System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving alarm information of a communication device; means for utilizing a generative artificial intelligence model to analyze the alarm information; means for generating a cause of the alarm information and a handling method based on an analysis result; means for transmitting the generated information to a user terminal; and means for displaying the generated information at the user terminal.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] In the construction and management of telecommunications equipment, alarm messages are often displayed in English, making it difficult for inexperienced engineers or newly transferred personnel to understand the alarm content and respond appropriately. This results in delayed responses to alarms and reduced overall work efficiency. Furthermore, interpretation of alarms and countermeasures are highly dependent on the individual, making standardization difficult. To solve this problem, a system is needed that automatically analyzes alarm content and presents specific response methods. [Means for solving the problem]

[0005] The present invention provides a system that receives alarm information from a communication device, analyzes the content of the alarm information, and presents an appropriate response method. This system includes the following means: a means for receiving alarm information from the communication device, a means for using a generative artificial intelligence model to analyze the alarm information, a means for generating a cause of the alarm information and a response method based on the analysis results, a means for transmitting the generated information to a user terminal, and a means for displaying the generated information on the user terminal. The system also includes a means for translating the alarm information if it is in English, and a generative artificial intelligence model that analyzes the alarm information using a machine learning algorithm, enabling even new employees or those who have been transferred to the system to quickly respond to alarms.

[0006] A "communications device" is a device that includes hardware and software for performing network communications.

[0007] "Alarm information" is a warning message that occurs when monitoring the status of communication equipment, and indicates an error or abnormality.

[0008] A "generative artificial intelligence model" is an artificial intelligence model that uses machine learning algorithms to analyze data and make predictions and classifications.

[0009] "Analysis" refers to the process of understanding input data and identifying meaning and patterns.

[0010] "Cause" indicates the event or condition that is the basis for generating alarm information.

[0011] "Response method" is an instruction indicating the specific measures and procedures to be taken in response to specific alarm information.

[0012] A "user terminal" is an electronic device that can be operated by a user, and includes a personal computer, a smartphone, and the like.

[0013] "Display" refers to providing information visually on the screen of a user terminal.

[0014] "Translation" refers to the process of converting information written in one language into another.

[0015] "Machine learning algorithms" refer to computational methods for learning from data and recognizing patterns, and are the foundation of generative artificial intelligence models. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

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

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is a system that automatically analyzes alarm information from communication devices and presents appropriate response methods. This system receives alarm information from communication devices, uses a generative artificial intelligence model to analyze it, generates response methods based on the analysis results, and transmits and displays them on the user's terminal, enabling smooth response.

[0038] Overall system overview

[0039] The system is comprised of a terminal operated by the user, a server that receives and analyzes alarm information, and a generative artificial intelligence model. The specific configuration and operation of this system are explained below.

[0040] System configuration

[0041] 1. Terminal

[0042] These are electronic devices such as PCs and smartphones that are operated by users.

[0043] It provides an interface for inputting alarm information through a dedicated application and communicating with the server.

[0044] 2. Server

[0045] It is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model.

[0046] It is responsible for generating analysis results and sending them to the user terminal.

[0047] 3. Generative AI Model

[0048] This model analyzes alarm information based on machine learning algorithms and identifies causes and countermeasures.

[0049] It runs on the server side and returns the analysis results to the server.

[0050] Program processing

[0051] Input phase (user operation)

[0052] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[0053] Receiving alarm information (server)

[0054] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[0055] Alarm analysis (generative artificial intelligence model)

[0056] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[0057] Generation and transmission of analysis results (server)

[0058] The server formats the analysis results obtained from the generative AI model and returns them to the user's device. The formatted analysis results are arranged in a form that is easy for the user to understand.

[0059] Displaying the results (terminal)

[0060] The user's device displays the analysis results received from the server. The user can check the analysis results on the screen along with specific countermeasures. For example, specific steps such as "wireless communication failure, turn the BBU on / off or remove and insert the SFP" are displayed.

[0061] Specific examples

[0062] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[0063] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[0064] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[0065] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[0066] 5. The server formats the analysis results and sends them to the user's device.

[0067] 6. The user's device displays the analysis results and how to respond.

[0068] 7. The user follows the displayed instructions to fix the problem.

[0069] In this way, the present invention enables efficient analysis of alarm information from communication devices and rapid response. Users can respond appropriately by simply following the system's instructions, even if they do not have special expertise, thereby significantly improving work efficiency.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] User enters alarm information

[0073] The user opens the dedicated application on their device and inputs the alarm message (e.g., "Link Failure RiLink=BBU-B") The user clicks the "Send" button to send the alarm information to the server.

[0074] Step 2:

[0075] The device sends alarm information to the server

[0076] The terminal creates an API request to send the alarm message entered by the user to the server, and then sends the created API request to the server's analysis endpoint.

[0077] Step 3:

[0078] The server receives the alarm information

[0079] The server receives alarm messages sent from the terminals and prepares to pass the received alarm messages to the analysis process.

[0080] Step 4:

[0081] The server inputs alarm information into the artificial intelligence model

[0082] The server inputs the received alarm message into the artificial intelligence model to start the analysis.

[0083] Step 5:

[0084] Generative AI model analysis

[0085] The generative AI model analyzes the alarm information, understands the content, and identifies the cause and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," it generates a specific response such as "Radio communication NG, turn the BBU on / off, or remove and insert the SFP."

[0086] Step 6:

[0087] The server formats the parsed results

[0088] The server receives the analysis results obtained from the generative AI model and formats them in a format that is easy for users to understand, such as JSON or text.

[0089] Step 7:

[0090] The server sends the analysis results to the user's device

[0091] Create an API response to send the formatted analysis results to the user's device. Send the created API response to the user's device.

[0092] Step 8:

[0093] The user device receives and displays the analysis results

[0094] The user's device displays the analysis results received from the server. Specific countermeasures such as "wireless communication failure, turn the BBU on / off, or remove and reinsert the SFP" are displayed on the screen.

[0095] Step 9:

[0096] The user checks and executes the response method.

[0097] The user checks the displayed analysis results and the countermeasures, and then actually takes corrective action based on that information. For example, to deal with a wireless communication problem, the user can reboot the BBU or remove and reinsert the SFP.

[0098] In this way, the system of the present invention assists users in quickly and appropriately interpreting and responding to alarm information.

[0099] Example 1

[0100] 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."

[0101] Responding quickly and appropriately to failures or abnormalities in communication equipment requires specialized knowledge and experience. In many cases, there are only a limited number of engineers with such knowledge, which can result in time-consuming problem resolution. Furthermore, when alarm information is provided in multiple languages, translation work and the risk of mistranslation can further delay response. Furthermore, analyzing alarm information and identifying how to respond can be complex and difficult for some engineers to understand, making it difficult to respond quickly.

[0102] 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.

[0103] In this invention, the server includes means for a user to input alarm information of a communication device into a terminal, means for the terminal to transmit the alarm information to the server, means for the server to receive the alarm information transmitted from the terminal, means for the server to use a generative artificial intelligence model to analyze the alarm information, means for the generative artificial intelligence model to analyze the alarm information and identify the cause and a response method, means for the server to format the analysis results and return them to the user terminal, and means for the terminal to display the analysis results received from the server. This enables efficient analysis of alarm information of communication devices and rapid and accurate response.

[0104] "Communication devices" are electronic devices for sending and receiving data and voice.

[0105] "Alarm information" is a message or notification indicating an abnormality or malfunction in a communication device.

[0106] A "terminal" is an electronic device used to input alarm information, such as a PC or smartphone operated by a user.

[0107] A "server" is a central processing unit for receiving and analyzing alarm information.

[0108] A "generative artificial intelligence model" is a model that uses machine learning algorithms to analyze alarm information and identify causes and response methods.

[0109] A "user terminal" is an electronic device that displays alarm information and is operated by a user.

[0110] The "analysis results" are analysis results that include the cause of the alarm information and how to respond to it.

[0111] "Formatting" is the process of arranging the analysis results in a form that is easy for the user to understand.

[0112] "Multilingual" refers to the inclusion of multiple different languages.

[0113] This invention relates to a system that automatically analyzes alarm information from communication devices and suggests appropriate countermeasures. The system mainly consists of a terminal operated by the user, a server that receives and analyzes alarm information, and a generative artificial intelligence model.

[0114] System configuration

[0115] 1. Terminal

[0116] Hardware: Electronic devices such as PCs and smartphones operated by users

[0117] Software: Dedicated application

[0118] Function: Provides an interface for users to input alarm information and communicate with the server.

[0119] 2. Server

[0120] Hardware: Central Processing Unit

[0121] Software: A program for receiving alarm information and analyzing it using a generative artificial intelligence model

[0122] function:

[0123] Receive alarm information sent from the device

[0124] Alarm information is passed to the generated AI model to begin analysis

[0125] Format the analysis results and return them to the user's device

[0126] 3. Generative AI Model

[0127] Software: Models that implement machine learning algorithms (APIs built using Python, etc.)

[0128] Function: Analyzes alarm information and identifies causes and countermeasures

[0129] process:

[0130] Takes alarm information as input

[0131] The cause and specific countermeasures are analyzed and sent back to the server

[0132] How it works

[0133] Input phase (user operation)

[0134] The user inputs the alarm information generated by the communication device into the dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[0135] Receiving alarm information (server)

[0136] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[0137] Specific processing details

[0138] Alarm analysis (generative artificial intelligence model)

[0139] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[0140] Generation and transmission of analysis results (server)

[0141] The server formats the analysis results obtained from the generative AI model and returns them to the user's device. The formatted analysis results are arranged in a form that is easy for the user to understand.

[0142] Displaying the results (terminal)

[0143] The user's device displays the analysis results received from the server. The user can check the analysis results on the screen along with specific countermeasures. For example, specific steps such as "wireless communication failure, turn the BBU on / off or remove and insert the SFP" are displayed.

[0144] Specific examples

[0145] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[0146] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[0147] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[0148] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[0149] 5. The server formats the analysis results and sends them to the user's device.

[0150] 6. The user's device displays the analysis results and how to respond.

[0151] 7. The user follows the displayed instructions to fix the problem.

[0152] In this way, the present invention enables efficient analysis of alarm information from communication devices and rapid response. Users can respond appropriately by simply following the system's instructions, even without special expertise, greatly improving work efficiency.

[0153] Prompt Sentence Examples

[0154] "What should I do if the communication device alarm 'Link Failure RiLink=BBU-B' occurs?"

[0155] "Please analyze the cause of the alarm message 'Link Failure RiLink=BBU-B' and what to do about it."

[0156] The present invention can be carried out as described above.

[0157] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0158] Step 1:

[0159] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the user inputs an alarm message such as "Link Failure RiLink=BBU-B" and presses the "Send" button. The terminal then formats the input data and generates packet data to be sent to the server.

[0160] Input: Alarm information "Link Failure RiLink=BBU-B"

[0161] Output: Packet data for transmission

[0162] Step 2:

[0163] The terminal sends the generated packet data to the server. This transmission is performed using API calls and HTTP requests on a dedicated application. The terminal monitors the transmission status and performs retransmission if an error occurs.

[0164] Input: Packet data to send

[0165] Output: HTTP request to the server

[0166] Step 3:

[0167] The server receives HTTP requests from the terminal. The receiving module analyzes the data and extracts alarm information. This data is stored in a database and passed on to the next analysis process.

[0168] Input: HTTP request from the terminal

[0169] Output: Extracted alarm information

[0170] Step 4:

[0171] The server inputs the extracted alarm information into the generative AI model. At this time, the server passes data to the model using a specific API endpoint. For example, it calls an API written in Python and passes the data "Link Failure RiLink=BBU-B."

[0172] Input: Extracted alarm information

[0173] Output: Input data to a generative artificial intelligence model

[0174] Step 5:

[0175] The generative artificial intelligence model analyzes the alarm information. The model uses a machine learning algorithm to identify the cause of the alarm and how to respond, and returns the results in JSON format to the server. For example, it identifies countermeasures such as "wireless communication failure, turn the BBU on / off, or remove and insert the SFP."

[0176] Input: Input data to the generative artificial intelligence model

[0177] Output: Analysis result (JSON format)

[0178] Step 6:

[0179] The server receives the analysis results obtained from the generative AI model. The server formats this data and arranges it in a form that is easy for the user to understand. For example, it converts it to include explanatory text in Japanese. The server then sends the formatted results to the user's device.

[0180] Input: Analysis result (JSON format)

[0181] Output: Formatted analysis results

[0182] Step 7:

[0183] The terminal displays the analysis results received from the server. A dedicated application displays the analysis results on the screen, and the user can check the specific steps to take. For example, specific steps such as "wireless communication failed, turn the BBU on / off or remove and insert the SFP."

[0184] Input: Formatted analysis results

[0185] Output: On-screen instructions

[0186] Step 8:

[0187] The user can then take corrective action according to the displayed solution. For example, the user can try turning the BBU on and off, or removing and reinserting the SFP. This will resolve the problem.

[0188] Input: The response displayed on the screen

[0189] Output: Communication equipment repair work

[0190] (Application example 1)

[0191] 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."

[0192] There is a demand for a system that can quickly and accurately analyze alarm information from communication devices and propose effective responses based on the analysis results. It is particularly important to provide actionable information in real time for complex devices and systems within factories, thereby improving work efficiency. However, with conventional systems, analyzing alarm information takes time, which can lead to reduced work efficiency and incorrect responses.

[0193] 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.

[0194] In this invention, the server includes means for receiving alarm information from communication devices, means for analyzing the alarm information using a generative artificial intelligence model, means for generating the cause of the alarm information and a response method based on the analysis results, means for transmitting the generated information to a user terminal, and means for analyzing alarm information from devices and systems in the factory in real time and presenting the response method on a visualization device that can be heard by workers. This makes it possible to quickly provide effective response methods and improve work efficiency.

[0195] "Communications equipment" refers to devices that send and receive data over a network. Examples include routers, switches, and modems.

[0196] "Alarm information" refers to warning messages that are generated and sent when an abnormality or error occurs in communication equipment. This includes error messages, warning notifications, and failure reports.

[0197] A "generative artificial intelligence model" is a model designed to perform data analysis using machine learning algorithms, which can identify patterns in the data and automate certain tasks.

[0198] "Analysis" is the act of examining data or information in detail to understand its meaning and patterns. The analysis process involves verifying, classifying, and identifying causes of data.

[0199] "User terminal" refers to an electronic device that is directly operated and used by a user, such as a smartphone, tablet, or PC.

[0200] "Response methods" are specific procedures and techniques for resolving problems based on analyzed alarm information. By following these methods, users can take appropriate action.

[0201] A "visualization device" is a device for visually displaying information. Examples include smart glasses and head-mounted displays.

[0202] "Work efficiency" is an indicator of how much work or tasks can be completed within a certain amount of time. Improving work efficiency can save time and costs.

[0203] A system for implementing this invention receives alarm information from a communication device, uses a generative artificial intelligence model to analyze the alarm information, and generates an appropriate response method based on the analysis results. Specific embodiments of this system are described below.

[0204] System configuration

[0205] 1. Terminal

[0206] An electronic device operated by a user, including PCs, smartphones, and smart glasses. A dedicated application is installed on the device, which provides an interface for inputting alarm information and communicating with the server. For example, when using smart glasses, alarm information can be input through a camera or voice input function.

[0207] 2. Server

[0208] This is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model. The server is responsible for sending the generated analysis results to the user's terminal. It also formats the analysis results in a form that is easy for the user to understand.

[0209] 3. Generative AI Model

[0210] This model analyzes alarm information based on machine learning algorithms. It is used to identify the cause and response method for alarm information from communication devices. The generative artificial intelligence model runs on the server and returns the analysis results to the server.

[0211] Program processing

[0212] Input phase (user operation)

[0213] When a problem occurs with equipment in the factory, users can input alarm information using voice input or a camera on a visualization device (e.g., smart glasses). For example, they can input "Robot arm E37 has stopped" by voice.

[0214] Receiving alarm information (server)

[0215] The alarm information sent from the terminal is received by the server. The server passes this information to the analysis process, and the analysis begins. For example, information such as "Robot arm E37 has stopped" is sent to the server.

[0216] Alarm analysis (generative artificial intelligence model)

[0217] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, the analysis result may identify a countermeasure such as "check the power and restart."

[0218] Generation and transmission of analysis results (server)

[0219] The server formats the analysis results obtained from the generative AI model and sends them back to the user device (e.g., smart glasses). The formatted analysis results are presented in a form that is easy for the user to understand.

[0220] Displaying the results (terminal)

[0221] The user terminal displays the analysis results received from the server. The user can check the analysis results and specific countermeasures on the display of the visualization device. For example, the smart glasses may display specific instructions such as "Check the power and restart."

[0222] Examples and prompts

[0223] Specific examples

[0224] Example 1: "Robot arm F45 has stopped working"

[0225] Solution: Check the sensor status and restart it.

[0226] Prompt Sentence Examples

[0227] What should I do if the message "Robot arm stopped" is displayed?

[0228] In this way, on-site workers can take appropriate action quickly, significantly improving work efficiency within the factory.

[0229] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0230] Step 1:

[0231] When a problem occurs with equipment in a factory, a user can input alarm information using voice input or a camera on a visualization device (e.g., smart glasses). The user can input "Robot arm E37 has stopped" by voice. This input data is first imported into a dedicated application on the terminal.

[0232] Step 2:

[0233] The terminal converts the alarm information input by voice from the user into text data. For example, voice data such as "Robot arm E37 has stopped" is converted into text data. This converted data is then sent to the server.

[0234] Step 3:

[0235] The server receives alarm information sent from the terminal, stores the received alarm information (e.g., "Robot arm E37 has stopped") in a database, and provides the data to the generative artificial intelligence model to start the analysis process.

[0236] Step 4:

[0237] The generative artificial intelligence model analyzes the provided alarm information. For example, it analyzes the message "Robot arm E37 has stopped" and identifies the cause and how to respond. This analysis identifies the countermeasure, "Check the power supply and restart." The analysis results are returned to the server.

[0238] Step 5:

[0239] The server receives the analyzed result (e.g., "Check the power and reboot"), formats it into a user-friendly format, and sends the formatted analysis result to the display of the user device (e.g., smart glasses).

[0240] Step 6:

[0241] The terminal receives the formatted analysis results sent from the server, for example, a message saying "Check power and reboot", which is then displayed on the display of the visualization device.

[0242] Step 7:

[0243] The user checks the analysis results and the corresponding solutions displayed on the terminal. For example, an instruction such as "Check the power supply and restart" is displayed on the display of the visualization device. The user then follows the displayed solutions to fix the equipment problem.

[0244] By taking these steps, alarm information from communication devices can be analyzed efficiently and a prompt response can be achieved.

[0245] 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.

[0246] This invention combines a system that automatically analyzes alarm information from communication devices and suggests appropriate responses with an emotion engine that recognizes the user's emotions. In addition to analyzing alarm information and suggesting appropriate responses, this system adjusts the display method based on the user's emotional state, supporting appropriate responses according to the user's experience and situation.

[0247] Overall system overview

[0248] The system consists of a terminal operated by the user, a server that receives and analyzes alarm information, a generative AI model, and an emotion engine. The specific configuration and operation of this system are described below.

