generated by the emotion engine. For example, if it determines that the user's input indicates stress, it will modify the diagram based on that information to make the work process go more smoothly.

[0554] As a specific example, consider the case where a user inputs a command to start a new project into a terminal. The input text data is sent to the server, which analyzes the command using NLP technology and an emotion engine. Based on the analysis results, the server generates a flow diagram for starting the project. The generated flow diagram is shared with the requester via email, who then confirms its contents. The confirmed diagram can support the user's work progress and prevent misunderstandings. Furthermore, emotion data is used to adjust the business process so that it takes the user's emotions into consideration.

[0555] The main features of this system are that the process of analyzing natural language data and visualizing it is automated, and that emotion recognition and the results are reflected in the visualization. This allows even new graduates and junior employees to smoothly understand and proceed with their work. This invention contributes to the efficiency of business communication and the promotion of accurate understanding.

[0556] The processing flow will be explained below.

[0557] Step 1:

[0558] The user inputs instructions or requests into the terminal. Input can be selected as either text input via a text box or voice input using a microphone.

[0559] Step 2:

[0560] In the case of voice input, the device captures the voice and converts it into text data using voice recognition technology, which is then used in subsequent processing steps.

[0561] Step 3:

[0562] The terminal sends the entered text data to the server, which receives the text data and starts the analysis process.

[0563] Step 4:

[0564] The server analyzes the received text data using natural language processing (NLP) technology. This analysis includes grammatical analysis, word recognition, and semantic analysis. Specifically, it extracts the purpose of the instruction or request, the required actions, and related elements (people, processes, etc.).

[0565] Step 5:

[0566] The server performs emotion analysis on the extracted data using an emotion engine, which recognizes the user's emotional state (e.g., stress, satisfaction, excitement, etc.) and incorporates this information into the subsequent visualization process.

[0567] Step 6:

[0568] The server visualizes business processes based on the analysis results and emotional data. The server references past business frameworks and existing graphic data to automatically generate appropriate flow diagrams and structure diagrams. These diagrams include the relationships between related elements and the order of actions. Furthermore, the diagrams are adjusted according to the user's emotional state, incorporating elements that reduce stress and increase motivation.

[0569] Step 7:

[0570] The generated flow diagrams and structure diagrams are saved as files by the server in common image formats (e.g. PNG, JPEG) or vector data formats (e.g. SVG).

[0571] Step 8:

[0572] The server shares the generated shape with the user and the requester. Sharing can be done via email or using a file sharing service. The generated shape file is attached to the email and sent to the requester.

[0573] Step 9:

[0574] The user and requester review the diagram and check the accuracy of the content. They provide corrections and additional feedback as needed. If there are any problems with the diagram content, the user can re-enter instructions or requests for a revised version, and the server will regenerate it.

[0575] Step 10:

[0576] The final confirmed diagram is used as a tool to support the user's work progress. The generated diagram helps users to understand the work process more easily and is used as a means to improve work efficiency. In addition, the diagram created using emotion data takes into account the user's emotional state, contributing to an improvement of the work environment.

[0577] Example 2

[0578] 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."

[0579] Conventional business process management systems lack the ability to efficiently analyze natural language data and automatically visualize it. Furthermore, they do not optimize business processes by taking into account user emotions. This has led to problems such as reduced work efficiency, stress, and increased misunderstandings.

[0580] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for analyzing extracted natural language data, a means for diagramming a business process based on the analysis result, a means for sharing the generated diagram with the user and the requester, a means for recognizing the user's emotion, and a means for adjusting the diagram of the business process based on the emotion recognition. This enables efficient analysis of natural language data and automatic diagramming of the business process taking emotion into consideration.

[0581] "Extracted natural language data" refers to human language data input in speech or text format.

[0582] The "means for analyzing" is the part of the system that includes natural language processing technology to identify important information from natural language data (such as the purpose of the instruction, the action required, and the people and processes involved).

[0583] "Means for visualizing business processes" refers to an automatically generated module that visually represents business steps and flows based on analyzed information.

[0584] "Generated shapes" refers to flow diagrams and structure diagrams that visually represent business processes.

[0585] "Means for sharing with users and requesters" refers to a function for sending the generated graphic files via email or file sharing services.

[0586] "Means for recognizing emotions" refers to a sentiment analysis engine for extracting emotional information from the user's natural language data.

[0587] "Means for adjusting the business process diagram based on emotion recognition" refers to an adjustment function for adapting the business process to the user's emotions using the extracted emotion data.

[0588] The present invention combines a system that analyzes extracted natural language data, visualizes business processes, and shares the generated diagrams with users and clients, with an emotion engine that recognizes user emotions. This system can be implemented as follows.

[0589] First, the server, which plays a central role in this system, receives natural language data input by the user from their device. Natural language data can be input in the form of voice or text, but if it is voice, it must be converted into text data using voice recognition technology. This conversion is achieved using voice recognition services such as Google Cloud Speech-to-Text and IBM Watson Speech to Text.

[0590] The server is then equipped with an analysis module that includes natural language processing (NLP) technology. This module uses Python NLP libraries such as spaCy and NLTK to analyze the input natural language data and extract important information, such as the purpose of the instruction, required actions, and related people and processes.

[0591] The server then runs a module that visualizes the business process based on the analysis results. This module uses a deep learning model to learn from past business data and business frameworks, and generates appropriate flow diagrams and structure diagrams. The visualization can be done using tools such as Diagram or Lucidchart API.

[0592] The generated shape is saved as a shape file and then shared with the user and the requester. The server sends this shape file via email or a file sharing service (e.g., Google Drive, Dropbox, etc.), making it easy to check the contents.

[0593] Furthermore, the present invention incorporates an emotion engine that extracts emotions from the user's natural language data using Microsoft Azure's Text Analytics for sentiment analysis, IBM Watson Tone Analyzer, etc. The extracted emotion data is reflected in the visualization of business processes and is used to generate, for example, flows that reduce stress or diagrams that increase motivation.

[0594] As a specific example, consider the case where a user inputs text into a terminal to instruct the start of a new project. For example, if the user inputs "Please set up a testing process for new software," the terminal sends this data to the server. The server uses NLP technology and an emotion engine to analyze the instruction and generate a flow diagram for starting the project. The generated flow diagram is shared with the requester via email, who then confirms its contents. This diagram supports the user's work progress and prevents misunderstandings. Emotional data is also used to adjust the business process to take the user's emotions into consideration.

[0595] A concrete example of a prompt is:

[0596] Set up a testing process for new software.

[0597] Examples include:

[0598] This system is capable of efficiently analyzing natural language data and automatically diagramming business processes that take emotions into account, achieving both improved business efficiency and consideration for user emotions.

[0599] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0600] The flow of this system's program processing

[0601] Step 1:

[0602] User

[0603] The user inputs instructions and requests through the terminal, either via text input (using a text box or chat interface) or voice input (using a microphone).

[0604] Input: User instructions or requests (natural language data)

[0605] Output: Text or audio data on the device

[0606] Specific behavior:

[0607] The user inputs a text into the terminal saying, "I want you to create a promotion plan for a new product."

[0608] Step 2:

[0609] Terminal

[0610] When the device receives voice data, it converts it into text data using voice recognition technology, using voice recognition services such as Google Cloud Speech-to-Text and IBM Watson Speech to Text.

[0611] Input: User's voice data

[0612] Output: Text data

[0613] Specific behavior:

[0614] The user types "Please automatically generate the agenda for the next meeting" by voice, and the device converts this into text data.

[0615] Step 3:

[0616] Terminal

[0617] The device sends the acquired text data to the server, which then uses it as input data for analysis.

[0618] Input: Text data

[0619] Output: Text data sent to the server

[0620] Specific behavior:

[0621] The terminal transmits text data saying "Please prepare a campaign to launch a new product" to the server.

[0622] Step 4:

[0623] server

[0624] The server passes the received text data to an NLP analysis module that extracts important information using the Python libraries spaCy and NLTK.

[0625] Input: Text data

[0626] Output: Analysis results (purpose of instruction, required actions, related elements)

[0627] Specific behavior:

[0628] The server analyzes the instruction "Prepare a campaign to launch a new product" and extracts keywords such as "new product," "launch," "campaign," and "preparation."

[0629] Step 5:

[0630] server

[0631] The server runs a module that visualizes business processes based on the analysis results, generating flow diagrams and structure diagrams using Diagram and Lucidchart APIs.

[0632] Input: Analysis results

[0633] Output: Graphic files (flow diagrams and structure diagrams)

[0634] Specific behavior:

[0635] The server generates a flow diagram for "preparing for a new product launch campaign" and creates figures showing steps (e.g., procuring products, creating advertising materials, planning a marketing strategy, etc.).

[0636] Step 6:

[0637] server

[0638] The server stores the generated graphic files and then shares them with users and requesters via email or file sharing services (e.g., Google Drive, Dropbox).

[0639] Input: Shape file

[0640] Output: Shared link or shared file

[0641] Specific behavior:

[0642] The server uploads the generated shape file to Google Drive and sends the shared link to the user via email.

[0643] Step 7:

[0644] server

[0645] The server uses a sentiment engine to extract sentiment from the user's natural language data, such as Microsoft Azure's Text Analytics for sentiment analysis or IBM Watson Tone Analyzer.

[0646] Input: Natural language data

[0647] Output: Emotional information

[0648] Specific behavior:

[0649] The server detects high stress levels from the user's input and outputs emotional data such as "Stress: High."

[0650] Step 8:

[0651] server

[0652] The server adjusts the business process diagram based on the emotion recognition, reflecting the extracted emotion data and providing a business process that is adapted to the user's emotions.

[0653] Input: Emotion information, generated shapes

[0654] Output: adjusted geometry file

[0655] Specific behavior:

[0656] To reduce the high stress on the server, a new flow diagram is generated in which a low-priority task is inserted instead of the normal business flow.

[0657] (Application example 2)

[0658] 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."

[0659] Modern business processes require systems that can automatically analyze and visualize instructions and reports in natural language. However, conventional systems are unable to consider the user's emotions, making it difficult to appropriately alleviate stress and tension. Furthermore, there are limited means for visualizing the generated diagrams and business process flows in real time and providing them to users. This can increase the workload of workers during work and potentially reduce productivity.

[0660] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0661] In this invention, the server includes means for analyzing extracted natural language data, means for diagramming a business process based on the analysis results, means for sharing the generated diagram with the user and the client, means including an emotion engine for recognizing the user's emotions, means for optimizing the business process based on the user's input content and emotion data, and means for displaying the generated diagram and advice within the user's field of view. This not only automatically analyzes and diagrams instructions and reports in natural language, but also enables process optimization that takes the user's emotions into consideration, thereby reducing worker stress and improving the efficiency of work execution.

[0662] "Means for analyzing extracted natural language data" refers to technology that analyzes natural language data input in voice or text format, understands the content of instructions or reports, and extracts important information.

[0663] "Means of visualizing business processes based on analysis results" refers to a technology that automatically generates diagrams such as flowcharts and structural diagrams that visually represent the flow and structure of business processes in an easy-to-understand manner based on the extracted information.

[0664] "Means for sharing the generated graphics with the user and the requester" refers to a technology for sending the generated graphics file to the user and the requester via email or a file sharing service, so that they can easily check it.

[0665] "Means including an emotion engine that recognizes user emotions" refers to technology that identifies emotions from natural language data entered by a user and incorporates them into analysis. For example, it is technology that determines whether a user is feeling stressed.

