system

Abstract

We provide the system. [Solution] A means for receiving image data obtained from a user, A means for analyzing received image data to determine the operating status of an electronic device, A means for generating and presenting a solution based on the operating state, A means of automatically transferring information to an external management center as needed, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] When a failure or abnormal operation occurs in an electronic device, there is a problem that it is difficult to quickly identify the cause of the problem and present an appropriate solution. For this reason, especially for users without specialized knowledge, a lot of time and labor are required for problem diagnosis and countermeasures, which causes a significant reduction in work efficiency. In addition, when the problem is serious, rapid information sharing with an external management center is required, but the process is complicated and time-consuming, which is also a problem.

Means for Solving the Problems

[0005] This invention provides a system that includes means for receiving image data acquired from a user and determines the operating status of an electronic device by analyzing the received image data. Furthermore, it includes means for generating and presenting solutions based on the operating status. This allows the user to easily identify problems and obtain appropriate solutions. In addition, if the problem cannot be resolved or a serious failure occurs, the system includes means for automatically transferring the solution to an external management center, enabling rapid information sharing and response.

[0006] A "user" is an individual or legal entity that attempts to solve a problem using the system.

[0007] "Image data" refers to image files acquired by a user using their device to visually capture the state of an electronic device.

[0008] "Means of receiving" refers to a function or device for acquiring data transmitted by a user and supplying it to the system.

[0009] "Analysis" refers to the process of verifying the content of received image data to identify problems and determine the operating status.

[0010] "Electronic equipment" refers to devices with network connectivity, including communication equipment and related devices used for business purposes.

[0011] "Operating status" refers to the state in which an electronic device is currently functioning or is experiencing any malfunctions.

[0012] A "solution" refers to the actions or procedures that can be taken to address an identified problem or anomaly.

[0013] "Means of presentation" refers to the method or device used to communicate the solution generated by the system to the user.

[0014] "External management center" refers to a specialized facility or organization that monitors the network to which users and systems are connected and supports problem-solving.

[0015] "Means for automatically transferring information" refers to a function or method by which a system transmits information to a management center as needed.

Brief Description of the Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It 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 the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

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

[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] This invention provides an embodiment of a system that enables users to identify and efficiently resolve malfunctions and operational abnormalities in electronic devices. The implementation method is described in detail below.

[0038] First, the user takes a picture of the malfunctioning device with their terminal to diagnose its condition using the system. This image includes screenshots of the device's indicator lights and error messages. The terminal then sends this image data to the server.

[0039] The server analyzes the received image data using AI-based algorithms. This allows it to determine the operating status of electronic devices from the color of lights, flashing patterns, and other visual characteristics. Based on this determination, the server identifies problems and generates solutions. These solutions include specific steps and guides in text and video formats.

[0040] The generated solutions are presented to the user via the terminal, allowing the user to quickly address the problem. The information presented includes video-based operation guides, making it easy for even users unfamiliar with the equipment to understand and implement the solutions.

[0041] Furthermore, if the problem persists or is deemed a serious failure, the server automatically forwards detailed information about the issue to an external management center. This allows the management center to monitor the overall network status and provide direct support as needed.

[0042] As a concrete example, consider a scenario where a user notices a red light illuminated, indicating a communication error. The user takes a picture of the situation, sends the image from their device to the server, and the server analyzes it. As a result, a video is presented showing the procedure for checking the cable's functionality and the specific meaning of the light. In this way, the user can quickly and efficiently resolve equipment problems.

[0043] Through the embodiments described above, the present invention provides a system that enables users to quickly address a variety of problems that occur in electronic devices without requiring specialized knowledge.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user uses the device to check for malfunctions or abnormalities in electronic devices and to photograph their condition. They take photos or screenshots and prepare them as image data.

[0047] Step 2:

[0048] The device sends image data captured by the user to the server. A communication protocol is used to ensure security and speed during transmission.

[0049] Step 3:

[0050] The server acquires the received image data and begins analysis using an AI algorithm. It analyzes visual information such as the color and flashing pattern of the lights and error messages displayed on the screen.

[0051] Step 4:

[0052] Based on the analysis results, the server determines the current operating status of the electronic devices and the cause of any malfunctions.

[0053] Step 5:

[0054] The server generates a solution to the problem. The solution includes a text guide with detailed instructions and specific operating procedures, as well as video links.

[0055] Step 6:

[0056] The server sends the generated solution to the terminal. The terminal receives it and presents the solution to the user. The user can then resolve the problem by following the instructions.

[0057] Step 7:

[0058] If the problem persists or the server detects a critical failure, it automatically forwards information to an external management center. This forwarding includes a detailed failure report.

[0059] Step 8:

[0060] The user enters the results of their attempted solutions on their device. The device sends this feedback to the server, which uses the information to determine if further assistance is needed.

[0061] (Example 1)

[0062] 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."

[0063] There is a need to quickly and efficiently identify and resolve malfunctions and abnormalities in electronic equipment. In particular, there is a need for users without specialized technical knowledge to be able to understand the status of the equipment based on visual information and take appropriate action. Conventional systems have made it difficult to provide accurate diagnoses and effective solutions, and problem resolution has sometimes been time-consuming.

[0064] 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.

[0065] In this invention, the server includes means for receiving image information acquired from a user, means for analyzing the received image information and determining the operating state of the device, and means for generating and presenting a solution guideline based on the operating state. This enables users, even without specialized knowledge, to quickly diagnose malfunctions in electronic devices and effectively resolve problems through the presented solution guideline.

[0066] A "user" is the entity that identifies and resolves malfunctions and operational abnormalities in electronic devices by using the system.

[0067] "Image information" refers to visual data captured by a user using their device to indicate the status of an electronic device.

[0068] "Means of receiving" refers to the function that allows the server to acquire image information sent by the user.

[0069] "Means of analysis and judgment" refers to processing functions that use machine learning algorithms and the like to determine the operating state of electronic devices based on received image information.

[0070] "Solution guidelines" refer to information that provides specific solutions and guidelines for identified problems in text and video formats.

[0071] The "automatic forwarding mechanism" refers to a function that automatically sends resolution guidelines and incident information, generated according to their importance, to an external management organization.

[0072] A "generative AI model" is an artificial intelligence technology used to analyze received image information and generate the type of problem and its solution.

[0073] A "prompt sentence" is an input sentence given to a generative AI model, containing instructions that enable the model to generate appropriate information for problem solving.

[0074] This invention provides a system for users to identify and quickly and efficiently resolve malfunctions and operational abnormalities that occur in electronic devices. Specific embodiments are described below.

[0075] The user first uses a smartphone or tablet to capture images of the electronic device in question to understand its status. These images may include indicator lights, error messages, and other visual information. The device then sends the captured images to a server. This transmission takes place over the internet, utilizing Wi-Fi or mobile data communication technologies.

[0076] The server analyzes the received image information using an AI-based algorithm. This analysis utilizes a generative AI model to identify the color, blinking pattern, and textual information of the lamps in the image, and to determine the type and scope of the problem. Based on this determination, the server generates a solution guide. This guide includes a detailed analysis of the problem, necessary steps, and even a video guide.

[0077] The generated troubleshooting guidelines are presented to the user via the terminal. Based on this information, the user can resolve the device problem by following the instructions. The video guides are designed to be easy for even unfamiliar users to understand, and they visually demonstrate specific steps.

[0078] Furthermore, if the problem remains unresolved or if it is determined that further assistance is needed, the server automatically forwards the information to an external management organization. This allows the management organization to monitor network-wide failures and provide expert support as needed.

[0079] As a concrete example, if a user experiences a communication error and notices a red light flashing, they can take a picture of the situation with their device. The image is sent to a server, which analyzes it and provides a video tutorial explaining how to check cable connections and the specific meaning of the light. This process allows the user to identify the problem and quickly resolve it.

[0080] As an example of a prompt, by inputting a text-based instruction such as "Please tell me what the red light means when a communication error occurs" into the AI ​​model, it is possible to obtain information that is useful for solving the problem.

[0081] Thus, the invention provides a system that allows users to efficiently resolve problems in electronic devices without requiring specialized technical knowledge.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user takes images of the malfunctioning electronic device with their device. The device generates image data, including screenshots of indicator lights and error messages, as input. The device then sends this image data to the server via Wi-Fi or a mobile network.

[0085] Step 2:

[0086] The server receives image data sent from the terminal. It takes the image data as input and uses image processing algorithms to extract features such as lamp color, blinking pattern, and error messages. As output, it generates feature data to be passed to a machine learning model.

[0087] Step 3:

[0088] The server uses a generative AI model to analyze feature data and determine the operating state of electronic devices. This process involves data calculations based on the input feature data to identify the type and scope of the problem. The output generates the identified problem and its solution guidelines.

[0089] Step 4:

[0090] The server generates solution guidelines and sends them to the terminal in text and video formats. Using data on the identified problem and its solution as input, it creates an easy-to-understand guide. The output provided to the user includes a text guide showing the operating procedures and a video guide for visual support.

[0091] Step 5:

[0092] The terminal displays the user with the solution guidelines received from the server. The user reviews the displayed information and corrects the problem with the electronic device according to the suggested solution. Here, the input is the solution guidelines received from the server, and the output is the specific steps the user takes to actually perform the action.

[0093] Step 6:

[0094] If a problem remains unresolved or a critical failure is detected, the server automatically forwards detailed information about the problem to an external management authority. This step takes unresolved problem information as input and generates detailed data accessible to the management authority as output, thereby providing more advanced support.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] When machinery and robots used in factories malfunction, on-site workers are required to quickly and accurately detect the abnormality and take appropriate countermeasures. However, with conventional systems, it is difficult for workers without specialized knowledge to identify the problem and find a solution. A system is needed to solve this problem and improve work efficiency.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for receiving visual data acquired from a user, means for analyzing the received visual data to determine the operating state of a machine, and means for generating and presenting a solution based on the operating state. This enables field workers to intuitively and quickly perform everything from detecting anomalies to obtaining solutions through a visual device.

[0100] "Visual data" refers to information in still image or video format acquired by a user using a visual device, intended to understand the status of a machine or device.

[0101] "Mechanical equipment" refers to devices and robots used in industrial settings such as factories, which perform specific tasks through their actions.

[0102] "Operating status" refers to information indicating the current operating status and presence or absence of abnormalities in machinery and equipment, and is necessary to confirm the normal operation of the equipment.

[0103] A "solution" refers to the specific procedures and methods provided to correct malfunctions in machinery and restore them to normal operation.

[0104] A "visual device" is a device that, when worn by a user, can acquire video information and display the analysis results. It is used for anomaly detection in work sites.

[0105] An "external management body" refers to an organization or system that monitors the status of machinery and equipment and provides support as needed, and can receive information via a network.

[0106] "Audio and visual guides" are features that use sound and visual information to inform users how to proceed with a task, helping them understand the solution.

[0107] To implement this invention, a system utilizing a visual device, a server, and an AI model is required. First, the user wears the visual device and observes the mechanical device. The visual device is equipped with a high-performance camera and can acquire visual data indicating normal operation or abnormalities. The acquired visual data is transmitted to the server in real time.

[0108] The server analyzes received visual data using AI models suitable for image recognition and anomaly detection. This analysis utilizes deep learning frameworks such as TENSORFLOW® and PyTorch to accurately determine the operating state of mechanical devices. Based on the identified anomalies, the server generates solutions. These solutions include information to be presented as audio and visual guides.

[0109] The solution is then transmitted to a visual device, and the user is shown the solution digitally along with voice instructions. The user proceeds with the work according to these instructions and can, if necessary, collaborate with external management organizations for support.

[0110] As a concrete example, consider a case where a malfunction occurs in the arm of a factory robot. The user uses a vision device to take an image of the arm and sends the data to a server. An AI model identifies the malfunction and provides the user with a video and step-by-step audio guide showing how to repair the worn joint.

[0111] An example of a prompt message for a generated AI model is, "Analyze the image of the joint of the factory robot, identify the anomaly, and instruct it on the repair procedure." In this way, users can solve problems quickly and accurately, even without high technical knowledge.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user wears a visual device and photographs the target part of the mechanical equipment. The input includes visual data, i.e., still or video images. By acquiring this data, the status of the equipment on site can be visually understood. The output is the acquired visual data itself.

[0115] Step 2:

[0116] The terminal sends the acquired visual data to the server. The input includes the visual data acquired by the user. By sending the data to the server using a data transfer protocol, the device status analysis begins. The output is the completion of data transmission to the server.

