system

Abstract

To provide a system.SOLUTION: The system includes: means for linking a ticket management system and a generative artificial intelligence to each other; means for analyzing the emotion of a user from the content of a ticket by using an emotion analysis engine and providing the analyzed emotion of the user to the generative artificial intelligence; means for generating a summary of the ticket according to the information of the emotion of the user on the basis of the information of the provided emotion of the user by using the generative artificial intelligence; and means for preparing a summary of the content of a new ticket when the new ticket is issued and updating the original summary; and means for storing the updated summary into the thicket management system.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] There are problems such as not being able to grasp discussions that occurred during past development, problems where the exchanges per ticket are long and it takes time to understand the background and conclusions, and problems where it is necessary to check the ticket every time. [Means for solving the problem]

[0005] The ticket management system and generative AI are linked to provide a means to create summaries for each ticket. When a new ticket is created, a summary of its contents is created and the original summary information is updated. The generative AI uses natural language processing technology to analyze the ticket contents and generate a summary. The ticket management system manages the creation, updating, and viewing of tickets, and provides the ticket contents to the generative AI. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One embodiment of the present invention is a system that links a ticket management system with a generative AI. This system sends the contents of tickets acquired from the ticket management system to the generative AI, which then uses natural language processing technology to analyze the ticket contents and generate a summary. The generated summary is sent back to the ticket management system and saved as the ticket's summary information. When a new ticket is created, the contents of that ticket are also sent to the generative AI, which then generates a summary. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[0029] "Example 2"

[0030] As a specific example, GPT-3 (registered trademark) can be used as the generative AI. The contents of tickets obtained from a ticket management system are sent to GPT-3, which uses natural language processing technology to analyze the contents of the ticket and generate a summary. The generated summary is sent back to the ticket management system and saved as the ticket's summary information. When a new ticket is created, the contents of that ticket are also sent to GPT-3, and a summary is generated. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[0031] The processing flow of each embodiment will be described below.

[0032] "Example 1"

[0033] Step 1: Get the ticket contents from the ticket management system.

[0034] Step 2: Send the acquired ticket contents to the generation AI.

[0035] Step 3: The generative AI uses natural language processing technology to analyze the ticket content and generate a summary.

[0036] Step 4: The generated summary is sent back to the ticket management system and stored as the ticket summary information.

[0037] Step 5: When a new ticket is created, its contents are also sent to the generative AI, which generates a summary.

[0038] Step 6: The generated new ticket summary is saved in the ticket management system along with the original summary information.

[0039] "Example 2"

[0040] Step 1: Obtain the ticket details from the ticket management system. Step 2: Send the obtained ticket details to the generative AI "GPT-3."

[0041] Step 3: GPT-3 uses natural language processing techniques to analyze the ticket content and generate a summary.

[0042] Step 4: The generated summary is sent back to the ticket management system and stored as the ticket summary information.

[0043] Step 5: When a new ticket is created, its contents are also sent to GPT-3, and a summary is generated.

[0044] Step 6: The generated new ticket summary is saved in the ticket management system along with the original summary information.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of Form 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."

[0047] In traditional ticket management systems, ticket content had to be summarized manually, which was time-consuming and labor-intensive. Furthermore, when a new ticket was created, it was difficult to quickly summarize its contents and update the original summary information. This reduced the efficiency of ticket management and could lead to important information being overlooked.

[0048] 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.

[0049] In this invention, the server includes means for linking the ticket management system with the generative artificial intelligence, means for creating a summary of each ticket, means for creating a summary of the contents of a new ticket when it is created and updating the original summary information, means for acquiring the contents of the newly created ticket from the ticket management system, means for sending the acquired ticket contents to the generative artificial intelligence, means for receiving the summary generated from the generative artificial intelligence, and means for saving the received summary in the ticket management system. This makes it possible to automatically summarize the contents of tickets and manage them quickly.

[0050] A "ticket management system" is a software or hardware system for managing the creation, updating, and viewing of tickets.

[0051] "Generative AI" refers to an AI system that uses natural language processing techniques to analyze input text data and perform summarization and other generative tasks.

[0052] The "summary" is a concise summary of the ticket's contents, briefly expressing the main points and issues of the ticket.

[0053] A "new ticket" is a ticket that has been newly created by a user and for which no summary has yet been created.

[0054] A "server" is a computer system that connects the ticket management system with generative artificial intelligence and acquires, sends, receives, and stores data.

[0055] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language, and is used to analyze text data and generate summaries.

[0056] "API" stands for Application Programming Interface, an interface for exchanging data and functions between different software systems.

[0057] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and is a lightweight data exchange format for structuring and expressing data.

[0058] This invention is a system that links a ticket management system with generative artificial intelligence, automatically creating summaries for each ticket, generating summaries of the contents of new tickets when they are created, and updating the original summary information.

[0059] The server first retrieves the details of newly created tickets from the ticket management system. A ticket management system is a software or hardware system that manages the creation, updating, and viewing of tickets. The server periodically calls the ticket management system's API to check whether new tickets exist.

[0060] Next, the server sends the retrieved ticket contents to a generative AI. The generative AI uses a system that uses natural language processing technology to analyze text data and generate a summary. Specifically, a generative AI model such as "OpenAI (registered trademark) GPT-4 (registered trademark)" is applied. The server converts the ticket contents into JSON format and sends a POST request to the generative AI's API endpoint.

[0061] The generative AI analyzes the content of the ticket received and generates a summary. For example, in response to a ticket that states "High server memory usage," it generates a summary such as "A problem has occurred with high server memory usage."

[0062] The generated summary is sent back to the server, which analyzes the response from the generative AI and extracts the summary text. The server then saves the received summary in the ticket management system. Specifically, it calls the ticket management system's API and sends a request to update the summary information.

[0063] When a new ticket is created, the server sends the ticket contents to the generative AI in a similar manner to generate a summary. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[0064] As a concrete example, if a user submits a ticket stating that "server memory usage is high," the server retrieves the contents of this ticket and sends it to the generative AI. The generative AI analyzes the content of "server memory usage is high" and generates a summary such as "a problem has occurred with high server memory usage." The generated summary is sent back to the server and stored in the ticket management system.

[0065] An example of a prompt sentence might be:

[0066] "Please summarize the ticket below:

[0067] Ticket details: The server's memory usage is high. It often peaks especially at night. We are considering increasing the memory as a solution.

[0068] By sending this prompt to the generative AI, the AI ​​will analyze the ticket contents and generate a summary.

[0069] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0070] Step 1:

[0071] The server obtains the contents of the newly created ticket from the ticket management system.

[0072] Input: Ticket management system API endpoint

[0073] Output: New ticket content (text data)

[0074] Specific operation: The server periodically calls the API of the ticket management system to check whether a new ticket exists. If a new ticket exists, it retrieves its contents.

[0075] Step 2:

[0076] The server sends the contents of the acquired ticket to the generative artificial intelligence.

[0077] Input: New ticket content (text data)

[0078] Output: Request to the generative AI (JSON format)

[0079] Specific operation: The server converts the acquired ticket contents into JSON format and sends a POST request to the API endpoint of the generative artificial intelligence.

[0080] Step 3:

[0081] Generative AI analyzes the ticket contents and generates a summary.

[0082] Input: Ticket content (JSON format)

[0083] Output: Summary (text data)

[0084] How it works: The generative AI uses natural language processing technology to analyze the content of the ticket it receives and generate a summary. For example, in response to a ticket that says "Server memory usage is high," it generates a summary such as "A problem with high server memory usage has occurred."

[0085] Step 4:

[0086] The server receives the summary generated from the generative artificial intelligence.

[0087] Input: Response from generative AI (JSON format)

[0088] Output: Summary (text data)

[0089] Specific operation: The server analyzes the response from the generative artificial intelligence API and extracts summary text.

[0090] Step 5:

[0091] The server stores the received summary in the ticket management system.

[0092] Input: Summary (text data)

[0093] Output: Update request to ticket management system (API call)

[0094] Specific operation: The server calls the API of the ticket management system and sends a request to update the summary information.

[0095] Step 6:

[0096] When a user submits a new ticket, the server again obtains the contents of the new ticket from the ticket management system and sends them to the generative artificial intelligence in the same manner, causing a summary to be generated.

