system

Abstract

We provide the system. [Solution] A means of maintaining a database that allows access to all the functions of a specific automation tool, A means for receiving and analyzing structural information and operating procedures to be automated as input, A means of identifying business processes based on input information and generating optimal automation scenarios, A means of providing the generated automation scenario to the user in a specific format, A system that includes this.

Description

Technical Field

[0001] The technology of this 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional RPA development, there has been a problem that it is difficult for end users without specific knowledge to create complex automation scenarios. In particular, analysis of business processes and formulation of optimal automation procedures require advanced programming and domain knowledge, which often restricts the progress of development. By removing such technical hurdles, the aim is to enable more users to utilize RPA and improve business efficiency.

Means for Solving the Problems

[0005] This invention provides a means for receiving and analyzing structural information and operating procedures of automation targets as input, using image and natural language processing technologies, while maintaining a database that allows access to all functions of a specific automation tool. This enables a system that automatically identifies business processes based on the input information and generates optimal automation scenarios. Furthermore, by providing the generated automation scenarios to end users in a specific format, the burden on users is reduced, and efficient automation implementation is made possible.

[0006] An "automation tool" is a platform that uses specific software functions to automatically perform repetitive tasks in order to streamline business processes.

[0007] A "database" is a digital storage system that systematically organizes, stores, and makes easily accessible information.

[0008] "Image processing technology" is a general term for computer technologies used to extract, analyze, or manipulate information from digital images.

[0009] "Structural information" refers to information that describes the internal or external structure of the target system or data, indicating the arrangement and relationships of elements.

[0010] "Natural language processing technology" is a technology that enables computers to understand, interpret, and generate human language, and is applied to document analysis, information extraction, and other tasks.

[0011] A "business process" refers to a series of activities or workflows carried out within a company or organization to achieve a specific objective.

[0012] An "automation scenario" is a set of steps or action plans configured to automatically execute a specific business process.

[0013] "Format" refers to a way in which information is organized in a specific shape or structure to achieve a visual or functional purpose. [Brief explanation of the drawing]

[0014] [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, when an emotion engine is combined. [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]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] 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.

[0018] 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.

[0019] 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, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system that analyzes information according to specific procedures and constructs an optimal automation scenario for process automation using automation tools.

[0036] The server first receives screenshots and HTML structure information to be automated, provided by the end user. This allows it to accurately identify the digital interfaces involved in the business processes that the user is currently performing manually.

[0037] The server uses image processing technology to analyze the components of the digital interface based on the received image data. This allows it to understand the details of UI elements, such as which parts are input fields and which are buttons.

[0038] Next, the server analyzes the user's provided operating procedures using natural language processing technology, breaking them down and understanding them. This allows for an understanding of the sequence of operations and the intent of each step, enabling a precise understanding of the business process.

[0039] Subsequently, the server constructs the workflow and uses a generated AI model to design an efficient automation scenario. This AI model leverages a functional reference of automation tools stored in the database to determine the necessary actions and generate an optimized scenario.

[0040] Finally, the generated automation scenarios are formatted into a specific format and provided to the user. The user can then easily implement automated processes by downloading and applying these scenarios to their own environment.

[0041] As a concrete example, consider the automated generation of sales reports. The user provides the server with the spreadsheet template they currently use and the method for inputting data into it. The server analyzes this and generates a scenario that automates the appropriate data insertion procedure. As a result, the user's work efficiency improves because the automated tool handles the periodic report generation task at set intervals.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users use their devices to prepare screenshots, HTML files, and instructional documents related to the processes they wish to automate, and then upload them to the server.

[0045] Step 2:

[0046] The server analyzes the screenshot received from the user using image processing technology to identify UI elements on the screen. This analysis identifies the position and type of each element (e.g., text boxes and buttons).

[0047] Step 3:

[0048] The server parses the HTML file and understands the structure within the document. This allows for the extraction of attributes and hierarchical information of UI elements, leading to a deeper understanding of the screen layout.

[0049] Step 4:

[0050] The server analyzes the documentation regarding the operating procedures using natural language processing techniques. It extracts the flow of operations and key steps from the documents, understanding the meaning and sequence of the procedures.

[0051] Step 5:

[0052] The server integrates the information from steps 2 and 3 to build the business flow. Each step of the procedure is associated with a UI element, and the entire business process is modeled.

[0053] Step 6:

[0054] The server launches a generated AI model based on the constructed business flow and designs the optimal scenario for automation. This model refers to automation tool references in the database to determine the actions that can be taken.

[0055] Step 7:

[0056] The server formats the generated automation scenarios into a specific format and provides them to the user. This format is one that can be imported and executed by the selected automation tool.

[0057] Step 8:

[0058] Users review the scenario provided on their device, make any necessary modifications, and then apply it to their own system. This initiates the automated workflow.

[0059] (Example 1)

[0060] 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."

[0061] To achieve efficient automation of business processes, it is crucial to accurately analyze the tasks that users perform manually on digital interfaces and effectively generate automation scenarios. However, current technologies sometimes have low accuracy in accurately identifying UI elements and interpreting operating procedures, making it difficult to generate optimal automation scenarios. As a result, users have to manually configure complex processes, which is time-consuming and labor-intensive.

[0062] 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.

[0063] In this invention, the server includes means for receiving and storing image and structural information of a digital interface to be automated and converting it into a format suitable for analysis; means for identifying user interface elements within the digital interface and extracting structural information using image analysis technology; and means for analyzing user-provided operating procedures using natural language analysis technology, breaking down the procedure steps and interpreting the intent. This makes it possible to generate automation scenarios for business processes efficiently and accurately without requiring manual configuration by the user.

[0064] The term "automation target" refers to the digital interfaces and operating procedures that are subject to manual processes performed by users.

[0065] A "digital interface" refers to a system that includes screen elements and HTML structure that constitute a user interface on a computer.

[0066] "Image information" refers to screenshots and visual data captured from a digital interface screen.

[0067] "Structural information" refers to markup data such as HTML and XML that make up a digital interface, and is information that describes the arrangement and attributes of UI elements.

[0068] "Image analysis technology" refers to techniques that use image processing libraries and algorithms to identify user interface elements on digital interfaces.

[0069] "Natural language processing technology" is a technology that uses natural language processing techniques to understand the context and interpret the intent behind user-provided operating procedures.

[0070] A "business flowchart" refers to a diagram that visually represents business procedures based on analyzed data.

[0071] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to learn from data and design optimal automation scenarios.

[0072] An "automation scenario" refers to the procedures or flows generated by a server to automate tasks performed on a digital interface.

[0073] This invention is a system for automating business processes that are currently performed manually by users. The server is the central component of this system and operates using the following means: First, the server receives screenshots of digital interfaces and structural data from the user. This data is converted into a format suitable for subsequent analysis. For analysis, image processing libraries such as "OpenCV" and structural information analysis tools such as "BeautifulSoup" or "XPath" are used.

[0074] Next, the server uses image analysis techniques to identify the user interface elements of the digital interface based on the received data. UI element identification is performed using techniques such as edge detection and template matching.

[0075] Furthermore, the server analyzes the user's provided operating procedures using natural language processing (NLTK) technology. This involves using natural language processing tools such as "spaCy" and "NLTK" to break down the operating procedures in detail and interpret their intent.

[0076] Based on the analyzed information, the server constructs a business flowchart and uses a generated AI model to design the optimal automation scenario. This AI model employs technologies such as "GPT-4 (registered trademark)" and recommends the optimal procedure by utilizing the information stored in the database.

[0077] The generated automation scenarios are formatted into a specific format and provided to the user. The user can then easily implement automated processes by integrating this data into their own environment.

[0078] As a concrete example, consider a case where a user wants to automatically generate sales reports. The user provides the server with a spreadsheet template they are using and the method for inputting data into it. The server analyzes this and generates a scenario that automates the data insertion procedure. As a result, the user can automatically generate periodic reports based on the spreadsheet template at set intervals.

[0079] An example of a prompt message would be, "Create a scenario to automate the regular updating of sales data and streamline the process."

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The server receives screenshots and HTML structure information related to the digital interface to be automated from the user. Image files and text files are provided as input data. The server stores this data in internal storage and converts it into a format that can be parsed. The converted data becomes the input for the next processing step.

[0083] Step 2:

[0084] The server uses the received image data to identify user interface elements using image analysis techniques. Specifically, it uses OpenCV to perform edge detection or template matching to identify components such as input fields and buttons. The output data generated is the UI elements and their location information.

[0085] Step 3:

[0086] The server parses HTML data and structural information, extracting attributes of UI elements based on the DOM tree. It uses BeautifulSoup and XPath queries to identify the ID, class, and text of each element. The output is structured UI element information.

[0087] Step 4:

[0088] The server breaks down and understands the user-provided operating procedures using natural language processing (NLTK) techniques. The input is operating procedures written in text format. Grammatical analysis and intent interpretation are performed using spaCy and NLTK to clarify the order and purpose of each step. The output is a list of the interpreted procedure steps.

[0089] Step 5:

[0090] The server constructs a business flowchart based on the analyzed UI element information and operation procedures. This flowchart visually represents each step and its associated UI actions, constructing the optimal automation scenario. The output at this stage is a flowchart for scenario construction.

[0091] Step 6:

[0092] The server references the business flowchart and designs an optimized automation scenario using a generated AI model. The previously created flowchart and operation data are used as input, and the AI ​​model utilizes case studies and functional information stored in the database to generate an efficient scenario. The output is the final automation scenario.

[0093] Step 7:

[0094] The server formats the generated automation scenarios into a specific format (e.g., JSON or XML). This format conversion makes it easier for users to apply the scenarios later. The formatted automation scenario data is provided to the user as output.

[0095] (Application Example 1)

[0096] 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."

[0097] In today's logistics environment, optimizing inventory management and replenishment planning is essential for efficient operations. However, current manual processes consume a lot of time and manpower and are prone to errors. Therefore, it is necessary to improve operational efficiency by quickly and accurately analyzing inventory information and building optimal automation scenarios.

[0098] 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.

[0099] In this invention, the server includes means for holding an information storage area that allows access to all functions of a specific automation technology, means for receiving and analyzing structural data and operating procedures to be automated as input, and means for extracting screen components from visible data using image processing technology to optimize physical management. This enables more efficient inventory management and replenishment planning.

[0100] A "repository area that allows access to all functions of a specific automation technology" refers to a database that stores all functions of an automation technology and allows access as needed.

[0101] "Structural data to be automated" refers to data that shows the configuration information of applications and systems that are targeted for process automation.

[0102] "Means for receiving and analyzing operating procedures as input" refers to technologies and processes that receive operating instructions provided by users and analyze their content.

[0103] "Extracting screen elements from visible data using image processing technology" refers to a method of identifying each element of a user interface from visible information by utilizing image processing technology.

[0104] "Optimizing physical inventory management" is the process of optimizing the allocation of inventory and resources to achieve efficient management.

[0105] "Analyzing operating procedures using natural language processing technology and proposing efficiency improvements" means using natural language processing technology to understand the instructions provided and automatically generate suggestions for improving the efficiency of the work.

[0106] "Designing automation scenarios and proposing additional replenishment plans using generative AI models" refers to the process of using AI technology to design efficient automation procedures and propose optimization plans for logistics and resource management.

[0107] The system for implementing this invention primarily uses a server and a user terminal. The server maintains an information storage area that allows access to all functions of a specific automation technology, thereby providing a database that encompasses all the functions necessary for process automation. The terminal (e.g., a smartphone) is responsible for collecting data on the user's work activities and transmitting it to the server.

[0108] The server analyzes the structure data and operation procedures to be automated, which are sent from the terminal. The analysis uses image processing techniques (e.g., OpenCV) to identify UI components and natural language processing techniques (e.g., Google's NLP API) to gain a detailed understanding of the user-provided operation procedures.

[0109] Furthermore, the server utilizes generative AI models (e.g., OpenAI® GPT-3®) to design automation scenarios aimed at improving efficiency. These scenarios are used to optimize inventory management and replenishment planning in logistics centers. A concrete example is a process where a terminal scans the barcode of an inventory item, the information is immediately analyzed by the server, and the optimal inventory placement and replenishment instructions are automatically generated.

[0110] In this way, operational efficiency can be improved. A concrete example is a system in a logistics center where a terminal is used to scan barcodes in front of shelves, and an optimal replenishment plan is received on the spot. An example of a prompt message to implement the functionality of this system is: "Generate an automated scenario to optimize inventory management. This is a smartphone app concept that analyzes barcode information and proposes an efficient replenishment plan."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The user scans the barcode of an item in stock using a terminal. The input is image data of the barcode, which the terminal converts into text information and sends to the server. This process utilizes the terminal's camera and barcode reader functions.

[0114] Step 2:

[0115] The server receives text information sent from the terminal. Based on the received data, the server retrieves inventory information associated with the barcode from the database using an information storage area that allows access to all functions of a specific automation technology. The data processing performed here involves searching for the corresponding inventory data based on the entered barcode information and extracting the necessary information.

