system

Abstract

We provide the system. [Solution] A device with computer capabilities, which includes means for extracting text information from a screenshot using image analysis means, A means of identifying an error code from the text information and querying the database to determine the appropriate course of action, A means of generating a solution based on the identified error code and providing it to the user via a communication medium, A means for searching log data based on URLs and time information contained in the text information and obtaining additional information, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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] Inquiries from customers require smooth customer support and reduced problem-solving time by quickly analyzing error codes and other important character information displayed within screenshots. Additionally, there is a lack of a function to automatically provide detailed situation descriptions and specific solutions related to error codes, resulting in insufficient user convenience.

Means for Solving the Problems

[0005] This invention provides a system in which a computer-enabled device uses image analysis means to extract text information from screenshot images, identifies an error code from the text information, and determines a countermeasure using a database. It also includes means for providing the user with a solution generated based on the identified error code, and means for searching log data based on URLs and time information within the screenshot to obtain additional information. In this way, the system quickly provides the user with a detailed problem explanation and supplementary instructions.

[0006] A "device with computer functions" refers to equipment that processes and analyzes information and provides specific functions or services to users, including hardware and its control programs.

[0007] "Image analysis means" refers to technologies and algorithms that perform the process of extracting necessary information from image data and converting it into text or other data.

[0008] A "screenshot" is a digital image that records the contents of a screen displayed on a display device such as a computer or mobile phone as a still image.

[0009] "Text information" refers to data expressed as characters, and is information that is recognized as sentences or words.

[0010] An "error code" is a combination of numbers and letters used to identify a system failure or malfunction, indicating a specific state or problem.

[0011] A "database" is a collection of data structured according to a specific purpose, and is a data storage system that allows for searching and updating.

[0012] A "countermeasure" is a set of procedures or strategies designed to address a specific problem or situation.

[0013] "Communication medium" refers to the channels or platforms used to send and receive information, including forms such as the internet and telephone lines.

[0014] A URL is a unified resource identifier used to identify a specific resource on the internet and indicate its location.

[0015] "Log data" refers to operational data such as operation history and error messages that a system or application records while it is running. [Brief explanation of the drawing]

[0016] [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] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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.

[0020] 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.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The system of this invention has the function of quickly and accurately identifying problems and providing countermeasures based on screenshots from customers. The specific actions performed by the server, terminal, and user are described below.

[0038] First, when a problem occurs, the user takes a screenshot on their device and sends it to the server. This image data is sent to an analysis module on the server. The server uses OCR (Optical Character Recognition) technology to analyze the received screenshot and extract text information from the image. This text information includes important data such as error codes, URLs, and date and time information.

[0039] Next, the server uses the parsed text information to identify a specific error code by comparing it with its internal database. Based on the identified error code, the server automatically generates a corresponding solution, which may include specific instructions and links to FAQs.

[0040] Furthermore, the server uses URLs and date / time information within the text data to search server logs and obtain additional information. This information is used to provide supplementary explanations regarding the detailed background of the problem and the state of the system.

[0041] Finally, the server notifies the user of the generated solution and any additional information obtained. Based on this information, the user can take appropriate action to resolve the problem.

[0042] As a concrete example, consider a scenario where a user sends a screenshot showing an "Error 404" message while using an application. The server recognizes this error code and generates guidelines such as, "The specified webpage could not be found. Please verify that the URL is correct." Simultaneously, it analyzes the URL on the screen to verify from the server logs whether the page is accessible and provides the verification results to the user. This entire process significantly improves convenience because the user can quickly identify the cause of the problem and its solution.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user takes a screenshot of the application or system experiencing the problem on their device and sends it to the support system.

[0046] Step 2:

[0047] The server receives screenshots sent by users and stores them in an analysis queue. This process makes the screenshots ready for subsequent processing.

[0048] Step 3:

[0049] The server uses an OCR engine to analyze screenshot images and extract text information from the screen. This text information may include error codes, URLs, and date / time information.

[0050] Step 4:

[0051] The server identifies the error code from the extracted text information and compares it with a pre-registered database. This identifies the meaning of the error code and the associated solution.

[0052] Step 5:

[0053] The server generates a solution based on the identified error code. This solution includes specific steps the user should take and links to FAQs they should refer to.

[0054] Step 6:

[0055] The server identifies the URL and date / time information within the screenshot and searches the server logs based on that information. Using this information, it collects detailed background information about the relevant problem from the log data.

[0056] Step 7:

[0057] The server integrates the generated solution and additional information obtained from the logs to construct a message to provide to the user.

[0058] Step 8:

[0059] The server sends a pre-constructed message to the user. Based on this information, the user can then take specific actions to resolve the problem.

[0060] (Example 1)

[0061] 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."

[0062] Existing problem-solving systems have the challenge of being unable to respond quickly and accurately to the technical problems that users face. In particular, the cumbersome and time-consuming process of identifying errors and obtaining additional information is considered a problem, as it lacks convenience.

[0063] 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.

[0064] In this invention, the server includes means for extracting character information from image data using image analysis means, means for identifying identification information from the character information and querying a storage device to determine countermeasures, and means for generating countermeasures based on the identified identification information and providing them to the user via a communication path. This makes it possible to quickly and accurately identify the cause and provide solutions to technical problems faced by the user.

[0065] An "information processing device" is an electronic device designed to perform data analysis and calculations, and capable of executing various tasks instructed by a software program.

[0066] "Image analysis means" refers to techniques and methods for analyzing digital images and extracting text or specific information from them, and generally includes optical character recognition technology.

[0067] "Character information" refers to information composed of alphabets, numbers, or other symbols extracted from image data, and is electronically processable data.

[0068] "Identification information" refers to codes or tokens used to indicate a specific problem or condition, and serves as the basis for a system to determine a specific course of action.

[0069] "Storage device" is a general term for hardware and software used to store electronic data, and includes database management systems.

[0070] A "communication path" refers to a network or interface used to send and receive information, enabling data transfer between a user and a server.

[0071] "Natural language processing means" refers to technologies and methods for understanding, analyzing, and generating human language (natural language), and which are capable of extracting the meaning and context of information.

[0072] This invention is an information processing system that automates the problem-solving process. It efficiently solves technical problems encountered by users using a terminal by analyzing image data and acquiring related information.

[0073] The user saves a screenshot of the problem on their device and sends the image data to the server. The device is equipped with basic image processing software for capturing images and editing them as needed. In this case, the image files are often saved in PNG or JPEG format.

[0074] The server receives the transmitted image data and performs image analysis using OCR (Optical Character Recognition) technology. Specifically, the open-source OCR engine Tesseract can be used. This extracts text information from the image data, identifying important data such as error codes, date and time information, and URLs.

[0075] Next, the server queries the database system in storage using the extracted text information. Commonly used databases such as MySQL® or PostgreSQL are employed for database management. The server automatically generates a corresponding solution based on the identified error code. During this generation process, guidelines are created using system manuals and FAQ links.

[0076] Furthermore, the system searches server logs using URLs and date / time information within the text data to obtain additional information about incidents and conditions related to the problem. ELK Stack, a widely used log analysis tool, is used for this operation.

[0077] Ultimately, the server notifies the user of the generated solution and retrieved details via the communication channel. Based on this information, the user resolves their problem by taking the instructed actions.

[0078] For example, if a user takes a screenshot of an "Error 404" message displayed while using an application and sends it, the server recognizes this error code and automatically generates a guideline such as "The specified webpage could not be found. Please verify that the URL is correct." Furthermore, by checking the connection status to the specified URL and informing the user of the result, the user can quickly identify the cause of the problem and take appropriate action.

[0079] An example of a prompt for a generative AI model is, "Identify errors based on the submitted screenshot and automatically suggest solutions." This prompt is used to form an initial response to user input.

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

[0081] Step 1:

[0082] The user takes a screenshot using their device when the problem occurs. This is done using a keyboard shortcut or by clicking the capture icon on the screen. The input is the screen displaying the problem, and the output is image data in PNG or JPEG format. The device saves this screen data to its local disk and allows for image editing as needed.

[0083] Step 2:

[0084] The terminal bundles the captured screenshots into data packets and sends them to the server. The input is an image file stored locally, and the output is image data transferred to the server over the network. The terminal uses a communication module here to efficiently transmit the data.

[0085] Step 3:

[0086] The server inputs the received screenshot into an analysis module and extracts text information from the image using OCR technology. The input is screenshot data, and the output is text information including error codes, date and time information, and URLs. The server uses an OCR engine (e.g., Tesseract) to perform character recognition on the image and converts it into structured data.

[0087] Step 4:

[0088] The server identifies identifying information from the extracted text data by querying a database. The input is text data obtained from an image, and the output is related error codes and procedural information for resolving the problem. The server uses a database management system (e.g., MySQL) to execute queries and quickly retrieve the information.

[0089] Step 5:

[0090] The server automatically generates solutions and creates guidelines to provide to the user based on the identified error code. The input is identified problem information, and the output is documentation and FAQ links related to the solutions. The server uses pre-prepared templates to create guidelines that combine the necessary information.

[0091] Step 6:

[0092] The server searches the logs based on the URLs and date / time information contained in the retrieved text information and obtains relevant additional information. The input is link information within the text information, and the output is relevant event information recorded in the logs. The server uses log analysis tools (e.g., ELK Stack) to extract the necessary data and prepare information to supplement the background of the problem.

[0093] Step 7:

[0094] The server notifies the user of the generated solution and additional information, using email notifications or system notification functions as needed. Input is the solution and related information, and output is a communication message to the user. The server sends this information through the communication path and provides an easily accessible interface for the user.

[0095] Step 8:

[0096] The user reviews the received information and performs appropriate troubleshooting based on it. The input is solution information from the server, and the output is the actual problem-solving action. The user follows the provided guidelines to resolve the problem.

[0097] (Application Example 1)

[0098] 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."

[0099] In electronic payment systems, there is a challenge in that when users encounter transaction errors, the system cannot immediately identify the problem and propose an appropriate solution. Traditional methods require users to manually search for error details and find solutions on their own, which is time-consuming and laborious. This can lead to decreased user satisfaction. Furthermore, because it is difficult for users to understand the technical details behind the errors, effective support is required.

[0100] 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.

[0101] In this invention, the server includes a device having computing capabilities, means for extracting character information from image data using image analysis means, means for identifying identification data from the character information and comparing it with an information storage device to determine a course of action, and means for generating a course of action based on the identified identification data and providing it to the user via communication means. This makes it possible to automatically provide quick and accurate solutions to electronic payment errors faced by the user.

[0102] A "device with computing capabilities" is a machine that has the ability to process information and can perform advanced calculations such as image analysis and data extraction.

[0103] "Image analysis means" refers to a technology or apparatus that performs a process of extracting visual information from image data and identifying necessary elements.

[0104] "Textual information" refers to text data such as letters, numbers, and symbols extracted from image data.

[0105] "Identification data" refers to data used to recognize specific problems or codes from extracted character information.

[0106] An "information storage device" is a device or system that structures and stores various types of data, enabling retrieval and matching.

[0107] An "action plan" is a plan that outlines solutions and procedures for specific conditions or problems.

[0108] "Communication methods" refer to the systems and technologies used to transmit data and information between different locations.

[0109] A "user" is an individual or group that uses a system or device and utilizes its functions.

[0110] The invention will now be described in terms of embodiments for carrying out the invention. This invention is a system for troubleshooting in electronic payment systems that identifies transaction errors based on screenshots and quickly proposes solutions. This system primarily functions through the mutual cooperation of three parties: a server, a terminal, and a user.

[0111] The server is a computing device that uses image analysis software such as Tesseract OCR to analyze screenshots sent by users and extract text information. This process reads error messages and other relevant information from the image. Simultaneously, the server compares the extracted text information against a database and determines the appropriate course of action based on the identification data. The necessary information is stored in an information storage device, and the comparison process is performed using a relational database management system such as PostgreSQL.

[0112] A terminal is a device such as a smartphone or tablet that serves as a user interface. When an error occurs using this terminal, the user takes a screenshot and sends it to the server. This action aims to resolve the problem quickly.

[0113] Users solve problems based on the suggested actions and supplementary information received from their device. They can also receive additional support and feedback via communication channels.

[0114] For example, if a user receives an "Error 502" message while making an online payment, they can take a screenshot and send it, at which point the server will suggest a solution such as, "The server is overloaded. Please try again later." Simultaneously, the server's status logs are used to provide technical details about the background of the error.

[0115] In this way, the system supports users in quickly and efficiently resolving their problems. An example of a prompt to the generated AI model is: "Please suggest solutions that the user should try for this electronic payment error 'Error 502'. Please also include log information showing the server status for additional reference."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] When an error occurs in the electronic payment system, the user takes a screenshot with their terminal and sends that data to the server. The input data is the screenshot image file, which is sent to the server's image analysis module. The output is the server receiving the image file. Specifically, the user uses a camera application or the system's built-in capture function.