[0249] System configuration

[0250] 1. Terminal

[0251] These are electronic devices such as PCs and smartphones that are operated by users.

[0252] It provides an interface for inputting alarm information through a dedicated application and communicating with the server.

[0253] 2. Server

[0254] It is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model.

[0255] It is responsible for generating analysis results and sending them to the user terminal.

[0256] 3. Generative AI Model

[0257] This model analyzes alarm information based on machine learning algorithms and identifies causes and countermeasures.

[0258] It runs on the server side and returns the analysis results to the server.

[0259] 4. Emotion Engine

[0260] It is an engine that analyzes a user's voice and text input to identify their emotional state.

[0261] The system adjusts the presentation of how to respond when the user is feeling stressed or nervous.

[0262] Program processing

[0263] Input phase (user operation)

[0264] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[0265] Receiving alarm information (server)

[0266] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[0267] Alarm analysis (generative artificial intelligence model)

[0268] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[0269] Emotion Recognition (Emotion Engine)

[0270] The emotion engine analyzes the user's voice and text input to identify their emotional state. For example, if the user types "What should I do? I don't know," the emotion engine will recognize that the user is feeling anxious.

[0271] Generation and adjustment of analysis results (server)

[0272] The server formats the analysis results obtained from the generative AI model and, based on the analysis results of the emotion engine, tailors the display to the user's emotional state. For example, a user who is feeling anxious might be provided with more detailed and easy-to-understand step-by-step instructions.

[0273] Sending and displaying analysis results (terminal)

[0274] The server sends the adjusted analysis results to the user's device. The user's device displays the received analysis results and can check specific countermeasures on the screen. For example, specific steps such as "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[0275] Specific examples

[0276] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[0277] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[0278] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[0279] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[0280] 5. The emotion engine recognizes anxiety from the user's text input.

[0281] 6. The server formats the analysis results and prepares detailed instructions based on the user's emotional state.

[0282] 7. The user's device displays the analysis results and how to respond.

[0283] 8. The user follows the displayed instructions to fix the problem.

[0284] In this way, the present invention is a system that efficiently analyzes alarm information from communication devices and supports appropriate responses according to the user's emotional state, allowing users to quickly take appropriate responses without having special expertise.

[0285] The processing flow will be explained below.

[0286] Step 1:

[0287] User enters alarm information

[0288] The user opens the dedicated application on their device and inputs the alarm message (e.g., "Link Failure RiLink=BBU-B") that occurred. The user clicks the "Send" button to send the alarm information to the server.

[0289] Step 2:

[0290] The device sends alarm information to the server

[0291] The device creates an API request to send the alarm message entered by the user to the server, and then sends the created API request to the server's analysis endpoint.

[0292] Step 3:

[0293] The server receives the alarm information

[0294] The server receives alarm messages sent from the terminals and prepares to pass the received alarm messages to the analysis process.

[0295] Step 4:

[0296] The server inputs alarm information into the artificial intelligence model

[0297] The server inputs the received alarm message into the artificial intelligence model to start the analysis.

[0298] Step 5:

[0299] Generative AI model analysis

[0300] The generative AI model analyzes the alarm information, understands the content, and identifies the cause and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," it generates a specific response such as "Radio communication NG, turn the BBU on / off, or remove and insert the SFP."

[0301] Step 6:

[0302] Emotion engine recognizes user emotions

[0303] The emotion engine analyzes the user's voice and text input (e.g., "What should I do? I don't know") to determine if the user is feeling anxious or stressed.

[0304] Step 7:

[0305] The server adjusts the response based on the analysis results and the emotional state.

[0306] The server evaluates the analysis results obtained from the artificial intelligence model and the emotion recognition results from the emotion engine, and adjusts the response method according to the user's emotional state. For example, if a user feels anxious, more detailed instructions or supplementary explanations will be added.

[0307] Step 8:

[0308] The server formats the adjusted analysis results

[0309] The server formats the tuned analysis results in a user-friendly format, such as JSON or text.

[0310] Step 9:

[0311] The server sends the analysis results to the user's device

[0312] Create an API response to send the formatted analysis results to the user's device. Send the created API response to the user's device.

[0313] Step 10:

[0314] The user device receives and displays the analysis results

[0315] The user's device displays the analysis results received from the server. Specific solutions, such as "wireless communication failure, turn the BBU on / off or remove and reinsert the SFP," are displayed on the screen.

[0316] Step 11:

[0317] The user checks and executes the response method.

[0318] The user checks the displayed analysis results and the countermeasures, and then actually takes corrective action based on that information. For example, to deal with a wireless communication problem, the user can reboot the BBU or remove and reinsert the SFP.

[0319] In this way, the system of the present invention helps users to quickly and appropriately interpret alarm information and then respond optimally to it in accordance with their emotional state.

[0320] Example 2

[0321] 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."

[0322] Conventional alarm systems for communication devices simply provide the cause of the alarm information and how to respond, but do not take into account the user's emotional state or level of understanding, making it difficult to take an appropriate response quickly. Furthermore, if there is no means to translate alarm information issued in English into Japanese, this becomes an additional burden for users who are not good at English.

[0323] 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.

[0324] In this invention, the server includes means for receiving alarm information from a communication device, means for analyzing the alarm information using a generative artificial intelligence model, means including an emotion engine that recognizes the user's emotional state, means for generating a cause of the alarm information and a response method based on the analysis results, means for transmitting the generated information to a user terminal, means for adjusting the display method based on the analysis results of the emotion engine, and means for displaying the generated information on the user terminal. This makes it possible to provide an appropriate and detailed response method according to the user's emotional state and level of understanding, enabling quick and effective problem solving.

[0325] A "communications device" is an electronic device for transmitting and receiving data.

[0326] "Alarm information" refers to a warning message that is sent when a failure or abnormality occurs in a communication device.

[0327] A "generative artificial intelligence model" is a model that uses machine learning algorithms to analyze data and provide solutions to specific problems.

[0328] An "emotion engine" is a system that analyzes a user's voice and text input to identify their emotional state.

[0329] "Analysis results" are the results of analysis and judgment generated by the generative artificial intelligence model or emotion engine.

[0330] A "user terminal" is an electronic device such as a computer or smartphone that is operated by a user.

[0331] "Display method" refers to the specific format and procedure for presenting analysis results and instructions to the user.

[0332] A "machine learning algorithm" is a mathematical technique for learning from data and making predictions or classifications based on that data.

[0333] The present invention is a system that automatically analyzes alarm information from communication devices and suggests appropriate response methods, and also combines it with an emotion engine that recognizes the user's emotions. In addition to analyzing alarm information and suggesting response methods, this system adjusts the display method based on the user's emotional state, thereby supporting appropriate responses according to the user's experience and situation. The specific configuration and operation of this system are described below.

[0334] System configuration

[0335] The system consists of a terminal operated by the user, a server that receives and analyzes alarm information, a generative AI model, and an emotion engine.

[0336] 1. Terminal

[0337] An electronic device such as a computer or smartphone operated by a user, which inputs alarm information through a dedicated application and provides an interface for communicating with a server.

[0338] 2. Server

[0339] This is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model. It is responsible for generating analysis results and sending them to user terminals.

[0340] 3. Generative AI Model

[0341] This model analyzes alarm information based on machine learning algorithms, identifies causes and countermeasures, and runs on the server side, returning the analysis results to the server.

[0342] 4. Emotion Engine

[0343] The engine analyzes the user's voice and text input to identify their emotional state, and adjusts the suggested response if the user is feeling stressed or nervous.

[0344] Program processing

[0345] Input phase (user operation)

[0346] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[0347] Receiving alarm information (server)

[0348] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[0349] Alarm analysis (generative artificial intelligence model)

[0350] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[0351] Emotion Recognition (Emotion Engine)

[0352] The emotion engine analyzes the user's voice and text input to identify their emotional state. For example, if the user types "What should I do? I don't know," the emotion engine will recognize that the user is feeling anxious.

[0353] Generation and adjustment of analysis results (server)

[0354] The server formats the analysis results obtained from the generative AI model and, based on the analysis results of the emotion engine, tailors the display to the user's emotional state. For example, a user who is feeling anxious might be provided with more detailed and easy-to-understand step-by-step instructions.

[0355] Sending and displaying analysis results (terminal)

[0356] The server sends the adjusted analysis results to the user's device. The user's device displays the received analysis results and can check specific countermeasures on the screen. For example, specific steps such as "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[0357] Specific examples

[0358] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[0359] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[0360] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[0361] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[0362] 5. The emotion engine recognizes anxiety from the user's text input.

[0363] 6. The server formats the analysis results and prepares detailed instructions based on the user's emotional state.

[0364] 7. The user's device displays the analysis results and how to respond.

[0365] 8. The user follows the displayed instructions to fix the problem.

[0366] Prompt Sentence Examples

[0367] "An alarm "Link Failure RiLink=BBU-B" has occurred. What should I do?"

[0368] As described above, this system efficiently analyzes alarm information from communication devices and supports appropriate responses according to the user's emotional state, allowing users to quickly and effectively take appropriate responses without requiring specialized knowledge.

[0369] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0370] Step 1:

[0371] The user inputs the alarm information generated by the communication device into a dedicated application on the terminal. For example, the user inputs the message "Link Failure RiLink=BBU-B."

[0372] Input: Alarm information (e.g. "Link Failure RiLink=BBU-B")

[0373] Operation: The user enters alarm information into the dedicated application and presses the "Send" button.

[0374] Output: Alarm information is sent from the terminal to the server.

[0375] Step 2:

[0376] The terminal transmits the alarm information to the server.

[0377] Input: Alarm information (e.g. "Link Failure RiLink=BBU-B")

[0378] Operation: The terminal converts the input alarm information into packets and sends them to the server via the network.

[0379] Output: Alarm information arrives at the server.

[0380] Step 3:

[0381] The server receives the alarm information and formats the data.

[0382] Input: Alarm information (e.g. "Link Failure RiLink=BBU-B")

[0383] Operation: Before the server passes the received alarm information to the analysis process, it performs any necessary pre-processing (for example, normalizing the data or removing unnecessary information).

[0384] Output: Formatted alarm information

[0385] Step 4:

[0386] The server inputs the formatted alarm information into the artificial intelligence model to begin analysis.

[0387] Input: Formatted alarm information

[0388] Operation: The generative AI model analyzes the alarm information and identifies the cause and how to respond. For example, it identifies "wireless communication failure, turn the BBU on / off, or remove and insert the SFP."

[0389] Output: Analysis results (e.g. "Radio communication NG, try turning the BBU on / off or removing and inserting the SFP")

[0390] Step 5:

[0391] The user provides additional text input, for example, "What should I do, I don't know."

[0392] Input: User-supplied text (e.g., "What should I do? I don't know.")

[0393] Action: The user types some additional text into the terminal and sends it.

[0394] Output: Additional text is sent from the terminal to the server.

[0395] Step 6:

[0396] The server receives the user's additional text input and passes it to the emotion engine.

[0397] Input: Additional text (e.g. "What should I do? I don't know")

[0398] How it works: The server inputs the received additional text into the emotion engine and analyzes the emotional state, e.g., recognizing "anxiety."

[0399] Output: Sentiment analysis result (e.g., "anxiety")

[0400] Step 7:

[0401] The server integrates the analysis results of the generated artificial intelligence model and the emotion engine and adjusts the display method.

[0402] Input: Analysis results (e.g., "Radio communication failed, try turning the BBU on / off and removing the SFP"), Emotion analysis results (e.g., "Anxiety")

[0403] How it works: The server formats the analysis results and tailors them into detailed, step-by-step instructions for anxious users.

[0404] Output: Adjusted analysis results (e.g. "Step 1: Turn the BBU power OFF and then ON again. Step 2: Remove the SFP module and then insert it again.")

[0405] Step 8:

[0406] The server transmits the adjusted analysis results to the user terminal.

[0407] Input: Adjusted analysis results

[0408] Operation: The server converts the adjusted analysis results into packets for transmission and sends them to the user terminal via the network.

[0409] Output: The adjusted analysis results arrive at the terminal.

[0410] Step 9:

[0411] The device receives the adjusted analysis results and displays them on the screen.

[0412] Input: Adjusted analysis results

[0413] Operation: The device displays the results it receives on the user interface, and the user can check the specific steps on the screen.

[0414] Output: The user checks the displayed analysis results and how to respond.

[0415] This series of processing flows not only enables users to efficiently solve problems with their communication devices, but also allows them to receive support that takes into consideration their own emotional state.

[0416] (Application example 2)

[0417] 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."

[0418] In modern factories, robots and machines perform complex tasks, generating a wide variety of alarms during operation. Rapidly analyzing these alarms and providing response methods is crucial for maintaining productivity. However, it is not realistic for specialized engineers to respond quickly every time an alarm occurs. Furthermore, on-site workers sometimes find themselves unable to respond appropriately to alarms. Responding quickly and accurately is particularly difficult when workers are emotionally anxious or tense. To resolve this situation, in addition to analyzing alarm information and providing response methods, it is necessary to provide response methods that take into account the worker's emotional state.

[0419] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving alarm information from a communication device, means for using a generative artificial intelligence model to analyze the alarm information, means for generating the cause of the alarm information and a response method based on the analysis results, means for recognizing the user's emotional state and reflecting that information in the display of the analysis results, and means for transmitting the generated information to the user terminal. This enables efficient analysis of alarm information and the presentation of an appropriate response method according to the user's emotional state.

[0420] "Communication device alarm information" is a warning message issued when a communication device detects an abnormality or error.

[0421] A "generative artificial intelligence model" is a computer program that uses machine learning algorithms to analyze alarm information and identify its cause and how to respond.

[0422] The "means for recognizing the user's emotional state" is a function that analyzes the user's voice, text input, etc., and identifies the user's emotional state.

[0423] The "means for reflecting the analysis results in the display" is a function for adjusting the display method of the analysis results based on the emotional state of the user.

[0424] A "user terminal" is an electronic device (e.g., a smartphone or head-mounted display) operated by a user, which is used to input alarm information and display analysis results.

[0425] The "receiving means" is a function for receiving alarm information transmitted from a communication device.

[0426] "Means for generating" refers to a function that identifies the cause and response method based on the results of analyzed alarm information and creates the information.

[0427] The "transmitting means" is a function for sending the generated information to the user terminal.

[0428] The "display means" is a function for visually displaying information generated on a user terminal.

[0429] This invention relates to the analysis of alarm information in the management of robots in a factory and the presentation of appropriate countermeasures, and in particular to the realization of a system that takes into account the emotional state of the user.

[0430] System configuration

[0431] 1. Server

[0432] The server receives alarm information transmitted from the communication device.

[0433] The server uses a generative artificial intelligence model to analyze the alarm information and generate the cause and response method.

[0434] The server uses an emotion engine to recognize the user's emotional state, analyzes the user's input information (voice or text), and identifies the user's emotional state.

[0435] The server adjusts the analysis results according to the user's emotional state and transmits them to the user's terminal.

[0436] 2. User Device

[0437] A user terminal is an electronic device (such as a smartphone or a head-mounted display) operated by a user.

[0438] Alarm information is entered through a dedicated application and communicates with the server.

[0439] The terminal receives the analysis results sent from the server and displays the information.

[0440] Hardware and software used

[0441] 1. Hardware

[0442] Server: Something with high-speed processing and large data storage capacity (e.g., cloud servers).

[0443] User devices: smartphones, tablets, head-mounted displays (e.g., Microsoft HoloLens), etc.

[0444] 2. Software

[0445] Generative artificial intelligence model: Software that uses machine learning algorithms to analyze alarm information.

[0446] Emotion engine: An algorithm that analyzes a user's voice or text input to identify their emotional state (e.g., emotion recognition software).

[0447] Dedicated application: An application installed on the user's terminal that inputs alarm information and displays analysis results.

[0448] What the program does

[0449] The server first receives alarm information sent from the communication device. Next, it inputs the alarm information into a generative artificial intelligence model for analysis. Once the analysis results indicate the cause of the alarm and a response method, the emotion engine analyzes the user's input information to identify the user's emotional state. The analysis results are adjusted by the server based on the user's emotional state and are finally sent to the user's terminal. The user's terminal receives and displays this adjusted information.

[0450] Specific examples

[0451] For example, consider the case where a robot in a factory generates an alarm that reads "Motor Overheat W1." A user inputs this alarm information using a smartphone application and sends it to the server. The server passes this alarm information to a generative AI model, which identifies a response, such as "The cooling fan needs to be cleaned." At the same time, if the user inputs "What should I do?", the emotion engine recognizes that the user is feeling anxious. Based on this recognition result, the server provides the user with more detailed step-by-step instructions, which are displayed on the smartphone or HMD.

[0452] Prompt Sentence Examples

[0453] Alarm analysis prompt:

[0454] Alarm Message: Motor Overheat W1

[0455] Emotion recognition prompts:

[0456] User Input: What should I do?

[0457] As a result, a system is realized that allows the user to quickly and accurately find a way to respond even in emotional situations.

[0458] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0459] Step 1:

[0460] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. At this time, the user inputs the alarm message (e.g., "Motor Overheat W1") in text format and presses the send button. The input alarm information is sent from the terminal to the server.

[0461] Step 2:

[0462] The server receives the alarm information sent from the terminal. The received data is passed to the server as "Alarm Message: Motor Overheat W1". This alarm information is input into the next analysis process.

[0463] Step 3:

[0464] The server passes the received alarm information to the generative AI model, which begins analysis. The generative AI model analyzes the alarm message and identifies the cause and how to respond. Data processing involves tokenizing the alarm message and comparing it with existing learning datasets to output the cause and how to respond. For example, in the case of "Motor Overheat W1," the response generated is "The cooling fan needs to be cleaned."

[0465] Step 4:

[0466] After sending the alarm information, the user can input additional voice or text through the terminal, such as "What should I do?", which indicates an emotion. This input is sent to the server as an emotion-recognition prompt.

[0467] Step 5:

[0468] The server uses the emotion engine to analyze the user's additional input and identify their emotional state. The input data is "What should I do?", and the emotion engine analyzes it and identifies "anxiety" as the emotional state. This outputs the user's emotional state as data.

[0469] Step 6:

[0470] The server combines the analysis results with the emotional state and adjusts the display method. In data calculations, the cause of the alarm and the solution are identified as "The cooling fan needs to be cleaned." Since the emotional state is "anxious," detailed step-by-step instructions are generated. For example, specific steps such as "1. Turn off the power. 2. Remove the cooling fan..." are generated.

[0471] Step 7:

[0472] The server then sends the adjusted analysis results to the user's device, which includes detailed instructions and how to respond.

[0473] Step 8:

[0474] The user device visually displays the received analysis results and how to respond on the screen. Detailed step-by-step instructions are displayed to help the user stay calm and take action. The user can follow these instructions to take action.

[0475] This allows the user to respond to alarm information quickly and appropriately. In addition, since consideration is given to the user's emotional state, the user can work while reducing stress.

[0476] As described above, the processing flow of the entire system is performed consistently and continuously.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] [Second embodiment]

[0481] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0482] 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.

[0483] 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).