[0666] "Means for optimizing business processes based on user input and emotional data" refers to technology that adjusts the flow and structure of business processes based on user instructions and emotional data, allowing work to proceed efficiently while reducing the stress and tension felt by users.

[0667] "Means for displaying generated diagrams and advice in the user's field of vision" refers to technology that uses a device such as smart glasses to display generated business process diagrams and advice for the user on the screen in real time.

[0668] The present invention is a system that analyzes natural language data, visualizes the user's work process, and recognizes the user's emotions to optimize the work, using a "security support smart glasses app" applied to security services as an example. The specific configuration and processing procedure of this system are described below.

[0669] Server Roles

[0670] The server plays a central role in the system. The main processes performed by the server are shown below.

[0671] 1. Speech Recognition:

[0672] When a user gives instructions or reports by voice, the voice data is sent to the server through the microphone in the smart glasses.

[0673] The server uses the speech_recognition library to convert the audio data into text data.

[0674] 2. Natural Language Processing:

[0675] The converted text data is then analyzed using spaCy's Japanese model, which extracts the instructions and relevant important information.

[0676] 3. Business process visualization:

[0677] Based on the extracted information, we generate a flowchart of the business process using networkx and matplotlib, which helps users to quickly understand the business process.

[0678] 4. Emotion recognition:

[0679] The server uses the emotion_recognition library to identify emotions from the natural language data entered by the user and incorporate them into the analysis.

[0680] 5. Business process optimization:

[0681] The server optimizes business processes based on the user's instructions and emotional data. For example, if the user is feeling stressed, it generates processes and advice to alleviate the stress.

[0682] 6. Data Sharing:

[0683] The generated diagrams and advice are displayed to the user in real time through the smart glasses display.

[0684] Device Role

[0685] The device (smart glasses) provides an interface with the user. Specifically, it plays the following roles:

[0686] Input acceptance:

[0687] It is equipped with a microphone that allows users to give voice instructions and reports, and transmits voice data to the server.

[0688] display:

[0689] The generated figures and advice sent from the server are displayed on the display of the smart glasses and provided to the user.

[0690] User Roles

[0691] The user needs to operate the system.

[0692] 1. Voice input:

[0693] The user gives voice instructions and reports to the smart glasses, for example, "There was a strange noise in the south corridor on the third floor. We will begin investigating."

[0694] 2. Check and respond:

[0695] The user can view the generated business process flowchart on the smart glasses display and take appropriate action.

[0696] Specific use cases

[0697] A security guard on patrol at a commercial facility at night might say, "I heard a strange noise in the south corridor on the third floor. I will begin investigating." This voice data is sent to a server via smart glasses and analyzed using natural language processing technology. Based on the analysis results, a flowchart of the business process is generated and displayed in real time on the smart glasses' display. Furthermore, an emotion engine detects the user's stress and displays appropriate advice.

[0698] Specific prompt examples:

[0699] "We heard a strange noise in the south corridor on the third floor. We are beginning an investigation."

[0700] This system allows users to visually check business processes and respond appropriately, and also makes it easier to respond to changes in emotions.

[0701] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0702] Step 1:

[0703] The user provides voice input via the smart glasses.

[0704] Input: User voice command: "We've heard a strange noise in the south hallway on the third floor. We're beginning an investigation."

[0705] How it works: The microphone captures the user's voice and sends it to the device as audio data.

[0706] Output: Audio data is collected on the device.

[0707] Step 2:

[0708] The device sends the collected voice data to the server.

[0709] Input: Audio data collected on the device

[0710] Operation: The device sends voice data to a server via the Internet.

[0711] Output: The audio data arrives at the server.

[0712] Step 3:

[0713] The server performs voice recognition and converts the voice data into text data.

[0714] Input: Audio data sent to the server

[0715] How it works: The server uses the speech_recognition library to convert the audio data into text data.

[0716] Output: The audio data is converted to text data that reads, "A strange noise was heard in the south corridor on the third floor. An investigation will begin."

[0717] Step 4:

[0718] The server performs natural language processing to extract important information from the text data.

[0719] Input: Text data obtained by speech recognition

[0720] How it works: The server uses spaCy's Japanese model to analyze the text data and extract instructions (location of the abnormal noise and start of investigation).

[0721] Output: Analysis result data will be generated, including the location of the noise ("South Corridor on the 3rd floor") and the action "Begin investigation."

[0722] Step 5:

[0723] The server visualizes the business process.

[0724] Input: Analysis result data

[0725] How it works: The server uses networkx and matplotlib to generate a flowchart of a business process. For example, it creates a graph structure containing the nodes "abnormal noise detected" and "investigation started" and draws a visual flowchart.

[0726] Output: An image file of the business process flowchart is generated.

[0727] Step 6:

[0728] The server performs analysis to recognize the user's emotions.

[0729] Input: Text data

[0730] How it works: The server uses the emotion_recognition library to detect the user's emotions (e.g., stress level) from text data.

[0731] Output: The detected emotion data is generated.

[0732] Step 7:

[0733] The server optimizes business processes based on emotion data.

[0734] Input: Analysis result data and emotion data

[0735] Action: If the emotion data indicates high stress, the server adds relaxation advice (e.g., take a deep breath) to the business process. This advice is also included in the flowchart.

[0736] Output: An image file of the optimized business process flowchart is generated.

[0737] Step 8:

[0738] The terminal displays the generated graphics and advice in the user's field of view.

[0739] Input: Image file of optimized business process flowchart

[0740] Operation: The server sends an image file of the generated flowchart to the terminal, which displays the flowchart and advice on the display of the smart glasses.

[0741] Output: A business process flowchart and relaxation advice are displayed in the user's field of vision.

[0742] This allows users to visually check business processes and respond appropriately, and creates a system that can easily respond to changes in emotions.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] [Third embodiment]

[0747] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0748] 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.

[0749] 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).

[0750] 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.

[0751] 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.

[0752] 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).

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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."

[0759] The present invention provides a system that analyzes extracted natural language data, visualizes business processes based on the analysis results, and shares the generated diagrams with users and clients. An embodiment of this system will be described in detail.

[0760] Server Roles

[0761] The server plays a central role in the system. First, it receives natural language data entered by the user from the terminal. Natural language data can be entered in the form of voice or text, but in the case of voice, it must first be converted into text data using voice recognition technology.

[0762] The server is equipped with an analysis module that incorporates natural language processing (NLP) technology. This analysis module analyzes the input natural language data and extracts important information, including the purpose of the instruction or request, the required action, and the elements involved (people, processes, etc.).

[0763] Next, the server uses the analyzed data to generate flow diagrams and structure diagrams using a module that visualizes business processes. This module learns from past business frameworks and visualized data, and automatically creates appropriate diagrams.

[0764] The generated diagram is saved as a diagram file and then shared with the user and requester. The server sends this diagram file via email or a file sharing service, making it easy to check the configuration.

[0765] Device Role

[0766] The terminal used by the user provides an interface for inputting instructions or requests. Input methods include text input, and instructions can be input through a text box or a chat interface, for example.

[0767] There is also a voice input option, where the user can use the microphone to input commands by voice, and the device will use voice recognition technology to convert this speech into text and send it to the server.

[0768] User Roles

[0769] The user's role begins by inputting instructions and requests via the terminal. The user then receives and checks the diagram generated by the server. The user checks whether there are any misunderstandings or missing information in the generated diagram, and requests corrections as necessary.

[0770] As a specific example, consider the case where a user inputs text into a terminal to instruct the start of a new project. The input text data is sent to a server, which uses NLP technology to analyze the content of the instruction. Based on the analysis results, the server generates a flow diagram for starting the project. The generated flow diagram is shared with the requester via email, who then confirms its contents. The confirmed diagram can support the user's work progress and prevent misunderstandings.

[0771] The main feature of this system is that the process of analyzing and visualizing natural language data is automated. This allows even new graduates and junior employees to smoothly understand and proceed with their work. This invention contributes to the efficiency of business communication and the promotion of accurate understanding.

[0772] The processing flow will be explained below.

[0773] Step 1:

[0774] The user inputs instructions or requests into the terminal. Input can be selected as either text input via a text box or voice input using a microphone.

[0775] Step 2:

[0776] In the case of voice input, the device captures the voice and converts it into text data using voice recognition technology, which is then used in subsequent processing steps.

[0777] Step 3:

[0778] The terminal sends the entered text data to the server, which receives the text data and starts the analysis process.

[0779] Step 4:

[0780] The server analyzes the received text data using natural language processing (NLP) technology. This analysis includes grammatical analysis, word recognition, and semantic analysis. Specifically, it extracts the purpose of the instruction or request, the required actions, and related elements (people, processes, etc.).

[0781] Step 5:

[0782] The server creates a diagram of the business process based on the analysis results. The server references past business frameworks and existing diagram data to automatically generate appropriate flow diagrams and structure diagrams. This diagram definition includes the relationships between related elements and the order of actions.

[0783] Step 6:

[0784] The generated flow diagrams and structure diagrams are saved as files by the server in common image formats (e.g. PNG, JPEG) or vector data formats (e.g. SVG).

[0785] Step 7:

[0786] The server shares the generated shape with the user and the requester. Sharing can be done via email or using a file sharing service. The generated shape file is attached to the email and sent to the requester.

[0787] Step 8:

[0788] The user and requester review the diagram and check the accuracy of the content. They provide corrections and additional feedback as needed. If there are any problems with the diagram content, the user can re-enter instructions or requests for a revised version, and the server will regenerate it.

[0789] Step 9:

[0790] The final confirmed diagram is used as a tool to support the user in progressing with their work. The generated diagram is used as a means to help users understand the business process and improve work efficiency.

[0791] Example 1

[0792] 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."

[0793] Modern business processes contain a large amount of information, and efficiently analyzing and visualizing it is important for smooth communication between teams and individuals. However, current systems often require manual analysis and visualization of natural language data, which is time-consuming and prone to human error. This invention provides a system that eliminates these inefficiencies and enables accurate and rapid visualization and sharing of business processes.

[0794] 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.

[0795] In this invention, the server includes: a means for a user to input natural language data from a terminal; a means for transmitting the input natural language data to the server; a means for the server to analyze the natural language data using natural language processing technology; a means for diagramming a business process based on the analysis results; and a means for sharing the generated diagram with the user and the requester. This makes it possible to automatically analyze the natural language data input by the user, generate an appropriate business process diagram, and quickly share it.

[0796] "Natural language data" refers to information expressed by humans using normal language, such as text or speech.

[0797] "Terminal" refers to an electronic device through which a user inputs natural language data.

[0798] "Server" refers to a central computer system that receives input natural language data and performs analysis and visualization.

[0799] "Natural language processing technology" refers to information processing technology for analyzing human language and understanding meaning and intent.

[0800] "Analysis" refers to the process of extracting and understanding important information from natural language data.

[0801] A "business process" refers to a series of activities and procedures for carrying out a specific business task.

[0802] "Graphing" refers to the presentation of analytical results in the form of graphs or figures that are visually easy to understand.

[0803] "Sharing" refers to the act of sharing the generated graphics and information with other users or requesters.

[0804] "Speech recognition technology" refers to technology for converting voice data into text.

[0805] "Business framework data" refers to data that records past business procedures and activities.

[0806] A "shape generation library" refers to a collection of software for generating shapes programmatically.