[0097] Input: New ticket content (text data)

[0098] Output: Summary (text data)

[0099] Specific operation: When a user submits a new ticket, the server retrieves the content from the ticket management system and sends it to the generative AI. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[0100] (Application example 1)

[0101] Next, a description will be given of Application Example 1 of Embodiment 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."

[0102] In factory maintenance work, the detailed content of tickets makes it difficult for workers to quickly understand and respond to them. Furthermore, because the content of tickets is so diverse, they need to be summarized, but manual summarization takes time and effort. This reduces the efficiency of maintenance work and reduces productivity.

[0103] 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.

[0104] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating summaries of each ticket, a means for creating a summary of the contents of a new ticket when it is created and updating the original summary information, and a means for acquiring the contents of maintenance tickets within the factory, generating summaries using the generative AI, and saving them. This allows the contents of maintenance tickets to be summarized quickly and accurately, enabling workers to perform maintenance work efficiently.

[0105] A "ticket management system" is a system that manages the creation, updating, and viewing of tickets.

[0106] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze text data and perform summarization and generation.

[0107] The "summary" is a concise summary of the ticket contents.

[0108] A "maintenance ticket" is a ticket that contains information about maintenance work that occurs within a factory.

[0109] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[0110] "In-plant" means the interior of a facility where manufacturing operations take place.

[0111] A "server" is a computer system that processes and stores data on a network.

[0112] As an embodiment of the present invention, a system is constructed that automatically generates ticket summaries using generative AI in a maintenance ticket management system within a factory. A specific embodiment of this system is shown below.

[0113] System configuration

[0114] 1. Hardware

[0115] Server: A computer system that processes and stores data. It houses the ticket management system and generative AI.

[0116] Factory Robot: A robot that performs maintenance work in a factory. It communicates with the server to obtain maintenance ticket information and receive a summary.

[0117] 2. Software

[0118] Ticket management system: A system that manages ticket creation, updates, and viewing. Ticket content is provided to the generative AI via API.

[0119] Generative AI: This is an AI that uses natural language processing technology to analyze text data and generate summaries. It receives ticket content via API and generates summaries.

[0120] Data processing and calculation

[0121] 1. Obtaining ticket details

[0122] The server obtains the contents of maintenance tickets generated within the factory through the ticket management system's API.

[0123] 2. Summary Generation Using Generative AI

[0124] The server sends the acquired ticket contents to the generative AI API, which uses natural language processing technology to analyze the ticket contents and generate a summary.

[0125] 3. Save the summary

[0126] The server saves the generated summary through the API of the ticket management system, so that the summary information of the maintenance ticket is saved in the ticket management system.

[0127] Specific examples

[0128] Ticket details: "The belt on machine A is loose and needs to be replaced."

[0129] Produced summary: "The belt on machine A needs to be replaced."

[0130] Prompt Sentence Examples

[0131] Summarize the following text:

[0132] "The belt on machine A is loose and needs to be replaced."

[0133] In this way, a system can be realized that supports factory robots in efficiently performing maintenance work.

[0134] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0135] Step 1:

[0136] The server obtains the contents of maintenance tickets generated within the factory through the ticket management system's API.

[0137] Input: Maintenance ticket information from your ticket management system

[0138] Output: The contents of the retrieved maintenance ticket

[0139] What happens: The server sends an HTTP request to retrieve ticket data from the ticket management system's API. The retrieved data is received in JSON format.

[0140] Step 2:

[0141] The server sends the acquired ticket contents to the API of the generation AI.

[0142] Input: The content of the acquired maintenance ticket

[0143] Output: Ticket content sent to the generative AI

[0144] Specific operation: The server sends an HTTP POST request to the generative AI API and sends the ticket contents in JSON format.

[0145] Step 3:

[0146] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[0147] Input: Ticket content sent to the generative AI

[0148] Output: A summary of the generated tickets

[0149] How it works: The generative AI analyzes the content of the received ticket, extracts important information, and generates a summary, which is then returned to the server in JSON format.

[0150] Step 4:

[0151] The server stores the generated summary through the API of the ticket management system.

[0152] Input: Summary of generated ticket

[0153] Output: Summary information stored in the ticket management system

[0154] What happens: The server sends an HTTP POST request to the ticket management system's API and saves the generated summary in JSON format.

[0155] Step 5:

[0156] A user views summarized maintenance ticket information through a ticket management system.

[0157] Input: Summary information stored in the ticket management system

[0158] Output: Summarized maintenance ticket information for user viewing

[0159] Specific operation: A user views summarized maintenance ticket information through the ticket management system interface. The system displays the saved summary information.

[0160] Example 2

[0161] Next, a description will be given of Example 2 of Form 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."

[0162] Conventional information management systems have the problem that information summaries must be created manually, which takes time and effort. In addition, when new information is registered, it is difficult to quickly and accurately summarize the content and update existing summary information. This makes information management cumbersome and hinders efficient operation.

[0163] 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.

[0164] In this invention, the server includes means for acquiring new information from the information management system, means for transmitting the acquired information to the generative AI, means for receiving summaries generated by the generative AI, and means for storing the generated summaries in the information management system, thereby enabling automatic generation of summaries of new information and rapid and accurate updating of existing summary information.

[0165] An "information management system" is a software or hardware system for managing the registration, updating, and viewing of information.

[0166] "Generative AI" refers to an AI system that uses natural language processing techniques to analyze input information and perform summarization and other generative tasks.

[0167] A "summary" is a short summary of the main points of information, intended to convey the content of the original information concisely.

[0168] "New information" refers to information that has been newly registered in the information management system and is to be distinguished from existing information.

[0169] A "server" is a computer system that sends and receives data between an information management system and generative artificial intelligence.

[0170] "Means of acquisition" refers to the methods and processes for acquiring new information from the information management system.

[0171] "Means for transmitting" refers to the method or process for transmitting acquired information to generative artificial intelligence.

[0172] "Means for receiving" refers to a method or process for receiving a summary generated by a generative artificial intelligence.

[0173] "Storage means" refers to the method or process for storing the generated summary in an information management system.

[0174] This invention is a system that automatically generates summaries of new information and updates existing summaries quickly and accurately by linking an information management system with generative artificial intelligence. Specific embodiments of this system are described below.

[0175] Hardware and software used

[0176] Information management systems: These are software or hardware systems used to manage the registration, updating, and viewing of information. Examples include database management systems and cloud-based information management platforms.

[0177] Generative AI: This is an AI system that uses natural language processing techniques to analyze input information and perform summarization and other generative tasks. A specific example is an AI service equipped with a natural language processing model.

[0178] System Operation

[0179] 1. A user registers new information in the information management system.

[0180] A user uses the interface of the information management system to register new information, including a title, a detailed description, and a priority.

[0181] Example: User enters the subject "System bug fix request" with the details "Error occurs on login screen."

[0182] 2. The server retrieves new information from the information management system.

[0183] The server periodically calls the information management system's API to obtain new information. The obtained data is received in JSON format.

[0184] Example: A server retrieves information from an information management system with the title "System bug fix request" and the details "Error occurs on login screen."

[0185] 3. The server sends the acquired information to the generative AI

[0186] The server generates an appropriate prompt to send the acquired information to the generative AI, which then sends the prompt as an API request to the generative AI.

[0187] Example: The server sends a request to the generative artificial intelligence to summarize the information content "Request to fix a system bug."

[0188] 4. Generative AI analyzes the content of information and generates summaries

[0189] The generative AI analyzes the prompt and uses natural language processing techniques to summarize the information, which is short and to the point.

[0190] Example: A generative AI summarizes the details "An error occurs on the login screen" as "Request to fix the error on the login screen."

[0191] 5. The generative AI sends the generated summary back to the server

[0192] The generative AI sends the generated summary back to the server as an API response, which the server receives and proceeds to the next step.

[0193] Example: The generative AI sends back to the server a summary titled "Request to fix login screen error."

[0194] 6. The server stores the generated summary in the information management system and displays it as a summary of the information.

[0195] The server adds the generated summary to the original information using the information management system's API. The user can view the generated summary on the information management system's interface.

[0196] Example: The server stores a summary "Request to fix login screen error" in the information management system and allows the user to view this summary on the information details screen.

[0197] Prompt Sentence Examples

[0198] An example of a prompt to be sent to the generative artificial intelligence is as follows:

[0199] Please summarize the information content of "Request for system bug fix."

[0200] By inputting this prompt into a generative artificial intelligence, the AI ​​analyzes the information content and generates an appropriate summary.