[0116] Step 3:

[0117] The server uses image processing technology (e.g., OpenCV) to identify the components of the UI. In this step, it analyzes the information obtained from the visible data to determine which information is important inventory information. As a result, customer information is output to optimize the information placement in the UI.

[0118] Step 4:

[0119] The server analyzes user-provided operating procedures using natural language processing (e.g., Google's NLP API). It understands the user's prepared procedures and instructions and determines what supplementary plans are needed. The input is text data from the user, and the output generates specific supplementary instructions.

[0120] Step 5:

[0121] The server designs automated inventory management scenarios for efficiency improvements using a generation AI model (e.g., OpenAI GPT-3). Inputs include past analysis data and operating procedures, while output is an optimized replenishment scenario. Specifically, the AI ​​generates the scenario.

[0122] Step 6:

[0123] Finally, the server returns the generated replenishment plan to the user in a specific format. Upon receiving this output data, the replenishment plan is displayed in an actionable format on the user's terminal. This allows the user to immediately perform the optimal replenishment task.

[0124] 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.

[0125] This invention provides a system that enables efficient business processes using automation tools, and in particular, by combining it with an emotion engine, it offers flexible automation scenarios that take into account the user's emotional state.

[0126] First, the user uploads information related to the process they want to automate to the server via their device. This information includes screenshots of the digital interface, HTML structure, and manual operating instructions.

[0127] The server uses image processing technology to extract screen elements from uploaded screenshots. This process clarifies the position and nature of each UI element within the interface. It also analyzes the HTML structure to understand the hierarchical information of the screen elements.

[0128] Subsequently, the server utilizes natural language processing technology to analyze the operating procedures provided by the user. This analysis understands the meaning and sequence of each step in the procedure, and the business process is modeled.

[0129] Furthermore, it's worth noting that the inclusion of an emotion engine makes it possible to detect the user's emotional state while they are interacting with the user interface. The emotion engine uses cameras and voice sensors to analyze the user's facial expressions and tone of voice in real time and recognize their emotions. This information is fed back into the workflow modeling process, providing the basis for building appropriate automation scenarios tailored to the user's state.

[0130] The generating AI model selects the optimal action from the automation tool's function reference based on the analysis results and generates an automation scenario. This scenario is then converted into a specific format and provided to the user.

[0131] As a concrete example, consider customer support operations. When a user experiences dissatisfaction, the emotion engine detects this, and the automated system executes a script to provide attentive support. In this way, dynamic automation based on user emotions becomes possible, improving the quality of service.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user uses a terminal to upload screenshots, HTML data, and documentation of the operating procedures related to the process to be automated to the server.

[0135] Step 2:

[0136] The server analyzes the uploaded screenshots using image processing techniques to identify UI elements. This includes the location and type of interface elements such as text fields, buttons, and checkboxes.

[0137] Step 3:

[0138] The server parses the HTML data to understand the document structure and the hierarchy between elements. This analysis allows the server to grasp the overall layout of the user interface.

[0139] Step 4:

[0140] The server uses natural language processing techniques to analyze the operating procedures provided by the user. This clarifies the sequence of operations and the specific meaning of each step.

[0141] Step 5:

[0142] The emotion engine monitors the user's facial expressions and voice in real time through the device's camera and microphone to recognize their emotional state. This information is used to understand the user's stress level and satisfaction level.

[0143] Step 6:

[0144] The server integrates collected sentiment data and UI analysis results to dynamically model the flow of business processes. This allows for the creation of flexible automation scenarios that respond to user emotions.

[0145] Step 7:

[0146] The generative AI model designs the optimal automation scenario based on all the input data. This model proposes actions that take into account the normal functions of the automation tool and the user's emotional state.

[0147] Step 8:

[0148] The server converts the designed automation scenario into a specific format usable by the user and provides it to the terminal. This scenario enables the user's work to be smoothly automated.

[0149] Step 9:

[0150] Users review the automation scenarios provided on their devices, adjust the settings as needed, and apply them to the system. This enables advanced automation, including emotion recognition.

[0151] (Example 2)

[0152] 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 will be referred to as the "terminal."

[0153] In recent years, various automation tools have been used to improve business efficiency, but these tools generally only provide simple scenarios that do not take into account the user's emotions or circumstances. As a result, it is difficult to reduce user stress and dissatisfaction, and there is a lack of flexibility in situations where a quick response is required. Furthermore, conventional automation tools have limited capabilities in analyzing user interfaces and operating procedures, making complete business automation difficult.

[0154] 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.

[0155] In this invention, the server includes means for holding an information set that allows reference to all functions of a specific processing device; means for receiving and analyzing structural information and operating procedures to be automated as input; means for extracting user interface elements using image analysis technology; means for analyzing procedures and modeling business processes using language processing technology; and means for recognizing the user's emotional state and adjusting the business process accordingly. This enables the generation of flexible and appropriate automation scenarios based on the user's emotions.

[0156] "A set of information that allows reference to all functions of a specific processing unit" refers to a collection of information that comprehensively describes and records all functions and procedures implemented in the processing unit.

[0157] "Structural information to be automated" refers to detailed information about the user interface and the arrangement and hierarchy of its components.

[0158] "Operating procedures" refer to a series of specific steps or steps required to carry out a particular business process.

[0159] "Image analysis technology" refers to the technology of processing image data and extracting meaningful information from it.

[0160] "User interface elements" refer to components in a computer or application that can be interacted with by the user, such as buttons and input fields.

[0161] "Language processing technology" refers to the technologies used to analyze, understand, and generate natural language.

[0162] "Modeling a business process" is the process of transforming a specific business process into a logical structure, making it reproducible within a system.

[0163] "User emotional state" refers to the emotions and moods a user experiences at a particular point in time, and is often measured in terms of stress levels or satisfaction levels.

[0164] An "automation scenario" is a plan or procedure document that describes how an automated process should be executed.

[0165] This invention is a system aimed at automating flexible business processes while taking into account the user's emotional state. The system's main role is to generate effective automation scenarios based on the structural information, operating procedures, and the user's emotional state of the process to be automated. The specific implementation method of this system is described below.

[0166] Users upload information related to the process they want to automate to the server using a device. This information includes screenshots of the digital interface, HTML structured data, and manual operating instructions. A standard computer or smartphone can be used as the device.

[0167] The server first uses image processing technology to extract user interface elements from uploaded screenshots. Specifically, it utilizes image processing libraries such as OpenCV to identify the position and attributes of each element in the image. Furthermore, to analyze the HTML structure data, it uses tools like BeautifulSoup to clarify the hierarchy and interrelationships of each element.

[0168] Furthermore, the server uses natural language processing techniques to analyze the operating procedures and model the business process. This involves using natural language processing libraries such as NLTK and SpaCy to analyze the text and understand the intent and sequence of each step. As a result, the business process is defined as a logical model.

[0169] For real-time emotion recognition, the server uses cameras and voice sensors to evaluate the user's emotional state. Emotion recognition technology could utilize facial recognition APIs or voice analysis APIs. This allows for real-time determination of the user's stress level and satisfaction level.

[0170] Finally, the generating AI model constructs an automation scenario based on all these analysis results. It selects the optimal automation actions and writes automation scripts suitable for each process step. This scenario is provided in a specific format that is easy for the user to understand.

[0171] As a concrete example, consider automating data entry in customer support. If a user expresses dissatisfaction with the data entry process, the emotion engine detects this state, and the automation system automatically generates a script to streamline the entry process. This reduces the burden on the user, improves work efficiency, and increases satisfaction.

[0172] An example of a prompt might be: "When a user clicks a specific button on a webpage, how would you leverage the emotion engine to build an automated scenario that enhances the click experience of that button?"

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The user uploads information about the business process they want to automate to the server using a terminal. Specific inputs include screenshots of digital interfaces, HTML structured data, and manual operation instructions. In this step, the user prepares the necessary information on the terminal and sends it to the server. The output is that the information is stored on the server and ready for the next analysis step.

[0176] Step 2:

[0177] The server extracts user interface elements from uploaded screenshots using image analysis technology. Based on the screenshot image as input, it performs image analysis using libraries such as OpenCV to identify the position and shape of each UI element. The output is metadata (position, attribute information) of the UI elements, and this data is used for subsequent analysis.

[0178] Step 3:

[0179] The server parses the HTML structure data. In this step, it receives an HTML file as input and parses the DOM tree using a tool like BeautifulSoup. This extracts the hierarchical structure and attribute information of each element. The output is the parsed HTML structure information, which generates data useful for identifying UI elements.

[0180] Step 4:

[0181] The server uses natural language processing (NLTK) techniques to analyze user instructions. The input is the user's text-based instructions, which are parsed using natural language processing libraries such as NLTK and SpaCy. Data processing and calculations are then used to model the intent and sequence of the instructions. The output is the structure of the modeled business process.

[0182] Step 5:

[0183] The server detects the user's emotional state using real-time emotion recognition technology. Input data comes from cameras and audio sensors, which are then analyzed using an emotion recognition API. The analysis results measure the degree of stress and emotion the user is experiencing. The output data represents the user's current emotional state and is used to adjust business processes.

[0184] Step 6:

[0185] The generating AI model integrates the analysis results and constructs the optimal automation scenario. The inputs are UI element information, HTML structure, operation procedures, and sentiment data obtained in the previous steps. Based on this, the AI ​​model designs the optimal process flow and generates a specific automation script. The output is an automation scenario in a specific format provided to the user. This scenario then facilitates business operations.

[0186] (Application Example 2)

[0187] 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 device 14 will be referred to as the "terminal."

[0188] In automated systems, a challenge exists in that standardized responses are performed without considering the user's emotional state, resulting in insufficient automation tailored to individual situations. In particular, in home environments, there is a need for more personalized and adaptable services that utilize emotion recognition.

[0189] 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.

[0190] In this invention, the server includes means for holding information resources that allow reference to all functions of a specific automation support device, means for receiving and analyzing configuration information and operating procedures of the target of automation as input, and means for understanding the emotional state of an individual using an emotion recognition function and dynamically changing the automation scenario accordingly. This makes it possible to provide a customized automation scenario that responds to the user's emotions.

[0191] An "automation support device" is a technical means for performing routine tasks through program control, with the aim of improving the efficiency of business processes.

[0192] "Information resources" refer to the data sets necessary for a system's operation, as well as the collection of information that is maintained in a form that allows for the reference and use of that data.

[0193] "Configuration information" refers to information that describes the logical and physical structure of data and operations within or outside a system or application.

[0194] "Operating instructions" refer to a set of sequential instructions outlining the series of actions necessary for a system or application to perform a specific operation.

[0195] "Emotion recognition function" refers to a technological means of identifying and analyzing an individual's emotional state based on sensor data such as images and sounds.

[0196] "Dynamically adaptable" means that the system's behavior and state are automatically adapted and adjusted in response to specific situations or inputs.

[0197] A "customized automation scenario" refers to an automation procedure or workflow that is individually designed and generated according to the user's specific needs and circumstances.

[0198] Embodiments of this invention will now be described. In this system, a server plays a central role in performing various processes to support automation. First, the server is responsible for receiving digital information, configuration information, and operating procedures transmitted from the user's terminal. This information includes, for example, screenshots of the interface to be automated and detailed operating procedures.

[0199] The server extracts UI elements from the received digital image using image processing technology. Specifically, it uses image analysis tools such as OpenCV to identify the location and characteristics of elements on the screen. Next, the server uses natural language processing technology based on the received information to analyze the operation procedure. This analysis includes understanding the meaning and order of each step in the procedure, which is useful for modeling business processes.

[0200] Furthermore, the server is equipped with emotion recognition capabilities, allowing it to capture the user's emotions in real time through cameras and microphones. This enables it to generate the most appropriate automated scenario based on the user's emotional state at any given time. A generative AI model is used to generate these scenarios, allowing it to respond to a variety of situations.

[0201] For example, consider a home support robot. If a user feels stressed, the server recognizes that emotion and instructs the robot to play music to promote relaxation. It can also provide a warm drink in the kitchen. This enables the provision of personalized services tailored to the individual user's situation.

[0202] An example of a prompt message is, "The user's current emotional state is 'tired'. Please suggest the most suitable relaxation action." In this way, the invention aims to provide a comfortable and efficient living environment by enabling flexible responses that reflect the user's emotions.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] First, the user's device collects configuration information and operating procedures to be automated and sends them to the server. This information includes screenshots, HTML structure, and manual operating procedures. This data provided by the user serves as input.

[0206] Step 2:

[0207] The server performs image analysis on the received digital image data using OpenCV. This process identifies UI elements and extracts their position and characteristics on the screen. As a result of this analysis, the placement information of each UI element is output.

[0208] Step 3:

[0209] Next, the server analyzes the hierarchical structure based on the HTML structure data. This extracts parent-child relationships and hierarchical information of screen elements from the input, and the output data includes an understanding of the structure.

[0210] Step 4:

[0211] The server analyzes the input procedure using natural language processing technology. This analysis helps understand the meaning and sequence of each step in the procedure, and models the business process. The output includes a structured model of the procedure.

[0212] Step 5:

[0213] In parallel, the server uses a camera and microphone to monitor the user's emotional state in real time. Using emotion recognition technology, it identifies emotions from facial expressions and voice, and outputs the user's current emotional state.