[0807] A "flow diagram" is a diagram that visually shows a series of steps or processes using arrows and symbols.

[0808] The present invention provides a system for analyzing natural language data, graphically representing a business process, and sharing the generated graphical representation with users and clients. Specific embodiments of the system are described below.

[0809] Server Roles

[0810] The server plays a central role in the system. First, it receives the natural language data entered by the user from the device. If the natural language data is voice data, it is converted into text data using voice recognition technology such as the Google Cloud Speech-to-Text API.

[0811] The server is equipped with an analysis module that uses natural language processing (NLP) techniques such as BERT and the Transformers library. This analysis module analyzes the input natural language data and extracts important information, such as the purpose of the instruction or request, the required action, and the elements involved (people, processes, etc.).

[0812] Next, the server generates flow diagrams and structure diagrams based on the analyzed data using a diagram generation library such as Graphviz. This module learns from past business framework data and automatically creates appropriate diagrams.

[0813] The generated diagram is saved as an image file (e.g., PNG format) and then shared with the user and requester. The server sends this diagram file via email, Google Drive, or Dropbox to make it easier to check the configuration.

[0814] Device Role

[0815] The device used by the user provides an interface for inputting instructions or requests. In the case of text input, instructions are entered through a text box or a chat interface, for example, a web form or a messaging app interface.

[0816] It also offers a voice input option: the user can use the microphone to input commands by voice, and the device will convert this speech into text using speech recognition technology (e.g., Amazon Transcribe) and send it to the server.

[0817] User Roles

[0818] The user's role begins by inputting instructions and requests via the terminal. Then, the user receives the diagram generated by the server and checks its contents. The user checks whether there are any misunderstandings or missing information in the generated diagram, and requests corrections as necessary.

[0819] Specific examples

[0820] For example, consider the case where a user inputs a command to start a new project into a terminal as text. The user inputs "Please start a new project" into the text box. This text data is sent to the server.

[0821] The server uses NLP technology to analyze the instructions and generate a "Start a New Project" flow chart based on the analysis results. The generated flow chart is shared with the requester via email. The requester can review the content and provide feedback if necessary.

[0822] An example of a prompt for a generative AI model is:

[0823] "Generate a flow diagram showing the steps to take when starting a new project. Analyze the following instructions: Project name: AI system development, Start date: October 1, 2023, First task: Hold a requirements definition meeting."

[0824] The above is a specific embodiment of the present invention. Through this system, the process of analyzing and visualizing natural language data is automated, making it possible to improve the efficiency of business communication and promote accurate understanding.

[0825] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0826] Step 1:

[0827] Input of natural language data via terminal

[0828] The user inputs natural language data from the device. Input methods include text input and voice input. For example, the user types "Start a new project" into a text box. In the case of voice input, the user speaks the same content into the microphone. The device converts the voice data into text data using voice recognition technology (for example, Google Cloud Speech-to-Text API).

[0829] Input: Natural language data: "Start a new project"

[0830] Output: Natural language data in text format

[0831] Step 2:

[0832] Sending data from the device to the server

[0833] The device sends the generated text data to the server. The HTTPS protocol is used for communication to ensure data security. For example, the device sends the text data "Start a new project" to the server via HTTPS.

[0834] Input: Natural language data in text format

[0835] Output: Natural language data sent to the server

[0836] Step 3:

[0837] Data analysis by server

[0838] The server passes the received text data to a Natural Language Processing (NLP) module, which uses BERT and Transformers libraries to analyze the data and extract key information. For example, it can analyze a request like "Start a new project" to extract the goal, required actions, and related elements.

[0839] Input: Natural language data received by the server

[0840] Output: Analyzed key information (objectives, actions, stakeholders, etc.)

[0841] Step 4:

[0842] Server-based business process visualization

[0843] The server uses a diagram generation library such as Graphviz to visualize the business process based on the analyzed data. For example, it generates a flow diagram for "Starting a new project" based on the analysis results. The flow diagram includes specific steps such as "Hold a requirements definition meeting."

[0844] Input: Parsed data (critical information)

[0845] Output: Business process graphic file (e.g. PNG format)

[0846] Step 5:

[0847] Saving and sharing generated shape files

[0848] The server saves the generated graphic files and shares them with the user or requester via email, Google Drive, or Dropbox. For example, the generated flow diagram is saved as a PNG file and sent to the requester's email address.

[0849] Input: Business process shape file

[0850] Output: Shared geometry files (sent to users and requesters)

[0851] Step 6:

[0852] User shape confirmation and feedback

[0853] The user checks the received graphic file and provides feedback as necessary. If there is a misunderstanding or missing information, they can request corrections again. For example, if the user checks the received flow diagram and determines that there is an error in the details of the "Requirements Definition Meeting," they will send an email with feedback to that effect.

[0854] Input: Received shape file

[0855] Output: Feedback provided (requests for corrections if necessary)

[0856] (Application example 1)

[0857] 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."

[0858] Currently, many factories are automating their business processes, but there is no system that can understand instructions in natural language, accurately visualize the business process as a flow diagram, and control robots based on that diagram. There is also a need to quickly and accurately share the generated diagrams and instructions with users and clients. The objective of this invention is to solve this technical deficiency.

[0859] 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.

[0860] In this invention, the server includes means for analyzing the extracted natural language data, means for diagramming the business process based on the analysis results, means for sharing the generated diagram with the user and the client, and means for controlling the robot based on the generated diagram, thereby enabling accurate understanding of instructions in natural language, automatic generation of a process diagram, and automatic control of the robot based on the diagram.

[0861] "Extracted natural language data" refers to instructions or requests entered by a user in voice or text format.

[0862] The "analysis means" refers to natural language processing technology used to interpret input natural language data and understand its content.

[0863] "Means for visualizing business processes" refers to technology that generates flow diagrams and structure diagrams that visually represent business flows and processes based on analyzed data.

[0864] "Generated diagrams" are flow diagrams or structure diagrams that are automatically created based on analyzed natural language data.

[0865] "Means for sharing with users and requesters" refers to the technology and functions for sending the generated figures to users and requesters via email or file sharing services.

[0866] "Means for controlling robots" refers to the technology and functions that give specific operational instructions to robots in the factory based on the information in the generated flow diagrams and structural diagrams.

[0867] The present invention is a system that analyzes extracted natural language data, visualizes business processes, and shares the generated diagrams with users and clients. An embodiment of this system will be described in detail below.

[0868] Server Roles

[0869] The server plays a central role in the system. First, the server receives natural language data input by the user from their device. The natural language data is input in the form of voice or text, and in the case of voice, it is converted into text data using voice recognition technology. For voice recognition, common voice recognition technology or APIs are used.

[0870] The server then uses an analysis module that includes natural language processing (NLP) technology to analyze the input natural language data and extract key information, such as the purpose of the instruction or request, the required action, and the elements involved (people, processes, etc.). The NLP technology used can be the Google Cloud Natural Language API.

[0871] Based on the analyzed data, a module that visualizes business processes is used to generate flow diagrams and structure diagrams. This module learns from past business frameworks and visualized data, and automatically creates appropriate diagrams. Visualization tools such as Graphviz are used for visualization.

[0872] The generated diagram is saved as a diagram file and then shared with the user and the requester. The server sends this diagram file via email or file sharing service, making it easier to check the configuration. It also includes a means to control the robot based on the generated diagram. ROS (Robot Operating System) can be used to control the robot.

[0873] Device Role

[0874] The device used by the user provides an interface for inputting instructions and requests. Input methods include text input, for example, inputting instructions through a text box or a chat interface. Voice input is also available. When the user inputs instructions by voice using a microphone, the device uses voice recognition technology to convert this voice into text and send it to the server.

[0875] User Roles

[0876] The user's role begins by inputting instructions and requests via a terminal. The user then receives and checks the diagram generated by the server. The robot begins to operate based on the generated diagram, and the business process progresses. The user checks the diagram for misunderstandings or missing information, and requests corrections as necessary.

[0877] As a specific example, when a new production line is added in a factory, instructions are input by voice, such as "Please start the inspection process on production line 1. Next, please pack the finished products." The voice data is converted into text and, after NLP analysis, a business flow diagram for the inspection process and packaging process is generated. The factory manager reviews the generated flow diagram and requests any necessary revisions. The following are examples of prompt sentences that could be considered:

[0878] "Please parse the following natural language instructions and generate a workflow diagram.

[0879] Instructions: Start the inspection process on production line 1. Then, package the finished products.

[0880] Generate a flow diagram using Graphviz based on the analysis results of the generative AI.

[0881] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0882] Step 1:

[0883] A user uses a device to input natural language data. This data can be input in the form of voice or text. For voice input, the user uses a microphone to input instructions, and the device converts the speech to text using speech recognition technology such as Google Speech-to-Text. For text input, the user inputs instructions through a chat interface or text box.

[0884] Input: Natural language audio or text data

[0885] Output: Text data

[0886] Step 2:

[0887] The device sends text data to the server, which then analyzes it using the Google Cloud Natural Language API. The API analyzes the instruction and extracts important information (the purpose of the instruction, the required action, and the relevant elements). The server receives the analysis results and continues processing.

[0888] Input: Text data

[0889] Output: Analysis results (extracted important information)

[0890] Step 3:

[0891] The server visualizes the business process based on the analysis results. Here, a visualization tool such as Graphviz is used to automatically generate a flow diagram and structure diagram of the business process. The server saves the generated flow diagram and structure diagram and proceeds to the next step.

[0892] Input: Analysis results

[0893] Output: Flow diagram or structure diagram

[0894] Step 4:

[0895] The server shares the generated graphic files with the user and requester. To do this, the server sends the graphic files via email or a file sharing system. The recipients check the graphic files and provide feedback if necessary.

[0896] Input: Flow diagram or structure diagram

[0897] Output: Sharing shape files

[0898] Step 5:

[0899] The server executes the means to control the robot based on the generated diagram, and sends specific operation instructions to the robot using ROS (Robot Operating System). The server controls the robot so that it automatically executes the business process according to each step of the generated flow diagram.

[0900] Input: Flow diagram or structure diagram

[0901] Output: Control instructions to the robot

[0902] Through the above steps, a system is realized in which a user inputs instructions in natural language, a business process is visualized based on the instructions, and a robot automatically operates based on the visualized instructions.

[0903] 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.

[0904] The present invention combines a system that analyzes extracted natural language data, visualizes business processes based on the analysis results, and shares the generated diagrams with users and clients, with an emotion engine that recognizes user emotions. An embodiment of this system will be described in detail.

[0905] Server Roles

[0906] The server plays a central role in the system. First, it receives natural language data entered by the user from the terminal. Natural language data can be entered in the form of voice or text, but in the case of voice, it must first be converted into text data using voice recognition technology.

[0907] The server is equipped with an analysis module that incorporates natural language processing (NLP) technology. This analysis module analyzes the input natural language data and extracts important information, including the purpose of the instruction or request, the required action, and related elements (people, processes, etc.).

[0908] Next, the server uses the analysis results to generate flow diagrams and structure diagrams using a module that visualizes business processes. This module learns from past business frameworks and visualized data, and automatically creates appropriate diagrams.

[0909] The generated diagram is saved as a diagram file and then shared with the user and requester. The server sends this diagram file via email or a file sharing service, making it easy to check the configuration.