[0201] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0202] Step 1:

[0203] A user registers new information in the information management system.

[0204] The user registers new information using the information management system interface, entering information such as a title, detailed description, and priority, and the new information is then saved in the information management system.

[0205] Specifically, the user enters details such as "An error occurs on the login screen" under the title "Request to fix a system bug" and registers it in the information management system.

[0206] Step 2:

[0207] The server retrieves new information from the information management system.

[0208] The server periodically calls the information management system's API to retrieve new information data. As input, it receives the API response from the information management system. As output, the retrieved data is stored on the server in JSON format.

[0209] Specifically, the server retrieves information from the information management system, including the title "Request to fix a system bug" and the details "An error occurs on the login screen."

[0210] Step 3:

[0211] The server sends the acquired information to the generative artificial intelligence.

[0212] The server generates an appropriate prompt to send the acquired information to the generative AI. As input, it creates a prompt based on the acquired information. As output, the prompt is sent to the generative AI as an API request.

[0213] Specifically, the server sends a request to the generative artificial intelligence to summarize the information content, "a request to fix a system bug."

[0214] Step 4:

[0215] Generative AI analyzes the content of information and generates summaries

[0216] The generative AI analyzes the received prompt and summarizes the information content using natural language processing techniques. The prompt is received as input, and the generated summary is generated as output.

[0217] As a specific operation, the generative artificial intelligence summarizes the details of "an error occurs on the login screen" as "a request to fix the error on the login screen."

[0218] Step 5:

[0219] The generative AI sends the generated summary back to the server

[0220] The generative AI sends the generated summary back to the server as an API response. The generated summary is received as input. The summary is sent back to the server as output.

[0221] Specifically, the generative artificial intelligence sends a summary to the server titled "Request to fix an error on the login screen."

[0222] Step 6:

[0223] The server stores the generated summary in the information management system and displays it as summary information of the information.

[0224] The server adds the generated summary to the original information using the information management system's API. As input, it receives the generated summary. As output, it stores the summary in the information management system and makes it available for the user to review.

[0225] Specifically, the server stores a summary titled "Request to correct login screen error" in the information management system, and the user can check this summary on the information details screen.

[0226] (Application example 2)

[0227] Next, a description will be given of Application Example 2 of Form 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."

[0228] When managing tickets for maintenance and repairs that occur within a factory, it is necessary to efficiently understand the contents of the tickets and respond quickly. However, with conventional systems, ticket contents must be manually analyzed and summarized, which is time-consuming and labor-intensive. Furthermore, if the ticket contents are complex, the quality of the summary may decline. This can reduce work efficiency and disrupt factory operations.

[0229] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0230] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating summaries for each ticket, a means for creating a summary of the contents of a new ticket when it is created and updating the original summary information, a means for managing maintenance and repair tickets generated in the factory, a means for analyzing the contents of the ticket and generating summaries using the generative AI, a means for saving the generated summaries in the ticket management system, and a means for being installed in the factory robot. This makes it possible to efficiently analyze the contents of tickets and generate summaries.

[0231] A "ticket management system" is a system that manages maintenance and repair tickets that occur within a factory, and creates, updates, and views tickets.

[0232] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze the contents of tickets and generate summaries.

[0233] A "means for creating a summary" is a means that has the function of analyzing the contents of a ticket, extracting important information, and summarizing it concisely.

[0234] "A means for creating a summary of the contents of a new ticket when it is created and updating the original summary information" is a means that has the function of analyzing the contents of a newly created ticket, generating a summary, and updating the existing summary information.

[0235] "A means for managing tickets for maintenance and repairs generated within a factory" refers to a means that has the function of centrally managing tickets related to maintenance and repairs generated within a factory.

[0236] "Means for saving the generated summary in the ticket management system" refers to means that has the function of saving the summary generated by the generative AI in the ticket management system.

[0237] "Means to be installed on factory robots" refers to a means to install an application on a robot used in a factory and have the function of linking the ticket management system with generative AI.

[0238] As an embodiment of the present invention, a system is constructed that efficiently manages maintenance and repair tickets generated in a factory and generates summaries. A specific embodiment of this system is shown below.

[0239] System configuration

[0240] The system consists of the following major components:

[0241] 1. Ticket Management System: A system for managing maintenance and repair tickets that occur within the factory. Tickets can be created, updated, and viewed.

[0242] 2. Generative AI: This is artificial intelligence that uses natural language processing technology to analyze the contents of tickets and generate summaries.

[0243] 3. Factory robot: A robot used in a factory, on which an application that connects the ticket management system with generative AI is installed.

[0244] Program processing

[0245] The server first retrieves maintenance or repair tickets from the ticket management system. Next, it sends the contents of the retrieved tickets to a generative AI system, which generates summaries using natural language processing technology. The generated summaries are then saved back into the ticket management system.

[0246] Hardware and software used

[0247] Hardware: Factory robots

[0248] Software: Ticket management system, generative AI (e.g., GPT-3), Python (registered trademark)

[0249] Data processing and calculation

[0250] 1. Data Acquisition: Acquire ticket contents from the ticket management system.

[0251] 2. Data analysis: The ticket contents are sent to a generative AI, which generates a summary using natural language processing technology.

[0252] 3. Data storage: The generated summary is stored in the ticket management system.

[0253] Specific examples

[0254] For example, when a machine malfunctions in a factory, a maintenance ticket is registered in the ticket management system. A factory robot retrieves the ticket and sends it to a generative AI to generate a summary. The summary is then stored in the ticket management system, allowing workers to efficiently understand the contents of the ticket.

[0255] Prompt Sentence Examples

[0256] "Please summarize the contents of the maintenance ticket obtained from the ticket management system. The contents are as follows: {Ticket content}"

[0257] In this way, a system can be realized that can significantly improve the efficiency of maintenance and repair within a factory.

[0258] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0259] Step 1:

[0260] The server retrieves maintenance and repair tickets from the ticket management system.

[0261] Input: Ticket information registered in the ticket management system

[0262] Data processing: Use the ticket management system's API to obtain the ticket contents.

[0263] Output: The retrieved ticket details

[0264] Step 2:

[0265] The server sends the contents of the acquired ticket to the generation AI.

[0266] Input: The content of the ticket obtained

[0267] Data processing: Send the ticket contents using a generative AI API.

[0268] Output: The ticket content sent to the generative AI

[0269] Step 3:

[0270] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[0271] Input: Ticket content sent to the generative AI

[0272] Data Computing: Using natural language processing techniques, the content of tickets is analyzed, key information is extracted, and a summary is generated.

[0273] Output: The generated summary

[0274] Step 4:

[0275] The server stores the generated summary in the ticket management system.

[0276] Input: Generated summary

[0277] Data processing: Use the ticket management system's API to store the generated summary.

[0278] Output: Summary stored in ticket management system

[0279] Step 5:

[0280] The user views the generated summary through the ticket management system.

[0281] Input: Summary stored in ticket management system

[0282] Data processing: Display summaries through the ticket management system interface.

[0283] Output: A summary that the user sees

[0284] In this way, maintenance and repair tickets within the factory can be managed efficiently and workers can respond quickly.

[0285] 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.

[0286] "Example 1"

[0287] The present invention is a system that links a ticket management system with a generative AI to create summaries of each ticket, and when a new ticket is created, creates a summary of the ticket's contents and updates the original summary information. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, the generative AI uses natural language processing technology to analyze the ticket contents and generate a summary. The emotion engine also analyzes the user's emotions from the ticket contents and provides the results to the generative AI. The generative AI generates a summary that takes the user's emotions into account based on the emotion analysis results provided by the emotion engine.

[0288] "Example 2"

[0289] As a concrete example, we have a system that uses a ticket management system, generative AI "GPT-3", and emotion engine "IBM Watson (registered trademark) Tone Analyzer".

[0290] Consider the following: Get the contents of tickets from the ticket management system and compare them with GPT-3 and IBM

[0291] The ticket content is then sent to the IBM Watson Tone Analyzer. GPT-3 uses natural language processing technology to analyze the ticket content and generate a summary. Meanwhile, IBM Watson Tone Analyzer analyzes the user's emotions from the ticket content and provides the results to GPT-3. GPT-3 generates a summary that takes the user's emotions into account based on the emotion analysis results provided by IBM Watson Tone Analyzer. The generated summary is sent back to the ticket management system and saved as the ticket summary information. When a new ticket is created, its content is also sent to GPT-3 and IBM Watson Tone Analyzer, and a summary is generated.