[0484] 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.

[0485] 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.

[0486] 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).

[0487] 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.

[0488] 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.

[0489] 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.

[0490] 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.

[0491] 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.

[0492] 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."

[0493] This invention is a system that automatically analyzes alarm information from communication devices and presents appropriate response methods. This system receives alarm information from communication devices, uses a generative artificial intelligence model to analyze it, generates response methods based on the analysis results, and transmits and displays them on the user's terminal, enabling smooth response.

[0494] Overall system overview

[0495] The system is comprised of a terminal operated by the user, a server that receives and analyzes alarm information, and a generative artificial intelligence model. The specific configuration and operation of this system are explained below.

[0496] System configuration

[0497] 1. Terminal

[0498] These are electronic devices such as PCs and smartphones that are operated by users.

[0499] It provides an interface for inputting alarm information through a dedicated application and communicating with the server.

[0500] 2. Server

[0501] It is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model.

[0502] It is responsible for generating analysis results and sending them to the user terminal.

[0503] 3. Generative AI Model

[0504] This model analyzes alarm information based on machine learning algorithms and identifies causes and countermeasures.

[0505] It runs on the server side and returns the analysis results to the server.

[0506] Program processing

[0507] Input phase (user operation)

[0508] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[0509] Receiving alarm information (server)

[0510] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[0511] Alarm analysis (generative artificial intelligence model)

[0512] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[0513] Generation and transmission of analysis results (server)

[0514] The server formats the analysis results obtained from the generative AI model and returns them to the user's device. The formatted analysis results are arranged in a form that is easy for the user to understand.

[0515] Displaying the results (terminal)

[0516] The user's device displays the analysis results received from the server. The user can check the analysis results on the screen along with specific countermeasures. For example, specific steps such as "wireless communication failure, turn the BBU on / off or remove and insert the SFP" are displayed.

[0517] Specific examples

[0518] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[0519] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[0520] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[0521] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[0522] 5. The server formats the analysis results and sends them to the user's device.

[0523] 6. The user's device displays the analysis results and how to respond.

[0524] 7. The user follows the displayed instructions to fix the problem.

[0525] In this way, the present invention enables efficient analysis of alarm information from communication devices and rapid response. Users can respond appropriately by simply following the system's instructions, even if they do not have special expertise, thereby significantly improving work efficiency.

[0526] The processing flow will be explained below.

[0527] Step 1:

[0528] User enters alarm information

[0529] The user opens the dedicated application on their device and inputs the alarm message (e.g., "Link Failure RiLink=BBU-B") The user clicks the "Send" button to send the alarm information to the server.

[0530] Step 2:

[0531] The device sends alarm information to the server

[0532] The terminal creates an API request to send the alarm message entered by the user to the server, and then sends the created API request to the server's analysis endpoint.

[0533] Step 3:

[0534] The server receives the alarm information

[0535] The server receives alarm messages sent from the terminals and prepares to pass the received alarm messages to the analysis process.

[0536] Step 4:

[0537] The server inputs alarm information into the artificial intelligence model

[0538] The server inputs the received alarm message into the artificial intelligence model to start the analysis.

[0539] Step 5:

[0540] Generative AI model analysis

[0541] The generative AI model analyzes the alarm information, understands the content, and identifies the cause and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," it generates a specific response such as "Radio communication NG, turn the BBU on / off, or remove and insert the SFP."

[0542] Step 6:

[0543] The server formats the parsed results

[0544] The server receives the analysis results obtained from the generative AI model and formats them in a format that is easy for users to understand, such as JSON or text.

[0545] Step 7:

[0546] The server sends the analysis results to the user's device

[0547] Create an API response to send the formatted analysis results to the user's device. Send the created API response to the user's device.

[0548] Step 8:

[0549] The user device receives and displays the analysis results

[0550] The user's device displays the analysis results received from the server. Specific countermeasures such as "wireless communication failure, turn the BBU on / off, or remove and reinsert the SFP" are displayed on the screen.

[0551] Step 9:

[0552] The user checks and executes the response method.

[0553] The user checks the displayed analysis results and the countermeasures, and then actually takes corrective action based on that information. For example, to deal with a wireless communication problem, the user can reboot the BBU or remove and reinsert the SFP.

[0554] In this way, the system of the present invention assists users in quickly and appropriately interpreting and responding to alarm information.

[0555] Example 1

[0556] 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."

[0557] Responding quickly and appropriately to failures or abnormalities in communication equipment requires specialized knowledge and experience. In many cases, there are only a limited number of engineers with such knowledge, which can result in time-consuming problem resolution. Furthermore, when alarm information is provided in multiple languages, translation work and the risk of mistranslation can further delay response. Furthermore, analyzing alarm information and identifying how to respond can be complex and difficult for some engineers to understand, making it difficult to respond quickly.

[0558] 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.

[0559] In this invention, the server includes means for a user to input alarm information of a communication device into a terminal, means for the terminal to transmit the alarm information to the server, means for the server to receive the alarm information transmitted from the terminal, means for the server to use a generative artificial intelligence model to analyze the alarm information, means for the generative artificial intelligence model to analyze the alarm information and identify the cause and a response method, means for the server to format the analysis results and return them to the user terminal, and means for the terminal to display the analysis results received from the server. This enables efficient analysis of alarm information of communication devices and rapid and accurate response.

[0560] "Communication devices" are electronic devices for sending and receiving data and voice.

[0561] "Alarm information" is a message or notification indicating an abnormality or malfunction in a communication device.

[0562] A "terminal" is an electronic device used to input alarm information, such as a PC or smartphone operated by a user.

[0563] A "server" is a central processing unit for receiving and analyzing alarm information.

[0564] A "generative artificial intelligence model" is a model that uses machine learning algorithms to analyze alarm information and identify causes and response methods.

[0565] A "user terminal" is an electronic device that displays alarm information and is operated by a user.

[0566] The "analysis results" are analysis results that include the cause of the alarm information and how to respond to it.

[0567] "Formatting" is the process of arranging the analysis results in a form that is easy for the user to understand.

[0568] "Multilingual" refers to the inclusion of multiple different languages.

[0569] This invention relates to a system that automatically analyzes alarm information from communication devices and suggests appropriate countermeasures. The system mainly consists of a terminal operated by the user, a server that receives and analyzes alarm information, and a generative artificial intelligence model.

[0570] System configuration

[0571] 1. Terminal

[0572] Hardware: Electronic devices such as PCs and smartphones operated by users

[0573] Software: Dedicated application

[0574] Function: Provides an interface for users to input alarm information and communicate with the server.

[0575] 2. Server

[0576] Hardware: Central Processing Unit

[0577] Software: A program for receiving alarm information and analyzing it using a generative artificial intelligence model

[0578] function:

[0579] Receive alarm information sent from the device

[0580] Alarm information is passed to the generated AI model to begin analysis

[0581] Format the analysis results and return them to the user's device

[0582] 3. Generative AI Model

[0583] Software: Models that implement machine learning algorithms (APIs built using Python, etc.)

[0584] Function: Analyzes alarm information and identifies causes and countermeasures

[0585] process:

[0586] Takes alarm information as input

[0587] The cause and specific countermeasures are analyzed and sent back to the server

[0588] How it works

[0589] Input phase (user operation)

[0590] The user inputs the alarm information generated by the communication device into the dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[0591] Receiving alarm information (server)

[0592] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[0593] Specific processing details

[0594] Alarm analysis (generative artificial intelligence model)

[0595] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[0596] Generation and transmission of analysis results (server)

[0597] The server formats the analysis results obtained from the generative AI model and returns them to the user's device. The formatted analysis results are arranged in a form that is easy for the user to understand.

[0598] Displaying the results (terminal)

[0599] The user's device displays the analysis results received from the server. The user can check the analysis results on the screen along with specific countermeasures. For example, specific steps such as "wireless communication failure, turn the BBU on / off or remove and insert the SFP" are displayed.

[0600] Specific examples

[0601] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[0602] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[0603] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[0604] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[0605] 5. The server formats the analysis results and sends them to the user's device.

[0606] 6. The user's device displays the analysis results and how to respond.

[0607] 7. The user follows the displayed instructions to fix the problem.

[0608] In this way, the present invention enables efficient analysis of alarm information from communication devices and rapid response. Users can respond appropriately by simply following the system's instructions, even without special expertise, greatly improving work efficiency.

[0609] Prompt Sentence Examples

[0610] "What should I do if the communication device alarm 'Link Failure RiLink=BBU-B' occurs?"

[0611] "Please analyze the cause of the alarm message 'Link Failure RiLink=BBU-B' and what to do about it."

[0612] The present invention can be carried out as described above.

[0613] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0614] Step 1:

[0615] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the user inputs an alarm message such as "Link Failure RiLink=BBU-B" and presses the "Send" button. The terminal then formats the input data and generates packet data to be sent to the server.

[0616] Input: Alarm information "Link Failure RiLink=BBU-B"

[0617] Output: Packet data for transmission

[0618] Step 2:

[0619] The terminal sends the generated packet data to the server. This transmission is performed using API calls and HTTP requests on a dedicated application. The terminal monitors the transmission status and performs retransmission if an error occurs.

[0620] Input: Packet data to send

[0621] Output: HTTP request to the server

[0622] Step 3:

[0623] The server receives HTTP requests from the terminal. The receiving module analyzes the data and extracts alarm information. This data is stored in a database and passed on to the next analysis process.

[0624] Input: HTTP request from the terminal

[0625] Output: Extracted alarm information

[0626] Step 4:

[0627] The server inputs the extracted alarm information into the generative AI model. At this time, the server passes data to the model using a specific API endpoint. For example, it calls an API written in Python and passes the data "Link Failure RiLink=BBU-B."

[0628] Input: Extracted alarm information

[0629] Output: Input data to a generative artificial intelligence model

[0630] Step 5:

[0631] The generative artificial intelligence model analyzes the alarm information. The model uses a machine learning algorithm to identify the cause of the alarm and how to respond, and returns the results in JSON format to the server. For example, it identifies countermeasures such as "wireless communication failure, turn the BBU on / off, or remove and insert the SFP."

[0632] Input: Input data to the generative artificial intelligence model

[0633] Output: Analysis result (JSON format)

[0634] Step 6:

[0635] The server receives the analysis results obtained from the generative AI model. The server formats this data and arranges it in a form that is easy for the user to understand. For example, it converts it to include explanatory text in Japanese. The server then sends the formatted results to the user's device.

[0636] Input: Analysis result (JSON format)

[0637] Output: Formatted analysis results

[0638] Step 7:

[0639] The terminal displays the analysis results received from the server. A dedicated application displays the analysis results on the screen, and the user can check the specific steps to take. For example, specific steps such as "wireless communication failed, turn the BBU on / off or remove and insert the SFP."

[0640] Input: Formatted analysis results

[0641] Output: On-screen instructions

[0642] Step 8:

[0643] The user can then take corrective action according to the displayed solution. For example, the user can try turning the BBU on and off, or removing and reinserting the SFP. This will resolve the problem.

[0644] Input: The response displayed on the screen

[0645] Output: Communication equipment repair work

[0646] (Application example 1)

[0647] 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."

[0648] There is a demand for a system that can quickly and accurately analyze alarm information from communication devices and propose effective responses based on the analysis results. It is particularly important to provide actionable information in real time for complex devices and systems within factories, thereby improving work efficiency. However, with conventional systems, analyzing alarm information takes time, which can lead to reduced work efficiency and incorrect responses.

[0649] 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.

[0650] In this invention, the server includes means for receiving alarm information from communication devices, means for analyzing the alarm information using a generative artificial intelligence model, means for generating the cause of the alarm information and a response method based on the analysis results, means for transmitting the generated information to a user terminal, and means for analyzing alarm information from devices and systems in the factory in real time and presenting the response method on a visualization device that can be heard by workers. This makes it possible to quickly provide effective response methods and improve work efficiency.

[0651] "Communications equipment" refers to devices that send and receive data over a network. Examples include routers, switches, and modems.

[0652] "Alarm information" refers to warning messages that are generated and sent when an abnormality or error occurs in communication equipment. This includes error messages, warning notifications, and failure reports.

[0653] A "generative artificial intelligence model" is a model designed to perform data analysis using machine learning algorithms, which can identify patterns in the data and automate certain tasks.

[0654] "Analysis" is the act of examining data or information in detail to understand its meaning and patterns. The analysis process involves verifying, classifying, and identifying causes of data.

[0655] "User terminal" refers to an electronic device that is directly operated and used by a user, such as a smartphone, tablet, or PC.

[0656] "Response methods" are specific procedures and techniques for resolving problems based on analyzed alarm information. By following these methods, users can take appropriate action.

[0657] A "visualization device" is a device for visually displaying information. Examples include smart glasses and head-mounted displays.

[0658] "Work efficiency" is an indicator of how much work or tasks can be completed within a certain amount of time. Improving work efficiency can save time and costs.

[0659] A system for implementing this invention receives alarm information from a communication device, uses a generative artificial intelligence model to analyze the alarm information, and generates an appropriate response method based on the analysis results. Specific embodiments of this system are described below.

[0660] System configuration

[0661] 1. Terminal

[0662] An electronic device operated by a user, including PCs, smartphones, and smart glasses. A dedicated application is installed on the device, which provides an interface for inputting alarm information and communicating with the server. For example, when using smart glasses, alarm information can be input through a camera or voice input function.

[0663] 2. Server

[0664] This is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model. The server is responsible for sending the generated analysis results to the user's terminal. It also formats the analysis results in a form that is easy for the user to understand.

[0665] 3. Generative AI Model

[0666] This model analyzes alarm information based on machine learning algorithms. It is used to identify the cause and response method for alarm information from communication devices. The generative artificial intelligence model runs on the server and returns the analysis results to the server.

[0667] Program processing

[0668] Input phase (user operation)

[0669] When a problem occurs with equipment in the factory, users can input alarm information using voice input or a camera on a visualization device (e.g., smart glasses). For example, they can input "Robot arm E37 has stopped" by voice.

[0670] Receiving alarm information (server)

[0671] The alarm information sent from the terminal is received by the server. The server passes this information to the analysis process, and the analysis begins. For example, information such as "Robot arm E37 has stopped" is sent to the server.

[0672] Alarm analysis (generative artificial intelligence model)

[0673] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, the analysis result may identify a countermeasure such as "check the power and restart."

[0674] Generation and transmission of analysis results (server)

[0675] The server formats the analysis results obtained from the generative AI model and sends them back to the user device (e.g., smart glasses). The formatted analysis results are presented in a form that is easy for the user to understand.

[0676] Displaying the results (terminal)

[0677] The user terminal displays the analysis results received from the server. The user can check the analysis results and specific countermeasures on the display of the visualization device. For example, the smart glasses may display specific instructions such as "Check the power and restart."

[0678] Examples and prompts

[0679] Specific examples

[0680] Example 1: "Robot arm F45 has stopped working"

[0681] Solution: Check the sensor status and restart it.

[0682] Prompt Sentence Examples

[0683] What should I do if the message "Robot arm stopped" is displayed?

[0684] In this way, on-site workers can take appropriate action quickly, significantly improving work efficiency within the factory.

[0685] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0686] Step 1:

[0687] When a problem occurs with equipment in a factory, a user can input alarm information using voice input or a camera on a visualization device (e.g., smart glasses). The user can input "Robot arm E37 has stopped" by voice. This input data is first imported into a dedicated application on the terminal.

[0688] Step 2:

[0689] The terminal converts the alarm information input by voice from the user into text data. For example, voice data such as "Robot arm E37 has stopped" is converted into text data. This converted data is then sent to the server.

[0690] Step 3:

[0691] The server receives alarm information sent from the terminal, stores the received alarm information (e.g., "Robot arm E37 has stopped") in a database, and provides the data to the generative artificial intelligence model to start the analysis process.

[0692] Step 4:

[0693] The generative artificial intelligence model analyzes the provided alarm information. For example, it analyzes the message "Robot arm E37 has stopped" and identifies the cause and how to respond. This analysis identifies the countermeasure, "Check the power supply and restart." The analysis results are returned to the server.

[0694] Step 5:

[0695] The server receives the analyzed result (e.g., "Check the power and reboot"), formats it into a user-friendly format, and sends the formatted analysis result to the display of the user device (e.g., smart glasses).

[0696] Step 6:

[0697] The terminal receives the formatted analysis results sent from the server, for example, a message saying "Check power and reboot", which is then displayed on the display of the visualization device.

[0698] Step 7:

[0699] The user checks the analysis results and the corresponding solutions displayed on the terminal. For example, an instruction such as "Check the power supply and restart" is displayed on the display of the visualization device. The user then follows the displayed solutions to fix the equipment problem.

[0700] By taking these steps, alarm information from communication devices can be analyzed efficiently and a prompt response can be achieved.

[0701] 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.

[0702] This invention combines a system that automatically analyzes alarm information from communication devices and suggests appropriate responses with an emotion engine that recognizes the user's emotions. In addition to analyzing alarm information and suggesting appropriate responses, this system adjusts the display method based on the user's emotional state, supporting appropriate responses according to the user's experience and situation.

[0703] Overall system overview

[0704] The system consists of a terminal operated by the user, a server that receives and analyzes alarm information, a generative AI model, and an emotion engine. The specific configuration and operation of this system are described below.

[0705] System configuration

[0706] 1. Terminal

[0707] These are electronic devices such as PCs and smartphones that are operated by users.

[0708] It provides an interface for inputting alarm information through a dedicated application and communicating with the server.

[0709] 2. Server

[0710] It is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model.

[0711] It is responsible for generating analysis results and sending them to the user terminal.

[0712] 3. Generative AI Model

[0713] This model analyzes alarm information based on machine learning algorithms and identifies causes and countermeasures.

[0714] It runs on the server side and returns the analysis results to the server.

[0715] 4. Emotion Engine

[0716] It is an engine that analyzes a user's voice and text input to identify their emotional state.

[0717] The system adjusts the presentation of how to respond when the user is feeling stressed or nervous.

[0718] Program processing

[0719] Input phase (user operation)

[0720] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[0721] Receiving alarm information (server)

[0722] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[0723] Alarm analysis (generative artificial intelligence model)

[0724] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[0725] Emotion Recognition (Emotion Engine)

[0726] The emotion engine analyzes the user's voice and text input to identify their emotional state. For example, if the user types "What should I do? I don't know," the emotion engine will recognize that the user is feeling anxious.

[0727] Generation and adjustment of analysis results (server)

[0728] The server formats the analysis results obtained from the generative AI model and, based on the analysis results of the emotion engine, tailors the display to the user's emotional state. For example, a user who is feeling anxious might be provided with more detailed and easy-to-understand step-by-step instructions.

[0729] Sending and displaying analysis results (terminal)

[0730] The server sends the adjusted analysis results to the user's device. The user's device displays the received analysis results and can check specific countermeasures on the screen. For example, specific steps such as "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[0731] Specific examples

[0732] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[0733] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[0734] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[0735] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[0736] 5. The emotion engine recognizes anxiety from the user's text input.

[0737] 6. The server formats the analysis results and prepares detailed instructions based on the user's emotional state.

[0738] 7. The user's device displays the analysis results and how to respond.