[0117] Step 3:

[0118] The server analyzes the received visual data using an AI model. The input includes visual data received from the terminal. This involves image recognition and anomaly detection, with frameworks such as TensorFlow or PyTorch used for data processing and analysis. The output is the analysis result, i.e., a determination of the operating state of the machine or device.

[0119] Step 4:

[0120] The server generates solutions to mechanical device malfunctions based on the analysis results. The analysis results from step 3 are used as input. The solutions are processed into a format that can be presented via audio or visual guides. The output is solution information to be presented to the user.

[0121] Step 5:

[0122] The server sends the generated solution to the terminal and presents it to the user through a visual device. The input is the solution information generated in step 4. The solution returned to the terminal is displayed on the visual device in both audio and visual formats, providing support in a format that is easy for the user to understand. The output is the solution received by the user.

[0123] Step 6:

[0124] The user addresses the machine's problem by following the provided solutions. Specific actions include following audio guidance and replacing parts by referring to visual guides. The inputs are the provided solution information and the user's actions, while the output is the resolution of the machine's problem.

[0125] 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.

[0126] This invention relates to a system for users to identify and quickly and efficiently resolve malfunctions and anomalies in electronic devices, and is particularly characterized by its incorporation of an emotion engine. This system consists of a terminal, a server, and a program running on it.

[0127] When a user detects a malfunction in an electronic device, they use a terminal to take an image showing the device's status and send it to the server. The terminal is equipped with an interface to smoothly process user input and communicate with the server.

[0128] The server analyzes received images and uses AI-based algorithms to determine the operating status of electronic devices. This analysis takes into account factors such as the status of the device's indicator lights and the content of the display screen. Furthermore, an emotion engine makes it possible to recognize user emotions from comments and feedback entered by the user, as well as from voice input and images.

[0129] The emotion engine has the ability to adjust how solutions are presented when it recognizes that the user's emotions are negative, such as frustration or anxiety. For example, it reduces the user's mental burden by making the solution text more helpful and polite, or by providing reassuring voice guidance.

[0130] The generated solutions are sent to the terminal and presented to the user. The information presented includes video guides to help the user quickly resolve the device problem. If the problem is serious and difficult to resolve on the server side, the server automatically transfers the data to an external management center to request further assistance. In this process, the user's emotional information is also transmitted, enabling the management center to provide a more appropriate response to the user.

[0131] For example, if a user reports a communication problem with a device, they can take a video of the situation and send it to the server. The server will then detect that a light is red and instruct the user to check the network connection. Furthermore, if the user leaves a comment expressing frustration, the emotion engine will detect this and provide a voice guide that gently explains the document to help the user understand it.

[0132] This embodiment allows the present invention to provide a rapid and effective solution to various problems faced by users, while also realizing a service that takes users' feelings into consideration.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user takes a picture of the problematic electronic device with their device. The device prepares the captured image data and sends it to the server.

[0136] Step 2:

[0137] The server analyzes the received image data. Using AI algorithms, it recognizes the status of the device's indicator lights and displayed messages to determine its operating status.

[0138] Step 3:

[0139] The server uses an emotion engine to analyze emotions from user-submitted comments and voice input. For example, it can detect nuances of frustration or anxiety from text.

[0140] Step 4:

[0141] The server generates an appropriate solution based on the analysis of its operational status and the user's emotional state. This solution includes text or videos illustrating the operating procedure, as well as emotionally sensitive explanations.

[0142] Step 5:

[0143] The server sends a solution to the terminal. The terminal presents the solution to the user and helps the user understand the problem by providing video guides and audio instructions.

[0144] Step 6:

[0145] If a user adds new images or comments to their device while attempting to solve a problem, the information is sent back to the server. This allows for further analysis and feedback.

[0146] Step 7:

[0147] If a problem persists or a critical failure is detected, the server automatically transfers information to the management center. This data includes user sentiment analysis results.

[0148] Step 8:

[0149] After a user successfully resolves a problem, they input feedback into their device. This information is sent to a server, which evaluates the effectiveness of the solution. This feedback is then used for future improvements and database updates.

[0150] (Example 2)

[0151] 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".

[0152] When users need to quickly and accurately identify malfunctions or abnormalities in electronic devices and obtain effective solutions, it is essential to efficiently resolve the problem while considering the user's emotional state. Therefore, it is necessary to build a system that can reduce the user's psychological burden while accelerating problem resolution.

[0153] 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.

[0154] In this invention, the server includes means for receiving visual information acquired from the user, means for analyzing the received visual information and determining the operating state of the electronic device, and means for analyzing the user's emotional state using an emotion recognition engine. This makes it possible to generate and present solutions based on the user's emotional state.

[0155] "Visual information" refers to information that indicates the state of electronic devices, such as images and videos obtained from the user.

[0156] "Electronic devices" refer to electrical equipment used by users, such as computers and routers.

[0157] "Operating status" refers to the current operating status of an electronic device and includes normal, abnormal, and specific malfunctions.

[0158] An "emotion recognition engine" is a software component that analyzes user input data to determine their emotional state.

[0159] A "solution" is a prescribed course of action or procedure proposed to resolve a malfunction or abnormality in an electronic device.

[0160] A "control center" is a support organization that provides external expertise and technology to help resolve problems with electronic devices.

[0161] This invention provides a system for users to efficiently identify and quickly resolve malfunctions and abnormalities in electronic devices. This system mainly consists of terminals, servers, and software components that run on them.

[0162] When a user discovers an electronic device showing signs of malfunction, they use the terminal to photograph its condition. This terminal features a high-resolution camera and user interface, allowing for immediate transmission of the acquired visual information to a server. Specifically, the user enters a prompt message in the terminal's input field, such as, "The router's light is blinking red, and the connection is unstable. What should I do?" This prompt message, along with the captured image data, is then sent to the server.

[0163] The server analyzes the received data using an AI-based algorithm and diagnoses the operating status of electronic devices using a generative AI model. This process utilizes image analysis technology to diagnose based on the external characteristics of the electronic devices, the color of the lamps, the status of the display, etc. Furthermore, the server uses an emotion recognition engine to analyze the emotional state from the user's comments and voice data. This analysis detects negative emotions such as frustration and anxiety.

[0164] Based on the emotional state expressed by the user, the server adjusts how it presents solutions. Solutions are displayed as helpful and easy-to-understand text messages, and in some cases, voice guidance is also provided. This allows users to solve problems efficiently while reducing their emotional burden.

[0165] If the problem is serious and difficult to resolve on the server side, the server automatically transfers information to an external control center. This information includes not only data on the failure of the electronic device but also user sentiment data, enabling the control center to take more appropriate action.

[0166] As described above, the present invention combines AI technology and emotion recognition to support problem resolution and realize flexible service provision that is considerate of the user.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user uses the terminal's camera to capture visual information indicating an abnormal state of an electronic device. At the same time, the user enters a prompt message into the terminal's input field. For example, "The router's light is blinking red." This information is sent to the server via the terminal's transmission function. The input consists of image data and a prompt message, while the output is the transmission of data to the server.

[0170] Step 2:

[0171] The server inputs the received visual information and prompt messages into an AI-based image analysis algorithm to determine the operating status of the electronic device. This analysis process detects lamp colors and display information within the image, identifying specific problems with the electronic device. The input is image data, and the output is a diagnostic result of the operating status.

[0172] Step 3:

[0173] The server uses an emotion recognition engine to analyze the user's emotions from the prompt text. Through text analysis, it determines whether the user is experiencing emotions such as frustration or anxiety. The input is the prompt text, and the output is the user's emotional state. This prepares the server to adjust how solutions are presented.

[0174] Step 4:

[0175] The server uses a generative AI model to generate solutions based on the operating state of electronic devices and the user's emotional state. For example, if a network problem is identified, the solution will include steps to verify the connection. Based on the results of the emotional analysis, the solution's explanation is adjusted to be more user-friendly and easy to understand. The input is the operating state and emotional state, and the output is the adjusted solution.

[0176] Step 5:

[0177] The server sends the generated solution to the terminal and presents it to the user. The solution is provided in text format or as an audio guide, and the user solves the problem by following these instructions. The input is the adjusted solution, and the output is the presentation of the solution to the user.

[0178] Step 6:

[0179] If the problem is severe and difficult to handle automatically, the server will transfer relevant information to an external control center. This information includes detailed fault data and user sentiment data. This allows external experts to provide more accurate support. The input is information about the problem that is difficult to resolve, and the output is the transfer of information to the control center.

[0180] (Application Example 2)

[0181] 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".

[0182] Users often find it difficult to quickly and accurately identify and resolve problems when they encounter malfunctions with electronic devices. Furthermore, depending on the user's emotional state, the method of presenting solutions may not be effective, potentially leading to decreased user satisfaction. To address these challenges, there is a need for effective systems that provide smoother support to users and reduce stress.

[0183] 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.

[0184] In this invention, the server includes means for receiving image data acquired from a user, means for analyzing the received image data to determine the operating state of an electronic device, and means for analyzing the user's emotional data and adjusting the method of presenting solutions according to the user's emotional state. This makes it possible to quickly identify problems with electronic devices and provide appropriate support according to the user's emotional state.

[0185] "Image data" refers to visual information that shows the operating status of an electronic device, acquired by a user using their device.

[0186] "Operating status of electronic equipment" refers to status information that indicates how electronic equipment is currently functioning, and includes indicator light colors and display screen content.

[0187] A "solution" refers to specific steps or instructions provided to improve the operational status of an electronic device.

[0188] "Emotional data" refers to information analyzed to understand a user's emotional state, and includes voice input, facial expressions, comments, and other data.

[0189] An "external management center" is a third-party organization that provides further support based on information automatically transferred from the server.

[0190] An "audio guide" is an audio guidance system designed to clearly communicate solutions to users, delivered in a tone that matches the user's emotions.

[0191] "Feedback" refers to information and opinions collected from users, which are used to evaluate the effectiveness of the solutions provided.

[0192] A system implementing this invention mainly consists of a user's terminal, a cloud server, and a responsive interface program.

[0193] The user's device is equipped with a camera for acquiring image data from electronic devices. The device has the function to send the image data captured by the user to a server. This transmitted image data is analyzed on a server in the cloud via Google Cloud's Vision API. This analysis determines the operating status of the electronic device.

[0194] The server also uses Google Cloud's Natural Language API to analyze sentiment data from voice input and comments obtained from users. This allows it to understand the user's emotional state and provide flexible responses if the user is showing signs of anxiety or frustration.

[0195] The solutions are generated in the cloud and presented after being adjusted according to the user's emotional state. For example, if a user reports a communication problem, the server provides an audio guide with a gentle tone of voice based on the emotional information. Furthermore, the solutions are designed to aid understanding, such as showing specific operating procedures with videos and diagrams.

[0196] This system includes a mechanism for collecting user feedback and evaluating the effectiveness of solutions. Based on this information, the solutions provided are continuously improved. For example, if a user reports a problem where "the device won't restart," the cloud server generates a guide, including specific restart steps, based on the analysis results, and provides instructions via both voice and text.

[0197] The following is an example of a prompt message to input into the generative AI model.

[0198] "A user has reported a communication failure with their electronic payment terminal. They have sent a picture of the terminal along with a frustrated expression and comment. Please generate a guide to alleviate the user's anxiety."

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The user takes an image of an electronic device using the terminal. This input data (image file) is sent to a cloud server via the terminal's interface. Specifically, the terminal converts the image to the appropriate resolution and format and sends it via the internet connection.

[0202] Step 2:

[0203] The server inputs the received image data into Google Cloud's Vision API to analyze the operating status of electronic devices. This analysis identifies features and lamp colors in the image to determine the operating status. This step outputs a list of identified operating statuses.

[0204] Step 3:

[0205] The user inputs comments and voice messages through the terminal. The terminal sends this input data to the server as text data. At this time, the terminal performs the specific action of converting voice data to text, packaging it in a usable format, and sending it.

[0206] Step 4:

[0207] The server processes the received comments and audio data through Google Cloud's Natural Language API to analyze the user's sentiment data. Based on this analysis, it outputs information indicating the user's emotional state. The server then starts generating prompts based on this output.

[0208] Step 5:

[0209] The server generates solutions based on the analyzed operational and emotional states. Using a generative AI model, it creates user-appropriate solutions based on prompt text. Specifically, it outputs a combination of voice guidance in a soft tone that matches the user's emotions and text containing specific operating instructions.