[0214] Step 6:

[0215] The server uses a generative AI model to generate the optimal automation scenario based on the input operation steps and the user's emotional state. In this process, prompts tailored to specific work situations are input to the AI, and an appropriate action plan is output.

[0216] Step 7:

[0217] Finally, the server converts the generated automation scenario into a suitable format and provides it to the user. This output consists of customized guidelines and automated task procedures tailored to the user's specific situation.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] This invention is a system that analyzes information according to specific procedures and constructs an optimal automation scenario for process automation using automation tools.

[0235] The server first receives screenshots and HTML structure information to be automated, provided by the end user. This allows it to accurately identify the digital interfaces involved in the business processes that the user is currently performing manually.

[0236] The server uses image processing technology to analyze the components of the digital interface based on the received image data. This allows it to understand the details of UI elements, such as which parts are input fields and which are buttons.

[0237] Next, the server analyzes the user's provided operating procedures using natural language processing technology, breaking them down and understanding them. This allows for an understanding of the sequence of operations and the intent of each step, enabling a precise understanding of the business process.

[0238] Subsequently, the server constructs the workflow and uses a generated AI model to design an efficient automation scenario. This AI model leverages a functional reference of automation tools stored in the database to determine the necessary actions and generate an optimized scenario.

[0239] Finally, the generated automation scenarios are formatted into a specific format and provided to the user. The user can then easily implement automated processes by downloading and applying these scenarios to their own environment.

[0240] As a concrete example, consider the automated generation of sales reports. The user provides the server with the spreadsheet template they currently use and the method for inputting data into it. The server analyzes this and generates a scenario that automates the appropriate data insertion procedure. As a result, the user's work efficiency improves because the automated tool handles the periodic report generation task at set intervals.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] Users use their devices to prepare screenshots, HTML files, and instructional documents related to the processes they wish to automate, and then upload them to the server.

[0244] Step 2:

[0245] The server analyzes the screenshot received from the user using image processing technology to identify UI elements on the screen. This analysis identifies the position and type of each element (e.g., text boxes and buttons).

[0246] Step 3:

[0247] The server parses the HTML file and understands the structure within the document. This allows for the extraction of attributes and hierarchical information of UI elements, leading to a deeper understanding of the screen layout.

[0248] Step 4:

[0249] The server analyzes the documentation regarding the operating procedures using natural language processing techniques. It extracts the flow of operations and key steps from the documents, understanding the meaning and sequence of the procedures.

[0250] Step 5:

[0251] The server integrates the information from steps 2 and 3 to build the business flow. Each step of the procedure is associated with a UI element, and the entire business process is modeled.

[0252] Step 6:

[0253] The server launches a generated AI model based on the constructed business flow and designs the optimal scenario for automation. This model refers to automation tool references in the database to determine the actions that can be taken.

[0254] Step 7:

[0255] The server formats the generated automation scenarios into a specific format and provides them to the user. This format is one that can be imported and executed by the selected automation tool.

[0256] Step 8:

[0257] Users review the scenario provided on their device, make any necessary modifications, and then apply it to their own system. This initiates the automated workflow.

[0258] (Example 1)

[0259] 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."

[0260] To achieve efficient automation of business processes, it is crucial to accurately analyze the tasks that users perform manually on digital interfaces and effectively generate automation scenarios. However, current technologies sometimes have low accuracy in accurately identifying UI elements and interpreting operating procedures, making it difficult to generate optimal automation scenarios. As a result, users have to manually configure complex processes, which is time-consuming and labor-intensive.

[0261] 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.

[0262] In this invention, the server includes means for receiving and storing image and structural information of a digital interface to be automated and converting it into a format suitable for analysis; means for identifying user interface elements within the digital interface and extracting structural information using image analysis technology; and means for analyzing user-provided operating procedures using natural language analysis technology, breaking down the procedure steps and interpreting the intent. This makes it possible to generate automation scenarios for business processes efficiently and accurately without requiring manual configuration by the user.

[0263] The term "automation target" refers to the digital interfaces and operating procedures that are subject to manual processes performed by users.

[0264] A "digital interface" refers to a system that includes screen elements and HTML structure that constitute a user interface on a computer.

[0265] "Image information" refers to screenshots and visual data captured from a digital interface screen.

[0266] "Structural information" refers to markup data such as HTML and XML that make up a digital interface, and is information that describes the arrangement and attributes of UI elements.

[0267] "Image analysis technology" refers to techniques that use image processing libraries and algorithms to identify user interface elements on digital interfaces.

[0268] "Natural language processing technology" is a technology that uses natural language processing techniques to understand the context and interpret the intent behind user-provided operating procedures.

[0269] A "business flowchart" refers to a diagram that visually represents business procedures based on analyzed data.

[0270] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to learn from data and design optimal automation scenarios.

[0271] An "automation scenario" refers to the procedures or flows generated by a server to automate tasks performed on a digital interface.

[0272] This invention is a system for automating business processes that are currently performed manually by users. The server is the central component of this system and operates using the following means: First, the server receives screenshots of digital interfaces and structural data from the user. This data is converted into a format suitable for subsequent analysis. For analysis, image processing libraries such as "OpenCV" and structural information analysis tools such as "BeautifulSoup" or "XPath" are used.

[0273] Next, the server uses image analysis techniques to identify the user interface elements of the digital interface based on the received data. UI element identification is performed using techniques such as edge detection and template matching.

[0274] Furthermore, the server analyzes the user's provided operating procedures using natural language processing (NLTK) technology. This involves using natural language processing tools such as "spaCy" and "NLTK" to break down the operating procedures in detail and interpret their intent.

[0275] Based on the analyzed information, the server constructs a business flowchart and uses a generated AI model to design the optimal automation scenario. This AI model employs technologies such as "GPT-4" and recommends the optimal procedure by utilizing the information stored in the database.

[0276] The generated automation scenarios are formatted into a specific format and provided to the user. The user can then easily implement automated processes by integrating this data into their own environment.

[0277] As a concrete example, consider a case where a user wants to automatically generate sales reports. The user provides the server with a spreadsheet template they are using and the method for inputting data into it. The server analyzes this and generates a scenario that automates the data insertion procedure. As a result, the user can automatically generate periodic reports based on the spreadsheet template at set intervals.

[0278] As an example of a prompt sentence, it can be instructed in the form of "Please create a scenario that automates the regular operation data update work and realizes the process efficiency."

[0279] The flow of the specific process in Example 1 will be described using FIG. 11.

[0280] Step 1:

[0281] The server receives a screenshot and HTML structure information related to the digital interface to be automated from the user. As input data, an image file or a text file is provided. The server stores these data in the internal storage and converts them into a format that can be analyzed. The converted data becomes the input for the next processing step.

[0282] Step 2:

[0283] The server uses the received image data and employs image analysis techniques to identify user interface elements. Specifically, by performing edge detection or template matching using OpenCV, components such as input fields and buttons are identified. As output data, UI elements and their position information are generated.

[0284] Step 3:

[0285] The server analyzes the HTML data and structure information and extracts the attributes of the UI elements based on the DOM tree. Using BeautifulSoup or XPath queries, the ID, class, and text of each element are identified. The output is structured UI element information.

[0286] Step 4:

[0287] The server breaks down and understands the user-provided operating procedures using natural language processing (NLTK) techniques. The input is operating procedures written in text format. Grammatical analysis and intent interpretation are performed using spaCy and NLTK to clarify the order and purpose of each step. The output is a list of the interpreted procedure steps.

[0288] Step 5:

[0289] The server constructs a business flowchart based on the analyzed UI element information and operation procedures. This flowchart visually represents each step and its associated UI actions, constructing the optimal automation scenario. The output at this stage is a flowchart for scenario construction.

[0290] Step 6:

[0291] The server references the business flowchart and designs an optimized automation scenario using a generated AI model. The previously created flowchart and operation data are used as input, and the AI ​​model utilizes case studies and functional information stored in the database to generate an efficient scenario. The output is the final automation scenario.

[0292] Step 7:

[0293] The server formats the generated automation scenarios into a specific format (e.g., JSON or XML). This format conversion makes it easier for users to apply the scenarios later. The formatted automation scenario data is provided to the user as output.

[0294] (Application Example 1)

[0295] 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."

[0296] In today's logistics environment, optimizing inventory management and replenishment planning is essential for efficient operations. However, current manual processes consume a lot of time and manpower and are prone to errors. Therefore, it is necessary to improve operational efficiency by quickly and accurately analyzing inventory information and building optimal automation scenarios.

[0297] 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.

[0298] In this invention, the server includes means for holding an information storage area that allows access to all functions of a specific automation technology, means for receiving and analyzing structural data and operating procedures to be automated as input, and means for extracting screen components from visible data using image processing technology to optimize physical management. This enables more efficient inventory management and replenishment planning.

[0299] A "repository area that allows access to all functions of a specific automation technology" refers to a database that stores all functions of an automation technology and allows access as needed.

[0300] "Structural data to be automated" refers to data that shows the configuration information of applications and systems that are targeted for process automation.

[0301] "Means for receiving and analyzing operating procedures as input" refers to technologies and processes that receive operating instructions provided by users and analyze their content.

[0302] "Extracting screen elements from visible data using image processing technology" refers to a method of identifying each element of a user interface from visible information by utilizing image processing technology.

[0303] "Optimizing physical inventory management" is the process of optimizing the allocation of inventory and resources to achieve efficient management.

[0304] "Analyze operation procedures using natural language processing technology and propose efficiency improvements" means understanding the instructions provided using natural language processing technology and automatically generating proposals regarding business efficiency improvements.

[0305] "Design automation scenarios using generative AI models and present additional replenishment plans" refers to the process of using AI technology to design efficient automation procedures and propose optimization plans in logistics and resource management.

[0306] The system for implementing this invention mainly uses a server and a user's terminal. The server holds an information storage area that can refer to all functions of a specific automation technology, thereby providing a database that encompasses all the functions necessary for process automation. The terminal (e.g., smartphone) has the role of collecting data on the business activities being performed by the user and sending this to the server.

[0307] The server analyzes the structural data and operation procedures of the automation target sent from the terminal. The analysis enables the identification of UI components using image processing technology (e.g., OpenCV) and a detailed understanding of the operation procedures provided by the user using natural language processing technology (e.g., Google's NLP API).

[0308] Furthermore, the server utilizes a generative AI model (e.g., OpenAI GPT-3) to design automation scenarios aimed at efficiency improvements. These scenarios are used for optimizing inventory management and replenishment plans in a logistics center. As a specific example, there is the process where the terminal scans the barcode of an inventory item, and the information is immediately analyzed by the server, automatically generating an optimal inventory arrangement and replenishment instructions.

[0309] In this way, operational efficiency can be improved. A concrete example is a system in a logistics center where a terminal is used to scan barcodes in front of shelves, and an optimal replenishment plan is received on the spot. An example of a prompt message to implement the functionality of this system is: "Generate an automated scenario to optimize inventory management. This is a smartphone app concept that analyzes barcode information and proposes an efficient replenishment plan."

[0310] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0311] Step 1:

[0312] The user scans the barcode of an item in stock using a terminal. The input is image data of the barcode, which the terminal converts into text information and sends to the server. This process utilizes the terminal's camera and barcode reader functions.

[0313] Step 2:

[0314] The server receives text information sent from the terminal. Based on the received data, the server retrieves inventory information associated with the barcode from the database using an information storage area that allows access to all functions of a specific automation technology. The data processing performed here involves searching for the corresponding inventory data based on the entered barcode information and extracting the necessary information.

[0315] Step 3:

[0316] The server uses image processing technology (e.g., OpenCV) to identify the components of the UI. In this step, it analyzes the information obtained from the visible data to determine which information is important inventory information. As a result, customer information is output to optimize the information placement in the UI.

[0317] Step 4:

[0318] The server analyzes user-provided operating procedures using natural language processing (e.g., Google's NLP API). It understands the user's prepared procedures and instructions and determines what supplementary plans are needed. The input is text data from the user, and the output generates specific supplementary instructions.

[0319] Step 5:

[0320] The server designs automated inventory management scenarios for efficiency improvements using a generation AI model (e.g., OpenAI GPT-3). Inputs include past analysis data and operating procedures, while output is an optimized replenishment scenario. Specifically, the AI ​​generates the scenario.

[0321] Step 6:

[0322] Finally, the server returns the generated replenishment plan to the user in a specific format. Upon receiving this output data, the replenishment plan is displayed in an actionable format on the user's terminal. This allows the user to immediately perform the optimal replenishment task.

[0323] 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.

[0324] This invention provides a system that enables efficient business processes using automation tools, and in particular, by combining it with an emotion engine, it offers flexible automation scenarios that take into account the user's emotional state.

[0325] First, the user uploads information related to the process they want to automate to the server via their device. This information includes screenshots of the digital interface, HTML structure, and manual operating instructions.

[0326] The server uses image processing technology to extract screen elements from uploaded screenshots. This process clarifies the position and nature of each UI element within the interface. It also analyzes the HTML structure to understand the hierarchical information of the screen elements.