[0119] Step 2:

[0120] The server analyzes the received screenshot using Tesseract OCR software to extract text information from the image, such as error codes and explanatory text. The input is an image file, and the output is text data. At this stage, data processing involves text extraction through character recognition within the image. Specifically, the OCR software analyzes character patterns within the image and generates digital text information.

[0121] Step 3:

[0122] The server uses the extracted character data to compare it with an internal database and identify identification data, namely error codes and related information. The input is character data from OCR, and the output is the identified error code and related information. A matching algorithm is used as the data calculation to search for entries in the database that match the information. Specifically, this involves performing database matching using SQL queries.

[0123] Step 4:

[0124] The server generates a course of action for the identified error code and communicates with the user to provide it. Inputs are identification data and solutions obtained from the database, while output is the generated course of action. Data processing includes assembling a resolution procedure based on the error code. Specifically, the solution is formatted as a message to the user and sent to the user's terminal.

[0125] Step 5:

[0126] The server searches log data based on resource location and time information within the character data obtained by OCR, and retrieves necessary supplementary information. The input is URLs and time data as character data, and the output is related login information and supplementary information. Data processing involves picking up relevant data using a log search algorithm. Specifically, the search process includes time-series analysis of log files and periodic data extraction.

[0127] Step 6:

[0128] The user reviews the suggested actions and supplementary information displayed on the terminal and executes the problem-solving steps. The input is the suggested actions and supplementary information sent from the server, and the output is the user's completion of problem-solving. Specifically, the user performs the actions necessary to solve the problem according to the instructions.

[0129] 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.

[0130] The system of this invention not only quickly identifies problems and provides solutions based on screenshots from customers, but also provides advanced support that takes into account the user's emotional state. This system is equipped with an emotion engine that accurately grasps the user's intentions, enabling more effective dialogue.

[0131] When a problem occurs, the user takes a screenshot on their device and sends it to the server. The server analyzes the received screenshot using image recognition technology and extracts text information from the screen. This information may include error codes, URLs, and date and time information.

[0132] The server identifies an error code using the extracted text and checks it against its internal database. Based on the identified error code, it generates an appropriate solution and determines the specific steps to be provided to the user. Furthermore, it searches the server logs based on the URL and time information in the text to obtain additional contextual information.

[0133] With the introduction of an emotion engine, the server recognizes the user's emotions from the received text. Based on this, it evaluates the user's psychological state and adjusts the system response. For example, if the server senses that the user is particularly irritated, it adjusts the tone of support to be kinder and more polite, and provides messages that will help the user relax.

[0134] As a concrete example, suppose a user sends a screenshot to the server showing the error message "Error 404". The server identifies a solution related to the error code, and if the sentiment engine determines from the user's post that the user is confused, it will provide instructions in a gentle tone such as, "The page could not be found, but don't worry. There's a good chance that simply checking this will resolve the issue."

[0135] Thus, this invention combines emotion recognition with conventional technology to achieve a richer user experience and faster problem solving. It is a system that is expected to improve customer satisfaction and efficiency in support services.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] Users take screenshots on their devices showing problems that occur while using the application and send them to the server through the support system.

[0139] Step 2:

[0140] The server receives screenshots sent by the user and forwards them to the image analysis module. This process prepares the image for detailed analysis of its contents.

[0141] Step 3:

[0142] The server uses OCR technology to extract text information from the screenshot. This information may include error codes and other relevant text.

[0143] Step 4:

[0144] The server identifies the error code from the extracted text and accesses its internal database to check for the corresponding problem and its solution. This reveals the nature of the error and how to address it.

[0145] Step 5:

[0146] The server searches the server logs based on the URLs and date / time information contained in the text. This operation allows the server to confirm the specific circumstances surrounding the error and gather additional information.

[0147] Step 6:

[0148] The server sends text information to the sentiment engine, which then evaluates the user's emotions. Sentiment analysis determines whether the user is feeling confused, angry, or confused.

[0149] Step 7:

[0150] The server adjusts the content and tone of its responses to the user based on the emotion engine's evaluation. For example, if the user is dissatisfied, a polite and calm message will be generated.

[0151] Step 8:

[0152] The server sends the user a message that integrates a solution based on the error code, additional information, and a coordinated response.

[0153] Step 9:

[0154] Based on the detailed instructions and supplementary information received, users can move on to specific problem-solving tasks. The information provided by the server allows users to proceed efficiently.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] Existing technologies have limitations in extracting text information from screenshots and providing error handling solutions, and furthermore, providing appropriate support that takes into account the user's feelings presents challenges. There is also a need to provide quick and accurate solutions based on a thorough understanding of the detailed context behind the problem.

[0158] 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.

[0159] In this invention, the server includes means for extracting text information from screenshots using image analysis means, means for analyzing the user's emotions from the acquired text information using emotion analysis means and adjusting the response, and means for searching log data based on identification information and obtaining additional information. This makes it possible to provide prompt and accurate support that is contextual and takes into account the user's emotions.

[0160] A "device with computer capabilities" refers to an electronic device that can process digital information and is designed to perform a specific task.

[0161] "Image analysis means" refers to techniques for processing and analyzing image data, enabling the extraction of useful information from images such as screenshots.

[0162] "Text information" refers to character data extracted from an image, including error codes and other identifying information.

[0163] An "error code" is a number or string of characters used to identify a problem that has occurred within a computer system.

[0164] A "database" refers to a collection of information that is organized and stored, and can be quickly searched and updated as needed.

[0165] "Communication medium" refers to a means for sending and receiving information, including wired and wireless methods.

[0166] "Log data" refers to data that compiles a chronological record of system operations and errors.

[0167] "Additional information" refers to detailed data obtained through further investigations and other means, in addition to the information obtained from the initial data analysis.

[0168] "Emotional analysis techniques" are technologies that analyze users' emotions from text and audio data, enabling adjustments to appropriate communication.

[0169] "Natural language processing" refers to technologies for processing human language using computers, enabling text analysis and semantic understanding.

[0170] "Users" refer to individuals or organizations that use the system and are responsible for reporting errors and receiving support.

[0171] The system of the present invention consists of a device with computer functions and terminals and a server connected via a network. When a problem occurs, the user takes a screenshot from the terminal and sends this image to the server. The server processes the image data in the received screenshot using optical character recognition (OCR) software as an image analysis means and extracts text information from the image. This text information includes error codes and other identification information.

[0172] The server can compare the extracted text information with a database, generate a corresponding solution based on the identified error code, and provide it to the user via a communication medium. Furthermore, the server can search log data based on the identification information contained in the text information to obtain additional information for a more detailed understanding of the problem's background. This additional information improves the accuracy of the solution provided to the user.

[0173] Furthermore, the server uses emotion analysis techniques to analyze the user's emotions from the acquired text information. This technology utilizes natural language processing (NLP) to estimate the user's psychological state and appropriately adjust the tone of its response. For example, if the user is feeling stressed, the server will generate a message that encourages relaxation.

[0174] For example, if a user sends a screenshot containing the error code "404" to the server, the server uses OCR technology to detect the error code. The server then searches its internal database to find a solution to the problem of the webpage not being found. Furthermore, if sentiment analysis determines the user is confused, the server will provide a message in a pleasant tone such as, "This error is usually temporary. Please clear your cache and try again."

[0175] An example of a prompt message would be: "Generate a suggested solution when a user submits a screenshot of error code 404. Also, please provide suggestions for constructing a kind and polite message if the user is feeling confused."

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] When a problem occurs, the user takes a screenshot using their device and sends the image file to the server. The input is the screenshot image taken by the device, and the output is the image data sent to the server. The user's actions include taking a screenshot and clicking the upload button.

[0179] Step 2:

[0180] The server processes the received screenshots using OCR software, which is an image analysis tool. The input is the screenshot image, and the output is the text information contained within the image. Specifically, the OCR engine recognizes and analyzes the characters in the screenshot to extract the text.

[0181] Step 3:

[0182] The server identifies an error code from the extracted text information and compares it against an internal database. The input is text information extracted by OCR, and the output is the identified error code and its corresponding solution. The server executes a database query to identify the most appropriate solution.

[0183] Step 4:

[0184] The server searches log data based on identifying information (e.g., URL and date / time) contained in the acquired text information and retrieves additional information. The input is identifying information obtained from a screenshot, and the output is additional information from the related log data. The server searches the log files and extracts details of relevant events and errors.

[0185] Step 5:

[0186] The server uses sentiment analysis tools to analyze the user's emotions from acquired text information. The input is text information, and the output is an estimated result of the user's emotional state. Specifically, the server's operation includes analyzing the text using natural language processing techniques and identifying emotional patterns.

[0187] Step 6:

[0188] The server generates a response message for the user based on the collected information and delivers it to the user via the communication medium. The input consists of identified error codes, additional information, and sentiment analysis results, while the output is the generated response message. The server constructs the wording, crafts the message in a friendly and polite tone, and sends it to the user.

[0189] (Application Example 2)

[0190] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0191] Modern information processing systems are required to respond quickly and effectively to technical problems and errors encountered by users. In particular, providing appropriate support that takes into account the user's emotional state is crucial for improving the user experience. However, many current systems only offer solutions to technical problems and suffer from a lack of consideration for the user's emotions.

[0192] 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.

[0193] In this invention, the server includes a computer-enabled device that includes means for extracting textual information from visual information using an image analysis function, means for identifying error codes from the textual information and querying an information storage means to determine appropriate countermeasures, and means for analyzing the user's psychological state using an emotion analysis function and generating support messages tailored to the user's emotions. This makes it possible to quickly identify problems from screenshots sent by the user and provide appropriate solutions that take into account the user's emotional state.

[0194] "A device with computer functionality" refers to hardware or software that has the ability to perform calculations and processing using electronic or digital technology.

[0195] "Image analysis function" refers to the technology of extracting information from image data and identifying or classifying its content.

[0196] "Visual information" refers to image and video data that is perceived by human vision.

[0197] "Textual information" refers to data expressed as text, used for the purpose of transmitting information.

[0198] An "error code" is an identifier used by a system or program to indicate a specific problem or error.

[0199] "Information storage means" refers to a system or device for storing data and keeping it in a state where it can be retrieved or used as needed.

[0200] "Response measures" refer to solutions or actions taken in response to a specific problem or situation.

[0201] "Emotion analysis function" is a technology that infers and analyzes human emotions from text, voice, facial expressions, etc.

[0202] "Psychological state" refers to the emotional and cognitive state that an individual experiences in a given situation or environment.

[0203] A "support message" is information or instructions provided to guide users and facilitate problem-solving.

[0204] The system for implementing the present invention consists of a user terminal, a server, and communication between them. When a user encounters a problem, the process is carried out according to the following procedure.

[0205] First, the user's device takes a screenshot of the screen where the problem occurred and sends the data to the server. The server analyzes this screenshot using image analysis technologies such as Google® Cloud Vision API and extracts text information from the visual information. The obtained text information includes error codes and related text.

[0206] The server identifies the extracted error code and queries its internal information storage system to determine the appropriate course of action. During this process, it uses sentiment analysis technologies such as Microsoft® Azure® Emotion API to analyze the user's psychological state from the transmitted text and other related data.

[0207] Based on these analysis results, a supportive message that takes the user's emotions into consideration is generated and sent to the user's device via a communication medium. For example, if it is determined that the user is facing an "Error 1001" and is feeling anxious, a message such as "Error 1001 has occurred, but this is a common problem. Don't worry, we can quickly resolve it by following these steps!" will be provided.

[0208] A key element in implementing this invention is the use of a generative AI model, which customizes and provides appropriate support messages for each individual user.

[0209] Examples of prompt messages include: "Analyze the error code in the screenshot and suggest a solution. Analyze the user's emotional state and generate a message with an appropriate tone." Based on these prompts, the AI ​​can provide optimal support tailored to the user's situation.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The user takes a screenshot of the screen where the problem occurred using their device. The input is the screenshot taken on the device. The user sends this screenshot to the server. The output is the image data sent to the server.

[0213] Step 2:

[0214] The server receives the submitted screenshot. The input is the image data of the screenshot. The server uses the Google Cloud Vision API to analyze the image and extract text information from the screenshot. The data processing involves extracting text from the image using OCR (Optical Character Recognition) technology, and the output is the extracted text information (text data).

[0215] Step 3:

[0216] The server identifies the error code from the extracted text information. The input is text information. String processing is performed to identify the error code, and the database is queried to determine the corrective action. The output is data related to the identified error code and the corrective action.

[0217] Step 4:

[0218] The server analyzes the user's psychological state using the Microsoft Azure Emotion API, based on text information and error codes. The input is text information. The server estimates the user's emotions using emotion analysis technology and obtains emotional state data as output.

[0219] Step 5:

[0220] The server generates support messages using a generative AI model based on the obtained emotional state data and corresponding action data. The input consists of emotional state data and corresponding action data. The AI ​​model is used with prompts to create emotionally sensitive support messages as output.