[0910] Furthermore, the present invention incorporates an emotion engine that extracts emotions from the user's natural language data and incorporates them as part of the analysis. This emotion data is used to influence the visualization of business processes, for example, to generate diagrams that reduce stress or increase motivation.

[0911] Device Role

[0912] The terminal used by the user provides an interface for inputting instructions or requests. Input methods include text input, and instructions can be input through a text box or a chat interface, for example.

[0913] There is also a voice input option, where the user can use the microphone to input commands by voice, and the device will use voice recognition technology to convert this speech into text and send it to the server.

[0914] User Roles

[0915] The user's role begins by inputting instructions and requests via the terminal. The user then receives and checks the diagram generated by the server. The user checks whether there are any misunderstandings or missing information in the generated diagram, and requests corrections as necessary.

[0916] Furthermore, the system also checks the analysis results generated by the emotion engine. For example, if it determines that the user's input indicates stress, it will modify the diagram based on that information to make the work process go more smoothly.

[0917] As a specific example, consider the case where a user inputs a command to start a new project into a terminal. The input text data is sent to the server, which analyzes the command using NLP technology and an emotion engine. Based on the analysis results, the server generates a flow diagram for starting the project. The generated flow diagram is shared with the requester via email, who then confirms its contents. The confirmed diagram can support the user's work progress and prevent misunderstandings. Furthermore, emotion data is used to adjust the business process so that it takes the user's emotions into consideration.

[0918] The main features of this system are that the process of analyzing natural language data and visualizing it is automated, and that emotion recognition and the results are reflected in the visualization. This allows even new graduates and junior employees to smoothly understand and proceed with their work. This invention contributes to the efficiency of business communication and the promotion of accurate understanding.

[0919] The processing flow will be explained below.

[0920] Step 1:

[0921] The user inputs instructions or requests into the terminal. Input can be selected as either text input via a text box or voice input using a microphone.

[0922] Step 2:

[0923] In the case of voice input, the device captures the voice and converts it into text data using voice recognition technology, which is then used in subsequent processing steps.

[0924] Step 3:

[0925] The terminal sends the entered text data to the server, which receives the text data and starts the analysis process.

[0926] Step 4:

[0927] The server analyzes the received text data using natural language processing (NLP) technology. This analysis includes grammatical analysis, word recognition, and semantic analysis. Specifically, it extracts the purpose of the instruction or request, the required actions, and related elements (people, processes, etc.).

[0928] Step 5:

[0929] The server performs emotion analysis on the extracted data using an emotion engine, which recognizes the user's emotional state (e.g., stress, satisfaction, excitement, etc.) and incorporates this information into the subsequent visualization process.

[0930] Step 6:

[0931] The server visualizes business processes based on the analysis results and emotional data. The server references past business frameworks and existing graphic data to automatically generate appropriate flow diagrams and structure diagrams. These diagrams include the relationships between related elements and the order of actions. Furthermore, the diagrams are adjusted according to the user's emotional state, incorporating elements that reduce stress and increase motivation.

[0932] Step 7:

[0933] The generated flow diagrams and structure diagrams are saved as files by the server in common image formats (e.g. PNG, JPEG) or vector data formats (e.g. SVG).

[0934] Step 8:

[0935] The server shares the generated shape with the user and the requester. Sharing can be done via email or using a file sharing service. The generated shape file is attached to the email and sent to the requester.

[0936] Step 9:

[0937] The user and requester review the diagram and check the accuracy of the content. They provide corrections and additional feedback as needed. If there are any problems with the diagram content, the user can re-enter instructions or requests for a revised version, and the server will regenerate it.

[0938] Step 10:

[0939] The final confirmed diagram is used as a tool to support the user's work progress. The generated diagram helps users to understand the work process more easily and is used as a means to improve work efficiency. In addition, the diagram created using emotion data takes into account the user's emotional state, contributing to an improvement of the work environment.

[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] Conventional business process management systems lack the ability to efficiently analyze natural language data and automatically visualize it. Furthermore, they do not optimize business processes by taking into account user emotions. This has led to problems such as reduced work efficiency, stress, and increased misunderstandings.

[0943] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for analyzing extracted natural language data, a means for diagramming a business process based on the analysis result, a means for sharing the generated diagram with the user and the requester, a means for recognizing the user's emotion, and a means for adjusting the diagram of the business process based on the emotion recognition. This enables efficient analysis of natural language data and automatic diagramming of the business process taking emotion into consideration.

[0944] "Extracted natural language data" refers to human language data input in speech or text format.

[0945] The "means for analyzing" is the part of the system that includes natural language processing technology to identify important information from natural language data (such as the purpose of the instruction, the action required, and the people and processes involved).

[0946] "Means for visualizing business processes" refers to an automatically generated module that visually represents business steps and flows based on analyzed information.

[0947] "Generated shapes" refers to flow diagrams and structure diagrams that visually represent business processes.

[0948] "Means for sharing with users and requesters" refers to a function for sending the generated graphic files via email or file sharing services.

[0949] "Means for recognizing emotions" refers to a sentiment analysis engine for extracting emotional information from the user's natural language data.

[0950] "Means for adjusting the business process diagram based on emotion recognition" refers to an adjustment function for adapting the business process to the user's emotions using the extracted emotion data.

[0951] The present invention combines a system that analyzes extracted natural language data, visualizes business processes, and shares the generated diagrams with users and clients, with an emotion engine that recognizes user emotions. This system can be implemented as follows.

[0952] First, the server, which plays a central role in this system, receives natural language data input by the user from their device. Natural language data can be input in the form of voice or text, but if it is voice, it must be converted into text data using voice recognition technology. This conversion is achieved using voice recognition services such as Google Cloud Speech-to-Text and IBM Watson Speech to Text.

[0953] The server is then equipped with an analysis module that includes natural language processing (NLP) technology. This module uses Python NLP libraries such as spaCy and NLTK to analyze the input natural language data and extract important information, such as the purpose of the instruction, required actions, and related people and processes.

[0954] The server then runs a module that visualizes the business process based on the analysis results. This module uses a deep learning model to learn from past business data and business frameworks, and generates appropriate flow diagrams and structure diagrams. The visualization can be done using tools such as Diagram or Lucidchart API.

[0955] The generated shape is saved as a shape file and then shared with the user and the requester. The server sends this shape file via email or a file sharing service (e.g., Google Drive, Dropbox, etc.), making it easy to check the contents.

[0956] Furthermore, the present invention incorporates an emotion engine that extracts emotions from the user's natural language data using Microsoft Azure's Text Analytics for sentiment analysis, IBM Watson Tone Analyzer, etc. The extracted emotion data is reflected in the visualization of business processes and is used to generate, for example, flows that reduce stress or diagrams that increase motivation.

[0957] As a specific example, consider the case where a user inputs text into a terminal to instruct the start of a new project. For example, if the user inputs "Please set up a testing process for new software," the terminal sends this data to the server. The server uses NLP technology and an emotion engine to analyze the instruction and generate a flow diagram for starting the project. The generated flow diagram is shared with the requester via email, who then confirms its contents. This diagram supports the user's work progress and prevents misunderstandings. Emotional data is also used to adjust the business process to take the user's emotions into consideration.

[0958] A concrete example of a prompt is:

[0959] Set up a testing process for new software.

[0960] Examples include:

[0961] This system is capable of efficiently analyzing natural language data and automatically diagramming business processes that take emotions into account, achieving both improved business efficiency and consideration for user emotions.

[0962] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0963] The flow of this system's program processing

[0964] Step 1:

[0965] User

[0966] The user inputs instructions and requests through the terminal, either via text input (using a text box or chat interface) or voice input (using a microphone).

[0967] Input: User instructions or requests (natural language data)

[0968] Output: Text or audio data on the device

[0969] Specific behavior:

[0970] The user inputs a text into the terminal saying, "I want you to create a promotion plan for a new product."

[0971] Step 2:

[0972] Terminal

[0973] When the device receives voice data, it converts it into text data using voice recognition technology, using voice recognition services such as Google Cloud Speech-to-Text and IBM Watson Speech to Text.

[0974] Input: User's voice data

[0975] Output: Text data

[0976] Specific behavior:

[0977] The user types "Please automatically generate the agenda for the next meeting" by voice, and the device converts this into text data.

[0978] Step 3:

[0979] Terminal

[0980] The device sends the acquired text data to the server, which then uses it as input data for analysis.

[0981] Input: Text data

[0982] Output: Text data sent to the server

[0983] Specific behavior:

[0984] The terminal transmits text data saying "Please prepare a campaign to launch a new product" to the server.

[0985] Step 4:

[0986] server

[0987] The server passes the received text data to an NLP analysis module that extracts important information using the Python libraries spaCy and NLTK.

[0988] Input: Text data

[0989] Output: Analysis results (purpose of instruction, required actions, related elements)

[0990] Specific behavior:

[0991] The server analyzes the instruction "Prepare a campaign to launch a new product" and extracts keywords such as "new product," "launch," "campaign," and "preparation."

[0992] Step 5:

[0993] server

[0994] The server runs a module that visualizes business processes based on the analysis results, generating flow diagrams and structure diagrams using Diagram and Lucidchart APIs.

[0995] Input: Analysis results

[0996] Output: Graphic files (flow diagrams and structure diagrams)

[0997] Specific behavior:

[0998] The server generates a flow diagram for "preparing for a new product launch campaign" and creates figures showing steps (e.g., procuring products, creating advertising materials, planning a marketing strategy, etc.).

[0999] Step 6:

[1000] server

[1001] The server stores the generated graphic files and then shares them with users and requesters via email or file sharing services (e.g., Google Drive, Dropbox).

[1002] Input: Shape file

[1003] Output: Shared link or shared file

[1004] Specific behavior:

[1005] The server uploads the generated shape file to Google Drive and sends the shared link to the user via email.

[1006] Step 7:

[1007] server

[1008] The server uses a sentiment engine to extract sentiment from the user's natural language data, such as Microsoft Azure's Text Analytics for sentiment analysis or IBM Watson Tone Analyzer.

[1009] Input: Natural language data

[1010] Output: Emotional information

[1011] Specific behavior:

[1012] The server detects high stress levels from the user's input and outputs emotional data such as "Stress: High."

[1013] Step 8:

[1014] server

[1015] The server adjusts the business process diagram based on the emotion recognition, reflecting the extracted emotion data and providing a business process that is adapted to the user's emotions.

[1016] Input: Emotion information, generated shapes

[1017] Output: adjusted geometry file

[1018] Specific behavior:

[1019] To reduce the high stress on the server, a new flow diagram is generated in which a low-priority task is inserted instead of the normal business flow.

[1020] (Application example 2)

[1021] 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."

[1022] Modern business processes require systems that can automatically analyze and visualize instructions and reports in natural language. However, conventional systems are unable to consider the user's emotions, making it difficult to appropriately alleviate stress and tension. Furthermore, there are limited means for visualizing the generated diagrams and business process flows in real time and providing them to users. This can increase the workload of workers during work and potentially reduce productivity.

[1023] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1024] In this invention, the server includes means for analyzing extracted natural language data, means for diagramming a business process based on the analysis results, means for sharing the generated diagram with the user and the client, means including an emotion engine for recognizing the user's emotions, means for optimizing the business process based on the user's input content and emotion data, and means for displaying the generated diagram and advice within the user's field of view. This not only automatically analyzes and diagrams instructions and reports in natural language, but also enables process optimization that takes the user's emotions into consideration, thereby reducing worker stress and improving the efficiency of work execution.