[0210] Step 6:

[0211] The server sends the generated solution to the terminal. The terminal provides audio guidance using its audio playback function and simultaneously displays the solution in text format on its screen. Specifically, it plays an audio file and displays the text in an easy-to-read format on the screen.

[0212] Step 7:

[0213] The user implements the suggested solution and, if necessary, sends feedback back to the server via their device. Based on this feedback, the server evaluates the effectiveness of the solution and uses it to make future improvements.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] This invention provides an embodiment of a system that enables users to identify and efficiently resolve malfunctions and operational abnormalities in electronic devices. The implementation method is described in detail below.

[0231] First, the user takes a picture of the malfunctioning device with their terminal to diagnose its condition using the system. This image includes screenshots of the device's indicator lights and error messages. The terminal then sends this image data to the server.

[0232] The server analyzes the received image data using AI-based algorithms. This allows it to determine the operating status of electronic devices from the color of lights, flashing patterns, and other visual characteristics. Based on this determination, the server identifies problems and generates solutions. These solutions include specific steps and guides in text and video formats.

[0233] The generated solutions are presented to the user via the terminal, allowing the user to quickly address the problem. The information presented includes video-based operation guides, making it easy for even users unfamiliar with the equipment to understand and implement the solutions.

[0234] Furthermore, if the problem persists or is deemed a serious failure, the server automatically forwards detailed information about the issue to an external management center. This allows the management center to monitor the overall network status and provide direct support as needed.

[0235] As a concrete example, consider a scenario where a user notices a red light illuminated, indicating a communication error. The user takes a picture of the situation, sends the image from their device to the server, and the server analyzes it. As a result, a video is presented showing the procedure for checking the cable's functionality and the specific meaning of the light. In this way, the user can quickly and efficiently resolve equipment problems.

[0236] Through the embodiments described above, the present invention provides a system that enables users to quickly address a variety of problems that occur in electronic devices without requiring specialized knowledge.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The user uses the device to check for malfunctions or abnormalities in electronic devices and to photograph their condition. They take photos or screenshots and prepare them as image data.

[0240] Step 2:

[0241] The device sends image data captured by the user to the server. A communication protocol is used to ensure security and speed during transmission.

[0242] Step 3:

[0243] The server acquires the received image data and begins analysis using an AI algorithm. It analyzes visual information such as the color and flashing pattern of the lights and error messages displayed on the screen.

[0244] Step 4:

[0245] Based on the analysis results, the server determines the current operating status of the electronic devices and the cause of any malfunctions.

[0246] Step 5:

[0247] The server generates a solution to the problem. The solution includes a text guide with detailed instructions and specific operating procedures, as well as video links.

[0248] Step 6:

[0249] The server sends the generated solution to the terminal. The terminal receives it and presents the solution to the user. The user can then resolve the problem by following the instructions.

[0250] Step 7:

[0251] If the problem persists or the server detects a critical failure, it automatically forwards information to an external management center. This forwarding includes a detailed failure report.

[0252] Step 8:

[0253] The user enters the results of their attempted solutions on their device. The device sends this feedback to the server, which uses the information to determine if further assistance is needed.

[0254] (Example 1)

[0255] 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."

[0256] There is a need to quickly and efficiently identify and resolve malfunctions and abnormalities in electronic equipment. In particular, there is a need for users without specialized technical knowledge to be able to understand the status of the equipment based on visual information and take appropriate action. Conventional systems have made it difficult to provide accurate diagnoses and effective solutions, and problem resolution has sometimes been time-consuming.

[0257] 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.

[0258] In this invention, the server includes means for receiving image information acquired from a user, means for analyzing the received image information and determining the operating state of the device, and means for generating and presenting a solution guideline based on the operating state. This enables users, even without specialized knowledge, to quickly diagnose malfunctions in electronic devices and effectively resolve problems through the presented solution guideline.

[0259] A "user" is the entity that identifies and resolves malfunctions and operational abnormalities in electronic devices by using the system.

[0260] "Image information" refers to visual data captured by a user using their device to indicate the status of an electronic device.

[0261] "Means of receiving" refers to the function that allows the server to acquire image information sent by the user.

[0262] "Means of analysis and judgment" refers to processing functions that use machine learning algorithms and the like to determine the operating state of electronic devices based on received image information.

[0263] "Solution guidelines" refer to information that provides specific solutions and guidelines for identified problems in text and video formats.

[0264] The "automatic forwarding mechanism" refers to a function that automatically sends resolution guidelines and incident information, generated according to their importance, to an external management organization.

[0265] A "generative AI model" is an artificial intelligence technology used to analyze received image information and generate the type of problem and its solution.

[0266] A "prompt sentence" is an input sentence given to a generative AI model, containing instructions that enable the model to generate appropriate information for problem solving.

[0267] This invention provides a system for users to identify and quickly and efficiently resolve malfunctions and operational abnormalities that occur in electronic devices. Specific embodiments are described below.

[0268] The user first uses a smartphone or tablet to capture images of the electronic device in question to understand its status. These images may include indicator lights, error messages, and other visual information. The device then sends the captured images to a server. This transmission takes place over the internet, utilizing Wi-Fi or mobile data communication technologies.

[0269] The server analyzes the received image information using an AI-based algorithm. This analysis utilizes a generative AI model to identify the color, blinking pattern, and textual information of the lamps in the image, and to determine the type and scope of the problem. Based on this determination, the server generates a solution guide. This guide includes a detailed analysis of the problem, necessary steps, and even a video guide.

[0270] The generated troubleshooting guidelines are presented to the user via the terminal. Based on this information, the user can resolve the device problem by following the instructions. The video guides are designed to be easy for even unfamiliar users to understand, and they visually demonstrate specific steps.

[0271] Furthermore, if the problem remains unresolved or if it is determined that further assistance is needed, the server automatically forwards the information to an external management organization. This allows the management organization to monitor network-wide failures and provide expert support as needed.

[0272] As a concrete example, if a user experiences a communication error and notices a red light flashing, they can take a picture of the situation with their device. The image is sent to a server, which analyzes it and provides a video tutorial explaining how to check cable connections and the specific meaning of the light. This process allows the user to identify the problem and quickly resolve it.

[0273] As an example of a prompt, by inputting a text-based instruction such as "Please tell me what the red light means when a communication error occurs" into the AI ​​model, it is possible to obtain information that is useful for solving the problem.

[0274] Thus, the invention provides a system that allows users to efficiently resolve problems in electronic devices without requiring specialized technical knowledge.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The user takes a picture of the image information of the electronic device with problems using a terminal. Image data including the state of the lamp and a screenshot of the error message is generated as input. The terminal transmits this image data to the server using Wi-Fi or a mobile network.

[0278] Step 2:

[0279] The server receives the image data transmitted from the terminal. The image data is acquired as input, and features such as the color of the lamp, the blinking pattern, and the error message are extracted using an image processing algorithm. Feature data for passing to a machine learning model is generated as output.

[0280] Step 3:

[0281] The server analyzes the feature data using a generated AI model to determine the operating state of the electronic device. In this process, data calculations are performed to identify the type and scope of the problem based on the input feature data. As output, the identified problem and its solution guidelines are generated.

[0282] Step 4:

[0283] The server generates solution guidelines and transmits them to the terminal in text and video formats. Using the data regarding the identified problem and its solution as input, an easy-to-understand guide is created. The output provided to the user is a text guide showing the operation procedure and a video guide for visual support.

[0284] Step 5:

[0285] The terminal displays the solution guidelines received from the server to the user. The user checks the displayed information and corrects the problem of the electronic device according to the shown solution. The input here is the solution guidelines received from the server, and the output is the specific procedure for the user to actually take action.

[0286] Step 6:

[0287] If the problem is not solved or a serious obstacle is detected, the server automatically transfers the detailed information of the problem to an external management agency. In this step, the unsolved problem information is handled as input, and detailed data accessible to the management agency is generated as output. This provides further advanced support.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] When a mechanical device or robot used in a factory malfunctions, it is required that on-site workers quickly and accurately detect the abnormality and take appropriate measures. However, in conventional systems, it is difficult for workers without specialized knowledge to identify problems and find solutions. There is a need for a system to solve this problem and improve work efficiency.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0292] In this invention, the server includes means for receiving visual data acquired from a user, means for analyzing the received visual data to determine the operating state of the mechanical device, and means for generating and presenting a solution based on the operating state. As a result, on-site workers can intuitively and quickly perform from detecting an abnormality to obtaining a solution through the visual device.

[0293] "Visual data" is information in the form of a still image or video acquired by a user using a visual device and is for grasping the state of a mechanical device.

[0294] "Mechanical device" refers to equipment and robots used in industrial sites such as factories and performs specific operations by means of motion.

[0295] "Operating status" refers to information indicating the current operating status and presence or absence of abnormalities in machinery and equipment, and is necessary to confirm the normal operation of the equipment.

[0296] A "solution" refers to the specific procedures and methods provided to correct malfunctions in machinery and restore them to normal operation.

[0297] A "visual device" is a device that, when worn by a user, can acquire video information and display the analysis results. It is used for anomaly detection in work sites.

[0298] An "external management body" refers to an organization or system that monitors the status of machinery and equipment and provides support as needed, and can receive information via a network.

[0299] "Audio and visual guides" are features that use sound and visual information to inform users how to proceed with a task, helping them understand the solution.

[0300] To implement this invention, a system utilizing a visual device, a server, and an AI model is required. First, the user wears the visual device and observes the mechanical device. The visual device is equipped with a high-performance camera and can acquire visual data indicating normal operation or abnormalities. The acquired visual data is transmitted to the server in real time.

[0301] The server analyzes received visual data using AI models suitable for image recognition and anomaly detection. This analysis utilizes deep learning frameworks such as TensorFlow and PyTorch to accurately determine the operating state of mechanical devices. Based on the identified anomalies, the server generates solutions. These solutions include information to be presented as audio and visual guides.

[0302] After that, the solution is sent to the visual device, and the user is presented with the solution in digital video along with voice instructions. The user can proceed with the work according to this and can receive assistance in cooperation with external regulatory agencies if necessary.

[0303] As a specific example, consider the case where an abnormality occurs in the arm part of a factory robot. The user uses the visual device to take an image of the arm part and sends the data to the server. The AI model identifies the abnormal location, and a video showing the correction method for the worn joint part and a step-by-step voice guide are provided to the user.

[0304] As an example of the prompt text for the generated AI model, "Analyze the image of the joint part of the factory robot, identify the abnormality, and instruct the repair procedure" can be cited. In this way, even without high technical knowledge, the user can solve problems quickly and accurately.

[0305] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0306] Step 1:

[0307] The user wears the visual device and takes a picture of the target part of the mechanical device. The input includes visual data, that is, an image in still or video format. By acquiring this data, the state of the device on-site is visually grasped. The output is the acquired visual data itself.

[0308] Step 2:

[0309] The terminal sends the acquired visual data to the server. The input includes the visual data acquired by the user. By using the data transfer protocol to send the data to the server, the state analysis of the device is started. The output is the completion of the data transmission to the server.

[0310] Step 3:

[0311] The server analyzes the received visual data using an AI model. The input includes visual data received from the terminal. This involves image recognition and anomaly detection, with frameworks such as TensorFlow or PyTorch used for data processing and analysis. The output is the analysis result, i.e., a determination of the operating state of the machine or device.

[0312] Step 4:

[0313] The server generates solutions to mechanical device malfunctions based on the analysis results. The analysis results from step 3 are used as input. The solutions are processed into a format that can be presented via audio or visual guides. The output is solution information to be presented to the user.

[0314] Step 5:

[0315] The server sends the generated solution to the terminal and presents it to the user through a visual device. The input is the solution information generated in step 4. The solution returned to the terminal is displayed on the visual device in both audio and visual formats, providing support in a format that is easy for the user to understand. The output is the solution received by the user.

[0316] Step 6:

[0317] The user addresses the machine's problem by following the provided solutions. Specific actions include following audio guidance and replacing parts by referring to visual guides. The inputs are the provided solution information and the user's actions, while the output is the resolution of the machine's problem.

[0318] 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.

[0319] This invention relates to a system for users to identify and quickly and efficiently resolve malfunctions and anomalies in electronic devices, and is particularly characterized by its incorporation of an emotion engine. This system consists of a terminal, a server, and a program running on it.

[0320] When a user detects a malfunction in an electronic device, they use a terminal to take an image showing the device's status and send it to the server. The terminal is equipped with an interface to smoothly process user input and communicate with the server.