[0221] Step 6:

[0222] The server sends the generated support message to the user's terminal. The input is the generated support message. The message is transmitted via communication and the output is the support message displayed on the user's terminal.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] The system of this invention has the function of quickly and accurately identifying problems and providing countermeasures based on screenshots from customers. The specific actions performed by the server, terminal, and user are described below.

[0240] First, when a problem occurs, the user takes a screenshot on their device and sends it to the server. This image data is sent to an analysis module on the server. The server uses OCR (Optical Character Recognition) technology to analyze the received screenshot and extract text information from the image. This text information includes important data such as error codes, URLs, and date and time information.

[0241] Next, the server uses the parsed text information to identify a specific error code by comparing it with its internal database. Based on the identified error code, the server automatically generates a corresponding solution, which may include specific instructions and links to FAQs.

[0242] Furthermore, the server uses URLs and date / time information within the text data to search server logs and obtain additional information. This information is used to provide supplementary explanations regarding the detailed background of the problem and the state of the system.

[0243] Finally, the server notifies the user of the generated solution and any additional information obtained. Based on this information, the user can take appropriate action to resolve the problem.

[0244] As a concrete example, consider a scenario where a user sends a screenshot showing an "Error 404" message while using an application. The server recognizes this error code and generates guidelines such as, "The specified webpage could not be found. Please verify that the URL is correct." Simultaneously, it analyzes the URL on the screen to verify from the server logs whether the page is accessible and provides the verification results to the user. This entire process significantly improves convenience because the user can quickly identify the cause of the problem and its solution.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The user takes a screenshot of the application or system experiencing the problem on their device and sends it to the support system.

[0248] Step 2:

[0249] The server receives screenshots sent by users and stores them in an analysis queue. This process makes the screenshots ready for subsequent processing.

[0250] Step 3:

[0251] The server uses an OCR engine to analyze screenshot images and extract text information from the screen. This text information may include error codes, URLs, and date / time information.

[0252] Step 4:

[0253] The server identifies the error code from the extracted text information and compares it with a pre-registered database. This identifies the meaning of the error code and the associated solution.

[0254] Step 5:

[0255] The server generates a solution based on the identified error code. This solution includes specific steps the user should take and links to FAQs they should refer to.

[0256] Step 6:

[0257] The server identifies the URL and date / time information within the screenshot and searches the server logs based on that information. Using this information, it collects detailed background information about the relevant problem from the log data.

[0258] Step 7:

[0259] The server integrates the generated solution and additional information obtained from the logs to construct a message to provide to the user.

[0260] Step 8:

[0261] The server sends a pre-constructed message to the user. Based on this information, the user can then take specific actions to resolve the problem.

[0262] (Example 1)

[0263] 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."

[0264] Existing problem-solving systems have the challenge of being unable to respond quickly and accurately to the technical problems that users face. In particular, the cumbersome and time-consuming process of identifying errors and obtaining additional information is considered a problem, as it lacks convenience.

[0265] 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.

[0266] In this invention, the server includes means for extracting character information from image data using image analysis means, means for identifying identification information from the character information and querying a storage device to determine countermeasures, and means for generating countermeasures based on the identified identification information and providing them to the user via a communication path. This makes it possible to quickly and accurately identify the cause and provide solutions to technical problems faced by the user.

[0267] An "information processing device" is an electronic device designed to perform data analysis and calculations, and capable of executing various tasks instructed by a software program.

[0268] "Image analysis means" refers to techniques and methods for analyzing digital images and extracting text or specific information from them, and generally includes optical character recognition technology.

[0269] "Character information" refers to information composed of alphabets, numbers, or other symbols extracted from image data, and is electronically processable data.

[0270] "Identification information" refers to codes or tokens used to indicate a specific problem or condition, and serves as the basis for a system to determine a specific course of action.

[0271] "Storage device" is a general term for hardware and software used to store electronic data, and includes database management systems.

[0272] A "communication path" refers to a network or interface used to send and receive information, enabling data transfer between a user and a server.

[0273] "Natural language processing means" refers to technologies and methods for understanding, analyzing, and generating human language (natural language), and which are capable of extracting the meaning and context of information.

[0274] This invention is an information processing system that automates the problem-solving process. It efficiently solves technical problems encountered by users using a terminal by analyzing image data and acquiring related information.

[0275] The user saves a screenshot of the problem on their device and sends the image data to the server. The device is equipped with basic image processing software for capturing images and editing them as needed. In this case, the image files are often saved in PNG or JPEG format.

[0276] The server receives the transmitted image data and performs image analysis using OCR (Optical Character Recognition) technology. Specifically, the open-source OCR engine Tesseract can be used. This extracts text information from the image data, identifying important data such as error codes, date and time information, and URLs.

[0277] Next, the server queries the database system in storage using the extracted text information. Commonly used databases such as MySQL and PostgreSQL are employed for database management. The server automatically generates corresponding solutions based on the identified error codes. During this generation process, guidelines are created using system manuals and FAQ links.

[0278] Furthermore, the system searches server logs using URLs and date / time information within the text data to obtain additional information about incidents and conditions related to the problem. ELK Stack, a widely used log analysis tool, is used for this operation.

[0279] Ultimately, the server notifies the user of the generated solution and retrieved details via the communication channel. Based on this information, the user resolves their problem by taking the instructed actions.

[0280] For example, if a user takes a screenshot of an "Error 404" message displayed while using an application and sends it, the server recognizes this error code and automatically generates a guideline such as "The specified webpage could not be found. Please verify that the URL is correct." Furthermore, by checking the connection status to the specified URL and informing the user of the result, the user can quickly identify the cause of the problem and take appropriate action.

[0281] As an example of a prompt sentence for a generative AI model, there is a sentence such as "Based on the sent screenshot, identify the error and automatically present a solution." This prompt is used to form an initial response to the input from the user.

[0282] The flow of the specific process in Example 1 will be described using FIG. 11.

[0283] Step 1:

[0284] The user takes a screenshot using a terminal in a situation where a problem has occurred. This operation is performed by clicking on a keyboard shortcut or a capture icon on the screen. As input, there is a screen on which the problem is displayed, and as output, image data in the PNG or JPEG format is generated. The terminal saves this screen data to the local disk and also performs image editing if necessary.

[0285] Step 2:

[0286] The terminal assembles the captured screenshot into data packets and sends it to the server. The input is the image file saved locally, and the output is the image data transferred to the server via the network. Here, the terminal uses a communication module to perform data transmission efficiently.

[0287] Step 3:

[0288] The server takes in the received screenshot into an analysis module and extracts text information from the image using OCR technology. The input is the screenshot data, and the output is text information including error codes, date and time information, URLs, etc. The server uses an OCR engine (e.g., Tesseract) to perform character recognition on the image and convert it into structured data.

[0289] Step 4:

[0290] The server identifies identifying information from the extracted text data by querying a database. The input is text data obtained from an image, and the output is related error codes and procedural information for resolving the problem. The server uses a database management system (e.g., MySQL) to execute queries and quickly retrieve the information.

[0291] Step 5:

[0292] The server automatically generates solutions and creates guidelines to provide to the user based on the identified error code. The input is identified problem information, and the output is documentation and FAQ links related to the solutions. The server uses pre-prepared templates to create guidelines that combine the necessary information.

[0293] Step 6:

[0294] The server searches the logs based on the URLs and date / time information contained in the retrieved text information and obtains relevant additional information. The input is link information within the text information, and the output is relevant event information recorded in the logs. The server uses log analysis tools (e.g., ELK Stack) to extract the necessary data and prepare information to supplement the background of the problem.

[0295] Step 7:

[0296] The server notifies the user of the generated solution and additional information, using email notifications or system notification functions as needed. Input is the solution and related information, and output is a communication message to the user. The server sends this information through the communication path and provides an easily accessible interface for the user.

[0297] Step 8:

[0298] The user reviews the received information and performs appropriate troubleshooting based on it. The input is solution information from the server, and the output is the actual problem-solving action. The user follows the provided guidelines to resolve the problem.

[0299] (Application Example 1)

[0300] 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."

[0301] In electronic payment systems, there is a challenge in that when users encounter transaction errors, the system cannot immediately identify the problem and propose an appropriate solution. Traditional methods require users to manually search for error details and find solutions on their own, which is time-consuming and laborious. This can lead to decreased user satisfaction. Furthermore, because it is difficult for users to understand the technical details behind the errors, effective support is required.

[0302] 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.

[0303] In this invention, the server includes a device having computing capabilities, means for extracting character information from image data using image analysis means, means for identifying identification data from the character information and comparing it with an information storage device to determine a course of action, and means for generating a course of action based on the identified identification data and providing it to the user via communication means. This makes it possible to automatically provide quick and accurate solutions to electronic payment errors faced by the user.

[0304] A "device with computing capabilities" is a machine that has the ability to process information and can perform advanced calculations such as image analysis and data extraction.

[0305] The "image analysis means" is a technology or device that extracts visual information from image data and executes a process of identifying necessary elements.

[0306] The "character information" is text data such as alphabets, numbers, symbols, etc. extracted from image data.

[0307] The "identification data" is data for recognizing specific problems or codes from the extracted character information.

[0308] The "information storage device" is a device or system that structures and stores various data and enables search and collation.

[0309] The "action plan" is a plan indicating solutions or procedures for specific conditions or problems.

[0310] The "communication means" is a mechanism or technology for transmitting data and information between different locations.

[0311] The "user" is an individual or group that uses a system or device and utilizes its functions.

[0312] The mode for carrying out the invention will be described. This invention is a system that aims at troubleshooting in an electronic payment system, identifies transaction errors based on screenshots, and quickly proposes solutions. This system mainly functions with the mutual cooperation of three parties: the server, the terminal, and the user.

[0313] The server is a computing device that uses image analysis software such as Tesseract OCR to analyze screenshots sent by users and extract text information. This process reads error messages and other relevant information from the image. Simultaneously, the server compares the extracted text information against a database and determines the appropriate course of action based on the identification data. The necessary information is stored in an information storage device, and the comparison process is performed using a relational database management system such as PostgreSQL.

[0314] A terminal is a device such as a smartphone or tablet that serves as a user interface. When an error occurs using this terminal, the user takes a screenshot and sends it to the server. This action aims to resolve the problem quickly.

[0315] Users solve problems based on the suggested actions and supplementary information received from their device. They can also receive additional support and feedback via communication channels.

[0316] For example, if a user receives an "Error 502" message while making an online payment, they can take a screenshot and send it, at which point the server will suggest a solution such as, "The server is overloaded. Please try again later." Simultaneously, the server's status logs are used to provide technical details about the background of the error.

[0317] In this way, the system supports users in quickly and efficiently resolving their problems. An example of a prompt to the generated AI model is: "Please suggest solutions that the user should try for this electronic payment error 'Error 502'. Please also include log information showing the server status for additional reference."

[0318] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0319] Step 1:

[0320] When an error occurs in the electronic payment system, the user takes a screenshot with their terminal and sends that data to the server. The input data is the screenshot image file, which is sent to the server's image analysis module. The output is the server receiving the image file. Specifically, the user uses a camera application or the system's built-in capture function.

[0321] Step 2:

[0322] The server analyzes the received screenshot using Tesseract OCR software to extract text information from the image, such as error codes and explanatory text. The input is an image file, and the output is text data. At this stage, data processing involves text extraction through character recognition within the image. Specifically, the OCR software analyzes character patterns within the image and generates digital text information.

[0323] Step 3:

[0324] The server uses the extracted character data to compare it with an internal database and identify identification data, namely error codes and related information. The input is character data from OCR, and the output is the identified error code and related information. A matching algorithm is used as the data calculation to search for entries in the database that match the information. Specifically, this involves performing database matching using SQL queries.

[0325] Step 4:

[0326] The server generates a course of action for the identified error code and communicates with the user to provide it. Inputs are identification data and solutions obtained from the database, while output is the generated course of action. Data processing includes assembling a resolution procedure based on the error code. Specifically, the solution is formatted as a message to the user and sent to the user's terminal.

[0327] Step 5:

[0328] The server searches log data based on resource location and time information within the character data obtained by OCR, and retrieves necessary supplementary information. The input is URLs and time data as character data, and the output is related login information and supplementary information. Data processing involves picking up relevant data using a log search algorithm. Specifically, the search process includes time-series analysis of log files and periodic data extraction.

[0329] Step 6:

[0330] The user reviews the suggested actions and supplementary information displayed on the terminal and executes the problem-solving steps. The input is the suggested actions and supplementary information sent from the server, and the output is the user's completion of problem-solving. Specifically, the user performs the actions necessary to solve the problem according to the instructions.

[0331] 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.

[0332] The system of this invention not only quickly identifies problems and provides solutions based on screenshots from customers, but also provides advanced support that takes into account the user's emotional state. This system is equipped with an emotion engine that accurately grasps the user's intentions, enabling more effective dialogue.