[1025] "Means for analyzing extracted natural language data" refers to technology that analyzes natural language data input in voice or text format, understands the content of instructions or reports, and extracts important information.

[1026] "Means of visualizing business processes based on analysis results" refers to a technology that automatically generates diagrams such as flowcharts and structural diagrams that visually represent the flow and structure of business processes in an easy-to-understand manner based on the extracted information.

[1027] "Means for sharing the generated graphics with the user and the requester" refers to a technology for sending the generated graphics file to the user and the requester via email or a file sharing service, so that they can easily check it.

[1028] "Means including an emotion engine that recognizes user emotions" refers to technology that identifies emotions from natural language data entered by a user and incorporates them into analysis. For example, it is technology that determines whether a user is feeling stressed.

[1029] "Means for optimizing business processes based on user input and emotional data" refers to technology that adjusts the flow and structure of business processes based on user instructions and emotional data, allowing work to proceed efficiently while reducing the stress and tension felt by users.

[1030] "Means for displaying generated diagrams and advice in the user's field of vision" refers to technology that uses a device such as smart glasses to display generated business process diagrams and advice for the user on the screen in real time.

[1031] The present invention is a system that analyzes natural language data, visualizes the user's work process, and recognizes the user's emotions to optimize the work, using a "security support smart glasses app" applied to security services as an example. The specific configuration and processing procedure of this system are described below.

[1032] Server Roles

[1033] The server plays a central role in the system. The main processes performed by the server are shown below.

[1034] 1. Speech Recognition:

[1035] When a user gives instructions or reports by voice, the voice data is sent to the server through the microphone in the smart glasses.

[1036] The server uses the speech_recognition library to convert the audio data into text data.

[1037] 2. Natural Language Processing:

[1038] The converted text data is then analyzed using spaCy's Japanese model, which extracts the instructions and relevant important information.

[1039] 3. Business process visualization:

[1040] Based on the extracted information, we generate a flowchart of the business process using networkx and matplotlib, which helps users to quickly understand the business process.

[1041] 4. Emotion recognition:

[1042] The server uses the emotion_recognition library to identify emotions from the natural language data entered by the user and incorporate them into the analysis.

[1043] 5. Business process optimization:

[1044] The server optimizes business processes based on the user's instructions and emotional data. For example, if the user is feeling stressed, it generates processes and advice to alleviate the stress.

[1045] 6. Data Sharing:

[1046] The generated diagrams and advice are displayed to the user in real time through the smart glasses display.

[1047] Device Role

[1048] The device (smart glasses) provides an interface with the user. Specifically, it plays the following roles:

[1049] Input acceptance:

[1050] It is equipped with a microphone that allows users to give voice instructions and reports, and transmits voice data to the server.

[1051] display:

[1052] The generated figures and advice sent from the server are displayed on the display of the smart glasses and provided to the user.

[1053] User Roles

[1054] The user needs to operate the system.

[1055] 1. Voice input:

[1056] The user gives voice instructions and reports to the smart glasses, for example, "There was a strange noise in the south corridor on the third floor. We will begin investigating."

[1057] 2. Check and respond:

[1058] The user can view the generated business process flowchart on the smart glasses display and take appropriate action.

[1059] Specific use cases

[1060] A security guard on patrol at a commercial facility at night might say, "I heard a strange noise in the south corridor on the third floor. I will begin investigating." This voice data is sent to a server via smart glasses and analyzed using natural language processing technology. Based on the analysis results, a flowchart of the business process is generated and displayed in real time on the smart glasses' display. Furthermore, an emotion engine detects the user's stress and displays appropriate advice.

[1061] Specific prompt examples:

[1062] "We heard a strange noise in the south corridor on the third floor. We are beginning an investigation."

[1063] This system allows users to visually check business processes and respond appropriately, and also makes it easier to respond to changes in emotions.

[1064] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1065] Step 1:

[1066] The user provides voice input via the smart glasses.

[1067] Input: User voice command: "We've heard a strange noise in the south hallway on the third floor. We're beginning an investigation."

[1068] How it works: The microphone captures the user's voice and sends it to the device as audio data.

[1069] Output: Audio data is collected on the device.

[1070] Step 2:

[1071] The device sends the collected voice data to the server.

[1072] Input: Audio data collected on the device

[1073] Operation: The device sends voice data to a server via the Internet.

[1074] Output: The audio data arrives at the server.

[1075] Step 3:

[1076] The server performs voice recognition and converts the voice data into text data.

[1077] Input: Audio data sent to the server

[1078] How it works: The server uses the speech_recognition library to convert the audio data into text data.

[1079] Output: The audio data is converted to text data that reads, "A strange noise was heard in the south corridor on the third floor. An investigation will begin."

[1080] Step 4:

[1081] The server performs natural language processing to extract important information from the text data.

[1082] Input: Text data obtained by speech recognition

[1083] How it works: The server uses spaCy's Japanese model to analyze the text data and extract instructions (location of the abnormal noise and start of investigation).

[1084] Output: Analysis result data will be generated, including the location of the noise ("South Corridor on the 3rd floor") and the action "Begin investigation."

[1085] Step 5:

[1086] The server visualizes the business process.

[1087] Input: Analysis result data

[1088] How it works: The server uses networkx and matplotlib to generate a flowchart of a business process. For example, it creates a graph structure containing the nodes "abnormal noise detected" and "investigation started" and draws a visual flowchart.

[1089] Output: An image file of the business process flowchart is generated.

[1090] Step 6:

[1091] The server performs analysis to recognize the user's emotions.

[1092] Input: Text data

[1093] How it works: The server uses the emotion_recognition library to detect the user's emotions (e.g., stress level) from text data.

[1094] Output: The detected emotion data is generated.

[1095] Step 7:

[1096] The server optimizes business processes based on emotion data.

[1097] Input: Analysis result data and emotion data

[1098] Action: If the emotion data indicates high stress, the server adds relaxation advice (e.g., take a deep breath) to the business process. This advice is also included in the flowchart.

[1099] Output: An image file of the optimized business process flowchart is generated.

[1100] Step 8:

[1101] The terminal displays the generated graphics and advice in the user's field of view.

[1102] Input: Image file of optimized business process flowchart

[1103] Operation: The server sends an image file of the generated flowchart to the terminal, which displays the flowchart and advice on the display of the smart glasses.

[1104] Output: A business process flowchart and relaxation advice are displayed in the user's field of vision.

[1105] This allows users to visually check business processes and respond appropriately, and creates a system that can easily respond to changes in emotions.

[1106] 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.

[1107] 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.

[1108] 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.

[1109] [Fourth embodiment]

[1110] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1111] 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.

[1112] 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).

[1113] 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.

[1114] 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.

[1115] 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).

[1116] 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.

[1117] 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.

[1118] 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.

[1119] 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.

[1120] 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.

[1121] 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.

[1122] 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."

[1123] The present invention provides a system that analyzes extracted natural language data, visualizes business processes based on the analysis results, and shares the generated diagrams with users and clients. An embodiment of this system will be described in detail.

[1124] Server Roles

[1125] The server plays a central role in the system. First, it receives natural language data entered by the user from the terminal. Natural language data can be entered in the form of voice or text, but in the case of voice, it must first be converted into text data using voice recognition technology.

[1126] The server is equipped with an analysis module that incorporates natural language processing (NLP) technology. This analysis module analyzes the input natural language data and extracts important information, including the purpose of the instruction or request, the required action, and the elements involved (people, processes, etc.).

[1127] Next, the server uses the analyzed data to generate flow diagrams and structure diagrams using a module that visualizes business processes. This module learns from past business frameworks and visualized data, and automatically creates appropriate diagrams.

[1128] The generated diagram is saved as a diagram file and then shared with the user and requester. The server sends this diagram file via email or a file sharing service, making it easy to check the configuration.

[1129] Device Role

[1130] The terminal used by the user provides an interface for inputting instructions or requests. Input methods include text input, and instructions can be input through a text box or a chat interface, for example.

[1131] There is also a voice input option, where the user can use the microphone to input commands by voice, and the device will use voice recognition technology to convert this speech into text and send it to the server.

[1132] User Roles

[1133] The user's role begins by inputting instructions and requests via the terminal. The user then receives and checks the diagram generated by the server. The user checks whether there are any misunderstandings or missing information in the generated diagram, and requests corrections as necessary.

[1134] As a specific example, consider the case where a user inputs text into a terminal to instruct the start of a new project. The input text data is sent to a server, which uses NLP technology to analyze the content of the instruction. Based on the analysis results, the server generates a flow diagram for starting the project. The generated flow diagram is shared with the requester via email, who then confirms its contents. The confirmed diagram can support the user's work progress and prevent misunderstandings.

[1135] The main feature of this system is that the process of analyzing and visualizing natural language data is automated. This allows even new graduates and junior employees to smoothly understand and proceed with their work. This invention contributes to the efficiency of business communication and the promotion of accurate understanding.

[1136] The processing flow will be explained below.

[1137] Step 1:

[1138] The user inputs instructions or requests into the terminal. Input can be selected as either text input via a text box or voice input using a microphone.

[1139] Step 2:

[1140] In the case of voice input, the device captures the voice and converts it into text data using voice recognition technology, which is then used in subsequent processing steps.

[1141] Step 3:

[1142] The terminal sends the entered text data to the server, which receives the text data and starts the analysis process.

[1143] Step 4:

[1144] The server analyzes the received text data using natural language processing (NLP) technology. This analysis includes grammatical analysis, word recognition, and semantic analysis. Specifically, it extracts the purpose of the instruction or request, the required actions, and related elements (people, processes, etc.).

[1145] Step 5:

[1146] The server creates a diagram of the business process based on the analysis results. The server references past business frameworks and existing diagram data to automatically generate appropriate flow diagrams and structure diagrams. This diagram definition includes the relationships between related elements and the order of actions.

[1147] Step 6:

[1148] The generated flow diagrams and structure diagrams are saved as files by the server in common image formats (e.g. PNG, JPEG) or vector data formats (e.g. SVG).

[1149] Step 7:

[1150] The server shares the generated shape with the user and the requester. Sharing can be done via email or using a file sharing service. The generated shape file is attached to the email and sent to the requester.

[1151] Step 8:

[1152] The user and requester review the diagram and check the accuracy of the content. They provide corrections and additional feedback as needed. If there are any problems with the diagram content, the user can re-enter instructions or requests for a revised version, and the server will regenerate it.

[1153] Step 9:

[1154] The final confirmed diagram is used as a tool to support the user in progressing with their work. The generated diagram is used as a means to help users understand the business process and improve work efficiency.

[1155] Example 1

[1156] 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."

[1157] Modern business processes contain a large amount of information, and efficiently analyzing and visualizing it is important for smooth communication between teams and individuals. However, current systems often require manual analysis and visualization of natural language data, which is time-consuming and prone to human error. This invention provides a system that eliminates these inefficiencies and enables accurate and rapid visualization and sharing of business processes.

[1158] 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.

[1159] In this invention, the server includes: a means for a user to input natural language data from a terminal; a means for transmitting the input natural language data to the server; a means for the server to analyze the natural language data using natural language processing technology; a means for diagramming a business process based on the analysis results; and a means for sharing the generated diagram with the user and the requester. This makes it possible to automatically analyze the natural language data input by the user, generate an appropriate business process diagram, and quickly share it.