[0321] The server analyzes received images and uses AI-based algorithms to determine the operating status of electronic devices. This analysis takes into account factors such as the status of the device's indicator lights and the content of the display screen. Furthermore, an emotion engine makes it possible to recognize user emotions from comments and feedback entered by the user, as well as from voice input and images.

[0322] The emotion engine has the ability to adjust how solutions are presented when it recognizes that the user's emotions are negative, such as frustration or anxiety. For example, it reduces the user's mental burden by making the solution text more helpful and polite, or by providing reassuring voice guidance.

[0323] The generated solutions are sent to the terminal and presented to the user. The information presented includes video guides to help the user quickly resolve the device problem. If the problem is serious and difficult to resolve on the server side, the server automatically transfers the data to an external management center to request further assistance. In this process, the user's emotional information is also transmitted, enabling the management center to provide a more appropriate response to the user.

[0324] For example, if a user reports a communication problem with a device, they can take a video of the situation and send it to the server. The server will then detect that a light is red and instruct the user to check the network connection. Furthermore, if the user leaves a comment expressing frustration, the emotion engine will detect this and provide a voice guide that gently explains the document to help the user understand it.

[0325] This embodiment allows the present invention to provide a rapid and effective solution to various problems faced by users, while also realizing a service that takes users' feelings into consideration.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The user takes a picture of the problematic electronic device with their device. The device prepares the captured image data and sends it to the server.

[0329] Step 2:

[0330] The server analyzes the received image data. Using AI algorithms, it recognizes the status of the device's indicator lights and displayed messages to determine its operating status.

[0331] Step 3:

[0332] The server uses an emotion engine to analyze emotions from user-submitted comments and voice input. For example, it can detect nuances of frustration or anxiety from text.

[0333] Step 4:

[0334] The server generates an appropriate solution based on the analysis of its operational status and the user's emotional state. This solution includes text or videos illustrating the operating procedure, as well as emotionally sensitive explanations.

[0335] Step 5:

[0336] The server sends a solution to the terminal. The terminal presents the solution to the user and helps the user understand the problem by providing video guides and audio instructions.

[0337] Step 6:

[0338] If a user adds new images or comments to their device while attempting to solve a problem, the information is sent back to the server. This allows for further analysis and feedback.

[0339] Step 7:

[0340] If a problem persists or a critical failure is detected, the server automatically transfers information to the management center. This data includes user sentiment analysis results.

[0341] Step 8:

[0342] After a user successfully resolves a problem, they input feedback into their device. This information is sent to a server, which evaluates the effectiveness of the solution. This feedback is then used for future improvements and database updates.

[0343] (Example 2)

[0344] 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".

[0345] When users need to quickly and accurately identify malfunctions or abnormalities in electronic devices and obtain effective solutions, it is essential to efficiently resolve the problem while considering the user's emotional state. Therefore, it is necessary to build a system that can reduce the user's psychological burden while accelerating problem resolution.

[0346] 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.

[0347] In this invention, the server includes means for receiving visual information acquired from the user, means for analyzing the received visual information and determining the operating state of the electronic device, and means for analyzing the user's emotional state using an emotion recognition engine. This makes it possible to generate and present solutions based on the user's emotional state.

[0348] "Visual information" refers to information that indicates the state of electronic devices, such as images and videos obtained from the user.

[0349] "Electronic devices" refer to electrical equipment used by users, such as computers and routers.

[0350] "Operating status" refers to the current operating status of an electronic device and includes normal, abnormal, and specific malfunctions.

[0351] An "emotion recognition engine" is a software component that analyzes user input data to determine their emotional state.

[0352] A "solution" is a prescribed course of action or procedure proposed to resolve a malfunction or abnormality in an electronic device.

[0353] A "control center" is a support organization that provides external expertise and technology to help resolve problems with electronic devices.

[0354] This invention provides a system for users to efficiently identify and quickly resolve malfunctions and abnormalities in electronic devices. This system mainly consists of terminals, servers, and software components that run on them.

[0355] When a user discovers an electronic device showing signs of malfunction, they use the terminal to photograph its condition. This terminal features a high-resolution camera and user interface, allowing for immediate transmission of the acquired visual information to a server. Specifically, the user enters a prompt message in the terminal's input field, such as, "The router's light is blinking red, and the connection is unstable. What should I do?" This prompt message, along with the captured image data, is then sent to the server.

[0356] The server analyzes the received data using an AI-based algorithm and diagnoses the operating status of electronic devices using a generative AI model. This process utilizes image analysis technology to diagnose based on the external characteristics of the electronic devices, the color of the lamps, the status of the display, etc. Furthermore, the server uses an emotion recognition engine to analyze the emotional state from the user's comments and voice data. This analysis detects negative emotions such as frustration and anxiety.

[0357] Based on the emotional state expressed by the user, the server adjusts how it presents solutions. Solutions are displayed as helpful and easy-to-understand text messages, and in some cases, voice guidance is also provided. This allows users to solve problems efficiently while reducing their emotional burden.

[0358] If the problem is serious and difficult to resolve on the server side, the server automatically transfers information to an external control center. This information includes not only data on the failure of the electronic device but also user sentiment data, enabling the control center to take more appropriate action.

[0359] As described above, the present invention combines AI technology and emotion recognition to support problem resolution and realize flexible service provision that is considerate of the user.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The user uses the terminal's camera to capture visual information indicating an abnormal state of an electronic device. At the same time, the user enters a prompt message into the terminal's input field. For example, "The router's light is blinking red." This information is sent to the server via the terminal's transmission function. The input consists of image data and a prompt message, while the output is the transmission of data to the server.

[0363] Step 2:

[0364] The server inputs the received visual information and prompt messages into an AI-based image analysis algorithm to determine the operating status of the electronic device. This analysis process detects lamp colors and display information within the image, identifying specific problems with the electronic device. The input is image data, and the output is a diagnostic result of the operating status.

[0365] Step 3:

[0366] The server uses an emotion recognition engine to analyze the user's emotions from the prompt text. Through text analysis, it determines whether the user is experiencing emotions such as frustration or anxiety. The input is the prompt text, and the output is the user's emotional state. This prepares the server to adjust how solutions are presented.

[0367] Step 4:

[0368] The server uses a generative AI model to generate solutions based on the operating state of electronic devices and the user's emotional state. For example, if a network problem is identified, the solution will include steps to verify the connection. Based on the results of the emotional analysis, the solution's explanation is adjusted to be more user-friendly and easy to understand. The input is the operating state and emotional state, and the output is the adjusted solution.

[0369] Step 5:

[0370] The server sends the generated solution to the terminal and presents it to the user. The solution is provided in text format or as an audio guide, and the user solves the problem by following these instructions. The input is the adjusted solution, and the output is the presentation of the solution to the user.

[0371] Step 6:

[0372] If the problem is severe and difficult to handle automatically, the server will transfer relevant information to an external control center. This information includes detailed fault data and user sentiment data. This allows external experts to provide more accurate support. The input is information about the problem that is difficult to resolve, and the output is the transfer of information to the control center.

[0373] (Application Example 2)

[0374] 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."

[0375] Users often find it difficult to quickly and accurately identify and resolve problems when they encounter malfunctions with electronic devices. Furthermore, depending on the user's emotional state, the method of presenting solutions may not be effective, potentially leading to decreased user satisfaction. To address these challenges, there is a need for effective systems that provide smoother support to users and reduce stress.

[0376] 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.

[0377] In this invention, the server includes means for receiving image data acquired from a user, means for analyzing the received image data to determine the operating state of an electronic device, and means for analyzing the user's emotional data and adjusting the method of presenting solutions according to the user's emotional state. This makes it possible to quickly identify problems with electronic devices and provide appropriate support according to the user's emotional state.

[0378] "Image data" refers to visual information that shows the operating status of an electronic device, acquired by a user using their device.

[0379] "Operating status of electronic equipment" refers to status information that indicates how electronic equipment is currently functioning, and includes indicator light colors and display screen content.

[0380] A "solution" refers to specific steps or instructions provided to improve the operational status of an electronic device.

[0381] "Emotional data" refers to information analyzed to understand a user's emotional state, and includes voice input, facial expressions, comments, and other data.

[0382] An "external management center" is a third-party organization that provides further support based on information automatically transferred from the server.

[0383] An "audio guide" is an audio guidance system designed to clearly communicate solutions to users, delivered in a tone that matches the user's emotions.

[0384] "Feedback" refers to information and opinions collected from users, which are used to evaluate the effectiveness of the solutions provided.

[0385] A system implementing this invention mainly consists of a user's terminal, a cloud server, and a responsive interface program.

[0386] The user's device is equipped with a camera for acquiring image data from electronic devices. The device has the function to send the image data captured by the user to a server. This transmitted image data is analyzed on a server in the cloud via the Google Cloud Vision API. This analysis determines the operating status of the electronic device.

[0387] The server also uses Google Cloud's Natural Language API to analyze sentiment data from voice input and comments obtained from users. This allows it to understand the user's emotional state and provide flexible responses if the user is showing signs of anxiety or frustration.

[0388] The solutions are generated in the cloud and presented after being adjusted according to the user's emotional state. For example, if a user reports a communication problem, the server provides an audio guide with a gentle tone of voice based on the emotional information. Furthermore, the solutions are designed to aid understanding, such as showing specific operating procedures with videos and diagrams.

[0389] This system includes a mechanism for collecting user feedback and evaluating the effectiveness of solutions. Based on this information, the solutions provided are continuously improved. For example, if a user reports a problem where "the device won't restart," the cloud server generates a guide, including specific restart steps, based on the analysis results, and provides instructions via both voice and text.

[0390] The following is an example of a prompt message to input into the generative AI model.

[0391] "A user has reported a communication failure with their electronic payment terminal. They have sent a picture of the terminal along with a frustrated expression and comment. Please generate a guide to alleviate the user's anxiety."

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The user takes an image of an electronic device using the terminal. This input data (image file) is sent to a cloud server via the terminal's interface. Specifically, the terminal converts the image to the appropriate resolution and format and sends it via the internet connection.

[0395] Step 2:

[0396] The server inputs the received image data into Google Cloud's Vision API to analyze the operating status of electronic devices. This analysis identifies features and lamp colors in the image to determine the operating status. This step outputs a list of identified operating statuses.

[0397] Step 3:

[0398] The user inputs comments and voice messages through the terminal. The terminal sends this input data to the server as text data. At this time, the terminal performs the specific action of converting voice data to text, packaging it in a usable format, and sending it.

[0399] Step 4:

[0400] The server processes the received comments and audio data through Google Cloud's Natural Language API to analyze the user's sentiment data. Based on this analysis, it outputs information indicating the user's emotional state. The server then starts generating prompts based on this output.

[0401] Step 5:

[0402] The server generates solutions based on the analyzed operational and emotional states. Using a generative AI model, it creates user-appropriate solutions based on prompt text. Specifically, it outputs a combination of voice guidance in a soft tone that matches the user's emotions and text containing specific operating instructions.

[0403] Step 6:

[0404] The server sends the generated solution to the terminal. The terminal provides audio guidance using its audio playback function and simultaneously displays the solution in text format on its screen. Specifically, it plays an audio file and displays the text in an easy-to-read format on the screen.

[0405] Step 7:

[0406] The user implements the suggested solution and, if necessary, sends feedback back to the server via their device. Based on this feedback, the server evaluates the effectiveness of the solution and uses it to make future improvements.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] This invention provides an embodiment of a system that enables users to identify and efficiently resolve malfunctions and operational abnormalities in electronic devices. The implementation method is described in detail below.

[0424] First, the user takes a picture of the malfunctioning device with their terminal to diagnose its condition using the system. This image includes screenshots of the device's indicator lights and error messages. The terminal then sends this image data to the server.

[0425] The server analyzes the received image data using AI-based algorithms. This allows it to determine the operating status of electronic devices from the color of lights, flashing patterns, and other visual characteristics. Based on this determination, the server identifies problems and generates solutions. These solutions include specific steps and guides in text and video formats.

[0426] The generated solutions are presented to the user via the terminal, allowing the user to quickly address the problem. The information presented includes video-based operation guides, making it easy for even users unfamiliar with the equipment to understand and implement the solutions.

[0427] Furthermore, if the problem persists or is deemed a serious failure, the server automatically forwards detailed information about the issue to an external management center. This allows the management center to monitor the overall network status and provide direct support as needed.