[0327] Subsequently, the server utilizes natural language processing technology to analyze the operating procedures provided by the user. This analysis understands the meaning and sequence of each step in the procedure, and the business process is modeled.

[0328] Furthermore, it's worth noting that the inclusion of an emotion engine makes it possible to detect the user's emotional state while they are interacting with the user interface. The emotion engine uses cameras and voice sensors to analyze the user's facial expressions and tone of voice in real time and recognize their emotions. This information is fed back into the workflow modeling process, providing the basis for building appropriate automation scenarios tailored to the user's state.

[0329] The generating AI model selects the optimal action from the automation tool's function reference based on the analysis results and generates an automation scenario. This scenario is then converted into a specific format and provided to the user.

[0330] As a concrete example, consider customer support operations. When a user experiences dissatisfaction, the emotion engine detects this, and the automated system executes a script to provide attentive support. In this way, dynamic automation based on user emotions becomes possible, improving the quality of service.

[0331] The following describes the processing flow.

[0332] Step 1:

[0333] The user uses a terminal to upload screenshots, HTML data, and documentation of the operating procedures related to the process to be automated to the server.

[0334] Step 2:

[0335] The server analyzes the uploaded screenshots using image processing techniques to identify UI elements. This includes the location and type of interface elements such as text fields, buttons, and checkboxes.

[0336] Step 3:

[0337] The server parses the HTML data to understand the document structure and the hierarchy between elements. This analysis allows the server to grasp the overall layout of the user interface.

[0338] Step 4:

[0339] The server uses natural language processing techniques to analyze the operating procedures provided by the user. This clarifies the sequence of operations and the specific meaning of each step.

[0340] Step 5:

[0341] The emotion engine monitors the user's facial expressions and voice in real time through the device's camera and microphone to recognize their emotional state. This information is used to understand the user's stress level and satisfaction level.

[0342] Step 6:

[0343] The server integrates collected sentiment data and UI analysis results to dynamically model the flow of business processes. This allows for the creation of flexible automation scenarios that respond to user emotions.

[0344] Step 7:

[0345] The generative AI model designs the optimal automation scenario based on all the input data. This model proposes actions that take into account the normal functions of the automation tool and the user's emotional state.

[0346] Step 8:

[0347] The server converts the designed automation scenario into a specific format usable by the user and provides it to the terminal. This scenario enables the user's work to be smoothly automated.

[0348] Step 9:

[0349] Users review the automation scenarios provided on their devices, adjust the settings as needed, and apply them to the system. This enables advanced automation, including emotion recognition.

[0350] (Example 2)

[0351] 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".

[0352] In recent years, various automation tools have been used to improve business efficiency, but these tools generally only provide simple scenarios that do not take into account the user's emotions or circumstances. As a result, it is difficult to reduce user stress and dissatisfaction, and there is a lack of flexibility in situations where a quick response is required. Furthermore, conventional automation tools have limited capabilities in analyzing user interfaces and operating procedures, making complete business automation difficult.

[0353] 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.

[0354] In this invention, the server includes means for holding an information set that allows reference to all functions of a specific processing device; means for receiving and analyzing structural information and operating procedures to be automated as input; means for extracting user interface elements using image analysis technology; means for analyzing procedures and modeling business processes using language processing technology; and means for recognizing the user's emotional state and adjusting the business process accordingly. This enables the generation of flexible and appropriate automation scenarios based on the user's emotions.

[0355] "A set of information that allows reference to all functions of a specific processing unit" refers to a collection of information that comprehensively describes and records all functions and procedures implemented in the processing unit.

[0356] "Structural information to be automated" refers to detailed information about the user interface and the arrangement and hierarchy of its components.

[0357] "Operating procedures" refer to a series of specific steps or steps required to carry out a particular business process.

[0358] "Image analysis technology" refers to the technology of processing image data and extracting meaningful information from it.

[0359] "User interface elements" refer to components in a computer or application that can be interacted with by the user, such as buttons and input fields.

[0360] "Language processing technology" refers to the technologies used to analyze, understand, and generate natural language.

[0361] "Modeling a business process" is the process of transforming a specific business process into a logical structure, making it reproducible within a system.

[0362] "User emotional state" refers to the emotions and moods a user experiences at a particular point in time, and is often measured in terms of stress levels or satisfaction levels.

[0363] An "automation scenario" is a plan or procedure document that describes how an automated process should be executed.

[0364] This invention is a system aimed at automating flexible business processes while taking into account the user's emotional state. The system's main role is to generate effective automation scenarios based on the structural information, operating procedures, and the user's emotional state of the process to be automated. The specific implementation method of this system is described below.

[0365] Users upload information related to the process they want to automate to the server using a device. This information includes screenshots of the digital interface, HTML structured data, and manual operating instructions. A standard computer or smartphone can be used as the device.

[0366] The server first uses image processing technology to extract user interface elements from uploaded screenshots. Specifically, it utilizes image processing libraries such as OpenCV to identify the position and attributes of each element in the image. Furthermore, to analyze the HTML structure data, it uses tools like BeautifulSoup to clarify the hierarchy and interrelationships of each element.

[0367] Furthermore, the server uses natural language processing techniques to analyze the operating procedures and model the business process. This involves using natural language processing libraries such as NLTK and SpaCy to analyze the text and understand the intent and sequence of each step. As a result, the business process is defined as a logical model.

[0368] For real-time emotion recognition, the server uses cameras and voice sensors to evaluate the user's emotional state. Emotion recognition technology could utilize facial recognition APIs or voice analysis APIs. This allows for real-time determination of the user's stress level and satisfaction level.

[0369] Finally, the generating AI model constructs an automation scenario based on all these analysis results. It selects the optimal automation actions and writes automation scripts suitable for each process step. This scenario is provided in a specific format that is easy for the user to understand.

[0370] As a concrete example, consider automating data entry in customer support. If a user expresses dissatisfaction with the data entry process, the emotion engine detects this state, and the automation system automatically generates a script to streamline the entry process. This reduces the burden on the user, improves work efficiency, and increases satisfaction.

[0371] An example of a prompt might be: "When a user clicks a specific button on a webpage, how would you leverage the emotion engine to build an automated scenario that enhances the click experience of that button?"

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The user uploads information about the business process they want to automate to the server using a terminal. Specific inputs include screenshots of digital interfaces, HTML structured data, and manual operation instructions. In this step, the user prepares the necessary information on the terminal and sends it to the server. The output is that the information is stored on the server and ready for the next analysis step.

[0375] Step 2:

[0376] The server extracts user interface elements from uploaded screenshots using image analysis technology. Based on the screenshot image as input, it performs image analysis using libraries such as OpenCV to identify the position and shape of each UI element. The output is metadata (position, attribute information) of the UI elements, and this data is used for subsequent analysis.

[0377] Step 3:

[0378] The server parses the HTML structure data. In this step, it receives an HTML file as input and parses the DOM tree using a tool like BeautifulSoup. This extracts the hierarchical structure and attribute information of each element. The output is the parsed HTML structure information, which generates data useful for identifying UI elements.

[0379] Step 4:

[0380] The server uses natural language processing (NLTK) techniques to analyze user instructions. The input is the user's text-based instructions, which are parsed using natural language processing libraries such as NLTK and SpaCy. Data processing and calculations are then used to model the intent and sequence of the instructions. The output is the structure of the modeled business process.

[0381] Step 5:

[0382] The server detects the user's emotional state using real-time emotion recognition technology. Input data comes from cameras and audio sensors, which are then analyzed using an emotion recognition API. The analysis results measure the degree of stress and emotion the user is experiencing. The output data represents the user's current emotional state and is used to adjust business processes.

[0383] Step 6:

[0384] The generating AI model integrates the analysis results and constructs the optimal automation scenario. The inputs are UI element information, HTML structure, operation procedures, and sentiment data obtained in the previous steps. Based on this, the AI ​​model designs the optimal process flow and generates a specific automation script. The output is an automation scenario in a specific format provided to the user. This scenario then facilitates business operations.

[0385] (Application Example 2)

[0386] 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."

[0387] In automated systems, a challenge exists in that standardized responses are performed without considering the user's emotional state, resulting in insufficient automation tailored to individual situations. In particular, in home environments, there is a need for more personalized and adaptable services that utilize emotion recognition.

[0388] 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.

[0389] In this invention, the server includes means for holding information resources that allow reference to all functions of a specific automation support device, means for receiving and analyzing configuration information and operating procedures of the target of automation as input, and means for understanding the emotional state of an individual using an emotion recognition function and dynamically changing the automation scenario accordingly. This makes it possible to provide a customized automation scenario that responds to the user's emotions.

[0390] An "automation support device" is a technical means for performing routine tasks through program control, with the aim of improving the efficiency of business processes.

[0391] "Information resources" refer to the data sets necessary for a system's operation, as well as the collection of information that is maintained in a form that allows for the reference and use of that data.

[0392] "Configuration information" refers to information that describes the logical and physical structure of data and operations within or outside a system or application.

[0393] "Operating instructions" refer to a set of sequential instructions outlining the series of actions necessary for a system or application to perform a specific operation.

[0394] "Emotion recognition function" refers to a technological means of identifying and analyzing an individual's emotional state based on sensor data such as images and sounds.

[0395] "Dynamically adaptable" means that the system's behavior and state are automatically adapted and adjusted in response to specific situations or inputs.

[0396] A "customized automation scenario" refers to an automation procedure or workflow that is individually designed and generated according to the user's specific needs and circumstances.

[0397] Embodiments of this invention will now be described. In this system, a server plays a central role in performing various processes to support automation. First, the server is responsible for receiving digital information, configuration information, and operating procedures transmitted from the user's terminal. This information includes, for example, screenshots of the interface to be automated and detailed operating procedures.

[0398] The server extracts UI elements from the received digital image using image processing technology. Specifically, it uses image analysis tools such as OpenCV to identify the location and characteristics of elements on the screen. Next, the server uses natural language processing technology based on the received information to analyze the operation procedure. This analysis includes understanding the meaning and order of each step in the procedure, which is useful for modeling business processes.

[0399] Furthermore, the server is equipped with emotion recognition capabilities, allowing it to capture the user's emotions in real time through cameras and microphones. This enables it to generate the most appropriate automated scenario based on the user's emotional state at any given time. A generative AI model is used to generate these scenarios, allowing it to respond to a variety of situations.

[0400] For example, consider a home support robot. If a user feels stressed, the server recognizes that emotion and instructs the robot to play music to promote relaxation. It can also provide a warm drink in the kitchen. This enables the provision of personalized services tailored to the individual user's situation.

[0401] An example of a prompt message is, "The user's current emotional state is 'tired'. Please suggest the most suitable relaxation action." In this way, the invention aims to provide a comfortable and efficient living environment by enabling flexible responses that reflect the user's emotions.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] First, the user's device collects configuration information and operating procedures to be automated and sends them to the server. This information includes screenshots, HTML structure, and manual operating procedures. This data provided by the user serves as input.

[0405] Step 2:

[0406] The server performs image analysis on the received digital image data using OpenCV. This process identifies UI elements and extracts their position and characteristics on the screen. As a result of this analysis, the placement information of each UI element is output.

[0407] Step 3:

[0408] Next, the server analyzes the hierarchical structure based on the HTML structure data. This extracts parent-child relationships and hierarchical information of screen elements from the input, and the output data includes an understanding of the structure.

[0409] Step 4:

[0410] The server analyzes the input procedure using natural language processing technology. This analysis helps understand the meaning and sequence of each step in the procedure, and models the business process. The output includes a structured model of the procedure.

[0411] Step 5:

[0412] In parallel, the server uses a camera and microphone to monitor the user's emotional state in real time. Using emotion recognition technology, it identifies emotions from facial expressions and voice, and outputs the user's current emotional state.

[0413] Step 6:

[0414] The server uses a generative AI model to generate the optimal automation scenario based on the input operation steps and the user's emotional state. In this process, prompts tailored to specific work situations are input to the AI, and an appropriate action plan is output.

[0415] Step 7:

[0416] Finally, the server converts the generated automation scenario into a suitable format and provides it to the user. This output consists of customized guidelines and automated task procedures tailored to the user's specific situation.

[0417] 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.

[0418] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] This invention is a system that analyzes information according to specific procedures and constructs an optimal automation scenario for process automation using automation tools.

[0434] The server first receives screenshots and HTML structure information to be automated, provided by the end user. This allows it to accurately identify the digital interfaces involved in the business processes that the user is currently performing manually.

[0435] The server uses image processing technology to analyze the components of the digital interface based on the received image data. This allows it to understand the details of UI elements, such as which parts are input fields and which are buttons.

[0436] Next, the server analyzes the user's provided operating procedures using natural language processing technology, breaking them down and understanding them. This allows for an understanding of the sequence of operations and the intent of each step, enabling a precise understanding of the business process.

[0437] Subsequently, the server constructs the workflow and uses a generated AI model to design an efficient automation scenario. This AI model leverages a functional reference of automation tools stored in the database to determine the necessary actions and generate an optimized scenario.

[0438] Finally, the generated automation scenarios are formatted into a specific format and provided to the user. The user can then easily implement automated processes by downloading and applying these scenarios to their own environment.