[1160] "Natural language data" refers to information expressed by humans using normal language, such as text or speech.

[1161] "Terminal" refers to an electronic device through which a user inputs natural language data.

[1162] "Server" refers to a central computer system that receives input natural language data and performs analysis and visualization.

[1163] "Natural language processing technology" refers to information processing technology for analyzing human language and understanding meaning and intent.

[1164] "Analysis" refers to the process of extracting and understanding important information from natural language data.

[1165] A "business process" refers to a series of activities and procedures for carrying out a specific business task.

[1166] "Graphing" refers to the presentation of analytical results in the form of graphs or figures that are visually easy to understand.

[1167] "Sharing" refers to the act of sharing the generated graphics and information with other users or requesters.

[1168] "Speech recognition technology" refers to technology for converting voice data into text.

[1169] "Business framework data" refers to data that records past business procedures and activities.

[1170] A "shape generation library" refers to a collection of software for generating shapes programmatically.

[1171] A "flow diagram" is a diagram that visually shows a series of steps or processes using arrows and symbols.

[1172] The present invention provides a system for analyzing natural language data, graphically representing a business process, and sharing the generated graphical representation with users and clients. Specific embodiments of the system are described below.

[1173] Server Roles

[1174] The server plays a central role in the system. First, it receives the natural language data entered by the user from the device. If the natural language data is voice data, it is converted into text data using voice recognition technology such as the Google Cloud Speech-to-Text API.

[1175] The server is equipped with an analysis module that uses natural language processing (NLP) techniques such as BERT and the Transformers library. This analysis module analyzes the input natural language data and extracts important information, such as the purpose of the instruction or request, the required action, and the elements involved (people, processes, etc.).

[1176] Next, the server generates flow diagrams and structure diagrams based on the analyzed data using a diagram generation library such as Graphviz. This module learns from past business framework data and automatically creates appropriate diagrams.

[1177] The generated diagram is saved as an image file (e.g., PNG format) and then shared with the user and requester. The server sends this diagram file via email, Google Drive, or Dropbox to make it easier to check the configuration.

[1178] Device Role

[1179] The device used by the user provides an interface for inputting instructions or requests. In the case of text input, instructions are entered through a text box or a chat interface, for example, a web form or a messaging app interface.

[1180] It also offers a voice input option: the user can use the microphone to input commands by voice, and the device will convert this speech into text using speech recognition technology (e.g., Amazon Transcribe) and send it to the server.

[1181] User Roles

[1182] The user's role begins by inputting instructions and requests via the terminal. Then, the user receives the diagram generated by the server and checks its contents. The user checks whether there are any misunderstandings or missing information in the generated diagram, and requests corrections as necessary.

[1183] Specific examples

[1184] For example, consider the case where a user inputs a command to start a new project into a terminal as text. The user inputs "Please start a new project" into the text box. This text data is sent to the server.

[1185] The server uses NLP technology to analyze the instructions and generate a "Start a New Project" flow chart based on the analysis results. The generated flow chart is shared with the requester via email. The requester can review the content and provide feedback if necessary.

[1186] An example of a prompt for a generative AI model is:

[1187] "Generate a flow diagram showing the steps to take when starting a new project. Analyze the following instructions: Project name: AI system development, Start date: October 1, 2023, First task: Hold a requirements definition meeting."

[1188] The above is a specific embodiment of the present invention. Through this system, the process of analyzing and visualizing natural language data is automated, making it possible to improve the efficiency of business communication and promote accurate understanding.

[1189] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1190] Step 1:

[1191] Input of natural language data via terminal

[1192] The user inputs natural language data from the device. Input methods include text input and voice input. For example, the user types "Start a new project" into a text box. In the case of voice input, the user speaks the same content into the microphone. The device converts the voice data into text data using voice recognition technology (for example, Google Cloud Speech-to-Text API).

[1193] Input: Natural language data: "Start a new project"

[1194] Output: Natural language data in text format

[1195] Step 2:

[1196] Sending data from the device to the server

[1197] The device sends the generated text data to the server. The HTTPS protocol is used for communication to ensure data security. For example, the device sends the text data "Start a new project" to the server via HTTPS.

[1198] Input: Natural language data in text format

[1199] Output: Natural language data sent to the server

[1200] Step 3:

[1201] Data analysis by server

[1202] The server passes the received text data to a Natural Language Processing (NLP) module, which uses BERT and Transformers libraries to analyze the data and extract key information. For example, it can analyze a request like "Start a new project" to extract the goal, required actions, and related elements.

[1203] Input: Natural language data received by the server

[1204] Output: Analyzed key information (objectives, actions, stakeholders, etc.)

[1205] Step 4:

[1206] Server-based business process visualization

[1207] The server uses a diagram generation library such as Graphviz to visualize the business process based on the analyzed data. For example, it generates a flow diagram for "Starting a new project" based on the analysis results. The flow diagram includes specific steps such as "Hold a requirements definition meeting."

[1208] Input: Parsed data (critical information)

[1209] Output: Business process graphic file (e.g. PNG format)

[1210] Step 5:

[1211] Saving and sharing generated shape files

[1212] The server saves the generated graphic files and shares them with the user or requester via email, Google Drive, or Dropbox. For example, the generated flow diagram is saved as a PNG file and sent to the requester's email address.

[1213] Input: Business process shape file

[1214] Output: Shared geometry files (sent to users and requesters)

[1215] Step 6:

[1216] User shape confirmation and feedback

[1217] The user checks the received graphic file and provides feedback as necessary. If there is a misunderstanding or missing information, they can request corrections again. For example, if the user checks the received flow diagram and determines that there is an error in the details of the "Requirements Definition Meeting," they will send an email with feedback to that effect.

[1218] Input: Received shape file

[1219] Output: Feedback provided (requests for corrections if necessary)

[1220] (Application example 1)

[1221] 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."

[1222] Currently, many factories are automating their business processes, but there is no system that can understand instructions in natural language, accurately visualize the business process as a flow diagram, and control robots based on that diagram. There is also a need to quickly and accurately share the generated diagrams and instructions with users and clients. The objective of this invention is to solve this technical deficiency.

[1223] 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.

[1224] In this invention, the server includes means for analyzing the extracted natural language data, means for diagramming the business process based on the analysis results, means for sharing the generated diagram with the user and the client, and means for controlling the robot based on the generated diagram, thereby enabling accurate understanding of instructions in natural language, automatic generation of a process diagram, and automatic control of the robot based on the diagram.

[1225] "Extracted natural language data" refers to instructions or requests entered by a user in voice or text format.

[1226] The "analysis means" refers to natural language processing technology used to interpret input natural language data and understand its content.

[1227] "Means for visualizing business processes" refers to technology that generates flow diagrams and structure diagrams that visually represent business flows and processes based on analyzed data.

[1228] "Generated diagrams" are flow diagrams or structure diagrams that are automatically created based on analyzed natural language data.

[1229] "Means for sharing with users and requesters" refers to the technology and functions for sending the generated figures to users and requesters via email or file sharing services.

[1230] "Means for controlling robots" refers to the technology and functions that give specific operational instructions to robots in the factory based on the information in the generated flow diagrams and structural diagrams.

[1231] The present invention is a system that analyzes extracted natural language data, visualizes business processes, and shares the generated diagrams with users and clients. An embodiment of this system will be described in detail below.

[1232] Server Roles

[1233] The server plays a central role in the system. First, the server receives natural language data input by the user from their device. The natural language data is input in the form of voice or text, and in the case of voice, it is converted into text data using voice recognition technology. For voice recognition, common voice recognition technology or APIs are used.

[1234] The server then uses an analysis module that includes natural language processing (NLP) technology to analyze the input natural language data and extract key information, such as the purpose of the instruction or request, the required action, and the elements involved (people, processes, etc.). The NLP technology used can be the Google Cloud Natural Language API.

[1235] Based on the analyzed data, a module that visualizes business processes is used to generate flow diagrams and structure diagrams. This module learns from past business frameworks and visualized data, and automatically creates appropriate diagrams. Visualization tools such as Graphviz are used for visualization.

[1236] The generated diagram is saved as a diagram file and then shared with the user and the requester. The server sends this diagram file via email or file sharing service, making it easier to check the configuration. It also includes a means to control the robot based on the generated diagram. ROS (Robot Operating System) can be used to control the robot.

[1237] Device Role

[1238] The device used by the user provides an interface for inputting instructions and requests. Input methods include text input, for example, inputting instructions through a text box or a chat interface. Voice input is also available. When the user inputs instructions by voice using a microphone, the device uses voice recognition technology to convert this voice into text and send it to the server.

[1239] User Roles

[1240] The user's role begins by inputting instructions and requests via a terminal. The user then receives and checks the diagram generated by the server. The robot begins to operate based on the generated diagram, and the business process progresses. The user checks the diagram for misunderstandings or missing information, and requests corrections as necessary.

[1241] As a specific example, when a new production line is added in a factory, instructions are input by voice, such as "Please start the inspection process on production line 1. Next, please pack the finished products." The voice data is converted into text and, after NLP analysis, a business flow diagram for the inspection process and packaging process is generated. The factory manager reviews the generated flow diagram and requests any necessary revisions. The following are examples of prompt sentences that could be considered:

[1242] "Please parse the following natural language instructions and generate a workflow diagram.

[1243] Instructions: Start the inspection process on production line 1. Then, package the finished products.

[1244] Generate a flow diagram using Graphviz based on the analysis results of the generative AI.

[1245] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1246] Step 1:

[1247] A user uses a device to input natural language data. This data can be input in the form of voice or text. For voice input, the user uses a microphone to input instructions, and the device converts the speech to text using speech recognition technology such as Google Speech-to-Text. For text input, the user inputs instructions through a chat interface or text box.

[1248] Input: Natural language audio or text data

[1249] Output: Text data

[1250] Step 2:

[1251] The device sends text data to the server, which then analyzes it using the Google Cloud Natural Language API. The API analyzes the instruction and extracts important information (the purpose of the instruction, the required action, and the relevant elements). The server receives the analysis results and continues processing.

[1252] Input: Text data

[1253] Output: Analysis results (extracted important information)

[1254] Step 3:

[1255] The server visualizes the business process based on the analysis results. Here, a visualization tool such as Graphviz is used to automatically generate a flow diagram and structure diagram of the business process. The server saves the generated flow diagram and structure diagram and proceeds to the next step.

[1256] Input: Analysis results

[1257] Output: Flow diagram or structure diagram

[1258] Step 4:

[1259] The server shares the generated graphic files with the user and requester. To do this, the server sends the graphic files via email or a file sharing system. The recipients check the graphic files and provide feedback if necessary.

[1260] Input: Flow diagram or structure diagram

[1261] Output: Sharing shape files

[1262] Step 5:

[1263] The server executes the means to control the robot based on the generated diagram, and sends specific operation instructions to the robot using ROS (Robot Operating System). The server controls the robot so that it automatically executes the business process according to each step of the generated flow diagram.

[1264] Input: Flow diagram or structure diagram

[1265] Output: Control instructions to the robot

[1266] Through the above steps, a system is realized in which a user inputs instructions in natural language, a business process is visualized based on the instructions, and a robot automatically operates based on the visualized instructions.

[1267] 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.

[1268] The present invention combines a system that analyzes extracted natural language data, visualizes business processes based on the analysis results, and shares the generated diagrams with users and clients, with an emotion engine that recognizes user emotions. An embodiment of this system will be described in detail.

