system

Abstract

We provide the system. [Solution] A means for analyzing electronic documents using natural language processing means, A means of proposing the optimal automation tool based on the analysis results, A means for generating program code using the proposed automation tool, A means of executing the generated program code, A means of reporting the execution results, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional business procedures, there are problems that the manual work efficiency is low, especially it is difficult to automate the process based on the procedure manual. Furthermore, specialized knowledge is required when selecting appropriate automation tools, and the technical burden is large. In such a situation, manual work often remains, and there is a problem that the improvement of the work quality and efficiency is hindered.

Means for Solving the Problems

[0005] To solve this problem, this invention provides the following means. First, it analyzes an electronic document, including a procedure manual, using natural language processing means, and proposes the optimal automation tool based on the analysis results. It generates program code using the proposed automation tool and reduces the burden of manual work by executing that code. Furthermore, by reporting the execution results of the generated program code, it realizes a system that improves efficiency while ensuring the quality of the work. Through this series of processes, it becomes possible to automate manual work and obtain appropriate results in a short period of time.

[0006] "Natural language processing means" refers to methods that use techniques to analyze natural language text within electronic documents and extract structured information.

[0007] An "electronic document" refers to a document containing information recorded in a digital format, including text files and PDF files.

[0008] "Automation tools" refer to tools and software used to automate manual tasks, and are means of automatically executing specific processes.

[0009] "Program code" refers to a set of instructions written in a format executable by a computer, and includes scripts and source code for performing specific operations or tasks.

[0010] A "virtual environment" refers to a simulated environment that differs from the actual physical environment and is used to test the operation of software.

[0011] "Execution result" refers to the output or effect obtained after program code has been executed, and is used as an indicator of its success or failure. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the 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 processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system for analyzing manual work procedures and providing appropriate automation. First, the user uploads an electronic document describing the business process to a server using a terminal. The server analyzes the document using natural language processing and extracts specific tasks from the procedure. This analysis determines which automation tool is most suitable.

[0034] As a specific embodiment, the server selects, for example, "data transfer software" as an automation tool for file backup procedures. Based on the selected automation tool, the server generates relevant program code, such as a "file synchronization script." This generated program code is then tested in a virtual environment to verify its executableness.

[0035] The user receives reports of the execution results via their terminal and verifies whether the system is functioning correctly. If any problems occur during the final execution, the server notifies the user and requests manual intervention if necessary.

[0036] For example, if a user uploads a procedure manual from their terminal to the server for "regular server log file backups," the server uses natural language processing to identify the "scheduled operation system utility" suitable for log file backups and generates a script for it. The generated script is configured to run automatically on the server, and the user is notified of the execution results via email or a dashboard. This significantly reduces manual work for the user and improves work efficiency.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] Users access the system using a terminal, select an electronic document describing a business process, and upload it to the server. The server receives the file and verifies the document's format.

[0040] Step 2:

[0041] The server activates natural language processing capabilities to analyze the uploaded electronic document. This analysis identifies each process and task within the document and organizes them as structured data.

[0042] Step 3:

[0043] The server evaluates the automation feasibility for each task identified from the analysis data. It consults historical databases and known automation tool libraries to determine the optimal automation tool. The server logs this selection result.

[0044] Step 4:

[0045] The server generates program code based on the selected automation tools. It utilizes generative AI to create detailed scripts and code corresponding to the procedures. During this process, explanatory comments are added to each part of the code.

[0046] Step 5:

[0047] The server tests the generated program code in a virtual environment. The test execution verifies that the code functions as expected. The test results are recorded as logs, and if problems are found, a detailed error report is generated.

[0048] Step 6:

[0049] The server notifies the user of the test results and the execution plan for the generated code. The user reviews this information via their terminal and provides feedback to the server with any necessary instructions.

[0050] Step 7:

[0051] After obtaining user approval, the server will deploy to the production environment. The script will run according to a predetermined schedule and be monitored periodically. Upon completion, the execution results will be reported to the user.

[0052] Step 8:

[0053] Users evaluate the execution results using their terminals and send feedback to the server as needed. This allows the server to accumulate data to further optimize system performance.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In modern business processes, automating tasks based on manually written procedures is essential. However, efficiently executing these automations, accurately and quickly selecting automation tools, and generating corresponding programs remains a challenge. In particular, providing the optimal automation method for various procedures is not easy, and the resulting complexity for users is a significant problem.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for analyzing electronic information using natural language processing technology, means for selecting the optimal automation equipment based on the analysis results, and means for generating a program description based on the selected automation equipment. This enables efficient and automated automation of tasks from work procedure manuals.

[0059] "Natural language processing technology" is a technology that enables computers to understand and analyze human language and convert it into usable information.

[0060] "Electronic information" refers to all information that is stored and processed in digital format, and includes text, images, audio data, and other similar data.

[0061] "Automated equipment" refers to devices and software that perform specific tasks without human intervention.

[0062] A "program description" is a text document that contains instructions and commands necessary for a computer to perform a specific action.

[0063] A "virtual domain" refers to a digital environment created by software, regardless of whether it involves physical resources or space, and is a domain used for testing and experimentation.

[0064] "Generation standards" are guidelines and rules that define how programs and data are generated.

[0065] "Control" refers to coordinating and supervising the progress of a process or action in order to achieve a specific objective.

[0066] This invention is a system that analyzes manual work procedures and provides automation. Specifically, the user uploads an electronic document describing the work process to a server using a terminal. The server analyzes the uploaded electronic information using natural language processing technology. The analysis uses the Python programming language and natural language processing libraries such as NLTK and spaCy.

[0067] Based on the analysis results, the server selects the optimal automation equipment. This process utilizes pre-defined rule sets and trained generative AI models to choose the most appropriate automation method for the given business procedure. Based on the selected automation equipment, the server generates the corresponding program description. For program description generation, the AI ​​model processes given prompts and automatically creates, for example, Python scripts or shell scripts.

[0068] As a concrete example, if a user uploads a work procedure document stating "back up customer data at the end of the month," the server analyzes this information and selects data transfer software as the automated backup device. A script is then generated to execute the backup process. This script is tested in a virtual environment and executed if there are no problems.

[0069] Users receive detailed execution results from the server via their terminal. This notification is delivered via email or a dashboard, allowing users to immediately verify the success or failure of a process and improve work efficiency. An example of a prompt for the generated AI model is, "Generate a script to automate periodic server log file backups." Using this prompt, the relevant program code is dynamically generated, automating the task.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The user uploads an electronic document describing a business process to the server using a terminal. The input is a text file recording the steps. The output is an electronic document stored on the server. Here, the user selects the file through the interface and performs the upload.

[0073] Step 2:

[0074] The server receives the uploaded electronic document and analyzes it using natural language processing techniques. The input is the electronic document saved in step 1. The server uses the Python programming language, NLTK, and spaCy to break down the steps within the document and identify each step. The output is a list of individual tasks defined within the document. The server converts each task into structured data.

[0075] Step 3:

[0076] The server selects the optimal automation equipment based on the analysis results. The input is a list of tasks obtained in step 2. The output is a list of automation equipment corresponding to each task. The server refers to pre-configured rules and trained AI models to assign the optimal automation tool for each task. For example, for file transfer, it selects a data transfer tool.

[0077] Step 4:

[0078] The server generates program descriptions based on the selected automation equipment. The input is a list of automation equipment selected in step 3. The output is a set of program code corresponding to each task. Using a generation AI model, Python scripts and shell scripts are automatically generated based on the given prompts.

[0079] Step 5:

[0080] The server tests the generated program description in a virtual environment. The input is the program code generated in step 4. The output is the test log and results. The server uses Docker or virtualization tools to execute the script in a secure virtual environment and perform error checking and operational verification.

[0081] Step 6:

[0082] The user receives test results and execution results reports via their terminal. The input is the test results from step 5. The output is a report provided to the user detailing the execution results. The server displays the results via email or on a dashboard, notifying the user whether it was a success or failure and the reason.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] In the manufacturing industry, many manual manufacturing processes are time-consuming and inefficient. Furthermore, manual process setting based on work procedures carries the risk of human error. Against this backdrop, there is a need for methods to streamline and automate manufacturing processes.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes means for analyzing electronic documents using natural language processing means, means for proposing the optimal automation tool based on the analysis results, means for generating program code using the proposed automation tool, means for executing the generated program code, means for reporting the execution results, means for analyzing manufacturing work instructions to generate machine operation commands, and means for controlling operating equipment using machine operation commands. This enables automation and efficiency improvements in the manufacturing process.

[0088] "Natural language processing means" refers to technologies for analyzing electronic documents and extracting meaningful information.

[0089] An "electronic document" is document data that is created, stored, and displayed on a computer.

[0090] "Automation tools" refer to software or hardware used to streamline business processes.

[0091] "Program code" refers to source code that describes the instructions a computer executes.

[0092] "Execution result" refers to the output information or state obtained during the implementation of the generated program code.

[0093] A "manufacturing work instruction sheet" is a document that outlines the necessary procedures and conditions for the manufacturing process.

[0094] A "machine operation command" is a command given to a device such as manufacturing equipment or a robot to perform a specific action.

[0095] "Operating equipment" refers to devices or machines used to perform machine operations.

[0096] Regarding the embodiment for carrying out the invention, the system that realizes this application example is as follows:

[0097] The server analyzes electronic documents, such as manufacturing work instructions, uploaded by users, using natural language processing technology. This natural language processing is performed using, for example, Python with libraries such as nltk and spaCy. Based on the analysis results, the server selects the most suitable automation equipment. Specifically, it uses an algorithm to determine which machine operation is appropriate for the manufacturing process.

[0098] Furthermore, the necessary program code is generated using the proposed automation tools. This is done using pyautogui or a custom script generation library to create machine operation commands for operating the actual production equipment. The program code thus generated is tested in a virtual environment beforehand to confirm its effectiveness before execution.

[0099] Users can verify the system's operation by receiving reports of analysis results and the execution results of the generated program code via their terminal. For example, if a manufacturing line includes a task called "screw installation," this system will generate motion commands for a robotic arm to streamline that task, ensuring efficient work.

[0100] As a concrete example, a prompt might read: "Analyze the following procedure manual to generate a program for operating the robot arm: 1. Position part A 2. Attach the screw 3. Position part B." Based on this prompt, the system generates optimized machine operation commands.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The user uploads electronic documents, such as manufacturing work instructions, to the server using a terminal. These documents become the input data. The server receives these documents and prepares to begin analysis.

[0104] Step 2:

[0105] The server analyzes uploaded electronic documents using natural language processing (NLTK) tools. This analysis utilizes Python and libraries such as nltk and spaCy. It extracts key manufacturing procedures and parameters from the received electronic documents, generating structured data. The output is the analysis result data for use in the next step.

[0106] Step 3:

[0107] The server performs a process to propose the optimal automation tool based on the analysis results. In this step, an algorithm is used to determine the optimal machine operation method corresponding to the manufacturing process. The input is the analysis result data, and the output is information on which automation platform is suitable.

[0108] Step 4:

[0109] The server generates program code using the proposed automation tools. This code generation utilizes pyautogui or a custom script generation library. The input consists of the automation tools and analysis results data from the previous step, and the final output is the operational instruction code to actually operate the machine.

[0110] Step 5:

[0111] The server tests the program code generated in the virtual environment. The testing process uses libraries such as Python's unittest to verify that the generated code functions correctly. The input is the generated program code, and the output is the success or failure status of the test.

[0112] Step 6:

[0113] The server implements the program code that passed the test into the machine and notifies the user of the results via a terminal. This notification allows the user to verify that the system is functioning correctly. The input is the result of the program code tested in the virtual environment, and the output is the execution status report sent to the user.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention is a system that combines natural language processing means and an emotion engine, and is designed to support the automation of tasks based on user input. The system can efficiently automate manual work procedures while recognizing the user's emotions and performing optimal processing.

