System

Abstract

A system is provided.SOLUTION: A system for handling an error occurring during a line opening work, comprising: means for detecting an error code; means for transmitting the detected error code to a server; means for specifying a handling procedure based on the error code in the server; means for converting the specified handling procedure into an automatic voice; and means for reproducing the automatic voice to a worker.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Errors frequently occur during line activation work, and since activation support members must deal with them each time, this increases the workload and reduces efficiency. In particular, there is a need for a means to quickly and automatically deal with errors that occur frequently and are easy to deal with. [Means for solving the problem]

[0005] In order to solve these problems, the present invention provides the following means. That is, a system for dealing with errors that occur during line installation work, the system including means for detecting error codes, means for transmitting the detected error codes to a server, means for identifying a response procedure based on the error code in the server, means for converting the identified response procedure into automated voice, and means for playing the automated voice to a worker. As a result, when an error occurs, AI automatically identifies the response procedure and issues instructions to the worker, making it possible to efficiently resolve the error.

[0006] An "error code" is a digital representation used to identify the type of malfunction or fault that occurs during line installation work.

[0007] A "server" is a central computer system that receives error codes over a network, analyzes them, and identifies procedures to follow.

[0008] The "AI engine" is an artificial intelligence system that refers to a database, analyzes error codes, and generates response procedures.

[0009] "Line installation" refers to a series of processes carried out to install a new communication line and verify its correct operation.

[0010] An "IVR system" is a type of automated telephone response system that receives input from users through voice prompts.

[0011] "Response procedures" are specific actions or instructions to be taken to resolve an error based on a specific error code.

[0012] The "automatic speech generation module" is a component that converts text-based instructions into speech and makes that speech replayable.

[0013] "Workers" refer to engineers who carry out line installation work and respond by following automated voice instructions when errors occur. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention relates to a system that uses AI to automatically handle errors that occur during line installation work. The purpose of this system is to reduce the workload of workers and improve overall work efficiency by quickly and efficiently handling errors when they occur. Detailed embodiments of this system are described below.

[0036] System Configuration

[0037] This system consists of the following main components:

[0038] 1. Terminal: A device used by technicians when installing lines. The terminal uses an IVR system to perform installation tests and detect error codes.

[0039] 2. Server: A central computer system that receives error codes and uses an AI engine to analyze and identify response procedures.

[0040] 3. AI Engine: An artificial intelligence system that checks the database based on the error code and generates appropriate response procedures.

[0041] 4. Automatic speech generation module: This component converts the response procedures identified on the server into speech and conveys them to workers.

[0042] Program processing

[0043] 1. Error detection and transmission

[0044] The terminal detects errors that occur during the line installation test by the technician. For example, suppose the terminal detects the error code "E101" indicating a communication failure. The terminal sends this error code to the server.

[0045] 2. Error Code Analysis

[0046] The server receives the error code. After receiving it, the server analyzes the error code using an AI engine and identifies the appropriate response procedure. For example, it identifies "restarting the modem" as the response procedure for error code "E101."

[0047] 3. Automatic voice generation

[0048] The server passes the identified response procedure to an automatic voice generation module to generate voice instructions. In this case, the instruction "A communication error has occurred. Please restart your modem" is converted into voice.

[0049] 4. Instructions to workers

[0050] The generated voice instructions are sent to a terminal, which then plays the instructions back to the worker in an automated voice. The worker receives these instructions, follows the response procedures, and takes the necessary action on site.

[0051] Specific examples

[0052] For example, consider the case where a worker detects error code "E202" (configuration error) while performing a line installation test. In this case, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings," and passes this to the automatic voice generation module. The generated voice instruction, "A configuration error has occurred. Please check the VLAN settings," is sent to the terminal, and the terminal plays the voice instruction to the worker. The worker checks the VLAN settings on-site and corrects the error by taking corrective action.

[0053] In this way, the operations at each processing step are performed in coordination, enabling a quick response when an error occurs, thereby improving the efficiency of line installation work and reducing the burden on workers.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The terminal performs an activation test using the IVR system during the line activation work. If an error occurs, the terminal detects the error code. For example, the terminal detects the error code "E101" (communication failure).

[0057] Step 2:

[0058] The terminal transmits the detected error code "E101" to the server using a communication means for transmitting the error code to the server.

[0059] Step 3:

[0060] The server records and saves the error code received from the device, then passes it to the AI ​​engine to begin analysis.

[0061] Step 4:

[0062] The AI ​​engine analyzes the error code "E101" it receives and checks it against a database, which contains past error codes and the corresponding procedures. The AI ​​engine identifies "restarting the modem" as the procedure for resolving the "poor communication" issue.

[0063] Step 5:

[0064] Based on the response procedure "restart the modem" obtained from the AI ​​engine, the server uses an automatic voice generation module to convert the response instructions into a voice file. Specifically, it generates the instruction "A communication error has occurred. Please restart the modem."

[0065] Step 6:

[0066] The server sends the generated audio file to the terminal, which then plays the received audio file for the worker.

[0067] Step 7:

[0068] The user (worker) hears the automated voice instruction played from the terminal: "A communication error has occurred. Please restart the modem," and follows the instructions to restart the modem on-site.

[0069] Step 8:

[0070] After the modem has been restarted, the user (service technician) uses the terminal to perform the line activation test again. The terminal sends the results of the retest to the server to confirm whether the error has been resolved.

[0071] Step 9:

[0072] The server receives the results of the retest and, if the error is resolved, ends the process. If the error is not resolved even after the retest, the server runs the AI ​​engine again to analyze the new error code.

[0073] Example 1

[0074] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0075] It is necessary to reduce the workload of workers and improve overall work efficiency by quickly and efficiently responding to errors that occur during line installation work. However, with the conventional system, error analysis and response took time, causing work delays. In addition, there were cases where workers were unable to respond appropriately because response procedures were not clear.

[0076] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0077] In this invention, the server includes a means for analyzing error codes, a means for generating response procedures using an AI engine, and a means for converting the response procedures into automated voice, which enables quick and appropriate response procedures to be provided to workers when an error occurs.

[0078] A "terminal" is a device used by a worker that has the function of detecting error codes during line installation work.

[0079] An "error code" is a code used to identify problems that occur during line installation work, and indicates a specific error condition.

[0080] "Server" is a central computer system that receives error codes, analyzes them, identifies appropriate response procedures, and communicates them to personnel.

[0081] The "AI engine" is an artificial intelligence system that analyzes error codes and generates appropriate response procedures by referencing a past database.

[0082] "Response procedures" are specific procedures for resolving a problem that are identified based on the error code.

[0083] The "automatic voice generation module" is a component that converts the response procedures identified by the server into voice and conveys them to workers.

[0084] "Internet connection" is a communication means by which the terminal sends error codes to the server, and is used to transmit information in real time.

[0085] A "line activation test" is a test conducted to confirm that the line is operating normally, and is carried out as part of error detection.

[0086] "IVR system" is an abbreviation for interactive voice response system, which allows workers to perform operations by following voice instructions.

[0087] A "database" is a collection of information that stores error codes and response procedure information, and is a data source referenced by the AI ​​engine.

[0088] This invention relates to a system that uses AI to automatically handle errors that occur during line installation work. The purpose of this system is to reduce the workload of workers and improve overall work efficiency by quickly and efficiently handling errors when they occur. To specifically implement this invention, the following hardware and software are used.

[0089] System Configuration

[0090] This system consists of the following main components:

[0091] 1. Terminal: A device used by technicians when installing lines. The terminal uses an IVR system to perform installation tests and detect error codes.

[0092] 2. Server: A central computer system that receives error codes and uses an AI engine to analyze and identify response procedures.

[0093] 3. AI Engine: An artificial intelligence system that checks the database based on the error code and generates appropriate response procedures.

[0094] 4. Automatic speech generation module: This component converts the response procedures identified on the server into speech and conveys them to workers.

[0095] Program processing

[0096] When a technician performs a line installation test, the terminal uses an IVR system to perform an error check. For example, suppose a communication failure is discovered and the error code "E101" is detected. The terminal sends the detected error code to the server. When the error code "E101" is detected, the terminal sends this error code to the server via an internet connection using a dedicated protocol.

[0097] The server analyzes the received error code. When the server receives the error code "E101," it inputs it into the AI ​​engine running on the backend. The AI ​​engine checks this error code against a database and interprets the detailed contents of the error. It generates an appropriate response procedure based on the analysis results. For example, "E101" indicates a communication failure, and the recommended response procedure is to "restart the modem." The AI ​​engine identifies this procedure and returns it to the server.

[0098] The server then passes this response procedure to an automatic voice generation module, which generates a voice instruction saying, "A communication error has occurred. Please restart the modem." The server then sends this voice instruction to the terminal. The terminal then plays the received voice instruction back to the worker. The worker follows this instruction and takes the necessary measures on site.

[0099] Specific examples

[0100] For example, consider the case where a worker detects error code "E202" (configuration error) while performing a line installation test. In this case, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings," and passes this to the automatic voice generation module. The generated voice instruction, "A configuration error has occurred. Please check the VLAN settings," is sent to the terminal, and the terminal plays the voice instruction to the worker. The worker checks the VLAN settings on-site and corrects the error by taking corrective action.

[0101] Prompt Sentence Examples

[0102] The following are examples of prompt sentences that explain the detailed processing of the system:

[0103] Please explain the detailed processing of the system.

[0104] 1. If an error code occurs when a technician performs a line installation test, the terminal sends the error code to the server.

[0105] 2. The server passes the received error code to the AI ​​engine, which analyzes the error code and identifies the appropriate response procedure.

[0106] 3. The server passes the identified response steps to the automatic voice generation module to generate voice instructions.

[0107] 4. The generated voice instructions are sent to the terminal, which then plays the voice instructions to the worker.

[0108] 5. Workers follow the voice instructions and take the necessary action on site.

[0109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0110] Step 1: Error detection

[0111] The terminal uses the IVR system to perform error checks when a technician performs a line installation test. For example, if a communication error occurs while the technician is testing the line, the terminal detects the error code "E101." The input here is the communication status during the line installation test, and the output is the detected error code "E101."

[0112] Step 2: Send Error

[0113] The terminal sends the detected error code "E101" to the server. The error code is encrypted and sent to the server via an Internet connection. At this time, the input is the error code "E101" and the output is the error data sent to the server. The terminal confirms the success of the error transmission by recording the transmission log.

[0114] Step 3: Error analysis

[0115] The server immediately analyzes the received error code "E101." The server inputs the error code into an AI engine, which then checks the error code against a database. The input is the error code "E101," and the output is the analyzed error details. This analysis includes a database query that references historical data.

[0116] Step 4: Generate response procedures

[0117] The AI ​​engine generates appropriate response procedures based on the error code analysis results. For example, "E101" indicates a communication failure, and the recommended response procedure is "restarting the modem." The input is the error analysis result, and the output is the identified response procedure, "restarting the modem."

[0118] Step 5: Generate voice instructions

[0119] The server passes the response procedures received from the AI ​​engine to the automatic voice generation module, which generates voice instructions. For example, it generates a voice instruction such as, "A communication error has occurred. Please restart the modem." The input here is the identified response procedures, and the output is the generated voice instruction data. High-resolution voice synthesis technology is used in the voice generation process.

[0120] Step 6: Send voice instructions

[0121] The server sends the generated voice instruction data to the terminal. The server transfers the corresponding procedure voice file to the terminal through a real-time communication protocol. The input is the voice instruction data, and the output is the voice data sent to the terminal. The data packets being sent use a checksum to ensure quality.

[0122] Step 7: Implement a response

[0123] After receiving the voice data, the terminal plays it back to the worker. The worker hears the voice instruction "A communication problem has occurred. Please restart the modem," and takes the necessary measures on-site according to the response procedure. The input here is the received voice data, and the output is the voice instruction played back to the worker. In concrete terms, the worker restarts the modem on-site and resolves the communication problem.

[0124] (Application example 1)

[0125] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0126] The problem to be solved by this invention is to provide a system that can respond quickly and efficiently when errors or abnormalities occur during line installation work and anomaly detection work for autonomous vehicles, thereby reducing the workload of workers and drivers and improving overall work efficiency and safety.

[0127] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0128] In this invention, the server includes a means for identifying a response procedure based on an error code or an abnormality signal, a means for converting the identified response procedure into an automated voice, and a means for playing back the automated voice to the worker or driver, thereby enabling an appropriate response procedure to be presented immediately when an error or abnormality occurs.

[0129] "Line activation work" refers to a series of tasks required to install a new communication line and ensure that it operates normally.

[0130] "Anomaly detection work" refers to a series of tasks to monitor whether equipment and systems are operating normally and to detect abnormalities early.

[0131] An "error code" is a combination of numbers and characters used to identify an abnormality or failure detected by a system or device.

[0132] An "abnormal signal" is a specific signal that is sent to indicate an abnormal condition of a system or device.

[0133] A "server" is a computer system for processing data and communicating with other devices over a network.

[0134] A "response procedure" is a series of steps to be taken when a specific error or abnormality occurs.

[0135] "Automatic speech" is a synthesized speech generated from text or other input data.

[0136] "Worker" refers to a person in charge of tasks such as line installation work and troubleshooting.

[0137] A "driver" is a person whose role is to monitor and control an automated vehicle.

[0138] "Automatic speech generation means" refers to technology or devices for converting text data into speech data.

[0139] An "AI engine" is a system that uses artificial intelligence technology to analyze data and solve problems.

[0140] The present invention is a system for responding to errors and abnormalities that occur during line installation work and anomaly detection work for autonomous vehicles. The system aims to detect error codes or abnormality signals in real time, automatically generate response procedures, and communicate them to the worker or driver via voice. A specific embodiment of the present invention is described below.

[0141] System Configuration

[0142] This system consists of the following main components:

[0143] 1. Error detection device

[0144] These are devices for detecting error codes or abnormal signals in real time, and examples include terminals used in line installation work and sensor units in self-driving vehicles.

[0145] 2. Server System

[0146] A central computer system that receives error codes or abnormal signals and identifies response procedures. The server system includes an AI engine (e.g., TensorFlow or PyTorch) and a voice generation system (e.g., Google Text-to-Speech).

[0147] 3. Communication Module

[0148] This module transmits and receives data between the detection device and the server, and between the server and the user device. This module is used to transmit error codes or abnormality signals and receive response procedures.

[0149] 4. User Devices

[0150] A device used to communicate response procedures to workers or drivers, such as a smartphone or head-mounted display.

[0151] Program processing

[0152] The server performs the following processing based on the error code or abnormal signal.

[0153] 1. Data Reception

[0154] Receive error codes or abnormal signals via the communication module.

[0155] 2. Analysis by AI engine

[0156] The AI ​​engine analyzes the received error code or abnormal signal and identifies the corrective action. For example, if the error code "E301" is received, the AI ​​engine will identify "recalibration of the sensor" as the corrective action.

[0157] 3. Automatic speech generation

[0158] The identified response procedure is converted into automated speech by a speech generation system, for example, generating instructions such as "A sensor malfunction has occurred. Please recalibrate the sensor."

[0159] 4. Sending voice commands

[0160] Automated voice instructions are sent to the user device via the communications module.

[0161] Examples of specific examples and prompts

[0162] For example, if an autonomous vehicle detects the error code "E301," which indicates a sensor malfunction, the following process takes place: The server receives the error code "E301," and uses an AI engine to identify the corrective action, "recalibrate the sensor." The voice generation system then generates a voice instruction saying, "A sensor malfunction has occurred. Please recalibrate the sensor," and sends it to the user device.

[0163] Example prompts for generative AI models:

[0164] Error code E301 detected. Please auto-generate appropriate response steps. If recommending sensor recalibration, please provide text to generate voice prompts.

[0165] In this way, the present invention can provide efficient and rapid response procedures for line installation work and abnormality detection work for autonomously driven vehicles.

[0166] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0167] Step 1:

[0168] The terminal detects an error code or abnormal signal during line installation work or while an autonomous vehicle is in operation. For example, the error code "E101" indicating poor communication or an abnormal signal indicating a sensor malfunction is detected. The input at this point is the error code or abnormal signal generated on-site, which is acquired in real time by the terminal's sensor unit, etc. The output is the data of the detected error code or abnormal signal.

[0169] Step 2:

[0170] The error code or abnormal signal data detected by the terminal is sent to the server. A communication module is used for this transmission. The input is the error code or abnormal signal data obtained in step 1, and the output is the data sent to the server.

[0171] Step 3:

[0172] The AI ​​engine analyzes the error code or abnormal signal received by the server. First, it checks against a database to see past response history for the same error or abnormality. The input is the data of the error code or abnormal signal that was sent, and the output is the identified candidate response procedure. For example, for "E101," "restart the modem" is identified as the response procedure.

[0173] Step 4:

[0174] The server passes the response procedures identified by the AI ​​engine to the speech generation system, which converts them into automated speech. For example, the text instruction "Please restart your modem" is converted into speech data. The input is the text data of the response procedures, and the output is automated speech data.

[0175] Step 5:

[0176] The server transmits the generated automated voice data to the terminal via the communication module. The input is the generated automated voice data, and the output is the automated voice data transmitted to the terminal.