[0439] As a concrete example, consider the automated generation of sales reports. The user provides the server with the spreadsheet template they currently use and the method for inputting data into it. The server analyzes this and generates a scenario that automates the appropriate data insertion procedure. As a result, the user's work efficiency improves because the automated tool handles the periodic report generation task at set intervals.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] Users use their devices to prepare screenshots, HTML files, and instructional documents related to the processes they wish to automate, and then upload them to the server.

[0443] Step 2:

[0444] The server analyzes the screenshot received from the user using image processing technology to identify UI elements on the screen. This analysis identifies the position and type of each element (e.g., text boxes and buttons).

[0445] Step 3:

[0446] The server parses the HTML file and understands the structure within the document. This allows for the extraction of attributes and hierarchical information of UI elements, leading to a deeper understanding of the screen layout.

[0447] Step 4:

[0448] The server analyzes the documentation regarding the operating procedures using natural language processing techniques. It extracts the flow of operations and key steps from the documents, understanding the meaning and sequence of the procedures.

[0449] Step 5:

[0450] The server integrates the information from steps 2 and 3 to build the business flow. Each step of the procedure is associated with a UI element, and the entire business process is modeled.

[0451] Step 6:

[0452] The server launches a generated AI model based on the constructed business flow and designs the optimal scenario for automation. This model refers to automation tool references in the database to determine the actions that can be taken.

[0453] Step 7:

[0454] The server formats the generated automation scenarios into a specific format and provides them to the user. This format is one that can be imported and executed by the selected automation tool.

[0455] Step 8:

[0456] Users review the scenario provided on their device, make any necessary modifications, and then apply it to their own system. This initiates the automated workflow.

[0457] (Example 1)

[0458] 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."

[0459] To achieve efficient automation of business processes, it is crucial to accurately analyze the tasks that users perform manually on digital interfaces and effectively generate automation scenarios. However, current technologies sometimes have low accuracy in accurately identifying UI elements and interpreting operating procedures, making it difficult to generate optimal automation scenarios. As a result, users have to manually configure complex processes, which is time-consuming and labor-intensive.

[0460] 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.

[0461] In this invention, the server includes means for receiving and storing image and structural information of a digital interface to be automated and converting it into a format suitable for analysis; means for identifying user interface elements within the digital interface and extracting structural information using image analysis technology; and means for analyzing user-provided operating procedures using natural language analysis technology, breaking down the procedure steps and interpreting the intent. This makes it possible to generate automation scenarios for business processes efficiently and accurately without requiring manual configuration by the user.

[0462] The term "automation target" refers to the digital interfaces and operating procedures that are subject to manual processes performed by users.

[0463] A "digital interface" refers to a system that includes screen elements and HTML structure that constitute a user interface on a computer.

[0464] "Image information" refers to screenshots and visual data captured from a digital interface screen.

[0465] "Structural information" refers to markup data such as HTML and XML that make up a digital interface, and is information that describes the arrangement and attributes of UI elements.

[0466] "Image analysis technology" refers to techniques that use image processing libraries and algorithms to identify user interface elements on digital interfaces.

[0467] "Natural language processing technology" is a technology that uses natural language processing techniques to understand the context and interpret the intent behind user-provided operating procedures.

[0468] A "business flowchart" refers to a diagram that visually represents business procedures based on analyzed data.

[0469] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to learn from data and design optimal automation scenarios.

[0470] An "automation scenario" refers to the procedures or flows generated by a server to automate tasks performed on a digital interface.

[0471] This invention is a system for automating business processes that are currently performed manually by users. The server is the central component of this system and operates using the following means: First, the server receives screenshots of digital interfaces and structural data from the user. This data is converted into a format suitable for subsequent analysis. For analysis, image processing libraries such as "OpenCV" and structural information analysis tools such as "BeautifulSoup" or "XPath" are used.

[0472] Next, the server uses image analysis techniques to identify the user interface elements of the digital interface based on the received data. UI element identification is performed using techniques such as edge detection and template matching.

[0473] Furthermore, the server analyzes the user's provided operating procedures using natural language processing (NLTK) technology. This involves using natural language processing tools such as "spaCy" and "NLTK" to break down the operating procedures in detail and interpret their intent.

[0474] Based on the analyzed information, the server constructs a business flowchart and uses a generated AI model to design the optimal automation scenario. This AI model employs technologies such as "GPT-4" and recommends the optimal procedure by utilizing the information stored in the database.

[0475] The generated automation scenarios are formatted into a specific format and provided to the user. The user can then easily implement automated processes by integrating this data into their own environment.

[0476] As a concrete example, consider a case where a user wants to automatically generate sales reports. The user provides the server with a spreadsheet template they are using and the method for inputting data into it. The server analyzes this and generates a scenario that automates the data insertion procedure. As a result, the user can automatically generate periodic reports based on the spreadsheet template at set intervals.

[0477] An example of a prompt message would be, "Create a scenario to automate the regular updating of sales data and streamline the process."

[0478] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0479] Step 1:

[0480] The server receives screenshots and HTML structure information related to the digital interface to be automated from the user. Image files and text files are provided as input data. The server stores this data in internal storage and converts it into a format that can be parsed. The converted data becomes the input for the next processing step.

[0481] Step 2:

[0482] The server uses the received image data to identify user interface elements using image analysis techniques. Specifically, it uses OpenCV to perform edge detection or template matching to identify components such as input fields and buttons. The output data generated is the UI elements and their location information.

[0483] Step 3:

[0484] The server parses HTML data and structural information, extracting attributes of UI elements based on the DOM tree. It uses BeautifulSoup and XPath queries to identify the ID, class, and text of each element. The output is structured UI element information.

[0485] Step 4:

[0486] The server breaks down and understands the user-provided operating procedures using natural language processing (NLTK) techniques. The input is operating procedures written in text format. Grammatical analysis and intent interpretation are performed using spaCy and NLTK to clarify the order and purpose of each step. The output is a list of the interpreted procedure steps.

[0487] Step 5:

[0488] The server constructs a business flowchart based on the analyzed UI element information and operation procedures. This flowchart visually represents each step and its associated UI actions, constructing the optimal automation scenario. The output at this stage is a flowchart for scenario construction.

[0489] Step 6:

[0490] The server references the business flowchart and designs an optimized automation scenario using a generated AI model. The previously created flowchart and operation data are used as input, and the AI ​​model utilizes case studies and functional information stored in the database to generate an efficient scenario. The output is the final automation scenario.

[0491] Step 7:

[0492] The server formats the generated automation scenarios into a specific format (e.g., JSON or XML). This format conversion makes it easier for users to apply the scenarios later. The formatted automation scenario data is provided to the user as output.

[0493] (Application Example 1)

[0494] 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."

[0495] In today's logistics environment, optimizing inventory management and replenishment planning is essential for efficient operations. However, current manual processes consume a lot of time and manpower and are prone to errors. Therefore, it is necessary to improve operational efficiency by quickly and accurately analyzing inventory information and building optimal automation scenarios.

[0496] 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.

[0497] In this invention, the server includes means for holding an information storage area that allows access to all functions of a specific automation technology, means for receiving and analyzing structural data and operating procedures to be automated as input, and means for extracting screen components from visible data using image processing technology to optimize physical management. This enables more efficient inventory management and replenishment planning.

[0498] A "repository area that allows access to all functions of a specific automation technology" refers to a database that stores all functions of an automation technology and allows access as needed.

[0499] "Structural data to be automated" refers to data that shows the configuration information of applications and systems that are targeted for process automation.

[0500] "Means for receiving and analyzing operating procedures as input" refers to technologies and processes that receive operating instructions provided by users and analyze their content.

[0501] "Extracting screen elements from visible data using image processing technology" refers to a method of identifying each element of a user interface from visible information by utilizing image processing technology.

[0502] "Optimizing physical inventory management" is the process of optimizing the allocation of inventory and resources to achieve efficient management.

[0503] "Analyzing operating procedures using natural language processing technology and proposing efficiency improvements" means using natural language processing technology to understand the instructions provided and automatically generate suggestions for improving the efficiency of the work.

[0504] "Designing automation scenarios and proposing additional replenishment plans using generative AI models" refers to the process of using AI technology to design efficient automation procedures and propose optimization plans for logistics and resource management.

[0505] The system for implementing this invention primarily uses a server and a user terminal. The server maintains an information storage area that allows access to all functions of a specific automation technology, thereby providing a database that encompasses all the functions necessary for process automation. The terminal (e.g., a smartphone) is responsible for collecting data on the user's work activities and transmitting it to the server.

[0506] The server analyzes the structure data and operation procedures to be automated, which are sent from the terminal. The analysis uses image processing techniques (e.g., OpenCV) to identify UI components and natural language processing techniques (e.g., Google's NLP API) to gain a detailed understanding of the user-provided operation procedures.

[0507] Furthermore, the server utilizes generative AI models (e.g., OpenAI GPT-3) to design automation scenarios aimed at improving efficiency. These scenarios are used to optimize inventory management and replenishment planning in logistics centers. A specific example is a process where a terminal scans the barcode of an inventory item, the information is immediately analyzed by the server, and the optimal inventory placement and replenishment instructions are automatically generated.

[0508] In this way, operational efficiency can be improved. A concrete example is a system in a logistics center where a terminal is used to scan barcodes in front of shelves, and an optimal replenishment plan is received on the spot. An example of a prompt message to implement the functionality of this system is: "Generate an automated scenario to optimize inventory management. This is a smartphone app concept that analyzes barcode information and proposes an efficient replenishment plan."

[0509] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0510] Step 1:

[0511] The user scans the barcode of an item in stock using a terminal. The input is image data of the barcode, which the terminal converts into text information and sends to the server. This process utilizes the terminal's camera and barcode reader functions.

[0512] Step 2:

[0513] The server receives text information sent from the terminal. Based on the received data, the server retrieves inventory information associated with the barcode from the database using an information storage area that allows access to all functions of a specific automation technology. The data processing performed here involves searching for the corresponding inventory data based on the entered barcode information and extracting the necessary information.

[0514] Step 3:

[0515] The server uses image processing technology (e.g., OpenCV) to identify the components of the UI. In this step, it analyzes the information obtained from the visible data to determine which information is important inventory information. As a result, customer information is output to optimize the information placement in the UI.

[0516] Step 4:

[0517] The server analyzes user-provided operating procedures using natural language processing (e.g., Google's NLP API). It understands the user's prepared procedures and instructions and determines what supplementary plans are needed. The input is text data from the user, and the output generates specific supplementary instructions.

[0518] Step 5:

[0519] The server designs automated inventory management scenarios for efficiency improvements using a generation AI model (e.g., OpenAI GPT-3). Inputs include past analysis data and operating procedures, while output is an optimized replenishment scenario. Specifically, the AI ​​generates the scenario.

[0520] Step 6:

[0521] Finally, the server returns the generated replenishment plan to the user in a specific format. Upon receiving this output data, the replenishment plan is displayed in an actionable format on the user's terminal. This allows the user to immediately perform the optimal replenishment task.

[0522] 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.

[0523] This invention provides a system that enables efficient business processes using automation tools, and in particular, by combining it with an emotion engine, it offers flexible automation scenarios that take into account the user's emotional state.

[0524] First, the user uploads information related to the process they want to automate to the server via their device. This information includes screenshots of the digital interface, HTML structure, and manual operating instructions.

[0525] The server uses image processing technology to extract screen elements from uploaded screenshots. This process clarifies the position and nature of each UI element within the interface. It also analyzes the HTML structure to understand the hierarchical information of the screen elements.

[0526] Subsequently, the server utilizes natural language processing technology to analyze the operating procedures provided by the user. This analysis understands the meaning and sequence of each step in the procedure, and the business process is modeled.

[0527] Furthermore, it's worth noting that the inclusion of an emotion engine makes it possible to detect the user's emotional state while they are interacting with the user interface. The emotion engine uses cameras and voice sensors to analyze the user's facial expressions and tone of voice in real time and recognize their emotions. This information is fed back into the workflow modeling process, providing the basis for building appropriate automation scenarios tailored to the user's state.

[0528] The generating AI model selects the optimal action from the automation tool's function reference based on the analysis results and generates an automation scenario. This scenario is then converted into a specific format and provided to the user.

[0529] As a concrete example, consider customer support operations. When a user experiences dissatisfaction, the emotion engine detects this, and the automated system executes a script to provide attentive support. In this way, dynamic automation based on user emotions becomes possible, improving the quality of service.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] The user uses a terminal to upload screenshots, HTML data, and documentation of the operating procedures related to the process to be automated to the server.

[0533] Step 2:

[0534] The server analyzes the uploaded screenshots using image processing techniques to identify UI elements. This includes the location and type of interface elements such as text fields, buttons, and checkboxes.

[0535] Step 3:

[0536] The server parses the HTML data to understand the document structure and the hierarchy between elements. This analysis allows the server to grasp the overall layout of the user interface.

[0537] Step 4:

[0538] The server uses natural language processing techniques to analyze the operating procedures provided by the user. This clarifies the sequence of operations and the specific meaning of each step.