[0116] As a specific implementation, the user first uses a terminal to upload an electronic document describing the work procedure to the server. The server analyzes this document using natural language processing, extracting each task from the procedure and listing them. In addition, the server utilizes an emotion engine to evaluate the user's emotions at the time of input. This emotion data is fed back into subsequent processes and reflected in the suggestion and implementation of automation.

[0117] For example, if a user rates a task as "highly urgent," the server can prioritize suggesting automation tools that can be executed quickly for that task. Similarly, if a user's emotion is perceived as "anxious," the server will provide more detailed execution instructions and results reports to alleviate their anxiety.

[0118] Based on the selected automation tools, the server generates program code. This code is tested in a virtual environment and then executed in the production environment according to the user's settings. The execution results are reported to the terminal in a format that suits the user's emotional state. This allows users to confidently engage in business automation and enables efficient operations.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] Users prepare electronic documents describing work procedures via their terminals and upload them to the system. The server receives these documents and stores them securely in storage.

[0122] Step 2:

[0123] The server activates natural language processing to analyze the uploaded document. This analysis extracts each work step within the document and stores it as structured data.

[0124] Step 3:

[0125] The server uses an emotion engine to recognize the user's emotions based on the analyzed data. It infers the emotional state from the user's responses and keywords entered via the terminal and records this data as a log.

[0126] Step 4:

[0127] The server suggests the most suitable automation tools based on emotional data. If it determines that the user is anxious, it prioritizes selecting tools capable of high-speed processing.

[0128] Step 5:

[0129] The server generates program code based on the selected automation tools. The generated code includes explanations and annotations that take user sentiment into consideration, and details procedures as needed.

[0130] Step 6:

[0131] The generated code is first tested in a virtual environment. The server evaluates the execution results of the code and records any corrections found.

[0132] Step 7:

[0133] The server reports the test results and final execution plan to the user. The user reviews this using their terminal, provides feedback, and approves the execution.

[0134] Step 8:

[0135] The server, upon user approval, begins executing the code in the production environment. Execution is monitored periodically, and upon completion, detailed results are sent to the user's device in a format tailored to their emotional state.

[0136] Step 9:

[0137] Users review the final execution results via their terminal and submit feedback for further improvements through the system's feedback function. The server continuously optimizes the system based on this evaluation.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] In today's work environment, inefficiencies arise from the manual execution of work procedures, and users have emotional anxieties about automating tasks. Therefore, when automation and optimization of tasks are required, the challenge lies in easily facilitating the selection of appropriate tools and methods, reducing the psychological burden on users, and efficiently implementing the automation process.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes a device that analyzes information using natural language processing means, a device that selects the optimal automatic control equipment based on the analysis results, a device that generates operation commands using the selected automatic control equipment, a device that evaluates the user's emotional state and implements appropriate countermeasures, a device that confirms the generated operation commands in the behavioral environment, and a device that reports the confirmation results. This makes it possible to efficiently automate work procedures and provide optimal support that responds to the user's emotions.

[0143] "Natural language processing methods" are technologies for analyzing text data and understanding its structure and meaning.

[0144] "Information" refers to documents and electronic data, including business procedures and data, which are the objects that the system analyzes.

[0145] "Automated control equipment" refers to software and tools used to automate business processes.

[0146] "Action commands" refer to a set of commands or scripts necessary to execute an automated task.

[0147] "User emotional state" refers to the psychological and emotional state that users experience when using the system, and is evaluated by the emotion engine.

[0148] A "virtual space" is a simulated workspace separated from the actual hardware and work environment, and is used for verifying program code.

[0149] In the scope of these claims, "device" refers to an integrated set of components, including specific hardware and software modules.

[0150] "Operating environment" refers to the actual operating environment in which the action commands generated by the system are executed.

[0151] "Confirmation results" refer to the evaluation or output obtained after the execution of an action command, and are notified to the user through a reporting mechanism.

[0152] This invention automates business procedures through a three-tiered system primarily consisting of a user, a terminal, and a server. The process begins with the user creating an electronic document describing the business procedure using a terminal and uploading it to the server.

[0153] The server uses natural language processing (NLP) techniques to analyze this electronic document. Specifically, it uses common libraries useful for natural language processing (e.g., SpaCy and NLTK) to extract individual tasks included in the business procedures from the electronic document and organize them as structured data. This process involves tokenization of information and extraction of keywords.

[0154] Next, the server uses an emotion engine to evaluate the emotions contained in the user's input. This is important to reduce the user's psychological burden and provide more appropriate automated suggestions. The emotion engine utilizes existing sentiment analysis technology to visualize emotional indicators such as "urgency," "anxiety," and "satisfaction" from the input text.

[0155] Subsequently, the server selects the optimal automated control equipment based on the analysis results, taking into account the nature and urgency of the task and the user's emotional state. This includes automation tools such as Ansible and Terraform, which the server uses to generate operational commands. The generated operational commands are then tested in a virtual environment before being safely operated in the execution environment.

[0156] Generative AI models are utilized throughout this entire process, and this technology is particularly useful for the automatic generation of program code. This allows users to efficiently execute automated processes while reducing the burden of manual coding.

[0157] For example, when writing a prompt message, based on the instruction "Automate the following business procedure," the system consistently performs tasks ranging from selecting automated control equipment to generating operation commands and providing appropriate feedback tailored to the user's emotions. This specification allows users to seamlessly automate their own tasks.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] Users use their terminals to create electronic documents describing work procedures and upload them to the server. Inputs include electronic documents such as work procedure manuals and related materials. Outputs are document data that the server receives for analysis. Specific actions include the user selecting files via a web browser and pressing a button to send to the server.

[0161] Step 2:

[0162] When the server receives an electronic document, it analyzes it using natural language processing (NLP) tools. The input is an electronic document containing business procedures uploaded by the user. During data processing, the server uses a natural language processing library to tokenize the document, recognizing and extracting business tasks at the sentence and paragraph level. The final output is a structured task list. The server then uses this list for subsequent processing, including keyword extraction and grammatical analysis.

[0163] Step 3:

[0164] The server uses an emotion engine to evaluate the user's emotional state based on the extracted task list. Input includes the task list obtained through natural language processing and user comments related to those tasks. The server uses emotion analysis techniques to calculate emotional indicators associated with each task and generates an emotional evaluation result as output. Specific operations include emotion scoring and tone analysis.

[0165] Step 4:

[0166] The server selects the optimal automated control equipment based on the analysis results and sentiment evaluation. Inputs include the task list obtained in step 2 and the sentiment evaluation results from step 3. For data calculation, an optimization algorithm that considers task content, urgency, and the user's emotional state is used to select the automation tool. The output is a list of selected automated control equipment. Specific operations include executing the selection algorithm and generating a recommendation list.

[0167] Step 5:

[0168] The server generates action commands based on selected automated control equipment. Inputs include a selection list and a database referenced by the generating AI model. The server utilizes the generating AI model to generate action commands suitable for the business procedure and prepares them for testing in a virtual environment. Outputs are the generated action commands, and specific actions include code generation and simulation preparation.

[0169] Step 6:

[0170] The server executes the generated operation commands in a virtual environment and verifies and confirms their operation. The required inputs are the generated operation commands and the virtual environment configuration information. Data calculations include simulation execution within the virtual environment and result analysis. The output is the operation verification result. Specific operations include error detection and health checks.

[0171] Step 7:

[0172] The server executes the program in the production environment and reports the results to the user based on the operational verification results. The input consists of the operational verification results and the production environment configuration information. The server deploys the operational commands it deems executable to the production environment. The output consists of the final execution results and a report for the user. Specific operations include saving the execution results as a log and generating a report for the user.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0175] Modern information systems demand the automation and optimization of vast amounts of business procedures, but this lacks the ability to respond appropriately to user emotions and urgency. Conventional technologies struggle to analyze user feedback and cannot respond quickly and flexibly to emotional states. Therefore, there is a need for solutions that improve user satisfaction and enable efficient business operations.

[0176] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0177] In this invention, the server includes means for analyzing electronic documents using natural language processing means, means for recognizing emotional states from input data and adjusting suggestions, and means for presenting execution results in a format based on the emotional state. This makes it possible to quickly and accurately automate and optimize business procedures while taking the user's emotional state into consideration.

[0178] "Natural language processing means" refers to methods that analyze input electronic documents and extract and perform semantic analysis on sentences and words within the document.

[0179] "Automation tools" refer to a set of software or hardware used to efficiently execute specific business procedures.

[0180] "Emotional state" refers to the psychological state inferred from the user's input and feedback, which allows the system to adjust its responses and suggestions.

[0181] "Program code generation" refers to the automatic creation of working software code based on analyzed data.

[0182] "Execution results" refer to the outcomes and data obtained when the generated program code is executed, and these are reported to the user.

[0183] "Format" refers to the way execution results are displayed and their layout presented to the user, and is communicated in an appropriate manner according to their emotional state.

[0184] This invention provides a system that combines natural language processing means and an emotion engine to efficiently analyze user feedback and automate and optimize business procedures. The system uses a server to analyze information entered on the user's terminal and automates appropriate processing.

[0185] The server first analyzes the input electronic document using the Spacy natural language processing library. This allows it to extract each task from the document and understand its meaning. Next, it uses an emotion recognition library called Emotion Engine to evaluate the user's emotional state from the input data. Based on this emotion evaluation, the priority and content of the proposed automation tools are adjusted. For example, if the feedback includes "danger" or "anxiety," detailed responses will be quickly proposed.

[0186] The generated program code is tested in a virtual environment through execution using an automation tool called Task Automation, and modifications are made as needed based on the results. Program code that passes the test is executed in the production environment, and the execution results are reported to the user in a format appropriate to their emotional state. This allows users to automate tasks with confidence and enables them to respond quickly and accurately to citizen feedback.

[0187] For example, if a citizen provides feedback that "the park lighting is dim," and the system recognizes the urgency of the issue, it will immediately propose inspecting the lighting and report the procedure in detail. Furthermore, prompts such as, "We will propose a response based on citizen feedback. We will analyze the emotional state of the feedback to determine if an emergency response is necessary," can be input into the generating AI model.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The user inputs feedback from their device and sends that data to the server. The input is natural language text data, and the server receives this data.

[0191] Step 2:

[0192] The server analyzes the received text data using Spacy, a natural language processing library. It divides the input text into sentences and words, and extracts business tasks from their context. As a result of this analysis, a list of tasks to be performed is created.

[0193] Step 3:

[0194] The server uses the Emotion Engine to perform emotional assessment on the analyzed task. Text data is used as input for emotion recognition, and the urgency and the user's psychological state are evaluated. As a result, data indicating the emotional state is generated.

[0195] Step 4:

[0196] Based on the sentiment assessment results, the server uses Task Automation to select the most suitable automation tools and adjust the suggestions. In this step, the priority of tasks to be automated is determined based on the input task list and sentiment data. This results in a suggested list including the execution order.

[0197] Step 5:

[0198] The server generates program code using selected automation tools. Here, the optimal code for each task is generated and prepared in a testable format within the virtual environment. The output is test program code.

[0199] Step 6:

[0200] The program code is tested in a virtual environment, and the results are evaluated. The tests verify that the execution results meet expectations, and the code is modified as needed. The final output is program code that can be applied to the production environment.

[0201] Step 7:

[0202] The program code is executed in a production environment, and the results and corresponding emotional states are reported to the user in an appropriate format. The execution results are formatted according to the emotional data and provided to the user in a way that is easy to understand and trustworthy. The final output is an emotionally sensitive report.

[0203] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0204] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0210] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0212] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0214] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0216] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0219] This invention is a system for analyzing manual work procedures and providing appropriate automation. First, the user uploads an electronic document describing the business process to a server using a terminal. The server analyzes the document using natural language processing and extracts specific tasks from the procedure. This analysis determines which automation tool is most suitable.