[0292] The processing flow of each embodiment will be described below.

[0293] "Example 1"

[0294] Step 1: Get the ticket contents from the ticket management system.

[0295] Step 2: The acquired ticket content is sent to the generative AI, which analyzes the content and generates a summary.

[0296] Step 3: At the same time, the content of the acquired ticket is sent to the emotion engine to analyze the user's emotions.

[0297] Step 4: Provide the emotion analysis results provided by the emotion engine to the generative AI.

[0298] Step 5: The generative AI generates a summary that takes the user's emotions into account based on the provided emotion analysis results.

[0299] "Example 2"

[0300] Step 1: Obtain the ticket contents from the ticket management system. Step 2: Send the obtained ticket contents to the generative AI "GPT-3", which analyzes the contents and generates a summary.

[0301] Step 3: At the same time, the contents of the acquired ticket are sent to the emotion engine "IBM Watson Tone Analyzer" to analyze the user's emotions.

[0302] Step 4: Sentiment analysis provided by IBM Watson Tone Analyzer

[0303] The results are provided to GPT-3.

[0304] Step 5: GPT-3 is provided by IBM Watson Tone Analyzer

[0305] Based on the emotion analysis results, a summary is generated that takes the user's emotions into consideration.

[0306] Step 6: The generated summary is sent back to the ticket management system and stored as the ticket summary information.

[0307] Step 7: When a new ticket is created, the content is also updated with GPT-3 and IBM.

[0308] It is sent to the Watson Tone Analyzer and a summary is generated.

[0309] Example 1

[0310] Next, a description will be given of Example 1 of Form 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."

[0311] Conventional ticket management systems have the problem that ticket contents must be summarized manually, which is time-consuming and labor-intensive. In addition, they are unable to generate summaries that take into account the user's feelings, making it difficult to properly reflect the user's complaints and requests. This can lead to reduced ticket management efficiency and lower user satisfaction.

[0312] 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.

[0313] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating a summary of each ticket, a means for creating a summary of the contents of a new ticket when it is opened and updating the original summary information, a means including an emotion engine for analyzing user emotions from the ticket contents, a means for generating a summary based on the emotion analysis results provided by the emotion engine, and a means for saving the generated summary in the ticket management system. This makes it possible to automatically summarize the contents of tickets and generate summaries that take user emotions into consideration.

[0314] A "ticket management system" is a system for managing problems and requests reported by users and recording and tracking them as tickets.

[0315] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze input data and generate summaries and other information.

[0316] The "means for creating a summary" is a function for automatically generating a summary that concisely summarizes the contents of the ticket.

[0317] "A means for creating a summary of the contents of a new ticket when it is created and updating the original summary information" is a function for summarizing the contents of a newly created ticket and adding or updating the summary to existing summary information.

[0318] The "emotion engine" is a technology that analyzes user emotions from the contents of tickets and provides the results.

[0319] The "means for generating a summary based on the emotion analysis result" is a function for generating a summary that reflects the user's emotions, taking into account the emotion analysis result provided by the emotion engine.

[0320] The "means for saving the generated summary in the ticket management system" is a function for sending the generated summary to the ticket management system and saving it therein.

[0321] This invention is a system that links a ticket management system with a generative AI, automatically generating summaries for each ticket and providing summaries that take user emotions into consideration. Specific embodiments of this system are described below.

[0322] System configuration

[0323] The system consists of the following main components:

[0324] 1. Ticket Management System: A system for managing problems and requests reported by users and recording and tracking them as tickets.

[0325] 2. Generative AI: This is artificial intelligence that uses natural language processing techniques to analyze input data and generate summaries and other information.

[0326] 3. Emotion Engine: A technology that analyzes user emotions from the contents of tickets and provides the results.

[0327] Data flow

[0328] 1. Get Ticket: The server retrieves the newly created ticket from the ticket management system, including the details entered by the user.

[0329] 2. Data transmission: The server sends the acquired ticket contents to the generative AI, which then analyzes the data using natural language processing technology.

[0330] 3. Ticket content analysis: The generative AI analyzes the ticket content and generates a summary. At the same time, the emotion engine works to analyze the user's emotions from the ticket content.

[0331] 4. Providing emotion analysis results: The emotion engine identifies the user's emotions and provides the results to the generative AI.

[0332] 5. Summary generation: The generative AI generates a summary that takes into account the user's emotions based on the emotion analysis results provided by the emotion engine.

[0333] 6. Save Abstract: The generated abstract is sent back to the ticket management system via the server, which saves it along with the original ticket information.

[0334] Specific examples

[0335] For example, if a user submits a ticket stating that "the system is slow," the server sends this information to the generative AI. The generative AI uses natural language processing technology to analyze the content, "The system is slow," and the emotion engine analyzes that the user is dissatisfied. The generative AI takes this emotion into account and generates a summary that reads, "The user is dissatisfied with the system's slowness." This summary is then saved in the ticket management system.

[0336] Prompt Sentence Examples

[0337] Below are some example prompts to input to a generative AI model:

[0338] Generate summaries for tickets where users report that the system is running slowly. Consider the user's feelings and create appropriate summaries.

[0339] By using this prompt, the generative AI can generate a summary that reflects the user's emotions.

[0340] In this way, by linking a ticket management system with generative AI, it becomes possible to automatically summarize the contents of tickets and generate summaries that take user emotions into account. This is expected to improve ticket management efficiency and user satisfaction.

[0341] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0342] Step 1:

[0343] The server retrieves the details of newly created tickets from the ticket management system. As input, it receives new ticket information from the ticket management system database. Specifically, when a new ticket is created in response to a user reporting that the system is running slowly, it retrieves the details. As output, it passes the retrieved ticket details to the next processing step.

[0344] Step 2:

[0345] The server sends the acquired ticket contents to the generative AI. As input, it receives the ticket contents acquired in step 1. Specifically, it sends the ticket contents to the generative AI in a standard data format such as JSON. For example, the following JSON data is sent to the generative AI:

[0346] json

[0347] {

[0348] "ticket_id": "12345",

[0349] "content": "System is running slow"

[0350] }

[0351] As an output, the data sent to the generative AI is passed on to the next processing step.

[0352] Step 3:

[0353] The generative AI analyzes the content of the received ticket. As input, it receives the content of the ticket sent in step 2. Specifically, it uses natural language processing technology to understand the content of the ticket and prepares to generate a summary. As output, the analysis results are passed to the next processing step.

[0354] Step 4:

[0355] The emotion engine analyzes the user's emotions from the ticket content. As input, it receives the ticket content analyzed by the generative AI in step 3. Specifically, it analyzes that the user is dissatisfied based on the content that "the system is running slowly." As output, the emotion analysis results are provided to the generative AI.

[0356] Step 5:

[0357] The generative AI generates a summary that takes into account the user's emotions based on the emotion analysis results provided by the emotion engine. As input, it receives the emotion analysis results provided in step 4. Specifically, it generates the summary "The user is frustrated by the system's slow performance." As output, the generated summary is passed to the next processing step.

[0358] Step 6:

[0359] The server sends the generated summary back to the ticket management system. As input, it receives the summary generated in step 5. Specifically, it sends the generated summary to the ticket management system and stores it. For example, the generated summary for ticket ID "12345" is stored. As output, the summary is stored in the ticket management system.

[0360] Step 7:

[0361] When a new ticket is created, the contents of the ticket are sent to the generative AI in a similar procedure, and a summary is generated. The contents of the newly created ticket are received as input. Specifically, steps 1 to 6 are repeated. As output, the generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[0362] (Application example 1)

[0363] Next, a description will be given of Application Example 1 of Embodiment 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."

[0364] Conventional ticket management systems have problems in that it takes a lot of time and effort to summarize the contents of tickets, and it is difficult to respond in a way that takes the user's feelings into account. In particular, solving these problems is important for online shopping sites, where customers make a wide variety of inquiries and require quick and appropriate responses.

[0365] 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.

[0366] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating a summary of each ticket, a means for creating a summary of the contents of a new ticket when it is opened and updating the original summary information, a means for analyzing user emotions from the ticket contents using a sentiment analysis engine, a means for the generative AI to generate a summary that takes user emotions into consideration based on the sentiment analysis result, and a means for saving the generated summary and the sentiment analysis result in the ticket management system. This makes it possible to quickly summarize the contents of tickets and respond to them in a way that takes user emotions into consideration.