[0428] As a concrete example, consider a scenario where a user notices a red light illuminated, indicating a communication error. The user takes a picture of the situation, sends the image from their device to the server, and the server analyzes it. As a result, a video is presented showing the procedure for checking the cable's functionality and the specific meaning of the light. In this way, the user can quickly and efficiently resolve equipment problems.

[0429] Through the embodiments described above, the present invention provides a system that enables users to quickly address a variety of problems that occur in electronic devices without requiring specialized knowledge.

[0430] The following describes the processing flow.

[0431] Step 1:

[0432] The user uses the device to check for malfunctions or abnormalities in electronic devices and to photograph their condition. They take photos or screenshots and prepare them as image data.

[0433] Step 2:

[0434] The device sends image data captured by the user to the server. A communication protocol is used to ensure security and speed during transmission.

[0435] Step 3:

[0436] The server acquires the received image data and begins analysis using an AI algorithm. It analyzes visual information such as the color and flashing pattern of the lights and error messages displayed on the screen.

[0437] Step 4:

[0438] Based on the analysis results, the server determines the current operating status of the electronic devices and the cause of any malfunctions.

[0439] Step 5:

[0440] The server generates a solution to the problem. The solution includes a text guide with detailed instructions and specific operating procedures, as well as video links.

[0441] Step 6:

[0442] The server sends the generated solution to the terminal. The terminal receives it and presents the solution to the user. The user can then resolve the problem by following the instructions.

[0443] Step 7:

[0444] If the problem persists or the server detects a critical failure, it automatically forwards information to an external management center. This forwarding includes a detailed failure report.

[0445] Step 8:

[0446] The user enters the results of their attempted solutions on their device. The device sends this feedback to the server, which uses the information to determine if further assistance is needed.

[0447] (Example 1)

[0448] 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."

[0449] There is a need to quickly and efficiently identify and resolve malfunctions and abnormalities in electronic equipment. In particular, there is a need for users without specialized technical knowledge to be able to understand the status of the equipment based on visual information and take appropriate action. Conventional systems have made it difficult to provide accurate diagnoses and effective solutions, and problem resolution has sometimes been time-consuming.

[0450] 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.

[0451] In this invention, the server includes means for receiving image information acquired from a user, means for analyzing the received image information and determining the operating state of the device, and means for generating and presenting a solution guideline based on the operating state. This enables users, even without specialized knowledge, to quickly diagnose malfunctions in electronic devices and effectively resolve problems through the presented solution guideline.

[0452] A "user" is the entity that identifies and resolves malfunctions and operational abnormalities in electronic devices by using the system.

[0453] "Image information" refers to visual data captured by a user using their device to indicate the status of an electronic device.

[0454] "Means of receiving" refers to the function that allows the server to acquire image information sent by the user.

[0455] "Means of analysis and judgment" refers to processing functions that use machine learning algorithms and the like to determine the operating state of electronic devices based on received image information.

[0456] "Solution guidelines" refer to information that provides specific solutions and guidelines for identified problems in text and video formats.

[0457] The "automatic forwarding mechanism" refers to a function that automatically sends resolution guidelines and incident information, generated according to their importance, to an external management organization.

[0458] A "generative AI model" is an artificial intelligence technology used to analyze received image information and generate the type of problem and its solution.

[0459] A "prompt sentence" is an input sentence given to a generative AI model, containing instructions that enable the model to generate appropriate information for problem solving.

[0460] This invention provides a system for users to identify and quickly and efficiently resolve malfunctions and operational abnormalities that occur in electronic devices. Specific embodiments are described below.

[0461] The user first uses a smartphone or tablet to capture images of the electronic device in question to understand its status. These images may include indicator lights, error messages, and other visual information. The device then sends the captured images to a server. This transmission takes place over the internet, utilizing Wi-Fi or mobile data communication technologies.

[0462] The server analyzes the received image information using an AI-based algorithm. This analysis utilizes a generative AI model to identify the color, blinking pattern, and textual information of the lamps in the image, and to determine the type and scope of the problem. Based on this determination, the server generates a solution guide. This guide includes a detailed analysis of the problem, necessary steps, and even a video guide.

[0463] The generated troubleshooting guidelines are presented to the user via the terminal. Based on this information, the user can resolve the device problem by following the instructions. The video guides are designed to be easy for even unfamiliar users to understand, and they visually demonstrate specific steps.

[0464] Furthermore, if the problem remains unresolved or if it is determined that further assistance is needed, the server automatically forwards the information to an external management organization. This allows the management organization to monitor network-wide failures and provide expert support as needed.

[0465] As a concrete example, if a user experiences a communication error and notices a red light flashing, they can take a picture of the situation with their device. The image is sent to a server, which analyzes it and provides a video tutorial explaining how to check cable connections and the specific meaning of the light. This process allows the user to identify the problem and quickly resolve it.

[0466] As an example of a prompt, by inputting a text-based instruction such as "Please tell me what the red light means when a communication error occurs" into the AI ​​model, it is possible to obtain information that is useful for solving the problem.

[0467] Thus, the invention provides a system that allows users to efficiently resolve problems in electronic devices without requiring specialized technical knowledge.

[0468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0469] Step 1:

[0470] The user takes images of the malfunctioning electronic device with their device. The device generates image data, including screenshots of indicator lights and error messages, as input. The device then sends this image data to the server via Wi-Fi or a mobile network.

[0471] Step 2:

[0472] The server receives image data sent from the terminal. It takes the image data as input and uses image processing algorithms to extract features such as lamp color, blinking pattern, and error messages. As output, it generates feature data to be passed to a machine learning model.

[0473] Step 3:

[0474] The server uses a generative AI model to analyze feature data and determine the operating state of electronic devices. This process involves data calculations based on the input feature data to identify the type and scope of the problem. The output generates the identified problem and its solution guidelines.

[0475] Step 4:

[0476] The server generates solution guidelines and sends them to the terminal in text and video formats. Using data on the identified problem and its solution as input, it creates an easy-to-understand guide. The output provided to the user includes a text guide showing the operating procedures and a video guide for visual support.

[0477] Step 5:

[0478] The terminal displays the user with the solution guidelines received from the server. The user reviews the displayed information and corrects the problem with the electronic device according to the suggested solution. Here, the input is the solution guidelines received from the server, and the output is the specific steps the user takes to actually perform the action.

[0479] Step 6:

[0480] If a problem remains unresolved or a critical failure is detected, the server automatically forwards detailed information about the problem to an external management authority. This step takes unresolved problem information as input and generates detailed data accessible to the management authority as output, thereby providing more advanced support.

[0481] (Application Example 1)

[0482] 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."

[0483] When machinery and robots used in factories malfunction, on-site workers are required to quickly and accurately detect the abnormality and take appropriate countermeasures. However, with conventional systems, it is difficult for workers without specialized knowledge to identify the problem and find a solution. A system is needed to solve this problem and improve work efficiency.

[0484] 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.

[0485] In this invention, the server includes means for receiving visual data acquired from a user, means for analyzing the received visual data to determine the operating state of a machine, and means for generating and presenting a solution based on the operating state. This enables field workers to intuitively and quickly perform everything from detecting anomalies to obtaining solutions through a visual device.

[0486] "Visual data" refers to information in still image or video format acquired by a user using a visual device, intended to understand the status of a machine or device.

[0487] "Mechanical equipment" refers to devices and robots used in industrial settings such as factories, which perform specific tasks through their actions.

[0488] "Operating status" refers to information indicating the current operating status and presence or absence of abnormalities in machinery and equipment, and is necessary to confirm the normal operation of the equipment.

[0489] A "solution" refers to the specific procedures and methods provided to correct malfunctions in machinery and restore them to normal operation.

[0490] A "visual device" is a device that, when worn by a user, can acquire video information and display the analysis results. It is used for anomaly detection in work sites.

[0491] An "external management body" refers to an organization or system that monitors the status of machinery and equipment and provides support as needed, and can receive information via a network.

[0492] "Audio and visual guides" are features that use sound and visual information to inform users how to proceed with a task, helping them understand the solution.

[0493] To implement this invention, a system utilizing a visual device, a server, and an AI model is required. First, the user wears the visual device and observes the mechanical device. The visual device is equipped with a high-performance camera and can acquire visual data indicating normal operation or abnormalities. The acquired visual data is transmitted to the server in real time.

[0494] The server analyzes received visual data using AI models suitable for image recognition and anomaly detection. This analysis utilizes deep learning frameworks such as TensorFlow and PyTorch to accurately determine the operating state of mechanical devices. Based on the identified anomalies, the server generates solutions. These solutions include information to be presented as audio and visual guides.

[0495] The solution is then transmitted to a visual device, and the user is shown the solution digitally along with voice instructions. The user proceeds with the work according to these instructions and can, if necessary, collaborate with external management organizations for support.

[0496] As a concrete example, consider a case where a malfunction occurs in the arm of a factory robot. The user uses a vision device to take an image of the arm and sends the data to a server. An AI model identifies the malfunction and provides the user with a video and step-by-step audio guide showing how to repair the worn joint.

[0497] An example of a prompt message for a generated AI model is, "Analyze the image of the joint of the factory robot, identify the anomaly, and instruct it on the repair procedure." In this way, users can solve problems quickly and accurately, even without high technical knowledge.

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] The user wears a visual device and photographs the target part of the mechanical equipment. The input includes visual data, i.e., still or video images. By acquiring this data, the status of the equipment on site can be visually understood. The output is the acquired visual data itself.

[0501] Step 2:

[0502] The terminal sends the acquired visual data to the server. The input includes the visual data acquired by the user. By sending the data to the server using a data transfer protocol, the device status analysis begins. The output is the completion of data transmission to the server.

[0503] Step 3:

[0504] The server analyzes the received visual data using an AI model. The input includes visual data received from the terminal. This involves image recognition and anomaly detection, with frameworks such as TensorFlow or PyTorch used for data processing and analysis. The output is the analysis result, i.e., a determination of the operating state of the machine or device.

[0505] Step 4:

[0506] The server generates solutions to mechanical device malfunctions based on the analysis results. The analysis results from step 3 are used as input. The solutions are processed into a format that can be presented via audio or visual guides. The output is solution information to be presented to the user.

[0507] Step 5:

[0508] The server sends the generated solution to the terminal and presents it to the user through a visual device. The input is the solution information generated in step 4. The solution returned to the terminal is displayed on the visual device in both audio and visual formats, providing support in a format that is easy for the user to understand. The output is the solution received by the user.

[0509] Step 6:

[0510] The user addresses the machine's problem by following the provided solutions. Specific actions include following audio guidance and replacing parts by referring to visual guides. The inputs are the provided solution information and the user's actions, while the output is the resolution of the machine's problem.

[0511] 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.

[0512] This invention relates to a system for users to identify and quickly and efficiently resolve malfunctions and anomalies in electronic devices, and is particularly characterized by its incorporation of an emotion engine. This system consists of a terminal, a server, and a program running on it.

[0513] When a user detects a malfunction in an electronic device, they use a terminal to take an image showing the device's status and send it to the server. The terminal is equipped with an interface to smoothly process user input and communicate with the server.

[0514] The server analyzes received images and uses AI-based algorithms to determine the operating status of electronic devices. This analysis takes into account factors such as the status of the device's indicator lights and the content of the display screen. Furthermore, an emotion engine makes it possible to recognize user emotions from comments and feedback entered by the user, as well as from voice input and images.

[0515] The emotion engine has the ability to adjust how solutions are presented when it recognizes that the user's emotions are negative, such as frustration or anxiety. For example, it reduces the user's mental burden by making the solution text more helpful and polite, or by providing reassuring voice guidance.

[0516] The generated solutions are sent to the terminal and presented to the user. The information presented includes video guides to help the user quickly resolve the device problem. If the problem is serious and difficult to resolve on the server side, the server automatically transfers the data to an external management center to request further assistance. In this process, the user's emotional information is also transmitted, enabling the management center to provide a more appropriate response to the user.

[0517] For example, if a user reports a communication problem with a device, they can take a video of the situation and send it to the server. The server will then detect that a light is red and instruct the user to check the network connection. Furthermore, if the user leaves a comment expressing frustration, the emotion engine will detect this and provide a voice guide that gently explains the document to help the user understand it.

[0518] This embodiment allows the present invention to provide a rapid and effective solution to various problems faced by users, while also realizing a service that takes users' feelings into consideration.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] The user takes a picture of the problematic electronic device with their device. The device prepares the captured image data and sends it to the server.