[0220] As a specific embodiment, the server selects, for example, "data transfer software" as an automation tool for file backup procedures. Based on the selected automation tool, the server generates relevant program code, such as a "file synchronization script." This generated program code is then tested in a virtual environment to verify its executableness.

[0221] The user receives reports of the execution results via their terminal and verifies whether the system is functioning correctly. If any problems occur during the final execution, the server notifies the user and requests manual intervention if necessary.

[0222] For example, if a user uploads a procedure manual from their terminal to the server for "regular server log file backups," the server uses natural language processing to identify the "scheduled operation system utility" suitable for log file backups and generates a script for it. The generated script is configured to run automatically on the server, and the user is notified of the execution results via email or a dashboard. This significantly reduces manual work for the user and improves work efficiency.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] Users access the system using a terminal, select an electronic document describing a business process, and upload it to the server. The server receives the file and verifies the document's format.

[0226] Step 2:

[0227] The server activates natural language processing capabilities to analyze the uploaded electronic document. This analysis identifies each process and task within the document and organizes them as structured data.

[0228] Step 3:

[0229] The server evaluates the automation feasibility for each task identified from the analysis data. It consults historical databases and known automation tool libraries to determine the optimal automation tool. The server logs this selection result.

[0230] Step 4:

[0231] The server generates program code based on the selected automation tools. It utilizes generative AI to create detailed scripts and code corresponding to the procedures. During this process, explanatory comments are added to each part of the code.

[0232] Step 5:

[0233] The server tests the generated program code in a virtual environment. The test execution verifies that the code functions as expected. The test results are recorded as logs, and if problems are found, a detailed error report is generated.

[0234] Step 6:

[0235] The server notifies the user of the test results and the execution plan for the generated code. The user reviews this information via their terminal and provides feedback to the server with any necessary instructions.

[0236] Step 7:

[0237] After obtaining user approval, the server will deploy to the production environment. The script will run according to a predetermined schedule and be monitored periodically. Upon completion, the execution results will be reported to the user.

[0238] Step 8:

[0239] Users evaluate the execution results using their terminals and send feedback to the server as needed. This allows the server to accumulate data to further optimize system performance.

[0240] (Example 1)

[0241] Next, we will describe Example 1. 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."

[0242] In modern business processes, automating tasks based on manually written procedures is essential. However, efficiently executing these automations, accurately and quickly selecting automation tools, and generating corresponding programs remains a challenge. In particular, providing the optimal automation method for various procedures is not easy, and the resulting complexity for users is a significant problem.

[0243] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0244] In this invention, the server includes means for analyzing electronic information using natural language processing technology, means for selecting the optimal automation equipment based on the analysis results, and means for generating a program description based on the selected automation equipment. This enables efficient and automated automation of tasks from work procedure manuals.

[0245] "Natural language processing technology" is a technology that enables computers to understand and analyze human language and convert it into usable information.

[0246] "Electronic information" refers to all information that is stored and processed in digital format, and includes text, images, audio data, and other similar data.

[0247] "Automated equipment" refers to devices and software that perform specific tasks without human intervention.

[0248] A "program description" is a text document that contains instructions and commands necessary for a computer to perform a specific action.

[0249] A "virtual domain" refers to a digital environment created by software, regardless of whether it involves physical resources or space, and is a domain used for testing and experimentation.

[0250] "Generation standards" are guidelines and rules that define how programs and data are generated.

[0251] "Control" refers to coordinating and supervising the progress of a process or action in order to achieve a specific objective.

[0252] This invention is a system that analyzes manual work procedures and provides automation. Specifically, the user uploads an electronic document describing the work process to a server using a terminal. The server analyzes the uploaded electronic information using natural language processing technology. The analysis uses the Python programming language and natural language processing libraries such as NLTK and spaCy.

[0253] Based on the analysis results, the server selects the optimal automation equipment. This process utilizes pre-defined rule sets and trained generative AI models to choose the most appropriate automation method for the given business procedure. Based on the selected automation equipment, the server generates the corresponding program description. For program description generation, the AI ​​model processes given prompts and automatically creates, for example, Python scripts or shell scripts.

[0254] As a concrete example, if a user uploads a work procedure document stating "back up customer data at the end of the month," the server analyzes this information and selects data transfer software as the automated backup device. A script is then generated to execute the backup process. This script is tested in a virtual environment and executed if there are no problems.

[0255] Users receive detailed execution results from the server via their terminal. This notification is delivered via email or a dashboard, allowing users to immediately verify the success or failure of a process and improve work efficiency. An example of a prompt for the generated AI model is, "Generate a script to automate periodic server log file backups." Using this prompt, the relevant program code is dynamically generated, automating the task.

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] The user uploads an electronic document describing a business process to the server using a terminal. The input is a text file recording the steps. The output is an electronic document stored on the server. Here, the user selects the file through the interface and performs the upload.

[0259] Step 2:

[0260] The server receives the uploaded electronic document and analyzes it using natural language processing techniques. The input is the electronic document saved in step 1. The server uses the Python programming language, NLTK, and spaCy to break down the steps within the document and identify each step. The output is a list of individual tasks defined within the document. The server converts each task into structured data.

[0261] Step 3:

[0262] The server selects the optimal automation equipment based on the analysis results. The input is a list of tasks obtained in step 2. The output is a list of automation equipment corresponding to each task. The server refers to pre-configured rules and trained AI models to assign the optimal automation tool for each task. For example, for file transfer, it selects a data transfer tool.

[0263] Step 4:

[0264] The server generates program descriptions based on the selected automation equipment. The input is a list of automation equipment selected in step 3. The output is a set of program code corresponding to each task. Using a generation AI model, Python scripts and shell scripts are automatically generated based on the given prompts.

[0265] Step 5:

[0266] The server tests the generated program description in a virtual environment. The input is the program code generated in step 4. The output is the test log and results. The server uses Docker or virtualization tools to execute the script in a secure virtual environment and perform error checking and operational verification.

[0267] Step 6:

[0268] The user receives test results and execution results reports via their terminal. The input is the test results from step 5. The output is a report provided to the user detailing the execution results. The server displays the results via email or on a dashboard, notifying the user whether it was a success or failure and the reason.

[0269] (Application Example 1)

[0270] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] In the manufacturing industry, many manual manufacturing processes are time-consuming and inefficient. Furthermore, manual process setting based on work procedures carries the risk of human error. Against this backdrop, there is a need for methods to streamline and automate manufacturing processes.

[0272] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0273] In this invention, the server includes means for analyzing electronic documents using natural language processing means, means for proposing the optimal automation tool based on the analysis results, means for generating program code using the proposed automation tool, means for executing the generated program code, means for reporting the execution results, means for analyzing manufacturing work instructions to generate machine operation commands, and means for controlling operating equipment using machine operation commands. This enables automation and efficiency improvements in the manufacturing process.

[0274] "Natural language processing means" refers to technologies for analyzing electronic documents and extracting meaningful information.

[0275] An "electronic document" is document data that is created, stored, and displayed on a computer.

[0276] "Automation tools" refer to software or hardware used to streamline business processes.

[0277] "Program code" refers to source code that describes the instructions a computer executes.

[0278] "Execution result" refers to the output information or state obtained during the implementation of the generated program code.

[0279] A "manufacturing work instruction sheet" is a document that outlines the necessary procedures and conditions for the manufacturing process.

[0280] A "machine operation instruction" is an instruction for causing a device such as manufacturing equipment or a robot to execute a specific operation.

[0281] An "operating device" refers to a device or machine for executing a machine operation.

[0282] Regarding the embodiments for implementing the invention, the system for realizing this application example is as follows.

[0283] The server analyzes electronic documents such as manufacturing work instructions uploaded by the user using natural language processing technology. This natural language processing is performed using, for example, Python and libraries such as nltk and spaCy. Also, based on the analysis results, an optimal automation tool is selected. Specifically, an algorithm for determining what kind of machine operation is suitable for the manufacturing process is used.

[0284] Furthermore, using the proposed automation tool, the necessary program code is generated. This is done using pyautogui and a custom script generation library to create machine operation instructions for operating the actual production equipment. The program code generated in this way is tested in a virtual environment in advance, and after its effectiveness is confirmed, the operation is executed.

[0285] The user can confirm the operation of the system by being reported the analysis results and the execution results of the generated program code through the terminal. For example, if the instruction contains an operation of "screwing on" used in a certain production line, this system generates an operation command for the robot arm to streamline the task and enables the work to proceed efficiently.

[0286] As a specific example, an example of a prompt sentence is "Please analyze the following procedure manual and generate a program for robot arm operation: 1. Positioning of part A 2. Screw attachment 3. Positioning of part B." Based on this prompt, the system is a mechanism that generates optimized machine operation instructions.

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The user uploads an electronic document such as a manufacturing work instruction to the server using a terminal. This document becomes the input data. The server receives this document and prepares to start analysis.

[0290] Step 2:

[0291] The server analyzes the uploaded electronic document using natural language processing means. In this analysis, libraries such as Python and nltk or spaCy are used. Important manufacturing procedures and parameters are extracted from the received electronic document as input, and structured data is generated. The output is the analysis result data for use in the next step.

[0292] Step 3:

[0293] The server performs a process of proposing an optimal automation tool based on the analysis result. In this step, an algorithm is used to determine the optimal machine operation means corresponding to the manufacturing process. The input is the analysis result data, and as the output, information on which automation platform is suitable is obtained.

[0294] Step 4:

[0295] The server generates program code using the proposed automation tools. This code generation utilizes pyautogui or a custom script generation library. The input consists of the automation tools and analysis results data from the previous step, and the final output is the operational instruction code to actually operate the machine.

[0296] Step 5:

[0297] The server tests the program code generated in the virtual environment. The testing process uses libraries such as Python's unittest to verify that the generated code functions correctly. The input is the generated program code, and the output is the success or failure status of the test.

[0298] Step 6:

[0299] The server implements the program code that passed the test into the machine and notifies the user of the results via a terminal. This notification allows the user to verify that the system is functioning correctly. The input is the result of the program code tested in the virtual environment, and the output is the execution status report sent to the user.

[0300] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0301] This invention is a system that combines natural language processing means and an emotion engine, and is designed to support the automation of tasks based on user input. The system can efficiently automate manual work procedures while recognizing the user's emotions and performing optimal processing.

[0302] As a specific embodiment, the user first uses the terminal to upload an electronic document describing the business procedures to the server. The server analyzes this document by means of natural language processing, extracts each task from the procedures, and lists them. In addition, the server utilizes an emotion engine to evaluate the emotion of the user at the time of input. This emotion data is fed back to subsequent processes and reflected in the proposal and implementation of automation.

[0303] For example, when the user's evaluation is "high urgency", the server can prioritize the proposal of automation tools that can be quickly executed for that task. Also, when the user's emotion is recognized as "uneasy", more detailed execution procedures and result reports are provided to try to reduce the uneasiness.

[0304] Based on the selected automation tools, the server generates program code. This code is tested in a virtual environment and then executed in the production environment according to the user's settings. The execution results are reported to the terminal in a format corresponding to the user's emotional state. As a result, the user can work on business automation with confidence, and efficient operations are realized.

[0305] The following describes the processing flow.

[0306] Step 1:

[0307] The user prepares an electronic document describing the business procedures through the terminal and uploads it to the system. The server receives this document and stores it in a secure storage.

[0308] Step 2:

[0309] The server activates the natural language processing means and analyzes the uploaded document. In this analysis, each operation procedure in the document is extracted and stored as structured data.

[0310] Step 3:

[0311] The server uses an emotion engine to recognize the user's emotions based on the analyzed data. It infers the emotional state from the user's responses and keywords entered via the terminal and records this data as a log.