[0367] A "ticket management system" is a system for managing customer inquiries and problem reports, and recording and tracking them as tickets.

[0368] "Generative AI" is artificial intelligence that uses natural language processing techniques to analyze text data and perform summarization and other generative tasks.

[0369] A "summary" is information that concisely summarizes the contents of a ticket, extracting and expressing the important points in a short form.

[0370] An "emotion analysis engine" is software or algorithms that analyze a user's emotions from text data and identify their emotional state.

[0371] "User's emotions" refers to the user's psychological state and emotions that can be inferred from the text included in the ticket content.

[0372] "Storage" refers to recording the generated summary and sentiment analysis results in a storage device such as a database or file system.

[0373] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[0374] "Raising a ticket" means creating a new ticket and registering it in the system.

[0375] "Ticket update" means changing or adding content to an existing ticket.

[0376] "Viewing a ticket" means viewing and checking the contents of a ticket registered in the system.

[0377] A system for implementing the present invention includes a ticket management system, a generative AI, and an emotion analysis engine. Specific embodiments of the system will be described below.

[0378] System configuration

[0379] The server has a means to link the ticket management system with the generative AI. The ticket management system is a system for recording and tracking customer inquiries and problem reports as tickets. The generative AI is an artificial intelligence that uses natural language processing technology to analyze the contents of the ticket and generate a summary. The sentiment analysis engine is software or an algorithm that analyzes user emotions from text data and identifies their emotional state.

[0380] Program processing

[0381] The server first retrieves a new ticket from the ticket management system. The contents of the retrieved ticket are sent to a generative AI, which generates a summary using natural language processing technology. The generated summary is then sent to a sentiment analysis engine, which analyzes the user's emotions. The results of the sentiment analysis are provided to the generative AI, which then regenerates a summary that takes the user's emotions into account. This summary and the results of the sentiment analysis are stored in the ticket management system.

[0382] Hardware and software used

[0383] Hardware: Servers, smartphones, PCs

[0384] Software: ticket management systems, generative AI (e.g., OpenAI API), sentiment analysis engines (e.g., SentimentAnalyzer)

[0385] Specific examples

[0386] For example, if a customer inquires that "the product has not arrived," the following prompt sentence is input into the generative AI model.

[0387] Example prompt sentence:

[0388] Please summarize the following:

[0389] I haven't received my item. My order number is 12345. Please deal with this as soon as possible.

[0390] When this prompt is input into the generative AI model, the summary generated is "Inquiry regarding non-delivery of product. Order number 12345." Sentiment analysis also detects emotions such as "anger" and "dissatisfaction." This allows customer support representatives to respond quickly and appropriately.

[0391] This system allows for quick summarization of ticket contents and responses that take into account the user's feelings, which is expected to improve customer satisfaction.

[0392] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0393] Step 1:

[0394] The server obtains a new ticket from the ticket management system.

[0395] Input: A new ticket entered into the ticket management system.

[0396] Output: New ticket content.

[0397] Specific behavior: Calls the ticket management system API to retrieve new ticket data, including information such as ticket ID, customer inquiry, and order number.

[0398] Step 2:

[0399] The server sends the contents of the acquired ticket to the generative AI and generates a summary.

[0400] Input: New ticket content.

[0401] Output: The generated summary.

[0402] Specific operation: The ticket contents are sent as a prompt to the generative AI (e.g., OpenAI API). The generative AI uses natural language processing technology to generate a summary and return it to the server.

[0403] Step 3:

[0404] The server sends the generated summary to a sentiment analysis engine to analyze the user's sentiment.

[0405] Input: The generated summary.

[0406] Output: Emotion analysis results.

[0407] Specific operation: Send the summary to a sentiment analysis engine (e.g., SentimentAnalyzer) to analyze the user's sentiment from the text data. The type of sentiment (e.g., anger, frustration, joy, etc.) is obtained as the analysis result.

[0408] Step 4:

[0409] The server provides the emotion analysis results to the generative AI, which then regenerates a summary that takes the user's emotions into account.

[0410] Input: Sentiment analysis results, original summary.

[0411] Output: A new sentiment-aware summary.

[0412] Specific operation: A prompt containing the results of emotion analysis is sent to the generative AI, which then generates a summary that reflects the user's emotions. For example, it generates a summary that includes information such as "The customer is angry, so a quick response is required."

[0413] Step 5:

[0414] The server stores the generated summary and sentiment analysis results in the ticket management system.

[0415] Input: New emotion-aware summarization, sentiment analysis results.

[0416] Output: Summary and sentiment analysis results stored in the ticket management system.

[0417] What it does: It calls the API of the ticket management system and saves the generated summary and sentiment analysis results to the corresponding ticket, so that support agents can view the summary and sentiment information when viewing the ticket.

[0418] Example 2

[0419] Next, a description will be given of Example 2 of Form 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."

[0420] Conventional ticket management systems were unable to take the user's emotions into account when summarizing the contents of a ticket, making it difficult to respond appropriately based on the user's emotions. Furthermore, when a new ticket was created, there was a lack of a way to quickly and accurately summarize its contents and update the existing summary information. This could lead to a decrease in ticket management efficiency and user satisfaction.

[0421] 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.

[0422] In this invention, the server includes means for linking the ticket management system with the generative artificial intelligence, means for creating a summary of each ticket, means for creating a summary of the contents of a new ticket when it is opened and updating the original summary information, means for analyzing user emotions from the contents of the ticket using an emotion analysis engine and providing the results to the generative artificial intelligence, means for the generative artificial intelligence to generate a summary that takes user emotions into consideration based on the emotion analysis results, and means for saving the generated summary in the ticket management system. This enables the generation of summaries that take user emotions into consideration and the rapid and accurate updating of summary information.

[0423] "Ticket Management System" means software or a platform for managing the opening, updating, and viewing of tickets.

[0424] "Generative AI" refers to an AI system that uses natural language processing techniques to analyze text data and generate summaries and other information.

[0425] An "emotion analysis engine" is software or a system that analyzes user emotions from text data and provides the results.

[0426] A "summary" is a text that concisely summarizes the contents of a ticket, extracting and shortening important information.

[0427] "New tickets" are tickets that have been newly added to the system and have not yet been processed.

[0428] "Natural language processing technology" is a technology that allows computers to understand, interpret, and generate human language.

[0429] "User emotion" refers to the user's psychological state or emotion analyzed from text data, and examples include dissatisfaction, joy, anger, etc.

[0430] "API" stands for Application Programming Interface, an interface that allows different software systems to communicate with each other.

[0431] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for structuring and representing data.

[0432] This invention is a system that summarizes the contents of tickets and generates summaries that take into account the emotions of users by linking a ticket management system with generative artificial intelligence and an emotion analysis engine. Specific embodiments of this system are described below.

[0433] Hardware and software used

[0434] Ticket Management System: Software or platform for managing ticket creation, updates, and viewing.

[0435] Generative AI: An AI system that uses natural language processing techniques to analyze text data and generate summaries and other information.

[0436] Sentiment analysis engine: Software or system for analyzing user sentiment from text data and providing the results.

[0437] System Operation Overview

[0438] The server obtains the ticket contents from the ticket management system and sends them to the generative artificial intelligence and sentiment analysis engine. The generative artificial intelligence uses natural language processing technology to analyze the ticket contents and generate a summary. Meanwhile, the sentiment analysis engine analyzes the user's sentiment from the ticket contents and provides the results to the generative artificial intelligence. The generative artificial intelligence generates a summary that takes the user's sentiment into consideration based on the sentiment analysis results. The generated summary is saved in the ticket management system via the server.

[0439] Specific examples

[0440] Ticket contents

[0441] Ticket ID: 12345

[0442] What it says: "We're having trouble with users logging in. The error message is 'Invalid credentials'."

[0443] Prompt Sentence Examples

[0444] "Please summarize the ticket below: 'We are experiencing issues with users being unable to log in. The error message is 'Invalid credentials'.'"

[0445] Example

[0446] 1. Obtaining a ticket

[0447] The server retrieves the details of ticket ID 12345 from the ticket management system.

[0448] Specifically, the server utilizes the API of the ticket management system to request ticket information corresponding to a specific ticket ID.