[0739] 8. The user follows the displayed instructions to fix the problem.

[0740] In this way, the present invention is a system that efficiently analyzes alarm information from communication devices and supports appropriate responses according to the user's emotional state, allowing users to quickly take appropriate responses without having special expertise.

[0741] The processing flow will be explained below.

[0742] Step 1:

[0743] User enters alarm information

[0744] The user opens the dedicated application on their device and inputs the alarm message (e.g., "Link Failure RiLink=BBU-B") that occurred. The user clicks the "Send" button to send the alarm information to the server.

[0745] Step 2:

[0746] The device sends alarm information to the server

[0747] The device creates an API request to send the alarm message entered by the user to the server, and then sends the created API request to the server's analysis endpoint.

[0748] Step 3:

[0749] The server receives the alarm information

[0750] The server receives alarm messages sent from the terminals and prepares to pass the received alarm messages to the analysis process.

[0751] Step 4:

[0752] The server inputs alarm information into the artificial intelligence model

[0753] The server inputs the received alarm message into the artificial intelligence model to start the analysis.

[0754] Step 5:

[0755] Generative AI model analysis

[0756] The generative AI model analyzes the alarm information, understands the content, and identifies the cause and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," it generates a specific response such as "Radio communication NG, turn the BBU on / off, or remove and insert the SFP."

[0757] Step 6:

[0758] Emotion engine recognizes user emotions

[0759] The emotion engine analyzes the user's voice and text input (e.g., "What should I do? I don't know") to determine if the user is feeling anxious or stressed.

[0760] Step 7:

[0761] The server adjusts the response based on the analysis results and the emotional state.

[0762] The server evaluates the analysis results obtained from the artificial intelligence model and the emotion recognition results from the emotion engine, and adjusts the response method according to the user's emotional state. For example, if a user feels anxious, more detailed instructions or supplementary explanations will be added.

[0763] Step 8:

[0764] The server formats the adjusted analysis results

[0765] The server formats the tuned analysis results in a user-friendly format, such as JSON or text.

[0766] Step 9:

[0767] The server sends the analysis results to the user's device

[0768] Create an API response to send the formatted analysis results to the user's device. Send the created API response to the user's device.

[0769] Step 10:

[0770] The user device receives and displays the analysis results

[0771] The user's device displays the analysis results received from the server. Specific solutions, such as "wireless communication failure, turn the BBU on / off or remove and reinsert the SFP," are displayed on the screen.

[0772] Step 11:

[0773] The user checks and executes the response method.

[0774] The user checks the displayed analysis results and the countermeasures, and then actually takes corrective action based on that information. For example, to deal with a wireless communication problem, the user can reboot the BBU or remove and reinsert the SFP.

[0775] In this way, the system of the present invention helps users to quickly and appropriately interpret alarm information and then respond optimally to it in accordance with their emotional state.

[0776] Example 2

[0777] 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."

[0778] Conventional alarm systems for communication devices simply provide the cause of the alarm information and how to respond, but do not take into account the user's emotional state or level of understanding, making it difficult to take an appropriate response quickly. Furthermore, if there is no means to translate alarm information issued in English into Japanese, this becomes an additional burden for users who are not good at English.

[0779] 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.

[0780] In this invention, the server includes means for receiving alarm information from a communication device, means for analyzing the alarm information using a generative artificial intelligence model, means including an emotion engine that recognizes the user's emotional state, means for generating a cause of the alarm information and a response method based on the analysis results, means for transmitting the generated information to a user terminal, means for adjusting the display method based on the analysis results of the emotion engine, and means for displaying the generated information on the user terminal. This makes it possible to provide an appropriate and detailed response method according to the user's emotional state and level of understanding, enabling quick and effective problem solving.

[0781] A "communications device" is an electronic device for transmitting and receiving data.

[0782] "Alarm information" refers to a warning message that is sent when a failure or abnormality occurs in a communication device.

[0783] A "generative artificial intelligence model" is a model that uses machine learning algorithms to analyze data and provide solutions to specific problems.

[0784] An "emotion engine" is a system that analyzes a user's voice and text input to identify their emotional state.

[0785] "Analysis results" are the results of analysis and judgment generated by the generative artificial intelligence model or emotion engine.

[0786] A "user terminal" is an electronic device such as a computer or smartphone that is operated by a user.

[0787] "Display method" refers to the specific format and procedure for presenting analysis results and instructions to the user.

[0788] A "machine learning algorithm" is a mathematical technique for learning from data and making predictions or classifications based on that data.

[0789] The present invention is a system that automatically analyzes alarm information from communication devices and suggests appropriate response methods, and also combines it with an emotion engine that recognizes the user's emotions. In addition to analyzing alarm information and suggesting response methods, this system adjusts the display method based on the user's emotional state, thereby supporting appropriate responses according to the user's experience and situation. The specific configuration and operation of this system are described below.

[0790] System configuration

[0791] The system consists of a terminal operated by the user, a server that receives and analyzes alarm information, a generative AI model, and an emotion engine.

[0792] 1. Terminal

[0793] An electronic device such as a computer or smartphone operated by a user, which inputs alarm information through a dedicated application and provides an interface for communicating with a server.

[0794] 2. Server

[0795] This is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model. It is responsible for generating analysis results and sending them to user terminals.

[0796] 3. Generative AI Model

[0797] This model analyzes alarm information based on machine learning algorithms, identifies causes and countermeasures, and runs on the server side, returning the analysis results to the server.

[0798] 4. Emotion Engine

[0799] The engine analyzes the user's voice and text input to identify their emotional state, and adjusts the suggested response if the user is feeling stressed or nervous.

[0800] Program processing

[0801] Input phase (user operation)

[0802] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[0803] Receiving alarm information (server)

[0804] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[0805] Alarm analysis (generative artificial intelligence model)

[0806] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[0807] Emotion Recognition (Emotion Engine)

[0808] The emotion engine analyzes the user's voice and text input to identify their emotional state. For example, if the user types "What should I do? I don't know," the emotion engine will recognize that the user is feeling anxious.

[0809] Generation and adjustment of analysis results (server)

[0810] The server formats the analysis results obtained from the generative AI model and, based on the analysis results of the emotion engine, tailors the display to the user's emotional state. For example, a user who is feeling anxious might be provided with more detailed and easy-to-understand step-by-step instructions.

[0811] Sending and displaying analysis results (terminal)

[0812] The server sends the adjusted analysis results to the user's device. The user's device displays the received analysis results and can check specific countermeasures on the screen. For example, specific steps such as "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[0813] Specific examples

[0814] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[0815] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[0816] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[0817] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[0818] 5. The emotion engine recognizes anxiety from the user's text input.

[0819] 6. The server formats the analysis results and prepares detailed instructions based on the user's emotional state.

[0820] 7. The user's device displays the analysis results and how to respond.

[0821] 8. The user follows the displayed instructions to fix the problem.

[0822] Prompt Sentence Examples

[0823] "An alarm "Link Failure RiLink=BBU-B" has occurred. What should I do?"

[0824] As described above, this system efficiently analyzes alarm information from communication devices and supports appropriate responses according to the user's emotional state, allowing users to quickly and effectively take appropriate responses without requiring specialized knowledge.

[0825] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0826] Step 1:

[0827] The user inputs the alarm information generated by the communication device into a dedicated application on the terminal. For example, the user inputs the message "Link Failure RiLink=BBU-B."

[0828] Input: Alarm information (e.g. "Link Failure RiLink=BBU-B")

[0829] Operation: The user enters alarm information into the dedicated application and presses the "Send" button.

[0830] Output: Alarm information is sent from the terminal to the server.

[0831] Step 2:

[0832] The terminal transmits the alarm information to the server.

[0833] Input: Alarm information (e.g. "Link Failure RiLink=BBU-B")

[0834] Operation: The terminal converts the input alarm information into packets and sends them to the server via the network.

[0835] Output: Alarm information arrives at the server.

[0836] Step 3:

[0837] The server receives the alarm information and formats the data.

[0838] Input: Alarm information (e.g. "Link Failure RiLink=BBU-B")

[0839] Operation: Before the server passes the received alarm information to the analysis process, it performs any necessary pre-processing (for example, normalizing the data or removing unnecessary information).

[0840] Output: Formatted alarm information

[0841] Step 4:

[0842] The server inputs the formatted alarm information into the artificial intelligence model to begin analysis.

[0843] Input: Formatted alarm information

[0844] Operation: The generative AI model analyzes the alarm information and identifies the cause and how to respond. For example, it identifies "wireless communication failure, turn the BBU on / off, or remove and insert the SFP."

[0845] Output: Analysis results (e.g. "Radio communication NG, try turning the BBU on / off or removing and inserting the SFP")

[0846] Step 5:

[0847] The user provides additional text input, for example, "What should I do, I don't know."

[0848] Input: User-supplied text (e.g., "What should I do? I don't know.")

[0849] Action: The user types some additional text into the terminal and sends it.

[0850] Output: Additional text is sent from the terminal to the server.

[0851] Step 6:

[0852] The server receives the user's additional text input and passes it to the emotion engine.

[0853] Input: Additional text (e.g. "What should I do? I don't know")

[0854] How it works: The server inputs the received additional text into the emotion engine and analyzes the emotional state, e.g., recognizing "anxiety."

[0855] Output: Sentiment analysis result (e.g., "anxiety")

[0856] Step 7:

[0857] The server integrates the analysis results of the generated artificial intelligence model and the emotion engine and adjusts the display method.

[0858] Input: Analysis results (e.g., "Radio communication failed, try turning the BBU on / off and removing the SFP"), Emotion analysis results (e.g., "Anxiety")

[0859] How it works: The server formats the analysis results and tailors them into detailed, step-by-step instructions for anxious users.

[0860] Output: Adjusted analysis results (e.g. "Step 1: Turn the BBU power OFF and then ON again. Step 2: Remove the SFP module and then insert it again.")

[0861] Step 8:

[0862] The server transmits the adjusted analysis results to the user terminal.

[0863] Input: Adjusted analysis results

[0864] Operation: The server converts the adjusted analysis results into packets for transmission and sends them to the user terminal via the network.

[0865] Output: The adjusted analysis results arrive at the terminal.

[0866] Step 9:

[0867] The device receives the adjusted analysis results and displays them on the screen.

[0868] Input: Adjusted analysis results

[0869] Operation: The device displays the results it receives on the user interface, and the user can check the specific steps on the screen.

[0870] Output: The user checks the displayed analysis results and how to respond.

[0871] This series of processing flows not only enables users to efficiently solve problems with their communication devices, but also allows them to receive support that takes into consideration their own emotional state.

[0872] (Application example 2)

[0873] 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."

[0874] In modern factories, robots and machines perform complex tasks, generating a wide variety of alarms during operation. Rapidly analyzing these alarms and providing response methods is crucial for maintaining productivity. However, it is not realistic for specialized engineers to respond quickly every time an alarm occurs. Furthermore, on-site workers sometimes find themselves unable to respond appropriately to alarms. Responding quickly and accurately is particularly difficult when workers are emotionally anxious or tense. To resolve this situation, in addition to analyzing alarm information and providing response methods, it is necessary to provide response methods that take into account the worker's emotional state.

[0875] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving alarm information from a communication device, means for using a generative artificial intelligence model to analyze the alarm information, means for generating the cause of the alarm information and a response method based on the analysis results, means for recognizing the user's emotional state and reflecting that information in the display of the analysis results, and means for transmitting the generated information to the user terminal. This enables efficient analysis of alarm information and the presentation of an appropriate response method according to the user's emotional state.

[0876] "Communication device alarm information" is a warning message issued when a communication device detects an abnormality or error.

[0877] A "generative artificial intelligence model" is a computer program that uses machine learning algorithms to analyze alarm information and identify its cause and how to respond.

[0878] The "means for recognizing the user's emotional state" is a function that analyzes the user's voice, text input, etc., and identifies the user's emotional state.

[0879] The "means for reflecting the analysis results in the display" is a function for adjusting the display method of the analysis results based on the emotional state of the user.

[0880] A "user terminal" is an electronic device (e.g., a smartphone or head-mounted display) operated by a user, which is used to input alarm information and display analysis results.

[0881] The "receiving means" is a function for receiving alarm information transmitted from a communication device.

[0882] "Means for generating" refers to a function that identifies the cause and response method based on the results of analyzed alarm information and creates the information.

[0883] The "transmitting means" is a function for sending the generated information to the user terminal.

[0884] The "display means" is a function for visually displaying information generated on a user terminal.

[0885] This invention relates to the analysis of alarm information in the management of robots in a factory and the presentation of appropriate countermeasures, and in particular to the realization of a system that takes into account the emotional state of the user.

[0886] System configuration

[0887] 1. Server

[0888] The server receives alarm information transmitted from the communication device.

[0889] The server uses a generative artificial intelligence model to analyze the alarm information and generate the cause and response method.

[0890] The server uses an emotion engine to recognize the user's emotional state, analyzes the user's input information (voice or text), and identifies the user's emotional state.

[0891] The server adjusts the analysis results according to the user's emotional state and transmits them to the user's terminal.

[0892] 2. User Device

[0893] A user terminal is an electronic device (such as a smartphone or a head-mounted display) operated by a user.

[0894] Alarm information is entered through a dedicated application and communicates with the server.

[0895] The terminal receives the analysis results sent from the server and displays the information.

[0896] Hardware and software used

[0897] 1. Hardware

[0898] Server: Something with high-speed processing and large data storage capacity (e.g., cloud servers).

[0899] User devices: smartphones, tablets, head-mounted displays (e.g., Microsoft HoloLens), etc.

[0900] 2. Software

[0901] Generative artificial intelligence model: Software that uses machine learning algorithms to analyze alarm information.

[0902] Emotion engine: An algorithm that analyzes a user's voice or text input to identify their emotional state (e.g., emotion recognition software).

[0903] Dedicated application: An application installed on the user's terminal that inputs alarm information and displays analysis results.

[0904] What the program does

[0905] The server first receives alarm information sent from the communication device. Next, it inputs the alarm information into a generative artificial intelligence model for analysis. Once the analysis results indicate the cause of the alarm and a response method, the emotion engine analyzes the user's input information to identify the user's emotional state. The analysis results are adjusted by the server based on the user's emotional state and are finally sent to the user's terminal. The user's terminal receives and displays this adjusted information.

[0906] Specific examples

[0907] For example, consider the case where a robot in a factory generates an alarm that reads "Motor Overheat W1." A user inputs this alarm information using a smartphone application and sends it to the server. The server passes this alarm information to a generative AI model, which identifies a response, such as "The cooling fan needs to be cleaned." At the same time, if the user inputs "What should I do?", the emotion engine recognizes that the user is feeling anxious. Based on this recognition result, the server provides the user with more detailed step-by-step instructions, which are displayed on the smartphone or HMD.

[0908] Prompt Sentence Examples

[0909] Alarm analysis prompt:

[0910] Alarm Message: Motor Overheat W1

[0911] Emotion recognition prompts:

[0912] User Input: What should I do?

[0913] As a result, a system is realized that allows the user to quickly and accurately find a way to respond even in emotional situations.

[0914] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0915] Step 1:

[0916] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. At this time, the user inputs the alarm message (e.g., "Motor Overheat W1") in text format and presses the send button. The input alarm information is sent from the terminal to the server.

[0917] Step 2:

[0918] The server receives the alarm information sent from the terminal. The received data is passed to the server as "Alarm Message: Motor Overheat W1". This alarm information is input into the next analysis process.

[0919] Step 3:

[0920] The server passes the received alarm information to the generative AI model, which begins analysis. The generative AI model analyzes the alarm message and identifies the cause and how to respond. Data processing involves tokenizing the alarm message and comparing it with existing learning datasets to output the cause and how to respond. For example, in the case of "Motor Overheat W1," the response generated is "The cooling fan needs to be cleaned."

[0921] Step 4:

[0922] After sending the alarm information, the user can input additional voice or text through the terminal, such as "What should I do?", which indicates an emotion. This input is sent to the server as an emotion-recognition prompt.

[0923] Step 5:

[0924] The server uses the emotion engine to analyze the user's additional input and identify their emotional state. The input data is "What should I do?", and the emotion engine analyzes it and identifies "anxiety" as the emotional state. This outputs the user's emotional state as data.

[0925] Step 6:

[0926] The server combines the analysis results with the emotional state and adjusts the display method. In data calculations, the cause of the alarm and the solution are identified as "The cooling fan needs to be cleaned." Since the emotional state is "anxious," detailed step-by-step instructions are generated. For example, specific steps such as "1. Turn off the power. 2. Remove the cooling fan..." are generated.

[0927] Step 7:

[0928] The server then sends the adjusted analysis results to the user's device, which includes detailed instructions and how to respond.

[0929] Step 8:

[0930] The user device visually displays the received analysis results and how to respond on the screen. Detailed step-by-step instructions are displayed to help the user stay calm and take action. The user can follow these instructions to take action.

[0931] This allows the user to respond to alarm information quickly and appropriately. In addition, since consideration is given to the user's emotional state, the user can work while reducing stress.

[0932] As described above, the processing flow of the entire system is performed consistently and continuously.

[0933] 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.

[0934] 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.

[0935] 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.

[0936] [Third embodiment]

[0937] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0938] 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.

[0939] 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).

[0940] 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.

[0941] 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.

[0942] 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).

[0943] 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.

[0944] 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.

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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."

[0949] This invention is a system that automatically analyzes alarm information from communication devices and presents appropriate response methods. This system receives alarm information from communication devices, uses a generative artificial intelligence model to analyze it, generates response methods based on the analysis results, and transmits and displays them on the user's terminal, enabling smooth response.

[0950] Overall system overview

[0951] The system is comprised of a terminal operated by the user, a server that receives and analyzes alarm information, and a generative artificial intelligence model. The specific configuration and operation of this system are explained below.

[0952] System configuration

[0953] 1. Terminal

[0954] These are electronic devices such as PCs and smartphones that are operated by users.

[0955] It provides an interface for inputting alarm information through a dedicated application and communicating with the server.

[0956] 2. Server

[0957] It is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model.

[0958] It is responsible for generating analysis results and sending them to the user terminal.

[0959] 3. Generative AI Model

[0960] This model analyzes alarm information based on machine learning algorithms and identifies causes and countermeasures.

[0961] It runs on the server side and returns the analysis results to the server.

[0962] Program processing

[0963] Input phase (user operation)

[0964] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[0965] Receiving alarm information (server)

[0966] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[0967] Alarm analysis (generative artificial intelligence model)

[0968] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[0969] Generation and transmission of analysis results (server)

[0970] The server formats the analysis results obtained from the generative AI model and returns them to the user's device. The formatted analysis results are arranged in a form that is easy for the user to understand.

[0971] Displaying the results (terminal)

[0972] The user's device displays the analysis results received from the server. The user can check the analysis results on the screen along with specific countermeasures. For example, specific steps such as "wireless communication failure, turn the BBU on / off or remove and insert the SFP" are displayed.

[0973] Specific examples

[0974] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[0975] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[0976] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[0977] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[0978] 5. The server formats the analysis results and sends them to the user's device.

[0979] 6. The user's device displays the analysis results and how to respond.