[0333] When a problem occurs, the user takes a screenshot on their device and sends it to the server. The server analyzes the received screenshot using image recognition technology and extracts text information from the screen. This information may include error codes, URLs, and date and time information.

[0334] The server identifies an error code using the extracted text and checks it against its internal database. Based on the identified error code, it generates an appropriate solution and determines the specific steps to be provided to the user. Furthermore, it searches the server logs based on the URL and time information in the text to obtain additional contextual information.

[0335] With the introduction of an emotion engine, the server recognizes the user's emotions from the received text. Based on this, it evaluates the user's psychological state and adjusts the system response. For example, if the server senses that the user is particularly irritated, it adjusts the tone of support to be kinder and more polite, and provides messages that will help the user relax.

[0336] As a concrete example, suppose a user sends a screenshot to the server showing the error message "Error 404". The server identifies a solution related to the error code, and if the sentiment engine determines from the user's post that the user is confused, it will provide instructions in a gentle tone such as, "The page could not be found, but don't worry. There's a good chance that simply checking this will resolve the issue."

[0337] Thus, this invention combines emotion recognition with conventional technology to achieve a richer user experience and faster problem solving. It is a system that is expected to improve customer satisfaction and efficiency in support services.

[0338] The following describes the processing flow.

[0339] Step 1:

[0340] Users take screenshots on their devices showing problems that occur while using the application and send them to the server through the support system.

[0341] Step 2:

[0342] The server receives screenshots sent by the user and forwards them to the image analysis module. This process prepares the image for detailed analysis of its contents.

[0343] Step 3:

[0344] The server uses OCR technology to extract text information from the screenshot. This information may include error codes and other relevant text.

[0345] Step 4:

[0346] The server identifies the error code from the extracted text and accesses its internal database to check for the corresponding problem and its solution. This reveals the nature of the error and how to address it.

[0347] Step 5:

[0348] The server searches the server logs based on the URLs and date / time information contained in the text. This operation allows the server to confirm the specific circumstances surrounding the error and gather additional information.

[0349] Step 6:

[0350] The server sends text information to the sentiment engine, which then evaluates the user's emotions. Sentiment analysis determines whether the user is feeling confused, angry, or confused.

[0351] Step 7:

[0352] The server adjusts the content and tone of its responses to the user based on the emotion engine's evaluation. For example, if the user is dissatisfied, a polite and calm message will be generated.

[0353] Step 8:

[0354] The server sends the user a message that integrates a solution based on the error code, additional information, and a coordinated response.

[0355] Step 9:

[0356] Based on the detailed instructions and supplementary information received, users can move on to specific problem-solving tasks. The information provided by the server allows users to proceed efficiently.

[0357] (Example 2)

[0358] 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".

[0359] Existing technologies have limitations in extracting text information from screenshots and providing error handling solutions, and furthermore, providing appropriate support that takes into account the user's feelings presents challenges. There is also a need to provide quick and accurate solutions based on a thorough understanding of the detailed context behind the problem.

[0360] 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.

[0361] In this invention, the server includes means for extracting text information from screenshots using image analysis means, means for analyzing the user's emotions from the acquired text information using emotion analysis means and adjusting the response, and means for searching log data based on identification information and obtaining additional information. This makes it possible to provide prompt and accurate support that is contextual and takes into account the user's emotions.

[0362] A "device with computer capabilities" refers to an electronic device that can process digital information and is designed to perform a specific task.

[0363] "Image analysis means" refers to techniques for processing and analyzing image data, enabling the extraction of useful information from images such as screenshots.

[0364] "Text information" refers to character data extracted from an image, including error codes and other identifying information.

[0365] An "error code" is a number or string of characters used to identify a problem that has occurred within a computer system.

[0366] A "database" refers to a collection of information that is organized and stored, and can be quickly searched and updated as needed.

[0367] "Communication medium" refers to a means for sending and receiving information, including wired and wireless methods.

[0368] "Log data" refers to data that compiles a chronological record of system operations and errors.

[0369] "Additional information" refers to detailed data obtained through further investigations and other means, in addition to the information obtained from the initial data analysis.

[0370] "Emotional analysis techniques" are technologies that analyze users' emotions from text and audio data, enabling adjustments to appropriate communication.

[0371] "Natural language processing" refers to technologies for processing human language using computers, enabling text analysis and semantic understanding.

[0372] "Users" refer to individuals or organizations that use the system and are responsible for reporting errors and receiving support.

[0373] The system of the present invention consists of a device with computer functions and terminals and a server connected via a network. When a problem occurs, the user takes a screenshot from the terminal and sends this image to the server. The server processes the image data in the received screenshot using optical character recognition (OCR) software as an image analysis means and extracts text information from the image. This text information includes error codes and other identification information.

[0374] The server can compare the extracted text information with a database, generate a corresponding solution based on the identified error code, and provide it to the user via a communication medium. Furthermore, the server can search log data based on the identification information contained in the text information to obtain additional information for a more detailed understanding of the problem's background. This additional information improves the accuracy of the solution provided to the user.

[0375] Furthermore, the server uses emotion analysis techniques to analyze the user's emotions from the acquired text information. This technology utilizes natural language processing (NLP) to estimate the user's psychological state and appropriately adjust the tone of its response. For example, if the user is feeling stressed, the server will generate a message that encourages relaxation.

[0376] For example, if a user sends a screenshot containing the error code "404" to the server, the server uses OCR technology to detect the error code. The server then searches its internal database to find a solution to the problem of the webpage not being found. Furthermore, if sentiment analysis determines the user is confused, the server will provide a message in a pleasant tone such as, "This error is usually temporary. Please clear your cache and try again."

[0377] An example of a prompt message would be: "Generate a suggested solution when a user submits a screenshot of error code 404. Also, please provide suggestions for constructing a kind and polite message if the user is feeling confused."

[0378] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0379] Step 1:

[0380] When a problem occurs, the user takes a screenshot using their device and sends the image file to the server. The input is the screenshot image taken by the device, and the output is the image data sent to the server. The user's actions include taking a screenshot and clicking the upload button.

[0381] Step 2:

[0382] The server processes the received screenshots using OCR software, which is an image analysis tool. The input is the screenshot image, and the output is the text information contained within the image. Specifically, the OCR engine recognizes and analyzes the characters in the screenshot to extract the text.

[0383] Step 3:

[0384] The server identifies an error code from the extracted text information and compares it against an internal database. The input is text information extracted by OCR, and the output is the identified error code and its corresponding solution. The server executes a database query to identify the most appropriate solution.

[0385] Step 4:

[0386] The server searches log data based on identifying information (e.g., URL and date / time) contained in the acquired text information and retrieves additional information. The input is identifying information obtained from a screenshot, and the output is additional information from the related log data. The server searches the log files and extracts details of relevant events and errors.

[0387] Step 5:

[0388] The server uses sentiment analysis tools to analyze the user's emotions from acquired text information. The input is text information, and the output is an estimated result of the user's emotional state. Specifically, the server's operation includes analyzing the text using natural language processing techniques and identifying emotional patterns.

[0389] Step 6:

[0390] The server generates a response message for the user based on the collected information and delivers it to the user via the communication medium. The input consists of identified error codes, additional information, and sentiment analysis results, while the output is the generated response message. The server constructs the wording, crafts the message in a friendly and polite tone, and sends it to the user.

[0391] (Application Example 2)

[0392] 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."

[0393] Modern information processing systems are required to respond quickly and effectively to technical problems and errors encountered by users. In particular, providing appropriate support that takes into account the user's emotional state is crucial for improving the user experience. However, many current systems only offer solutions to technical problems and suffer from a lack of consideration for the user's emotions.

[0394] 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.

[0395] In this invention, the server includes a computer-enabled device that includes means for extracting textual information from visual information using an image analysis function, means for identifying error codes from the textual information and querying an information storage means to determine appropriate countermeasures, and means for analyzing the user's psychological state using an emotion analysis function and generating support messages tailored to the user's emotions. This makes it possible to quickly identify problems from screenshots sent by the user and provide appropriate solutions that take into account the user's emotional state.

[0396] "A device with computer functionality" refers to hardware or software that has the ability to perform calculations and processing using electronic or digital technology.

[0397] "Image analysis function" refers to the technology of extracting information from image data and identifying or classifying its content.

[0398] "Visual information" refers to image and video data that is perceived by human vision.

[0399] "Textual information" refers to data expressed as text, used for the purpose of transmitting information.

[0400] An "error code" is an identifier used by a system or program to indicate a specific problem or error.

[0401] "Information storage means" refers to a system or device for storing data and keeping it in a state where it can be retrieved or used as needed.

[0402] "Response measures" refer to solutions or actions taken in response to a specific problem or situation.

[0403] "Emotion analysis function" is a technology that infers and analyzes human emotions from text, voice, facial expressions, etc.

[0404] "Psychological state" refers to the emotional and cognitive state that an individual experiences in a given situation or environment.

[0405] A "support message" is information or instructions provided to guide users and facilitate problem-solving.

[0406] The system for implementing the present invention consists of a user terminal, a server, and communication between them. When a user encounters a problem, the process is carried out according to the following procedure.

[0407] First, the user's device takes a screenshot of the screen where the problem occurred and sends the data to the server. The server analyzes this screenshot using image analysis technologies such as the Google Cloud Vision API and extracts text information from the visual information. The resulting text information includes error codes and related text.

[0408] The server identifies the extracted error code and queries its internal information storage system to determine the appropriate course of action. During this process, it uses sentiment analysis technologies such as the Microsoft Azure Emotion API to analyze the user's psychological state from the transmitted text and other relevant data.

[0409] Based on these analysis results, a supportive message that takes the user's emotions into consideration is generated and sent to the user's device via a communication medium. For example, if it is determined that the user is facing an "Error 1001" and is feeling anxious, a message such as "Error 1001 has occurred, but this is a common problem. Don't worry, we can quickly resolve it by following these steps!" will be provided.

[0410] A key element in implementing this invention is the use of a generative AI model, which customizes and provides appropriate support messages for each individual user.

[0411] Examples of prompt messages include: "Analyze the error code in the screenshot and suggest a solution. Analyze the user's emotional state and generate a message with an appropriate tone." Based on these prompts, the AI ​​can provide optimal support tailored to the user's situation.

[0412] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0413] Step 1:

[0414] The user takes a screenshot of the screen where the problem occurred using their device. The input is the screenshot taken on the device. The user sends this screenshot to the server. The output is the image data sent to the server.

[0415] Step 2:

[0416] The server receives the submitted screenshot. The input is the image data of the screenshot. The server uses the Google Cloud Vision API to analyze the image and extract text information from the screenshot. The data processing involves extracting text from the image using OCR (Optical Character Recognition) technology, and the output is the extracted text information (text data).

[0417] Step 3:

[0418] The server identifies the error code from the extracted text information. The input is text information. String processing is performed to identify the error code, and the database is queried to determine the corrective action. The output is data related to the identified error code and the corrective action.

[0419] Step 4:

[0420] The server analyzes the user's psychological state using the Microsoft Azure Emotion API, based on text information and error codes. The input is text information. The server estimates the user's emotions using emotion analysis technology and obtains emotional state data as output.

[0421] Step 5:

[0422] The server generates support messages using a generative AI model based on the obtained emotional state data and corresponding action data. The input consists of emotional state data and corresponding action data. The AI ​​model is used with prompts to create emotionally sensitive support messages as output.

[0423] Step 6:

[0424] The server sends the generated support message to the user's terminal. The input is the generated support message. The message is transmitted via communication and the output is the support message displayed on the user's terminal.

[0425] 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.

[0426] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0427] 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.

[0428] [Third Embodiment]

[0429] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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".

[0441] The system of this invention has the function of quickly and accurately identifying problems and providing countermeasures based on screenshots from customers. The specific actions performed by the server, terminal, and user are described below.

[0442] First, when a problem occurs, the user takes a screenshot on their device and sends it to the server. This image data is sent to an analysis module on the server. The server uses OCR (Optical Character Recognition) technology to analyze the received screenshot and extract text information from the image. This text information includes important data such as error codes, URLs, and date and time information.

[0443] Next, the server uses the parsed text information to identify a specific error code by comparing it with its internal database. Based on the identified error code, the server automatically generates a corresponding solution, which may include specific instructions and links to FAQs.

[0444] Furthermore, the server uses URLs and date / time information within the text data to search server logs and obtain additional information. This information is used to provide supplementary explanations regarding the detailed background of the problem and the state of the system.

[0445] Finally, the server notifies the user of the generated solution and any additional information obtained. Based on this information, the user can take appropriate action to resolve the problem.

[0446] As a concrete example, consider a scenario where a user sends a screenshot showing an "Error 404" message while using an application. The server recognizes this error code and generates guidelines such as, "The specified webpage could not be found. Please verify that the URL is correct." Simultaneously, it analyzes the URL on the screen to verify from the server logs whether the page is accessible and provides the verification results to the user. This entire process significantly improves convenience because the user can quickly identify the cause of the problem and its solution.