[0449] 2. Submitting ticket details

[0450] The server sends the contents of the acquired ticket to the generative artificial intelligence and sentiment analysis engine.

[0451] Specifically, the server sends the ticket contents in JSON format as a POST request to the API of the generative artificial intelligence and sentiment analysis engine.

[0452] 3. Summary Generation

[0453] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[0454] Example: Produces the summary "A problem occurred where the user was unable to log in. The error message was 'Invalid credentials'."

[0455] 4. Emotion analysis

[0456] The emotion analysis engine analyzes user emotions from the contents of the ticket and provides the results to generative artificial intelligence.

[0457] Example: Detect the emotion of "dissatisfaction" from the ticket content and send the results to generative artificial intelligence.

[0458] 5. Emotion-Aware Summary Generation

[0459] Generative AI generates summaries that take into account the user's emotions based on the results of emotion analysis.

[0460] Example: Produces the summary "Users are having trouble logging in. The error message is 'Invalid credentials'. Users are frustrated."

[0461] 6. Save the summary

[0462] The server sends the generated summary back to the ticket management system and stores it as summary information for ticket ID 12345.

[0463] In this way, it is possible to generate summaries that take into account the user's feelings and to update the summary information quickly and accurately.

[0464] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0465] Step 1:

[0466] Obtaining a ticket

[0467] The server retrieves the ticket contents from the ticket management system.

[0468] Input: Ticket ID (e.g. 12345)

[0469] Specific operation: The server uses the ticket management system's API to send the request "GET / k / v1 / record.json?app=APP_ID&id=12345".

[0470] Output: Ticket details (e.g. "We're having trouble with users logging in. The error message is 'Invalid credentials'.")

[0471] Step 2:

[0472] Submitting ticket details

[0473] The server sends the contents of the acquired ticket to the generative artificial intelligence and sentiment analysis engine.

[0474] Input: Ticket details (e.g. "We are experiencing issues with users being unable to log in. The error message is 'Invalid credentials'.")

[0475] Specific operation: The server sends the ticket contents in JSON format as a POST request to the generative AI and sentiment analysis engine APIs.

[0476] Output: Ticket content is sent to the generative artificial intelligence and sentiment analysis engine.

[0477] Step 3:

[0478] Generate a summary

[0479] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[0480] Input: Ticket details (e.g. "We are experiencing issues with users being unable to log in. The error message is 'Invalid credentials'.")

[0481] Specific operation: The generative AI analyzes the content of the received ticket, extracts important information, and generates a short summary.

[0482] Output: Summary (e.g. "There was a problem with the user being unable to log in. The error message was 'Invalid credentials'")

[0483] Step 4:

[0484] Emotion analysis

[0485] The emotion analysis engine analyzes user emotions from the contents of the ticket and provides the results to generative artificial intelligence.

[0486] Input: Ticket details (e.g. "We are experiencing issues with users being unable to log in. The error message is 'Invalid credentials'.")

[0487] What it does: The sentiment analysis engine analyzes ticket content and detects emotions (e.g., dissatisfaction).

[0488] Output: Sentiment analysis result (e.g., "dissatisfied")

[0489] Step 5:

[0490] Emotion-aware summary generation

[0491] Generative AI generates summaries that take into account the user's emotions based on the results of emotion analysis.

[0492] Input: Summary (e.g., "A user is having trouble logging in. The error message is 'Invalid credentials'"), Sentiment analysis results (e.g., "Dissatisfied")

[0493] Specific operation: The generative AI takes into account the results of sentiment analysis and adds emotional information to the summary text.

[0494] Output: A summary that takes sentiment into account (e.g., "The user is having trouble logging in. The error message is 'Invalid credentials'. The user is frustrated.")

[0495] Step 6:

[0496] Save Summary

[0497] The server sends the generated summary back to the ticket management system and stores it as the ticket summary information.

[0498] Input: A sentiment-based summary (e.g., "Users are having trouble logging in. The error message is 'Invalid credentials'. Users are frustrated.")

[0499] Specific operation: The server sends the generated summary in JSON format to the ticket management system API as a POST request to update the ticket information.

[0500] Output: Summary information is saved in the ticket management system.

[0501] (Application example 2)

[0502] Next, a description will be given of Application Example 2 of Form 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."

[0503] With conventional ticket management systems, it was difficult to efficiently analyze and summarize the content of user inquiries and complaints. Furthermore, they were unable to respond in a way that took user feelings into consideration, which led to a decline in the quality of customer support. This resulted in issues such as a decline in user satisfaction and delayed responses.

[0504] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0505] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating a summary of each ticket, a means for creating a summary of the contents of a new ticket when it is opened and updating the original summary information, a means for analyzing user emotions, a means for generating a summary based on the results of the emotion analysis, and a means for saving the generated summary in the ticket management system. This makes it possible to generate summaries that take user emotions into consideration, improving the quality of customer support and enabling quick and appropriate responses.

[0506] A "ticket management system" is a system that manages tickets such as user inquiries and complaints, and creates, updates, and views tickets.

[0507] "Generative AI" is artificial intelligence that uses natural language processing techniques to analyze text data and perform summarization and other generative tasks.

[0508] A "means for creating a summary" is a means that has the function of analyzing the contents of a ticket, extracting important information, and summarizing it concisely.

[0509] "A means for creating a summary of the contents of a new ticket when it is created and updating the original summary information" is a means that has the function of analyzing the contents of a newly created ticket, generating a summary of it, and keeping the existing summary information up to date.

[0510] The "means for analyzing user emotions" refers to a means having the function of identifying a user's emotions from text data and analyzing that emotional state.

[0511] The "means for generating a summary based on the result of sentiment analysis" refers to a means that has the function of generating a more appropriate summary by taking into account the result of sentiment analysis of the user.

[0512] The "means for saving the generated summary in the ticket management system" is a means having a function for saving the generated summary in the ticket management system and making it available for later reference.

[0513] The system for implementing this invention includes a ticket management system, a generative AI, an emotion analysis engine, and a server for linking these. Specifically, it has the following configuration and performs the following processes.

[0514] System Configuration

[0515] 1. Ticket Management System:

[0516] This is a system for managing inquiries and complaints from users, and has the ability to create, update, and view tickets.

[0517] As a concrete example, we will use a common ticket management system.

[0518] 2. Generative AI:

[0519] It is an artificial intelligence that uses natural language processing technology to analyze the contents of tickets and generate summaries.

[0520] As a concrete example, we will use a generative AI model.

[0521] 3. Sentiment Analysis Engine:

[0522] It is an engine that analyzes user emotions from text data and identifies their emotional state.

[0523] As a concrete example, we use a sentiment analysis engine.

[0524] 4. Server:

[0525] It is a central processing unit that links the ticket management system, generative AI, and sentiment analysis engine.

[0526] The server acquires data from each system, performs the necessary processing, and stores the results.

[0527] Processing flow

[0528] 1. Getting a ticket:

[0529] A user submits an inquiry or complaint to the ticket management system.

[0530] The server obtains the new ticket contents from the ticket management system.

[0531] 2. Emotion analysis:

[0532] The server sends the acquired ticket contents to a sentiment analysis engine to analyze the user's sentiment.

[0533] The emotion analysis engine identifies emotions from the text data and returns the results to the server.

[0534] 3. Summary generation:

[0535] The server sends the ticket content and the results of emotion analysis to the generative AI, which then generates a summary.

[0536] Generative AI uses natural language processing technology to analyze ticket content and generate summaries that take emotions into account.

[0537] 4. Save the summary:

[0538] The server stores the generated summary in the ticket management system.

[0539] The ticket management system stores the summary information along with the original ticket information for future reference.

[0540] Specific examples

[0541] For example, if a user sends an inquiry saying "the product has not arrived," the following processing will be performed.

[0542] 1. An inquiry is posted in the ticket management system saying, "My product hasn't arrived yet. What's going on? I'm very unhappy."

[0543] 2. The server receives the query and sends it to the emotion analysis engine.

[0544] 3. The emotion analysis engine analyzes the emotion of "dissatisfaction" and returns the results to the server.

[0545] 4. The server sends the inquiry content and the results of the sentiment analysis to the generative AI, which generates a summary such as, "There was an inquiry about the product not arriving, and the user is dissatisfied."

[0546] 5. The generated summary is stored in the ticket management system for quick response by customer support representatives.