[0177] Step 6:

[0178] The terminal plays an automated voice message and gives instructions to the worker or driver. The worker or driver follows these voice instructions to carry out the appropriate procedures. The input is the automated voice data sent to the terminal, and the output is voice instructions to the worker or driver. Specific actions include the worker restarting the modem and checking the settings.

[0179] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0180] The present invention relates to a system for dealing with errors that occur during line installation work. In particular, this system provides more flexible and effective response procedures by incorporating an emotion engine that recognizes the user's emotions. The purpose of this system is to respond quickly and efficiently when an error occurs, reduce the workload of workers, improve overall work efficiency, and also reduce the emotional burden on users. Detailed embodiments of this system are described below.

[0181] System Configuration

[0182] This system consists of the following main components:

[0183] 1. Terminal: A device used by technicians when installing lines. The terminal uses an IVR system to perform installation tests and detect error codes.

[0184] 2. Server: A central computer system that receives error codes and uses an AI engine to analyze and identify response procedures. It also includes an emotion engine that recognizes user emotions and adjusts instructions as needed.

[0185] 3. AI Engine: An artificial intelligence system that checks the database based on the error code and generates appropriate response procedures.

[0186] 4. Automatic speech generation module: This component converts the response procedures identified on the server into speech and conveys them to workers.

[0187] 5. Emotion engine: It has the ability to recognize the user's emotions and adjust the instructions based on those emotions.

[0188] Program processing

[0189] 1. Error detection and transmission

[0190] The terminal uses the IVR system to perform an activation test during the line activation process. If an error occurs, the terminal detects the error code. For example, it detects the error code "E101" (poor communication). The terminal then sends this error code to the server.

[0191] 2. Error Code Analysis

[0192] The server receives the transmitted error code. Next, the server passes the received error code to the AI ​​engine and begins analysis. The AI ​​engine checks the database based on the error code and identifies the appropriate response procedure. For example, it identifies "restarting the modem" as the response procedure for error code "E101."

[0193] 3. Emotional Recognition

[0194] The server uses an emotion engine to recognize the user's (worker's) emotions. For example, it may recognize that the user is feeling stressed. Based on this information, the server adjusts the instructions for the response procedure. For example, if the user is feeling stressed, the instructions may be explained more carefully.

[0195] 4. Automatic voice generation

[0196] Based on the identified response procedure, the server uses an automatic voice generation module to convert the response instructions into a voice file, for example, "A communication error has occurred. Please do not worry. Please restart your modem."

[0197] 5. Instructions to workers

[0198] The generated voice instructions are sent to a terminal, which then plays the instructions back to the worker in an automated voice format. The worker receives these instructions and takes the necessary action on-site.

[0199] Specific examples

[0200] For example, consider the case where a worker detects error code "E202" (setting error) while performing a line installation test. At this time, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings." If the emotion engine then recognizes the user's emotions and determines that the worker is feeling anxious, it generates voice instructions that include additional explanations and encouraging words, such as "It's okay. Please check the VLAN settings."

[0201] In this way, the operations at each processing step are coordinated, enabling a quick response when an error occurs. This makes line installation work more efficient, reduces the burden on workers, and provides emotional support.

[0202] The processing flow will be explained below.

[0203] Step 1:

[0204] The terminal performs an activation test using the IVR system during the line activation work. If an error occurs, the terminal detects the error code. For example, the terminal detects the error code "E101" (communication failure).

[0205] Step 2:

[0206] The terminal transmits the detected error code "E101" to the server using a communication means for transmitting the error code to the server.

[0207] Step 3:

[0208] The server receives the error code sent from the device, then passes it to the AI ​​engine and begins analysis.

[0209] Step 4:

[0210] The AI ​​engine analyzes the error code "E101" it receives and checks it against a database, which contains past error codes and the corresponding procedures. The AI ​​engine identifies "restarting the modem" as the procedure for resolving the "poor communication" issue.

[0211] Step 5:

[0212] The server uses an automatic voice generation module to convert the response instruction "restart the modem" obtained from the AI ​​engine into a voice file. For example, it generates an instruction such as "A communication error has occurred. Please restart the modem."

[0213] Step 6:

[0214] The server sends the generated audio file to the terminal, which then prepares to play the received audio file for the worker.

[0215] Step 7:

[0216] The terminal uses an emotion engine to recognize the user's (worker's) emotions. For example, if it recognizes that the user is feeling stressed, it sends that information to the server.

[0217] Step 8:

[0218] The server can then adjust the voice prompts based on the emotional information, for example, if the user is feeling stressed, it might say something like, "Don't worry, it's a very simple task. Just restart your modem."

[0219] Step 9:

[0220] The terminal plays the adjusted audio file sent from the server to the worker, who then hears an automated voice instruction saying, "A communication error has occurred. Don't worry, it's a very simple task. Please restart the modem." The user then follows the instructions to restart the modem on-site.

[0221] Step 10:

[0222] After the modem has been restarted, the user (service technician) uses the terminal to perform the line activation test again. The terminal sends the results of the retest to the server to confirm whether the error has been resolved.

[0223] Step 11:

[0224] The server receives the results of the retest and, if the error is resolved, ends the process. If the error is not resolved even after the retest, the server runs the AI ​​engine again to analyze the new error code.

[0225] Example 2

[0226] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0227] There is a need for a method to quickly and efficiently respond to errors that occur during line installation work. There is also a need to reduce the emotional burden on users (workers) and improve overall work efficiency.

[0228] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0229] In this invention, the server includes a means for recognizing the user's emotions and adjusting the response procedure, a means for identifying the response procedure based on the error code, and a means for converting the identified response procedure into automated speech, thereby enabling a quick response when an error occurs and emotional support for the user.

[0230] An "error code" is a specific code used to identify problems or abnormalities that occur during line installation work.

[0231] The "means for recognizing the user's emotions and adjusting the response procedure" is a means for analyzing the emotions felt by the user and changing or adapting the instructions based on the analysis.

[0232] The "server" is a central computer system that receives and analyzes data related to line installation work and generates instructions.

[0233] "Means for identifying response procedures" refers to a means for checking a database or knowledge base based on the error code to derive appropriate countermeasures or procedures.

[0234] The "means for converting into automatic speech" is a means for converting the contents of instructions such as text into speech format and conveying it to the user.

[0235] The "means for playing an automated voice to a worker" is a means for playing back the generated voice instruction to a worker who is a user, and providing a specific method of dealing with the problem.

[0236] The present invention relates to a system for dealing with errors that occur during line installation work. The system detects error codes, identifies appropriate response procedures based on the error codes, and flexibly adjusts instructions according to the user's emotions. The specific configuration and operation for implementing the present invention are described in detail below.

[0237] System Configuration

[0238] The terminal is a device used by technicians when performing line installation work. The terminal uses an interactive voice response (IVR) system to perform installation tests and has the function of detecting error codes. When the terminal detects an error code, it sends it to the server in real time.

[0239] The server is a central computer system that receives error codes and uses an AI engine to analyze and identify appropriate response procedures. The server uses the AI ​​engine to match the error code with a database and derive appropriate response procedures. The server also includes an emotion engine that recognizes the user's emotions and adjusts instructions based on those emotions.

[0240] The AI ​​engine is an artificial intelligence system used to identify response procedures based on error codes. The AI ​​engine analyzes error codes based on pre-trained models and extracts the optimal response procedures from a database.

[0241] The automatic voice generation module is a component that converts the response procedures identified by the server into voice and conveys them to the operator. For example, it generates instructions such as, "A communication error has occurred. Please be at ease. Please restart your modem."

[0242] The emotion engine has the ability to recognize the user's emotions and adjust the instructions based on those emotions. The emotion engine analyzes the user's tone of voice and vocabulary to determine the user's emotions, and adjusts the instructions based on that information.

[0243] Specific examples

[0244] For example, consider the case where a worker detects error code "E202" (setting error) while performing a line installation test. At this time, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings." If the emotion engine then recognizes the user's emotions and determines that the worker is feeling anxious, it generates voice instructions that include additional explanations and encouraging words, such as "It's okay. Please check the VLAN settings."

[0245] Prompt Sentence Examples

[0246] Here are some examples of prompts to input to a generative AI model:

[0247] "Error code 'E202' occurred during the line installation test. Analysis of the emotion engine indicates that the user is feeling anxious. Please explain the appropriate response procedure for the error code and suggest ways to calm the user."

[0248] As described above, the system's components work together to provide rapid response when errors occur and emotional support to users, thereby improving the efficiency of line installation work and reducing the burden on workers.

[0249] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0250] Step 1:

[0251] The terminal performs an activation test using an interactive voice response (IVR) system during line activation work.

[0252] Input: IVR system prompts to check line status.

[0253] Data processing / data calculation: The IVR system checks the line connection status and outputs various test results.

[0254] Output: If an error occurs, the error code is detected. For example, the error code "E101" (communication failure) is detected.

[0255] Step 2:

[0256] The device sends the detected error code to the server.

[0257] Input: Detected error code "E101".

[0258] Data calculation: Sends the error code to the server according to the communication protocol.

[0259] Output: The server receives the error code "E101".

[0260] Step 3:

[0261] The server passes the received error code to the AI ​​engine.

[0262] Input: Received error code "E101".

[0263] Data calculation: Input the error code into the AI ​​engine and start analysis.

[0264] Output: As a result of the analysis, the corrective action to be taken is identified as "restarting the modem."

[0265] Step 4:

[0266] The server uses an emotion engine to recognize the user's emotion.

[0267] Input: User voice information and dialogue.

[0268] Data calculation: The emotion engine analyzes voice tone, wording, etc. to determine the user's emotions.

[0269] Output: The user is aware that they are stressed.

[0270] Step 5:

[0271] The server adjusts the response procedure instructions based on the recognized emotion.

[0272] Input: Recognized user emotion information and the corresponding procedure "restart modem."

[0273] Data processing / data calculation: Adjust the text to explain the instructions more clearly.

[0274] Output: Generates the instruction "Don't worry, this is easy to fix. First, restart your modem."

[0275] Step 6:

[0276] The server uses an automatic voice generation module to convert the response instructions into a voice file.

[0277] Input: The adjusted instruction text.

[0278] Data processing / data calculation: Convert text into an audio file using speech synthesis technology.

[0279] Output: Generates a sound file saying "A communication error has occurred. Please do not worry. Please restart your modem."

[0280] Step 7:

[0281] The server transmits the generated voice instructions to the terminal.

[0282] Input: The generated audio file.

[0283] Data calculation: The audio file is sent to the terminal according to the communication protocol.

[0284] Output: The device receives the audio file.

[0285] Step 8:

[0286] The terminal plays automated voice instructions to the worker.

[0287] Input: Received audio file.

[0288] Data processing / data calculation: Play the audio file on an audio playback device.

[0289] Output: The worker can hear the voice instructions.

[0290] Step 9:

[0291] The user (worker) follows the automated voice prompts and takes the necessary measures on-site.

[0292] Input: Automated voice instructions.

[0293] Data operation: Performing a physical operation based on instructions (e.g., rebooting a modem).

[0294] Output: Corrective action completed and error resolved.

[0295] (Application example 2)

[0296] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0297] When an error occurs during line installation work or at a work site such as a factory, a quick and appropriate response is required. However, currently, there are no standardized response procedures when an error occurs, which places a heavy burden on workers. In addition, the emotional burden on workers is not taken into consideration, which can increase worker stress and reduce work efficiency. To solve these issues, a system is needed that can respond quickly and appropriately when an error occurs, reduce the work burden on workers, improve overall work efficiency, and also reduce the emotional burden on workers.

[0298] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0299] In this invention, the server includes a means for identifying a response procedure based on an error code, a means for converting the identified response procedure into an automated voice, an emotion recognition means for recognizing the emotion of a worker, and a means for adjusting the response procedure based on the emotion recognition, thereby making it possible to provide an appropriate response procedure that takes into account the emotional state of the worker when an error occurs.

[0300] An "error code" is an identification number used to identify problems or failures that occur during work.

[0301] A "server" is a central computer system that receives error codes and performs analysis and generates response procedures.

[0302] The "response procedure" is a procedure that instructs the specific actions and operations that the worker should take based on the error code.

[0303] "Automatic voice" is a voice that has the function of converting text information into voice and playing it back.

[0304] "Workers" refers to people who actually perform work on-site, such as installing lines or working in factories.

[0305] "Emotion recognition" is a technology that automatically detects a worker's emotions and understands their state.

[0306] An "emotion recognition means" is a device or system that uses a camera or sensor to detect the emotions of workers.

[0307] An "AI engine" is a system that uses artificial intelligence to analyze data and automatically determine the appropriate response.

[0308] A "database" is an information management system that stores information such as error codes and response procedures and can be referenced as needed.

[0309] The present invention provides a system for quickly and appropriately dealing with errors that occur during line installation work or at a factory work site, thereby improving work efficiency. Specific embodiments of the system will be described below.

[0310] System Configuration

[0311] This system consists of the following main components:

[0312] 1. Terminal: A device used by a worker, equipped with an IVR system to detect errors and send an error code to the server when an error occurs.

[0313] 2. Server: A central computer system that receives and analyzes error codes and identifies response procedures. It has emotion recognition capabilities and adjusts instructions based on the emotions of workers.

[0314] 3. AI Engine: An artificial intelligence system that references a database based on the error code and generates appropriate response procedures.

[0315] 4. Automatic speech generation module: Converts the response procedures identified by the server into speech and conveys it to the worker.

[0316] 5. Emotion recognition means: Recognize the emotional state of workers using cameras and other sensors.

[0317] Program processing

[0318] The server receives information sent from each component and performs the following data processing and calculations.

[0319] 1. Error detection and transmission: The terminal detects an error code using the IVR system during the line installation process and transmits it to the server.

[0320] 2. Error code analysis: The server passes the received error code to the AI ​​engine, which checks the database to identify the corrective action. For example, if the error code "E101" is detected, "restart the modem" is identified as the corrective action.

[0321] 3. Emotion recognition: Emotion recognition means analyzes data obtained from cameras and sensors to understand the emotional state of the worker. For example, if stress is detected, the system will carefully explain the steps to take.

[0322] 4. Automatic voice generation: The server converts the analyzed response procedures into a voice file and generates instructions such as, "A communication error has occurred. Please do not worry. Please restart your modem."

[0323] 5. Instructions to workers: The generated voice instructions are sent to the terminal, which then plays the instructions to the worker in automated voice.

[0324] Hardware and software used

[0325] Camera: for worker emotion recognition

[0326] Smart glasses: devices for displaying visual instructions

[0327] Software for generating voice instructions (specific speech synthesis engines used include pyttsx3)

[0328] AI engine: for error analysis (e.g., a custom AI model implemented in Python)

[0329] Specific examples

[0330] For example, if an error code "E300" (part placement error) is detected while a work robot is installing a part in a factory, the following prompt sentence will be generated:

[0331] error: E300

[0332] Emotion: stress

[0333] response: Don't worry, please recheck the placement of the component.

[0334] Based on this prompt, the server will generate a voice instruction, "Don't worry, please double-check the placement of the parts," which will be displayed on the smart glasses, allowing the worker to immediately understand and take the appropriate response steps.

[0335] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0336] Step 1:

[0337] The terminal uses the IVR system to detect error codes during the line activation process. If an error code is detected, it is sent to the terminal as input data. The terminal then sends this error code to the server. Specifically, the terminal detects the error code and sends it to a specific API endpoint on the server.

[0338] Step 2:

[0339] The server receives the error code sent from the terminal. The received error code becomes input data and is passed to the AI ​​engine in the server. The AI ​​engine checks the database based on the error code and identifies the response procedure. For example, if the error code "E101" is the input data, the AI ​​engine generates the response procedure "restart the modem."

[0340] Step 3:

[0341] The server receives data from cameras and sensors and recognizes the emotional state of the worker using emotion recognition means. The emotion recognition means receives input data from camera footage and sensor data, and detects emotions based on this. The server adjusts instructions based on the analysis results.

[0342] Step 4:

[0343] The server integrates the response procedures from the AI ​​engine with the emotional data from the emotion recognition means. For example, if the server detects that a worker is feeling stressed, it adds additional explanations or words of encouragement to the response procedures. It inputs prompt sentences into the generative AI model and adds phrases such as "Don't worry" or "There's nothing to worry about."

[0344] Step 5:

[0345] The server passes the adjusted response instructions to an automatic speech generation module to generate an audio file, where the input data is the adjusted text instructions and the output data is the audio file. The automatic speech generation module converts the text into speech using a specific speech synthesis engine (e.g., pyttsx3).

[0346] Step 6:

[0347] The server sends the generated audio file to the terminal, and the terminal plays instructions to the worker in an automated voice. Specifically, the terminal plays the audio file, and the worker listens to it and executes the corresponding procedure. For example, the voice instruction "A communication error has occurred. Please do not worry. Please restart the modem" is played.

[0348] These steps allow for a quick and appropriate response when an error occurs, and provide effective instructions that take into account the emotional state of the worker.