[0980] 7. The user follows the displayed instructions to fix the problem.

[0981] In this way, the present invention enables efficient analysis of alarm information from communication devices and rapid response. Users can respond appropriately by simply following the system's instructions, even if they do not have special expertise, thereby significantly improving work efficiency.

[0982] The processing flow will be explained below.

[0983] Step 1:

[0984] User enters alarm information

[0985] The user opens the dedicated application on their device and inputs the alarm message (e.g., "Link Failure RiLink=BBU-B") The user clicks the "Send" button to send the alarm information to the server.

[0986] Step 2:

[0987] The device sends alarm information to the server

[0988] The terminal creates an API request to send the alarm message entered by the user to the server, and then sends the created API request to the server's analysis endpoint.

[0989] Step 3:

[0990] The server receives the alarm information

[0991] The server receives alarm messages sent from the terminals and prepares to pass the received alarm messages to the analysis process.

[0992] Step 4:

[0993] The server inputs alarm information into the artificial intelligence model

[0994] The server inputs the received alarm message into the artificial intelligence model to start the analysis.

[0995] Step 5:

[0996] Generative AI model analysis

[0997] The generative AI model analyzes the alarm information, understands the content, and identifies the cause and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," it generates a specific response such as "Radio communication NG, turn the BBU on / off, or remove and insert the SFP."

[0998] Step 6:

[0999] The server formats the parsed results

[1000] The server receives the analysis results obtained from the generative AI model and formats them in a format that is easy for users to understand, such as JSON or text.

[1001] Step 7:

[1002] The server sends the analysis results to the user's device

[1003] Create an API response to send the formatted analysis results to the user's device. Send the created API response to the user's device.

[1004] Step 8:

[1005] The user device receives and displays the analysis results

[1006] The user's device displays the analysis results received from the server. Specific countermeasures such as "wireless communication failure, turn the BBU on / off, or remove and reinsert the SFP" are displayed on the screen.

[1007] Step 9:

[1008] The user checks and executes the response method.

[1009] The user checks the displayed analysis results and the countermeasures, and then actually takes corrective action based on that information. For example, to deal with a wireless communication problem, the user can reboot the BBU or remove and reinsert the SFP.

[1010] In this way, the system of the present invention assists users in quickly and appropriately interpreting and responding to alarm information.

[1011] Example 1

[1012] 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."

[1013] Responding quickly and appropriately to failures or abnormalities in communication equipment requires specialized knowledge and experience. In many cases, there are only a limited number of engineers with such knowledge, which can result in time-consuming problem resolution. Furthermore, when alarm information is provided in multiple languages, translation work and the risk of mistranslation can further delay response. Furthermore, analyzing alarm information and identifying how to respond can be complex and difficult for some engineers to understand, making it difficult to respond quickly.

[1014] 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.

[1015] In this invention, the server includes means for a user to input alarm information of a communication device into a terminal, means for the terminal to transmit the alarm information to the server, means for the server to receive the alarm information transmitted from the terminal, means for the server to use a generative artificial intelligence model to analyze the alarm information, means for the generative artificial intelligence model to analyze the alarm information and identify the cause and a response method, means for the server to format the analysis results and return them to the user terminal, and means for the terminal to display the analysis results received from the server. This enables efficient analysis of alarm information of communication devices and rapid and accurate response.

[1016] "Communication devices" are electronic devices for sending and receiving data and voice.

[1017] "Alarm information" is a message or notification indicating an abnormality or malfunction in a communication device.

[1018] A "terminal" is an electronic device used to input alarm information, such as a PC or smartphone operated by a user.

[1019] A "server" is a central processing unit for receiving and analyzing alarm information.

[1020] A "generative artificial intelligence model" is a model that uses machine learning algorithms to analyze alarm information and identify causes and response methods.

[1021] A "user terminal" is an electronic device that displays alarm information and is operated by a user.

[1022] The "analysis results" are analysis results that include the cause of the alarm information and how to respond to it.

[1023] "Formatting" is the process of arranging the analysis results in a form that is easy for the user to understand.

[1024] "Multilingual" refers to the inclusion of multiple different languages.

[1025] This invention relates to a system that automatically analyzes alarm information from communication devices and suggests appropriate countermeasures. The system mainly consists of a terminal operated by the user, a server that receives and analyzes alarm information, and a generative artificial intelligence model.

[1026] System configuration

[1027] 1. Terminal

[1028] Hardware: Electronic devices such as PCs and smartphones operated by users

[1029] Software: Dedicated application

[1030] Function: Provides an interface for users to input alarm information and communicate with the server.

[1031] 2. Server

[1032] Hardware: Central Processing Unit

[1033] Software: A program for receiving alarm information and analyzing it using a generative artificial intelligence model

[1034] function:

[1035] Receive alarm information sent from the device

[1036] Alarm information is passed to the generated AI model to begin analysis

[1037] Format the analysis results and return them to the user's device

[1038] 3. Generative AI Model

[1039] Software: Models that implement machine learning algorithms (APIs built using Python, etc.)

[1040] Function: Analyzes alarm information and identifies causes and countermeasures

[1041] process:

[1042] Takes alarm information as input

[1043] The cause and specific countermeasures are analyzed and sent back to the server

[1044] How it works

[1045] Input phase (user operation)

[1046] The user inputs the alarm information generated by the communication device into the dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[1047] Receiving alarm information (server)

[1048] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[1049] Specific processing details

[1050] Alarm analysis (generative artificial intelligence model)

[1051] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[1052] Generation and transmission of analysis results (server)

[1053] The server formats the analysis results obtained from the generative AI model and returns them to the user's device. The formatted analysis results are arranged in a form that is easy for the user to understand.

[1054] Displaying the results (terminal)

[1055] The user's device displays the analysis results received from the server. The user can check the analysis results on the screen along with specific countermeasures. For example, specific steps such as "wireless communication failure, turn the BBU on / off or remove and insert the SFP" are displayed.

[1056] Specific examples

[1057] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[1058] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[1059] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[1060] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[1061] 5. The server formats the analysis results and sends them to the user's device.

[1062] 6. The user's device displays the analysis results and how to respond.

[1063] 7. The user follows the displayed instructions to fix the problem.

[1064] In this way, the present invention enables efficient analysis of alarm information from communication devices and rapid response. Users can respond appropriately by simply following the system's instructions, even without special expertise, greatly improving work efficiency.

[1065] Prompt Sentence Examples

[1066] "What should I do if the communication device alarm 'Link Failure RiLink=BBU-B' occurs?"

[1067] "Please analyze the cause of the alarm message 'Link Failure RiLink=BBU-B' and what to do about it."

[1068] The present invention can be carried out as described above.

[1069] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1070] Step 1:

[1071] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the user inputs an alarm message such as "Link Failure RiLink=BBU-B" and presses the "Send" button. The terminal then formats the input data and generates packet data to be sent to the server.

[1072] Input: Alarm information "Link Failure RiLink=BBU-B"

[1073] Output: Packet data for transmission

[1074] Step 2:

[1075] The terminal sends the generated packet data to the server. This transmission is performed using API calls and HTTP requests on a dedicated application. The terminal monitors the transmission status and performs retransmission if an error occurs.

[1076] Input: Packet data to send

[1077] Output: HTTP request to the server

[1078] Step 3:

[1079] The server receives HTTP requests from the terminal. The receiving module analyzes the data and extracts alarm information. This data is stored in a database and passed on to the next analysis process.

[1080] Input: HTTP request from the terminal

[1081] Output: Extracted alarm information

[1082] Step 4:

[1083] The server inputs the extracted alarm information into the generative AI model. At this time, the server passes data to the model using a specific API endpoint. For example, it calls an API written in Python and passes the data "Link Failure RiLink=BBU-B."

[1084] Input: Extracted alarm information

[1085] Output: Input data to a generative artificial intelligence model

[1086] Step 5:

[1087] The generative artificial intelligence model analyzes the alarm information. The model uses a machine learning algorithm to identify the cause of the alarm and how to respond, and returns the results in JSON format to the server. For example, it identifies countermeasures such as "wireless communication failure, turn the BBU on / off, or remove and insert the SFP."

[1088] Input: Input data to the generative artificial intelligence model

[1089] Output: Analysis result (JSON format)

[1090] Step 6:

[1091] The server receives the analysis results obtained from the generative AI model. The server formats this data and arranges it in a form that is easy for the user to understand. For example, it converts it to include explanatory text in Japanese. The server then sends the formatted results to the user's device.

[1092] Input: Analysis result (JSON format)

[1093] Output: Formatted analysis results

[1094] Step 7:

[1095] The terminal displays the analysis results received from the server. A dedicated application displays the analysis results on the screen, and the user can check the specific steps to take. For example, specific steps such as "wireless communication failed, turn the BBU on / off or remove and insert the SFP."

[1096] Input: Formatted analysis results

[1097] Output: On-screen instructions

[1098] Step 8:

[1099] The user can then take corrective action according to the displayed solution. For example, the user can try turning the BBU on and off, or removing and reinserting the SFP. This will resolve the problem.

[1100] Input: The response displayed on the screen

[1101] Output: Communication equipment repair work

[1102] (Application example 1)

[1103] 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."

[1104] There is a demand for a system that can quickly and accurately analyze alarm information from communication devices and propose effective responses based on the analysis results. It is particularly important to provide actionable information in real time for complex devices and systems within factories, thereby improving work efficiency. However, with conventional systems, analyzing alarm information takes time, which can lead to reduced work efficiency and incorrect responses.

[1105] 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.

[1106] In this invention, the server includes means for receiving alarm information from communication devices, means for analyzing the alarm information using a generative artificial intelligence model, means for generating the cause of the alarm information and a response method based on the analysis results, means for transmitting the generated information to a user terminal, and means for analyzing alarm information from devices and systems in the factory in real time and presenting the response method on a visualization device that can be heard by workers. This makes it possible to quickly provide effective response methods and improve work efficiency.

[1107] "Communications equipment" refers to devices that send and receive data over a network. Examples include routers, switches, and modems.

[1108] "Alarm information" refers to warning messages that are generated and sent when an abnormality or error occurs in communication equipment. This includes error messages, warning notifications, and failure reports.

[1109] A "generative artificial intelligence model" is a model designed to perform data analysis using machine learning algorithms, which can identify patterns in the data and automate certain tasks.

[1110] "Analysis" is the act of examining data or information in detail to understand its meaning and patterns. The analysis process involves verifying, classifying, and identifying causes of data.

[1111] "User terminal" refers to an electronic device that is directly operated and used by a user, such as a smartphone, tablet, or PC.

[1112] "Response methods" are specific procedures and techniques for resolving problems based on analyzed alarm information. By following these methods, users can take appropriate action.

[1113] A "visualization device" is a device for visually displaying information. Examples include smart glasses and head-mounted displays.

[1114] "Work efficiency" is an indicator of how much work or tasks can be completed within a certain amount of time. Improving work efficiency can save time and costs.

[1115] A system for implementing this invention receives alarm information from a communication device, uses a generative artificial intelligence model to analyze the alarm information, and generates an appropriate response method based on the analysis results. Specific embodiments of this system are described below.

[1116] System configuration

[1117] 1. Terminal

[1118] An electronic device operated by a user, including PCs, smartphones, and smart glasses. A dedicated application is installed on the device, which provides an interface for inputting alarm information and communicating with the server. For example, when using smart glasses, alarm information can be input through a camera or voice input function.

[1119] 2. Server

[1120] This is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model. The server is responsible for sending the generated analysis results to the user's terminal. It also formats the analysis results in a form that is easy for the user to understand.

[1121] 3. Generative AI Model

[1122] This model analyzes alarm information based on machine learning algorithms. It is used to identify the cause and response method for alarm information from communication devices. The generative artificial intelligence model runs on the server and returns the analysis results to the server.

[1123] Program processing

[1124] Input phase (user operation)

[1125] When a problem occurs with equipment in the factory, users can input alarm information using voice input or a camera on a visualization device (e.g., smart glasses). For example, they can input "Robot arm E37 has stopped" by voice.

[1126] Receiving alarm information (server)

[1127] The alarm information sent from the terminal is received by the server. The server passes this information to the analysis process, and the analysis begins. For example, information such as "Robot arm E37 has stopped" is sent to the server.

[1128] Alarm analysis (generative artificial intelligence model)

[1129] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, the analysis result may identify a countermeasure such as "check the power and restart."

[1130] Generation and transmission of analysis results (server)

[1131] The server formats the analysis results obtained from the generative AI model and sends them back to the user device (e.g., smart glasses). The formatted analysis results are presented in a form that is easy for the user to understand.

[1132] Displaying the results (terminal)

[1133] The user terminal displays the analysis results received from the server. The user can check the analysis results and specific countermeasures on the display of the visualization device. For example, the smart glasses may display specific instructions such as "Check the power and restart."

[1134] Examples and prompts

[1135] Specific examples

[1136] Example 1: "Robot arm F45 has stopped working"

[1137] Solution: Check the sensor status and restart it.

[1138] Prompt Sentence Examples

[1139] What should I do if the message "Robot arm stopped" is displayed?

[1140] In this way, on-site workers can take appropriate action quickly, significantly improving work efficiency within the factory.

[1141] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1142] Step 1:

[1143] When a problem occurs with equipment in a factory, a user can input alarm information using voice input or a camera on a visualization device (e.g., smart glasses). The user can input "Robot arm E37 has stopped" by voice. This input data is first imported into a dedicated application on the terminal.

[1144] Step 2:

[1145] The terminal converts the alarm information input by voice from the user into text data. For example, voice data such as "Robot arm E37 has stopped" is converted into text data. This converted data is then sent to the server.

[1146] Step 3:

[1147] The server receives alarm information sent from the terminal, stores the received alarm information (e.g., "Robot arm E37 has stopped") in a database, and provides the data to the generative artificial intelligence model to start the analysis process.

[1148] Step 4:

[1149] The generative artificial intelligence model analyzes the provided alarm information. For example, it analyzes the message "Robot arm E37 has stopped" and identifies the cause and how to respond. This analysis identifies the countermeasure, "Check the power supply and restart." The analysis results are returned to the server.

[1150] Step 5:

[1151] The server receives the analyzed result (e.g., "Check the power and reboot"), formats it into a user-friendly format, and sends the formatted analysis result to the display of the user device (e.g., smart glasses).

[1152] Step 6:

[1153] The terminal receives the formatted analysis results sent from the server, for example, a message saying "Check power and reboot", which is then displayed on the display of the visualization device.

[1154] Step 7:

[1155] The user checks the analysis results and the corresponding solutions displayed on the terminal. For example, an instruction such as "Check the power supply and restart" is displayed on the display of the visualization device. The user then follows the displayed solutions to fix the equipment problem.

[1156] By taking these steps, alarm information from communication devices can be analyzed efficiently and a prompt response can be achieved.

[1157] 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.

[1158] This invention combines a system that automatically analyzes alarm information from communication devices and suggests appropriate responses with an emotion engine that recognizes the user's emotions. In addition to analyzing alarm information and suggesting appropriate responses, this system adjusts the display method based on the user's emotional state, supporting appropriate responses according to the user's experience and situation.

[1159] Overall system overview

[1160] The system consists of a terminal operated by the user, a server that receives and analyzes alarm information, a generative AI model, and an emotion engine. The specific configuration and operation of this system are described below.

[1161] System configuration

[1162] 1. Terminal

[1163] These are electronic devices such as PCs and smartphones that are operated by users.

[1164] It provides an interface for inputting alarm information through a dedicated application and communicating with the server.

[1165] 2. Server

[1166] It is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model.

[1167] It is responsible for generating analysis results and sending them to the user terminal.

[1168] 3. Generative AI Model

[1169] This model analyzes alarm information based on machine learning algorithms and identifies causes and countermeasures.

[1170] It runs on the server side and returns the analysis results to the server.

[1171] 4. Emotion Engine

[1172] It is an engine that analyzes a user's voice and text input to identify their emotional state.

[1173] The system adjusts the presentation of how to respond when the user is feeling stressed or nervous.

[1174] Program processing

[1175] Input phase (user operation)

[1176] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[1177] Receiving alarm information (server)

[1178] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[1179] Alarm analysis (generative artificial intelligence model)

[1180] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[1181] Emotion Recognition (Emotion Engine)

[1182] The emotion engine analyzes the user's voice and text input to identify their emotional state. For example, if the user types "What should I do? I don't know," the emotion engine will recognize that the user is feeling anxious.

[1183] Generation and adjustment of analysis results (server)

[1184] The server formats the analysis results obtained from the generative AI model and, based on the analysis results of the emotion engine, tailors the display to the user's emotional state. For example, a user who is feeling anxious might be provided with more detailed and easy-to-understand step-by-step instructions.

[1185] Sending and displaying analysis results (terminal)

[1186] The server sends the adjusted analysis results to the user's device. The user's device displays the received analysis results and can check specific countermeasures on the screen. For example, specific steps such as "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[1187] Specific examples

[1188] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[1189] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[1190] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[1191] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[1192] 5. The emotion engine recognizes anxiety from the user's text input.

[1193] 6. The server formats the analysis results and prepares detailed instructions based on the user's emotional state.

[1194] 7. The user's device displays the analysis results and how to respond.

[1195] 8. The user follows the displayed instructions to fix the problem.

[1196] In this way, the present invention is a system that efficiently analyzes alarm information from communication devices and supports appropriate responses according to the user's emotional state, allowing users to quickly take appropriate responses without having special expertise.

[1197] The processing flow will be explained below.

[1198] Step 1:

[1199] User enters alarm information

[1200] The user opens the dedicated application on their device and inputs the alarm message (e.g., "Link Failure RiLink=BBU-B") that occurred. The user clicks the "Send" button to send the alarm information to the server.

[1201] Step 2:

[1202] The device sends alarm information to the server

[1203] The device creates an API request to send the alarm message entered by the user to the server, and then sends the created API request to the server's analysis endpoint.

[1204] Step 3:

[1205] The server receives the alarm information

[1206] The server receives alarm messages sent from the terminals and prepares to pass the received alarm messages to the analysis process.

[1207] Step 4:

[1208] The server inputs alarm information into the artificial intelligence model

[1209] The server inputs the received alarm message into the artificial intelligence model to start the analysis.

[1210] Step 5:

[1211] Generative AI model analysis

[1212] The generative AI model analyzes the alarm information, understands the content, and identifies the cause and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," it generates a specific response such as "Radio communication NG, turn the BBU on / off, or remove and insert the SFP."

[1213] Step 6:

[1214] Emotion engine recognizes user emotions

[1215] The emotion engine analyzes the user's voice and text input (e.g., "What should I do? I don't know") to determine if the user is feeling anxious or stressed.

[1216] Step 7:

[1217] The server adjusts the response based on the analysis results and the emotional state.

[1218] The server evaluates the analysis results obtained from the artificial intelligence model and the emotion recognition results from the emotion engine, and adjusts the response method according to the user's emotional state. For example, if a user feels anxious, more detailed instructions or supplementary explanations will be added.

[1219] Step 8:

[1220] The server formats the adjusted analysis results

[1221] The server formats the tuned analysis results in a user-friendly format, such as JSON or text.