[0312] Step 4:

[0313] The server suggests the most suitable automation tools based on emotional data. If it determines that the user is anxious, it prioritizes selecting tools capable of high-speed processing.

[0314] Step 5:

[0315] The server generates program code based on the selected automation tools. The generated code includes explanations and annotations that take user sentiment into consideration, and details procedures as needed.

[0316] Step 6:

[0317] The generated code is first tested in a virtual environment. The server evaluates the execution results of the code and records any corrections found.

[0318] Step 7:

[0319] The server reports the test results and final execution plan to the user. The user reviews this using their terminal, provides feedback, and approves the execution.

[0320] Step 8:

[0321] The server, upon user approval, begins executing the code in the production environment. Execution is monitored periodically, and upon completion, detailed results are sent to the user's device in a format tailored to their emotional state.

[0322] Step 9:

[0323] Users review the final execution results via their terminal and submit feedback for further improvements through the system's feedback function. The server continuously optimizes the system based on this evaluation.

[0324] (Example 2)

[0325] Next, we will describe Example 2. 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".

[0326] In today's work environment, inefficiencies arise from the manual execution of work procedures, and users have emotional anxieties about automating tasks. Therefore, when automation and optimization of tasks are required, the challenge lies in easily facilitating the selection of appropriate tools and methods, reducing the psychological burden on users, and efficiently implementing the automation process.

[0327] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0328] In this invention, the server includes a device that analyzes information using natural language processing means, a device that selects the optimal automatic control equipment based on the analysis results, a device that generates operation commands using the selected automatic control equipment, a device that evaluates the user's emotional state and implements appropriate countermeasures, a device that confirms the generated operation commands in the behavioral environment, and a device that reports the confirmation results. This makes it possible to efficiently automate work procedures and provide optimal support that responds to the user's emotions.

[0329] "Natural language processing methods" are technologies for analyzing text data and understanding its structure and meaning.

[0330] "Information" refers to documents and electronic data, including business procedures and data, which are the objects that the system analyzes.

[0331] "Automated control equipment" refers to software and tools used to automate business processes.

[0332] "Action commands" refer to a set of commands or scripts necessary to execute an automated task.

[0333] "User emotional state" refers to the psychological and emotional state that users experience when using the system, and is evaluated by the emotion engine.

[0334] A "virtual space" is a simulated workspace separated from the actual hardware and work environment, and is used for verifying program code.

[0335] In the scope of these claims, "device" refers to an integrated set of components, including specific hardware and software modules.

[0336] "Operating environment" refers to the actual operating environment in which the action commands generated by the system are executed.

[0337] "Confirmation results" refer to the evaluation or output obtained after the execution of an action command, and are notified to the user through a reporting mechanism.

[0338] This invention automates business procedures through a three-tiered system primarily consisting of a user, a terminal, and a server. The process begins with the user creating an electronic document describing the business procedure using a terminal and uploading it to the server.

[0339] The server uses natural language processing (NLP) techniques to analyze this electronic document. Specifically, it uses common libraries useful for natural language processing (e.g., SpaCy and NLTK) to extract individual tasks included in the business procedures from the electronic document and organize them as structured data. This process involves tokenization of information and extraction of keywords.

[0340] Next, the server uses an emotion engine to evaluate the emotions contained in the user's input. This is important to reduce the user's psychological burden and provide more appropriate automated suggestions. The emotion engine utilizes existing sentiment analysis technology to visualize emotional indicators such as "urgency," "anxiety," and "satisfaction" from the input text.

[0341] Subsequently, the server selects the optimal automated control equipment based on the analysis results, taking into account the nature and urgency of the task and the user's emotional state. This includes automation tools such as Ansible and Terraform, which the server uses to generate operational commands. The generated operational commands are then tested in a virtual environment before being safely operated in the execution environment.

[0342] Generative AI models are utilized throughout this entire process, and this technology is particularly useful for the automatic generation of program code. This allows users to efficiently execute automated processes while reducing the burden of manual coding.

[0343] For example, when writing a prompt message, based on the instruction "Automate the following business procedure," the system consistently performs tasks ranging from selecting automated control equipment to generating operation commands and providing appropriate feedback tailored to the user's emotions. This specification allows users to seamlessly automate their own tasks.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] Users use their terminals to create electronic documents describing work procedures and upload them to the server. Inputs include electronic documents such as work procedure manuals and related materials. Outputs are document data that the server receives for analysis. Specific actions include the user selecting files via a web browser and pressing a button to send to the server.

[0347] Step 2:

[0348] When the server receives an electronic document, it analyzes it using natural language processing (NLP) tools. The input is an electronic document containing business procedures uploaded by the user. During data processing, the server uses a natural language processing library to tokenize the document, recognizing and extracting business tasks at the sentence and paragraph level. The final output is a structured task list. The server then uses this list for subsequent processing, including keyword extraction and grammatical analysis.

[0349] Step 3:

[0350] The server uses an emotion engine to evaluate the user's emotional state based on the extracted task list. Input includes the task list obtained through natural language processing and user comments related to those tasks. The server uses emotion analysis techniques to calculate emotional indicators associated with each task and generates an emotional evaluation result as output. Specific operations include emotion scoring and tone analysis.

[0351] Step 4:

[0352] The server selects the optimal automated control equipment based on the analysis results and sentiment evaluation. Inputs include the task list obtained in step 2 and the sentiment evaluation results from step 3. For data calculation, an optimization algorithm that considers task content, urgency, and the user's emotional state is used to select the automation tool. The output is a list of selected automated control equipment. Specific operations include executing the selection algorithm and generating a recommendation list.

[0353] Step 5:

[0354] The server generates action commands based on selected automated control equipment. Inputs include a selection list and a database referenced by the generating AI model. The server utilizes the generating AI model to generate action commands suitable for the business procedure and prepares them for testing in a virtual environment. Outputs are the generated action commands, and specific actions include code generation and simulation preparation.

[0355] Step 6:

[0356] The server executes the generated operation commands in a virtual environment and verifies and confirms their operation. The required inputs are the generated operation commands and the virtual environment configuration information. Data calculations include simulation execution within the virtual environment and result analysis. The output is the operation verification result. Specific operations include error detection and health checks.

[0357] Step 7:

[0358] The server executes the program in the production environment and reports the results to the user based on the operational verification results. The input consists of the operational verification results and the production environment configuration information. The server deploys the operational commands it deems executable to the production environment. The output consists of the final execution results and a report for the user. Specific operations include saving the execution results as a log and generating a report for the user.

[0359] (Application Example 2)

[0360] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0361] Modern information systems demand the automation and optimization of vast amounts of business procedures, but this lacks the ability to respond appropriately to user emotions and urgency. Conventional technologies struggle to analyze user feedback and cannot respond quickly and flexibly to emotional states. Therefore, there is a need for solutions that improve user satisfaction and enable efficient business operations.

[0362] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0363] In this invention, the server includes means for analyzing electronic documents using natural language processing means, means for recognizing emotional states from input data and adjusting suggestions, and means for presenting execution results in a format based on the emotional state. This makes it possible to quickly and accurately automate and optimize business procedures while taking the user's emotional state into consideration.

[0364] "Natural language processing means" refers to methods that analyze input electronic documents and extract and perform semantic analysis on sentences and words within the document.

[0365] "Automation tools" refer to a set of software or hardware used to efficiently execute specific business procedures.

[0366] "Emotional state" refers to the psychological state inferred from the user's input and feedback, which allows the system to adjust its responses and suggestions.

[0367] "Program code generation" refers to the automatic creation of working software code based on analyzed data.

[0368] "Execution results" refer to the outcomes and data obtained when the generated program code is executed, and these are reported to the user.

[0369] "Format" refers to the way execution results are displayed and their layout presented to the user, and is communicated in an appropriate manner according to their emotional state.

[0370] This invention provides a system that combines natural language processing means and an emotion engine to efficiently analyze user feedback and automate and optimize business procedures. The system uses a server to analyze information entered on the user's terminal and automates appropriate processing.

[0371] The server first analyzes the input electronic document using the Spacy natural language processing library. This allows it to extract each task from the document and understand its meaning. Next, it uses an emotion recognition library called Emotion Engine to evaluate the user's emotional state from the input data. Based on this emotion evaluation, the priority and content of the proposed automation tools are adjusted. For example, if the feedback includes "danger" or "anxiety," detailed responses will be quickly proposed.

[0372] The generated program code is tested in a virtual environment through execution using an automation tool called Task Automation, and modifications are made as needed based on the results. Program code that passes the test is executed in the production environment, and the execution results are reported to the user in a format appropriate to their emotional state. This allows users to automate tasks with confidence and enables them to respond quickly and accurately to citizen feedback.

[0373] For example, if a citizen provides feedback that "the park lighting is dim," and the system recognizes the urgency of the issue, it will immediately propose inspecting the lighting and report the procedure in detail. Furthermore, prompts such as, "We will propose a response based on citizen feedback. We will analyze the emotional state of the feedback to determine if an emergency response is necessary," can be input into the generating AI model.

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The user inputs feedback from their device and sends that data to the server. The input is natural language text data, and the server receives this data.

[0377] Step 2:

[0378] The server analyzes the received text data using Spacy, a natural language processing library. It divides the input text into sentences and words, and extracts business tasks from their context. As a result of this analysis, a list of tasks to be performed is created.

[0379] Step 3:

[0380] The server uses the Emotion Engine to perform emotional assessment on the analyzed task. Text data is used as input for emotion recognition, and the urgency and the user's psychological state are evaluated. As a result, data indicating the emotional state is generated.

[0381] Step 4:

[0382] Based on the sentiment assessment results, the server uses Task Automation to select the most suitable automation tools and adjust the suggestions. In this step, the priority of tasks to be automated is determined based on the input task list and sentiment data. This results in a suggested list including the execution order.

[0383] Step 5:

[0384] The server generates program code using selected automation tools. Here, the optimal code for each task is generated and prepared in a testable format within the virtual environment. The output is test program code.

[0385] Step 6:

[0386] The program code is tested in a virtual environment, and the results are evaluated. The tests verify that the execution results meet expectations, and the code is modified as needed. The final output is program code that can be applied to the production environment.

[0387] Step 7:

[0388] The program code is executed in a production environment, and the results and corresponding emotional states are reported to the user in an appropriate format. The execution results are formatted according to the emotional data and provided to the user in a way that is easy to understand and reassuring. The final output is an emotionally sensitive report.

[0389] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0390] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0391] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0392] [Third Embodiment]

[0393] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0394] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0395] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0396] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0397] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0398] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0399] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0400] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0401] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0402] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0403] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0404] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0405] This invention is a system for analyzing manual work procedures and providing appropriate automation. First, the user uploads an electronic document describing the business process to a server using a terminal. The server analyzes the document using natural language processing and extracts specific tasks from the procedure. This analysis determines which automation tool is most suitable.

[0406] As a specific embodiment, the server selects, for example, "data transfer software" as an automation tool for file backup procedures. Based on the selected automation tool, the server generates relevant program code, such as a "file synchronization script." This generated program code is then tested in a virtual environment to verify its executableness.

[0407] The user receives reports of the execution results via their terminal and verifies whether the system is functioning correctly. If any problems occur during the final execution, the server notifies the user and requests manual intervention if necessary.

[0408] For example, if a user uploads a procedure manual from their terminal to the server for "regular server log file backups," the server uses natural language processing to identify the "scheduled operation system utility" suitable for log file backups and generates a script for it. The generated script is configured to run automatically on the server, and the user is notified of the execution results via email or a dashboard. This significantly reduces manual work for the user and improves work efficiency.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] Users access the system using a terminal, select an electronic document describing a business process, and upload it to the server. The server receives the file and verifies the document's format.

[0412] Step 2:

[0413] The server activates natural language processing capabilities to analyze the uploaded electronic document. This analysis identifies each process and task within the document and organizes them as structured data.