[0349] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0350] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0351] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0352] [Second embodiment]

[0353] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0354] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0355] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0356] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0357] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0358] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0359] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0360] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0361] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0362] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0363] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0364] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0365] The present invention relates to a system that uses AI to automatically handle errors that occur during line installation work. The purpose of this system is to reduce the workload of workers and improve overall work efficiency by quickly and efficiently handling errors when they occur. Detailed embodiments of this system are described below.

[0366] System Configuration

[0367] This system consists of the following main components:

[0368] 1. Terminal: A device used by technicians when installing lines. The terminal uses an IVR system to perform installation tests and detect error codes.

[0369] 2. Server: A central computer system that receives error codes and uses an AI engine to analyze and identify response procedures.

[0370] 3. AI Engine: An artificial intelligence system that checks the database based on the error code and generates appropriate response procedures.

[0371] 4. Automatic speech generation module: This component converts the response procedures identified on the server into speech and conveys them to workers.

[0372] Program processing

[0373] 1. Error detection and transmission

[0374] The terminal detects errors that occur during the line installation test by the technician. For example, suppose the terminal detects the error code "E101" indicating a communication failure. The terminal sends this error code to the server.

[0375] 2. Error Code Analysis

[0376] The server receives the error code. After receiving it, the server analyzes the error code using an AI engine and identifies the appropriate response procedure. For example, it identifies "restarting the modem" as the response procedure for error code "E101."

[0377] 3. Automatic voice generation

[0378] The server passes the identified response procedure to an automatic voice generation module to generate voice instructions. In this case, the instruction "A communication error has occurred. Please restart your modem" is converted into voice.

[0379] 4. Instructions to workers

[0380] The generated voice instructions are sent to a terminal, which then plays the instructions back to the worker in an automated voice. The worker receives these instructions, follows the response procedures, and takes the necessary action on site.

[0381] Specific examples

[0382] For example, consider the case where a worker detects error code "E202" (configuration error) while performing a line installation test. In this case, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings," and passes this to the automatic voice generation module. The generated voice instruction, "A configuration error has occurred. Please check the VLAN settings," is sent to the terminal, and the terminal plays the voice instruction to the worker. The worker checks the VLAN settings on-site and corrects the error by taking corrective action.

[0383] In this way, the operations at each processing step are performed in coordination, enabling a quick response when an error occurs, thereby improving the efficiency of line installation work and reducing the burden on workers.

[0384] The processing flow will be explained below.

[0385] Step 1:

[0386] The terminal performs an activation test using the IVR system during the line activation work. If an error occurs, the terminal detects the error code. For example, the terminal detects the error code "E101" (communication failure).

[0387] Step 2:

[0388] The terminal transmits the detected error code "E101" to the server using a communication means for transmitting the error code to the server.

[0389] Step 3:

[0390] The server records and saves the error code received from the device, then passes it to the AI ​​engine to begin analysis.

[0391] Step 4:

[0392] The AI ​​engine analyzes the error code "E101" it receives and checks it against a database, which contains past error codes and the corresponding procedures. The AI ​​engine identifies "restarting the modem" as the procedure for resolving the "poor communication" issue.

[0393] Step 5:

[0394] Based on the response procedure "restart the modem" obtained from the AI ​​engine, the server uses an automatic voice generation module to convert the response instructions into a voice file. Specifically, it generates the instruction "A communication error has occurred. Please restart the modem."

[0395] Step 6:

[0396] The server sends the generated audio file to the terminal, which then plays the received audio file for the worker.

[0397] Step 7:

[0398] The user (worker) hears the automated voice instruction played from the terminal: "A communication error has occurred. Please restart the modem," and follows the instructions to restart the modem on-site.

[0399] Step 8:

[0400] After the modem has been restarted, the user (service technician) uses the terminal to perform the line activation test again. The terminal sends the results of the retest to the server to confirm whether the error has been resolved.

[0401] Step 9:

[0402] The server receives the results of the retest and, if the error is resolved, ends the process. If the error is not resolved even after the retest, the server runs the AI ​​engine again to analyze the new error code.

[0403] Example 1

[0404] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0405] It is necessary to reduce the workload of workers and improve overall work efficiency by quickly and efficiently responding to errors that occur during line installation work. However, with the conventional system, error analysis and response took time, causing work delays. In addition, there were cases where workers were unable to respond appropriately because response procedures were not clear.

[0406] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0407] In this invention, the server includes a means for analyzing error codes, a means for generating response procedures using an AI engine, and a means for converting the response procedures into automated voice, which enables quick and appropriate response procedures to be provided to workers when an error occurs.

[0408] A "terminal" is a device used by a worker that has the function of detecting error codes during line installation work.

[0409] An "error code" is a code used to identify problems that occur during line installation work, and indicates a specific error condition.

[0410] "Server" is a central computer system that receives error codes, analyzes them, identifies appropriate response procedures, and communicates them to personnel.

[0411] The "AI engine" is an artificial intelligence system that analyzes error codes and generates appropriate response procedures by referencing a past database.

[0412] "Response procedures" are specific procedures for resolving a problem that are identified based on the error code.

[0413] The "automatic voice generation module" is a component that converts the response procedures identified by the server into voice and conveys them to workers.

[0414] "Internet connection" is a communication means by which the terminal sends error codes to the server, and is used to transmit information in real time.

[0415] A "line activation test" is a test conducted to confirm that the line is operating normally, and is carried out as part of error detection.

[0416] "IVR system" is an abbreviation for interactive voice response system, which allows workers to perform operations by following voice instructions.

[0417] A "database" is a collection of information that stores error codes and response procedure information, and is a data source referenced by the AI ​​engine.

[0418] This invention relates to a system that uses AI to automatically handle errors that occur during line installation work. The purpose of this system is to reduce the workload of workers and improve overall work efficiency by quickly and efficiently handling errors when they occur. To specifically implement this invention, the following hardware and software are used.

[0419] System Configuration

[0420] This system consists of the following main components:

[0421] 1. Terminal: A device used by technicians when installing lines. The terminal uses an IVR system to perform installation tests and detect error codes.

[0422] 2. Server: A central computer system that receives error codes and uses an AI engine to analyze and identify response procedures.

[0423] 3. AI Engine: An artificial intelligence system that checks the database based on the error code and generates appropriate response procedures.

[0424] 4. Automatic speech generation module: This component converts the response procedures identified on the server into speech and conveys them to workers.

[0425] Program processing

[0426] When a technician performs a line installation test, the terminal uses an IVR system to perform an error check. For example, suppose a communication failure is discovered and the error code "E101" is detected. The terminal sends the detected error code to the server. When the error code "E101" is detected, the terminal sends this error code to the server via an internet connection using a dedicated protocol.

[0427] The server analyzes the received error code. When the server receives the error code "E101," it inputs it into the AI ​​engine running on the backend. The AI ​​engine checks this error code against a database and interprets the detailed contents of the error. It generates an appropriate response procedure based on the analysis results. For example, "E101" indicates a communication failure, and the recommended response procedure is to "restart the modem." The AI ​​engine identifies this procedure and returns it to the server.

[0428] The server then passes this response procedure to an automatic voice generation module, which generates a voice instruction saying, "A communication error has occurred. Please restart the modem." The server then sends this voice instruction to the terminal. The terminal then plays the received voice instruction back to the worker. The worker follows this instruction and takes the necessary measures on site.

[0429] Specific examples

[0430] For example, consider the case where a worker detects error code "E202" (configuration error) while performing a line installation test. In this case, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings," and passes this to the automatic voice generation module. The generated voice instruction, "A configuration error has occurred. Please check the VLAN settings," is sent to the terminal, and the terminal plays the voice instruction to the worker. The worker checks the VLAN settings on-site and corrects the error by taking corrective action.

[0431] Prompt Sentence Examples

[0432] The following are examples of prompt sentences that explain the detailed processing of the system:

[0433] Please explain the detailed processing of the system.

[0434] 1. If an error code occurs when a technician performs a line installation test, the terminal sends the error code to the server.

[0435] 2. The server passes the received error code to the AI ​​engine, which analyzes the error code and identifies the appropriate response procedure.

[0436] 3. The server passes the identified response steps to the automatic voice generation module to generate voice instructions.

[0437] 4. The generated voice instructions are sent to the terminal, which then plays the voice instructions to the worker.

[0438] 5. Workers follow the voice instructions and take the necessary action on site.

[0439] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0440] Step 1: Error detection

[0441] The terminal uses the IVR system to perform error checks when a technician performs a line installation test. For example, if a communication error occurs while the technician is testing the line, the terminal detects the error code "E101." The input here is the communication status during the line installation test, and the output is the detected error code "E101."

[0442] Step 2: Send Error

[0443] The terminal sends the detected error code "E101" to the server. The error code is encrypted and sent to the server via an Internet connection. At this time, the input is the error code "E101" and the output is the error data sent to the server. The terminal confirms the success of the error transmission by recording the transmission log.

[0444] Step 3: Error analysis

[0445] The server immediately analyzes the received error code "E101." The server inputs the error code into an AI engine, which then checks the error code against a database. The input is the error code "E101," and the output is the analyzed error details. This analysis includes a database query that references historical data.

[0446] Step 4: Generate response procedures

[0447] The AI ​​engine generates appropriate response procedures based on the error code analysis results. For example, "E101" indicates a communication failure, and the recommended response procedure is "restarting the modem." The input is the error analysis result, and the output is the identified response procedure, "restarting the modem."

[0448] Step 5: Generate voice instructions

[0449] The server passes the response procedures received from the AI ​​engine to the automatic voice generation module, which generates voice instructions. For example, it generates a voice instruction such as, "A communication error has occurred. Please restart the modem." The input here is the identified response procedures, and the output is the generated voice instruction data. High-resolution voice synthesis technology is used in the voice generation process.

[0450] Step 6: Send voice instructions

[0451] The server sends the generated voice instruction data to the terminal. The server transfers the corresponding procedure voice file to the terminal through a real-time communication protocol. The input is the voice instruction data, and the output is the voice data sent to the terminal. The data packets being sent use a checksum to ensure quality.

[0452] Step 7: Implement a response

[0453] After receiving the voice data, the terminal plays it back to the worker. The worker hears the voice instruction "A communication problem has occurred. Please restart the modem," and takes the necessary measures on-site according to the response procedure. The input here is the received voice data, and the output is the voice instruction played back to the worker. In concrete terms, the worker restarts the modem on-site and resolves the communication problem.

[0454] (Application example 1)

[0455] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0456] The problem to be solved by this invention is to provide a system that can respond quickly and efficiently when errors or abnormalities occur during line installation work and anomaly detection work for autonomous vehicles, thereby reducing the workload of workers and drivers and improving overall work efficiency and safety.

[0457] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0458] In this invention, the server includes a means for identifying a response procedure based on an error code or an abnormality signal, a means for converting the identified response procedure into an automated voice, and a means for playing back the automated voice to the worker or driver, thereby enabling an appropriate response procedure to be presented immediately when an error or abnormality occurs.

[0459] "Line activation work" refers to a series of tasks required to install a new communication line and ensure that it operates normally.

[0460] "Anomaly detection work" refers to a series of tasks to monitor whether equipment and systems are operating normally and to detect abnormalities early.

[0461] An "error code" is a combination of numbers and characters used to identify an abnormality or failure detected by a system or device.

[0462] An "abnormal signal" is a specific signal that is sent to indicate an abnormal condition of a system or device.

[0463] A "server" is a computer system for processing data and communicating with other devices over a network.

[0464] A "response procedure" is a series of steps to be taken when a specific error or abnormality occurs.

[0465] "Automatic speech" is a synthesized speech generated from text or other input data.

[0466] "Worker" refers to a person in charge of tasks such as line installation work and troubleshooting.

[0467] A "driver" is a person whose role is to monitor and control an automated vehicle.

[0468] "Automatic speech generation means" refers to technology or devices for converting text data into speech data.

[0469] An "AI engine" is a system that uses artificial intelligence technology to analyze data and solve problems.

[0470] The present invention is a system for responding to errors and abnormalities that occur during line installation work and anomaly detection work for autonomous vehicles. The system aims to detect error codes or abnormality signals in real time, automatically generate response procedures, and communicate them to the worker or driver via voice. A specific embodiment of the present invention is described below.

[0471] System Configuration

[0472] This system consists of the following main components:

[0473] 1. Error detection device

[0474] These are devices for detecting error codes or abnormal signals in real time, and examples include terminals used in line installation work and sensor units in self-driving vehicles.

[0475] 2. Server System

[0476] A central computer system that receives error codes or abnormal signals and identifies response procedures. The server system includes an AI engine (e.g., TensorFlow or PyTorch) and a voice generation system (e.g., Google Text-to-Speech).

[0477] 3. Communication Module

[0478] This module transmits and receives data between the detection device and the server, and between the server and the user device. This module is used to transmit error codes or abnormality signals and receive response procedures.

[0479] 4. User Devices

[0480] A device used to communicate response procedures to workers or drivers, such as a smartphone or head-mounted display.

[0481] Program processing

[0482] The server performs the following processing based on the error code or abnormal signal.

[0483] 1. Data Reception

[0484] Receive error codes or abnormal signals via the communication module.

[0485] 2. Analysis by AI engine

[0486] The AI ​​engine analyzes the received error code or abnormal signal and identifies the corrective action. For example, if the error code "E301" is received, the AI ​​engine will identify "recalibration of the sensor" as the corrective action.

[0487] 3. Automatic speech generation

[0488] The identified response procedure is converted into automated speech by a speech generation system, for example, generating instructions such as "A sensor malfunction has occurred. Please recalibrate the sensor."

[0489] 4. Sending voice commands

[0490] Automated voice instructions are sent to the user device via the communications module.

[0491] Examples of specific examples and prompts

[0492] For example, if an autonomous vehicle detects the error code "E301," which indicates a sensor malfunction, the following process takes place: The server receives the error code "E301," and uses an AI engine to identify the corrective action, "recalibrate the sensor." The voice generation system then generates a voice instruction saying, "A sensor malfunction has occurred. Please recalibrate the sensor," and sends it to the user device.

[0493] Example prompts for generative AI models:

[0494] Error code E301 detected. Please auto-generate appropriate response steps. If recommending sensor recalibration, please provide text to generate voice prompts.

[0495] In this way, the present invention can provide efficient and rapid response procedures for line installation work and abnormality detection work for autonomously driven vehicles.

[0496] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0497] Step 1:

[0498] The terminal detects an error code or abnormal signal during line installation work or while an autonomous vehicle is in operation. For example, the error code "E101" indicating poor communication or an abnormal signal indicating a sensor malfunction is detected. The input at this point is the error code or abnormal signal generated on-site, which is acquired in real time by the terminal's sensor unit, etc. The output is the data of the detected error code or abnormal signal.

[0499] Step 2:

[0500] The error code or abnormal signal data detected by the terminal is sent to the server. A communication module is used for this transmission. The input is the error code or abnormal signal data obtained in step 1, and the output is the data sent to the server.

[0501] Step 3:

[0502] The AI ​​engine analyzes the error code or abnormal signal received by the server. First, it checks against a database to see past response history for the same error or abnormality. The input is the data of the error code or abnormal signal that was sent, and the output is the identified candidate response procedure. For example, for "E101," "restart the modem" is identified as the response procedure.

[0503] Step 4:

[0504] The server passes the response procedures identified by the AI ​​engine to the speech generation system, which converts them into automated speech. For example, the text instruction "Please restart your modem" is converted into speech data. The input is the text data of the response procedures, and the output is automated speech data.

[0505] Step 5:

[0506] The server transmits the generated automated voice data to the terminal via the communication module. The input is the generated automated voice data, and the output is the automated voice data transmitted to the terminal.

[0507] Step 6:

[0508] The terminal plays an automated voice message and gives instructions to the worker or driver. The worker or driver follows these voice instructions to carry out the appropriate procedures. The input is the automated voice data sent to the terminal, and the output is voice instructions to the worker or driver. Specific actions include the worker restarting the modem and checking the settings.

[0509] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0510] The present invention relates to a system for dealing with errors that occur during line installation work. In particular, this system provides more flexible and effective response procedures by incorporating an emotion engine that recognizes the user's emotions. The purpose of this system is to respond quickly and efficiently when an error occurs, reduce the workload of workers, improve overall work efficiency, and also reduce the emotional burden on users. Detailed embodiments of this system are described below.

[0511] System Configuration

[0512] This system consists of the following main components:

[0513] 1. Terminal: A device used by technicians when installing lines. The terminal uses an IVR system to perform installation tests and detect error codes.

[0514] 2. Server: A central computer system that receives error codes and uses an AI engine to analyze and identify response procedures. It also includes an emotion engine that recognizes user emotions and adjusts instructions as needed.

[0515] 3. AI Engine: An artificial intelligence system that checks the database based on the error code and generates appropriate response procedures.

[0516] 4. Automatic speech generation module: This component converts the response procedures identified on the server into speech and conveys them to workers.

[0517] 5. Emotion engine: It has the ability to recognize the user's emotions and adjust the instructions based on those emotions.

[0518] Program processing

[0519] 1. Error detection and transmission

[0520] The terminal uses the IVR system to perform an activation test during the line activation process. If an error occurs, the terminal detects the error code. For example, it detects the error code "E101" (poor communication). The terminal then sends this error code to the server.