[0522] Step 2:

[0523] The server analyzes the received image data. Using AI algorithms, it recognizes the status of the device's indicator lights and displayed messages to determine its operating status.

[0524] Step 3:

[0525] The server uses an emotion engine to analyze emotions from user-submitted comments and voice input. For example, it can detect nuances of frustration or anxiety from text.

[0526] Step 4:

[0527] The server generates an appropriate solution based on the analysis of its operational status and the user's emotional state. This solution includes text or videos illustrating the operating procedure, as well as emotionally sensitive explanations.

[0528] Step 5:

[0529] The server sends a solution to the terminal. The terminal presents the solution to the user and helps the user understand the problem by providing video guides and audio instructions.

[0530] Step 6:

[0531] If a user adds new images or comments to their device while attempting to solve a problem, the information is sent back to the server. This allows for further analysis and feedback.

[0532] Step 7:

[0533] If a problem persists or a critical failure is detected, the server automatically transfers information to the management center. This data includes user sentiment analysis results.

[0534] Step 8:

[0535] After a user successfully resolves a problem, they input feedback into their device. This information is sent to a server, which evaluates the effectiveness of the solution. This feedback is then used for future improvements and database updates.

[0536] (Example 2)

[0537] 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."

[0538] When users need to quickly and accurately identify malfunctions or abnormalities in electronic devices and obtain effective solutions, it is essential to efficiently resolve the problem while considering the user's emotional state. Therefore, it is necessary to build a system that can reduce the user's psychological burden while accelerating problem resolution.

[0539] 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.

[0540] In this invention, the server includes means for receiving visual information acquired from the user, means for analyzing the received visual information and determining the operating state of the electronic device, and means for analyzing the user's emotional state using an emotion recognition engine. This makes it possible to generate and present solutions based on the user's emotional state.

[0541] "Visual information" refers to information that indicates the state of electronic devices, such as images and videos obtained from the user.

[0542] "Electronic devices" refer to electrical equipment used by users, such as computers and routers.

[0543] "Operating status" refers to the current operating status of an electronic device and includes normal, abnormal, and specific malfunctions.

[0544] An "emotion recognition engine" is a software component that analyzes user input data to determine their emotional state.

[0545] A "solution" is a prescribed course of action or procedure proposed to resolve a malfunction or abnormality in an electronic device.

[0546] A "control center" is a support organization that provides external expertise and technology to help resolve problems with electronic devices.

[0547] This invention provides a system for users to efficiently identify and quickly resolve malfunctions and abnormalities in electronic devices. This system mainly consists of terminals, servers, and software components that run on them.

[0548] When a user discovers an electronic device showing signs of malfunction, they use the terminal to photograph its condition. This terminal features a high-resolution camera and user interface, allowing for immediate transmission of the acquired visual information to a server. Specifically, the user enters a prompt message in the terminal's input field, such as, "The router's light is blinking red, and the connection is unstable. What should I do?" This prompt message, along with the captured image data, is then sent to the server.

[0549] The server analyzes the received data using an AI-based algorithm and diagnoses the operating status of electronic devices using a generative AI model. This process utilizes image analysis technology to diagnose based on the external characteristics of the electronic devices, the color of the lamps, the status of the display, etc. Furthermore, the server uses an emotion recognition engine to analyze the emotional state from the user's comments and voice data. This analysis detects negative emotions such as frustration and anxiety.

[0550] Based on the emotional state expressed by the user, the server adjusts how it presents solutions. Solutions are displayed as helpful and easy-to-understand text messages, and in some cases, voice guidance is also provided. This allows users to solve problems efficiently while reducing their emotional burden.

[0551] If the problem is serious and difficult to resolve on the server side, the server automatically transfers information to an external control center. This information includes not only data on the failure of the electronic device but also user sentiment data, enabling the control center to take more appropriate action.

[0552] As described above, the present invention combines AI technology and emotion recognition to support problem resolution and realize flexible service provision that is considerate of the user.

[0553] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0554] Step 1:

[0555] The user uses the terminal's camera to capture visual information indicating an abnormal state of an electronic device. At the same time, the user enters a prompt message into the terminal's input field. For example, "The router's light is blinking red." This information is sent to the server via the terminal's transmission function. The input consists of image data and a prompt message, while the output is the transmission of data to the server.

[0556] Step 2:

[0557] The server inputs the received visual information and prompt messages into an AI-based image analysis algorithm to determine the operating status of the electronic device. This analysis process detects lamp colors and display information within the image, identifying specific problems with the electronic device. The input is image data, and the output is a diagnostic result of the operating status.

[0558] Step 3:

[0559] The server uses an emotion recognition engine to analyze the user's emotions from the prompt text. Through text analysis, it determines whether the user is experiencing emotions such as frustration or anxiety. The input is the prompt text, and the output is the user's emotional state. This prepares the server to adjust how solutions are presented.

[0560] Step 4:

[0561] The server uses a generative AI model to generate solutions based on the operating state of electronic devices and the user's emotional state. For example, if a network problem is identified, the solution will include steps to verify the connection. Based on the results of the emotional analysis, the solution's explanation is adjusted to be more user-friendly and easy to understand. The input is the operating state and emotional state, and the output is the adjusted solution.

[0562] Step 5:

[0563] The server sends the generated solution to the terminal and presents it to the user. The solution is provided in text format or as an audio guide, and the user solves the problem by following these instructions. The input is the adjusted solution, and the output is the presentation of the solution to the user.

[0564] Step 6:

[0565] If the problem is severe and difficult to handle automatically, the server will transfer relevant information to an external control center. This information includes detailed fault data and user sentiment data. This allows external experts to provide more accurate support. The input is information about the problem that is difficult to resolve, and the output is the transfer of information to the control center.

[0566] (Application Example 2)

[0567] 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."

[0568] Users often find it difficult to quickly and accurately identify and resolve problems when they encounter malfunctions with electronic devices. Furthermore, depending on the user's emotional state, the method of presenting solutions may not be effective, potentially leading to decreased user satisfaction. To address these challenges, there is a need for effective systems that provide smoother support to users and reduce stress.

[0569] 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.

[0570] In this invention, the server includes means for receiving image data acquired from a user, means for analyzing the received image data to determine the operating state of an electronic device, and means for analyzing the user's emotional data and adjusting the method of presenting solutions according to the user's emotional state. This makes it possible to quickly identify problems with electronic devices and provide appropriate support according to the user's emotional state.

[0571] "Image data" refers to visual information that shows the operating status of an electronic device, acquired by a user using their device.

[0572] "Operating status of electronic equipment" refers to status information that indicates how electronic equipment is currently functioning, and includes indicator light colors and display screen content.

[0573] A "solution" refers to specific steps or instructions provided to improve the operational status of an electronic device.

[0574] "Emotional data" refers to information analyzed to understand a user's emotional state, and includes voice input, facial expressions, comments, and other data.

[0575] An "external management center" is a third-party organization that provides further support based on information automatically transferred from the server.

[0576] An "audio guide" is an audio guidance system designed to clearly communicate solutions to users, delivered in a tone that matches the user's emotions.

[0577] "Feedback" refers to information and opinions collected from users, which are used to evaluate the effectiveness of the solutions provided.

[0578] A system implementing this invention mainly consists of a user's terminal, a cloud server, and a responsive interface program.

[0579] The user's device is equipped with a camera for acquiring image data from electronic devices. The device has the function to send the image data captured by the user to a server. This transmitted image data is analyzed on a server in the cloud via the Google Cloud Vision API. This analysis determines the operating status of the electronic device.

[0580] The server also uses Google Cloud's Natural Language API to analyze sentiment data from voice input and comments obtained from users. This allows it to understand the user's emotional state and provide flexible responses if the user is showing signs of anxiety or frustration.

[0581] The solutions are generated in the cloud and presented after being adjusted according to the user's emotional state. For example, if a user reports a communication problem, the server provides an audio guide with a gentle tone of voice based on the emotional information. Furthermore, the solutions are designed to aid understanding, such as showing specific operating procedures with videos and diagrams.

[0582] This system includes a mechanism for collecting user feedback and evaluating the effectiveness of solutions. Based on this information, the solutions provided are continuously improved. For example, if a user reports a problem where "the device won't restart," the cloud server generates a guide, including specific restart steps, based on the analysis results, and provides instructions via both voice and text.

[0583] The following is an example of a prompt message to input into the generative AI model.

[0584] "A user has reported a communication failure with their electronic payment terminal. They have sent a picture of the terminal along with a frustrated expression and comment. Please generate a guide to alleviate the user's anxiety."

[0585] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0586] Step 1:

[0587] The user takes an image of an electronic device using the terminal. This input data (image file) is sent to a cloud server via the terminal's interface. Specifically, the terminal converts the image to the appropriate resolution and format and sends it via the internet connection.

[0588] Step 2:

[0589] The server inputs the received image data into Google Cloud's Vision API to analyze the operating status of electronic devices. This analysis identifies features and lamp colors in the image to determine the operating status. This step outputs a list of identified operating statuses.

[0590] Step 3:

[0591] The user inputs comments and voice messages through the terminal. The terminal sends this input data to the server as text data. At this time, the terminal performs the specific action of converting voice data to text, packaging it in a usable format, and sending it.

[0592] Step 4:

[0593] The server processes the received comments and audio data through Google Cloud's Natural Language API to analyze the user's sentiment data. Based on this analysis, it outputs information indicating the user's emotional state. The server then starts generating prompts based on this output.

[0594] Step 5:

[0595] The server generates solutions based on the analyzed operational and emotional states. Using a generative AI model, it creates user-appropriate solutions based on prompt text. Specifically, it outputs a combination of voice guidance in a soft tone that matches the user's emotions and text containing specific operating instructions.

[0596] Step 6:

[0597] The server sends the generated solution to the terminal. The terminal provides audio guidance using its audio playback function and simultaneously displays the solution in text format on its screen. Specifically, it plays an audio file and displays the text in an easy-to-read format on the screen.

[0598] Step 7:

[0599] The user implements the suggested solution and, if necessary, sends feedback back to the server via their device. Based on this feedback, the server evaluates the effectiveness of the solution and uses it to make future improvements.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] [Fourth Embodiment]

[0604] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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".

[0617] This invention provides an embodiment of a system that enables users to identify and efficiently resolve malfunctions and operational abnormalities in electronic devices. The implementation method is described in detail below.

[0618] First, the user takes a picture of the malfunctioning device with their terminal to diagnose its condition using the system. This image includes screenshots of the device's indicator lights and error messages. The terminal then sends this image data to the server.

[0619] The server analyzes the received image data using AI-based algorithms. This allows it to determine the operating status of electronic devices from the color of lights, flashing patterns, and other visual characteristics. Based on this determination, the server identifies problems and generates solutions. These solutions include specific steps and guides in text and video formats.

[0620] The generated solutions are presented to the user via the terminal, allowing the user to quickly address the problem. The information presented includes video-based operation guides, making it easy for even users unfamiliar with the equipment to understand and implement the solutions.

[0621] Furthermore, if the problem persists or is deemed a serious failure, the server automatically forwards detailed information about the issue to an external management center. This allows the management center to monitor the overall network status and provide direct support as needed.

[0622] As a concrete example, consider a scenario where a user notices a red light illuminated, indicating a communication error. The user takes a picture of the situation, sends the image from their device to the server, and the server analyzes it. As a result, a video is presented showing the procedure for checking the cable's functionality and the specific meaning of the light. In this way, the user can quickly and efficiently resolve equipment problems.

[0623] Through the embodiments described above, the present invention provides a system that enables users to quickly address a variety of problems that occur in electronic devices without requiring specialized knowledge.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The user uses the device to check for malfunctions or abnormalities in electronic devices and to photograph their condition. They take photos or screenshots and prepare them as image data.

[0627] Step 2:

[0628] The device sends image data captured by the user to the server. A communication protocol is used to ensure security and speed during transmission.

[0629] Step 3:

[0630] The server acquires the received image data and begins analysis using an AI algorithm. It analyzes visual information such as the color and flashing pattern of the lights and error messages displayed on the screen.

[0631] Step 4:

[0632] Based on the analysis results, the server determines the current operating status of the electronic devices and the cause of any malfunctions.

[0633] Step 5:

[0634] The server generates a solution to the problem. The solution includes a text guide with detailed instructions and specific operating procedures, as well as video links.

[0635] Step 6:

[0636] The server sends the generated solution to the terminal. The terminal receives it and presents the solution to the user. The user can then resolve the problem by following the instructions.