[0547] Prompt Sentence Examples

[0548] Summarize the user's query and generate a sentiment-based summary. Analyze the following:

[0549] Inquiry: "I haven't received my item yet. What's going on? I'm very unhappy."

[0550] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0551] Step 1:

[0552] A user submits an inquiry or complaint to the ticket management system.

[0553] Input: The query entered by the user.

[0554] Output: A new ticket is created in the ticket management system.

[0555] Specific operation: The user uses a smartphone or PC to enter the inquiry details into the ticket management system interface and presses the send button.

[0556] Step 2:

[0557] The server retrieves the new ticket contents from the ticket management system.

[0558] Input: A new ticket saved in the ticket management system.

[0559] Output: The contents of the new ticket are forwarded to the server.

[0560] Specific operation: The server periodically polls the ticket management system to check whether a new ticket exists. If a new ticket exists, it retrieves its contents.

[0561] Step 3:

[0562] The server sends the acquired ticket contents to the emotion analysis engine to analyze the user's emotions.

[0563] Input: New ticket content.

[0564] Output: Sentiment analysis results from the sentiment analysis engine.

[0565] Specific operation: The server sends the ticket contents in text format to the sentiment analysis engine, which analyzes the text data to identify the emotional state, and returns the analysis result to the server.

[0566] Step 4:

[0567] The server sends the ticket content and sentiment analysis results to the generative AI, which then generates a summary.

[0568] Input: Ticket content and sentiment analysis results.

[0569] Output: Generative AI summary.

[0570] Specific operation: The server sends the ticket content and the results of sentiment analysis as a prompt to the generative AI, which then uses natural language processing technology to generate a summary, which is then returned to the server.

[0571] Step 5:

[0572] The server stores the generated summary in the ticket management system.

[0573] Input: Generative AI summary.

[0574] Output: A summary stored in the ticket management system.

[0575] What happens: The server adds the generated summary to the corresponding ticket in the ticket management system and saves it, making the summary information available to customer support agents.

[0576] 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.

[0577] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0578] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0579] 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.

[0580] [Second embodiment]

[0581] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0582] 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.

[0583] 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).

[0584] 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.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0593] "Example 1"

[0594] One embodiment of the present invention is a system that links a ticket management system with a generative AI. This system sends the contents of tickets acquired from the ticket management system to the generative AI, which then uses natural language processing technology to analyze the ticket contents and generate a summary. The generated summary is sent back to the ticket management system and saved as the ticket's summary information. When a new ticket is created, the contents of that ticket are also sent to the generative AI, which then generates a summary. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[0595] "Example 2"

[0596] As a specific example, GPT-3 can be used as a generative AI. The contents of tickets obtained from a ticket management system are sent to GPT-3, which then uses natural language processing technology to analyze the contents of the ticket and generate a summary. The generated summary is sent back to the ticket management system and saved as the ticket's summary information. When a new ticket is created, the contents of that ticket are also sent to GPT-3, and a summary is generated. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[0597] The processing flow of each embodiment will be described below.

[0598] "Example 1"

[0599] Step 1: Get the ticket contents from the ticket management system.

[0600] Step 2: Send the acquired ticket contents to the generation AI.

[0601] Step 3: The generative AI uses natural language processing technology to analyze the ticket content and generate a summary.

[0602] Step 4: The generated summary is sent back to the ticket management system and stored as the ticket summary information.

[0603] Step 5: When a new ticket is created, its contents are also sent to the generative AI, which generates a summary.

[0604] Step 6: The generated new ticket summary is saved in the ticket management system along with the original summary information.

[0605] "Example 2"

[0606] Step 1: Obtain the ticket details from the ticket management system. Step 2: Send the obtained ticket details to the generative AI "GPT-3."

[0607] Step 3: GPT-3 uses natural language processing techniques to analyze the ticket content and generate a summary.

[0608] Step 4: The generated summary is sent back to the ticket management system and stored as the ticket summary information.

[0609] Step 5: When a new ticket is created, its contents are also sent to GPT-3, and a summary is generated.

[0610] Step 6: The generated new ticket summary is saved in the ticket management system along with the original summary information.

[0611] Example 1

[0612] Next, a description will be given of Example 1 of Form 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."

[0613] In traditional ticket management systems, ticket content had to be summarized manually, which was time-consuming and labor-intensive. Furthermore, when a new ticket was created, it was difficult to quickly summarize its contents and update the original summary information. This reduced the efficiency of ticket management and could lead to important information being overlooked.

[0614] 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.

[0615] In this invention, the server includes means for linking the ticket management system with the generative artificial intelligence, means for creating a summary of each ticket, means for creating a summary of the contents of a new ticket when it is created and updating the original summary information, means for acquiring the contents of the newly created ticket from the ticket management system, means for sending the acquired ticket contents to the generative artificial intelligence, means for receiving the summary generated from the generative artificial intelligence, and means for saving the received summary in the ticket management system. This makes it possible to automatically summarize the contents of tickets and manage them quickly.

[0616] A "ticket management system" is a software or hardware system for managing the creation, updating, and viewing of tickets.

[0617] "Generative AI" refers to an AI system that uses natural language processing techniques to analyze input text data and perform summarization and other generative tasks.

[0618] The "summary" is a concise summary of the ticket's contents, briefly expressing the main points and issues of the ticket.

[0619] A "new ticket" is a ticket that has been newly created by a user and for which no summary has yet been created.

[0620] A "server" is a computer system that connects the ticket management system with generative artificial intelligence and acquires, sends, receives, and stores data.

[0621] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language, and is used to analyze text data and generate summaries.

[0622] "API" stands for Application Programming Interface, an interface for exchanging data and functions between different software systems.

[0623] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for structuring and representing data.

[0624] This invention is a system that links a ticket management system with generative artificial intelligence, automatically creating summaries for each ticket, generating summaries of the contents of new tickets when they are created, and updating the original summary information.

[0625] The server first retrieves the details of newly created tickets from the ticket management system. A ticket management system is a software or hardware system that manages the creation, updating, and viewing of tickets. The server periodically calls the ticket management system's API to check whether new tickets exist.

[0626] The server then sends the retrieved ticket contents to a generative AI. The generative AI uses a system that uses natural language processing technology to analyze text data and generate a summary. Specifically, a generative AI model such as "OpenAI GPT-4" is used. The server converts the ticket contents into JSON format and sends a POST request to the generative AI's API endpoint.

[0627] The generative AI analyzes the content of the ticket received and generates a summary. For example, in response to a ticket that states "High server memory usage," it generates a summary such as "A problem has occurred with high server memory usage."

[0628] The generated summary is sent back to the server, which analyzes the response from the generative AI and extracts the summary text. The server then saves the received summary in the ticket management system. Specifically, it calls the ticket management system's API and sends a request to update the summary information.

[0629] When a new ticket is created, the server sends the ticket contents to the generative AI in a similar manner to generate a summary. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[0630] As a concrete example, if a user submits a ticket stating that "server memory usage is high," the server retrieves the contents of this ticket and sends it to the generative AI. The generative AI analyzes the content of "server memory usage is high" and generates a summary such as "a problem has occurred with high server memory usage." The generated summary is sent back to the server and stored in the ticket management system.

[0631] An example of a prompt sentence might be:

[0632] "Please summarize the ticket below:

[0633] Ticket details: The server's memory usage is high. It often peaks especially at night. We are considering increasing the memory as a solution.

[0634] By sending this prompt to the generative AI, the AI ​​will analyze the ticket contents and generate a summary.

[0635] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0636] Step 1:

[0637] The server obtains the contents of the newly created ticket from the ticket management system.

[0638] Input: Ticket management system API endpoint

[0639] Output: New ticket content (text data)

[0640] Specific operation: The server periodically calls the API of the ticket management system to check whether a new ticket exists. If a new ticket exists, it retrieves its contents.

[0641] Step 2:

[0642] The server sends the contents of the acquired ticket to the generative artificial intelligence.

[0643] Input: New ticket content (text data)

[0644] Output: Request to the generative AI (JSON format)

[0645] Specific operation: The server converts the acquired ticket contents into JSON format and sends a POST request to the API endpoint of the generative artificial intelligence.

[0646] Step 3:

[0647] Generative AI analyzes the ticket contents and generates a summary.