[0521] 2. Error Code Analysis

[0522] The server receives the transmitted error code. Next, the server passes the received error code to the AI ​​engine and begins analysis. The AI ​​engine checks the database based on the error code and identifies the appropriate response procedure. For example, it identifies "restarting the modem" as the response procedure for error code "E101."

[0523] 3. Emotional Recognition

[0524] The server uses an emotion engine to recognize the user's (worker's) emotions. For example, it may recognize that the user is feeling stressed. Based on this information, the server adjusts the instructions for the response procedure. For example, if the user is feeling stressed, the instructions may be explained more carefully.

[0525] 4. Automatic voice generation

[0526] Based on the identified response procedure, the server uses an automatic voice generation module to convert the response instructions into a voice file, for example, "A communication error has occurred. Please do not worry. Please restart your modem."

[0527] 5. Instructions to workers

[0528] The generated voice instructions are sent to a terminal, which then plays the instructions back to the worker in an automated voice format. The worker receives these instructions and takes the necessary action on-site.

[0529] Specific examples

[0530] For example, consider the case where a worker detects error code "E202" (setting error) while performing a line installation test. At this time, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings." If the emotion engine then recognizes the user's emotions and determines that the worker is feeling anxious, it generates voice instructions that include additional explanations and encouraging words, such as "It's okay. Please check the VLAN settings."

[0531] In this way, the operations at each processing step are coordinated, enabling a quick response when an error occurs. This makes line installation work more efficient, reduces the burden on workers, and provides emotional support.

[0532] The processing flow will be explained below.

[0533] Step 1:

[0534] The terminal performs an activation test using the IVR system during the line activation work. If an error occurs, the terminal detects the error code. For example, the terminal detects the error code "E101" (communication failure).

[0535] Step 2:

[0536] The terminal transmits the detected error code "E101" to the server using a communication means for transmitting the error code to the server.

[0537] Step 3:

[0538] The server receives the error code sent from the device, then passes it to the AI ​​engine and begins analysis.

[0539] Step 4:

[0540] The AI ​​engine analyzes the error code "E101" it receives and checks it against a database, which contains past error codes and the corresponding procedures. The AI ​​engine identifies "restarting the modem" as the procedure for resolving the "poor communication" issue.

[0541] Step 5:

[0542] The server uses an automatic voice generation module to convert the response instruction "restart the modem" obtained from the AI ​​engine into a voice file. For example, it generates an instruction such as "A communication error has occurred. Please restart the modem."

[0543] Step 6:

[0544] The server sends the generated audio file to the terminal, which then prepares to play the received audio file for the worker.

[0545] Step 7:

[0546] The terminal uses an emotion engine to recognize the user's (worker's) emotions. For example, if it recognizes that the user is feeling stressed, it sends that information to the server.

[0547] Step 8:

[0548] The server can then adjust the voice prompts based on the emotional information, for example, if the user is feeling stressed, it might say something like, "Don't worry, it's a very simple task. Just restart your modem."

[0549] Step 9:

[0550] The terminal plays the adjusted audio file sent from the server to the worker, who then hears an automated voice instruction saying, "A communication error has occurred. Don't worry, it's a very simple task. Please restart the modem." The user then follows the instructions to restart the modem on-site.

[0551] Step 10:

[0552] After the modem has been restarted, the user (service technician) uses the terminal to perform the line activation test again. The terminal sends the results of the retest to the server to confirm whether the error has been resolved.

[0553] Step 11:

[0554] The server receives the results of the retest and, if the error is resolved, ends the process. If the error is not resolved even after the retest, the server runs the AI ​​engine again to analyze the new error code.

[0555] Example 2

[0556] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0557] There is a need for a method to quickly and efficiently respond to errors that occur during line installation work. There is also a need to reduce the emotional burden on users (workers) and improve overall work efficiency.

[0558] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0559] In this invention, the server includes a means for recognizing the user's emotions and adjusting the response procedure, a means for identifying the response procedure based on the error code, and a means for converting the identified response procedure into automated speech, thereby enabling a quick response when an error occurs and emotional support for the user.

[0560] An "error code" is a specific code used to identify problems or abnormalities that occur during line installation work.

[0561] The "means for recognizing the user's emotions and adjusting the response procedure" is a means for analyzing the emotions felt by the user and changing or adapting the instructions based on the analysis.

[0562] The "server" is a central computer system that receives and analyzes data related to line installation work and generates instructions.

[0563] "Means for identifying response procedures" refers to a means for checking a database or knowledge base based on the error code to derive appropriate countermeasures or procedures.

[0564] The "means for converting into automatic speech" is a means for converting the contents of instructions such as text into speech format and conveying it to the user.

[0565] The "means for playing an automated voice to a worker" is a means for playing back the generated voice instruction to a worker who is a user, and providing a specific method of dealing with the problem.

[0566] The present invention relates to a system for dealing with errors that occur during line installation work. The system detects error codes, identifies appropriate response procedures based on the error codes, and flexibly adjusts instructions according to the user's emotions. The specific configuration and operation for implementing the present invention are described in detail below.

[0567] System Configuration

[0568] The terminal is a device used by technicians when performing line installation work. The terminal uses an interactive voice response (IVR) system to perform installation tests and has the function of detecting error codes. When the terminal detects an error code, it sends it to the server in real time.

[0569] The server is a central computer system that receives error codes and uses an AI engine to analyze and identify appropriate response procedures. The server uses the AI ​​engine to match the error code with a database and derive appropriate response procedures. The server also includes an emotion engine that recognizes the user's emotions and adjusts instructions based on those emotions.

[0570] The AI ​​engine is an artificial intelligence system used to identify response procedures based on error codes. The AI ​​engine analyzes error codes based on pre-trained models and extracts the optimal response procedures from a database.

[0571] The automatic voice generation module is a component that converts the response procedures identified by the server into voice and conveys them to the operator. For example, it generates instructions such as, "A communication error has occurred. Please be at ease. Please restart your modem."

[0572] The emotion engine has the ability to recognize the user's emotions and adjust the instructions based on those emotions. The emotion engine analyzes the user's tone of voice and vocabulary to determine the user's emotions, and adjusts the instructions based on that information.

[0573] Specific examples

[0574] For example, consider the case where a worker detects error code "E202" (setting error) while performing a line installation test. At this time, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings." If the emotion engine then recognizes the user's emotions and determines that the worker is feeling anxious, it generates voice instructions that include additional explanations and encouraging words, such as "It's okay. Please check the VLAN settings."

[0575] Prompt Sentence Examples

[0576] Here are some examples of prompts to input to a generative AI model:

[0577] "Error code 'E202' occurred during the line installation test. Analysis of the emotion engine indicates that the user is feeling anxious. Please explain the appropriate response procedure for the error code and suggest ways to calm the user."

[0578] As described above, the system's components work together to provide rapid response when errors occur and emotional support to users, thereby improving the efficiency of line installation work and reducing the burden on workers.

[0579] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0580] Step 1:

[0581] The terminal performs an activation test using an interactive voice response (IVR) system during line activation work.

[0582] Input: IVR system prompts to check line status.

[0583] Data processing / data calculation: The IVR system checks the line connection status and outputs various test results.

[0584] Output: If an error occurs, the error code is detected. For example, the error code "E101" (communication failure) is detected.

[0585] Step 2:

[0586] The device sends the detected error code to the server.

[0587] Input: Detected error code "E101".

[0588] Data calculation: Sends the error code to the server according to the communication protocol.

[0589] Output: The server receives the error code "E101".

[0590] Step 3:

[0591] The server passes the received error code to the AI ​​engine.

[0592] Input: Received error code "E101".

[0593] Data calculation: Input the error code into the AI ​​engine and start analysis.

[0594] Output: As a result of the analysis, the corrective action to be taken is identified as "restarting the modem."

[0595] Step 4:

[0596] The server uses an emotion engine to recognize the user's emotion.

[0597] Input: User voice information and dialogue.

[0598] Data calculation: The emotion engine analyzes voice tone, wording, etc. to determine the user's emotions.

[0599] Output: The user is aware that they are stressed.

[0600] Step 5:

[0601] The server adjusts the response procedure instructions based on the recognized emotion.

[0602] Input: Recognized user emotion information and the corresponding procedure "restart modem."

[0603] Data processing / data calculation: Adjust the text to explain the instructions more clearly.

[0604] Output: Generates the instruction "Don't worry, this is easy to fix. First, restart your modem."

[0605] Step 6:

[0606] The server uses an automatic voice generation module to convert the response instructions into a voice file.

[0607] Input: The adjusted instruction text.

[0608] Data processing / data calculation: Convert text into an audio file using speech synthesis technology.

[0609] Output: Generates a sound file saying "A communication error has occurred. Please do not worry. Please restart your modem."

[0610] Step 7:

[0611] The server transmits the generated voice instructions to the terminal.

[0612] Input: The generated audio file.

[0613] Data calculation: The audio file is sent to the terminal according to the communication protocol.

[0614] Output: The device receives the audio file.

[0615] Step 8:

[0616] The terminal plays automated voice instructions to the worker.

[0617] Input: Received audio file.

[0618] Data processing / data calculation: Play the audio file on an audio playback device.

[0619] Output: The worker can hear the voice instructions.

[0620] Step 9:

[0621] The user (worker) follows the automated voice prompts and takes the necessary measures on-site.

[0622] Input: Automated voice instructions.

[0623] Data operation: Performing a physical operation based on instructions (e.g., rebooting a modem).

[0624] Output: Corrective action completed and error resolved.

[0625] (Application example 2)

[0626] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0627] When an error occurs during line installation work or at a work site such as a factory, a quick and appropriate response is required. However, currently, there are no standardized response procedures when an error occurs, which places a heavy burden on workers. In addition, the emotional burden on workers is not taken into consideration, which can increase worker stress and reduce work efficiency. To solve these issues, a system is needed that can respond quickly and appropriately when an error occurs, reduce the work burden on workers, improve overall work efficiency, and also reduce the emotional burden on workers.

[0628] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0629] In this invention, the server includes a means for identifying a response procedure based on an error code, a means for converting the identified response procedure into an automated voice, an emotion recognition means for recognizing the emotion of a worker, and a means for adjusting the response procedure based on the emotion recognition, thereby making it possible to provide an appropriate response procedure that takes into account the emotional state of the worker when an error occurs.

[0630] An "error code" is an identification number used to identify problems or failures that occur during work.

[0631] A "server" is a central computer system that receives error codes and performs analysis and generates response procedures.

[0632] The "response procedure" is a procedure that instructs the specific actions and operations that the worker should take based on the error code.

[0633] "Automatic voice" is a voice that has the function of converting text information into voice and playing it back.

[0634] "Workers" refers to people who actually perform work on-site, such as installing lines or working in factories.

[0635] "Emotion recognition" is a technology that automatically detects a worker's emotions and understands their state.

[0636] An "emotion recognition means" is a device or system that uses a camera or sensor to detect the emotions of workers.

[0637] An "AI engine" is a system that uses artificial intelligence to analyze data and automatically determine the appropriate response.

[0638] A "database" is an information management system that stores information such as error codes and response procedures and can be referenced as needed.

[0639] The present invention provides a system for quickly and appropriately dealing with errors that occur during line installation work or at a factory work site, thereby improving work efficiency. Specific embodiments of the system will be described below.

[0640] System Configuration

[0641] This system consists of the following main components:

[0642] 1. Terminal: A device used by a worker, equipped with an IVR system to detect errors and send an error code to the server when an error occurs.

[0643] 2. Server: A central computer system that receives and analyzes error codes and identifies response procedures. It has emotion recognition capabilities and adjusts instructions based on the emotions of workers.

[0644] 3. AI Engine: An artificial intelligence system that references a database based on the error code and generates appropriate response procedures.

[0645] 4. Automatic speech generation module: Converts the response procedures identified by the server into speech and conveys it to the worker.

[0646] 5. Emotion recognition means: Recognize the emotional state of workers using cameras and other sensors.

[0647] Program processing

[0648] The server receives information sent from each component and performs the following data processing and calculations.

[0649] 1. Error detection and transmission: The terminal detects an error code using the IVR system during the line installation process and transmits it to the server.

[0650] 2. Error code analysis: The server passes the received error code to the AI ​​engine, which checks the database to identify the corrective action. For example, if the error code "E101" is detected, "restart the modem" is identified as the corrective action.

[0651] 3. Emotion recognition: Emotion recognition means analyzes data obtained from cameras and sensors to understand the emotional state of the worker. For example, if stress is detected, the system will carefully explain the steps to take.

[0652] 4. Automatic voice generation: The server converts the analyzed response procedures into a voice file and generates instructions such as, "A communication error has occurred. Please do not worry. Please restart your modem."

[0653] 5. Instructions to workers: The generated voice instructions are sent to the terminal, which then plays the instructions to the worker in automated voice.

[0654] Hardware and software used

[0655] Camera: for worker emotion recognition

[0656] Smart glasses: devices for displaying visual instructions

[0657] Software for generating voice instructions (specific speech synthesis engines used include pyttsx3)

[0658] AI engine: for error analysis (e.g., a custom AI model implemented in Python)

[0659] Specific examples

[0660] For example, if an error code "E300" (part placement error) is detected while a work robot is installing a part in a factory, the following prompt sentence will be generated:

[0661] error: E300

[0662] Emotion: stress

[0663] response: Don't worry, please recheck the placement of the component.

[0664] Based on this prompt, the server will generate a voice instruction, "Don't worry, please double-check the placement of the parts," which will be displayed on the smart glasses, allowing the worker to immediately understand and take the appropriate response steps.

[0665] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0666] Step 1:

[0667] The terminal uses the IVR system to detect error codes during the line activation process. If an error code is detected, it is sent to the terminal as input data. The terminal then sends this error code to the server. Specifically, the terminal detects the error code and sends it to a specific API endpoint on the server.

[0668] Step 2:

[0669] The server receives the error code sent from the terminal. The received error code becomes input data and is passed to the AI ​​engine in the server. The AI ​​engine checks the database based on the error code and identifies the response procedure. For example, if the error code "E101" is the input data, the AI ​​engine generates the response procedure "restart the modem."

[0670] Step 3:

[0671] The server receives data from cameras and sensors and recognizes the emotional state of the worker using emotion recognition means. The emotion recognition means receives input data from camera footage and sensor data, and detects emotions based on this. The server adjusts instructions based on the analysis results.

[0672] Step 4:

[0673] The server integrates the response procedures from the AI ​​engine with the emotional data from the emotion recognition means. For example, if the server detects that a worker is feeling stressed, it adds additional explanations or words of encouragement to the response procedures. It inputs prompt sentences into the generative AI model and adds phrases such as "Don't worry" or "There's nothing to worry about."

[0674] Step 5:

[0675] The server passes the adjusted response instructions to an automatic speech generation module to generate an audio file, where the input data is the adjusted text instructions and the output data is the audio file. The automatic speech generation module converts the text into speech using a specific speech synthesis engine (e.g., pyttsx3).

[0676] Step 6:

[0677] The server sends the generated audio file to the terminal, and the terminal plays instructions to the worker in an automated voice. Specifically, the terminal plays the audio file, and the worker listens to it and executes the corresponding procedure. For example, the voice instruction "A communication error has occurred. Please do not worry. Please restart the modem" is played.

[0678] These steps allow for a quick and appropriate response when an error occurs, and provide effective instructions that take into account the emotional state of the worker.

[0679] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0680] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0681] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0682] [Third embodiment]

[0683] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0684] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0685] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0686] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0687] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0688] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0689] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0690] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0691] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0692] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0693] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0694] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0695] The present invention relates to a system that uses AI to automatically handle errors that occur during line installation work. The purpose of this system is to reduce the workload of workers and improve overall work efficiency by quickly and efficiently handling errors when they occur. Detailed embodiments of this system are described below.

[0696] System Configuration

[0697] This system consists of the following main components:

[0698] 1. Terminal: A device used by technicians when installing lines. The terminal uses an IVR system to perform installation tests and detect error codes.

[0699] 2. Server: A central computer system that receives error codes and uses an AI engine to analyze and identify response procedures.

[0700] 3. AI Engine: An artificial intelligence system that checks the database based on the error code and generates appropriate response procedures.

[0701] 4. Automatic speech generation module: This component converts the response procedures identified on the server into speech and conveys them to workers.

[0702] Program processing

[0703] 1. Error detection and transmission

[0704] The terminal detects errors that occur during the line installation test by the technician. For example, suppose the terminal detects the error code "E101" indicating a communication failure. The terminal sends this error code to the server.

[0705] 2. Error Code Analysis

[0706] The server receives the error code. After receiving it, the server analyzes the error code using an AI engine and identifies the appropriate response procedure. For example, it identifies "restarting the modem" as the response procedure for error code "E101."

[0707] 3. Automatic voice generation

[0708] The server passes the identified response procedure to an automatic voice generation module to generate voice instructions. In this case, the instruction "A communication error has occurred. Please restart your modem" is converted into voice.

[0709] 4. Instructions to workers

[0710] The generated voice instructions are sent to a terminal, which then plays the instructions back to the worker in an automated voice. The worker receives these instructions, follows the response procedures, and takes the necessary action on site.