[0637] Step 7:

[0638] If the problem persists or the server detects a critical failure, it automatically forwards information to an external management center. This forwarding includes a detailed failure report.

[0639] Step 8:

[0640] The user enters the results of their attempted solutions on their device. The device sends this feedback to the server, which uses the information to determine if further assistance is needed.

[0641] (Example 1)

[0642] 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".

[0643] There is a need to quickly and efficiently identify and resolve malfunctions and abnormalities in electronic equipment. In particular, there is a need for users without specialized technical knowledge to be able to understand the status of the equipment based on visual information and take appropriate action. Conventional systems have made it difficult to provide accurate diagnoses and effective solutions, and problem resolution has sometimes been time-consuming.

[0644] 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.

[0645] In this invention, the server includes means for receiving image information acquired from a user, means for analyzing the received image information and determining the operating state of the device, and means for generating and presenting a solution guideline based on the operating state. This enables users, even without specialized knowledge, to quickly diagnose malfunctions in electronic devices and effectively resolve problems through the presented solution guideline.

[0646] A "user" is the entity that identifies and resolves malfunctions and operational abnormalities in electronic devices by using the system.

[0647] "Image information" refers to visual data captured by a user using their device to indicate the status of an electronic device.

[0648] "Means of receiving" refers to the function that allows the server to acquire image information sent by the user.

[0649] "Means of analysis and judgment" refers to processing functions that use machine learning algorithms and the like to determine the operating state of electronic devices based on received image information.

[0650] "Solution guidelines" refer to information that provides specific solutions and guidelines for identified problems in text and video formats.

[0651] The "automatic forwarding mechanism" refers to a function that automatically sends resolution guidelines and incident information, generated according to their importance, to an external management organization.

[0652] A "generative AI model" is an artificial intelligence technology used to analyze received image information and generate the type of problem and its solution.

[0653] A "prompt sentence" is an input sentence given to a generative AI model, containing instructions that enable the model to generate appropriate information for problem solving.

[0654] This invention provides a system for users to identify and quickly and efficiently resolve malfunctions and operational abnormalities that occur in electronic devices. Specific embodiments are described below.

[0655] The user first uses a smartphone or tablet to capture images of the electronic device in question to understand its status. These images may include indicator lights, error messages, and other visual information. The device then sends the captured images to a server. This transmission takes place over the internet, utilizing Wi-Fi or mobile data communication technologies.

[0656] The server analyzes the received image information using an AI-based algorithm. This analysis utilizes a generative AI model to identify the color, blinking pattern, and textual information of the lamps in the image, and to determine the type and scope of the problem. Based on this determination, the server generates a solution guide. This guide includes a detailed analysis of the problem, necessary steps, and even a video guide.

[0657] The generated troubleshooting guidelines are presented to the user via the terminal. Based on this information, the user can resolve the device problem by following the instructions. The video guides are designed to be easy for even unfamiliar users to understand, and they visually demonstrate specific steps.

[0658] Furthermore, if the problem remains unresolved or if it is determined that further assistance is needed, the server automatically forwards the information to an external management organization. This allows the management organization to monitor network-wide failures and provide expert support as needed.

[0659] As a concrete example, if a user experiences a communication error and notices a red light flashing, they can take a picture of the situation with their device. The image is sent to a server, which analyzes it and provides a video tutorial explaining how to check cable connections and the specific meaning of the light. This process allows the user to identify the problem and quickly resolve it.

[0660] As an example of a prompt, by inputting a text-based instruction such as "Please tell me what the red light means when a communication error occurs" into the AI ​​model, it is possible to obtain information that is useful for solving the problem.

[0661] Thus, the invention provides a system that allows users to efficiently resolve problems in electronic devices without requiring specialized technical knowledge.

[0662] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0663] Step 1:

[0664] The user takes images of the malfunctioning electronic device with their device. The device generates image data, including screenshots of indicator lights and error messages, as input. The device then sends this image data to the server via Wi-Fi or a mobile network.

[0665] Step 2:

[0666] The server receives image data sent from the terminal. It takes the image data as input and uses image processing algorithms to extract features such as lamp color, blinking pattern, and error messages. As output, it generates feature data to be passed to a machine learning model.

[0667] Step 3:

[0668] The server uses a generative AI model to analyze feature data and determine the operating state of electronic devices. This process involves data calculations based on the input feature data to identify the type and scope of the problem. The output generates the identified problem and its solution guidelines.

[0669] Step 4:

[0670] The server generates solution guidelines and sends them to the terminal in text and video formats. Using data on the identified problem and its solution as input, it creates an easy-to-understand guide. The output provided to the user includes a text guide showing the operating procedures and a video guide for visual support.

[0671] Step 5:

[0672] The terminal displays the user with the solution guidelines received from the server. The user reviews the displayed information and corrects the problem with the electronic device according to the suggested solution. Here, the input is the solution guidelines received from the server, and the output is the specific steps the user takes to actually perform the action.

[0673] Step 6:

[0674] If a problem remains unresolved or a critical failure is detected, the server automatically forwards detailed information about the problem to an external management authority. This step takes unresolved problem information as input and generates detailed data accessible to the management authority as output, thereby providing more advanced support.

[0675] (Application Example 1)

[0676] 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".

[0677] When machinery and robots used in factories malfunction, on-site workers are required to quickly and accurately detect the abnormality and take appropriate countermeasures. However, with conventional systems, it is difficult for workers without specialized knowledge to identify the problem and find a solution. A system is needed to solve this problem and improve work efficiency.

[0678] 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.

[0679] In this invention, the server includes means for receiving visual data acquired from a user, means for analyzing the received visual data to determine the operating state of a machine, and means for generating and presenting a solution based on the operating state. This enables field workers to intuitively and quickly perform everything from detecting anomalies to obtaining solutions through a visual device.

[0680] "Visual data" refers to information in still image or video format acquired by a user using a visual device, intended to understand the status of a machine or device.

[0681] "Mechanical equipment" refers to devices and robots used in industrial settings such as factories, which perform specific tasks through their actions.

[0682] "Operating status" refers to information indicating the current operating status and presence or absence of abnormalities in machinery and equipment, and is necessary to confirm the normal operation of the equipment.

[0683] A "solution" refers to the specific procedures and methods provided to correct malfunctions in machinery and restore them to normal operation.

[0684] A "visual device" is a device that, when worn by a user, can acquire video information and display the analysis results. It is used for anomaly detection in work sites.

[0685] An "external management body" refers to an organization or system that monitors the status of machinery and equipment and provides support as needed, and can receive information via a network.

[0686] "Audio and visual guides" are features that use sound and visual information to inform users how to proceed with a task, helping them understand the solution.

[0687] To implement this invention, a system utilizing a visual device, a server, and an AI model is required. First, the user wears the visual device and observes the mechanical device. The visual device is equipped with a high-performance camera and can acquire visual data indicating normal operation or abnormalities. The acquired visual data is transmitted to the server in real time.

[0688] The server analyzes received visual data using AI models suitable for image recognition and anomaly detection. This analysis utilizes deep learning frameworks such as TensorFlow and PyTorch to accurately determine the operating state of mechanical devices. Based on the identified anomalies, the server generates solutions. These solutions include information to be presented as audio and visual guides.

[0689] The solution is then transmitted to a visual device, and the user is shown the solution digitally along with voice instructions. The user proceeds with the work according to these instructions and can, if necessary, collaborate with external management organizations for support.

[0690] As a concrete example, consider a case where a malfunction occurs in the arm of a factory robot. The user uses a vision device to take an image of the arm and sends the data to a server. An AI model identifies the malfunction and provides the user with a video and step-by-step audio guide showing how to repair the worn joint.

[0691] An example of a prompt message for a generated AI model is, "Analyze the image of the joint of the factory robot, identify the anomaly, and instruct it on the repair procedure." In this way, users can solve problems quickly and accurately, even without high technical knowledge.

[0692] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0693] Step 1:

[0694] The user wears a visual device and photographs the target part of the mechanical equipment. The input includes visual data, i.e., still or video images. By acquiring this data, the status of the equipment on site can be visually understood. The output is the acquired visual data itself.

[0695] Step 2:

[0696] The terminal sends the acquired visual data to the server. The input includes the visual data acquired by the user. By sending the data to the server using a data transfer protocol, the device status analysis begins. The output is the completion of data transmission to the server.

[0697] Step 3:

[0698] The server analyzes the received visual data using an AI model. The input includes visual data received from the terminal. This involves image recognition and anomaly detection, with frameworks such as TensorFlow or PyTorch used for data processing and analysis. The output is the analysis result, i.e., a determination of the operating state of the machine or device.

[0699] Step 4:

[0700] The server generates solutions to mechanical device malfunctions based on the analysis results. The analysis results from step 3 are used as input. The solutions are processed into a format that can be presented via audio or visual guides. The output is solution information to be presented to the user.

[0701] Step 5:

[0702] The server sends the generated solution to the terminal and presents it to the user through a visual device. The input is the solution information generated in step 4. The solution returned to the terminal is displayed on the visual device in both audio and visual formats, providing support in a format that is easy for the user to understand. The output is the solution received by the user.

[0703] Step 6:

[0704] The user addresses the machine's problem by following the provided solutions. Specific actions include following audio guidance and replacing parts by referring to visual guides. The inputs are the provided solution information and the user's actions, while the output is the resolution of the machine's problem.

[0705] 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.

[0706] This invention relates to a system for users to identify and quickly and efficiently resolve malfunctions and anomalies in electronic devices, and is particularly characterized by its incorporation of an emotion engine. This system consists of a terminal, a server, and a program running on it.

[0707] When a user detects a malfunction in an electronic device, they use a terminal to take an image showing the device's status and send it to the server. The terminal is equipped with an interface to smoothly process user input and communicate with the server.

[0708] The server analyzes received images and uses AI-based algorithms to determine the operating status of electronic devices. This analysis takes into account factors such as the status of the device's indicator lights and the content of the display screen. Furthermore, an emotion engine makes it possible to recognize user emotions from comments and feedback entered by the user, as well as from voice input and images.

[0709] The emotion engine has the ability to adjust how solutions are presented when it recognizes that the user's emotions are negative, such as frustration or anxiety. For example, it reduces the user's mental burden by making the solution text more helpful and polite, or by providing reassuring voice guidance.

[0710] The generated solutions are sent to the terminal and presented to the user. The information presented includes video guides to help the user quickly resolve the device problem. If the problem is serious and difficult to resolve on the server side, the server automatically transfers the data to an external management center to request further assistance. In this process, the user's emotional information is also transmitted, enabling the management center to provide a more appropriate response to the user.

[0711] For example, if a user reports a communication problem with a device, they can take a video of the situation and send it to the server. The server will then detect that a light is red and instruct the user to check the network connection. Furthermore, if the user leaves a comment expressing frustration, the emotion engine will detect this and provide a voice guide that gently explains the document to help the user understand it.

[0712] This embodiment allows the present invention to provide a rapid and effective solution to various problems faced by users, while also realizing a service that takes users' feelings into consideration.

[0713] The following describes the processing flow.

[0714] Step 1:

[0715] The user takes a picture of the problematic electronic device with their device. The device prepares the captured image data and sends it to the server.

[0716] Step 2:

[0717] The server analyzes the received image data. Using AI algorithms, it recognizes the status of the device's indicator lights and displayed messages to determine its operating status.

[0718] Step 3:

[0719] The server uses an emotion engine to analyze emotions from user-submitted comments and voice input. For example, it can detect nuances of frustration or anxiety from text.

[0720] Step 4:

[0721] The server generates an appropriate solution based on the analysis of its operational status and the user's emotional state. This solution includes text or videos illustrating the operating procedure, as well as emotionally sensitive explanations.

[0722] Step 5:

[0723] The server sends a solution to the terminal. The terminal presents the solution to the user and helps the user understand the problem by providing video guides and audio instructions.

[0724] Step 6:

[0725] If a user adds new images or comments to their device while attempting to solve a problem, the information is sent back to the server. This allows for further analysis and feedback.

[0726] Step 7:

[0727] If a problem persists or a critical failure is detected, the server automatically transfers information to the management center. This data includes user sentiment analysis results.

[0728] Step 8:

[0729] After a user successfully resolves a problem, they input feedback into their device. This information is sent to a server, which evaluates the effectiveness of the solution. This feedback is then used for future improvements and database updates.

[0730] (Example 2)

[0731] 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".