[0414] Step 3:

[0415] The server evaluates the automation feasibility for each task identified from the analysis data. It consults historical databases and known automation tool libraries to determine the optimal automation tool. The server logs this selection result.

[0416] Step 4:

[0417] The server generates program code based on the selected automation tools. It utilizes generative AI to create detailed scripts and code corresponding to the procedures. During this process, explanatory comments are added to each part of the code.

[0418] Step 5:

[0419] The server tests the generated program code in a virtual environment. The test execution verifies that the code functions as expected. The test results are recorded as logs, and if problems are found, a detailed error report is generated.

[0420] Step 6:

[0421] The server notifies the user of the test results and the execution plan for the generated code. The user reviews this information via their terminal and provides feedback to the server with any necessary instructions.

[0422] Step 7:

[0423] After obtaining user approval, the server will deploy to the production environment. The script will run according to a predetermined schedule and be monitored periodically. Upon completion, the execution results will be reported to the user.

[0424] Step 8:

[0425] Users evaluate the execution results using their terminals and send feedback to the server as needed. This allows the server to accumulate data to further optimize system performance.

[0426] (Example 1)

[0427] Next, we will describe Example 1. 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."

[0428] In modern business processes, automating tasks based on manually written procedures is essential. However, efficiently executing these automations, accurately and quickly selecting automation tools, and generating corresponding programs remains a challenge. In particular, providing the optimal automation method for various procedures is not easy, and the resulting complexity for users is a significant problem.

[0429] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0430] In this invention, the server includes means for analyzing electronic information using natural language processing technology, means for selecting the optimal automation equipment based on the analysis results, and means for generating a program description based on the selected automation equipment. This enables efficient and automated automation of tasks from work procedure manuals.

[0431] "Natural language processing technology" is a technology that enables computers to understand and analyze human language and convert it into usable information.

[0432] "Electronic information" refers to all information that is stored and processed in digital format, and includes text, images, audio data, and other similar data.

[0433] "Automated equipment" refers to devices and software that perform specific tasks without human intervention.

[0434] A "program description" is a text document that contains instructions and commands necessary for a computer to perform a specific action.

[0435] A "virtual domain" refers to a digital environment created by software, regardless of whether it involves physical resources or space, and is a domain used for testing and experimentation.

[0436] "Generation standards" are guidelines and rules that define how programs and data are generated.

[0437] "Control" refers to coordinating and supervising the progress of a process or action in order to achieve a specific objective.

[0438] This invention is a system that analyzes manual work procedures and provides automation. Specifically, the user uploads an electronic document describing the work process to a server using a terminal. The server analyzes the uploaded electronic information using natural language processing technology. The analysis uses the Python programming language and natural language processing libraries such as NLTK and spaCy.

[0439] Based on the analysis results, the server selects the optimal automation equipment. This process utilizes pre-defined rule sets and trained generative AI models to choose the most appropriate automation method for the given business procedure. Based on the selected automation equipment, the server generates the corresponding program description. For program description generation, the AI ​​model processes given prompts and automatically creates, for example, Python scripts or shell scripts.

[0440] As a concrete example, if a user uploads a work procedure document stating "back up customer data at the end of the month," the server analyzes this information and selects data transfer software as the automated backup device. A script is then generated to execute the backup process. This script is tested in a virtual environment and executed if there are no problems.

[0441] Users receive detailed execution results from the server via their terminal. This notification is delivered via email or a dashboard, allowing users to immediately verify the success or failure of a process and improve work efficiency. An example of a prompt for the generated AI model is, "Generate a script to automate periodic server log file backups." Using this prompt, the relevant program code is dynamically generated, automating the task.

[0442] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0443] Step 1:

[0444] The user uploads an electronic document describing a business process to the server using a terminal. The input is a text file recording the steps. The output is an electronic document stored on the server. Here, the user selects the file through the interface and performs the upload.

[0445] Step 2:

[0446] The server receives the uploaded electronic document and analyzes it using natural language processing techniques. The input is the electronic document saved in step 1. The server uses the Python programming language, NLTK, and spaCy to break down the steps within the document and identify each step. The output is a list of individual tasks defined within the document. The server converts each task into structured data.

[0447] Step 3:

[0448] The server selects the optimal automation equipment based on the analysis results. The input is a list of tasks obtained in step 2. The output is a list of automation equipment corresponding to each task. The server refers to pre-configured rules and trained AI models to assign the optimal automation tool for each task. For example, for file transfer, it selects a data transfer tool.

[0449] Step 4:

[0450] The server generates program descriptions based on the selected automation equipment. The input is a list of automation equipment selected in step 3. The output is a set of program code corresponding to each task. Using a generation AI model, Python scripts and shell scripts are automatically generated based on the given prompts.

[0451] Step 5:

[0452] The server tests the generated program description in a virtual environment. The input is the program code generated in step 4. The output is the test log and results. The server uses Docker or virtualization tools to execute the script in a secure virtual environment and perform error checking and operational verification.

[0453] Step 6:

[0454] The user receives test results and execution results reports via their terminal. The input is the test results from step 5. The output is a report provided to the user detailing the execution results. The server displays the results via email or on a dashboard, notifying the user whether it was a success or failure and the reason.

[0455] (Application Example 1)

[0456] Next, we will explain Application Example 1. In the following explanation, 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."

[0457] In the manufacturing industry, many manual manufacturing processes are time-consuming and inefficient. Furthermore, manual process setting based on work procedures carries the risk of human error. Against this backdrop, there is a need for methods to streamline and automate manufacturing processes.

[0458] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0459] In this invention, the server includes means for analyzing electronic documents using natural language processing means, means for proposing the optimal automation tool based on the analysis results, means for generating program code using the proposed automation tool, means for executing the generated program code, means for reporting the execution results, means for analyzing manufacturing work instructions to generate machine operation commands, and means for controlling operating equipment using machine operation commands. This enables automation and efficiency improvements in the manufacturing process.

[0460] "Natural language processing means" refers to technologies for analyzing electronic documents and extracting meaningful information.

[0461] An "electronic document" is document data that is created, stored, and displayed on a computer.

[0462] "Automation tools" refer to software or hardware used to streamline business processes.

[0463] "Program code" refers to source code that describes the instructions a computer executes.

[0464] "Execution result" refers to the output information or state obtained during the implementation of the generated program code.

[0465] A "manufacturing work instruction sheet" is a document that outlines the necessary procedures and conditions for the manufacturing process.

[0466] A "machine operation command" is a command given to a device such as manufacturing equipment or a robot to perform a specific action.

[0467] "Operating equipment" refers to devices or machines used to perform machine operations.

[0468] Regarding the embodiment for carrying out the invention, the system that realizes this application example is as follows:

[0469] The server analyzes electronic documents, such as manufacturing work instructions, uploaded by users, using natural language processing technology. This natural language processing is performed using, for example, Python with libraries such as nltk and spaCy. Based on the analysis results, the server selects the most suitable automation equipment. Specifically, it uses an algorithm to determine which machine operation is appropriate for the manufacturing process.

[0470] Furthermore, the necessary program code is generated using the proposed automation tools. This is done using pyautogui or a custom script generation library to create machine operation commands for operating the actual production equipment. The program code thus generated is tested in a virtual environment beforehand to confirm its effectiveness before execution.

[0471] Users can verify the system's operation by receiving reports of analysis results and the execution results of the generated program code via their terminal. For example, if a manufacturing line includes a task called "screw installation," this system will generate motion commands for a robotic arm to streamline that task, ensuring efficient work.

[0472] As a concrete example, a prompt might read: "Analyze the following procedure manual to generate a program for operating the robot arm: 1. Position part A 2. Attach the screw 3. Position part B." Based on this prompt, the system generates optimized machine operation commands.

[0473] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0474] Step 1:

[0475] The user uploads electronic documents, such as manufacturing work instructions, to the server using a terminal. These documents become the input data. The server receives these documents and prepares to begin analysis.

[0476] Step 2:

[0477] The server analyzes uploaded electronic documents using natural language processing (NLTK) tools. This analysis utilizes Python and libraries such as nltk and spaCy. It extracts key manufacturing procedures and parameters from the received electronic documents, generating structured data. The output is the analysis result data for use in the next step.

[0478] Step 3:

[0479] The server performs a process to propose the optimal automation tool based on the analysis results. In this step, an algorithm is used to determine the optimal machine operation method corresponding to the manufacturing process. The input is the analysis result data, and the output is information on which automation platform is suitable.

[0480] Step 4:

[0481] The server generates program code using the proposed automation tools. This code generation utilizes pyautogui or a custom script generation library. The input consists of the automation tools and analysis results data from the previous step, and the final output is the operational instruction code to actually operate the machine.

[0482] Step 5:

[0483] The server tests the program code generated in the virtual environment. The testing process uses libraries such as Python's unittest to verify that the generated code functions correctly. The input is the generated program code, and the output is the success or failure status of the test.

[0484] Step 6:

[0485] The server implements the program code that passed the test into the machine and notifies the user of the results via a terminal. This notification allows the user to verify that the system is functioning correctly. The input is the result of the program code tested in the virtual environment, and the output is the execution status report sent to the user.

[0486] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0487] This invention is a system that combines natural language processing means and an emotion engine, and is designed to support the automation of tasks based on user input. The system can efficiently automate manual work procedures while recognizing the user's emotions and performing optimal processing.

[0488] As a specific implementation, the user first uses a terminal to upload an electronic document describing the work procedure to the server. The server analyzes this document using natural language processing, extracting each task from the procedure and listing them. In addition, the server utilizes an emotion engine to evaluate the user's emotions at the time of input. This emotion data is fed back into subsequent processes and reflected in the suggestion and implementation of automation.

[0489] For example, if a user rates a task as "highly urgent," the server can prioritize suggesting automation tools that can be executed quickly for that task. Similarly, if a user's emotion is perceived as "anxious," the server will provide more detailed execution instructions and results reports to alleviate their anxiety.

[0490] Based on the selected automation tools, the server generates program code. This code is tested in a virtual environment and then executed in the production environment according to the user's settings. The execution results are reported to the terminal in a format that suits the user's emotional state. This allows users to confidently engage in business automation and enables efficient operations.

[0491] The following describes the processing flow.

[0492] Step 1:

[0493] Users prepare electronic documents describing work procedures via their terminals and upload them to the system. The server receives these documents and stores them securely in storage.

[0494] Step 2:

[0495] The server activates natural language processing to analyze the uploaded document. This analysis extracts each work step within the document and stores it as structured data.

[0496] Step 3:

[0497] The server uses an emotion engine to recognize the user's emotions based on the analyzed data. It infers the emotional state from the user's responses and keywords entered via the terminal and records this data as a log.

[0498] Step 4:

[0499] The server suggests the most suitable automation tools based on emotional data. If it determines that the user is anxious, it prioritizes selecting tools capable of high-speed processing.

[0500] Step 5:

[0501] The server generates program code based on the selected automation tools. The generated code includes explanations and annotations that take user sentiment into consideration, and details procedures as needed.

[0502] Step 6:

[0503] The generated code is first tested in a virtual environment. The server evaluates the execution results of the code and records any corrections found.

[0504] Step 7:

[0505] The server reports the test results and final execution plan to the user. The user reviews this using their terminal, provides feedback, and approves the execution.

[0506] Step 8:

[0507] The server, upon user approval, begins executing the code in the production environment. Execution is monitored periodically, and upon completion, detailed results are sent to the user's device in a format tailored to their emotional state.

[0508] Step 9:

[0509] Users review the final execution results via their terminal and submit feedback for further improvements through the system's feedback function. The server continuously optimizes the system based on this evaluation.

[0510] (Example 2)

[0511] Next, we will describe Example 2. 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."