[0711] Specific examples

[0712] For example, consider the case where a worker detects error code "E202" (configuration error) while performing a line installation test. In this case, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings," and passes this to the automatic voice generation module. The generated voice instruction, "A configuration error has occurred. Please check the VLAN settings," is sent to the terminal, and the terminal plays the voice instruction to the worker. The worker checks the VLAN settings on-site and corrects the error by taking corrective action.

[0713] In this way, the operations at each processing step are performed in coordination, enabling a quick response when an error occurs, thereby improving the efficiency of line installation work and reducing the burden on workers.

[0714] The processing flow will be explained below.

[0715] Step 1:

[0716] The terminal performs an activation test using the IVR system during the line activation work. If an error occurs, the terminal detects the error code. For example, the terminal detects the error code "E101" (communication failure).

[0717] Step 2:

[0718] The terminal transmits the detected error code "E101" to the server using a communication means for transmitting the error code to the server.

[0719] Step 3:

[0720] The server records and saves the error code received from the device, then passes it to the AI ​​engine to begin analysis.

[0721] Step 4:

[0722] The AI ​​engine analyzes the error code "E101" it receives and checks it against a database, which contains past error codes and the corresponding procedures. The AI ​​engine identifies "restarting the modem" as the procedure for resolving the "poor communication" issue.

[0723] Step 5:

[0724] Based on the response procedure "restart the modem" obtained from the AI ​​engine, the server uses an automatic voice generation module to convert the response instructions into a voice file. Specifically, it generates the instruction "A communication error has occurred. Please restart the modem."

[0725] Step 6:

[0726] The server sends the generated audio file to the terminal, which then plays the received audio file for the worker.

[0727] Step 7:

[0728] The user (worker) hears the automated voice instruction played from the terminal: "A communication error has occurred. Please restart the modem," and follows the instructions to restart the modem on-site.

[0729] Step 8:

[0730] After the modem has been restarted, the user (service technician) uses the terminal to perform the line activation test again. The terminal sends the results of the retest to the server to confirm whether the error has been resolved.

[0731] Step 9:

[0732] The server receives the results of the retest and, if the error is resolved, ends the process. If the error is not resolved even after the retest, the server runs the AI ​​engine again to analyze the new error code.

[0733] Example 1

[0734] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0735] It is necessary to reduce the workload of workers and improve overall work efficiency by quickly and efficiently responding to errors that occur during line installation work. However, with the conventional system, error analysis and response took time, causing work delays. In addition, there were cases where workers were unable to respond appropriately because response procedures were not clear.

[0736] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0737] In this invention, the server includes a means for analyzing error codes, a means for generating response procedures using an AI engine, and a means for converting the response procedures into automated voice, which enables quick and appropriate response procedures to be provided to workers when an error occurs.

[0738] A "terminal" is a device used by a worker that has the function of detecting error codes during line installation work.

[0739] An "error code" is a code used to identify problems that occur during line installation work, and indicates a specific error condition.

[0740] "Server" is a central computer system that receives error codes, analyzes them, identifies appropriate response procedures, and communicates them to personnel.

[0741] The "AI engine" is an artificial intelligence system that analyzes error codes and generates appropriate response procedures by referencing a past database.

[0742] "Response procedures" are specific procedures for resolving a problem that are identified based on the error code.

[0743] The "automatic voice generation module" is a component that converts the response procedures identified by the server into voice and conveys them to workers.

[0744] "Internet connection" is a communication means by which the terminal sends error codes to the server, and is used to transmit information in real time.

[0745] A "line activation test" is a test conducted to confirm that the line is operating normally, and is carried out as part of error detection.

[0746] "IVR system" is an abbreviation for interactive voice response system, which allows workers to perform operations by following voice instructions.

[0747] A "database" is a collection of information that stores error codes and response procedure information, and is a data source referenced by the AI ​​engine.

[0748] This invention relates to a system that uses AI to automatically handle errors that occur during line installation work. The purpose of this system is to reduce the workload of workers and improve overall work efficiency by quickly and efficiently handling errors when they occur. To specifically implement this invention, the following hardware and software are used.

[0749] System Configuration

[0750] This system consists of the following main components:

[0751] 1. Terminal: A device used by technicians when installing lines. The terminal uses an IVR system to perform installation tests and detect error codes.

[0752] 2. Server: A central computer system that receives error codes and uses an AI engine to analyze and identify response procedures.

[0753] 3. AI Engine: An artificial intelligence system that checks the database based on the error code and generates appropriate response procedures.

[0754] 4. Automatic speech generation module: This component converts the response procedures identified on the server into speech and conveys them to workers.

[0755] Program processing

[0756] When a technician performs a line installation test, the terminal uses an IVR system to perform an error check. For example, suppose a communication failure is discovered and the error code "E101" is detected. The terminal sends the detected error code to the server. When the error code "E101" is detected, the terminal sends this error code to the server via an internet connection using a dedicated protocol.

[0757] The server analyzes the received error code. When the server receives the error code "E101," it inputs it into the AI ​​engine running on the backend. The AI ​​engine checks this error code against a database and interprets the detailed contents of the error. It generates an appropriate response procedure based on the analysis results. For example, "E101" indicates a communication failure, and the recommended response procedure is to "restart the modem." The AI ​​engine identifies this procedure and returns it to the server.

[0758] The server then passes this response procedure to an automatic voice generation module, which generates a voice instruction saying, "A communication error has occurred. Please restart the modem." The server then sends this voice instruction to the terminal. The terminal then plays the received voice instruction back to the worker. The worker follows this instruction and takes the necessary measures on site.

[0759] Specific examples

[0760] For example, consider the case where a worker detects error code "E202" (configuration error) while performing a line installation test. In this case, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings," and passes this to the automatic voice generation module. The generated voice instruction, "A configuration error has occurred. Please check the VLAN settings," is sent to the terminal, and the terminal plays the voice instruction to the worker. The worker checks the VLAN settings on-site and corrects the error by taking corrective action.

[0761] Prompt Sentence Examples

[0762] The following are examples of prompt sentences that explain the detailed processing of the system:

[0763] Please explain the detailed processing of the system.

[0764] 1. If an error code occurs when a technician performs a line installation test, the terminal sends the error code to the server.

[0765] 2. The server passes the received error code to the AI ​​engine, which analyzes the error code and identifies the appropriate response procedure.

[0766] 3. The server passes the identified response steps to the automatic voice generation module to generate voice instructions.

[0767] 4. The generated voice instructions are sent to the terminal, which then plays the voice instructions to the worker.

[0768] 5. Workers follow the voice instructions and take the necessary action on site.

[0769] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0770] Step 1: Error detection

[0771] The terminal uses the IVR system to perform error checks when a technician performs a line installation test. For example, if a communication error occurs while the technician is testing the line, the terminal detects the error code "E101." The input here is the communication status during the line installation test, and the output is the detected error code "E101."

[0772] Step 2: Send Error

[0773] The terminal sends the detected error code "E101" to the server. The error code is encrypted and sent to the server via an Internet connection. At this time, the input is the error code "E101" and the output is the error data sent to the server. The terminal confirms the success of the error transmission by recording the transmission log.

[0774] Step 3: Error analysis

[0775] The server immediately analyzes the received error code "E101." The server inputs the error code into an AI engine, which then checks the error code against a database. The input is the error code "E101," and the output is the analyzed error details. This analysis includes a database query that references historical data.

[0776] Step 4: Generate response procedures

[0777] The AI ​​engine generates appropriate response procedures based on the error code analysis results. For example, "E101" indicates a communication failure, and the recommended response procedure is "restarting the modem." The input is the error analysis result, and the output is the identified response procedure, "restarting the modem."

[0778] Step 5: Generate voice instructions

[0779] The server passes the response procedures received from the AI ​​engine to the automatic voice generation module, which generates voice instructions. For example, it generates a voice instruction such as, "A communication error has occurred. Please restart the modem." The input here is the identified response procedures, and the output is the generated voice instruction data. High-resolution voice synthesis technology is used in the voice generation process.

[0780] Step 6: Send voice instructions

[0781] The server sends the generated voice instruction data to the terminal. The server transfers the corresponding procedure voice file to the terminal through a real-time communication protocol. The input is the voice instruction data, and the output is the voice data sent to the terminal. The data packets being sent use a checksum to ensure quality.

[0782] Step 7: Implement a response

[0783] After receiving the voice data, the terminal plays it back to the worker. The worker hears the voice instruction "A communication problem has occurred. Please restart the modem," and takes the necessary measures on-site according to the response procedure. The input here is the received voice data, and the output is the voice instruction played back to the worker. In concrete terms, the worker restarts the modem on-site and resolves the communication problem.

[0784] (Application example 1)

[0785] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0786] The problem to be solved by this invention is to provide a system that can respond quickly and efficiently when errors or abnormalities occur during line installation work and anomaly detection work for autonomous vehicles, thereby reducing the workload of workers and drivers and improving overall work efficiency and safety.

[0787] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0788] In this invention, the server includes a means for identifying a response procedure based on an error code or an abnormality signal, a means for converting the identified response procedure into an automated voice, and a means for playing back the automated voice to the worker or driver, thereby enabling an appropriate response procedure to be presented immediately when an error or abnormality occurs.

[0789] "Line activation work" refers to a series of tasks required to install a new communication line and ensure that it operates normally.

[0790] "Anomaly detection work" refers to a series of tasks to monitor whether equipment and systems are operating normally and to detect abnormalities early.

[0791] An "error code" is a combination of numbers and characters used to identify an abnormality or failure detected by a system or device.

[0792] An "abnormal signal" is a specific signal that is sent to indicate an abnormal condition of a system or device.

[0793] A "server" is a computer system for processing data and communicating with other devices over a network.

[0794] A "response procedure" is a series of steps to be taken when a specific error or abnormality occurs.

[0795] "Automatic speech" is a synthesized speech generated from text or other input data.

[0796] "Worker" refers to a person in charge of tasks such as line installation work and troubleshooting.

[0797] A "driver" is a person whose role is to monitor and control an automated vehicle.

[0798] "Automatic speech generation means" refers to technology or devices for converting text data into speech data.

[0799] An "AI engine" is a system that uses artificial intelligence technology to analyze data and solve problems.

[0800] The present invention is a system for responding to errors and abnormalities that occur during line installation work and anomaly detection work for autonomous vehicles. The system aims to detect error codes or abnormality signals in real time, automatically generate response procedures, and communicate them to the worker or driver via voice. A specific embodiment of the present invention is described below.

[0801] System Configuration

[0802] This system consists of the following main components:

[0803] 1. Error detection device

[0804] These are devices for detecting error codes or abnormal signals in real time, and examples include terminals used in line installation work and sensor units in self-driving vehicles.

[0805] 2. Server System

[0806] A central computer system that receives error codes or abnormal signals and identifies response procedures. The server system includes an AI engine (e.g., TensorFlow or PyTorch) and a voice generation system (e.g., Google Text-to-Speech).

[0807] 3. Communication Module

[0808] This module transmits and receives data between the detection device and the server, and between the server and the user device. This module is used to transmit error codes or abnormality signals and receive response procedures.

[0809] 4. User Devices

[0810] A device used to communicate response procedures to workers or drivers, such as a smartphone or head-mounted display.

[0811] Program processing

[0812] The server performs the following processing based on the error code or abnormal signal.

[0813] 1. Data Reception

[0814] Receive error codes or abnormal signals via the communication module.

[0815] 2. Analysis by AI engine

[0816] The AI ​​engine analyzes the received error code or abnormal signal and identifies the corrective action. For example, if the error code "E301" is received, the AI ​​engine will identify "recalibration of the sensor" as the corrective action.

[0817] 3. Automatic speech generation

[0818] The identified response procedure is converted into automated speech by a speech generation system, for example, generating instructions such as "A sensor malfunction has occurred. Please recalibrate the sensor."

[0819] 4. Sending voice commands

[0820] Automated voice instructions are sent to the user device via the communications module.

[0821] Examples of specific examples and prompts

[0822] For example, if an autonomous vehicle detects the error code "E301," which indicates a sensor malfunction, the following process takes place: The server receives the error code "E301," and uses an AI engine to identify the corrective action, "recalibrate the sensor." The voice generation system then generates a voice instruction saying, "A sensor malfunction has occurred. Please recalibrate the sensor," and sends it to the user device.

[0823] Example prompts for generative AI models:

[0824] Error code E301 detected. Please auto-generate appropriate response steps. If recommending sensor recalibration, please provide text to generate voice prompts.

[0825] In this way, the present invention can provide efficient and rapid response procedures for line installation work and abnormality detection work for autonomously driven vehicles.

[0826] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0827] Step 1:

[0828] The terminal detects an error code or abnormal signal during line installation work or while an autonomous vehicle is in operation. For example, the error code "E101" indicating poor communication or an abnormal signal indicating a sensor malfunction is detected. The input at this point is the error code or abnormal signal generated on-site, which is acquired in real time by the terminal's sensor unit, etc. The output is the data of the detected error code or abnormal signal.

[0829] Step 2:

[0830] The error code or abnormal signal data detected by the terminal is sent to the server. A communication module is used for this transmission. The input is the error code or abnormal signal data obtained in step 1, and the output is the data sent to the server.

[0831] Step 3:

[0832] The AI ​​engine analyzes the error code or abnormal signal received by the server. First, it checks against a database to see past response history for the same error or abnormality. The input is the data of the error code or abnormal signal that was sent, and the output is the identified candidate response procedure. For example, for "E101," "restart the modem" is identified as the response procedure.

[0833] Step 4:

[0834] The server passes the response procedures identified by the AI ​​engine to the speech generation system, which converts them into automated speech. For example, the text instruction "Please restart your modem" is converted into speech data. The input is the text data of the response procedures, and the output is automated speech data.

[0835] Step 5:

[0836] The server transmits the generated automated voice data to the terminal via the communication module. The input is the generated automated voice data, and the output is the automated voice data transmitted to the terminal.

[0837] Step 6:

[0838] The terminal plays an automated voice message and gives instructions to the worker or driver. The worker or driver follows these voice instructions to carry out the appropriate procedures. The input is the automated voice data sent to the terminal, and the output is voice instructions to the worker or driver. Specific actions include the worker restarting the modem and checking the settings.

[0839] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0840] The present invention relates to a system for dealing with errors that occur during line installation work. In particular, this system provides more flexible and effective response procedures by incorporating an emotion engine that recognizes the user's emotions. The purpose of this system is to respond quickly and efficiently when an error occurs, reduce the workload of workers, improve overall work efficiency, and also reduce the emotional burden on users. Detailed embodiments of this system are described below.

[0841] System Configuration

[0842] This system consists of the following main components:

[0843] 1. Terminal: A device used by technicians when installing lines. The terminal uses an IVR system to perform installation tests and detect error codes.

[0844] 2. Server: A central computer system that receives error codes and uses an AI engine to analyze and identify response procedures. It also includes an emotion engine that recognizes user emotions and adjusts instructions as needed.

[0845] 3. AI Engine: An artificial intelligence system that checks the database based on the error code and generates appropriate response procedures.

[0846] 4. Automatic speech generation module: This component converts the response procedures identified on the server into speech and conveys them to workers.

[0847] 5. Emotion engine: It has the ability to recognize the user's emotions and adjust the instructions based on those emotions.

[0848] Program processing

[0849] 1. Error detection and transmission

[0850] The terminal uses the IVR system to perform an activation test during the line activation process. If an error occurs, the terminal detects the error code. For example, it detects the error code "E101" (poor communication). The terminal then sends this error code to the server.

[0851] 2. Error Code Analysis

[0852] The server receives the transmitted error code. Next, the server passes the received error code to the AI ​​engine and begins analysis. The AI ​​engine checks the database based on the error code and identifies the appropriate response procedure. For example, it identifies "restarting the modem" as the response procedure for error code "E101."

[0853] 3. Emotional Recognition

[0854] The server uses an emotion engine to recognize the user's (worker's) emotions. For example, it may recognize that the user is feeling stressed. Based on this information, the server adjusts the instructions for the response procedure. For example, if the user is feeling stressed, the instructions may be explained more carefully.

[0855] 4. Automatic voice generation

[0856] Based on the identified response procedure, the server uses an automatic voice generation module to convert the response instructions into a voice file, for example, "A communication error has occurred. Please do not worry. Please restart your modem."

[0857] 5. Instructions to workers

[0858] The generated voice instructions are sent to a terminal, which then plays the instructions back to the worker in an automated voice format. The worker receives these instructions and takes the necessary action on-site.

[0859] Specific examples

[0860] For example, consider the case where a worker detects error code "E202" (setting error) while performing a line installation test. At this time, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings." If the emotion engine then recognizes the user's emotions and determines that the worker is feeling anxious, it generates voice instructions that include additional explanations and encouraging words, such as "It's okay. Please check the VLAN settings."

[0861] In this way, the operations at each processing step are coordinated, enabling a quick response when an error occurs. This makes line installation work more efficient, reduces the burden on workers, and provides emotional support.

[0862] The processing flow will be explained below.

[0863] Step 1:

[0864] The terminal performs an activation test using the IVR system during the line activation work. If an error occurs, the terminal detects the error code. For example, the terminal detects the error code "E101" (communication failure).