[1222] Step 9:

[1223] The server sends the analysis results to the user's device

[1224] Create an API response to send the formatted analysis results to the user's device. Send the created API response to the user's device.

[1225] Step 10:

[1226] The user device receives and displays the analysis results

[1227] The user's device displays the analysis results received from the server. Specific solutions, such as "wireless communication failure, turn the BBU on / off or remove and reinsert the SFP," are displayed on the screen.

[1228] Step 11:

[1229] The user checks and executes the response method.

[1230] The user checks the displayed analysis results and the countermeasures, and then actually takes corrective action based on that information. For example, to deal with a wireless communication problem, the user can reboot the BBU or remove and reinsert the SFP.

[1231] In this way, the system of the present invention helps users to quickly and appropriately interpret alarm information and then respond optimally to it in accordance with their emotional state.

[1232] Example 2

[1233] 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."

[1234] Conventional alarm systems for communication devices simply provide the cause of the alarm information and how to respond, but do not take into account the user's emotional state or level of understanding, making it difficult to take an appropriate response quickly. Furthermore, if there is no means to translate alarm information issued in English into Japanese, this becomes an additional burden for users who are not good at English.

[1235] 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.

[1236] In this invention, the server includes means for receiving alarm information from a communication device, means for analyzing the alarm information using a generative artificial intelligence model, means including an emotion engine that recognizes the user's emotional state, means for generating a cause of the alarm information and a response method based on the analysis results, means for transmitting the generated information to a user terminal, means for adjusting the display method based on the analysis results of the emotion engine, and means for displaying the generated information on the user terminal. This makes it possible to provide an appropriate and detailed response method according to the user's emotional state and level of understanding, enabling quick and effective problem solving.

[1237] A "communications device" is an electronic device for transmitting and receiving data.

[1238] "Alarm information" refers to a warning message that is sent when a failure or abnormality occurs in a communication device.

[1239] A "generative artificial intelligence model" is a model that uses machine learning algorithms to analyze data and provide solutions to specific problems.

[1240] An "emotion engine" is a system that analyzes a user's voice and text input to identify their emotional state.

[1241] "Analysis results" are the results of analysis and judgment generated by the generative artificial intelligence model or emotion engine.

[1242] A "user terminal" is an electronic device such as a computer or smartphone that is operated by a user.

[1243] "Display method" refers to the specific format and procedure for presenting analysis results and instructions to the user.

[1244] A "machine learning algorithm" is a mathematical technique for learning from data and making predictions or classifications based on that data.

[1245] The present invention is a system that automatically analyzes alarm information from communication devices and suggests appropriate response methods, and also combines it with an emotion engine that recognizes the user's emotions. In addition to analyzing alarm information and suggesting response methods, this system adjusts the display method based on the user's emotional state, thereby supporting appropriate responses according to the user's experience and situation. The specific configuration and operation of this system are described below.

[1246] System configuration

[1247] The system consists of a terminal operated by the user, a server that receives and analyzes alarm information, a generative AI model, and an emotion engine.

[1248] 1. Terminal

[1249] An electronic device such as a computer or smartphone operated by a user, which inputs alarm information through a dedicated application and provides an interface for communicating with a server.

[1250] 2. Server

[1251] This is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model. It is responsible for generating analysis results and sending them to user terminals.

[1252] 3. Generative AI Model

[1253] This model analyzes alarm information based on machine learning algorithms, identifies causes and countermeasures, and runs on the server side, returning the analysis results to the server.

[1254] 4. Emotion Engine

[1255] The engine analyzes the user's voice and text input to identify their emotional state, and adjusts the suggested response if the user is feeling stressed or nervous.

[1256] Program processing

[1257] Input phase (user operation)

[1258] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[1259] Receiving alarm information (server)

[1260] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[1261] Alarm analysis (generative artificial intelligence model)

[1262] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[1263] Emotion Recognition (Emotion Engine)

[1264] The emotion engine analyzes the user's voice and text input to identify their emotional state. For example, if the user types "What should I do? I don't know," the emotion engine will recognize that the user is feeling anxious.

[1265] Generation and adjustment of analysis results (server)

[1266] The server formats the analysis results obtained from the generative AI model and, based on the analysis results of the emotion engine, tailors the display to the user's emotional state. For example, a user who is feeling anxious might be provided with more detailed and easy-to-understand step-by-step instructions.

[1267] Sending and displaying analysis results (terminal)

[1268] The server sends the adjusted analysis results to the user's device. The user's device displays the received analysis results and can check specific countermeasures on the screen. For example, specific steps such as "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[1269] Specific examples

[1270] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[1271] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[1272] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[1273] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[1274] 5. The emotion engine recognizes anxiety from the user's text input.

[1275] 6. The server formats the analysis results and prepares detailed instructions based on the user's emotional state.

[1276] 7. The user's device displays the analysis results and how to respond.

[1277] 8. The user follows the displayed instructions to fix the problem.

[1278] Prompt Sentence Examples

[1279] "An alarm "Link Failure RiLink=BBU-B" has occurred. What should I do?"

[1280] As described above, this system efficiently analyzes alarm information from communication devices and supports appropriate responses according to the user's emotional state, allowing users to quickly and effectively take appropriate responses without requiring specialized knowledge.

[1281] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1282] Step 1:

[1283] The user inputs the alarm information generated by the communication device into a dedicated application on the terminal. For example, the user inputs the message "Link Failure RiLink=BBU-B."

[1284] Input: Alarm information (e.g. "Link Failure RiLink=BBU-B")

[1285] Operation: The user enters alarm information into the dedicated application and presses the "Send" button.

[1286] Output: Alarm information is sent from the terminal to the server.

[1287] Step 2:

[1288] The terminal transmits the alarm information to the server.

[1289] Input: Alarm information (e.g. "Link Failure RiLink=BBU-B")

[1290] Operation: The terminal converts the input alarm information into packets and sends them to the server via the network.

[1291] Output: Alarm information arrives at the server.

[1292] Step 3:

[1293] The server receives the alarm information and formats the data.

[1294] Input: Alarm information (e.g. "Link Failure RiLink=BBU-B")

[1295] Operation: Before the server passes the received alarm information to the analysis process, it performs any necessary pre-processing (for example, normalizing the data or removing unnecessary information).

[1296] Output: Formatted alarm information

[1297] Step 4:

[1298] The server inputs the formatted alarm information into the artificial intelligence model to begin analysis.

[1299] Input: Formatted alarm information

[1300] Operation: The generative AI model analyzes the alarm information and identifies the cause and how to respond. For example, it identifies "wireless communication failure, turn the BBU on / off, or remove and insert the SFP."

[1301] Output: Analysis results (e.g. "Radio communication NG, try turning the BBU on / off or removing and inserting the SFP")

[1302] Step 5:

[1303] The user provides additional text input, for example, "What should I do, I don't know."

[1304] Input: User-supplied text (e.g., "What should I do? I don't know.")

[1305] Action: The user types some additional text into the terminal and sends it.

[1306] Output: Additional text is sent from the terminal to the server.

[1307] Step 6:

[1308] The server receives the user's additional text input and passes it to the emotion engine.

[1309] Input: Additional text (e.g. "What should I do? I don't know")

[1310] How it works: The server inputs the received additional text into the emotion engine and analyzes the emotional state, e.g., recognizing "anxiety."

[1311] Output: Sentiment analysis result (e.g., "anxiety")

[1312] Step 7:

[1313] The server integrates the analysis results of the generated artificial intelligence model and the emotion engine and adjusts the display method.

[1314] Input: Analysis results (e.g., "Radio communication failed, try turning the BBU on / off and removing the SFP"), Emotion analysis results (e.g., "Anxiety")

[1315] How it works: The server formats the analysis results and tailors them into detailed, step-by-step instructions for anxious users.

[1316] Output: Adjusted analysis results (e.g. "Step 1: Turn the BBU power OFF and then ON again. Step 2: Remove the SFP module and then insert it again.")

[1317] Step 8:

[1318] The server transmits the adjusted analysis results to the user terminal.

[1319] Input: Adjusted analysis results

[1320] Operation: The server converts the adjusted analysis results into packets for transmission and sends them to the user terminal via the network.

[1321] Output: The adjusted analysis results arrive at the terminal.

[1322] Step 9:

[1323] The device receives the adjusted analysis results and displays them on the screen.

[1324] Input: Adjusted analysis results

[1325] Operation: The device displays the results it receives on the user interface, and the user can check the specific steps on the screen.

[1326] Output: The user checks the displayed analysis results and how to respond.

[1327] This series of processing flows not only enables users to efficiently solve problems with their communication devices, but also allows them to receive support that takes into consideration their own emotional state.

[1328] (Application example 2)

[1329] 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."

[1330] In modern factories, robots and machines perform complex tasks, generating a wide variety of alarms during operation. Rapidly analyzing these alarms and providing response methods is crucial for maintaining productivity. However, it is not realistic for specialized engineers to respond quickly every time an alarm occurs. Furthermore, on-site workers sometimes find themselves unable to respond appropriately to alarms. Responding quickly and accurately is particularly difficult when workers are emotionally anxious or tense. To resolve this situation, in addition to analyzing alarm information and providing response methods, it is necessary to provide response methods that take into account the worker's emotional state.

[1331] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving alarm information from a communication device, means for using a generative artificial intelligence model to analyze the alarm information, means for generating the cause of the alarm information and a response method based on the analysis results, means for recognizing the user's emotional state and reflecting that information in the display of the analysis results, and means for transmitting the generated information to the user terminal. This enables efficient analysis of alarm information and the presentation of an appropriate response method according to the user's emotional state.

[1332] "Communication device alarm information" is a warning message issued when a communication device detects an abnormality or error.

[1333] A "generative artificial intelligence model" is a computer program that uses machine learning algorithms to analyze alarm information and identify its cause and how to respond.

[1334] The "means for recognizing the user's emotional state" is a function that analyzes the user's voice, text input, etc., and identifies the user's emotional state.

[1335] The "means for reflecting the analysis results in the display" is a function for adjusting the display method of the analysis results based on the emotional state of the user.

[1336] A "user terminal" is an electronic device (e.g., a smartphone or head-mounted display) operated by a user, which is used to input alarm information and display analysis results.

[1337] The "receiving means" is a function for receiving alarm information transmitted from a communication device.

[1338] "Means for generating" refers to a function that identifies the cause and response method based on the results of analyzed alarm information and creates the information.

[1339] The "transmitting means" is a function for sending the generated information to the user terminal.

[1340] The "display means" is a function for visually displaying information generated on a user terminal.

[1341] This invention relates to the analysis of alarm information in the management of robots in a factory and the presentation of appropriate countermeasures, and in particular to the realization of a system that takes into account the emotional state of the user.

[1342] System configuration

[1343] 1. Server

[1344] The server receives alarm information transmitted from the communication device.

[1345] The server uses a generative artificial intelligence model to analyze the alarm information and generate the cause and response method.

[1346] The server uses an emotion engine to recognize the user's emotional state, analyzes the user's input information (voice or text), and identifies the user's emotional state.

[1347] The server adjusts the analysis results according to the user's emotional state and transmits them to the user's terminal.

[1348] 2. User Device

[1349] A user terminal is an electronic device (such as a smartphone or a head-mounted display) operated by a user.

[1350] Alarm information is entered through a dedicated application and communicates with the server.

[1351] The terminal receives the analysis results sent from the server and displays the information.

[1352] Hardware and software used

[1353] 1. Hardware

[1354] Server: Something with high-speed processing and large data storage capacity (e.g., cloud servers).

[1355] User devices: smartphones, tablets, head-mounted displays (e.g., Microsoft HoloLens), etc.

[1356] 2. Software

[1357] Generative artificial intelligence model: Software that uses machine learning algorithms to analyze alarm information.

[1358] Emotion engine: An algorithm that analyzes a user's voice or text input to identify their emotional state (e.g., emotion recognition software).

[1359] Dedicated application: An application installed on the user's terminal that inputs alarm information and displays analysis results.

[1360] What the program does

[1361] The server first receives alarm information sent from the communication device. Next, it inputs the alarm information into a generative artificial intelligence model for analysis. Once the analysis results indicate the cause of the alarm and a response method, the emotion engine analyzes the user's input information to identify the user's emotional state. The analysis results are adjusted by the server based on the user's emotional state and are finally sent to the user's terminal. The user's terminal receives and displays this adjusted information.

[1362] Specific examples

[1363] For example, consider the case where a robot in a factory generates an alarm that reads "Motor Overheat W1." A user inputs this alarm information using a smartphone application and sends it to the server. The server passes this alarm information to a generative AI model, which identifies a response, such as "The cooling fan needs to be cleaned." At the same time, if the user inputs "What should I do?", the emotion engine recognizes that the user is feeling anxious. Based on this recognition result, the server provides the user with more detailed step-by-step instructions, which are displayed on the smartphone or HMD.

[1364] Prompt Sentence Examples

[1365] Alarm analysis prompt:

[1366] Alarm Message: Motor Overheat W1

[1367] Emotion recognition prompts:

[1368] User Input: What should I do?

[1369] As a result, a system is realized that allows the user to quickly and accurately find a way to respond even in emotional situations.

[1370] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1371] Step 1:

[1372] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. At this time, the user inputs the alarm message (e.g., "Motor Overheat W1") in text format and presses the send button. The input alarm information is sent from the terminal to the server.

[1373] Step 2:

[1374] The server receives the alarm information sent from the terminal. The received data is passed to the server as "Alarm Message: Motor Overheat W1". This alarm information is input into the next analysis process.

[1375] Step 3:

[1376] The server passes the received alarm information to the generative AI model, which begins analysis. The generative AI model analyzes the alarm message and identifies the cause and how to respond. Data processing involves tokenizing the alarm message and comparing it with existing learning datasets to output the cause and how to respond. For example, in the case of "Motor Overheat W1," the response generated is "The cooling fan needs to be cleaned."

[1377] Step 4:

[1378] After sending the alarm information, the user can input additional voice or text through the terminal, such as "What should I do?", which indicates an emotion. This input is sent to the server as an emotion-recognition prompt.

[1379] Step 5:

[1380] The server uses the emotion engine to analyze the user's additional input and identify their emotional state. The input data is "What should I do?", and the emotion engine analyzes it and identifies "anxiety" as the emotional state. This outputs the user's emotional state as data.

[1381] Step 6:

[1382] The server combines the analysis results with the emotional state and adjusts the display method. In data calculations, the cause of the alarm and the solution are identified as "The cooling fan needs to be cleaned." Since the emotional state is "anxious," detailed step-by-step instructions are generated. For example, specific steps such as "1. Turn off the power. 2. Remove the cooling fan..." are generated.

[1383] Step 7:

[1384] The server then sends the adjusted analysis results to the user's device, which includes detailed instructions and how to respond.

[1385] Step 8:

[1386] The user device visually displays the received analysis results and how to respond on the screen. Detailed step-by-step instructions are displayed to help the user stay calm and take action. The user can follow these instructions to take action.

[1387] This allows the user to respond to alarm information quickly and appropriately. In addition, since consideration is given to the user's emotional state, the user can work while reducing stress.

[1388] As described above, the processing flow of the entire system is performed consistently and continuously.

[1389] 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.

[1390] 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.

[1391] 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.

[1392] [Fourth embodiment]

[1393] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1394] 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.

[1395] 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).

[1396] 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.

[1397] 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.

[1398] 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).

[1399] 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.

[1400] 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.

[1401] 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.

[1402] 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.

[1403] 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.

[1404] 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.

[1405] 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."

[1406] This invention is a system that automatically analyzes alarm information from communication devices and presents appropriate response methods. This system receives alarm information from communication devices, uses a generative artificial intelligence model to analyze it, generates response methods based on the analysis results, and transmits and displays them on the user's terminal, enabling smooth response.

[1407] Overall system overview

[1408] The system is comprised of a terminal operated by the user, a server that receives and analyzes alarm information, and a generative artificial intelligence model. The specific configuration and operation of this system are explained below.

[1409] System configuration

[1410] 1. Terminal

[1411] These are electronic devices such as PCs and smartphones that are operated by users.

[1412] It provides an interface for inputting alarm information through a dedicated application and communicating with the server.

[1413] 2. Server

[1414] It is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model.

[1415] It is responsible for generating analysis results and sending them to the user terminal.

[1416] 3. Generative AI Model

[1417] This model analyzes alarm information based on machine learning algorithms and identifies causes and countermeasures.

[1418] It runs on the server side and returns the analysis results to the server.

[1419] Program processing

[1420] Input phase (user operation)

[1421] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[1422] Receiving alarm information (server)

[1423] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[1424] Alarm analysis (generative artificial intelligence model)

[1425] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[1426] Generation and transmission of analysis results (server)

[1427] The server formats the analysis results obtained from the generative AI model and returns them to the user's device. The formatted analysis results are arranged in a form that is easy for the user to understand.

[1428] Displaying the results (terminal)

[1429] The user's device displays the analysis results received from the server. The user can check the analysis results on the screen along with specific countermeasures. For example, specific steps such as "wireless communication failure, turn the BBU on / off or remove and insert the SFP" are displayed.

[1430] Specific examples

[1431] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[1432] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[1433] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[1434] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[1435] 5. The server formats the analysis results and sends them to the user's device.

[1436] 6. The user's device displays the analysis results and how to respond.

[1437] 7. The user follows the displayed instructions to fix the problem.

[1438] In this way, the present invention enables efficient analysis of alarm information from communication devices and rapid response. Users can respond appropriately by simply following the system's instructions, even if they do not have special expertise, thereby significantly improving work efficiency.

[1439] The processing flow will be explained below.

[1440] Step 1:

[1441] User enters alarm information

[1442] The user opens the dedicated application on their device and inputs the alarm message (e.g., "Link Failure RiLink=BBU-B") The user clicks the "Send" button to send the alarm information to the server.

[1443] Step 2:

[1444] The device sends alarm information to the server

[1445] The terminal creates an API request to send the alarm message entered by the user to the server, and then sends the created API request to the server's analysis endpoint.

[1446] Step 3:

[1447] The server receives the alarm information

[1448] The server receives alarm messages sent from the terminals and prepares to pass the received alarm messages to the analysis process.

[1449] Step 4:

[1450] The server inputs alarm information into the artificial intelligence model

[1451] The server inputs the received alarm message into the artificial intelligence model to start the analysis.

[1452] Step 5:

[1453] Generative AI model analysis

[1454] The generative AI model analyzes the alarm information, understands the content, and identifies the cause and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," it generates a specific response such as "Radio communication NG, turn the BBU on / off, or remove and insert the SFP."

[1455] Step 6:

[1456] The server formats the parsed results

[1457] The server receives the analysis results obtained from the generative AI model and formats them in a format that is easy for users to understand, such as JSON or text.

[1458] Step 7:

[1459] The server sends the analysis results to the user's device

[1460] Create an API response to send the formatted analysis results to the user's device. Send the created API response to the user's device.

[1461] Step 8:

[1462] The user device receives and displays the analysis results

[1463] The user's device displays the analysis results received from the server. Specific countermeasures such as "wireless communication failure, turn the BBU on / off, or remove and reinsert the SFP" are displayed on the screen.