[1269] Server Roles

[1270] The server plays a central role in the system. First, it receives natural language data entered by the user from the terminal. Natural language data can be entered in the form of voice or text, but in the case of voice, it must first be converted into text data using voice recognition technology.

[1271] The server is equipped with an analysis module that incorporates natural language processing (NLP) technology. This analysis module analyzes the input natural language data and extracts important information, including the purpose of the instruction or request, the required action, and related elements (people, processes, etc.).

[1272] Next, the server uses the analysis results to generate flow diagrams and structure diagrams using a module that visualizes business processes. This module learns from past business frameworks and visualized data, and automatically creates appropriate diagrams.

[1273] The generated diagram is saved as a diagram file and then shared with the user and requester. The server sends this diagram file via email or a file sharing service, making it easy to check the configuration.

[1274] Furthermore, the present invention incorporates an emotion engine that extracts emotions from the user's natural language data and incorporates them as part of the analysis. This emotion data is used to influence the visualization of business processes, for example, to generate diagrams that reduce stress or increase motivation.

[1275] Device Role

[1276] The terminal used by the user provides an interface for inputting instructions or requests. Input methods include text input, and instructions can be input through a text box or a chat interface, for example.

[1277] There is also a voice input option, where the user can use the microphone to input commands by voice, and the device will use voice recognition technology to convert this speech into text and send it to the server.

[1278] User Roles

[1279] The user's role begins by inputting instructions and requests via the terminal. The user then receives and checks the diagram generated by the server. The user checks whether there are any misunderstandings or missing information in the generated diagram, and requests corrections as necessary.

[1280] Furthermore, the system also checks the analysis results generated by the emotion engine. For example, if it determines that the user's input indicates stress, it will modify the diagram based on that information to make the work process go more smoothly.

[1281] As a specific example, consider the case where a user inputs a command to start a new project into a terminal. The input text data is sent to the server, which analyzes the command using NLP technology and an emotion engine. Based on the analysis results, the server generates a flow diagram for starting the project. The generated flow diagram is shared with the requester via email, who then confirms its contents. The confirmed diagram can support the user's work progress and prevent misunderstandings. Furthermore, emotion data is used to adjust the business process so that it takes the user's emotions into consideration.

[1282] The main features of this system are that the process of analyzing natural language data and visualizing it is automated, and that emotion recognition and the results are reflected in the visualization. This allows even new graduates and junior employees to smoothly understand and proceed with their work. This invention contributes to the efficiency of business communication and the promotion of accurate understanding.

[1283] The processing flow will be explained below.

[1284] Step 1:

[1285] The user inputs instructions or requests into the terminal. Input can be selected as either text input via a text box or voice input using a microphone.

[1286] Step 2:

[1287] In the case of voice input, the device captures the voice and converts it into text data using voice recognition technology, which is then used in subsequent processing steps.

[1288] Step 3:

[1289] The terminal sends the entered text data to the server, which receives the text data and starts the analysis process.

[1290] Step 4:

[1291] The server analyzes the received text data using natural language processing (NLP) technology. This analysis includes grammatical analysis, word recognition, and semantic analysis. Specifically, it extracts the purpose of the instruction or request, the required actions, and related elements (people, processes, etc.).

[1292] Step 5:

[1293] The server performs emotion analysis on the extracted data using an emotion engine, which recognizes the user's emotional state (e.g., stress, satisfaction, excitement, etc.) and incorporates this information into the subsequent visualization process.

[1294] Step 6:

[1295] The server visualizes business processes based on the analysis results and emotional data. The server references past business frameworks and existing graphic data to automatically generate appropriate flow diagrams and structure diagrams. These diagrams include the relationships between related elements and the order of actions. Furthermore, the diagrams are adjusted according to the user's emotional state, incorporating elements that reduce stress and increase motivation.

[1296] Step 7:

[1297] The generated flow diagrams and structure diagrams are saved as files by the server in common image formats (e.g. PNG, JPEG) or vector data formats (e.g. SVG).

[1298] Step 8:

[1299] The server shares the generated shape with the user and the requester. Sharing can be done via email or using a file sharing service. The generated shape file is attached to the email and sent to the requester.

[1300] Step 9:

[1301] The user and requester review the diagram and check the accuracy of the content. They provide corrections and additional feedback as needed. If there are any problems with the diagram content, the user can re-enter instructions or requests for a revised version, and the server will regenerate it.

[1302] Step 10:

[1303] The final confirmed diagram is used as a tool to support the user's work progress. The generated diagram helps users to understand the work process more easily and is used as a means to improve work efficiency. In addition, the diagram created using emotion data takes into account the user's emotional state, contributing to an improvement of the work environment.

[1304] Example 2

[1305] 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."

[1306] Conventional business process management systems lack the ability to efficiently analyze natural language data and automatically visualize it. Furthermore, they do not optimize business processes by taking into account user emotions. This has led to problems such as reduced work efficiency, stress, and increased misunderstandings.

[1307] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for analyzing extracted natural language data, a means for diagramming a business process based on the analysis result, a means for sharing the generated diagram with the user and the requester, a means for recognizing the user's emotion, and a means for adjusting the diagram of the business process based on the emotion recognition. This enables efficient analysis of natural language data and automatic diagramming of the business process taking emotion into consideration.

[1308] "Extracted natural language data" refers to human language data input in speech or text format.

[1309] The "means for analyzing" is the part of the system that includes natural language processing technology to identify important information from natural language data (such as the purpose of the instruction, the action required, and the people and processes involved).

[1310] "Means for visualizing business processes" refers to an automatically generated module that visually represents business steps and flows based on analyzed information.

[1311] "Generated shapes" refers to flow diagrams and structure diagrams that visually represent business processes.

[1312] "Means for sharing with users and requesters" refers to a function for sending the generated graphic files via email or file sharing services.

[1313] "Means for recognizing emotions" refers to a sentiment analysis engine for extracting emotional information from the user's natural language data.

[1314] "Means for adjusting the business process diagram based on emotion recognition" refers to an adjustment function for adapting the business process to the user's emotions using the extracted emotion data.

[1315] The present invention combines a system that analyzes extracted natural language data, visualizes business processes, and shares the generated diagrams with users and clients, with an emotion engine that recognizes user emotions. This system can be implemented as follows.

[1316] First, the server, which plays a central role in this system, receives natural language data input by the user from their device. Natural language data can be input in the form of voice or text, but if it is voice, it must be converted into text data using voice recognition technology. This conversion is achieved using voice recognition services such as Google Cloud Speech-to-Text and IBM Watson Speech to Text.

[1317] The server is then equipped with an analysis module that includes natural language processing (NLP) technology. This module uses Python NLP libraries such as spaCy and NLTK to analyze the input natural language data and extract important information, such as the purpose of the instruction, required actions, and related people and processes.

[1318] The server then runs a module that visualizes the business process based on the analysis results. This module uses a deep learning model to learn from past business data and business frameworks, and generates appropriate flow diagrams and structure diagrams. The visualization can be done using tools such as Diagram or Lucidchart API.

[1319] The generated shape is saved as a shape file and then shared with the user and the requester. The server sends this shape file via email or a file sharing service (e.g., Google Drive, Dropbox, etc.), making it easy to check the contents.

[1320] Furthermore, the present invention incorporates an emotion engine that extracts emotions from the user's natural language data using Microsoft Azure's Text Analytics for sentiment analysis, IBM Watson Tone Analyzer, etc. The extracted emotion data is reflected in the visualization of business processes and is used to generate, for example, flows that reduce stress or diagrams that increase motivation.

[1321] As a specific example, consider the case where a user inputs text into a terminal to instruct the start of a new project. For example, if the user inputs "Please set up a testing process for new software," the terminal sends this data to the server. The server uses NLP technology and an emotion engine to analyze the instruction and generate a flow diagram for starting the project. The generated flow diagram is shared with the requester via email, who then confirms its contents. This diagram supports the user's work progress and prevents misunderstandings. Emotional data is also used to adjust the business process to take the user's emotions into consideration.

[1322] A concrete example of a prompt is:

[1323] Set up a testing process for new software.

[1324] Examples include:

[1325] This system is capable of efficiently analyzing natural language data and automatically diagramming business processes that take emotions into account, achieving both improved business efficiency and consideration for user emotions.

[1326] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1327] The flow of this system's program processing

[1328] Step 1:

[1329] User

[1330] The user inputs instructions and requests through the terminal, either via text input (using a text box or chat interface) or voice input (using a microphone).

[1331] Input: User instructions or requests (natural language data)

[1332] Output: Text or audio data on the device

[1333] Specific behavior:

[1334] The user inputs a text into the terminal saying, "I want you to create a promotion plan for a new product."

[1335] Step 2:

[1336] Terminal

[1337] When the device receives voice data, it converts it into text data using voice recognition technology, using voice recognition services such as Google Cloud Speech-to-Text and IBM Watson Speech to Text.

[1338] Input: User's voice data

[1339] Output: Text data

[1340] Specific behavior:

[1341] The user types "Please automatically generate the agenda for the next meeting" by voice, and the device converts this into text data.

[1342] Step 3:

[1343] Terminal

[1344] The device sends the acquired text data to the server, which then uses it as input data for analysis.

[1345] Input: Text data

[1346] Output: Text data sent to the server

[1347] Specific behavior:

[1348] The terminal transmits text data saying "Please prepare a campaign to launch a new product" to the server.

[1349] Step 4:

[1350] server

[1351] The server passes the received text data to an NLP analysis module that extracts important information using the Python libraries spaCy and NLTK.

[1352] Input: Text data

[1353] Output: Analysis results (purpose of instruction, required actions, related elements)

[1354] Specific behavior:

[1355] The server analyzes the instruction "Prepare a campaign to launch a new product" and extracts keywords such as "new product," "launch," "campaign," and "preparation."

[1356] Step 5:

[1357] server

[1358] The server runs a module that visualizes business processes based on the analysis results, generating flow diagrams and structure diagrams using Diagram and Lucidchart APIs.

[1359] Input: Analysis results

[1360] Output: Graphic files (flow diagrams and structure diagrams)

[1361] Specific behavior:

[1362] The server generates a flow diagram for "preparing for a new product launch campaign" and creates figures showing steps (e.g., procuring products, creating advertising materials, planning a marketing strategy, etc.).

[1363] Step 6:

[1364] server

[1365] The server stores the generated graphic files and then shares them with users and requesters via email or file sharing services (e.g., Google Drive, Dropbox).

[1366] Input: Shape file

[1367] Output: Shared link or shared file

[1368] Specific behavior:

[1369] The server uploads the generated shape file to Google Drive and sends the shared link to the user via email.

[1370] Step 7:

[1371] server

[1372] The server uses a sentiment engine to extract sentiment from the user's natural language data, such as Microsoft Azure's Text Analytics for sentiment analysis or IBM Watson Tone Analyzer.

[1373] Input: Natural language data

[1374] Output: Emotional information

[1375] Specific behavior:

[1376] The server detects high stress levels from the user's input and outputs emotional data such as "Stress: High."

[1377] Step 8:

[1378] server

[1379] The server adjusts the business process diagram based on the emotion recognition, reflecting the extracted emotion data and providing a business process that is adapted to the user's emotions.

[1380] Input: Emotion information, generated shapes

[1381] Output: adjusted geometry file

[1382] Specific behavior:

[1383] To reduce the high stress on the server, a new flow diagram is generated in which a low-priority task is inserted instead of the normal business flow.