[0539] Step 5:

[0540] The emotion engine monitors the user's facial expressions and voice in real time through the device's camera and microphone to recognize their emotional state. This information is used to understand the user's stress level and satisfaction level.

[0541] Step 6:

[0542] The server integrates collected sentiment data and UI analysis results to dynamically model the flow of business processes. This allows for the creation of flexible automation scenarios that respond to user emotions.

[0543] Step 7:

[0544] The generative AI model designs the optimal automation scenario based on all the input data. This model proposes actions that take into account the normal functions of the automation tool and the user's emotional state.

[0545] Step 8:

[0546] The server converts the designed automation scenario into a specific format usable by the user and provides it to the terminal. This scenario enables the user's work to be smoothly automated.

[0547] Step 9:

[0548] Users review the automation scenarios provided on their devices, adjust the settings as needed, and apply them to the system. This enables advanced automation, including emotion recognition.

[0549] (Example 2)

[0550] 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."

[0551] In recent years, various automation tools have been used to improve business efficiency, but these tools generally only provide simple scenarios that do not take into account the user's emotions or circumstances. As a result, it is difficult to reduce user stress and dissatisfaction, and there is a lack of flexibility in situations where a quick response is required. Furthermore, conventional automation tools have limited capabilities in analyzing user interfaces and operating procedures, making complete business automation difficult.

[0552] 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.

[0553] In this invention, the server includes means for holding an information set that allows reference to all functions of a specific processing device; means for receiving and analyzing structural information and operating procedures to be automated as input; means for extracting user interface elements using image analysis technology; means for analyzing procedures and modeling business processes using language processing technology; and means for recognizing the user's emotional state and adjusting the business process accordingly. This enables the generation of flexible and appropriate automation scenarios based on the user's emotions.

[0554] "A set of information that allows reference to all functions of a specific processing unit" refers to a collection of information that comprehensively describes and records all functions and procedures implemented in the processing unit.

[0555] "Structural information to be automated" refers to detailed information about the user interface and the arrangement and hierarchy of its components.

[0556] "Operating procedures" refer to a series of specific steps or steps required to carry out a particular business process.

[0557] "Image analysis technology" refers to the technology of processing image data and extracting meaningful information from it.

[0558] "User interface elements" refer to components in a computer or application that can be interacted with by the user, such as buttons and input fields.

[0559] "Language processing technology" refers to the technologies used to analyze, understand, and generate natural language.

[0560] "Modeling a business process" is the process of transforming a specific business process into a logical structure, making it reproducible within a system.

[0561] "User emotional state" refers to the emotions and moods a user experiences at a particular point in time, and is often measured in terms of stress levels or satisfaction levels.

[0562] An "automation scenario" is a plan or procedure document that describes how an automated process should be executed.

[0563] This invention is a system aimed at automating flexible business processes while taking into account the user's emotional state. The system's main role is to generate effective automation scenarios based on the structural information, operating procedures, and the user's emotional state of the process to be automated. The specific implementation method of this system is described below.

[0564] Users upload information related to the process they want to automate to the server using a device. This information includes screenshots of the digital interface, HTML structured data, and manual operating instructions. A standard computer or smartphone can be used as the device.

[0565] The server first uses image processing technology to extract user interface elements from uploaded screenshots. Specifically, it utilizes image processing libraries such as OpenCV to identify the position and attributes of each element in the image. Furthermore, to analyze the HTML structure data, it uses tools like BeautifulSoup to clarify the hierarchy and interrelationships of each element.

[0566] Furthermore, the server uses natural language processing techniques to analyze the operating procedures and model the business process. This involves using natural language processing libraries such as NLTK and SpaCy to analyze the text and understand the intent and sequence of each step. As a result, the business process is defined as a logical model.

[0567] For real-time emotion recognition, the server uses cameras and voice sensors to evaluate the user's emotional state. Emotion recognition technology could utilize facial recognition APIs or voice analysis APIs. This allows for real-time determination of the user's stress level and satisfaction level.

[0568] Finally, the generating AI model constructs an automation scenario based on all these analysis results. It selects the optimal automation actions and writes automation scripts suitable for each process step. This scenario is provided in a specific format that is easy for the user to understand.

[0569] As a concrete example, consider automating data entry in customer support. If a user expresses dissatisfaction with the data entry process, the emotion engine detects this state, and the automation system automatically generates a script to streamline the entry process. This reduces the burden on the user, improves work efficiency, and increases satisfaction.

[0570] An example of a prompt might be: "When a user clicks a specific button on a webpage, how would you leverage the emotion engine to build an automated scenario that enhances the click experience of that button?"

[0571] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0572] Step 1:

[0573] The user uploads information about the business process they want to automate to the server using a terminal. Specific inputs include screenshots of digital interfaces, HTML structured data, and manual operation instructions. In this step, the user prepares the necessary information on the terminal and sends it to the server. The output is that the information is stored on the server and ready for the next analysis step.

[0574] Step 2:

[0575] The server extracts user interface elements from uploaded screenshots using image analysis technology. Based on the screenshot image as input, it performs image analysis using libraries such as OpenCV to identify the position and shape of each UI element. The output is metadata (position, attribute information) of the UI elements, and this data is used for subsequent analysis.

[0576] Step 3:

[0577] The server parses the HTML structure data. In this step, it receives an HTML file as input and parses the DOM tree using a tool like BeautifulSoup. This extracts the hierarchical structure and attribute information of each element. The output is the parsed HTML structure information, which generates data useful for identifying UI elements.

[0578] Step 4:

[0579] The server uses natural language processing (NLTK) techniques to analyze user instructions. The input is the user's text-based instructions, which are parsed using natural language processing libraries such as NLTK and SpaCy. Data processing and calculations are then used to model the intent and sequence of the instructions. The output is the structure of the modeled business process.

[0580] Step 5:

[0581] The server detects the user's emotional state using real-time emotion recognition technology. Input data comes from cameras and audio sensors, which are then analyzed using an emotion recognition API. The analysis results measure the degree of stress and emotion the user is experiencing. The output data represents the user's current emotional state and is used to adjust business processes.

[0582] Step 6:

[0583] The generating AI model integrates the analysis results and constructs the optimal automation scenario. The inputs are UI element information, HTML structure, operation procedures, and sentiment data obtained in the previous steps. Based on this, the AI ​​model designs the optimal process flow and generates a specific automation script. The output is an automation scenario in a specific format provided to the user. This scenario then facilitates business operations.

[0584] (Application Example 2)

[0585] 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."

[0586] In automated systems, a challenge exists in that standardized responses are performed without considering the user's emotional state, resulting in insufficient automation tailored to individual situations. In particular, in home environments, there is a need for more personalized and adaptable services that utilize emotion recognition.

[0587] 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.

[0588] In this invention, the server includes means for holding information resources that allow reference to all functions of a specific automation support device, means for receiving and analyzing configuration information and operating procedures of the target of automation as input, and means for understanding the emotional state of an individual using an emotion recognition function and dynamically changing the automation scenario accordingly. This makes it possible to provide a customized automation scenario that responds to the user's emotions.

[0589] An "automation support device" is a technical means for performing routine tasks through program control, with the aim of improving the efficiency of business processes.

[0590] "Information resources" refer to the data sets necessary for a system's operation, as well as the collection of information that is maintained in a form that allows for the reference and use of that data.

[0591] "Configuration information" refers to information that describes the logical and physical structure of data and operations within or outside a system or application.

[0592] "Operating instructions" refer to a set of sequential instructions outlining the series of actions necessary for a system or application to perform a specific operation.

[0593] "Emotion recognition function" refers to a technological means of identifying and analyzing an individual's emotional state based on sensor data such as images and sounds.

[0594] "Dynamically adaptable" means that the system's behavior and state are automatically adapted and adjusted in response to specific situations or inputs.

[0595] A "customized automation scenario" refers to an automation procedure or workflow that is individually designed and generated according to the user's specific needs and circumstances.

[0596] Embodiments of this invention will now be described. In this system, a server plays a central role in performing various processes to support automation. First, the server is responsible for receiving digital information, configuration information, and operating procedures transmitted from the user's terminal. This information includes, for example, screenshots of the interface to be automated and detailed operating procedures.

[0597] The server extracts UI elements from the received digital image using image processing technology. Specifically, it uses image analysis tools such as OpenCV to identify the location and characteristics of elements on the screen. Next, the server uses natural language processing technology based on the received information to analyze the operation procedure. This analysis includes understanding the meaning and order of each step in the procedure, which is useful for modeling business processes.

[0598] Furthermore, the server is equipped with emotion recognition capabilities, allowing it to capture the user's emotions in real time through cameras and microphones. This enables it to generate the most appropriate automated scenario based on the user's emotional state at any given time. A generative AI model is used to generate these scenarios, allowing it to respond to a variety of situations.

[0599] For example, consider a home support robot. If a user feels stressed, the server recognizes that emotion and instructs the robot to play music to promote relaxation. It can also provide a warm drink in the kitchen. This enables the provision of personalized services tailored to the individual user's situation.

[0600] An example of a prompt message is, "The user's current emotional state is 'tired'. Please suggest the most suitable relaxation action." In this way, the invention aims to provide a comfortable and efficient living environment by enabling flexible responses that reflect the user's emotions.

[0601] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0602] Step 1:

[0603] First, the user's device collects configuration information and operating procedures to be automated and sends them to the server. This information includes screenshots, HTML structure, and manual operating procedures. This data provided by the user serves as input.

[0604] Step 2:

[0605] The server performs image analysis on the received digital image data using OpenCV. This process identifies UI elements and extracts their position and characteristics on the screen. As a result of this analysis, the placement information of each UI element is output.

[0606] Step 3:

[0607] Next, the server analyzes the hierarchical structure based on the HTML structure data. This extracts parent-child relationships and hierarchical information of screen elements from the input, and the output data includes an understanding of the structure.

[0608] Step 4:

[0609] The server analyzes the input procedure using natural language processing technology. This analysis helps understand the meaning and sequence of each step in the procedure, and models the business process. The output includes a structured model of the procedure.

[0610] Step 5:

[0611] In parallel, the server uses a camera and microphone to monitor the user's emotional state in real time. Using emotion recognition technology, it identifies emotions from facial expressions and voice, and outputs the user's current emotional state.

[0612] Step 6:

[0613] The server uses a generative AI model to generate the optimal automation scenario based on the input operation steps and the user's emotional state. In this process, prompts tailored to specific work situations are input to the AI, and an appropriate action plan is output.

[0614] Step 7:

[0615] Finally, the server converts the generated automation scenario into a suitable format and provides it to the user. This output consists of customized guidelines and automated task procedures tailored to the user's specific situation.

[0616] 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.

[0617] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0618] 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.

[0619] [Fourth Embodiment]

[0620] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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).

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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".

[0633] This invention is a system that analyzes information according to specific procedures and constructs an optimal automation scenario for process automation using automation tools.

[0634] The server first receives screenshots and HTML structure information to be automated, provided by the end user. This allows it to accurately identify the digital interfaces involved in the business processes that the user is currently performing manually.

[0635] The server uses image processing technology to analyze the components of the digital interface based on the received image data. This allows it to understand the details of UI elements, such as which parts are input fields and which are buttons.

[0636] Next, the server analyzes the user's provided operating procedures using natural language processing technology, breaking them down and understanding them. This allows for an understanding of the sequence of operations and the intent of each step, enabling a precise understanding of the business process.

[0637] Subsequently, the server constructs the workflow and uses a generated AI model to design an efficient automation scenario. This AI model leverages a functional reference of automation tools stored in the database to determine the necessary actions and generate an optimized scenario.

[0638] Finally, the generated automation scenarios are formatted into a specific format and provided to the user. The user can then easily implement automated processes by downloading and applying these scenarios to their own environment.

[0639] As a concrete example, consider the automated generation of sales reports. The user provides the server with the spreadsheet template they currently use and the method for inputting data into it. The server analyzes this and generates a scenario that automates the appropriate data insertion procedure. As a result, the user's work efficiency improves because the automated tool handles the periodic report generation task at set intervals.

[0640] The following describes the processing flow.

[0641] Step 1:

[0642] Users use their devices to prepare screenshots, HTML files, and instructional documents related to the processes they wish to automate, and then upload them to the server.

[0643] Step 2:

[0644] The server analyzes the screenshot received from the user using image processing technology to identify UI elements on the screen. This analysis identifies the position and type of each element (e.g., text boxes and buttons).

[0645] Step 3:

[0646] The server parses the HTML file and understands the structure within the document. This allows for the extraction of attributes and hierarchical information of UI elements, leading to a deeper understanding of the screen layout.

[0647] Step 4:

[0648] The server analyzes the documentation regarding the operating procedures using natural language processing techniques. It extracts the flow of operations and key steps from the documents, understanding the meaning and sequence of the procedures.

[0649] Step 5:

[0650] The server integrates the information from steps 2 and 3 to build the business flow. Each step of the procedure is associated with a UI element, and the entire business process is modeled.

[0651] Step 6:

[0652] The server launches a generated AI model based on the constructed business flow and designs the optimal scenario for automation. This model refers to automation tool references in the database to determine the actions that can be taken.