[0447] The following describes the processing flow.

[0448] Step 1:

[0449] The user takes a screenshot of the application or system experiencing the problem on their device and sends it to the support system.

[0450] Step 2:

[0451] The server receives screenshots sent by users and stores them in an analysis queue. This process makes the screenshots ready for subsequent processing.

[0452] Step 3:

[0453] The server uses an OCR engine to analyze screenshot images and extract text information from the screen. This text information may include error codes, URLs, and date / time information.

[0454] Step 4:

[0455] The server identifies the error code from the extracted text information and compares it with a pre-registered database. This identifies the meaning of the error code and the associated solution.

[0456] Step 5:

[0457] The server generates a solution based on the identified error code. This solution includes specific steps the user should take and links to FAQs they should refer to.

[0458] Step 6:

[0459] The server identifies the URL and date / time information within the screenshot and searches the server logs based on that information. Using this information, it collects detailed background information about the relevant problem from the log data.

[0460] Step 7:

[0461] The server integrates the generated solution and additional information obtained from the logs to construct a message to provide to the user.

[0462] Step 8:

[0463] The server sends a pre-constructed message to the user. Based on this information, the user can then take specific actions to resolve the problem.

[0464] (Example 1)

[0465] 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."

[0466] Existing problem-solving systems have the challenge of being unable to respond quickly and accurately to the technical problems that users face. In particular, the cumbersome and time-consuming process of identifying errors and obtaining additional information is considered a problem, as it lacks convenience.

[0467] 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.

[0468] In this invention, the server includes means for extracting character information from image data using image analysis means, means for identifying identification information from the character information and querying a storage device to determine countermeasures, and means for generating countermeasures based on the identified identification information and providing them to the user via a communication path. This makes it possible to quickly and accurately identify the cause and provide solutions to technical problems faced by the user.

[0469] An "information processing device" is an electronic device designed to perform data analysis and calculations, and capable of executing various tasks instructed by a software program.

[0470] "Image analysis means" refers to techniques and methods for analyzing digital images and extracting text or specific information from them, and generally includes optical character recognition technology.

[0471] "Character information" refers to information composed of alphabets, numbers, or other symbols extracted from image data, and is electronically processable data.

[0472] "Identification information" refers to codes or tokens used to indicate a specific problem or condition, and serves as the basis for a system to determine a specific course of action.

[0473] "Storage device" is a general term for hardware and software used to store electronic data, and includes database management systems.

[0474] A "communication path" refers to a network or interface used to send and receive information, enabling data transfer between a user and a server.

[0475] "Natural language processing means" refers to technologies and methods for understanding, analyzing, and generating human language (natural language), and which are capable of extracting the meaning and context of information.

[0476] This invention is an information processing system that automates the problem-solving process. It efficiently solves technical problems encountered by users using a terminal by analyzing image data and acquiring related information.

[0477] The user saves a screenshot of the problem on their device and sends the image data to the server. The device is equipped with basic image processing software for capturing images and editing them as needed. In this case, the image files are often saved in PNG or JPEG format.

[0478] The server receives the transmitted image data and performs image analysis using OCR (Optical Character Recognition) technology. Specifically, the open-source OCR engine Tesseract can be used. This extracts text information from the image data, identifying important data such as error codes, date and time information, and URLs.

[0479] Next, the server queries the database system in storage using the extracted text information. Commonly used databases such as MySQL and PostgreSQL are employed for database management. The server automatically generates corresponding solutions based on the identified error codes. During this generation process, guidelines are created using system manuals and FAQ links.

[0480] Furthermore, the system searches server logs using URLs and date / time information within the text data to obtain additional information about incidents and conditions related to the problem. ELK Stack, a widely used log analysis tool, is used for this operation.

[0481] Ultimately, the server notifies the user of the generated solution and retrieved details via the communication channel. Based on this information, the user resolves their problem by taking the instructed actions.

[0482] For example, if a user takes a screenshot of an "Error 404" message displayed while using an application and sends it, the server recognizes this error code and automatically generates a guideline such as "The specified webpage could not be found. Please verify that the URL is correct." Furthermore, by checking the connection status to the specified URL and informing the user of the result, the user can quickly identify the cause of the problem and take appropriate action.

[0483] An example of a prompt for a generative AI model is, "Identify errors based on the submitted screenshot and automatically suggest solutions." This prompt is used to form an initial response to user input.

[0484] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0485] Step 1:

[0486] The user takes a screenshot using their device when the problem occurs. This is done using a keyboard shortcut or by clicking the capture icon on the screen. The input is the screen displaying the problem, and the output is image data in PNG or JPEG format. The device saves this screen data to its local disk and allows for image editing as needed.

[0487] Step 2:

[0488] The terminal bundles the captured screenshots into data packets and sends them to the server. The input is an image file stored locally, and the output is image data transferred to the server over the network. The terminal uses a communication module here to efficiently transmit the data.

[0489] Step 3:

[0490] The server inputs the received screenshot into an analysis module and extracts text information from the image using OCR technology. The input is screenshot data, and the output is text information including error codes, date and time information, and URLs. The server uses an OCR engine (e.g., Tesseract) to perform character recognition on the image and converts it into structured data.

[0491] Step 4:

[0492] The server identifies identifying information from the extracted text data by querying a database. The input is text data obtained from an image, and the output is related error codes and procedural information for resolving the problem. The server uses a database management system (e.g., MySQL) to execute queries and quickly retrieve the information.

[0493] Step 5:

[0494] The server automatically generates solutions and creates guidelines to provide to the user based on the identified error code. The input is identified problem information, and the output is documentation and FAQ links related to the solutions. The server uses pre-prepared templates to create guidelines that combine the necessary information.

[0495] Step 6:

[0496] The server searches the logs based on the URLs and date / time information contained in the retrieved text information and obtains relevant additional information. The input is link information within the text information, and the output is relevant event information recorded in the logs. The server uses log analysis tools (e.g., ELK Stack) to extract the necessary data and prepare information to supplement the background of the problem.

[0497] Step 7:

[0498] The server notifies the user of the generated solution and additional information, using email notifications or system notification functions as needed. Input is the solution and related information, and output is a communication message to the user. The server sends this information through the communication path and provides an easily accessible interface for the user.

[0499] Step 8:

[0500] The user reviews the received information and performs appropriate troubleshooting based on it. The input is solution information from the server, and the output is the actual problem-solving action. The user follows the provided guidelines to resolve the problem.

[0501] (Application Example 1)

[0502] 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."

[0503] In electronic payment systems, there is a challenge in that when users encounter transaction errors, the system cannot immediately identify the problem and propose an appropriate solution. Traditional methods require users to manually search for error details and find solutions on their own, which is time-consuming and laborious. This can lead to decreased user satisfaction. Furthermore, because it is difficult for users to understand the technical details behind the errors, effective support is required.

[0504] 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.

[0505] In this invention, the server includes a device having computing capabilities, means for extracting character information from image data using image analysis means, means for identifying identification data from the character information and comparing it with an information storage device to determine a course of action, and means for generating a course of action based on the identified identification data and providing it to the user via communication means. This makes it possible to automatically provide quick and accurate solutions to electronic payment errors faced by the user.

[0506] A "device with computing capabilities" is a machine that has the ability to process information and can perform advanced calculations such as image analysis and data extraction.

[0507] "Image analysis means" refers to a technology or apparatus that performs a process of extracting visual information from image data and identifying necessary elements.

[0508] "Textual information" refers to text data such as letters, numbers, and symbols extracted from image data.

[0509] "Identification data" refers to data used to recognize specific problems or codes from extracted character information.

[0510] An "information storage device" is a device or system that structures and stores various types of data, enabling retrieval and matching.

[0511] An "action plan" is a plan that outlines solutions and procedures for specific conditions or problems.

[0512] "Communication methods" refer to the systems and technologies used to transmit data and information between different locations.

[0513] A "user" is an individual or group that uses a system or device and utilizes its functions.

[0514] The invention will now be described in terms of embodiments for carrying out the invention. This invention is a system for troubleshooting in electronic payment systems that identifies transaction errors based on screenshots and quickly proposes solutions. This system primarily functions through the mutual cooperation of three parties: a server, a terminal, and a user.

[0515] The server is a computing device that uses image analysis software such as Tesseract OCR to analyze screenshots sent by users and extract text information. This process reads error messages and other relevant information from the image. Simultaneously, the server compares the extracted text information against a database and determines the appropriate course of action based on the identification data. The necessary information is stored in an information storage device, and the comparison process is performed using a relational database management system such as PostgreSQL.

[0516] A terminal is a device such as a smartphone or tablet that serves as a user interface. When an error occurs using this terminal, the user takes a screenshot and sends it to the server. This action aims to resolve the problem quickly.

[0517] Users solve problems based on the suggested actions and supplementary information received from their device. They can also receive additional support and feedback via communication channels.

[0518] For example, if a user receives an "Error 502" message while making an online payment, they can take a screenshot and send it, at which point the server will suggest a solution such as, "The server is overloaded. Please try again later." Simultaneously, the server's status logs are used to provide technical details about the background of the error.

[0519] In this way, the system supports users in quickly and efficiently resolving their problems. An example of a prompt to the generated AI model is: "Please suggest solutions that the user should try for this electronic payment error 'Error 502'. Please also include log information showing the server status for additional reference."

[0520] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0521] Step 1:

[0522] When an error occurs in the electronic payment system, the user takes a screenshot with their terminal and sends that data to the server. The input data is the screenshot image file, which is sent to the server's image analysis module. The output is the server receiving the image file. Specifically, the user uses a camera application or the system's built-in capture function.

[0523] Step 2:

[0524] The server analyzes the received screenshot using Tesseract OCR software to extract text information from the image, such as error codes and explanatory text. The input is an image file, and the output is text data. At this stage, data processing involves text extraction through character recognition within the image. Specifically, the OCR software analyzes character patterns within the image and generates digital text information.

[0525] Step 3:

[0526] The server uses the extracted character data to compare it with an internal database and identify identification data, namely error codes and related information. The input is character data from OCR, and the output is the identified error code and related information. A matching algorithm is used as the data calculation to search for entries in the database that match the information. Specifically, this involves performing database matching using SQL queries.

[0527] Step 4:

[0528] The server generates a course of action for the identified error code and communicates with the user to provide it. Inputs are identification data and solutions obtained from the database, while output is the generated course of action. Data processing includes assembling a resolution procedure based on the error code. Specifically, the solution is formatted as a message to the user and sent to the user's terminal.

[0529] Step 5:

[0530] The server searches log data based on resource location and time information within the character data obtained by OCR, and retrieves necessary supplementary information. The input is URLs and time data as character data, and the output is related login information and supplementary information. Data processing involves picking up relevant data using a log search algorithm. Specifically, the search process includes time-series analysis of log files and periodic data extraction.

[0531] Step 6:

[0532] The user reviews the suggested actions and supplementary information displayed on the terminal and executes the problem-solving steps. The input is the suggested actions and supplementary information sent from the server, and the output is the user's completion of problem-solving. Specifically, the user performs the actions necessary to solve the problem according to the instructions.

[0533] 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.

[0534] The system of this invention not only quickly identifies problems and provides solutions based on screenshots from customers, but also provides advanced support that takes into account the user's emotional state. This system is equipped with an emotion engine that accurately grasps the user's intentions, enabling more effective dialogue.

[0535] When a problem occurs, the user takes a screenshot on their device and sends it to the server. The server analyzes the received screenshot using image recognition technology and extracts text information from the screen. This information may include error codes, URLs, and date and time information.

[0536] The server identifies an error code using the extracted text and checks it against its internal database. Based on the identified error code, it generates an appropriate solution and determines the specific steps to be provided to the user. Furthermore, it searches the server logs based on the URL and time information in the text to obtain additional contextual information.

[0537] With the introduction of an emotion engine, the server recognizes the user's emotions from the received text. Based on this, it evaluates the user's psychological state and adjusts the system response. For example, if the server senses that the user is particularly irritated, it adjusts the tone of support to be kinder and more polite, and provides messages that will help the user relax.

[0538] As a concrete example, suppose a user sends a screenshot to the server showing the error message "Error 404". The server identifies a solution related to the error code, and if the sentiment engine determines from the user's post that the user is confused, it will provide instructions in a gentle tone such as, "The page could not be found, but don't worry. There's a good chance that simply checking this will resolve the issue."

[0539] Thus, this invention combines emotion recognition with conventional technology to achieve a richer user experience and faster problem solving. It is a system that is expected to improve customer satisfaction and efficiency in support services.

[0540] The following describes the processing flow.

[0541] Step 1:

[0542] Users take screenshots on their devices showing problems that occur while using the application and send them to the server through the support system.

[0543] Step 2:

[0544] The server receives screenshots sent by the user and forwards them to the image analysis module. This process prepares the image for detailed analysis of its contents.