[0512] In today's work environment, inefficiencies arise from the manual execution of work procedures, and users have emotional anxieties about automating tasks. Therefore, when automation and optimization of tasks are required, the challenge lies in easily facilitating the selection of appropriate tools and methods, reducing the psychological burden on users, and efficiently implementing the automation process.

[0513] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0514] In this invention, the server includes a device that analyzes information using natural language processing means, a device that selects the optimal automatic control equipment based on the analysis results, a device that generates operation commands using the selected automatic control equipment, a device that evaluates the user's emotional state and implements appropriate countermeasures, a device that confirms the generated operation commands in the behavioral environment, and a device that reports the confirmation results. This makes it possible to efficiently automate work procedures and provide optimal support that responds to the user's emotions.

[0515] "Natural language processing methods" are technologies for analyzing text data and understanding its structure and meaning.

[0516] "Information" refers to documents and electronic data, including business procedures and data, which are the objects that the system analyzes.

[0517] "Automated control equipment" refers to software and tools used to automate business processes.

[0518] "Action commands" refer to a set of commands or scripts necessary to execute an automated task.

[0519] "User emotional state" refers to the psychological and emotional state that users experience when using the system, and is evaluated by the emotion engine.

[0520] A "virtual space" is a simulated workspace separated from the actual hardware and work environment, and is used for verifying program code.

[0521] In the scope of these claims, "device" refers to an integrated set of components, including specific hardware and software modules.

[0522] "Operating environment" refers to the actual operating environment in which the action commands generated by the system are executed.

[0523] "Confirmation results" refer to the evaluation or output obtained after the execution of an action command, and are notified to the user through a reporting mechanism.

[0524] This invention automates business procedures through a three-tiered system primarily consisting of a user, a terminal, and a server. The process begins with the user creating an electronic document describing the business procedure using a terminal and uploading it to the server.

[0525] The server uses natural language processing (NLP) techniques to analyze this electronic document. Specifically, it uses common libraries useful for natural language processing (e.g., SpaCy and NLTK) to extract individual tasks included in the business procedures from the electronic document and organize them as structured data. This process involves tokenization of information and extraction of keywords.

[0526] Next, the server uses an emotion engine to evaluate the emotions contained in the user's input. This is important to reduce the user's psychological burden and provide more appropriate automated suggestions. The emotion engine utilizes existing sentiment analysis technology to visualize emotional indicators such as "urgency," "anxiety," and "satisfaction" from the input text.

[0527] Subsequently, the server selects the optimal automated control equipment based on the analysis results, taking into account the nature and urgency of the task and the user's emotional state. This includes automation tools such as Ansible and Terraform, which the server uses to generate operational commands. The generated operational commands are then tested in a virtual environment before being safely operated in the execution environment.

[0528] Generative AI models are utilized throughout this entire process, and this technology is particularly useful for the automatic generation of program code. This allows users to efficiently execute automated processes while reducing the burden of manual coding.

[0529] For example, when writing a prompt message, based on the instruction "Automate the following business procedure," the system consistently performs tasks ranging from selecting automated control equipment to generating operation commands and providing appropriate feedback tailored to the user's emotions. This specification allows users to seamlessly automate their own tasks.

[0530] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0531] Step 1:

[0532] Users use their terminals to create electronic documents describing work procedures and upload them to the server. Inputs include electronic documents such as work procedure manuals and related materials. Outputs are document data that the server receives for analysis. Specific actions include the user selecting files via a web browser and pressing a button to send to the server.

[0533] Step 2:

[0534] When the server receives an electronic document, it analyzes it using natural language processing (NLP) tools. The input is an electronic document containing business procedures uploaded by the user. During data processing, the server uses a natural language processing library to tokenize the document, recognizing and extracting business tasks at the sentence and paragraph level. The final output is a structured task list. The server then uses this list for subsequent processing, including keyword extraction and grammatical analysis.

[0535] Step 3:

[0536] The server uses an emotion engine to evaluate the user's emotional state based on the extracted task list. Input includes the task list obtained through natural language processing and user comments related to those tasks. The server uses emotion analysis techniques to calculate emotional indicators associated with each task and generates an emotional evaluation result as output. Specific operations include emotion scoring and tone analysis.

[0537] Step 4:

[0538] The server selects the optimal automated control equipment based on the analysis results and sentiment evaluation. Inputs include the task list obtained in step 2 and the sentiment evaluation results from step 3. For data calculation, an optimization algorithm that considers task content, urgency, and the user's emotional state is used to select the automation tool. The output is a list of selected automated control equipment. Specific operations include executing the selection algorithm and generating a recommendation list.

[0539] Step 5:

[0540] The server generates action commands based on selected automated control equipment. Inputs include a selection list and a database referenced by the generating AI model. The server utilizes the generating AI model to generate action commands suitable for the business procedure and prepares them for testing in a virtual environment. Outputs are the generated action commands, and specific actions include code generation and simulation preparation.

[0541] Step 6:

[0542] The server executes the generated operation commands in a virtual environment and verifies and confirms their operation. The required inputs are the generated operation commands and the virtual environment configuration information. Data calculations include simulation execution within the virtual environment and result analysis. The output is the operation verification result. Specific operations include error detection and health checks.

[0543] Step 7:

[0544] The server executes the program in the production environment and reports the results to the user based on the operational verification results. The input consists of the operational verification results and the production environment configuration information. The server deploys the operational commands it deems executable to the production environment. The output consists of the final execution results and a report for the user. Specific operations include saving the execution results as a log and generating a report for the user.

[0545] (Application Example 2)

[0546] Next, we will explain application example 2. In the following explanation, 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."

[0547] Modern information systems demand the automation and optimization of vast amounts of business procedures, but this lacks the ability to respond appropriately to user emotions and urgency. Conventional technologies struggle to analyze user feedback and cannot respond quickly and flexibly to emotional states. Therefore, there is a need for solutions that improve user satisfaction and enable efficient business operations.

[0548] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0549] In this invention, the server includes means for analyzing electronic documents using natural language processing means, means for recognizing emotional states from input data and adjusting suggestions, and means for presenting execution results in a format based on the emotional state. This makes it possible to quickly and accurately automate and optimize business procedures while taking the user's emotional state into consideration.

[0550] "Natural language processing means" refers to methods that analyze input electronic documents and extract and perform semantic analysis on sentences and words within the document.

[0551] "Automation tools" refer to a set of software or hardware used to efficiently execute specific business procedures.

[0552] "Emotional state" refers to the psychological state inferred from the user's input and feedback, which allows the system to adjust its responses and suggestions.

[0553] "Program code generation" refers to the automatic creation of working software code based on analyzed data.

[0554] "Execution results" refer to the outcomes and data obtained when the generated program code is executed, and these are reported to the user.

[0555] "Format" refers to the way execution results are displayed and their layout presented to the user, and is communicated in an appropriate manner according to their emotional state.

[0556] This invention provides a system that combines natural language processing means and an emotion engine to efficiently analyze user feedback and automate and optimize business procedures. The system uses a server to analyze information entered on the user's terminal and automates appropriate processing.

[0557] The server first analyzes the input electronic document using the Spacy natural language processing library. This allows it to extract each task from the document and understand its meaning. Next, it uses an emotion recognition library called Emotion Engine to evaluate the user's emotional state from the input data. Based on this emotion evaluation, the priority and content of the proposed automation tools are adjusted. For example, if the feedback includes "danger" or "anxiety," detailed responses will be quickly proposed.

[0558] The generated program code is tested in a virtual environment through execution using an automation tool called Task Automation, and modifications are made as needed based on the results. Program code that passes the test is executed in the production environment, and the execution results are reported to the user in a format appropriate to their emotional state. This allows users to automate tasks with confidence and enables them to respond quickly and accurately to citizen feedback.

[0559] For example, if a citizen provides feedback that "the park lighting is dim," and the system recognizes the urgency of the issue, it will immediately propose inspecting the lighting and report the procedure in detail. Furthermore, prompts such as, "We will propose a response based on citizen feedback. We will analyze the emotional state of the feedback to determine if an emergency response is necessary," can be input into the generating AI model.

[0560] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0561] Step 1:

[0562] The user inputs feedback from their device and sends that data to the server. The input is natural language text data, and the server receives this data.

[0563] Step 2:

[0564] The server analyzes the received text data using Spacy, a natural language processing library. It divides the input text into sentences and words, and extracts business tasks from their context. As a result of this analysis, a list of tasks to be performed is created.

[0565] Step 3:

[0566] The server uses the Emotion Engine to perform emotional assessment on the analyzed task. Text data is used as input for emotion recognition, and the urgency and the user's psychological state are evaluated. As a result, data indicating the emotional state is generated.

[0567] Step 4:

[0568] Based on the sentiment assessment results, the server uses Task Automation to select the most suitable automation tools and adjust the suggestions. In this step, the priority of tasks to be automated is determined based on the input task list and sentiment data. This results in a suggested list including the execution order.

[0569] Step 5:

[0570] The server generates program code using selected automation tools. Here, the optimal code for each task is generated and prepared in a testable format within the virtual environment. The output is test program code.

[0571] Step 6:

[0572] The program code is tested in a virtual environment, and the results are evaluated. The tests verify that the execution results meet expectations, and the code is modified as needed. The final output is program code that can be applied to the production environment.

[0573] Step 7:

[0574] The program code is executed in a production environment, and the results and corresponding emotional states are reported to the user in an appropriate format. The execution results are formatted according to the emotional data and provided to the user in a way that is easy to understand and reassuring. The final output is an emotionally sensitive report.

[0575] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0576] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0577] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0578] [Fourth Embodiment]

[0579] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0580] As shown in Figure 7, the 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.

[0581] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0582] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0583] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0584] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0585] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0586] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0587] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0588] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0589] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0590] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0591] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0592] This invention is a system for analyzing manual work procedures and providing appropriate automation. First, the user uploads an electronic document describing the business process to a server using a terminal. The server analyzes the document using natural language processing and extracts specific tasks from the procedure. This analysis determines which automation tool is most suitable.

[0593] As a specific embodiment, the server selects, for example, "data transfer software" as an automation tool for file backup procedures. Based on the selected automation tool, the server generates relevant program code, such as a "file synchronization script." This generated program code is then tested in a virtual environment to verify its executableness.

[0594] The user receives reports of the execution results via their terminal and verifies whether the system is functioning correctly. If any problems occur during the final execution, the server notifies the user and requests manual intervention if necessary.

[0595] For example, if a user uploads a procedure manual from their terminal to the server for "regular server log file backups," the server uses natural language processing to identify the "scheduled operation system utility" suitable for log file backups and generates a script for it. The generated script is configured to run automatically on the server, and the user is notified of the execution results via email or a dashboard. This significantly reduces manual work for the user and improves work efficiency.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] Users access the system using a terminal, select an electronic document describing a business process, and upload it to the server. The server receives the file and verifies the document's format.

[0599] Step 2:

[0600] The server activates natural language processing capabilities to analyze the uploaded electronic document. This analysis identifies each process and task within the document and organizes them as structured data.

[0601] Step 3:

[0602] The server evaluates the automation feasibility for each task identified from the analysis data. It consults historical databases and known automation tool libraries to determine the optimal automation tool. The server logs this selection result.

[0603] Step 4:

[0604] The server generates program code based on the selected automation tools. It utilizes generative AI to create detailed scripts and code corresponding to the procedures. During this process, explanatory comments are added to each part of the code.

[0605] Step 5:

[0606] The server tests the generated program code in a virtual environment. The test execution verifies that the code functions as expected. The test results are recorded as logs, and if problems are found, a detailed error report is generated.

[0607] Step 6:

[0608] The server notifies the user of the test results and the execution plan for the generated code. The user reviews this information via their terminal and provides feedback to the server with any necessary instructions.

[0609] Step 7:

[0610] After obtaining user approval, the server will deploy to the production environment. The script will run according to a predetermined schedule and be monitored periodically. Upon completion, the execution results will be reported to the user.