[0732] When users need to quickly and accurately identify malfunctions or abnormalities in electronic devices and obtain effective solutions, it is essential to efficiently resolve the problem while considering the user's emotional state. Therefore, it is necessary to build a system that can reduce the user's psychological burden while accelerating problem resolution.

[0733] 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.

[0734] In this invention, the server includes means for receiving visual information acquired from the user, means for analyzing the received visual information and determining the operating state of the electronic device, and means for analyzing the user's emotional state using an emotion recognition engine. This makes it possible to generate and present solutions based on the user's emotional state.

[0735] "Visual information" refers to information that indicates the state of electronic devices, such as images and videos obtained from the user.

[0736] "Electronic devices" refer to electrical equipment used by users, such as computers and routers.

[0737] "Operating status" refers to the current operating status of an electronic device and includes normal, abnormal, and specific malfunctions.

[0738] An "emotion recognition engine" is a software component that analyzes user input data to determine their emotional state.

[0739] A "solution" is a prescribed course of action or procedure proposed to resolve a malfunction or abnormality in an electronic device.

[0740] A "control center" is a support organization that provides external expertise and technology to help resolve problems with electronic devices.

[0741] This invention provides a system for users to efficiently identify and quickly resolve malfunctions and abnormalities in electronic devices. This system mainly consists of terminals, servers, and software components that run on them.

[0742] When a user discovers an electronic device showing signs of malfunction, they use the terminal to photograph its condition. This terminal features a high-resolution camera and user interface, allowing for immediate transmission of the acquired visual information to a server. Specifically, the user enters a prompt message in the terminal's input field, such as, "The router's light is blinking red, and the connection is unstable. What should I do?" This prompt message, along with the captured image data, is then sent to the server.

[0743] The server analyzes the received data using an AI-based algorithm and diagnoses the operating status of electronic devices using a generative AI model. This process utilizes image analysis technology to diagnose based on the external characteristics of the electronic devices, the color of the lamps, the status of the display, etc. Furthermore, the server uses an emotion recognition engine to analyze the emotional state from the user's comments and voice data. This analysis detects negative emotions such as frustration and anxiety.

[0744] Based on the emotional state expressed by the user, the server adjusts how it presents solutions. Solutions are displayed as helpful and easy-to-understand text messages, and in some cases, voice guidance is also provided. This allows users to solve problems efficiently while reducing their emotional burden.

[0745] If the problem is serious and difficult to resolve on the server side, the server automatically transfers information to an external control center. This information includes not only data on the failure of the electronic device but also user sentiment data, enabling the control center to take more appropriate action.

[0746] As described above, the present invention combines AI technology and emotion recognition to support problem resolution and realize flexible service provision that is considerate of the user.

[0747] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0748] Step 1:

[0749] The user uses the terminal's camera to capture visual information indicating an abnormal state of an electronic device. At the same time, the user enters a prompt message into the terminal's input field. For example, "The router's light is blinking red." This information is sent to the server via the terminal's transmission function. The input consists of image data and a prompt message, while the output is the transmission of data to the server.

[0750] Step 2:

[0751] The server inputs the received visual information and prompt messages into an AI-based image analysis algorithm to determine the operating status of the electronic device. This analysis process detects lamp colors and display information within the image, identifying specific problems with the electronic device. The input is image data, and the output is a diagnostic result of the operating status.

[0752] Step 3:

[0753] The server uses an emotion recognition engine to analyze the user's emotions from the prompt text. Through text analysis, it determines whether the user is experiencing emotions such as frustration or anxiety. The input is the prompt text, and the output is the user's emotional state. This prepares the server to adjust how solutions are presented.

[0754] Step 4:

[0755] The server uses a generative AI model to generate solutions based on the operating state of electronic devices and the user's emotional state. For example, if a network problem is identified, the solution will include steps to verify the connection. Based on the results of the emotional analysis, the solution's explanation is adjusted to be more user-friendly and easy to understand. The input is the operating state and emotional state, and the output is the adjusted solution.

[0756] Step 5:

[0757] The server sends the generated solution to the terminal and presents it to the user. The solution is provided in text format or as an audio guide, and the user solves the problem by following these instructions. The input is the adjusted solution, and the output is the presentation of the solution to the user.

[0758] Step 6:

[0759] If the problem is severe and difficult to handle automatically, the server will transfer relevant information to an external control center. This information includes detailed fault data and user sentiment data. This allows external experts to provide more accurate support. The input is information about the problem that is difficult to resolve, and the output is the transfer of information to the control center.

[0760] (Application Example 2)

[0761] 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".

[0762] Users often find it difficult to quickly and accurately identify and resolve problems when they encounter malfunctions with electronic devices. Furthermore, depending on the user's emotional state, the method of presenting solutions may not be effective, potentially leading to decreased user satisfaction. To address these challenges, there is a need for effective systems that provide smoother support to users and reduce stress.

[0763] 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.

[0764] In this invention, the server includes means for receiving image data acquired from a user, means for analyzing the received image data to determine the operating state of an electronic device, and means for analyzing the user's emotional data and adjusting the method of presenting solutions according to the user's emotional state. This makes it possible to quickly identify problems with electronic devices and provide appropriate support according to the user's emotional state.

[0765] "Image data" refers to visual information that shows the operating status of an electronic device, acquired by a user using their device.

[0766] "Operating status of electronic equipment" refers to status information that indicates how electronic equipment is currently functioning, and includes indicator light colors and display screen content.

[0767] A "solution" refers to specific steps or instructions provided to improve the operational status of an electronic device.

[0768] "Emotional data" refers to information analyzed to understand a user's emotional state, and includes voice input, facial expressions, comments, and other data.

[0769] An "external management center" is a third-party organization that provides further support based on information automatically transferred from the server.

[0770] An "audio guide" is an audio guidance system designed to clearly communicate solutions to users, delivered in a tone that matches the user's emotions.

[0771] "Feedback" refers to information and opinions collected from users, which are used to evaluate the effectiveness of the solutions provided.

[0772] A system implementing this invention mainly consists of a user's terminal, a cloud server, and a responsive interface program.

[0773] The user's device is equipped with a camera for acquiring image data from electronic devices. The device has the function to send the image data captured by the user to a server. This transmitted image data is analyzed on a server in the cloud via the Google Cloud Vision API. This analysis determines the operating status of the electronic device.

[0774] The server also uses Google Cloud's Natural Language API to analyze sentiment data from voice input and comments obtained from users. This allows it to understand the user's emotional state and provide flexible responses if the user is showing signs of anxiety or frustration.

[0775] The solutions are generated in the cloud and presented after being adjusted according to the user's emotional state. For example, if a user reports a communication problem, the server provides an audio guide with a gentle tone of voice based on the emotional information. Furthermore, the solutions are designed to aid understanding, such as showing specific operating procedures with videos and diagrams.

[0776] This system includes a mechanism for collecting user feedback and evaluating the effectiveness of solutions. Based on this information, the solutions provided are continuously improved. For example, if a user reports a problem where "the device won't restart," the cloud server generates a guide, including specific restart steps, based on the analysis results, and provides instructions via both voice and text.

[0777] The following is an example of a prompt message to input into the generative AI model.

[0778] "A user has reported a communication failure with their electronic payment terminal. They have sent a picture of the terminal along with a frustrated expression and comment. Please generate a guide to alleviate the user's anxiety."

[0779] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0780] Step 1:

[0781] The user takes an image of an electronic device using the terminal. This input data (image file) is sent to a cloud server via the terminal's interface. Specifically, the terminal converts the image to the appropriate resolution and format and sends it via the internet connection.

[0782] Step 2:

[0783] The server inputs the received image data into Google Cloud's Vision API to analyze the operating status of electronic devices. This analysis identifies features and lamp colors in the image to determine the operating status. This step outputs a list of identified operating statuses.

[0784] Step 3:

[0785] The user inputs comments and voice messages through the terminal. The terminal sends this input data to the server as text data. At this time, the terminal performs the specific action of converting voice data to text, packaging it in a usable format, and sending it.

[0786] Step 4:

[0787] The server processes the received comments and audio data through Google Cloud's Natural Language API to analyze the user's sentiment data. Based on this analysis, it outputs information indicating the user's emotional state. The server then starts generating prompts based on this output.

[0788] Step 5:

[0789] The server generates solutions based on the analyzed operational and emotional states. Using a generative AI model, it creates user-appropriate solutions based on prompt text. Specifically, it outputs a combination of voice guidance in a soft tone that matches the user's emotions and text containing specific operating instructions.

[0790] Step 6:

[0791] The server sends the generated solution to the terminal. The terminal provides audio guidance using its audio playback function and simultaneously displays the solution in text format on its screen. Specifically, it plays an audio file and displays the text in an easy-to-read format on the screen.

[0792] Step 7:

[0793] The user implements the suggested solution and, if necessary, sends feedback back to the server via their device. Based on this feedback, the server evaluates the effectiveness of the solution and uses it to make future improvements.

[0794] 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.

[0795] 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.

[0796] 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 robot 414.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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."

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] The following is further disclosed regarding the embodiments described above.

[0816] (Claim 1)

[0817] A means for receiving image data obtained from a user,

[0818] A means for analyzing received image data to determine the operating status of an electronic device,

[0819] A means for generating and presenting a solution based on the operating state,

[0820] A means of automatically transferring information to an external management center as needed,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, comprising means for presenting the generated solution to the user as a video guide.

[0824] (Claim 3)

[0825] The system according to claim 1, comprising means for collecting user feedback and evaluating the effectiveness of the solution.

[0826] "Example 1"

[0827] (Claim 1)

[0828] A means for receiving image information acquired from a user,

[0829] A means for analyzing received image information and determining the operating status of the device,

[0830] A means for generating and presenting a solution guideline based on the operating state,

[0831] A means of presenting the generated solution guidelines to the user in text and video formats,

[0832] A means of automatically transferring information on the resolution guidelines to an external management body as needed,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, comprising means for collecting user feedback and evaluating the effectiveness of solution guidelines.

[0836] (Claim 3)

[0837] The system according to claim 1, comprising means for extracting features from received image information and identifying the type of problem using a generating AI model.

[0838] "Application Example 1"

[0839] (Claim 1)

[0840] A means for receiving visual data acquired from the user,

[0841] A means for analyzing received visual data to determine the operating status of a machine,

[0842] A means for generating and presenting a solution based on the operating state,

[0843] A means of automatically transferring information to an external management body as needed,

[0844] A means of presenting solutions to users via a visual device,

[0845] A means of presenting the generated solutions as audio and visual guides,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, comprising means for an operator to monitor and detect abnormalities in an operating device using a visual device.

[0849] (Claim 3)

[0850] The system according to claim 1, comprising means for collecting user feedback, evaluating the effectiveness of solutions, and reflecting this feedback in prompt generation.

[0851] "Example 2 of combining an emotion engine"

[0852] (Claim 1)

[0853] A means for receiving visual information acquired from the user,

[0854] A means for analyzing received visual information and determining the operating status of an electronic device,

[0855] A means of analyzing a user's emotional state using an emotion recognition engine,

[0856] A means for generating and presenting solutions based on the operational state and emotional state,

[0857] A means to flexibly adjust the generated solutions and present them in a format suitable for the user,

[0858] A means of automatically transferring information to an external control center as needed,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, comprising means for presenting the generated solution to the user as visual and audio guidance.

[0862] (Claim 3)

[0863] The system according to claim 1, comprising means for collecting user feedback and evaluating the effectiveness of the solution.

[0864] "Application example 2 when combining with an emotional engine"

[0865] (Claim 1)

[0866] A means for receiving image data obtained from a user,

[0867] A means for analyzing received image data to determine the operating status of an electronic device,

[0868] A means for generating and presenting a solution based on the operating state,

[0869] A means of analyzing user emotional data and adjusting the method of presenting solutions according to the user's emotional state,

[0870] A means of automatically transferring information to an external management center as needed,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, comprising means for presenting the generated solution as an audio guide that corresponds to the user's emotional state.

[0874] (Claim 3)

[0875] The system according to claim 1, comprising means for collecting user feedback and emotional states, evaluating the effectiveness of solutions, and improving the method of delivery. [Explanation of Symbols]

[0876] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving image data obtained from a user, A means for analyzing received image data to determine the operating status of an electronic device, A means for generating and presenting a solution based on the operating state, A means of automatically transferring information to an external management center as needed, A system that includes this.

2. The system according to claim 1, comprising means for presenting the generated solution to the user as a video guide.

3. The system according to claim 1, comprising means for collecting user feedback and evaluating the effectiveness of the solution.

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