[0653] Step 7:

[0654] The server formats the generated automation scenarios into a specific format and provides them to the user. This format is one that can be imported and executed by the selected automation tool.

[0655] Step 8:

[0656] Users review the scenario provided on their device, make any necessary modifications, and then apply it to their own system. This initiates the automated workflow.

[0657] (Example 1)

[0658] 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".

[0659] To achieve efficient automation of business processes, it is crucial to accurately analyze the tasks that users perform manually on digital interfaces and effectively generate automation scenarios. However, current technologies sometimes have low accuracy in accurately identifying UI elements and interpreting operating procedures, making it difficult to generate optimal automation scenarios. As a result, users have to manually configure complex processes, which is time-consuming and labor-intensive.

[0660] 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.

[0661] In this invention, the server includes means for receiving and storing image and structural information of a digital interface to be automated and converting it into a format suitable for analysis; means for identifying user interface elements within the digital interface and extracting structural information using image analysis technology; and means for analyzing user-provided operating procedures using natural language analysis technology, breaking down the procedure steps and interpreting the intent. This makes it possible to generate automation scenarios for business processes efficiently and accurately without requiring manual configuration by the user.

[0662] The term "automation target" refers to the digital interfaces and operating procedures that are subject to manual processes performed by users.

[0663] A "digital interface" refers to a system that includes screen elements and HTML structure that constitute a user interface on a computer.

[0664] "Image information" refers to screenshots and visual data captured from a digital interface screen.

[0665] "Structural information" refers to markup data such as HTML and XML that make up a digital interface, and is information that describes the arrangement and attributes of UI elements.

[0666] "Image analysis technology" refers to techniques that use image processing libraries and algorithms to identify user interface elements on digital interfaces.

[0667] "Natural language processing technology" is a technology that uses natural language processing techniques to understand the context and interpret the intent behind user-provided operating procedures.

[0668] A "business flowchart" refers to a diagram that visually represents business procedures based on analyzed data.

[0669] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to learn from data and design optimal automation scenarios.

[0670] An "automation scenario" refers to the procedures or flows generated by a server to automate tasks performed on a digital interface.

[0671] This invention is a system for automating business processes that are currently performed manually by users. The server is the central component of this system and operates using the following means: First, the server receives screenshots of digital interfaces and structural data from the user. This data is converted into a format suitable for subsequent analysis. For analysis, image processing libraries such as "OpenCV" and structural information analysis tools such as "BeautifulSoup" or "XPath" are used.

[0672] Next, the server uses image analysis techniques to identify the user interface elements of the digital interface based on the received data. UI element identification is performed using techniques such as edge detection and template matching.

[0673] Furthermore, the server analyzes the user's provided operating procedures using natural language processing (NLTK) technology. This involves using natural language processing tools such as "spaCy" and "NLTK" to break down the operating procedures in detail and interpret their intent.

[0674] Based on the analyzed information, the server constructs a business flowchart and uses a generated AI model to design the optimal automation scenario. This AI model employs technologies such as "GPT-4" and recommends the optimal procedure by utilizing the information stored in the database.

[0675] The generated automation scenarios are formatted into a specific format and provided to the user. The user can then easily implement automated processes by integrating this data into their own environment.

[0676] As a concrete example, consider a case where a user wants to automatically generate sales reports. The user provides the server with a spreadsheet template they are using and the method for inputting data into it. The server analyzes this and generates a scenario that automates the data insertion procedure. As a result, the user can automatically generate periodic reports based on the spreadsheet template at set intervals.

[0677] An example of a prompt message would be, "Create a scenario to automate the regular updating of sales data and streamline the process."

[0678] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0679] Step 1:

[0680] The server receives screenshots and HTML structure information related to the digital interface to be automated from the user. Image files and text files are provided as input data. The server stores this data in internal storage and converts it into a format that can be parsed. The converted data becomes the input for the next processing step.

[0681] Step 2:

[0682] The server uses the received image data to identify user interface elements using image analysis techniques. Specifically, it uses OpenCV to perform edge detection or template matching to identify components such as input fields and buttons. The output data generated is the UI elements and their location information.

[0683] Step 3:

[0684] The server parses HTML data and structural information, extracting attributes of UI elements based on the DOM tree. It uses BeautifulSoup and XPath queries to identify the ID, class, and text of each element. The output is structured UI element information.

[0685] Step 4:

[0686] The server breaks down and understands the user-provided operating procedures using natural language processing (NLTK) techniques. The input is operating procedures written in text format. Grammatical analysis and intent interpretation are performed using spaCy and NLTK to clarify the order and purpose of each step. The output is a list of the interpreted procedure steps.

[0687] Step 5:

[0688] The server constructs a business flowchart based on the analyzed UI element information and operation procedures. This flowchart visually represents each step and its associated UI actions, constructing the optimal automation scenario. The output at this stage is a flowchart for scenario construction.

[0689] Step 6:

[0690] The server references the business flowchart and designs an optimized automation scenario using a generated AI model. The previously created flowchart and operation data are used as input, and the AI ​​model utilizes case studies and functional information stored in the database to generate an efficient scenario. The output is the final automation scenario.

[0691] Step 7:

[0692] The server formats the generated automation scenarios into a specific format (e.g., JSON or XML). This format conversion makes it easier for users to apply the scenarios later. The formatted automation scenario data is provided to the user as output.

[0693] (Application Example 1)

[0694] 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".

[0695] In today's logistics environment, optimizing inventory management and replenishment planning is essential for efficient operations. However, current manual processes consume a lot of time and manpower and are prone to errors. Therefore, it is necessary to improve operational efficiency by quickly and accurately analyzing inventory information and building optimal automation scenarios.

[0696] 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.

[0697] In this invention, the server includes means for holding an information storage area that allows access to all functions of a specific automation technology, means for receiving and analyzing structural data and operating procedures to be automated as input, and means for extracting screen components from visible data using image processing technology to optimize physical management. This enables more efficient inventory management and replenishment planning.

[0698] A "repository area that allows access to all functions of a specific automation technology" refers to a database that stores all functions of an automation technology and allows access as needed.

[0699] "Structural data to be automated" refers to data that shows the configuration information of applications and systems that are targeted for process automation.

[0700] "Means for receiving and analyzing operating procedures as input" refers to technologies and processes that receive operating instructions provided by users and analyze their content.

[0701] "Extracting screen elements from visible data using image processing technology" refers to a method of identifying each element of a user interface from visible information by utilizing image processing technology.

[0702] "Optimizing physical inventory management" is the process of optimizing the allocation of inventory and resources to achieve efficient management.

[0703] "Analyzing operating procedures using natural language processing technology and proposing efficiency improvements" means using natural language processing technology to understand the instructions provided and automatically generate suggestions for improving the efficiency of the work.

[0704] "Designing automation scenarios and proposing additional replenishment plans using generative AI models" refers to the process of using AI technology to design efficient automation procedures and propose optimization plans for logistics and resource management.

[0705] The system for implementing this invention primarily uses a server and a user terminal. The server maintains an information storage area that allows access to all functions of a specific automation technology, thereby providing a database that encompasses all the functions necessary for process automation. The terminal (e.g., a smartphone) is responsible for collecting data on the user's work activities and transmitting it to the server.

[0706] The server analyzes the structure data and operation procedures to be automated, which are sent from the terminal. The analysis uses image processing techniques (e.g., OpenCV) to identify UI components and natural language processing techniques (e.g., Google's NLP API) to gain a detailed understanding of the user-provided operation procedures.

[0707] Furthermore, the server utilizes generative AI models (e.g., OpenAI GPT-3) to design automation scenarios aimed at improving efficiency. These scenarios are used to optimize inventory management and replenishment planning in logistics centers. A specific example is a process where a terminal scans the barcode of an inventory item, the information is immediately analyzed by the server, and the optimal inventory placement and replenishment instructions are automatically generated.

[0708] In this way, operational efficiency can be improved. A concrete example is a system in a logistics center where a terminal is used to scan barcodes in front of shelves, and an optimal replenishment plan is received on the spot. An example of a prompt message to implement the functionality of this system is: "Generate an automated scenario to optimize inventory management. This is a smartphone app concept that analyzes barcode information and proposes an efficient replenishment plan."

[0709] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0710] Step 1:

[0711] The user scans the barcode of an item in stock using a terminal. The input is image data of the barcode, which the terminal converts into text information and sends to the server. This process utilizes the terminal's camera and barcode reader functions.

[0712] Step 2:

[0713] The server receives text information sent from the terminal. Based on the received data, the server retrieves inventory information associated with the barcode from the database using an information storage area that allows access to all functions of a specific automation technology. The data processing performed here involves searching for the corresponding inventory data based on the entered barcode information and extracting the necessary information.

[0714] Step 3:

[0715] The server uses image processing technology (e.g., OpenCV) to identify the components of the UI. In this step, it analyzes the information obtained from the visible data to determine which information is important inventory information. As a result, customer information is output to optimize the information placement in the UI.

[0716] Step 4:

[0717] The server analyzes user-provided operating procedures using natural language processing (e.g., Google's NLP API). It understands the user's prepared procedures and instructions and determines what supplementary plans are needed. The input is text data from the user, and the output generates specific supplementary instructions.

[0718] Step 5:

[0719] The server designs automated inventory management scenarios for efficiency improvements using a generation AI model (e.g., OpenAI GPT-3). Inputs include past analysis data and operating procedures, while output is an optimized replenishment scenario. Specifically, the AI ​​generates the scenario.

[0720] Step 6:

[0721] Finally, the server returns the generated replenishment plan to the user in a specific format. Upon receiving this output data, the replenishment plan is displayed in an actionable format on the user's terminal. This allows the user to immediately perform the optimal replenishment task.

[0722] 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.

[0723] This invention provides a system that enables efficient business processes using automation tools, and in particular, by combining it with an emotion engine, it offers flexible automation scenarios that take into account the user's emotional state.

[0724] First, the user uploads information related to the process they want to automate to the server via their device. This information includes screenshots of the digital interface, HTML structure, and manual operating instructions.

[0725] The server uses image processing technology to extract screen elements from uploaded screenshots. This process clarifies the position and nature of each UI element within the interface. It also analyzes the HTML structure to understand the hierarchical information of the screen elements.

[0726] Subsequently, the server utilizes natural language processing technology to analyze the operating procedures provided by the user. This analysis understands the meaning and sequence of each step in the procedure, and the business process is modeled.

[0727] Furthermore, it's worth noting that the inclusion of an emotion engine makes it possible to detect the user's emotional state while they are interacting with the user interface. The emotion engine uses cameras and voice sensors to analyze the user's facial expressions and tone of voice in real time and recognize their emotions. This information is fed back into the workflow modeling process, providing the basis for building appropriate automation scenarios tailored to the user's state.

[0728] The generating AI model selects the optimal action from the automation tool's function reference based on the analysis results and generates an automation scenario. This scenario is then converted into a specific format and provided to the user.

[0729] As a concrete example, consider customer support operations. When a user experiences dissatisfaction, the emotion engine detects this, and the automated system executes a script to provide attentive support. In this way, dynamic automation based on user emotions becomes possible, improving the quality of service.

[0730] The following describes the processing flow.

[0731] Step 1:

[0732] The user uses a terminal to upload screenshots, HTML data, and documentation of the operating procedures related to the process to be automated to the server.

[0733] Step 2:

[0734] The server analyzes the uploaded screenshots using image processing techniques to identify UI elements. This includes the location and type of interface elements such as text fields, buttons, and checkboxes.

[0735] Step 3:

[0736] The server parses the HTML data to understand the document structure and the hierarchy between elements. This analysis allows the server to grasp the overall layout of the user interface.

[0737] Step 4:

[0738] The server uses natural language processing techniques to analyze the operating procedures provided by the user. This clarifies the sequence of operations and the specific meaning of each step.

[0739] Step 5:

[0740] The emotion engine monitors the user's facial expressions and voice in real time through the device's camera and microphone to recognize their emotional state. This information is used to understand the user's stress level and satisfaction level.

[0741] Step 6:

[0742] The server integrates collected sentiment data and UI analysis results to dynamically model the flow of business processes. This allows for the creation of flexible automation scenarios that respond to user emotions.

[0743] Step 7:

[0744] The generative AI model designs the optimal automation scenario based on all the input data. This model proposes actions that take into account the normal functions of the automation tool and the user's emotional state.

[0745] Step 8:

[0746] The server converts the designed automation scenario into a specific format usable by the user and provides it to the terminal. This scenario enables the user's work to be smoothly automated.

[0747] Step 9:

[0748] Users review the automation scenarios provided on their devices, adjust the settings as needed, and apply them to the system. This enables advanced automation, including emotion recognition.

[0749] (Example 2)

[0750] 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".

[0751] In recent years, various automation tools have been used to improve business efficiency, but these tools generally only provide simple scenarios that do not take into account the user's emotions or circumstances. As a result, it is difficult to reduce user stress and dissatisfaction, and there is a lack of flexibility in situations where a quick response is required. Furthermore, conventional automation tools have limited capabilities in analyzing user interfaces and operating procedures, making complete business automation difficult.

[0752] 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.