[0545] Step 3:

[0546] The server uses OCR technology to extract text information from the screenshot. This information may include error codes and other relevant text.

[0547] Step 4:

[0548] The server identifies the error code from the extracted text and accesses its internal database to check for the corresponding problem and its solution. This reveals the nature of the error and how to address it.

[0549] Step 5:

[0550] The server searches the server logs based on the URLs and date / time information contained in the text. This operation allows the server to confirm the specific circumstances surrounding the error and gather additional information.

[0551] Step 6:

[0552] The server sends text information to the sentiment engine, which then evaluates the user's emotions. Sentiment analysis determines whether the user is feeling confused, angry, or confused.

[0553] Step 7:

[0554] The server adjusts the content and tone of its responses to the user based on the emotion engine's evaluation. For example, if the user is dissatisfied, a polite and calm message will be generated.

[0555] Step 8:

[0556] The server sends the user a message that integrates a solution based on the error code, additional information, and a coordinated response.

[0557] Step 9:

[0558] Based on the detailed instructions and supplementary information received, users can move on to specific problem-solving tasks. The information provided by the server allows users to proceed efficiently.

[0559] (Example 2)

[0560] 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."

[0561] Existing technologies have limitations in extracting text information from screenshots and providing error handling solutions, and furthermore, providing appropriate support that takes into account the user's feelings presents challenges. There is also a need to provide quick and accurate solutions based on a thorough understanding of the detailed context behind the problem.

[0562] 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.

[0563] In this invention, the server includes means for extracting text information from screenshots using image analysis means, means for analyzing the user's emotions from the acquired text information using emotion analysis means and adjusting the response, and means for searching log data based on identification information and obtaining additional information. This makes it possible to provide prompt and accurate support that is contextual and takes into account the user's emotions.

[0564] A "device with computer capabilities" refers to an electronic device that can process digital information and is designed to perform a specific task.

[0565] "Image analysis means" refers to techniques for processing and analyzing image data, enabling the extraction of useful information from images such as screenshots.

[0566] "Text information" refers to character data extracted from an image, including error codes and other identifying information.

[0567] An "error code" is a number or string of characters used to identify a problem that has occurred within a computer system.

[0568] A "database" refers to a collection of information that is organized and stored, and can be quickly searched and updated as needed.

[0569] "Communication medium" refers to a means for sending and receiving information, including wired and wireless methods.

[0570] "Log data" refers to data that compiles a chronological record of system operations and errors.

[0571] "Additional information" refers to detailed data obtained through further investigations and other means, in addition to the information obtained from the initial data analysis.

[0572] "Emotional analysis techniques" are technologies that analyze users' emotions from text and audio data, enabling adjustments to appropriate communication.

[0573] "Natural language processing" refers to technologies for processing human language using computers, enabling text analysis and semantic understanding.

[0574] "Users" refer to individuals or organizations that use the system and are responsible for reporting errors and receiving support.

[0575] The system of the present invention consists of a device with computer functions and terminals and a server connected via a network. When a problem occurs, the user takes a screenshot from the terminal and sends this image to the server. The server processes the image data in the received screenshot using optical character recognition (OCR) software as an image analysis means and extracts text information from the image. This text information includes error codes and other identification information.

[0576] The server can compare the extracted text information with a database, generate a corresponding solution based on the identified error code, and provide it to the user via a communication medium. Furthermore, the server can search log data based on the identification information contained in the text information to obtain additional information for a more detailed understanding of the problem's background. This additional information improves the accuracy of the solution provided to the user.

[0577] Furthermore, the server uses emotion analysis techniques to analyze the user's emotions from the acquired text information. This technology utilizes natural language processing (NLP) to estimate the user's psychological state and appropriately adjust the tone of its response. For example, if the user is feeling stressed, the server will generate a message that encourages relaxation.

[0578] For example, if a user sends a screenshot containing the error code "404" to the server, the server uses OCR technology to detect the error code. The server then searches its internal database to find a solution to the problem of the webpage not being found. Furthermore, if sentiment analysis determines the user is confused, the server will provide a message in a pleasant tone such as, "This error is usually temporary. Please clear your cache and try again."

[0579] An example of a prompt message would be: "Generate a suggested solution when a user submits a screenshot of error code 404. Also, please provide suggestions for constructing a kind and polite message if the user is feeling confused."

[0580] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0581] Step 1:

[0582] When a problem occurs, the user takes a screenshot using their device and sends the image file to the server. The input is the screenshot image taken by the device, and the output is the image data sent to the server. The user's actions include taking a screenshot and clicking the upload button.

[0583] Step 2:

[0584] The server processes the received screenshots using OCR software, which is an image analysis tool. The input is the screenshot image, and the output is the text information contained within the image. Specifically, the OCR engine recognizes and analyzes the characters in the screenshot to extract the text.

[0585] Step 3:

[0586] The server identifies an error code from the extracted text information and compares it against an internal database. The input is text information extracted by OCR, and the output is the identified error code and its corresponding solution. The server executes a database query to identify the most appropriate solution.

[0587] Step 4:

[0588] The server searches log data based on identifying information (e.g., URL and date / time) contained in the acquired text information and retrieves additional information. The input is identifying information obtained from a screenshot, and the output is additional information from the related log data. The server searches the log files and extracts details of relevant events and errors.

[0589] Step 5:

[0590] The server uses sentiment analysis tools to analyze the user's emotions from acquired text information. The input is text information, and the output is an estimated result of the user's emotional state. Specifically, the server's operation includes analyzing the text using natural language processing techniques and identifying emotional patterns.

[0591] Step 6:

[0592] The server generates a response message for the user based on the collected information and delivers it to the user via the communication medium. The input consists of identified error codes, additional information, and sentiment analysis results, while the output is the generated response message. The server constructs the wording, crafts the message in a friendly and polite tone, and sends it to the user.

[0593] (Application Example 2)

[0594] 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."

[0595] Modern information processing systems are required to respond quickly and effectively to technical problems and errors encountered by users. In particular, providing appropriate support that takes into account the user's emotional state is crucial for improving the user experience. However, many current systems only offer solutions to technical problems and suffer from a lack of consideration for the user's emotions.

[0596] 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.

[0597] In this invention, the server includes a computer-enabled device that includes means for extracting textual information from visual information using an image analysis function, means for identifying error codes from the textual information and querying an information storage means to determine appropriate countermeasures, and means for analyzing the user's psychological state using an emotion analysis function and generating support messages tailored to the user's emotions. This makes it possible to quickly identify problems from screenshots sent by the user and provide appropriate solutions that take into account the user's emotional state.

[0598] "A device with computer functionality" refers to hardware or software that has the ability to perform calculations and processing using electronic or digital technology.

[0599] "Image analysis function" refers to the technology of extracting information from image data and identifying or classifying its content.

[0600] "Visual information" refers to image and video data that is perceived by human vision.

[0601] "Textual information" refers to data expressed as text, used for the purpose of transmitting information.

[0602] An "error code" is an identifier used by a system or program to indicate a specific problem or error.

[0603] "Information storage means" refers to a system or device for storing data and keeping it in a state where it can be retrieved or used as needed.

[0604] "Response measures" refer to solutions or actions taken in response to a specific problem or situation.

[0605] "Emotion analysis function" is a technology that infers and analyzes human emotions from text, voice, facial expressions, etc.

[0606] "Psychological state" refers to the emotional and cognitive state that an individual experiences in a given situation or environment.

[0607] A "support message" is information or instructions provided to guide users and facilitate problem-solving.

[0608] The system for implementing the present invention consists of a user terminal, a server, and communication between them. When a user encounters a problem, the process is carried out according to the following procedure.

[0609] First, the user's device takes a screenshot of the screen where the problem occurred and sends the data to the server. The server analyzes this screenshot using image analysis technologies such as the Google Cloud Vision API and extracts text information from the visual information. The resulting text information includes error codes and related text.

[0610] The server identifies the extracted error code and queries its internal information storage system to determine the appropriate course of action. During this process, it uses sentiment analysis technologies such as the Microsoft Azure Emotion API to analyze the user's psychological state from the transmitted text and other relevant data.

[0611] Based on these analysis results, a supportive message that takes the user's emotions into consideration is generated and sent to the user's device via a communication medium. For example, if it is determined that the user is facing an "Error 1001" and is feeling anxious, a message such as "Error 1001 has occurred, but this is a common problem. Don't worry, we can quickly resolve it by following these steps!" will be provided.

[0612] A key element in implementing this invention is the use of a generative AI model, which customizes and provides appropriate support messages for each individual user.

[0613] Examples of prompt messages include: "Analyze the error code in the screenshot and suggest a solution. Analyze the user's emotional state and generate a message with an appropriate tone." Based on these prompts, the AI ​​can provide optimal support tailored to the user's situation.

[0614] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0615] Step 1:

[0616] The user takes a screenshot of the screen where the problem occurred using their device. The input is the screenshot taken on the device. The user sends this screenshot to the server. The output is the image data sent to the server.

[0617] Step 2:

[0618] The server receives the submitted screenshot. The input is the image data of the screenshot. The server uses the Google Cloud Vision API to analyze the image and extract text information from the screenshot. The data processing involves extracting text from the image using OCR (Optical Character Recognition) technology, and the output is the extracted text information (text data).

[0619] Step 3:

[0620] The server identifies the error code from the extracted text information. The input is text information. String processing is performed to identify the error code, and the database is queried to determine the corrective action. The output is data related to the identified error code and the corrective action.

[0621] Step 4:

[0622] The server analyzes the user's psychological state using the Microsoft Azure Emotion API, based on text information and error codes. The input is text information. The server estimates the user's emotions using emotion analysis technology and obtains emotional state data as output.

[0623] Step 5:

[0624] The server generates support messages using a generative AI model based on the obtained emotional state data and corresponding action data. The input consists of emotional state data and corresponding action data. The AI ​​model is used with prompts to create emotionally sensitive support messages as output.

[0625] Step 6:

[0626] The server sends the generated support message to the user's terminal. The input is the generated support message. The message is transmitted via communication and the output is the support message displayed on the user's terminal.

[0627] 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.

[0628] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0629] 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.

[0630] [Fourth Embodiment]

[0631] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0632] 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.

[0633] 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).

[0634] 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.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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".

[0644] The system of this invention has the function of quickly and accurately identifying problems and providing countermeasures based on screenshots from customers. The specific actions performed by the server, terminal, and user are described below.

[0645] First, when a problem occurs, the user takes a screenshot on their device and sends it to the server. This image data is sent to an analysis module on the server. The server uses OCR (Optical Character Recognition) technology to analyze the received screenshot and extract text information from the image. This text information includes important data such as error codes, URLs, and date and time information.

[0646] Next, the server uses the parsed text information to identify a specific error code by comparing it with its internal database. Based on the identified error code, the server automatically generates a corresponding solution, which may include specific instructions and links to FAQs.

[0647] Furthermore, the server uses URLs and date / time information within the text data to search server logs and obtain additional information. This information is used to provide supplementary explanations regarding the detailed background of the problem and the state of the system.

[0648] Finally, the server notifies the user of the generated solution and any additional information obtained. Based on this information, the user can take appropriate action to resolve the problem.

[0649] As a concrete example, consider a scenario where a user sends a screenshot showing an "Error 404" message while using an application. The server recognizes this error code and generates guidelines such as, "The specified webpage could not be found. Please verify that the URL is correct." Simultaneously, it analyzes the URL on the screen to verify from the server logs whether the page is accessible and provides the verification results to the user. This entire process significantly improves convenience because the user can quickly identify the cause of the problem and its solution.

[0650] The following describes the processing flow.

[0651] Step 1:

[0652] The user takes a screenshot of the application or system experiencing the problem on their device and sends it to the support system.

[0653] Step 2:

[0654] The server receives screenshots sent by users and stores them in an analysis queue. This process makes the screenshots ready for subsequent processing.

[0655] Step 3:

[0656] The server uses an OCR engine to analyze screenshot images and extract text information from the screen. This text information may include error codes, URLs, and date / time information.

[0657] Step 4:

[0658] The server identifies the error code from the extracted text information and compares it with a pre-registered database. This identifies the meaning of the error code and the associated solution.

[0659] Step 5:

[0660] The server generates a solution based on the identified error code. This solution includes specific steps the user should take and links to FAQs they should refer to.

[0661] Step 6:

[0662] The server identifies the URL and date / time information within the screenshot and searches the server logs based on that information. Using this information, it collects detailed background information about the relevant problem from the log data.

[0663] Step 7:

[0664] The server integrates the generated solution and additional information obtained from the logs to construct a message to provide to the user.

[0665] Step 8:

[0666] The server sends a pre-constructed message to the user. Based on this information, the user can then take specific actions to resolve the problem.

[0667] (Example 1)

[0668] 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".

[0669] Existing problem-solving systems have the challenge of being unable to respond quickly and accurately to the technical problems that users face. In particular, the cumbersome and time-consuming process of identifying errors and obtaining additional information is considered a problem, as it lacks convenience.