[0865] Step 2:

[0866] The terminal transmits the detected error code "E101" to the server using a communication means for transmitting the error code to the server.

[0867] Step 3:

[0868] The server receives the error code sent from the device, then passes it to the AI ​​engine and begins analysis.

[0869] Step 4:

[0870] The AI ​​engine analyzes the error code "E101" it receives and checks it against a database, which contains past error codes and the corresponding procedures. The AI ​​engine identifies "restarting the modem" as the procedure for resolving the "poor communication" issue.

[0871] Step 5:

[0872] The server uses an automatic voice generation module to convert the response instruction "restart the modem" obtained from the AI ​​engine into a voice file. For example, it generates an instruction such as "A communication error has occurred. Please restart the modem."

[0873] Step 6:

[0874] The server sends the generated audio file to the terminal, which then prepares to play the received audio file for the worker.

[0875] Step 7:

[0876] The terminal uses an emotion engine to recognize the user's (worker's) emotions. For example, if it recognizes that the user is feeling stressed, it sends that information to the server.

[0877] Step 8:

[0878] The server can then adjust the voice prompts based on the emotional information, for example, if the user is feeling stressed, it might say something like, "Don't worry, it's a very simple task. Just restart your modem."

[0879] Step 9:

[0880] The terminal plays the adjusted audio file sent from the server to the worker, who then hears an automated voice instruction saying, "A communication error has occurred. Don't worry, it's a very simple task. Please restart the modem." The user then follows the instructions to restart the modem on-site.

[0881] Step 10:

[0882] After the modem has been restarted, the user (service technician) uses the terminal to perform the line activation test again. The terminal sends the results of the retest to the server to confirm whether the error has been resolved.

[0883] Step 11:

[0884] The server receives the results of the retest and, if the error is resolved, ends the process. If the error is not resolved even after the retest, the server runs the AI ​​engine again to analyze the new error code.

[0885] Example 2

[0886] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0887] There is a need for a method to quickly and efficiently respond to errors that occur during line installation work. There is also a need to reduce the emotional burden on users (workers) and improve overall work efficiency.

[0888] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0889] In this invention, the server includes a means for recognizing the user's emotions and adjusting the response procedure, a means for identifying the response procedure based on the error code, and a means for converting the identified response procedure into automated speech, thereby enabling a quick response when an error occurs and emotional support for the user.

[0890] An "error code" is a specific code used to identify problems or abnormalities that occur during line installation work.

[0891] The "means for recognizing the user's emotions and adjusting the response procedure" is a means for analyzing the emotions felt by the user and changing or adapting the instructions based on the analysis.

[0892] The "server" is a central computer system that receives and analyzes data related to line installation work and generates instructions.

[0893] "Means for identifying response procedures" refers to a means for checking a database or knowledge base based on the error code to derive appropriate countermeasures or procedures.

[0894] The "means for converting into automatic speech" is a means for converting the contents of instructions such as text into speech format and conveying it to the user.

[0895] The "means for playing an automated voice to a worker" is a means for playing back the generated voice instruction to a worker who is a user, and providing a specific method of dealing with the problem.

[0896] The present invention relates to a system for dealing with errors that occur during line installation work. The system detects error codes, identifies appropriate response procedures based on the error codes, and flexibly adjusts instructions according to the user's emotions. The specific configuration and operation for implementing the present invention are described in detail below.

[0897] System Configuration

[0898] The terminal is a device used by technicians when performing line installation work. The terminal uses an interactive voice response (IVR) system to perform installation tests and has the function of detecting error codes. When the terminal detects an error code, it sends it to the server in real time.

[0899] The server is a central computer system that receives error codes and uses an AI engine to analyze and identify appropriate response procedures. The server uses the AI ​​engine to match the error code with a database and derive appropriate response procedures. The server also includes an emotion engine that recognizes the user's emotions and adjusts instructions based on those emotions.

[0900] The AI ​​engine is an artificial intelligence system used to identify response procedures based on error codes. The AI ​​engine analyzes error codes based on pre-trained models and extracts the optimal response procedures from a database.

[0901] The automatic voice generation module is a component that converts the response procedures identified by the server into voice and conveys them to the operator. For example, it generates instructions such as, "A communication error has occurred. Please be at ease. Please restart your modem."

[0902] The emotion engine has the ability to recognize the user's emotions and adjust the instructions based on those emotions. The emotion engine analyzes the user's tone of voice and vocabulary to determine the user's emotions, and adjusts the instructions based on that information.

[0903] Specific examples

[0904] For example, consider the case where a worker detects error code "E202" (setting error) while performing a line installation test. At this time, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings." If the emotion engine then recognizes the user's emotions and determines that the worker is feeling anxious, it generates voice instructions that include additional explanations and encouraging words, such as "It's okay. Please check the VLAN settings."

[0905] Prompt Sentence Examples

[0906] Here are some examples of prompts to input to a generative AI model:

[0907] "Error code 'E202' occurred during the line installation test. Analysis of the emotion engine indicates that the user is feeling anxious. Please explain the appropriate response procedure for the error code and suggest ways to calm the user."

[0908] As described above, the system's components work together to provide rapid response when errors occur and emotional support to users, thereby improving the efficiency of line installation work and reducing the burden on workers.

[0909] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0910] Step 1:

[0911] The terminal performs an activation test using an interactive voice response (IVR) system during line activation work.

[0912] Input: IVR system prompts to check line status.

[0913] Data processing / data calculation: The IVR system checks the line connection status and outputs various test results.

[0914] Output: If an error occurs, the error code is detected. For example, the error code "E101" (communication failure) is detected.

[0915] Step 2:

[0916] The device sends the detected error code to the server.

[0917] Input: Detected error code "E101".

[0918] Data calculation: Sends the error code to the server according to the communication protocol.

[0919] Output: The server receives the error code "E101".

[0920] Step 3:

[0921] The server passes the received error code to the AI ​​engine.

[0922] Input: Received error code "E101".

[0923] Data calculation: Input the error code into the AI ​​engine and start analysis.

[0924] Output: As a result of the analysis, the corrective action to be taken is identified as "restarting the modem."

[0925] Step 4:

[0926] The server uses an emotion engine to recognize the user's emotion.

[0927] Input: User voice information and dialogue.

[0928] Data calculation: The emotion engine analyzes voice tone, wording, etc. to determine the user's emotions.

[0929] Output: The user is aware that they are stressed.

[0930] Step 5:

[0931] The server adjusts the response procedure instructions based on the recognized emotion.

[0932] Input: Recognized user emotion information and the corresponding procedure "restart modem."

[0933] Data processing / data calculation: Adjust the text to explain the instructions more clearly.

[0934] Output: Generates the instruction "Don't worry, this is easy to fix. First, restart your modem."

[0935] Step 6:

[0936] The server uses an automatic voice generation module to convert the response instructions into a voice file.

[0937] Input: The adjusted instruction text.

[0938] Data processing / data calculation: Convert text into an audio file using speech synthesis technology.

[0939] Output: Generates a sound file saying "A communication error has occurred. Please do not worry. Please restart your modem."

[0940] Step 7:

[0941] The server transmits the generated voice instructions to the terminal.

[0942] Input: The generated audio file.

[0943] Data calculation: The audio file is sent to the terminal according to the communication protocol.

[0944] Output: The device receives the audio file.

[0945] Step 8:

[0946] The terminal plays automated voice instructions to the worker.

[0947] Input: Received audio file.

[0948] Data processing / data calculation: Play the audio file on an audio playback device.

[0949] Output: The worker can hear the voice instructions.

[0950] Step 9:

[0951] The user (worker) follows the automated voice prompts and takes the necessary measures on-site.

[0952] Input: Automated voice instructions.

[0953] Data operation: Performing a physical operation based on instructions (e.g., rebooting a modem).

[0954] Output: Corrective action completed and error resolved.

[0955] (Application example 2)

[0956] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0957] When an error occurs during line installation work or at a work site such as a factory, a quick and appropriate response is required. However, currently, there are no standardized response procedures when an error occurs, which places a heavy burden on workers. In addition, the emotional burden on workers is not taken into consideration, which can increase worker stress and reduce work efficiency. To solve these issues, a system is needed that can respond quickly and appropriately when an error occurs, reduce the work burden on workers, improve overall work efficiency, and also reduce the emotional burden on workers.

[0958] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0959] In this invention, the server includes a means for identifying a response procedure based on an error code, a means for converting the identified response procedure into an automated voice, an emotion recognition means for recognizing the emotion of a worker, and a means for adjusting the response procedure based on the emotion recognition, thereby making it possible to provide an appropriate response procedure that takes into account the emotional state of the worker when an error occurs.

[0960] An "error code" is an identification number used to identify problems or failures that occur during work.

[0961] A "server" is a central computer system that receives error codes and performs analysis and generates response procedures.

[0962] The "response procedure" is a procedure that instructs the specific actions and operations that the worker should take based on the error code.

[0963] "Automatic voice" is a voice that has the function of converting text information into voice and playing it back.

[0964] "Workers" refers to people who actually perform work on-site, such as installing lines or working in factories.

[0965] "Emotion recognition" is a technology that automatically detects a worker's emotions and understands their state.

[0966] An "emotion recognition means" is a device or system that uses a camera or sensor to detect the emotions of workers.

[0967] An "AI engine" is a system that uses artificial intelligence to analyze data and automatically determine the appropriate response.

[0968] A "database" is an information management system that stores information such as error codes and response procedures and can be referenced as needed.

[0969] The present invention provides a system for quickly and appropriately dealing with errors that occur during line installation work or at a factory work site, thereby improving work efficiency. Specific embodiments of the system will be described below.

[0970] System Configuration

[0971] This system consists of the following main components:

[0972] 1. Terminal: A device used by a worker, equipped with an IVR system to detect errors and send an error code to the server when an error occurs.

[0973] 2. Server: A central computer system that receives and analyzes error codes and identifies response procedures. It has emotion recognition capabilities and adjusts instructions based on the emotions of workers.

[0974] 3. AI Engine: An artificial intelligence system that references a database based on the error code and generates appropriate response procedures.

[0975] 4. Automatic speech generation module: Converts the response procedures identified by the server into speech and conveys it to the worker.

[0976] 5. Emotion recognition means: Recognize the emotional state of workers using cameras and other sensors.

[0977] Program processing

[0978] The server receives information sent from each component and performs the following data processing and calculations.

[0979] 1. Error detection and transmission: The terminal detects an error code using the IVR system during the line installation process and transmits it to the server.

[0980] 2. Error code analysis: The server passes the received error code to the AI ​​engine, which checks the database to identify the corrective action. For example, if the error code "E101" is detected, "restart the modem" is identified as the corrective action.

[0981] 3. Emotion recognition: Emotion recognition means analyzes data obtained from cameras and sensors to understand the emotional state of the worker. For example, if stress is detected, the system will carefully explain the steps to take.

[0982] 4. Automatic voice generation: The server converts the analyzed response procedures into a voice file and generates instructions such as, "A communication error has occurred. Please do not worry. Please restart your modem."

[0983] 5. Instructions to workers: The generated voice instructions are sent to the terminal, which then plays the instructions to the worker in automated voice.

[0984] Hardware and software used

[0985] Camera: for worker emotion recognition

[0986] Smart glasses: devices for displaying visual instructions

[0987] Software for generating voice instructions (specific speech synthesis engines used include pyttsx3)

[0988] AI engine: for error analysis (e.g., a custom AI model implemented in Python)

[0989] Specific examples

[0990] For example, if an error code "E300" (part placement error) is detected while a work robot is installing a part in a factory, the following prompt sentence will be generated:

[0991] error: E300

[0992] Emotion: stress

[0993] response: Don't worry, please recheck the placement of the component.

[0994] Based on this prompt, the server will generate a voice instruction, "Don't worry, please double-check the placement of the parts," which will be displayed on the smart glasses, allowing the worker to immediately understand and take the appropriate response steps.

[0995] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0996] Step 1:

[0997] The terminal uses the IVR system to detect error codes during the line activation process. If an error code is detected, it is sent to the terminal as input data. The terminal then sends this error code to the server. Specifically, the terminal detects the error code and sends it to a specific API endpoint on the server.

[0998] Step 2:

[0999] The server receives the error code sent from the terminal. The received error code becomes input data and is passed to the AI ​​engine in the server. The AI ​​engine checks the database based on the error code and identifies the response procedure. For example, if the error code "E101" is the input data, the AI ​​engine generates the response procedure "restart the modem."

[1000] Step 3:

[1001] The server receives data from cameras and sensors and recognizes the emotional state of the worker using emotion recognition means. The emotion recognition means receives input data from camera footage and sensor data, and detects emotions based on this. The server adjusts instructions based on the analysis results.

[1002] Step 4:

[1003] The server integrates the response procedures from the AI ​​engine with the emotional data from the emotion recognition means. For example, if the server detects that a worker is feeling stressed, it adds additional explanations or words of encouragement to the response procedures. It inputs prompt sentences into the generative AI model and adds phrases such as "Don't worry" or "There's nothing to worry about."

[1004] Step 5:

[1005] The server passes the adjusted response instructions to an automatic speech generation module to generate an audio file, where the input data is the adjusted text instructions and the output data is the audio file. The automatic speech generation module converts the text into speech using a specific speech synthesis engine (e.g., pyttsx3).

[1006] Step 6:

[1007] The server sends the generated audio file to the terminal, and the terminal plays instructions to the worker in an automated voice. Specifically, the terminal plays the audio file, and the worker listens to it and executes the corresponding procedure. For example, the voice instruction "A communication error has occurred. Please do not worry. Please restart the modem" is played.

[1008] These steps allow for a quick and appropriate response when an error occurs, and provide effective instructions that take into account the emotional state of the worker.

[1009] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1010] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1011] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1012] [Fourth embodiment]

[1013] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1014] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1015] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1016] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1017] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1018] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1019] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1020] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1021] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1022] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1023] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1024] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1025] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1026] The present invention relates to a system that uses AI to automatically handle errors that occur during line installation work. The purpose of this system is to reduce the workload of workers and improve overall work efficiency by quickly and efficiently handling errors when they occur. Detailed embodiments of this system are described below.

[1027] System Configuration

[1028] This system consists of the following main components:

[1029] 1. Terminal: A device used by technicians when installing lines. The terminal uses an IVR system to perform installation tests and detect error codes.

[1030] 2. Server: A central computer system that receives error codes and uses an AI engine to analyze and identify response procedures.

[1031] 3. AI Engine: An artificial intelligence system that checks the database based on the error code and generates appropriate response procedures.

[1032] 4. Automatic speech generation module: This component converts the response procedures identified on the server into speech and conveys them to workers.

[1033] Program processing

[1034] 1. Error detection and transmission

[1035] The terminal detects errors that occur during the line installation test by the technician. For example, suppose the terminal detects the error code "E101" indicating a communication failure. The terminal sends this error code to the server.

[1036] 2. Error Code Analysis

[1037] The server receives the error code. After receiving it, the server analyzes the error code using an AI engine and identifies the appropriate response procedure. For example, it identifies "restarting the modem" as the response procedure for error code "E101."

[1038] 3. Automatic voice generation

[1039] The server passes the identified response procedure to an automatic voice generation module to generate voice instructions. In this case, the instruction "A communication error has occurred. Please restart your modem" is converted into voice.

[1040] 4. Instructions to workers

[1041] The generated voice instructions are sent to a terminal, which then plays the instructions back to the worker in an automated voice. The worker receives these instructions, follows the response procedures, and takes the necessary action on site.

[1042] Specific examples

[1043] For example, consider the case where a worker detects error code "E202" (configuration error) while performing a line installation test. In this case, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings," and passes this to the automatic voice generation module. The generated voice instruction, "A configuration error has occurred. Please check the VLAN settings," is sent to the terminal, and the terminal plays the voice instruction to the worker. The worker checks the VLAN settings on-site and corrects the error by taking corrective action.

[1044] In this way, the operations at each processing step are performed in coordination, enabling a quick response when an error occurs, thereby improving the efficiency of line installation work and reducing the burden on workers.

[1045] The processing flow will be explained below.

[1046] Step 1:

[1047] The terminal performs an activation test using the IVR system during the line activation work. If an error occurs, the terminal detects the error code. For example, the terminal detects the error code "E101" (communication failure).

[1048] Step 2:

[1049] The terminal transmits the detected error code "E101" to the server using a communication means for transmitting the error code to the server.

[1050] Step 3:

[1051] The server records and saves the error code received from the device, then passes it to the AI ​​engine to begin analysis.

[1052] Step 4:

[1053] The AI ​​engine analyzes the error code "E101" it receives and checks it against a database, which contains past error codes and the corresponding procedures. The AI ​​engine identifies "restarting the modem" as the procedure for resolving the "poor communication" issue.

[1054] Step 5:

[1055] Based on the response procedure "restart the modem" obtained from the AI ​​engine, the server uses an automatic voice generation module to convert the response instructions into a voice file. Specifically, it generates the instruction "A communication error has occurred. Please restart the modem."

[1056] Step 6:

[1057] The server sends the generated audio file to the terminal, which then plays the received audio file for the worker.