[0753] In this invention, the server includes means for holding an information set that allows reference to all functions of a specific processing device; means for receiving and analyzing structural information and operating procedures to be automated as input; means for extracting user interface elements using image analysis technology; means for analyzing procedures and modeling business processes using language processing technology; and means for recognizing the user's emotional state and adjusting the business process accordingly. This enables the generation of flexible and appropriate automation scenarios based on the user's emotions.

[0754] "A set of information that allows reference to all functions of a specific processing unit" refers to a collection of information that comprehensively describes and records all functions and procedures implemented in the processing unit.

[0755] "Structural information to be automated" refers to detailed information about the user interface and the arrangement and hierarchy of its components.

[0756] "Operating procedures" refer to a series of specific steps or steps required to carry out a particular business process.

[0757] "Image analysis technology" refers to the technology of processing image data and extracting meaningful information from it.

[0758] "User interface elements" refer to components in a computer or application that can be interacted with by the user, such as buttons and input fields.

[0759] "Language processing technology" refers to the technologies used to analyze, understand, and generate natural language.

[0760] "Modeling a business process" is the process of transforming a specific business process into a logical structure, making it reproducible within a system.

[0761] "User emotional state" refers to the emotions and moods a user experiences at a particular point in time, and is often measured in terms of stress levels or satisfaction levels.

[0762] An "automation scenario" is a plan or procedure document that describes how an automated process should be executed.

[0763] This invention is a system aimed at automating flexible business processes while taking into account the user's emotional state. The system's main role is to generate effective automation scenarios based on the structural information, operating procedures, and the user's emotional state of the process to be automated. The specific implementation method of this system is described below.

[0764] Users upload information related to the process they want to automate to the server using a device. This information includes screenshots of the digital interface, HTML structured data, and manual operating instructions. A standard computer or smartphone can be used as the device.

[0765] The server first uses image processing technology to extract user interface elements from uploaded screenshots. Specifically, it utilizes image processing libraries such as OpenCV to identify the position and attributes of each element in the image. Furthermore, to analyze the HTML structure data, it uses tools like BeautifulSoup to clarify the hierarchy and interrelationships of each element.

[0766] Furthermore, the server uses natural language processing techniques to analyze the operating procedures and model the business process. This involves using natural language processing libraries such as NLTK and SpaCy to analyze the text and understand the intent and sequence of each step. As a result, the business process is defined as a logical model.

[0767] For real-time emotion recognition, the server uses cameras and voice sensors to evaluate the user's emotional state. Emotion recognition technology could utilize facial recognition APIs or voice analysis APIs. This allows for real-time determination of the user's stress level and satisfaction level.

[0768] Finally, the generating AI model constructs an automation scenario based on all these analysis results. It selects the optimal automation actions and writes automation scripts suitable for each process step. This scenario is provided in a specific format that is easy for the user to understand.

[0769] As a concrete example, consider automating data entry in customer support. If a user expresses dissatisfaction with the data entry process, the emotion engine detects this state, and the automation system automatically generates a script to streamline the entry process. This reduces the burden on the user, improves work efficiency, and increases satisfaction.

[0770] An example of a prompt might be: "When a user clicks a specific button on a webpage, how would you leverage the emotion engine to build an automated scenario that enhances the click experience of that button?"

[0771] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0772] Step 1:

[0773] The user uploads information about the business process they want to automate to the server using a terminal. Specific inputs include screenshots of digital interfaces, HTML structured data, and manual operation instructions. In this step, the user prepares the necessary information on the terminal and sends it to the server. The output is that the information is stored on the server and ready for the next analysis step.

[0774] Step 2:

[0775] The server extracts user interface elements from uploaded screenshots using image analysis technology. Based on the screenshot image as input, it performs image analysis using libraries such as OpenCV to identify the position and shape of each UI element. The output is metadata (position, attribute information) of the UI elements, and this data is used for subsequent analysis.

[0776] Step 3:

[0777] The server parses the HTML structure data. In this step, it receives an HTML file as input and parses the DOM tree using a tool like BeautifulSoup. This extracts the hierarchical structure and attribute information of each element. The output is the parsed HTML structure information, which generates data useful for identifying UI elements.

[0778] Step 4:

[0779] The server uses natural language processing (NLTK) techniques to analyze user instructions. The input is the user's text-based instructions, which are parsed using natural language processing libraries such as NLTK and SpaCy. Data processing and calculations are then used to model the intent and sequence of the instructions. The output is the structure of the modeled business process.

[0780] Step 5:

[0781] The server detects the user's emotional state using real-time emotion recognition technology. Input data comes from cameras and audio sensors, which are then analyzed using an emotion recognition API. The analysis results measure the degree of stress and emotion the user is experiencing. The output data represents the user's current emotional state and is used to adjust business processes.

[0782] Step 6:

[0783] The generating AI model integrates the analysis results and constructs the optimal automation scenario. The inputs are UI element information, HTML structure, operation procedures, and sentiment data obtained in the previous steps. Based on this, the AI ​​model designs the optimal process flow and generates a specific automation script. The output is an automation scenario in a specific format provided to the user. This scenario then facilitates business operations.

[0784] (Application Example 2)

[0785] 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".

[0786] In automated systems, a challenge exists in that standardized responses are performed without considering the user's emotional state, resulting in insufficient automation tailored to individual situations. In particular, in home environments, there is a need for more personalized and adaptable services that utilize emotion recognition.

[0787] 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.

[0788] In this invention, the server includes means for holding information resources that allow reference to all functions of a specific automation support device, means for receiving and analyzing configuration information and operating procedures of the target of automation as input, and means for understanding the emotional state of an individual using an emotion recognition function and dynamically changing the automation scenario accordingly. This makes it possible to provide a customized automation scenario that responds to the user's emotions.

[0789] An "automation support device" is a technical means for performing routine tasks through program control, with the aim of improving the efficiency of business processes.

[0790] "Information resources" refer to the data sets necessary for a system's operation, as well as the collection of information that is maintained in a form that allows for the reference and use of that data.

[0791] "Configuration information" refers to information that describes the logical and physical structure of data and operations within or outside a system or application.

[0792] "Operating instructions" refer to a set of sequential instructions outlining the series of actions necessary for a system or application to perform a specific operation.

[0793] "Emotion recognition function" refers to a technological means of identifying and analyzing an individual's emotional state based on sensor data such as images and sounds.

[0794] "Dynamically adaptable" means that the system's behavior and state are automatically adapted and adjusted in response to specific situations or inputs.

[0795] A "customized automation scenario" refers to an automation procedure or workflow that is individually designed and generated according to the user's specific needs and circumstances.

[0796] Embodiments of this invention will now be described. In this system, a server plays a central role in performing various processes to support automation. First, the server is responsible for receiving digital information, configuration information, and operating procedures transmitted from the user's terminal. This information includes, for example, screenshots of the interface to be automated and detailed operating procedures.

[0797] The server extracts UI elements from the received digital image using image processing technology. Specifically, it uses image analysis tools such as OpenCV to identify the location and characteristics of elements on the screen. Next, the server uses natural language processing technology based on the received information to analyze the operation procedure. This analysis includes understanding the meaning and order of each step in the procedure, which is useful for modeling business processes.

[0798] Furthermore, the server is equipped with emotion recognition capabilities, allowing it to capture the user's emotions in real time through cameras and microphones. This enables it to generate the most appropriate automated scenario based on the user's emotional state at any given time. A generative AI model is used to generate these scenarios, allowing it to respond to a variety of situations.

[0799] For example, consider a home support robot. If a user feels stressed, the server recognizes that emotion and instructs the robot to play music to promote relaxation. It can also provide a warm drink in the kitchen. This enables the provision of personalized services tailored to the individual user's situation.

[0800] An example of a prompt message is, "The user's current emotional state is 'tired'. Please suggest the most suitable relaxation action." In this way, the invention aims to provide a comfortable and efficient living environment by enabling flexible responses that reflect the user's emotions.

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] First, the user's device collects configuration information and operating procedures to be automated and sends them to the server. This information includes screenshots, HTML structure, and manual operating procedures. This data provided by the user serves as input.

[0804] Step 2:

[0805] The server performs image analysis on the received digital image data using OpenCV. This process identifies UI elements and extracts their position and characteristics on the screen. As a result of this analysis, the placement information of each UI element is output.

[0806] Step 3:

[0807] Next, the server analyzes the hierarchical structure based on the HTML structure data. This extracts parent-child relationships and hierarchical information of screen elements from the input, and the output data includes an understanding of the structure.

[0808] Step 4:

[0809] The server analyzes the input procedure using natural language processing technology. This analysis helps understand the meaning and sequence of each step in the procedure, and models the business process. The output includes a structured model of the procedure.

[0810] Step 5:

[0811] In parallel, the server uses a camera and microphone to monitor the user's emotional state in real time. Using emotion recognition technology, it identifies emotions from facial expressions and voice, and outputs the user's current emotional state.

[0812] Step 6:

[0813] The server uses a generative AI model to generate the optimal automation scenario based on the input operation steps and the user's emotional state. In this process, prompts tailored to specific work situations are input to the AI, and an appropriate action plan is output.

[0814] Step 7:

[0815] Finally, the server converts the generated automation scenario into a suitable format and provides it to the user. This output consists of customized guidelines and automated task procedures tailored to the user's specific situation.

[0816] 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.

[0817] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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."

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] The following is further disclosed regarding the embodiments described above.

[0838] (Claim 1)

[0839] A means of maintaining a database that allows access to all the functions of a specific automation tool,

[0840] A means for receiving and analyzing structural information and operating procedures to be automated as input,

[0841] A means of identifying business processes based on input information and generating optimal automation scenarios,

[0842] A means of providing the generated automation scenario to the user in a specific format,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, which extracts screen elements using image processing technology.

[0846] (Claim 3)

[0847] The system according to claim 1, which analyzes operating procedures using natural language processing technology.

[0848] "Example 1"

[0849] (Claim 1)

[0850] A means for receiving image and structural information of a digital interface to be automated, saving it, and converting it into a format suitable for analysis,

[0851] A means for identifying user interface elements within a digital interface and extracting structural information using image analysis technology,

[0852] A means for analyzing user-provided operating procedures using natural language processing technology, breaking down the procedure steps, and interpreting the intent,

[0853] A means of constructing a business flowchart based on the analyzed data and designing the optimal automation scenario using a generated AI model,

[0854] A means of formatting the designed automation scenario into a specific data format and providing it to the user,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, which uses edge detection and template matching as image analysis techniques.

[0858] (Claim 3)

[0859] The system according to claim 1, which uses a machine learning algorithm as a natural language processing technique to understand the context of an operation procedure.

[0860] "Application Example 1"

[0861] (Claim 1)

[0862] A means for holding an information storage area that allows access to all functions of a specific automation technology,

[0863] A means for receiving and analyzing structural data and operating procedures to be automated as input,

[0864] A means of identifying business processes based on input data and generating optimal automation scenarios,

[0865] A means of providing the generated automation scenario to the user in a specific format,

[0866] A means for extracting screen elements from visible data using image processing technology and optimizing entity management,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, which analyzes operating procedures using natural language processing technology and proposes ways to improve efficiency.

[0870] (Claim 3)

[0871] The system according to claim 1, which utilizes a generative AI model to design automation scenarios and presents additional supplementary plans.

[0872] "Example 2 of combining an emotion engine"

[0873] (Claim 1)

[0874] A means for holding a set of information that allows reference to all functions of a specific processing device,

[0875] A means for receiving and analyzing structural information and operating procedures to be automated as input,

[0876] A means for extracting user interface elements using image analysis technology,

[0877] A means of analyzing procedures and modeling business processes using language processing technology,

[0878] A means of recognizing the user's emotional state and adjusting the work process accordingly,

[0879] A means of generating and presenting the optimal automation scenario based on the analysis results,

[0880] A means of providing the generated automation scenario to the user in a specific format,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, which analyzes screen elements using image analysis technology.

[0884] (Claim 3)

[0885] The system according to claim 1, which uses language processing technology to decipher the operating procedures provided by the user.

[0886] "Application example 2 when combining with an emotional engine"

[0887] (Claim 1)

[0888] A means for holding information resources that allow reference to all functions of a specific automation support device,

[0889] A means for receiving and analyzing configuration information and operating procedures to be automated,

[0890] A means of identifying task execution based on input information and generating an optimal automation scenario,

[0891] A means of providing the generated automation scenario to the user in a specific format,

[0892] A means of understanding an individual's emotional state using emotion recognition functionality and dynamically changing the automation scenario accordingly,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, which extracts display components using image analysis technology.

[0896] (Claim 3)

[0897] The system according to claim 1, which analyzes operating procedures using natural language processing technology. [Explanation of Symbols]

[0898] 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 of maintaining a database that allows access to all the functions of a specific automation tool, A means for receiving and analyzing structural information and operating procedures to be automated as input, A means of identifying business processes based on input information and generating optimal automation scenarios, A means of providing the generated automation scenario to the user in a specific format, A system that includes this.

2. The system according to claim 1, which extracts screen elements using image processing technology.

3. The system according to claim 1, which analyzes the operating procedure using natural language processing technology.

Images