[0670] 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.

[0671] In this invention, the server includes means for extracting character information from image data using image analysis means, means for identifying identification information from the character information and querying a storage device to determine countermeasures, and means for generating countermeasures based on the identified identification information and providing them to the user via a communication path. This makes it possible to quickly and accurately identify the cause and provide solutions to technical problems faced by the user.

[0672] An "information processing device" is an electronic device designed to perform data analysis and calculations, and capable of executing various tasks instructed by a software program.

[0673] "Image analysis means" refers to techniques and methods for analyzing digital images and extracting text or specific information from them, and generally includes optical character recognition technology.

[0674] "Character information" refers to information composed of alphabets, numbers, or other symbols extracted from image data, and is electronically processable data.

[0675] "Identification information" refers to codes or tokens used to indicate a specific problem or condition, and serves as the basis for a system to determine a specific course of action.

[0676] "Storage device" is a general term for hardware and software used to store electronic data, and includes database management systems.

[0677] A "communication path" refers to a network or interface used to send and receive information, enabling data transfer between a user and a server.

[0678] "Natural language processing means" refers to technologies and methods for understanding, analyzing, and generating human language (natural language), and which are capable of extracting the meaning and context of information.

[0679] This invention is an information processing system that automates the problem-solving process. It efficiently solves technical problems encountered by users using a terminal by analyzing image data and acquiring related information.

[0680] The user saves a screenshot of the problem on their device and sends the image data to the server. The device is equipped with basic image processing software for capturing images and editing them as needed. In this case, the image files are often saved in PNG or JPEG format.

[0681] The server receives the transmitted image data and performs image analysis using OCR (Optical Character Recognition) technology. Specifically, the open-source OCR engine Tesseract can be used. This extracts text information from the image data, identifying important data such as error codes, date and time information, and URLs.

[0682] Next, the server queries the database system in storage using the extracted text information. Commonly used databases such as MySQL and PostgreSQL are employed for database management. The server automatically generates corresponding solutions based on the identified error codes. During this generation process, guidelines are created using system manuals and FAQ links.

[0683] Furthermore, the system searches server logs using URLs and date / time information within the text data to obtain additional information about incidents and conditions related to the problem. ELK Stack, a widely used log analysis tool, is used for this operation.

[0684] Ultimately, the server notifies the user of the generated solution and retrieved details via the communication channel. Based on this information, the user resolves their problem by taking the instructed actions.

[0685] For example, if a user takes a screenshot of an "Error 404" message displayed while using an application and sends it, the server recognizes this error code and automatically generates a guideline such as "The specified webpage could not be found. Please verify that the URL is correct." Furthermore, by checking the connection status to the specified URL and informing the user of the result, the user can quickly identify the cause of the problem and take appropriate action.

[0686] An example of a prompt for a generative AI model is, "Identify errors based on the submitted screenshot and automatically suggest solutions." This prompt is used to form an initial response to user input.

[0687] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0688] Step 1:

[0689] The user takes a screenshot using their device when the problem occurs. This is done using a keyboard shortcut or by clicking the capture icon on the screen. The input is the screen displaying the problem, and the output is image data in PNG or JPEG format. The device saves this screen data to its local disk and allows for image editing as needed.

[0690] Step 2:

[0691] The terminal bundles the captured screenshots into data packets and sends them to the server. The input is an image file stored locally, and the output is image data transferred to the server over the network. The terminal uses a communication module here to efficiently transmit the data.

[0692] Step 3:

[0693] The server inputs the received screenshot into an analysis module and extracts text information from the image using OCR technology. The input is screenshot data, and the output is text information including error codes, date and time information, and URLs. The server uses an OCR engine (e.g., Tesseract) to perform character recognition on the image and converts it into structured data.

[0694] Step 4:

[0695] The server identifies identifying information from the extracted text data by querying a database. The input is text data obtained from an image, and the output is related error codes and procedural information for resolving the problem. The server uses a database management system (e.g., MySQL) to execute queries and quickly retrieve the information.

[0696] Step 5:

[0697] The server automatically generates solutions and creates guidelines to provide to the user based on the identified error code. The input is identified problem information, and the output is documentation and FAQ links related to the solutions. The server uses pre-prepared templates to create guidelines that combine the necessary information.

[0698] Step 6:

[0699] The server searches the logs based on the URLs and date / time information contained in the retrieved text information and obtains relevant additional information. The input is link information within the text information, and the output is relevant event information recorded in the logs. The server uses log analysis tools (e.g., ELK Stack) to extract the necessary data and prepare information to supplement the background of the problem.

[0700] Step 7:

[0701] The server notifies the user of the generated solution and additional information, using email notifications or system notification functions as needed. Input is the solution and related information, and output is a communication message to the user. The server sends this information through the communication path and provides an easily accessible interface for the user.

[0702] Step 8:

[0703] The user reviews the received information and performs appropriate troubleshooting based on it. The input is solution information from the server, and the output is the actual problem-solving action. The user follows the provided guidelines to resolve the problem.

[0704] (Application Example 1)

[0705] 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".

[0706] In electronic payment systems, there is a challenge in that when users encounter transaction errors, the system cannot immediately identify the problem and propose an appropriate solution. Traditional methods require users to manually search for error details and find solutions on their own, which is time-consuming and laborious. This can lead to decreased user satisfaction. Furthermore, because it is difficult for users to understand the technical details behind the errors, effective support is required.

[0707] 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.

[0708] In this invention, the server includes a device having computing capabilities, means for extracting character information from image data using image analysis means, means for identifying identification data from the character information and comparing it with an information storage device to determine a course of action, and means for generating a course of action based on the identified identification data and providing it to the user via communication means. This makes it possible to automatically provide quick and accurate solutions to electronic payment errors faced by the user.

[0709] A "device with computing capabilities" is a machine that has the ability to process information and can perform advanced calculations such as image analysis and data extraction.

[0710] "Image analysis means" refers to a technology or apparatus that performs a process of extracting visual information from image data and identifying necessary elements.

[0711] "Textual information" refers to text data such as letters, numbers, and symbols extracted from image data.

[0712] "Identification data" refers to data used to recognize specific problems or codes from extracted character information.

[0713] An "information storage device" is a device or system that structures and stores various types of data, enabling retrieval and matching.

[0714] An "action plan" is a plan that outlines solutions and procedures for specific conditions or problems.

[0715] "Communication methods" refer to the systems and technologies used to transmit data and information between different locations.

[0716] A "user" is an individual or group that uses a system or device and utilizes its functions.

[0717] The invention will now be described in terms of embodiments for carrying out the invention. This invention is a system for troubleshooting in electronic payment systems that identifies transaction errors based on screenshots and quickly proposes solutions. This system primarily functions through the mutual cooperation of three parties: a server, a terminal, and a user.

[0718] The server is a computing device that uses image analysis software such as Tesseract OCR to analyze screenshots sent by users and extract text information. This process reads error messages and other relevant information from the image. Simultaneously, the server compares the extracted text information against a database and determines the appropriate course of action based on the identification data. The necessary information is stored in an information storage device, and the comparison process is performed using a relational database management system such as PostgreSQL.

[0719] A terminal is a device such as a smartphone or tablet that serves as a user interface. When an error occurs using this terminal, the user takes a screenshot and sends it to the server. This action aims to resolve the problem quickly.

[0720] Users solve problems based on the suggested actions and supplementary information received from their device. They can also receive additional support and feedback via communication channels.

[0721] For example, if a user receives an "Error 502" message while making an online payment, they can take a screenshot and send it, at which point the server will suggest a solution such as, "The server is overloaded. Please try again later." Simultaneously, the server's status logs are used to provide technical details about the background of the error.

[0722] In this way, the system supports users in quickly and efficiently resolving their problems. An example of a prompt to the generated AI model is: "Please suggest solutions that the user should try for this electronic payment error 'Error 502'. Please also include log information showing the server status for additional reference."

[0723] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0724] Step 1:

[0725] When an error occurs in the electronic payment system, the user takes a screenshot with their terminal and sends that data to the server. The input data is the screenshot image file, which is sent to the server's image analysis module. The output is the server receiving the image file. Specifically, the user uses a camera application or the system's built-in capture function.

[0726] Step 2:

[0727] The server analyzes the received screenshot using Tesseract OCR software to extract text information from the image, such as error codes and explanatory text. The input is an image file, and the output is text data. At this stage, data processing involves text extraction through character recognition within the image. Specifically, the OCR software analyzes character patterns within the image and generates digital text information.

[0728] Step 3:

[0729] The server uses the extracted character data to compare it with an internal database and identify identification data, namely error codes and related information. The input is character data from OCR, and the output is the identified error code and related information. A matching algorithm is used as the data calculation to search for entries in the database that match the information. Specifically, this involves performing database matching using SQL queries.

[0730] Step 4:

[0731] The server generates a course of action for the identified error code and communicates with the user to provide it. Inputs are identification data and solutions obtained from the database, while output is the generated course of action. Data processing includes assembling a resolution procedure based on the error code. Specifically, the solution is formatted as a message to the user and sent to the user's terminal.

[0732] Step 5:

[0733] The server searches log data based on resource location and time information within the character data obtained by OCR, and retrieves necessary supplementary information. The input is URLs and time data as character data, and the output is related login information and supplementary information. Data processing involves picking up relevant data using a log search algorithm. Specifically, the search process includes time-series analysis of log files and periodic data extraction.

[0734] Step 6:

[0735] The user reviews the suggested actions and supplementary information displayed on the terminal and executes the problem-solving steps. The input is the suggested actions and supplementary information sent from the server, and the output is the user's completion of problem-solving. Specifically, the user performs the actions necessary to solve the problem according to the instructions.

[0736] 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.

[0737] The system of this invention not only quickly identifies problems and provides solutions based on screenshots from customers, but also provides advanced support that takes into account the user's emotional state. This system is equipped with an emotion engine that accurately grasps the user's intentions, enabling more effective dialogue.

[0738] When a problem occurs, the user takes a screenshot on their device and sends it to the server. The server analyzes the received screenshot using image recognition technology and extracts text information from the screen. This information may include error codes, URLs, and date and time information.

[0739] The server identifies an error code using the extracted text and checks it against its internal database. Based on the identified error code, it generates an appropriate solution and determines the specific steps to be provided to the user. Furthermore, it searches the server logs based on the URL and time information in the text to obtain additional contextual information.

[0740] With the introduction of an emotion engine, the server recognizes the user's emotions from the received text. Based on this, it evaluates the user's psychological state and adjusts the system response. For example, if the server senses that the user is particularly irritated, it adjusts the tone of support to be kinder and more polite, and provides messages that will help the user relax.

[0741] As a concrete example, suppose a user sends a screenshot to the server showing the error message "Error 404". The server identifies a solution related to the error code, and if the sentiment engine determines from the user's post that the user is confused, it will provide instructions in a gentle tone such as, "The page could not be found, but don't worry. There's a good chance that simply checking this will resolve the issue."

[0742] Thus, this invention combines emotion recognition with conventional technology to achieve a richer user experience and faster problem solving. It is a system that is expected to improve customer satisfaction and efficiency in support services.

[0743] The following describes the processing flow.

[0744] Step 1:

[0745] Users take screenshots on their devices showing problems that occur while using the application and send them to the server through the support system.

[0746] Step 2:

[0747] The server receives screenshots sent by the user and forwards them to the image analysis module. This process prepares the image for detailed analysis of its contents.

[0748] Step 3:

[0749] The server uses OCR technology to extract text information from the screenshot. This information may include error codes and other relevant text.

[0750] Step 4:

[0751] The server identifies the error code from the extracted text and accesses its internal database to check for the corresponding problem and its solution. This reveals the nature of the error and how to address it.

[0752] Step 5:

[0753] The server searches the server logs based on the URLs and date / time information contained in the text. This operation allows the server to confirm the specific circumstances surrounding the error and gather additional information.

[0754] Step 6:

[0755] The server sends text information to the sentiment engine, which then evaluates the user's emotions. Sentiment analysis determines whether the user is feeling confused, angry, or confused.

[0756] Step 7:

[0757] The server adjusts the content and tone of its responses to the user based on the emotion engine's evaluation. For example, if the user is dissatisfied, a polite and calm message will be generated.

[0758] Step 8:

[0759] The server sends the user a message that integrates a solution based on the error code, additional information, and a coordinated response.

[0760] Step 9:

[0761] Based on the detailed instructions and supplementary information received, users can move on to specific problem-solving tasks. The information provided by the server allows users to proceed efficiently.

[0762] (Example 2)

[0763] 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".

[0764] Existing technologies have limitations in extracting text information from screenshots and providing error handling solutions, and furthermore, providing appropriate support that takes into account the user's feelings presents challenges. There is also a need to provide quick and accurate solutions based on a thorough understanding of the detailed context behind the problem.

[0765] 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.