[1058] Step 7:

[1059] The user (worker) hears the automated voice instruction played from the terminal: "A communication error has occurred. Please restart the modem," and follows the instructions to restart the modem on-site.

[1060] Step 8:

[1061] After the modem has been restarted, the user (service technician) uses the terminal to perform the line activation test again. The terminal sends the results of the retest to the server to confirm whether the error has been resolved.

[1062] Step 9:

[1063] The server receives the results of the retest and, if the error is resolved, ends the process. If the error is not resolved even after the retest, the server runs the AI ​​engine again to analyze the new error code.

[1064] Example 1

[1065] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1066] It is necessary to reduce the workload of workers and improve overall work efficiency by quickly and efficiently responding to errors that occur during line installation work. However, with the conventional system, error analysis and response took time, causing work delays. In addition, there were cases where workers were unable to respond appropriately because response procedures were not clear.

[1067] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1068] In this invention, the server includes a means for analyzing error codes, a means for generating response procedures using an AI engine, and a means for converting the response procedures into automated voice, which enables quick and appropriate response procedures to be provided to workers when an error occurs.

[1069] A "terminal" is a device used by a worker that has the function of detecting error codes during line installation work.

[1070] An "error code" is a code used to identify problems that occur during line installation work, and indicates a specific error condition.

[1071] "Server" is a central computer system that receives error codes, analyzes them, identifies appropriate response procedures, and communicates them to personnel.

[1072] The "AI engine" is an artificial intelligence system that analyzes error codes and generates appropriate response procedures by referencing a past database.

[1073] "Response procedures" are specific procedures for resolving a problem that are identified based on the error code.

[1074] The "automatic voice generation module" is a component that converts the response procedures identified by the server into voice and conveys them to workers.

[1075] "Internet connection" is a communication means by which the terminal sends error codes to the server, and is used to transmit information in real time.

[1076] A "line activation test" is a test conducted to confirm that the line is operating normally, and is carried out as part of error detection.

[1077] "IVR system" is an abbreviation for interactive voice response system, which allows workers to perform operations by following voice instructions.

[1078] A "database" is a collection of information that stores error codes and response procedure information, and is a data source referenced by the AI ​​engine.

[1079] This invention relates to a system that uses AI to automatically handle errors that occur during line installation work. The purpose of this system is to reduce the workload of workers and improve overall work efficiency by quickly and efficiently handling errors when they occur. To specifically implement this invention, the following hardware and software are used.

[1080] System Configuration

[1081] This system consists of the following main components:

[1082] 1. Terminal: A device used by technicians when installing lines. The terminal uses an IVR system to perform installation tests and detect error codes.

[1083] 2. Server: A central computer system that receives error codes and uses an AI engine to analyze and identify response procedures.

[1084] 3. AI Engine: An artificial intelligence system that checks the database based on the error code and generates appropriate response procedures.

[1085] 4. Automatic speech generation module: This component converts the response procedures identified on the server into speech and conveys them to workers.

[1086] Program processing

[1087] When a technician performs a line installation test, the terminal uses an IVR system to perform an error check. For example, suppose a communication failure is discovered and the error code "E101" is detected. The terminal sends the detected error code to the server. When the error code "E101" is detected, the terminal sends this error code to the server via an internet connection using a dedicated protocol.

[1088] The server analyzes the received error code. When the server receives the error code "E101," it inputs it into the AI ​​engine running on the backend. The AI ​​engine checks this error code against a database and interprets the detailed contents of the error. It generates an appropriate response procedure based on the analysis results. For example, "E101" indicates a communication failure, and the recommended response procedure is to "restart the modem." The AI ​​engine identifies this procedure and returns it to the server.

[1089] The server then passes this response procedure to an automatic voice generation module, which generates a voice instruction saying, "A communication error has occurred. Please restart the modem." The server then sends this voice instruction to the terminal. The terminal then plays the received voice instruction back to the worker. The worker follows this instruction and takes the necessary measures on site.

[1090] Specific examples

[1091] For example, consider the case where a worker detects error code "E202" (configuration error) while performing a line installation test. In this case, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings," and passes this to the automatic voice generation module. The generated voice instruction, "A configuration error has occurred. Please check the VLAN settings," is sent to the terminal, and the terminal plays the voice instruction to the worker. The worker checks the VLAN settings on-site and corrects the error by taking corrective action.

[1092] Prompt Sentence Examples

[1093] The following are examples of prompt sentences that explain the detailed processing of the system:

[1094] Please explain the detailed processing of the system.

[1095] 1. If an error code occurs when a technician performs a line installation test, the terminal sends the error code to the server.

[1096] 2. The server passes the received error code to the AI ​​engine, which analyzes the error code and identifies the appropriate response procedure.

[1097] 3. The server passes the identified response steps to the automatic voice generation module to generate voice instructions.

[1098] 4. The generated voice instructions are sent to the terminal, which then plays the voice instructions to the worker.

[1099] 5. Workers follow the voice instructions and take the necessary action on site.

[1100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1101] Step 1: Error detection

[1102] The terminal uses the IVR system to perform error checks when a technician performs a line installation test. For example, if a communication error occurs while the technician is testing the line, the terminal detects the error code "E101." The input here is the communication status during the line installation test, and the output is the detected error code "E101."

[1103] Step 2: Send Error

[1104] The terminal sends the detected error code "E101" to the server. The error code is encrypted and sent to the server via an Internet connection. At this time, the input is the error code "E101" and the output is the error data sent to the server. The terminal confirms the success of the error transmission by recording the transmission log.

[1105] Step 3: Error analysis

[1106] The server immediately analyzes the received error code "E101." The server inputs the error code into an AI engine, which then checks the error code against a database. The input is the error code "E101," and the output is the analyzed error details. This analysis includes a database query that references historical data.

[1107] Step 4: Generate response procedures

[1108] The AI ​​engine generates appropriate response procedures based on the error code analysis results. For example, "E101" indicates a communication failure, and the recommended response procedure is "restarting the modem." The input is the error analysis result, and the output is the identified response procedure, "restarting the modem."

[1109] Step 5: Generate voice instructions

[1110] The server passes the response procedures received from the AI ​​engine to the automatic voice generation module, which generates voice instructions. For example, it generates a voice instruction such as, "A communication error has occurred. Please restart the modem." The input here is the identified response procedures, and the output is the generated voice instruction data. High-resolution voice synthesis technology is used in the voice generation process.

[1111] Step 6: Send voice instructions

[1112] The server sends the generated voice instruction data to the terminal. The server transfers the corresponding procedure voice file to the terminal through a real-time communication protocol. The input is the voice instruction data, and the output is the voice data sent to the terminal. The data packets being sent use a checksum to ensure quality.

[1113] Step 7: Implement a response

[1114] After receiving the voice data, the terminal plays it back to the worker. The worker hears the voice instruction "A communication problem has occurred. Please restart the modem," and takes the necessary measures on-site according to the response procedure. The input here is the received voice data, and the output is the voice instruction played back to the worker. In concrete terms, the worker restarts the modem on-site and resolves the communication problem.

[1115] (Application example 1)

[1116] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1117] The problem to be solved by this invention is to provide a system that can respond quickly and efficiently when errors or abnormalities occur during line installation work and anomaly detection work for autonomous vehicles, thereby reducing the workload of workers and drivers and improving overall work efficiency and safety.

[1118] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1119] In this invention, the server includes a means for identifying a response procedure based on an error code or an abnormality signal, a means for converting the identified response procedure into an automated voice, and a means for playing back the automated voice to the worker or driver, thereby enabling an appropriate response procedure to be presented immediately when an error or abnormality occurs.

[1120] "Line activation work" refers to a series of tasks required to install a new communication line and ensure that it operates normally.

[1121] "Anomaly detection work" refers to a series of tasks to monitor whether equipment and systems are operating normally and to detect abnormalities early.

[1122] An "error code" is a combination of numbers and characters used to identify an abnormality or failure detected by a system or device.

[1123] An "abnormal signal" is a specific signal that is sent to indicate an abnormal condition of a system or device.

[1124] A "server" is a computer system for processing data and communicating with other devices over a network.

[1125] A "response procedure" is a series of steps to be taken when a specific error or abnormality occurs.

[1126] "Automatic speech" is a synthesized speech generated from text or other input data.

[1127] "Worker" refers to a person in charge of tasks such as line installation work and troubleshooting.

[1128] A "driver" is a person whose role is to monitor and control an automated vehicle.

[1129] "Automatic speech generation means" refers to technology or devices for converting text data into speech data.

[1130] An "AI engine" is a system that uses artificial intelligence technology to analyze data and solve problems.

[1131] The present invention is a system for responding to errors and abnormalities that occur during line installation work and anomaly detection work for autonomous vehicles. The system aims to detect error codes or abnormality signals in real time, automatically generate response procedures, and communicate them to the worker or driver via voice. A specific embodiment of the present invention is described below.

[1132] System Configuration

[1133] This system consists of the following main components:

[1134] 1. Error detection device

[1135] These are devices for detecting error codes or abnormal signals in real time, and examples include terminals used in line installation work and sensor units in self-driving vehicles.

[1136] 2. Server System

[1137] A central computer system that receives error codes or abnormal signals and identifies response procedures. The server system includes an AI engine (e.g., TensorFlow or PyTorch) and a voice generation system (e.g., Google Text-to-Speech).

[1138] 3. Communication Module

[1139] This module transmits and receives data between the detection device and the server, and between the server and the user device. This module is used to transmit error codes or abnormality signals and receive response procedures.

[1140] 4. User Devices

[1141] A device used to communicate response procedures to workers or drivers, such as a smartphone or head-mounted display.

[1142] Program processing

[1143] The server performs the following processing based on the error code or abnormal signal.

[1144] 1. Data Reception

[1145] Receive error codes or abnormal signals via the communication module.

[1146] 2. Analysis by AI engine

[1147] The AI ​​engine analyzes the received error code or abnormal signal and identifies the corrective action. For example, if the error code "E301" is received, the AI ​​engine will identify "recalibration of the sensor" as the corrective action.

[1148] 3. Automatic speech generation

[1149] The identified response procedure is converted into automated speech by a speech generation system, for example, generating instructions such as "A sensor malfunction has occurred. Please recalibrate the sensor."

[1150] 4. Sending voice commands

[1151] Automated voice instructions are sent to the user device via the communications module.

[1152] Examples of specific examples and prompts

[1153] For example, if an autonomous vehicle detects the error code "E301," which indicates a sensor malfunction, the following process takes place: The server receives the error code "E301," and uses an AI engine to identify the corrective action, "recalibrate the sensor." The voice generation system then generates a voice instruction saying, "A sensor malfunction has occurred. Please recalibrate the sensor," and sends it to the user device.

[1154] Example prompts for generative AI models:

[1155] Error code E301 detected. Please auto-generate appropriate response steps. If recommending sensor recalibration, please provide text to generate voice prompts.

[1156] In this way, the present invention can provide efficient and rapid response procedures for line installation work and abnormality detection work for autonomously driven vehicles.

[1157] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1158] Step 1:

[1159] The terminal detects an error code or abnormal signal during line installation work or while an autonomous vehicle is in operation. For example, the error code "E101" indicating poor communication or an abnormal signal indicating a sensor malfunction is detected. The input at this point is the error code or abnormal signal generated on-site, which is acquired in real time by the terminal's sensor unit, etc. The output is the data of the detected error code or abnormal signal.

[1160] Step 2:

[1161] The error code or abnormal signal data detected by the terminal is sent to the server. A communication module is used for this transmission. The input is the error code or abnormal signal data obtained in step 1, and the output is the data sent to the server.

[1162] Step 3:

[1163] The AI ​​engine analyzes the error code or abnormal signal received by the server. First, it checks against a database to see past response history for the same error or abnormality. The input is the data of the error code or abnormal signal that was sent, and the output is the identified candidate response procedure. For example, for "E101," "restart the modem" is identified as the response procedure.

[1164] Step 4:

[1165] The server passes the response procedures identified by the AI ​​engine to the speech generation system, which converts them into automated speech. For example, the text instruction "Please restart your modem" is converted into speech data. The input is the text data of the response procedures, and the output is automated speech data.

[1166] Step 5:

[1167] The server transmits the generated automated voice data to the terminal via the communication module. The input is the generated automated voice data, and the output is the automated voice data transmitted to the terminal.

[1168] Step 6:

[1169] The terminal plays an automated voice message and gives instructions to the worker or driver. The worker or driver follows these voice instructions to carry out the appropriate procedures. The input is the automated voice data sent to the terminal, and the output is voice instructions to the worker or driver. Specific actions include the worker restarting the modem and checking the settings.

[1170] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1171] The present invention relates to a system for dealing with errors that occur during line installation work. In particular, this system provides more flexible and effective response procedures by incorporating an emotion engine that recognizes the user's emotions. The purpose of this system is to respond quickly and efficiently when an error occurs, reduce the workload of workers, improve overall work efficiency, and also reduce the emotional burden on users. Detailed embodiments of this system are described below.

[1172] System Configuration

[1173] This system consists of the following main components:

[1174] 1. Terminal: A device used by technicians when installing lines. The terminal uses an IVR system to perform installation tests and detect error codes.

[1175] 2. Server: A central computer system that receives error codes and uses an AI engine to analyze and identify response procedures. It also includes an emotion engine that recognizes user emotions and adjusts instructions as needed.

[1176] 3. AI Engine: An artificial intelligence system that checks the database based on the error code and generates appropriate response procedures.

[1177] 4. Automatic speech generation module: This component converts the response procedures identified on the server into speech and conveys them to workers.

[1178] 5. Emotion engine: It has the ability to recognize the user's emotions and adjust the instructions based on those emotions.

[1179] Program processing

[1180] 1. Error detection and transmission

[1181] The terminal uses the IVR system to perform an activation test during the line activation process. If an error occurs, the terminal detects the error code. For example, it detects the error code "E101" (poor communication). The terminal then sends this error code to the server.

[1182] 2. Error Code Analysis

[1183] The server receives the transmitted error code. Next, the server passes the received error code to the AI ​​engine and begins analysis. The AI ​​engine checks the database based on the error code and identifies the appropriate response procedure. For example, it identifies "restarting the modem" as the response procedure for error code "E101."

[1184] 3. Emotional Recognition

[1185] The server uses an emotion engine to recognize the user's (worker's) emotions. For example, it may recognize that the user is feeling stressed. Based on this information, the server adjusts the instructions for the response procedure. For example, if the user is feeling stressed, the instructions may be explained more carefully.

[1186] 4. Automatic voice generation

[1187] Based on the identified response procedure, the server uses an automatic voice generation module to convert the response instructions into a voice file, for example, "A communication error has occurred. Please do not worry. Please restart your modem."

[1188] 5. Instructions to workers

[1189] The generated voice instructions are sent to a terminal, which then plays the instructions back to the worker in an automated voice format. The worker receives these instructions and takes the necessary action on-site.

[1190] Specific examples

[1191] For example, consider the case where a worker detects error code "E202" (setting error) while performing a line installation test. At this time, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings." If the emotion engine then recognizes the user's emotions and determines that the worker is feeling anxious, it generates voice instructions that include additional explanations and encouraging words, such as "It's okay. Please check the VLAN settings."

[1192] In this way, the operations at each processing step are coordinated, enabling a quick response when an error occurs. This makes line installation work more efficient, reduces the burden on workers, and provides emotional support.

[1193] The processing flow will be explained below.

[1194] Step 1:

[1195] The terminal performs an activation test using the IVR system during the line activation work. If an error occurs, the terminal detects the error code. For example, the terminal detects the error code "E101" (communication failure).

[1196] Step 2:

[1197] The terminal transmits the detected error code "E101" to the server using a communication means for transmitting the error code to the server.

[1198] Step 3:

[1199] The server receives the error code sent from the device, then passes it to the AI ​​engine and begins analysis.

[1200] Step 4:

[1201] The AI ​​engine analyzes the error code "E101" it receives and checks it against a database, which contains past error codes and the corresponding procedures. The AI ​​engine identifies "restarting the modem" as the procedure for resolving the "poor communication" issue.

[1202] Step 5:

[1203] The server uses an automatic voice generation module to convert the response instruction "restart the modem" obtained from the AI ​​engine into a voice file. For example, it generates an instruction such as "A communication error has occurred. Please restart the modem."

[1204] Step 6:

[1205] The server sends the generated audio file to the terminal, which then prepares to play the received audio file for the worker.

[1206] Step 7:

[1207] The terminal uses an emotion engine to recognize the user's (worker's) emotions. For example, if it recognizes that the user is feeling stressed, it sends that information to the server.

[1208] Step 8:

[1209] The server can then adjust the voice prompts based on the emotional information, for example, if the user is feeling stressed, it might say something like, "Don't worry, it's a very simple task. Just restart your modem."

[1210] Step 9:

[1211] The terminal plays the adjusted audio file sent from the server to the worker, who then hears an automated voice instruction saying, "A communication error has occurred. Don't worry, it's a very simple task. Please restart the modem." The user then follows the instructions to restart the modem on-site.

[1212] Step 10:

[1213] After the modem has been restarted, the user (service technician) uses the terminal to perform the line activation test again. The terminal sends the results of the retest to the server to confirm whether the error has been resolved.