[0611] Step 8:

[0612] Users evaluate the execution results using their terminals and send feedback to the server as needed. This allows the server to accumulate data to further optimize system performance.

[0613] (Example 1)

[0614] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0615] In modern business processes, automating tasks based on manually written procedures is essential. However, efficiently executing these automations, accurately and quickly selecting automation tools, and generating corresponding programs remains a challenge. In particular, providing the optimal automation method for various procedures is not easy, and the resulting complexity for users is a significant problem.

[0616] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0617] In this invention, the server includes means for analyzing electronic information using natural language processing technology, means for selecting the optimal automation equipment based on the analysis results, and means for generating a program description based on the selected automation equipment. This enables efficient and automated automation of tasks from work procedure manuals.

[0618] "Natural language processing technology" is a technology that enables computers to understand and analyze human language and convert it into usable information.

[0619] "Electronic information" refers to all information that is stored and processed in digital format, and includes text, images, audio data, and other similar data.

[0620] "Automated equipment" refers to devices and software that perform specific tasks without human intervention.

[0621] A "program description" is a text document that contains instructions and commands necessary for a computer to perform a specific action.

[0622] A "virtual domain" refers to a digital environment created by software, regardless of whether it involves physical resources or space, and is a domain used for testing and experimentation.

[0623] "Generation standards" are guidelines and rules that define how programs and data are generated.

[0624] "Control" refers to coordinating and supervising the progress of a process or action in order to achieve a specific objective.

[0625] This invention is a system that analyzes manual work procedures and provides automation. Specifically, the user uploads an electronic document describing the work process to a server using a terminal. The server analyzes the uploaded electronic information using natural language processing technology. The analysis uses the Python programming language and natural language processing libraries such as NLTK and spaCy.

[0626] Based on the analysis results, the server selects the optimal automation equipment. This process utilizes pre-defined rule sets and trained generative AI models to choose the most appropriate automation method for the given business procedure. Based on the selected automation equipment, the server generates the corresponding program description. For program description generation, the AI ​​model processes given prompts and automatically creates, for example, Python scripts or shell scripts.

[0627] As a concrete example, if a user uploads a work procedure document stating "back up customer data at the end of the month," the server analyzes this information and selects data transfer software as the automated backup device. A script is then generated to execute the backup process. This script is tested in a virtual environment and executed if there are no problems.

[0628] Users receive detailed execution results from the server via their terminal. This notification is delivered via email or a dashboard, allowing users to immediately verify the success or failure of a process and improve work efficiency. An example of a prompt for the generated AI model is, "Generate a script to automate periodic server log file backups." Using this prompt, the relevant program code is dynamically generated, automating the task.

[0629] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0630] Step 1:

[0631] The user uploads an electronic document describing a business process to the server using a terminal. The input is a text file recording the steps. The output is an electronic document stored on the server. Here, the user selects the file through the interface and performs the upload.

[0632] Step 2:

[0633] The server receives the uploaded electronic document and analyzes it using natural language processing techniques. The input is the electronic document saved in step 1. The server uses the Python programming language, NLTK, and spaCy to break down the steps within the document and identify each step. The output is a list of individual tasks defined within the document. The server converts each task into structured data.

[0634] Step 3:

[0635] The server selects the optimal automation equipment based on the analysis results. The input is a list of tasks obtained in step 2. The output is a list of automation equipment corresponding to each task. The server refers to pre-configured rules and trained AI models to assign the optimal automation tool for each task. For example, for file transfer, it selects a data transfer tool.

[0636] Step 4:

[0637] The server generates program descriptions based on the selected automation equipment. The input is a list of automation equipment selected in step 3. The output is a set of program code corresponding to each task. Using a generation AI model, Python scripts and shell scripts are automatically generated based on the given prompts.

[0638] Step 5:

[0639] The server tests the generated program description in a virtual environment. The input is the program code generated in step 4. The output is the test log and results. The server uses Docker or virtualization tools to execute the script in a secure virtual environment and perform error checking and operational verification.

[0640] Step 6:

[0641] The user receives test results and execution results reports via their terminal. The input is the test results from step 5. The output is a report provided to the user detailing the execution results. The server displays the results via email or on a dashboard, notifying the user whether it was a success or failure and the reason.

[0642] (Application Example 1)

[0643] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0644] In the manufacturing industry, many manual manufacturing processes are time-consuming and inefficient. Furthermore, manual process setting based on work procedures carries the risk of human error. Against this backdrop, there is a need for methods to streamline and automate manufacturing processes.

[0645] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0646] In this invention, the server includes means for analyzing electronic documents using natural language processing means, means for proposing the optimal automation tool based on the analysis results, means for generating program code using the proposed automation tool, means for executing the generated program code, means for reporting the execution results, means for analyzing manufacturing work instructions to generate machine operation commands, and means for controlling operating equipment using machine operation commands. This enables automation and efficiency improvements in the manufacturing process.

[0647] "Natural language processing means" refers to technologies for analyzing electronic documents and extracting meaningful information.

[0648] An "electronic document" is document data that is created, stored, and displayed on a computer.

[0649] "Automation tools" refer to software or hardware used to streamline business processes.

[0650] "Program code" refers to source code that describes the instructions a computer executes.

[0651] "Execution result" refers to the output information or state obtained during the implementation of the generated program code.

[0652] A "manufacturing work instruction sheet" is a document that outlines the necessary procedures and conditions for the manufacturing process.

[0653] A "machine operation command" is a command given to a device such as manufacturing equipment or a robot to perform a specific action.

[0654] "Operating equipment" refers to devices or machines used to perform machine operations.

[0655] Regarding the embodiment for carrying out the invention, the system that realizes this application example is as follows:

[0656] The server analyzes electronic documents, such as manufacturing work instructions, uploaded by users, using natural language processing technology. This natural language processing is performed using, for example, Python with libraries such as nltk and spaCy. Based on the analysis results, the server selects the most suitable automation equipment. Specifically, it uses an algorithm to determine which machine operation is appropriate for the manufacturing process.

[0657] Furthermore, the necessary program code is generated using the proposed automation tools. This is done using pyautogui or a custom script generation library to create machine operation commands for operating the actual production equipment. The program code thus generated is tested in a virtual environment beforehand to confirm its effectiveness before execution.

[0658] Users can verify the system's operation by receiving reports of analysis results and the execution results of the generated program code via their terminal. For example, if a manufacturing line includes a task called "screw installation," this system will generate motion commands for a robotic arm to streamline that task, ensuring efficient work.

[0659] As a concrete example, a prompt might read: "Analyze the following procedure manual to generate a program for operating the robot arm: 1. Position part A 2. Attach the screw 3. Position part B." Based on this prompt, the system generates optimized machine operation commands.

[0660] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0661] Step 1:

[0662] The user uploads electronic documents, such as manufacturing work instructions, to the server using a terminal. These documents become the input data. The server receives these documents and prepares to begin analysis.

[0663] Step 2:

[0664] The server analyzes uploaded electronic documents using natural language processing (NLTK) tools. This analysis utilizes Python and libraries such as nltk and spaCy. It extracts key manufacturing procedures and parameters from the received electronic documents, generating structured data. The output is the analysis result data for use in the next step.

[0665] Step 3:

[0666] The server performs a process to propose the optimal automation tool based on the analysis results. In this step, an algorithm is used to determine the optimal machine operation method corresponding to the manufacturing process. The input is the analysis result data, and the output is information on which automation platform is suitable.

[0667] Step 4:

[0668] The server generates program code using the proposed automation tools. This code generation utilizes pyautogui or a custom script generation library. The input consists of the automation tools and analysis results data from the previous step, and the final output is the operational instruction code to actually operate the machine.

[0669] Step 5:

[0670] The server tests the program code generated in the virtual environment. The testing process uses libraries such as Python's unittest to verify that the generated code functions correctly. The input is the generated program code, and the output is the success or failure status of the test.

[0671] Step 6:

[0672] The server implements the program code that passed the test into the machine and notifies the user of the results via a terminal. This notification allows the user to verify that the system is functioning correctly. The input is the result of the program code tested in the virtual environment, and the output is the execution status report sent to the user.

[0673] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0674] This invention is a system that combines natural language processing means and an emotion engine, and is designed to support the automation of tasks based on user input. The system can efficiently automate manual work procedures while recognizing the user's emotions and performing optimal processing.

[0675] As a specific implementation, the user first uses a terminal to upload an electronic document describing the work procedure to the server. The server analyzes this document using natural language processing, extracting each task from the procedure and listing them. In addition, the server utilizes an emotion engine to evaluate the user's emotions at the time of input. This emotion data is fed back into subsequent processes and reflected in the suggestion and implementation of automation.

[0676] For example, if a user rates a task as "highly urgent," the server can prioritize suggesting automation tools that can be executed quickly for that task. Similarly, if a user's emotion is perceived as "anxious," the server will provide more detailed execution instructions and results reports to alleviate their anxiety.

[0677] Based on the selected automation tools, the server generates program code. This code is tested in a virtual environment and then executed in the production environment according to the user's settings. The execution results are reported to the terminal in a format that suits the user's emotional state. This allows users to confidently engage in business automation and enables efficient operations.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] Users prepare electronic documents describing work procedures via their terminals and upload them to the system. The server receives these documents and stores them securely in storage.

[0681] Step 2:

[0682] The server activates natural language processing to analyze the uploaded document. This analysis extracts each work step within the document and stores it as structured data.

[0683] Step 3:

[0684] The server uses an emotion engine to recognize the user's emotions based on the analyzed data. It infers the emotional state from the user's responses and keywords entered via the terminal and records this data as a log.

[0685] Step 4:

[0686] The server suggests the most suitable automation tools based on emotional data. If it determines that the user is anxious, it prioritizes selecting tools capable of high-speed processing.

[0687] Step 5:

[0688] The server generates program code based on the selected automation tools. The generated code includes explanations and annotations that take user sentiment into consideration, and details procedures as needed.

[0689] Step 6:

[0690] The generated code is first tested in a virtual environment. The server evaluates the execution results of the code and records any corrections found.

[0691] Step 7:

[0692] The server reports the test results and final execution plan to the user. The user reviews this using their terminal, provides feedback, and approves the execution.

[0693] Step 8:

[0694] The server, upon user approval, begins executing the code in the production environment. Execution is monitored periodically, and upon completion, detailed results are sent to the user's device in a format tailored to their emotional state.

[0695] Step 9:

[0696] Users review the final execution results via their terminal and submit feedback for further improvements through the system's feedback function. The server continuously optimizes the system based on this evaluation.

[0697] (Example 2)

[0698] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0699] In today's work environment, inefficiencies arise from the manual execution of work procedures, and users have emotional anxieties about automating tasks. Therefore, when automation and optimization of tasks are required, the challenge lies in easily facilitating the selection of appropriate tools and methods, reducing the psychological burden on users, and efficiently implementing the automation process.

[0700] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0701] In this invention, the server includes a device that analyzes information using natural language processing means, a device that selects the optimal automatic control equipment based on the analysis results, a device that generates operation commands using the selected automatic control equipment, a device that evaluates the user's emotional state and implements appropriate countermeasures, a device that confirms the generated operation commands in the behavioral environment, and a device that reports the confirmation results. This makes it possible to efficiently automate work procedures and provide optimal support that responds to the user's emotions.

[0702] "Natural language processing methods" are technologies for analyzing text data and understanding its structure and meaning.

[0703] "Information" refers to documents and electronic data, including business procedures and data, which are the objects that the system analyzes.

[0704] "Automated control equipment" refers to software and tools used to automate business processes.

[0705] "Action commands" refer to a set of commands or scripts necessary to execute an automated task.

[0706] "User emotional state" refers to the psychological and emotional state that users experience when using the system, and is evaluated by the emotion engine.