[1384] (Application example 2)

[1385] 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."

[1386] Modern business processes require systems that can automatically analyze and visualize instructions and reports in natural language. However, conventional systems are unable to consider the user's emotions, making it difficult to appropriately alleviate stress and tension. Furthermore, there are limited means for visualizing the generated diagrams and business process flows in real time and providing them to users. This can increase the workload of workers during work and potentially reduce productivity.

[1387] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1388] In this invention, the server includes means for analyzing extracted natural language data, means for diagramming a business process based on the analysis results, means for sharing the generated diagram with the user and the client, means including an emotion engine for recognizing the user's emotions, means for optimizing the business process based on the user's input content and emotion data, and means for displaying the generated diagram and advice within the user's field of view. This not only automatically analyzes and diagrams instructions and reports in natural language, but also enables process optimization that takes the user's emotions into consideration, thereby reducing worker stress and improving the efficiency of work execution.

[1389] "Means for analyzing extracted natural language data" refers to technology that analyzes natural language data input in voice or text format, understands the content of instructions or reports, and extracts important information.

[1390] "Means of visualizing business processes based on analysis results" refers to a technology that automatically generates diagrams such as flowcharts and structural diagrams that visually represent the flow and structure of business processes in an easy-to-understand manner based on the extracted information.

[1391] "Means for sharing the generated graphics with the user and the requester" refers to a technology for sending the generated graphics file to the user and the requester via email or a file sharing service, so that they can easily check it.

[1392] "Means including an emotion engine that recognizes user emotions" refers to technology that identifies emotions from natural language data entered by a user and incorporates them into analysis. For example, it is technology that determines whether a user is feeling stressed.

[1393] "Means for optimizing business processes based on user input and emotional data" refers to technology that adjusts the flow and structure of business processes based on user instructions and emotional data, allowing work to proceed efficiently while reducing the stress and tension felt by users.

[1394] "Means for displaying generated diagrams and advice in the user's field of vision" refers to technology that uses a device such as smart glasses to display generated business process diagrams and advice for the user on the screen in real time.

[1395] The present invention is a system that analyzes natural language data, visualizes the user's work process, and recognizes the user's emotions to optimize the work, using a "security support smart glasses app" applied to security services as an example. The specific configuration and processing procedure of this system are described below.

[1396] Server Roles

[1397] The server plays a central role in the system. The main processes performed by the server are shown below.

[1398] 1. Speech Recognition:

[1399] When a user gives instructions or reports by voice, the voice data is sent to the server through the microphone in the smart glasses.

[1400] The server uses the speech_recognition library to convert the audio data into text data.

[1401] 2. Natural Language Processing:

[1402] The converted text data is then analyzed using spaCy's Japanese model, which extracts the instructions and relevant important information.

[1403] 3. Business process visualization:

[1404] Based on the extracted information, we generate a flowchart of the business process using networkx and matplotlib, which helps users to quickly understand the business process.

[1405] 4. Emotion recognition:

[1406] The server uses the emotion_recognition library to identify emotions from the natural language data entered by the user and incorporate them into the analysis.

[1407] 5. Business process optimization:

[1408] The server optimizes business processes based on the user's instructions and emotional data. For example, if the user is feeling stressed, it generates processes and advice to alleviate the stress.

[1409] 6. Data Sharing:

[1410] The generated diagrams and advice are displayed to the user in real time through the smart glasses display.

[1411] Device Role

[1412] The device (smart glasses) provides an interface with the user. Specifically, it plays the following roles:

[1413] Input acceptance:

[1414] It is equipped with a microphone that allows users to give voice instructions and reports, and transmits voice data to the server.

[1415] display:

[1416] The generated figures and advice sent from the server are displayed on the display of the smart glasses and provided to the user.

[1417] User Roles

[1418] The user needs to operate the system.

[1419] 1. Voice input:

[1420] The user gives voice instructions and reports to the smart glasses, for example, "There was a strange noise in the south corridor on the third floor. We will begin investigating."

[1421] 2. Check and respond:

[1422] The user can view the generated business process flowchart on the smart glasses display and take appropriate action.

[1423] Specific use cases

[1424] A security guard on patrol at a commercial facility at night might say, "I heard a strange noise in the south corridor on the third floor. I will begin investigating." This voice data is sent to a server via smart glasses and analyzed using natural language processing technology. Based on the analysis results, a flowchart of the business process is generated and displayed in real time on the smart glasses' display. Furthermore, an emotion engine detects the user's stress and displays appropriate advice.

[1425] Specific prompt examples:

[1426] "We heard a strange noise in the south corridor on the third floor. We are beginning an investigation."

[1427] This system allows users to visually check business processes and respond appropriately, and also makes it easier to respond to changes in emotions.

[1428] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1429] Step 1:

[1430] The user provides voice input via the smart glasses.

[1431] Input: User voice command: "We've heard a strange noise in the south hallway on the third floor. We're beginning an investigation."

[1432] How it works: The microphone captures the user's voice and sends it to the device as audio data.

[1433] Output: Audio data is collected on the device.

[1434] Step 2:

[1435] The device sends the collected voice data to the server.

[1436] Input: Audio data collected on the device

[1437] Operation: The device sends voice data to a server via the Internet.

[1438] Output: The audio data arrives at the server.

[1439] Step 3:

[1440] The server performs voice recognition and converts the voice data into text data.

[1441] Input: Audio data sent to the server

[1442] How it works: The server uses the speech_recognition library to convert the audio data into text data.

[1443] Output: The audio data is converted to text data that reads, "A strange noise was heard in the south corridor on the third floor. An investigation will begin."

[1444] Step 4:

[1445] The server performs natural language processing to extract important information from the text data.

[1446] Input: Text data obtained by speech recognition

[1447] How it works: The server uses spaCy's Japanese model to analyze the text data and extract instructions (location of the abnormal noise and start of investigation).

[1448] Output: Analysis result data will be generated, including the location of the noise ("South Corridor on the 3rd floor") and the action "Begin investigation."

[1449] Step 5:

[1450] The server visualizes the business process.

[1451] Input: Analysis result data

[1452] How it works: The server uses networkx and matplotlib to generate a flowchart of a business process. For example, it creates a graph structure containing the nodes "abnormal noise detected" and "investigation started" and draws a visual flowchart.

[1453] Output: An image file of the business process flowchart is generated.

[1454] Step 6:

[1455] The server performs analysis to recognize the user's emotions.

[1456] Input: Text data

[1457] How it works: The server uses the emotion_recognition library to detect the user's emotions (e.g., stress level) from text data.

[1458] Output: The detected emotion data is generated.

[1459] Step 7:

[1460] The server optimizes business processes based on emotion data.

[1461] Input: Analysis result data and emotion data

[1462] Action: If the emotion data indicates high stress, the server adds relaxation advice (e.g., take a deep breath) to the business process. This advice is also included in the flowchart.

[1463] Output: An image file of the optimized business process flowchart is generated.

[1464] Step 8:

[1465] The terminal displays the generated graphics and advice in the user's field of view.

[1466] Input: Image file of optimized business process flowchart

[1467] Operation: The server sends an image file of the generated flowchart to the terminal, which displays the flowchart and advice on the display of the smart glasses.

[1468] Output: A business process flowchart and relaxation advice are displayed in the user's field of vision.

[1469] This allows users to visually check business processes and respond appropriately, and creates a system that can easily respond to changes in emotions.

[1470] 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.

[1471] 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.

[1472] 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.

[1473] 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.

[1474] 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.

[1475] 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.

[1476] 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).

[1477] 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.

[1478] 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."

[1479] 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.

[1480] 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).

[1481] 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.

[1482] 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.

[1483] 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.

[1484] 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.

[1485] 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.

[1486] 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.

[1487] 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.

[1488] 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.

[1489] 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.

[1490] 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.

[1491] The following is further disclosed regarding the above embodiment.

[1492] (Claim 1)

[1493] means for analyzing the extracted natural language data;

[1494] A means of visualizing business processes based on analysis results,

[1495] A means for sharing the generated diagram with the user and the requester;

[1496] A system including:

[1497] (Claim 2)

[1498] 10. The system of claim 1, further comprising means for inputting the extracted natural language data as speech and converting it to text.

[1499] (Claim 3)

[1500] 10. The system of claim 1, further comprising: means for employing natural language processing techniques in analyzing the extracted natural language data.

[1501] "Example 1"

[1502] (Claim 1)

[1503] a means for a user to input natural language data from a terminal;

[1504] means for transmitting the input natural language data to a server;

[1505] A means for the server to analyze the natural language data using natural language processing technology;

[1506] A means of visualizing business processes based on analysis results,

[1507] A means for sharing the generated diagram with the user and the requester;

[1508] A system including:

[1509] (Claim 2)

[1510] 10. The system of claim 1, further comprising means for converting natural language data input as speech into text using speech recognition technology.

[1511] (Claim 3)

[1512] 10. The system of claim 1, further comprising: means for the server to learn past business framework data and automatically generate appropriate graphics.

[1513] "Application Example 1"

[1514] (Claim 1)

[1515] means for analyzing the extracted natural language data;

[1516] A means of visualizing business processes based on analysis results,

[1517] A means for sharing the generated diagram with the user and the requester;

[1518] A means for controlling a robot based on the generated figure;

[1519] A system including:

[1520] (Claim 2)

[1521] 10. The system of claim 1, further comprising means for inputting the extracted natural language data as speech and converting it to text.

[1522] (Claim 3)

[1523] 10. The system of claim 1, further comprising: means for employing natural language processing techniques in analyzing the extracted natural language data.

[1524] "Example 2: Combining Emotion Engines"

[1525] (Claim 1)

[1526] means for analyzing the extracted natural language data;

[1527] A means of visualizing business processes based on analysis results,

[1528] A means for sharing the generated diagram with the user and the requester;

[1529] means for recognizing a user's emotion;

[1530] A means for adjusting the business process diagram based on emotion recognition;

[1531] A system including:

[1532] (Claim 2)

[1533] 10. The system of claim 1, further comprising means for inputting the extracted natural language data as speech and converting it to text.

[1534] (Claim 3)

[1535] 10. The system of claim 1, further comprising: means for employing natural language processing techniques in analyzing the extracted natural language data.

[1536] "Application example 2 when combining emotion engines"

[1537] (Claim 1)

[1538] means for analyzing the extracted natural language data;

[1539] A means of visualizing business processes based on analysis results,

[1540] A means for sharing the generated diagram with the user and the requester;

[1541] means including an emotion engine for recognizing an emotion of a user;

[1542] A means for optimizing business processes based on user input and emotion data;

[1543] a means for displaying the generated figure and advice in the user's field of view;

[1544] A system including:

[1545] (Claim 2)

[1546] 10. The system of claim 1, further comprising means for inputting the extracted natural language data as speech and converting it to text.

[1547] (Claim 3)

[1548] 10. The system of claim 1, further comprising: means for employing natural language processing techniques in analyzing the extracted natural language data. [Explanation of symbols]

[1549] 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. means for analyzing the extracted natural language data; A means of visualizing business processes based on analysis results, A means for sharing the generated diagram with the user and the requester; A system including:

2. The system of claim 1 further comprising means for inputting the extracted natural language data as speech and converting it to text.

3. The system of claim 1 , further comprising: means for employing natural language processing techniques in analyzing the extracted natural language data.

Images