[1214] Step 11:

[1215] The server receives the results of the retest and, if the error is resolved, ends the process. If the error is not resolved even after the retest, the server runs the AI ​​engine again to analyze the new error code.

[1216] Example 2

[1217] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1218] There is a need for a method to quickly and efficiently respond to errors that occur during line installation work. There is also a need to reduce the emotional burden on users (workers) and improve overall work efficiency.

[1219] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1220] In this invention, the server includes a means for recognizing the user's emotions and adjusting the response procedure, a means for identifying the response procedure based on the error code, and a means for converting the identified response procedure into automated speech, thereby enabling a quick response when an error occurs and emotional support for the user.

[1221] An "error code" is a specific code used to identify problems or abnormalities that occur during line installation work.

[1222] The "means for recognizing the user's emotions and adjusting the response procedure" is a means for analyzing the emotions felt by the user and changing or adapting the instructions based on the analysis.

[1223] The "server" is a central computer system that receives and analyzes data related to line installation work and generates instructions.

[1224] "Means for identifying response procedures" refers to a means for checking a database or knowledge base based on the error code to derive appropriate countermeasures or procedures.

[1225] The "means for converting into automatic speech" is a means for converting the contents of instructions such as text into speech format and conveying it to the user.

[1226] The "means for playing an automated voice to a worker" is a means for playing back the generated voice instruction to a worker who is a user, and providing a specific method of dealing with the problem.

[1227] The present invention relates to a system for dealing with errors that occur during line installation work. The system detects error codes, identifies appropriate response procedures based on the error codes, and flexibly adjusts instructions according to the user's emotions. The specific configuration and operation for implementing the present invention are described in detail below.

[1228] System Configuration

[1229] The terminal is a device used by technicians when performing line installation work. The terminal uses an interactive voice response (IVR) system to perform installation tests and has the function of detecting error codes. When the terminal detects an error code, it sends it to the server in real time.

[1230] The server is a central computer system that receives error codes and uses an AI engine to analyze and identify appropriate response procedures. The server uses the AI ​​engine to match the error code with a database and derive appropriate response procedures. The server also includes an emotion engine that recognizes the user's emotions and adjusts instructions based on those emotions.

[1231] The AI ​​engine is an artificial intelligence system used to identify response procedures based on error codes. The AI ​​engine analyzes error codes based on pre-trained models and extracts the optimal response procedures from a database.

[1232] The automatic voice generation module is a component that converts the response procedures identified by the server into voice and conveys them to the operator. For example, it generates instructions such as, "A communication error has occurred. Please be at ease. Please restart your modem."

[1233] The emotion engine has the ability to recognize the user's emotions and adjust the instructions based on those emotions. The emotion engine analyzes the user's tone of voice and vocabulary to determine the user's emotions, and adjusts the instructions based on that information.

[1234] Specific examples

[1235] For example, consider the case where a worker detects error code "E202" (setting error) while performing a line installation test. At this time, the terminal sends error code "E202" to the server. The server passes the received error code to the AI ​​engine, which checks the database to confirm the corrective action. The AI ​​engine identifies the corrective action, such as "Check the VLAN settings." If the emotion engine then recognizes the user's emotions and determines that the worker is feeling anxious, it generates voice instructions that include additional explanations and encouraging words, such as "It's okay. Please check the VLAN settings."

[1236] Prompt Sentence Examples

[1237] Here are some examples of prompts to input to a generative AI model:

[1238] "Error code 'E202' occurred during the line installation test. Analysis of the emotion engine indicates that the user is feeling anxious. Please explain the appropriate response procedure for the error code and suggest ways to calm the user."

[1239] As described above, the system's components work together to provide rapid response when errors occur and emotional support to users, thereby improving the efficiency of line installation work and reducing the burden on workers.

[1240] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1241] Step 1:

[1242] The terminal performs an activation test using an interactive voice response (IVR) system during line activation work.

[1243] Input: IVR system prompts to check line status.

[1244] Data processing / data calculation: The IVR system checks the line connection status and outputs various test results.

[1245] Output: If an error occurs, the error code is detected. For example, the error code "E101" (communication failure) is detected.

[1246] Step 2:

[1247] The device sends the detected error code to the server.

[1248] Input: Detected error code "E101".

[1249] Data calculation: Sends the error code to the server according to the communication protocol.

[1250] Output: The server receives the error code "E101".

[1251] Step 3:

[1252] The server passes the received error code to the AI ​​engine.

[1253] Input: Received error code "E101".

[1254] Data calculation: Input the error code into the AI ​​engine and start analysis.

[1255] Output: As a result of the analysis, the corrective action to be taken is identified as "restarting the modem."

[1256] Step 4:

[1257] The server uses an emotion engine to recognize the user's emotion.

[1258] Input: User voice information and dialogue.

[1259] Data calculation: The emotion engine analyzes voice tone, wording, etc. to determine the user's emotions.

[1260] Output: The user is aware that they are stressed.

[1261] Step 5:

[1262] The server adjusts the response procedure instructions based on the recognized emotion.

[1263] Input: Recognized user emotion information and the corresponding procedure "restart modem."

[1264] Data processing / data calculation: Adjust the text to explain the instructions more clearly.

[1265] Output: Generates the instruction "Don't worry, this is easy to fix. First, restart your modem."

[1266] Step 6:

[1267] The server uses an automatic voice generation module to convert the response instructions into a voice file.

[1268] Input: The adjusted instruction text.

[1269] Data processing / data calculation: Convert text into an audio file using speech synthesis technology.

[1270] Output: Generates a sound file saying "A communication error has occurred. Please do not worry. Please restart your modem."

[1271] Step 7:

[1272] The server transmits the generated voice instructions to the terminal.

[1273] Input: The generated audio file.

[1274] Data calculation: The audio file is sent to the terminal according to the communication protocol.

[1275] Output: The device receives the audio file.

[1276] Step 8:

[1277] The terminal plays automated voice instructions to the worker.

[1278] Input: Received audio file.

[1279] Data processing / data calculation: Play the audio file on an audio playback device.

[1280] Output: The worker can hear the voice instructions.

[1281] Step 9:

[1282] The user (worker) follows the automated voice prompts and takes the necessary measures on-site.

[1283] Input: Automated voice instructions.

[1284] Data operation: Performing a physical operation based on instructions (e.g., rebooting a modem).

[1285] Output: Corrective action completed and error resolved.

[1286] (Application example 2)

[1287] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1288] When an error occurs during line installation work or at a work site such as a factory, a quick and appropriate response is required. However, currently, there are no standardized response procedures when an error occurs, which places a heavy burden on workers. In addition, the emotional burden on workers is not taken into consideration, which can increase worker stress and reduce work efficiency. To solve these issues, a system is needed that can respond quickly and appropriately when an error occurs, reduce the work burden on workers, improve overall work efficiency, and also reduce the emotional burden on workers.

[1289] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1290] In this invention, the server includes a means for identifying a response procedure based on an error code, a means for converting the identified response procedure into an automated voice, an emotion recognition means for recognizing the emotion of a worker, and a means for adjusting the response procedure based on the emotion recognition, thereby making it possible to provide an appropriate response procedure that takes into account the emotional state of the worker when an error occurs.

[1291] An "error code" is an identification number used to identify problems or failures that occur during work.

[1292] A "server" is a central computer system that receives error codes and performs analysis and generates response procedures.

[1293] The "response procedure" is a procedure that instructs the specific actions and operations that the worker should take based on the error code.

[1294] "Automatic voice" is a voice that has the function of converting text information into voice and playing it back.

[1295] "Workers" refers to people who actually perform work on-site, such as installing lines or working in factories.

[1296] "Emotion recognition" is a technology that automatically detects a worker's emotions and understands their state.

[1297] An "emotion recognition means" is a device or system that uses a camera or sensor to detect the emotions of workers.

[1298] An "AI engine" is a system that uses artificial intelligence to analyze data and automatically determine the appropriate response.

[1299] A "database" is an information management system that stores information such as error codes and response procedures and can be referenced as needed.

[1300] The present invention provides a system for quickly and appropriately dealing with errors that occur during line installation work or at a factory work site, thereby improving work efficiency. Specific embodiments of the system will be described below.

[1301] System Configuration

[1302] This system consists of the following main components:

[1303] 1. Terminal: A device used by a worker, equipped with an IVR system to detect errors and send an error code to the server when an error occurs.

[1304] 2. Server: A central computer system that receives and analyzes error codes and identifies response procedures. It has emotion recognition capabilities and adjusts instructions based on the emotions of workers.

[1305] 3. AI Engine: An artificial intelligence system that references a database based on the error code and generates appropriate response procedures.

[1306] 4. Automatic speech generation module: Converts the response procedures identified by the server into speech and conveys it to the worker.

[1307] 5. Emotion recognition means: Recognize the emotional state of workers using cameras and other sensors.

[1308] Program processing

[1309] The server receives information sent from each component and performs the following data processing and calculations.

[1310] 1. Error detection and transmission: The terminal detects an error code using the IVR system during the line installation process and transmits it to the server.

[1311] 2. Error code analysis: The server passes the received error code to the AI ​​engine, which checks the database to identify the corrective action. For example, if the error code "E101" is detected, "restart the modem" is identified as the corrective action.

[1312] 3. Emotion recognition: Emotion recognition means analyzes data obtained from cameras and sensors to understand the emotional state of the worker. For example, if stress is detected, the system will carefully explain the steps to take.

[1313] 4. Automatic voice generation: The server converts the analyzed response procedures into a voice file and generates instructions such as, "A communication error has occurred. Please do not worry. Please restart your modem."

[1314] 5. Instructions to workers: The generated voice instructions are sent to the terminal, which then plays the instructions to the worker in automated voice.

[1315] Hardware and software used

[1316] Camera: for worker emotion recognition

[1317] Smart glasses: devices for displaying visual instructions

[1318] Software for generating voice instructions (specific speech synthesis engines used include pyttsx3)

[1319] AI engine: for error analysis (e.g., a custom AI model implemented in Python)

[1320] Specific examples

[1321] For example, if an error code "E300" (part placement error) is detected while a work robot is installing a part in a factory, the following prompt sentence will be generated:

[1322] error: E300

[1323] Emotion: stress

[1324] response: Don't worry, please recheck the placement of the component.

[1325] Based on this prompt, the server will generate a voice instruction, "Don't worry, please double-check the placement of the parts," which will be displayed on the smart glasses, allowing the worker to immediately understand and take the appropriate response steps.

[1326] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1327] Step 1:

[1328] The terminal uses the IVR system to detect error codes during the line activation process. If an error code is detected, it is sent to the terminal as input data. The terminal then sends this error code to the server. Specifically, the terminal detects the error code and sends it to a specific API endpoint on the server.

[1329] Step 2:

[1330] The server receives the error code sent from the terminal. The received error code becomes input data and is passed to the AI ​​engine in the server. The AI ​​engine checks the database based on the error code and identifies the response procedure. For example, if the error code "E101" is the input data, the AI ​​engine generates the response procedure "restart the modem."

[1331] Step 3:

[1332] The server receives data from cameras and sensors and recognizes the emotional state of the worker using emotion recognition means. The emotion recognition means receives input data from camera footage and sensor data, and detects emotions based on this. The server adjusts instructions based on the analysis results.

[1333] Step 4:

[1334] The server integrates the response procedures from the AI ​​engine with the emotional data from the emotion recognition means. For example, if the server detects that a worker is feeling stressed, it adds additional explanations or words of encouragement to the response procedures. It inputs prompt sentences into the generative AI model and adds phrases such as "Don't worry" or "There's nothing to worry about."

[1335] Step 5:

[1336] The server passes the adjusted response instructions to an automatic speech generation module to generate an audio file, where the input data is the adjusted text instructions and the output data is the audio file. The automatic speech generation module converts the text into speech using a specific speech synthesis engine (e.g., pyttsx3).

[1337] Step 6:

[1338] The server sends the generated audio file to the terminal, and the terminal plays instructions to the worker in an automated voice. Specifically, the terminal plays the audio file, and the worker listens to it and executes the corresponding procedure. For example, the voice instruction "A communication error has occurred. Please do not worry. Please restart the modem" is played.

[1339] These steps allow for a quick and appropriate response when an error occurs, and provide effective instructions that take into account the emotional state of the worker.

[1340] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1341] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1342] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1343] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1344] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1345] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1346] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1347] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1348] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1349] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1350] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1351] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1352] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1353] 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.

[1354] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1355] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1356] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1357] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1358] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1359] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1360] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1361] The following is further disclosed regarding the above embodiment.

[1362] (Claim 1)

[1363] A system for dealing with errors that occur during line installation work,

[1364] means for detecting an error code;

[1365] means for transmitting the detected error code to a server;

[1366] A means for identifying a response procedure based on the error code in the server;

[1367] a means for converting the identified response steps into automated speech;

[1368] means for playing an automated voice to the worker;

[1369] A system including:

[1370] (Claim 2)

[1371] 10. The system of claim 1, further comprising: means for referencing a database when generating automated voice instructions based on the error code.

[1372] (Claim 3)

[1373] 10. The system of claim 1, further comprising means for using an AI engine in identifying error codes and generating response procedures.

[1374] (Claim 4)

[1375] 2. The system according to claim 1, further comprising means for executing the line opening test again after the worker has completed the action in accordance with the instructions.

[1376] "Example 1"

[1377] (Claim 1)

[1378] means for detecting an error code at the terminal;

[1379] means for transmitting an error code to a server;

[1380] means for analyzing the error code at the server;

[1381] A means for generating response procedures using an AI engine;

[1382] A means for converting response procedures into automated voice;

[1383] means for transmitting voice instructions to the terminal;

[1384] A means for playing the automated voice on the device;

[1385] A system including:

[1386] (Claim 2)

[1387] 2. The system according to claim 1, further comprising means for referencing a database when generating a response procedure based on the error code.

[1388] (Claim 3)

[1389] 10. The system of claim 1, further comprising means for transmitting the error code to a server via an Internet connection when the terminal detects the error code.

[1390] "Application Example 1"

[1391] (Claim 1)

[1392] A system for dealing with errors that occur during line installation work or during abnormality detection work for autonomous driving vehicles,

[1393] means for detecting an error code or an abnormal signal;

[1394] means for transmitting the detected error code or abnormality signal to a server;

[1395] A means for identifying a response procedure based on the error code or abnormal signal in the server;

[1396] a means for converting the identified response steps into automated speech;

[1397] means for playing an automated voice message to the operator or driver;

[1398] A system including:

[1399] (Claim 2)

[1400] 10. The system of claim 1, further comprising: means for referencing a database when generating automated voice instructions based on the error code or abnormal signal.

[1401] (Claim 3)

[1402] 10. The system of claim 1, further comprising means for using an AI engine in identifying error codes or abnormal signals and generating response procedures.

[1403] "Example 2: Combining Emotion Engines"

[1404] (Claim 1)

[1405] A system for dealing with errors that occur during line installation work,

[1406] means for detecting an error code;

[1407] means for transmitting the detected error code to a server;

[1408] A means for identifying a response procedure based on the error code in the server;

[1409] a means for recognizing a user's emotions and adjusting response procedures;

[1410] a means for converting the identified response steps into automated speech;

[1411] means for playing an automated voice to the worker;

[1412] A system including:

[1413] (Claim 2)

[1414] 10. The system of claim 1, further comprising: means for referencing a database when generating automated voice instructions based on the error code.

[1415] (Claim 3)

[1416] 10. The system of claim 1, further comprising means for using an AI engine in identifying error codes and generating response procedures.

[1417] (Claim 4)

[1418] 10. The system of claim 1, further comprising means for recognizing a user's emotion and adjusting instructions based on the emotion.

[1419] "Application example 2 when combining emotion engines"

[1420] (Claim 1)

[1421] A system for dealing with errors that occur during line installation work,

[1422] means for detecting an error code;

[1423] means for transmitting the detected error code to a server;

[1424] A means for identifying a response procedure based on the error code in the server;

[1425] a means for converting the identified response steps into automated speech;

[1426] means for playing an automated voice to the worker;

[1427] an emotion recognition means for recognizing the emotion of a worker;

[1428] a means for adjusting response procedures based on emotion recognition;

[1429] A system including:

[1430] (Claim 2)

[1431] 10. The system of claim 1, further comprising: means for referencing a database when generating automated voice instructions based on the error code.

[1432] (Claim 3)

[1433] 10. The system of claim 1, further comprising means for using an AI engine in identifying error codes and generating response procedures. [Explanation of symbols]

[1434] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A system for dealing with errors that occur during line installation work, means for detecting an error code; means for transmitting the detected error code to a server; A means for identifying a response procedure based on the error code in the server; a means for converting the identified response steps into automated speech; means for playing an automated voice to the worker; A system including:

2. 10. The system of claim 1, further comprising means for referencing a database when generating automated voice instructions based on the error code.

3. 10. The system of claim 1, further comprising means for using an AI engine in identifying error codes and generating response procedures.

4. 2. The system according to claim 1, further comprising means for executing the line establishment test again after the worker has completed the procedure in accordance with the instructions.

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