[1464] Step 9:

[1465] The user checks and executes the response method.

[1466] The user checks the displayed analysis results and the countermeasures, and then actually takes corrective action based on that information. For example, to deal with a wireless communication problem, the user can reboot the BBU or remove and reinsert the SFP.

[1467] In this way, the system of the present invention assists users in quickly and appropriately interpreting and responding to alarm information.

[1468] Example 1

[1469] 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."

[1470] Responding quickly and appropriately to failures or abnormalities in communication equipment requires specialized knowledge and experience. In many cases, there are only a limited number of engineers with such knowledge, which can result in time-consuming problem resolution. Furthermore, when alarm information is provided in multiple languages, translation work and the risk of mistranslation can further delay response. Furthermore, analyzing alarm information and identifying how to respond can be complex and difficult for some engineers to understand, making it difficult to respond quickly.

[1471] 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.

[1472] In this invention, the server includes means for a user to input alarm information of a communication device into a terminal, means for the terminal to transmit the alarm information to the server, means for the server to receive the alarm information transmitted from the terminal, means for the server to use a generative artificial intelligence model to analyze the alarm information, means for the generative artificial intelligence model to analyze the alarm information and identify the cause and a response method, means for the server to format the analysis results and return them to the user terminal, and means for the terminal to display the analysis results received from the server. This enables efficient analysis of alarm information of communication devices and rapid and accurate response.

[1473] "Communication devices" are electronic devices for sending and receiving data and voice.

[1474] "Alarm information" is a message or notification indicating an abnormality or malfunction in a communication device.

[1475] A "terminal" is an electronic device used to input alarm information, such as a PC or smartphone operated by a user.

[1476] A "server" is a central processing unit for receiving and analyzing alarm information.

[1477] A "generative artificial intelligence model" is a model that uses machine learning algorithms to analyze alarm information and identify causes and response methods.

[1478] A "user terminal" is an electronic device that displays alarm information and is operated by a user.

[1479] The "analysis results" are analysis results that include the cause of the alarm information and how to respond to it.

[1480] "Formatting" is the process of arranging the analysis results in a form that is easy for the user to understand.

[1481] "Multilingual" refers to the inclusion of multiple different languages.

[1482] This invention relates to a system that automatically analyzes alarm information from communication devices and suggests appropriate countermeasures. The system mainly consists of a terminal operated by the user, a server that receives and analyzes alarm information, and a generative artificial intelligence model.

[1483] System configuration

[1484] 1. Terminal

[1485] Hardware: Electronic devices such as PCs and smartphones operated by users

[1486] Software: Dedicated application

[1487] Function: Provides an interface for users to input alarm information and communicate with the server.

[1488] 2. Server

[1489] Hardware: Central Processing Unit

[1490] Software: A program for receiving alarm information and analyzing it using a generative artificial intelligence model

[1491] function:

[1492] Receive alarm information sent from the device

[1493] Alarm information is passed to the generated AI model to begin analysis

[1494] Format the analysis results and return them to the user's device

[1495] 3. Generative AI Model

[1496] Software: Models that implement machine learning algorithms (APIs built using Python, etc.)

[1497] Function: Analyzes alarm information and identifies causes and countermeasures

[1498] process:

[1499] Takes alarm information as input

[1500] The cause and specific countermeasures are analyzed and sent back to the server

[1501] How it works

[1502] Input phase (user operation)

[1503] The user inputs the alarm information generated by the communication device into the dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[1504] Receiving alarm information (server)

[1505] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[1506] Specific processing details

[1507] Alarm analysis (generative artificial intelligence model)

[1508] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[1509] Generation and transmission of analysis results (server)

[1510] The server formats the analysis results obtained from the generative AI model and returns them to the user's device. The formatted analysis results are arranged in a form that is easy for the user to understand.

[1511] Displaying the results (terminal)

[1512] The user's device displays the analysis results received from the server. The user can check the analysis results on the screen along with specific countermeasures. For example, specific steps such as "wireless communication failure, turn the BBU on / off or remove and insert the SFP" are displayed.

[1513] Specific examples

[1514] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[1515] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[1516] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[1517] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[1518] 5. The server formats the analysis results and sends them to the user's device.

[1519] 6. The user's device displays the analysis results and how to respond.

[1520] 7. The user follows the displayed instructions to fix the problem.

[1521] In this way, the present invention enables efficient analysis of alarm information from communication devices and rapid response. Users can respond appropriately by simply following the system's instructions, even without special expertise, greatly improving work efficiency.

[1522] Prompt Sentence Examples

[1523] "What should I do if the communication device alarm 'Link Failure RiLink=BBU-B' occurs?"

[1524] "Please analyze the cause of the alarm message 'Link Failure RiLink=BBU-B' and what to do about it."

[1525] The present invention can be carried out as described above.

[1526] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1527] Step 1:

[1528] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the user inputs an alarm message such as "Link Failure RiLink=BBU-B" and presses the "Send" button. The terminal then formats the input data and generates packet data to be sent to the server.

[1529] Input: Alarm information "Link Failure RiLink=BBU-B"

[1530] Output: Packet data for transmission

[1531] Step 2:

[1532] The terminal sends the generated packet data to the server. This transmission is performed using API calls and HTTP requests on a dedicated application. The terminal monitors the transmission status and performs retransmission if an error occurs.

[1533] Input: Packet data to send

[1534] Output: HTTP request to the server

[1535] Step 3:

[1536] The server receives HTTP requests from the terminal. The receiving module analyzes the data and extracts alarm information. This data is stored in a database and passed on to the next analysis process.

[1537] Input: HTTP request from the terminal

[1538] Output: Extracted alarm information

[1539] Step 4:

[1540] The server inputs the extracted alarm information into the generative AI model. At this time, the server passes data to the model using a specific API endpoint. For example, it calls an API written in Python and passes the data "Link Failure RiLink=BBU-B."

[1541] Input: Extracted alarm information

[1542] Output: Input data to a generative artificial intelligence model

[1543] Step 5:

[1544] The generative artificial intelligence model analyzes the alarm information. The model uses a machine learning algorithm to identify the cause of the alarm and how to respond, and returns the results in JSON format to the server. For example, it identifies countermeasures such as "wireless communication failure, turn the BBU on / off, or remove and insert the SFP."

[1545] Input: Input data to the generative artificial intelligence model

[1546] Output: Analysis result (JSON format)

[1547] Step 6:

[1548] The server receives the analysis results obtained from the generative AI model. The server formats this data and arranges it in a form that is easy for the user to understand. For example, it converts it to include explanatory text in Japanese. The server then sends the formatted results to the user's device.

[1549] Input: Analysis result (JSON format)

[1550] Output: Formatted analysis results

[1551] Step 7:

[1552] The terminal displays the analysis results received from the server. A dedicated application displays the analysis results on the screen, and the user can check the specific steps to take. For example, specific steps such as "wireless communication failed, turn the BBU on / off or remove and insert the SFP."

[1553] Input: Formatted analysis results

[1554] Output: On-screen instructions

[1555] Step 8:

[1556] The user can then take corrective action according to the displayed solution. For example, the user can try turning the BBU on and off, or removing and reinserting the SFP. This will resolve the problem.

[1557] Input: The response displayed on the screen

[1558] Output: Communication equipment repair work

[1559] (Application example 1)

[1560] 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."

[1561] There is a demand for a system that can quickly and accurately analyze alarm information from communication devices and propose effective responses based on the analysis results. It is particularly important to provide actionable information in real time for complex devices and systems within factories, thereby improving work efficiency. However, with conventional systems, analyzing alarm information takes time, which can lead to reduced work efficiency and incorrect responses.

[1562] 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.

[1563] In this invention, the server includes means for receiving alarm information from communication devices, means for analyzing the alarm information using a generative artificial intelligence model, means for generating the cause of the alarm information and a response method based on the analysis results, means for transmitting the generated information to a user terminal, and means for analyzing alarm information from devices and systems in the factory in real time and presenting the response method on a visualization device that can be heard by workers. This makes it possible to quickly provide effective response methods and improve work efficiency.

[1564] "Communications equipment" refers to devices that send and receive data over a network. Examples include routers, switches, and modems.

[1565] "Alarm information" refers to warning messages that are generated and sent when an abnormality or error occurs in communication equipment. This includes error messages, warning notifications, and failure reports.

[1566] A "generative artificial intelligence model" is a model designed to perform data analysis using machine learning algorithms, which can identify patterns in the data and automate certain tasks.

[1567] "Analysis" is the act of examining data or information in detail to understand its meaning and patterns. The analysis process involves verifying, classifying, and identifying causes of data.

[1568] "User terminal" refers to an electronic device that is directly operated and used by a user, such as a smartphone, tablet, or PC.

[1569] "Response methods" are specific procedures and techniques for resolving problems based on analyzed alarm information. By following these methods, users can take appropriate action.

[1570] A "visualization device" is a device for visually displaying information. Examples include smart glasses and head-mounted displays.

[1571] "Work efficiency" is an indicator of how much work or tasks can be completed within a certain amount of time. Improving work efficiency can save time and costs.

[1572] A system for implementing this invention receives alarm information from a communication device, uses a generative artificial intelligence model to analyze the alarm information, and generates an appropriate response method based on the analysis results. Specific embodiments of this system are described below.

[1573] System configuration

[1574] 1. Terminal

[1575] An electronic device operated by a user, including PCs, smartphones, and smart glasses. A dedicated application is installed on the device, which provides an interface for inputting alarm information and communicating with the server. For example, when using smart glasses, alarm information can be input through a camera or voice input function.

[1576] 2. Server

[1577] This is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model. The server is responsible for sending the generated analysis results to the user's terminal. It also formats the analysis results in a form that is easy for the user to understand.

[1578] 3. Generative AI Model

[1579] This model analyzes alarm information based on machine learning algorithms. It is used to identify the cause and response method for alarm information from communication devices. The generative artificial intelligence model runs on the server and returns the analysis results to the server.

[1580] Program processing

[1581] Input phase (user operation)

[1582] When a problem occurs with equipment in the factory, users can input alarm information using voice input or a camera on a visualization device (e.g., smart glasses). For example, they can input "Robot arm E37 has stopped" by voice.

[1583] Receiving alarm information (server)

[1584] The alarm information sent from the terminal is received by the server. The server passes this information to the analysis process, and the analysis begins. For example, information such as "Robot arm E37 has stopped" is sent to the server.

[1585] Alarm analysis (generative artificial intelligence model)

[1586] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, the analysis result may identify a countermeasure such as "check the power and restart."

[1587] Generation and transmission of analysis results (server)

[1588] The server formats the analysis results obtained from the generative AI model and sends them back to the user device (e.g., smart glasses). The formatted analysis results are presented in a form that is easy for the user to understand.

[1589] Displaying the results (terminal)

[1590] The user terminal displays the analysis results received from the server. The user can check the analysis results and specific countermeasures on the display of the visualization device. For example, the smart glasses may display specific instructions such as "Check the power and restart."

[1591] Examples and prompts

[1592] Specific examples

[1593] Example 1: "Robot arm F45 has stopped working"

[1594] Solution: Check the sensor status and restart it.

[1595] Prompt Sentence Examples

[1596] What should I do if the message "Robot arm stopped" is displayed?

[1597] In this way, on-site workers can take appropriate action quickly, significantly improving work efficiency within the factory.

[1598] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1599] Step 1:

[1600] When a problem occurs with equipment in a factory, a user can input alarm information using voice input or a camera on a visualization device (e.g., smart glasses). The user can input "Robot arm E37 has stopped" by voice. This input data is first imported into a dedicated application on the terminal.

[1601] Step 2:

[1602] The terminal converts the alarm information input by voice from the user into text data. For example, voice data such as "Robot arm E37 has stopped" is converted into text data. This converted data is then sent to the server.

[1603] Step 3:

[1604] The server receives alarm information sent from the terminal, stores the received alarm information (e.g., "Robot arm E37 has stopped") in a database, and provides the data to the generative artificial intelligence model to start the analysis process.

[1605] Step 4:

[1606] The generative artificial intelligence model analyzes the provided alarm information. For example, it analyzes the message "Robot arm E37 has stopped" and identifies the cause and how to respond. This analysis identifies the countermeasure, "Check the power supply and restart." The analysis results are returned to the server.

[1607] Step 5:

[1608] The server receives the analyzed result (e.g., "Check the power and reboot"), formats it into a user-friendly format, and sends the formatted analysis result to the display of the user device (e.g., smart glasses).

[1609] Step 6:

[1610] The terminal receives the formatted analysis results sent from the server, for example, a message saying "Check power and reboot", which is then displayed on the display of the visualization device.

[1611] Step 7:

[1612] The user checks the analysis results and the corresponding solutions displayed on the terminal. For example, an instruction such as "Check the power supply and restart" is displayed on the display of the visualization device. The user then follows the displayed solutions to fix the equipment problem.

[1613] By taking these steps, alarm information from communication devices can be analyzed efficiently and a prompt response can be achieved.

[1614] 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.

[1615] This invention combines a system that automatically analyzes alarm information from communication devices and suggests appropriate responses with an emotion engine that recognizes the user's emotions. In addition to analyzing alarm information and suggesting appropriate responses, this system adjusts the display method based on the user's emotional state, supporting appropriate responses according to the user's experience and situation.

[1616] Overall system overview

[1617] The system consists of a terminal operated by the user, a server that receives and analyzes alarm information, a generative AI model, and an emotion engine. The specific configuration and operation of this system are described below.

[1618] System configuration

[1619] 1. Terminal

[1620] These are electronic devices such as PCs and smartphones that are operated by users.

[1621] It provides an interface for inputting alarm information through a dedicated application and communicating with the server.

[1622] 2. Server

[1623] It is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model.

[1624] It is responsible for generating analysis results and sending them to the user terminal.

[1625] 3. Generative AI Model

[1626] This model analyzes alarm information based on machine learning algorithms and identifies causes and countermeasures.

[1627] It runs on the server side and returns the analysis results to the server.

[1628] 4. Emotion Engine

[1629] It is an engine that analyzes a user's voice and text input to identify their emotional state.

[1630] The system adjusts the presentation of how to respond when the user is feeling stressed or nervous.

[1631] Program processing

[1632] Input phase (user operation)

[1633] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[1634] Receiving alarm information (server)

[1635] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[1636] Alarm analysis (generative artificial intelligence model)

[1637] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[1638] Emotion Recognition (Emotion Engine)

[1639] The emotion engine analyzes the user's voice and text input to identify their emotional state. For example, if the user types "What should I do? I don't know," the emotion engine will recognize that the user is feeling anxious.

[1640] Generation and adjustment of analysis results (server)

[1641] The server formats the analysis results obtained from the generative AI model and, based on the analysis results of the emotion engine, tailors the display to the user's emotional state. For example, a user who is feeling anxious might be provided with more detailed and easy-to-understand step-by-step instructions.

[1642] Sending and displaying analysis results (terminal)

[1643] The server sends the adjusted analysis results to the user's device. The user's device displays the received analysis results and can check specific countermeasures on the screen. For example, specific steps such as "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[1644] Specific examples

[1645] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[1646] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[1647] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[1648] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[1649] 5. The emotion engine recognizes anxiety from the user's text input.

[1650] 6. The server formats the analysis results and prepares detailed instructions based on the user's emotional state.

[1651] 7. The user's device displays the analysis results and how to respond.

[1652] 8. The user follows the displayed instructions to fix the problem.

[1653] In this way, the present invention is a system that efficiently analyzes alarm information from communication devices and supports appropriate responses according to the user's emotional state, allowing users to quickly take appropriate responses without having special expertise.

[1654] The processing flow will be explained below.

[1655] Step 1:

[1656] User enters alarm information

[1657] The user opens the dedicated application on their device and inputs the alarm message (e.g., "Link Failure RiLink=BBU-B") that occurred. The user clicks the "Send" button to send the alarm information to the server.

[1658] Step 2:

[1659] The device sends alarm information to the server

[1660] The device creates an API request to send the alarm message entered by the user to the server, and then sends the created API request to the server's analysis endpoint.

[1661] Step 3:

[1662] The server receives the alarm information

[1663] The server receives alarm messages sent from the terminals and prepares to pass the received alarm messages to the analysis process.

[1664] Step 4:

[1665] The server inputs alarm information into the artificial intelligence model

[1666] The server inputs the received alarm message into the artificial intelligence model to start the analysis.

[1667] Step 5:

[1668] Generative AI model analysis

[1669] The generative AI model analyzes the alarm information, understands the content, and identifies the cause and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," it generates a specific response such as "Radio communication NG, turn the BBU on / off, or remove and insert the SFP."

[1670] Step 6:

[1671] Emotion engine recognizes user emotions

[1672] The emotion engine analyzes the user's voice and text input (e.g., "What should I do? I don't know") to determine if the user is feeling anxious or stressed.

[1673] Step 7:

[1674] The server adjusts the response based on the analysis results and the emotional state.

[1675] The server evaluates the analysis results obtained from the artificial intelligence model and the emotion recognition results from the emotion engine, and adjusts the response method according to the user's emotional state. For example, if a user feels anxious, more detailed instructions or supplementary explanations will be added.

[1676] Step 8:

[1677] The server formats the adjusted analysis results

[1678] The server formats the tuned analysis results in a user-friendly format, such as JSON or text.

[1679] Step 9:

[1680] The server sends the analysis results to the user's device

[1681] Create an API response to send the formatted analysis results to the user's device. Send the created API response to the user's device.

[1682] Step 10:

[1683] The user device receives and displays the analysis results

[1684] The user's device displays the analysis results received from the server. Specific solutions, such as "wireless communication failure, turn the BBU on / off or remove and reinsert the SFP," are displayed on the screen.

[1685] Step 11:

[1686] The user checks and executes the response method.

[1687] The user checks the displayed analysis results and the countermeasures, and then actually takes corrective action based on that information. For example, to deal with a wireless communication problem, the user can reboot the BBU or remove and reinsert the SFP.

[1688] In this way, the system of the present invention helps users to quickly and appropriately interpret alarm information and then respond optimally to it in accordance with their emotional state.

[1689] Example 2

[1690] 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."

[1691] Conventional alarm systems for communication devices simply provide the cause of the alarm information and how to respond, but do not take into account the user's emotional state or level of understanding, making it difficult to take an appropriate response quickly. Furthermore, if there is no means to translate alarm information issued in English into Japanese, this becomes an additional burden for users who are not good at English.

[1692] 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.

[1693] In this invention, the server includes means for receiving alarm information from a communication device, means for analyzing the alarm information using a generative artificial intelligence model, means including an emotion engine that recognizes the user's emotional state, means for generating a cause of the alarm information and a response method based on the analysis results, means for transmitting the generated information to a user terminal, means for adjusting the display method based on the analysis results of the emotion engine, and means for displaying the generated information on the user terminal. This makes it possible to provide an appropriate and detailed response method according to the user's emotional state and level of understanding, enabling quick and effective problem solving.

[1694] A "communications device" is an electronic device for transmitting and receiving data.

[1695] "Alarm information" refers to a warning message that is sent when a failure or abnormality occurs in a communication device.