[0707] A "virtual space" is a simulated workspace separated from the actual hardware and work environment, and is used for verifying program code.

[0708] In the scope of these claims, "device" refers to an integrated set of components, including specific hardware and software modules.

[0709] "Operating environment" refers to the actual operating environment in which the action commands generated by the system are executed.

[0710] "Confirmation results" refer to the evaluation or output obtained after the execution of an action command, and are notified to the user through a reporting mechanism.

[0711] This invention automates business procedures through a three-tiered system primarily consisting of a user, a terminal, and a server. The process begins with the user creating an electronic document describing the business procedure using a terminal and uploading it to the server.

[0712] The server uses natural language processing (NLP) techniques to analyze this electronic document. Specifically, it uses common libraries useful for natural language processing (e.g., SpaCy and NLTK) to extract individual tasks included in the business procedures from the electronic document and organize them as structured data. This process involves tokenization of information and extraction of keywords.

[0713] Next, the server uses an emotion engine to evaluate the emotions contained in the user's input. This is important to reduce the user's psychological burden and provide more appropriate automated suggestions. The emotion engine utilizes existing sentiment analysis technology to visualize emotional indicators such as "urgency," "anxiety," and "satisfaction" from the input text.

[0714] Subsequently, the server selects the optimal automated control equipment based on the analysis results, taking into account the nature and urgency of the task and the user's emotional state. This includes automation tools such as Ansible and Terraform, which the server uses to generate operational commands. The generated operational commands are then tested in a virtual environment before being safely operated in the execution environment.

[0715] Generative AI models are utilized throughout this entire process, and this technology is particularly useful for the automatic generation of program code. This allows users to efficiently execute automated processes while reducing the burden of manual coding.

[0716] For example, when writing a prompt message, based on the instruction "Automate the following business procedure," the system consistently performs tasks ranging from selecting automated control equipment to generating operation commands and providing appropriate feedback tailored to the user's emotions. This specification allows users to seamlessly automate their own tasks.

[0717] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0718] Step 1:

[0719] Users use their terminals to create electronic documents describing work procedures and upload them to the server. Inputs include electronic documents such as work procedure manuals and related materials. Outputs are document data that the server receives for analysis. Specific actions include the user selecting files via a web browser and pressing a button to send to the server.

[0720] Step 2:

[0721] When the server receives an electronic document, it analyzes it using natural language processing (NLP) tools. The input is an electronic document containing business procedures uploaded by the user. During data processing, the server uses a natural language processing library to tokenize the document, recognizing and extracting business tasks at the sentence and paragraph level. The final output is a structured task list. The server then uses this list for subsequent processing, including keyword extraction and grammatical analysis.

[0722] Step 3:

[0723] The server uses an emotion engine to evaluate the user's emotional state based on the extracted task list. Input includes the task list obtained through natural language processing and user comments related to those tasks. The server uses emotion analysis techniques to calculate emotional indicators associated with each task and generates an emotional evaluation result as output. Specific operations include emotion scoring and tone analysis.

[0724] Step 4:

[0725] The server selects the optimal automated control equipment based on the analysis results and sentiment evaluation. Inputs include the task list obtained in step 2 and the sentiment evaluation results from step 3. For data calculation, an optimization algorithm that considers task content, urgency, and the user's emotional state is used to select the automation tool. The output is a list of selected automated control equipment. Specific operations include executing the selection algorithm and generating a recommendation list.

[0726] Step 5:

[0727] The server generates action commands based on selected automated control equipment. Inputs include a selection list and a database referenced by the generating AI model. The server utilizes the generating AI model to generate action commands suitable for the business procedure and prepares them for testing in a virtual environment. Outputs are the generated action commands, and specific actions include code generation and simulation preparation.

[0728] Step 6:

[0729] The server executes the generated operation commands in a virtual environment and verifies and confirms their operation. The required inputs are the generated operation commands and the virtual environment configuration information. Data calculations include simulation execution within the virtual environment and result analysis. The output is the operation verification result. Specific operations include error detection and health checks.

[0730] Step 7:

[0731] The server executes the program in the production environment and reports the results to the user based on the operational verification results. The input consists of the operational verification results and the production environment configuration information. The server deploys the operational commands it deems executable to the production environment. The output consists of the final execution results and a report for the user. Specific operations include saving the execution results as a log and generating a report for the user.

[0732] (Application Example 2)

[0733] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] Modern information systems demand the automation and optimization of vast amounts of business procedures, but this lacks the ability to respond appropriately to user emotions and urgency. Conventional technologies struggle to analyze user feedback and cannot respond quickly and flexibly to emotional states. Therefore, there is a need for solutions that improve user satisfaction and enable efficient business operations.

[0735] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0736] In this invention, the server includes means for analyzing electronic documents using natural language processing means, means for recognizing emotional states from input data and adjusting suggestions, and means for presenting execution results in a format based on the emotional state. This makes it possible to quickly and accurately automate and optimize business procedures while taking the user's emotional state into consideration.

[0737] "Natural language processing means" refers to methods that analyze input electronic documents and extract and perform semantic analysis on sentences and words within the document.

[0738] "Automation tools" refer to a set of software or hardware used to efficiently execute specific business procedures.

[0739] "Emotional state" refers to the psychological state inferred from the user's input and feedback, which allows the system to adjust its responses and suggestions.

[0740] "Program code generation" refers to the automatic creation of working software code based on analyzed data.

[0741] "Execution results" refer to the outcomes and data obtained when the generated program code is executed, and these are reported to the user.

[0742] "Format" refers to the way execution results are displayed and their layout presented to the user, and is communicated in an appropriate manner according to their emotional state.

[0743] This invention provides a system that combines natural language processing means and an emotion engine to efficiently analyze user feedback and automate and optimize business procedures. The system uses a server to analyze information entered on the user's terminal and automates appropriate processing.

[0744] The server first analyzes the input electronic document using the Spacy natural language processing library. This allows it to extract each task from the document and understand its meaning. Next, it uses an emotion recognition library called Emotion Engine to evaluate the user's emotional state from the input data. Based on this emotion evaluation, the priority and content of the proposed automation tools are adjusted. For example, if the feedback includes "danger" or "anxiety," detailed responses will be quickly proposed.

[0745] The generated program code is tested in a virtual environment through execution using an automation tool called Task Automation, and modifications are made as needed based on the results. Program code that passes the test is executed in the production environment, and the execution results are reported to the user in a format appropriate to their emotional state. This allows users to automate tasks with confidence and enables them to respond quickly and accurately to citizen feedback.

[0746] For example, if a citizen provides feedback that "the park lighting is dim," and the system recognizes the urgency of the issue, it will immediately propose inspecting the lighting and report the procedure in detail. Furthermore, prompts such as, "We will propose a response based on citizen feedback. We will analyze the emotional state of the feedback to determine if an emergency response is necessary," can be input into the generating AI model.

[0747] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0748] Step 1:

[0749] The user inputs feedback from their device and sends that data to the server. The input is natural language text data, and the server receives this data.

[0750] Step 2:

[0751] The server analyzes the received text data using Spacy, a natural language processing library. It divides the input text into sentences and words, and extracts business tasks from their context. As a result of this analysis, a list of tasks to be performed is created.

[0752] Step 3:

[0753] The server uses the Emotion Engine to perform emotional assessment on the analyzed task. Text data is used as input for emotion recognition, and the urgency and the user's psychological state are evaluated. As a result, data indicating the emotional state is generated.

[0754] Step 4:

[0755] Based on the sentiment assessment results, the server uses Task Automation to select the most suitable automation tools and adjust the suggestions. In this step, the priority of tasks to be automated is determined based on the input task list and sentiment data. This results in a suggested list including the execution order.

[0756] Step 5:

[0757] The server generates program code using selected automation tools. Here, the optimal code for each task is generated and prepared in a testable format within the virtual environment. The output is test program code.

[0758] Step 6:

[0759] The program code is tested in a virtual environment, and the results are evaluated. The tests verify that the execution results meet expectations, and the code is modified as needed. The final output is program code that can be applied to the production environment.

[0760] Step 7:

[0761] The program code is executed in a production environment, and the results and corresponding emotional states are reported to the user in an appropriate format. The execution results are formatted according to the emotional data and provided to the user in a way that is easy to understand and reassuring. The final output is an emotionally sensitive report.

[0762] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0763] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0764] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0765] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0766] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0767] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0768] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0769] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0770] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0771] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0772] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0773] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0774] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0775] 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.

[0776] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0777] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0778] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0779] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0780] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0781] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0782] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0783] The following is further disclosed regarding the embodiments described above.

[0784] (Claim 1)

[0785] A means for analyzing electronic documents using natural language processing means,

[0786] A means of proposing the optimal automation tool based on the analysis results,

[0787] A means for generating program code using the proposed automation tool,

[0788] A means of executing the generated program code,

[0789] A means of reporting the execution results,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, further comprising means for verifying the execution of generated program code in a virtual environment.

[0793] (Claim 3)

[0794] The system according to claim 1, comprising means for optimizing the generation rules in program code generation.

[0795] "Example 1"

[0796] (Claim 1)

[0797] A means of analyzing electronic information using natural language processing technology,

[0798] A means for selecting the optimal automation equipment based on the analysis results,

[0799] A means for generating a program description based on selected automation equipment,

[0800] Means for controlling the generated program description,

[0801] A means for notifying the control result,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, further comprising means for verifying the control of the generated program description in a virtual area.

[0805] (Claim 3)

[0806] The system according to claim 1, comprising means for optimizing the generation norm in program description generation.

[0807] "Application Example 1"

[0808] (Claim 1)

[0809] A means for analyzing electronic documents using natural language processing means,

[0810] A means of proposing the optimal automation tool based on the analysis results,

[0811] A means for generating program code using the proposed automation tool,

[0812] A means of executing the generated program code,

[0813] A means of reporting the execution results,

[0814] A means for analyzing manufacturing work instructions to generate machine operation commands,

[0815] A means for controlling operating equipment using machine operation commands,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising means for verifying the execution of generated program code in a virtual environment.

[0819] (Claim 3)

[0820] The system according to claim 1, comprising means for optimizing the generation rules in program code generation.

[0821] "Example 2 of combining an emotion engine"

[0822] (Claim 1)

[0823] A device that analyzes information using natural language processing means,

[0824] A device that selects the optimal automatic control equipment based on the analysis results,

[0825] A device that generates operation commands using selected automatic control equipment,

[0826] A device that evaluates the emotional state of users and implements appropriate countermeasures,

[0827] A device for verifying the generated action commands in the operating environment,

[0828] A device that reports the verification results,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, further comprising means for verifying the generated operation commands in a virtual space.

[0832] (Claim 3)

[0833] The system according to claim 1, comprising a device for optimizing generation rules in generating operation commands.

[0834] "Application example 2 when combining with an emotional engine"

[0835] (Claim 1)

[0836] A means for analyzing electronic documents using natural language processing means,

[0837] A means of proposing the optimal automation tool based on the analysis results,

[0838] A means of recognizing emotional states from input data and adjusting suggestions accordingly.

[0839] A means of generating program code using the proposed automation tool,

[0840] A means of executing the generated program code,

[0841] A means of reporting the execution results,

[0842] A means of presenting the results in a format based on emotional state,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising means for verifying the execution of generated program code in a virtual environment.

[0846] (Claim 3)

[0847] The system according to claim 1, comprising means for optimizing generation rules and means for reflecting feedback in program code generation. [Explanation of symbols]

[0848] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for analyzing electronic documents using natural language processing means, A means of proposing the optimal automation tool based on the analysis results, A means for generating program code using the proposed automation tool, A means of executing the generated program code, A means of reporting the execution results, A system that includes this.

2. The system according to claim 1, further comprising means for verifying the execution of generated program code in a virtual environment.

3. The system according to claim 1, comprising means for optimizing the generation rules in program code generation.

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