[0766] In this invention, the server includes means for extracting text information from screenshots using image analysis means, means for analyzing the user's emotions from the acquired text information using emotion analysis means and adjusting the response, and means for searching log data based on identification information and obtaining additional information. This makes it possible to provide prompt and accurate support that is contextual and takes into account the user's emotions.

[0767] A "device with computer capabilities" refers to an electronic device that can process digital information and is designed to perform a specific task.

[0768] "Image analysis means" refers to techniques for processing and analyzing image data, enabling the extraction of useful information from images such as screenshots.

[0769] "Text information" refers to character data extracted from an image, including error codes and other identifying information.

[0770] An "error code" is a number or string of characters used to identify a problem that has occurred within a computer system.

[0771] A "database" refers to a collection of information that is organized and stored, and can be quickly searched and updated as needed.

[0772] "Communication medium" refers to a means for sending and receiving information, including wired and wireless methods.

[0773] "Log data" refers to data that compiles a chronological record of system operations and errors.

[0774] "Additional information" refers to detailed data obtained through further investigations and other means, in addition to the information obtained from the initial data analysis.

[0775] "Emotional analysis techniques" are technologies that analyze users' emotions from text and audio data, enabling adjustments to appropriate communication.

[0776] "Natural language processing" refers to technologies for processing human language using computers, enabling text analysis and semantic understanding.

[0777] "Users" refer to individuals or organizations that use the system and are responsible for reporting errors and receiving support.

[0778] The system of the present invention consists of a device with computer functions and terminals and a server connected via a network. When a problem occurs, the user takes a screenshot from the terminal and sends this image to the server. The server processes the image data in the received screenshot using optical character recognition (OCR) software as an image analysis means and extracts text information from the image. This text information includes error codes and other identification information.

[0779] The server can compare the extracted text information with a database, generate a corresponding solution based on the identified error code, and provide it to the user via a communication medium. Furthermore, the server can search log data based on the identification information contained in the text information to obtain additional information for a more detailed understanding of the problem's background. This additional information improves the accuracy of the solution provided to the user.

[0780] Furthermore, the server uses emotion analysis techniques to analyze the user's emotions from the acquired text information. This technology utilizes natural language processing (NLP) to estimate the user's psychological state and appropriately adjust the tone of its response. For example, if the user is feeling stressed, the server will generate a message that encourages relaxation.

[0781] For example, if a user sends a screenshot containing the error code "404" to the server, the server uses OCR technology to detect the error code. The server then searches its internal database to find a solution to the problem of the webpage not being found. Furthermore, if sentiment analysis determines the user is confused, the server will provide a message in a pleasant tone such as, "This error is usually temporary. Please clear your cache and try again."

[0782] An example of a prompt message would be: "Generate a suggested solution when a user submits a screenshot of error code 404. Also, please provide suggestions for constructing a kind and polite message if the user is feeling confused."

[0783] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0784] Step 1:

[0785] When a problem occurs, the user takes a screenshot using their device and sends the image file to the server. The input is the screenshot image taken by the device, and the output is the image data sent to the server. The user's actions include taking a screenshot and clicking the upload button.

[0786] Step 2:

[0787] The server processes the received screenshots using OCR software, which is an image analysis tool. The input is the screenshot image, and the output is the text information contained within the image. Specifically, the OCR engine recognizes and analyzes the characters in the screenshot to extract the text.

[0788] Step 3:

[0789] The server identifies an error code from the extracted text information and compares it against an internal database. The input is text information extracted by OCR, and the output is the identified error code and its corresponding solution. The server executes a database query to identify the most appropriate solution.

[0790] Step 4:

[0791] The server searches log data based on identifying information (e.g., URL and date / time) contained in the acquired text information and retrieves additional information. The input is identifying information obtained from a screenshot, and the output is additional information from the related log data. The server searches the log files and extracts details of relevant events and errors.

[0792] Step 5:

[0793] The server uses sentiment analysis tools to analyze the user's emotions from acquired text information. The input is text information, and the output is an estimated result of the user's emotional state. Specifically, the server's operation includes analyzing the text using natural language processing techniques and identifying emotional patterns.

[0794] Step 6:

[0795] The server generates a response message for the user based on the collected information and delivers it to the user via the communication medium. The input consists of identified error codes, additional information, and sentiment analysis results, while the output is the generated response message. The server constructs the wording, crafts the message in a friendly and polite tone, and sends it to the user.

[0796] (Application Example 2)

[0797] 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".

[0798] Modern information processing systems are required to respond quickly and effectively to technical problems and errors encountered by users. In particular, providing appropriate support that takes into account the user's emotional state is crucial for improving the user experience. However, many current systems only offer solutions to technical problems and suffer from a lack of consideration for the user's emotions.

[0799] 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.

[0800] In this invention, the server includes a computer-enabled device that includes means for extracting textual information from visual information using an image analysis function, means for identifying error codes from the textual information and querying an information storage means to determine appropriate countermeasures, and means for analyzing the user's psychological state using an emotion analysis function and generating support messages tailored to the user's emotions. This makes it possible to quickly identify problems from screenshots sent by the user and provide appropriate solutions that take into account the user's emotional state.

[0801] "A device with computer functionality" refers to hardware or software that has the ability to perform calculations and processing using electronic or digital technology.

[0802] "Image analysis function" refers to the technology of extracting information from image data and identifying or classifying its content.

[0803] "Visual information" refers to image and video data that is perceived by human vision.

[0804] "Textual information" refers to data expressed as text, used for the purpose of transmitting information.

[0805] An "error code" is an identifier used by a system or program to indicate a specific problem or error.

[0806] "Information storage means" refers to a system or device for storing data and keeping it in a state where it can be retrieved or used as needed.

[0807] "Response measures" refer to solutions or actions taken in response to a specific problem or situation.

[0808] "Emotion analysis function" is a technology that infers and analyzes human emotions from text, voice, facial expressions, etc.

[0809] "Psychological state" refers to the emotional and cognitive state that an individual experiences in a given situation or environment.

[0810] A "support message" is information or instructions provided to guide users and facilitate problem-solving.

[0811] The system for implementing the present invention consists of a user terminal, a server, and communication between them. When a user encounters a problem, the process is carried out according to the following procedure.

[0812] First, the user's device takes a screenshot of the screen where the problem occurred and sends the data to the server. The server analyzes this screenshot using image analysis technologies such as the Google Cloud Vision API and extracts text information from the visual information. The resulting text information includes error codes and related text.

[0813] The server identifies the extracted error code and queries its internal information storage system to determine the appropriate course of action. During this process, it uses sentiment analysis technologies such as the Microsoft Azure Emotion API to analyze the user's psychological state from the transmitted text and other relevant data.

[0814] Based on these analysis results, a supportive message that takes the user's emotions into consideration is generated and sent to the user's device via a communication medium. For example, if it is determined that the user is facing an "Error 1001" and is feeling anxious, a message such as "Error 1001 has occurred, but this is a common problem. Don't worry, we can quickly resolve it by following these steps!" will be provided.

[0815] A key element in implementing this invention is the use of a generative AI model, which customizes and provides appropriate support messages for each individual user.

[0816] Examples of prompt messages include: "Analyze the error code in the screenshot and suggest a solution. Analyze the user's emotional state and generate a message with an appropriate tone." Based on these prompts, the AI ​​can provide optimal support tailored to the user's situation.

[0817] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0818] Step 1:

[0819] The user takes a screenshot of the screen where the problem occurred using their device. The input is the screenshot taken on the device. The user sends this screenshot to the server. The output is the image data sent to the server.

[0820] Step 2:

[0821] The server receives the submitted screenshot. The input is the image data of the screenshot. The server uses the Google Cloud Vision API to analyze the image and extract text information from the screenshot. The data processing involves extracting text from the image using OCR (Optical Character Recognition) technology, and the output is the extracted text information (text data).

[0822] Step 3:

[0823] The server identifies the error code from the extracted text information. The input is text information. String processing is performed to identify the error code, and the database is queried to determine the corrective action. The output is data related to the identified error code and the corrective action.

[0824] Step 4:

[0825] The server analyzes the user's psychological state using the Microsoft Azure Emotion API, based on text information and error codes. The input is text information. The server estimates the user's emotions using emotion analysis technology and obtains emotional state data as output.

[0826] Step 5:

[0827] The server generates support messages using a generative AI model based on the obtained emotional state data and corresponding action data. The input consists of emotional state data and corresponding action data. The AI ​​model is used with prompts to create emotionally sensitive support messages as output.

[0828] Step 6:

[0829] The server sends the generated support message to the user's terminal. The input is the generated support message. The message is transmitted via communication and the output is the support message displayed on the user's terminal.

[0830] 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.

[0831] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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."

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] The following is further disclosed regarding the embodiments described above.

[0852] (Claim 1)

[0853] A device with computer capabilities, which includes means for extracting text information from a screenshot using image analysis means,

[0854] A means of identifying an error code from the text information and querying the database to determine the appropriate course of action,

[0855] A means of generating a solution based on the identified error code and providing it to the user via a communication medium,

[0856] A means for searching log data based on URLs and time information contained in the text information and obtaining additional information,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, which uses acquired additional information to generate a detailed description of the problem and provides supplementary instructions to the user.

[0860] (Claim 3)

[0861] The system according to claim 1, which analyzes acquired text information using natural language processing means, estimates the user's intent, and provides supplementary information.

[0862] "Example 1"

[0863] (Claim 1)

[0864] A device having an information processing function includes means for extracting character information from image data using image analysis means,

[0865] A means for identifying identification information from the character information and querying the storage device to determine countermeasures,

[0866] A means of generating countermeasures based on identified identification information and providing them to the user via a communication path,

[0867] A means for searching recorded data based on link information and date / time information contained in the text information and obtaining additional information,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, which generates a detailed explanation of the problem using acquired additional information and provides supplementary instructions to the user.

[0871] (Claim 3)

[0872] The system according to claim 1, which analyzes acquired text information using natural language processing means, estimates the user's intent, and provides supplementary information.

[0873] "Application Example 1"

[0874] (Claim 1)

[0875] A device having a computing function, a means for extracting character information from image data using image analysis means,

[0876] A means for identifying identification data from the character information and determining a course of action by comparing it with an information storage device,

[0877] A means for generating action plans based on identified identification data and providing them to users via communication means,

[0878] A means for searching for recorded information based on resource location and time information contained in the character information and obtaining supplementary information,

[0879] A means for an information processing device with computing capabilities to present problem-solving procedures tailored to the user in natural language,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, which uses acquired supplementary information to generate a detailed explanation of the problem and provides further instructions to the user.

[0883] (Claim 3)

[0884] The system according to claim 1, which analyzes acquired text information using natural language processing means, estimates the user's intention, and provides supplementary information.

[0885] "Example 2 of combining an emotion engine"

[0886] (Claim 1)

[0887] A device with computer capabilities, which includes means for extracting text information from a screenshot using image analysis means,

[0888] A means of identifying an error code from the text information and querying the database to determine the appropriate course of action,

[0889] A means of generating a solution based on the identified error code and providing it to the user via a communication medium,

[0890] A means for searching log data based on identification information contained in the text information and obtaining additional information,

[0891] A means for analyzing the user's emotions from acquired text information using emotion analysis tools and adjusting the response accordingly,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, which uses acquired additional information to generate a detailed explanation of the problem and provides supplementary instructions to the user.

[0895] (Claim 3)

[0896] The system according to claim 1, which analyzes acquired text information using natural language processing means, estimates the user's intent, and provides supplementary information.

[0897] "Application example 2 when combining with an emotional engine"

[0898] (Claim 1)

[0899] A device with computer capabilities, a means for extracting textual information from visual information using an image analysis function,

[0900] A means for identifying error codes from the character information and querying the information storage means to determine the appropriate action,

[0901] A means for generating a solution based on identified error codes and providing it to the user via an information transmission means,

[0902] A means for searching for recorded information based on network addresses and time information contained in the character information and obtaining auxiliary information,

[0903] A means of analyzing the user's psychological state using emotion analysis functionality and generating support messages tailored to the user's emotions,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, which uses acquired auxiliary information to generate a detailed explanation of the problem and provides additional instructions to the user.

[0907] (Claim 3)

[0908] The system according to claim 1, which analyzes acquired text information using natural language processing functions, estimates the user's intent, and provides auxiliary information. [Explanation of Symbols]

[0909] 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 device with computer capabilities, which includes means for extracting text information from a screenshot using image analysis means, A means of identifying an error code from the text information and querying the database to determine the appropriate course of action, A means of generating a solution based on the identified error code and providing it to the user via a communication medium, A means for searching log data based on URLs and time information contained in the text information and obtaining additional information, A system that includes this.

2. The system according to claim 1, which generates a detailed explanation of the problem using acquired additional information and provides supplementary instructions to the user.

3. The system according to claim 1, which analyzes acquired text information using natural language processing means, estimates the user's intent, and provides supplementary information.

Images