[1696] A "generative artificial intelligence model" is a model that uses machine learning algorithms to analyze data and provide solutions to specific problems.

[1697] An "emotion engine" is a system that analyzes a user's voice and text input to identify their emotional state.

[1698] "Analysis results" are the results of analysis and judgment generated by the generative artificial intelligence model or emotion engine.

[1699] A "user terminal" is an electronic device such as a computer or smartphone that is operated by a user.

[1700] "Display method" refers to the specific format and procedure for presenting analysis results and instructions to the user.

[1701] A "machine learning algorithm" is a mathematical technique for learning from data and making predictions or classifications based on that data.

[1702] The present invention is a system that automatically analyzes alarm information from communication devices and suggests appropriate response methods, and also combines it with an emotion engine that recognizes the user's emotions. In addition to analyzing alarm information and suggesting response methods, this system adjusts the display method based on the user's emotional state, thereby supporting appropriate responses according to the user's experience and situation. The specific configuration and operation of this system are described below.

[1703] System configuration

[1704] The system consists of a terminal operated by the user, a server that receives and analyzes alarm information, a generative AI model, and an emotion engine.

[1705] 1. Terminal

[1706] An electronic device such as a computer or smartphone operated by a user, which inputs alarm information through a dedicated application and provides an interface for communicating with a server.

[1707] 2. Server

[1708] This is a central processing unit that receives alarm information from communication devices and analyzes that information using a generative artificial intelligence model. It is responsible for generating analysis results and sending them to user terminals.

[1709] 3. Generative AI Model

[1710] This model analyzes alarm information based on machine learning algorithms, identifies causes and countermeasures, and runs on the server side, returning the analysis results to the server.

[1711] 4. Emotion Engine

[1712] The engine analyzes the user's voice and text input to identify their emotional state, and adjusts the suggested response if the user is feeling stressed or nervous.

[1713] Program processing

[1714] Input phase (user operation)

[1715] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. For example, the alarm message "Link Failure RiLink=BBU-B" is input. When the user presses the "Send" button, the terminal sends this alarm information to the server.

[1716] Receiving alarm information (server)

[1717] The alarm information sent from the terminal is received by the server, which then passes the information to the analysis process, where the analysis begins.

[1718] Alarm analysis (generative artificial intelligence model)

[1719] The server inputs the received alarm information into the generative AI model. The generative AI model analyzes this information and identifies the cause of the alarm and how to respond. For example, in the case of "Link Failure RiLink=BBU-B," the countermeasure identified is "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[1720] Emotion Recognition (Emotion Engine)

[1721] The emotion engine analyzes the user's voice and text input to identify their emotional state. For example, if the user types "What should I do? I don't know," the emotion engine will recognize that the user is feeling anxious.

[1722] Generation and adjustment of analysis results (server)

[1723] The server formats the analysis results obtained from the generative AI model and, based on the analysis results of the emotion engine, tailors the display to the user's emotional state. For example, a user who is feeling anxious might be provided with more detailed and easy-to-understand step-by-step instructions.

[1724] Sending and displaying analysis results (terminal)

[1725] The server sends the adjusted analysis results to the user's device. The user's device displays the received analysis results and can check specific countermeasures on the screen. For example, specific steps such as "wireless communication failed, turn the BBU on / off, or remove and insert the SFP."

[1726] Specific examples

[1727] 1. The communication device generates an alarm "Link Failure RiLink=BBU-B".

[1728] 2. The user enters this alarm information into a dedicated application on the terminal and sends it to the server.

[1729] 3. The server passes the alarm information to the artificial intelligence model that generates it, and analysis begins.

[1730] 4. The generative AI model identifies the cause of the alarm and specific countermeasures, and returns them to the server.

[1731] 5. The emotion engine recognizes anxiety from the user's text input.

[1732] 6. The server formats the analysis results and prepares detailed instructions based on the user's emotional state.

[1733] 7. The user's device displays the analysis results and how to respond.

[1734] 8. The user follows the displayed instructions to fix the problem.

[1735] Prompt Sentence Examples

[1736] "An alarm "Link Failure RiLink=BBU-B" has occurred. What should I do?"

[1737] As described above, this system efficiently analyzes alarm information from communication devices and supports appropriate responses according to the user's emotional state, allowing users to quickly and effectively take appropriate responses without requiring specialized knowledge.

[1738] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1739] Step 1:

[1740] The user inputs the alarm information generated by the communication device into a dedicated application on the terminal. For example, the user inputs the message "Link Failure RiLink=BBU-B."

[1741] Input: Alarm information (e.g. "Link Failure RiLink=BBU-B")

[1742] Operation: The user enters alarm information into the dedicated application and presses the "Send" button.

[1743] Output: Alarm information is sent from the terminal to the server.

[1744] Step 2:

[1745] The terminal transmits the alarm information to the server.

[1746] Input: Alarm information (e.g. "Link Failure RiLink=BBU-B")

[1747] Operation: The terminal converts the input alarm information into packets and sends them to the server via the network.

[1748] Output: Alarm information arrives at the server.

[1749] Step 3:

[1750] The server receives the alarm information and formats the data.

[1751] Input: Alarm information (e.g. "Link Failure RiLink=BBU-B")

[1752] Operation: Before the server passes the received alarm information to the analysis process, it performs any necessary pre-processing (for example, normalizing the data or removing unnecessary information).

[1753] Output: Formatted alarm information

[1754] Step 4:

[1755] The server inputs the formatted alarm information into the artificial intelligence model to begin analysis.

[1756] Input: Formatted alarm information

[1757] Operation: The generative AI model analyzes the alarm information and identifies the cause and how to respond. For example, it identifies "wireless communication failure, turn the BBU on / off, or remove and insert the SFP."

[1758] Output: Analysis results (e.g. "Radio communication NG, try turning the BBU on / off or removing and inserting the SFP")

[1759] Step 5:

[1760] The user provides additional text input, for example, "What should I do, I don't know."

[1761] Input: User-supplied text (e.g., "What should I do? I don't know.")

[1762] Action: The user types some additional text into the terminal and sends it.

[1763] Output: Additional text is sent from the terminal to the server.

[1764] Step 6:

[1765] The server receives the user's additional text input and passes it to the emotion engine.

[1766] Input: Additional text (e.g. "What should I do? I don't know")

[1767] How it works: The server inputs the received additional text into the emotion engine and analyzes the emotional state, e.g., recognizing "anxiety."

[1768] Output: Sentiment analysis result (e.g., "anxiety")

[1769] Step 7:

[1770] The server integrates the analysis results of the generated artificial intelligence model and the emotion engine and adjusts the display method.

[1771] Input: Analysis results (e.g., "Radio communication failed, try turning the BBU on / off and removing the SFP"), Emotion analysis results (e.g., "Anxiety")

[1772] How it works: The server formats the analysis results and tailors them into detailed, step-by-step instructions for anxious users.

[1773] Output: Adjusted analysis results (e.g. "Step 1: Turn the BBU power OFF and then ON again. Step 2: Remove the SFP module and then insert it again.")

[1774] Step 8:

[1775] The server transmits the adjusted analysis results to the user terminal.

[1776] Input: Adjusted analysis results

[1777] Operation: The server converts the adjusted analysis results into packets for transmission and sends them to the user terminal via the network.

[1778] Output: The adjusted analysis results arrive at the terminal.

[1779] Step 9:

[1780] The device receives the adjusted analysis results and displays them on the screen.

[1781] Input: Adjusted analysis results

[1782] Operation: The device displays the results it receives on the user interface, and the user can check the specific steps on the screen.

[1783] Output: The user checks the displayed analysis results and how to respond.

[1784] This series of processing flows not only enables users to efficiently solve problems with their communication devices, but also allows them to receive support that takes into consideration their own emotional state.

[1785] (Application example 2)

[1786] 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."

[1787] In modern factories, robots and machines perform complex tasks, generating a wide variety of alarms during operation. Rapidly analyzing these alarms and providing response methods is crucial for maintaining productivity. However, it is not realistic for specialized engineers to respond quickly every time an alarm occurs. Furthermore, on-site workers sometimes find themselves unable to respond appropriately to alarms. Responding quickly and accurately is particularly difficult when workers are emotionally anxious or tense. To resolve this situation, in addition to analyzing alarm information and providing response methods, it is necessary to provide response methods that take into account the worker's emotional state.

[1788] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving alarm information from a communication device, means for using a generative artificial intelligence model to analyze the alarm information, means for generating the cause of the alarm information and a response method based on the analysis results, means for recognizing the user's emotional state and reflecting that information in the display of the analysis results, and means for transmitting the generated information to the user terminal. This enables efficient analysis of alarm information and the presentation of an appropriate response method according to the user's emotional state.

[1789] "Communication device alarm information" is a warning message issued when a communication device detects an abnormality or error.

[1790] A "generative artificial intelligence model" is a computer program that uses machine learning algorithms to analyze alarm information and identify its cause and how to respond.

[1791] The "means for recognizing the user's emotional state" is a function that analyzes the user's voice, text input, etc., and identifies the user's emotional state.

[1792] The "means for reflecting the analysis results in the display" is a function for adjusting the display method of the analysis results based on the emotional state of the user.

[1793] A "user terminal" is an electronic device (e.g., a smartphone or head-mounted display) operated by a user, which is used to input alarm information and display analysis results.

[1794] The "receiving means" is a function for receiving alarm information transmitted from a communication device.

[1795] "Means for generating" refers to a function that identifies the cause and response method based on the results of analyzed alarm information and creates the information.

[1796] The "transmitting means" is a function for sending the generated information to the user terminal.

[1797] The "display means" is a function for visually displaying information generated on a user terminal.

[1798] This invention relates to the analysis of alarm information in the management of robots in a factory and the presentation of appropriate countermeasures, and in particular to the realization of a system that takes into account the emotional state of the user.

[1799] System configuration

[1800] 1. Server

[1801] The server receives alarm information transmitted from the communication device.

[1802] The server uses a generative artificial intelligence model to analyze the alarm information and generate the cause and response method.

[1803] The server uses an emotion engine to recognize the user's emotional state, analyzes the user's input information (voice or text), and identifies the user's emotional state.

[1804] The server adjusts the analysis results according to the user's emotional state and transmits them to the user's terminal.

[1805] 2. User Device

[1806] A user terminal is an electronic device (such as a smartphone or a head-mounted display) operated by a user.

[1807] Alarm information is entered through a dedicated application and communicates with the server.

[1808] The terminal receives the analysis results sent from the server and displays the information.

[1809] Hardware and software used

[1810] 1. Hardware

[1811] Server: Something with high-speed processing and large data storage capacity (e.g., cloud servers).

[1812] User devices: smartphones, tablets, head-mounted displays (e.g., Microsoft HoloLens), etc.

[1813] 2. Software

[1814] Generative artificial intelligence model: Software that uses machine learning algorithms to analyze alarm information.

[1815] Emotion engine: An algorithm that analyzes a user's voice or text input to identify their emotional state (e.g., emotion recognition software).

[1816] Dedicated application: An application installed on the user's terminal that inputs alarm information and displays analysis results.

[1817] What the program does

[1818] The server first receives alarm information sent from the communication device. Next, it inputs the alarm information into a generative artificial intelligence model for analysis. Once the analysis results indicate the cause of the alarm and a response method, the emotion engine analyzes the user's input information to identify the user's emotional state. The analysis results are adjusted by the server based on the user's emotional state and are finally sent to the user's terminal. The user's terminal receives and displays this adjusted information.

[1819] Specific examples

[1820] For example, consider the case where a robot in a factory generates an alarm that reads "Motor Overheat W1." A user inputs this alarm information using a smartphone application and sends it to the server. The server passes this alarm information to a generative AI model, which identifies a response, such as "The cooling fan needs to be cleaned." At the same time, if the user inputs "What should I do?", the emotion engine recognizes that the user is feeling anxious. Based on this recognition result, the server provides the user with more detailed step-by-step instructions, which are displayed on the smartphone or HMD.

[1821] Prompt Sentence Examples

[1822] Alarm analysis prompt:

[1823] Alarm Message: Motor Overheat W1

[1824] Emotion recognition prompts:

[1825] User Input: What should I do?

[1826] As a result, a system is realized that allows the user to quickly and accurately find a way to respond even in emotional situations.

[1827] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1828] Step 1:

[1829] The user inputs alarm information generated by the communication device into a dedicated application on the terminal. At this time, the user inputs the alarm message (e.g., "Motor Overheat W1") in text format and presses the send button. The input alarm information is sent from the terminal to the server.

[1830] Step 2:

[1831] The server receives the alarm information sent from the terminal. The received data is passed to the server as "Alarm Message: Motor Overheat W1". This alarm information is input into the next analysis process.

[1832] Step 3:

[1833] The server passes the received alarm information to the generative AI model, which begins analysis. The generative AI model analyzes the alarm message and identifies the cause and how to respond. Data processing involves tokenizing the alarm message and comparing it with existing learning datasets to output the cause and how to respond. For example, in the case of "Motor Overheat W1," the response generated is "The cooling fan needs to be cleaned."

[1834] Step 4:

[1835] After sending the alarm information, the user can input additional voice or text through the terminal, such as "What should I do?", which indicates an emotion. This input is sent to the server as an emotion-recognition prompt.

[1836] Step 5:

[1837] The server uses the emotion engine to analyze the user's additional input and identify their emotional state. The input data is "What should I do?", and the emotion engine analyzes it and identifies "anxiety" as the emotional state. This outputs the user's emotional state as data.

[1838] Step 6:

[1839] The server combines the analysis results with the emotional state and adjusts the display method. In data calculations, the cause of the alarm and the solution are identified as "The cooling fan needs to be cleaned." Since the emotional state is "anxious," detailed step-by-step instructions are generated. For example, specific steps such as "1. Turn off the power. 2. Remove the cooling fan..." are generated.

[1840] Step 7:

[1841] The server then sends the adjusted analysis results to the user's device, which includes detailed instructions and how to respond.

[1842] Step 8:

[1843] The user device visually displays the received analysis results and how to respond on the screen. Detailed step-by-step instructions are displayed to help the user stay calm and take action. The user can follow these instructions to take action.

[1844] This allows the user to respond to alarm information quickly and appropriately. In addition, since consideration is given to the user's emotional state, the user can work while reducing stress.

[1845] As described above, the processing flow of the entire system is performed consistently and continuously.

[1846] 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.

[1847] 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.

[1848] 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.

[1849] 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.

[1850] 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.

[1851] 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.

[1852] 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).

[1853] 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.

[1854] 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."

[1855] 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.

[1856] 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).

[1857] 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.

[1858] 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.

[1859] 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.

[1860] 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.

[1861] 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.

[1862] 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.

[1863] 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.

[1864] 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.

[1865] 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.

[1866] 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.

[1867] The following is further disclosed regarding the above embodiment.

[1868] (Claim 1)

[1869] means for receiving alarm information of a communication device;

[1870] a means for utilizing a generative artificial intelligence model to analyze the alarm information;

[1871] A means for generating a cause of alarm information and a response method based on the analysis result;

[1872] means for transmitting the generated information to a user terminal;

[1873] A system including means for displaying information generated at a user terminal.

[1874] (Claim 2)

[1875] 10. The system of claim 1, further comprising means for translating the alarm information of the communication device if the alarm information is in English.

[1876] (Claim 3)

[1877] 10. The system of claim 1, wherein the generative artificial intelligence model uses a machine learning algorithm to analyze the alarm information.

[1878] "Example 1"

[1879] (Claim 1)

[1880] A means for a user to input alarm information of a communication device into a terminal;

[1881] A means for the terminal to transmit alarm information to a server;

[1882] a means for the server to receive alarm information transmitted from the terminal;

[1883] a means for the server to use the generative artificial intelligence model to analyze the alarm information;

[1884] A means for the generative artificial intelligence model to analyze the alarm information and identify the cause and a response method;

[1885] means for the server to format the analysis results and return them to the user terminal;

[1886] The system includes means for displaying the analysis results received by the terminal from the server.

[1887] (Claim 2)

[1888] 2. The system according to claim 1, further comprising means for translating alarm information of the communication device when the alarm information is in multiple languages.

[1889] (Claim 3)

[1890] 10. The system of claim 1, wherein the generative artificial intelligence model uses a machine learning algorithm to analyze the alarm information.

[1891] "Application Example 1"

[1892] (Claim 1)

[1893] means for receiving alarm information of a communication device;

[1894] a means for utilizing a generative artificial intelligence model to analyze the alarm information;

[1895] A means for generating a cause of alarm information and a response method based on the analysis result;

[1896] means for transmitting the generated information to a user terminal;

[1897] means for displaying the generated information at the user terminal;

[1898] A means for analyzing alarm information from devices and systems in a factory in real time and presenting countermeasures on a visualization device that has an audible function for workers;

[1899] A system including:

[1900] (Claim 2)

[1901] 10. The system of claim 1, further comprising means for translating alarm information of the communication device if the alarm information is in a foreign language.

[1902] (Claim 3)

[1903] 10. The system of claim 1, wherein the generative artificial intelligence model includes means for analyzing the alarm information using a machine learning algorithm.

[1904] "Example 2: Combining Emotion Engines"

[1905] (Claim 1)

[1906] means for receiving alarm information of a communication device;

[1907] a means for utilizing a generative artificial intelligence model to analyze the alarm information;

[1908] A means for generating a cause of alarm information and a response method based on the analysis result;

[1909] means for transmitting the generated information to a user terminal;

[1910] means for displaying the generated information at the user terminal;

[1911] means including an emotion engine for recognizing an emotional state of a user;

[1912] A means for adjusting the display method based on the analysis result of the emotion engine;

[1913] A system including:

[1914] (Claim 2)

[1915] 10. The system of claim 1, further comprising means for translating the alarm information of the communication device if the alarm information is in English.

[1916] (Claim 3)

[1917] 10. The system of claim 1, wherein the generative artificial intelligence model uses a machine learning algorithm to analyze the alarm information.

[1918] "Application example 2 when combining emotion engines"

[1919] (Claim 1)

[1920] means for receiving alarm information of a communication device;

[1921] a means for utilizing a generative artificial intelligence model to analyze the alarm information;

[1922] A means for generating a cause of alarm information and a response method based on the analysis result;

[1923] A means for recognizing the emotional state of the user and reflecting that information in the analysis result display;

[1924] means for transmitting the generated information to a user terminal;

[1925] A system including means for displaying information generated at a user terminal.

[1926] (Claim 2)

[1927] 10. The system of claim 1, further comprising means for translating the alarm information of the communication device if the alarm information is in English.

[1928] (Claim 3)

[1929] 10. The system of claim 1, wherein the generative artificial intelligence model uses a machine learning algorithm to analyze the alarm information. [Explanation of symbols]

[1930] 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. means for receiving alarm information of a communication device; a means for utilizing a generative artificial intelligence model to analyze the alarm information; A means for generating a cause of alarm information and a response method based on the analysis result; means for transmitting the generated information to a user terminal; A system including means for displaying information generated at a user terminal.

2. 2. The system of claim 1, further comprising means for translating alarm information of the communication device if the alarm information is in English.

3. The system of claim 1 , wherein the generative artificial intelligence model analyzes the alarm information using a machine learning algorithm.

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