[0648] Input: Ticket content (JSON format)

[0649] Output: Summary (text data)

[0650] How it works: The generative AI uses natural language processing technology to analyze the content of the ticket it receives and generate a summary. For example, in response to a ticket that says "Server memory usage is high," it generates a summary such as "A problem with high server memory usage has occurred."

[0651] Step 4:

[0652] The server receives the summary generated from the generative artificial intelligence.

[0653] Input: Response from generative AI (JSON format)

[0654] Output: Summary (text data)

[0655] Specific operation: The server analyzes the response from the generative artificial intelligence API and extracts summary text.

[0656] Step 5:

[0657] The server stores the received summary in the ticket management system.

[0658] Input: Summary (text data)

[0659] Output: Update request to ticket management system (API call)

[0660] Specific operation: The server calls the API of the ticket management system and sends a request to update the summary information.

[0661] Step 6:

[0662] When a user submits a new ticket, the server again obtains the contents of the new ticket from the ticket management system and sends them to the generative artificial intelligence in the same manner, causing a summary to be generated.

[0663] Input: New ticket content (text data)

[0664] Output: Summary (text data)

[0665] Specific operation: When a user submits a new ticket, the server retrieves the content from the ticket management system and sends it to the generative AI. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[0666] (Application example 1)

[0667] Next, a description will be given of Application Example 1 of Form 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."

[0668] In factory maintenance work, the detailed content of tickets makes it difficult for workers to quickly understand and respond to them. Furthermore, because the content of tickets is so diverse, they need to be summarized, but manual summarization takes time and effort. This reduces the efficiency of maintenance work and reduces productivity.

[0669] 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.

[0670] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating summaries of each ticket, a means for creating a summary of the contents of a new ticket when it is created and updating the original summary information, and a means for acquiring the contents of maintenance tickets within the factory, generating summaries using the generative AI, and saving them. This allows the contents of maintenance tickets to be summarized quickly and accurately, enabling workers to perform maintenance work efficiently.

[0671] A "ticket management system" is a system that manages the creation, updating, and viewing of tickets.

[0672] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze text data and perform summarization and generation.

[0673] The "summary" is a concise summary of the ticket contents.

[0674] A "maintenance ticket" is a ticket that contains information about maintenance work that occurs within a factory.

[0675] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[0676] "In-plant" means the interior of a facility where manufacturing operations take place.

[0677] A "server" is a computer system that processes and stores data on a network.

[0678] As an embodiment of the present invention, a system is constructed that automatically generates ticket summaries using generative AI in a maintenance ticket management system within a factory. A specific embodiment of this system is shown below.

[0679] System configuration

[0680] 1. Hardware

[0681] Server: A computer system that processes and stores data. It houses the ticket management system and generative AI.

[0682] Factory Robot: A robot that performs maintenance work in a factory. It communicates with the server to obtain maintenance ticket information and receive a summary.

[0683] 2. Software

[0684] Ticket management system: A system that manages ticket creation, updates, and viewing. Ticket content is provided to the generative AI via API.

[0685] Generative AI: This is an AI that uses natural language processing technology to analyze text data and generate summaries. It receives ticket content via API and generates summaries.

[0686] Data processing and calculation

[0687] 1. Obtaining ticket details

[0688] The server obtains the contents of maintenance tickets generated within the factory through the ticket management system's API.

[0689] 2. Summary Generation Using Generative AI

[0690] The server sends the acquired ticket contents to the generative AI API, which uses natural language processing technology to analyze the ticket contents and generate a summary.

[0691] 3. Save the summary

[0692] The server saves the generated summary through the API of the ticket management system, so that the summary information of the maintenance ticket is saved in the ticket management system.

[0693] Specific examples

[0694] Ticket details: "The belt on machine A is loose and needs to be replaced."

[0695] Produced summary: "The belt on machine A needs to be replaced."

[0696] Prompt Sentence Examples

[0697] Summarize the following text:

[0698] "The belt on machine A is loose and needs to be replaced."

[0699] In this way, a system can be realized that supports factory robots in efficiently performing maintenance work.

[0700] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0701] Step 1:

[0702] The server obtains the contents of maintenance tickets generated within the factory through the ticket management system's API.

[0703] Input: Maintenance ticket information from your ticket management system

[0704] Output: The contents of the retrieved maintenance ticket

[0705] What happens: The server sends an HTTP request to retrieve ticket data from the ticket management system's API. The retrieved data is received in JSON format.

[0706] Step 2:

[0707] The server sends the acquired ticket contents to the API of the generation AI.

[0708] Input: The content of the acquired maintenance ticket

[0709] Output: Ticket content sent to the generative AI

[0710] Specific operation: The server sends an HTTP POST request to the generative AI API and sends the ticket contents in JSON format.

[0711] Step 3:

[0712] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[0713] Input: Ticket content sent to the generative AI

[0714] Output: A summary of the generated tickets

[0715] How it works: The generative AI analyzes the content of the received ticket, extracts important information, and generates a summary, which is then returned to the server in JSON format.

[0716] Step 4:

[0717] The server stores the generated summary through the API of the ticket management system.

[0718] Input: Summary of generated ticket

[0719] Output: Summary information stored in the ticket management system

[0720] What happens: The server sends an HTTP POST request to the ticket management system's API and saves the generated summary in JSON format.

[0721] Step 5:

[0722] A user views summarized maintenance ticket information through a ticket management system.

[0723] Input: Summary information stored in the ticket management system

[0724] Output: Summarized maintenance ticket information for user viewing

[0725] Specific operation: A user views summarized maintenance ticket information through the ticket management system interface. The system displays the saved summary information.

[0726] Example 2

[0727] Next, a description will be given of Example 2 of Form 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."

[0728] Conventional information management systems have the problem that information summaries must be created manually, which takes time and effort. In addition, when new information is registered, it is difficult to quickly and accurately summarize the content and update existing summary information. This makes information management cumbersome and hinders efficient operation.

[0729] 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.

[0730] In this invention, the server includes means for acquiring new information from the information management system, means for transmitting the acquired information to the generative AI, means for receiving summaries generated by the generative AI, and means for storing the generated summaries in the information management system, thereby enabling automatic generation of summaries of new information and rapid and accurate updating of existing summary information.

[0731] An "information management system" is a software or hardware system for managing the registration, updating, and viewing of information.

[0732] "Generative AI" refers to an AI system that uses natural language processing techniques to analyze input information and perform summarization and other generative tasks.

[0733] A "summary" is a short summary of the main points of information, intended to convey the content of the original information concisely.

[0734] "New information" refers to information that has been newly registered in the information management system and is to be distinguished from existing information.

[0735] A "server" is a computer system that sends and receives data between an information management system and generative artificial intelligence.

[0736] "Means of acquisition" refers to the methods and processes for acquiring new information from the information management system.

[0737] "Means for transmitting" refers to the method or process for transmitting acquired information to generative artificial intelligence.

[0738] "Means for receiving" refers to a method or process for receiving a summary generated by a generative artificial intelligence.

[0739] "Storage means" refers to the method or process for storing the generated summary in an information management system.

[0740] This invention is a system that automatically generates summaries of new information and updates existing summaries quickly and accurately by linking an information management system with generative artificial intelligence. Specific embodiments of this system are described below.

[0741] Hardware and software used

[0742] Information management systems: These are software or hardware systems used to manage the registration, updating, and viewing of information. Examples include database management systems and cloud-based information management platforms.

[0743] Generative AI: This is an AI system that uses natural language processing techniques to analyze input information and perform summarization and other generative tasks. A specific example is an AI service equipped with a natural language processing model.

[0744] System Operation

[0745] 1. A user registers new information in the information management system.

[0746] A user uses the interface of the information management system to register new information, including a title, a detailed description, and a priority.

[0747] Example: User enters the subject "System bug fix request" with the details "Error occurs on login screen."

[0748] 2. The server retrieves new information from the information management system.

[0749] The server periodically calls the information management system's API to obtain new information. The obtained data is received in JSON format.

[0750] Example: A server retrieves information from an information management system with the title "System bug fix request" and the details "Error occurs on login screen."

[0751] 3. The server sends the acquired information to the generative AI

[0752] The server generates an appropriate prompt to send the acquired information to the generative AI, which then sends the prompt as an API request to the generative AI.

[0753] Example: The server sends a request to the generative artificial intelligence to summarize the information content "Request to fix a system bug."

[0754] 4. Generative AI analyzes the content of information and generates summaries

[0755] The generative AI analyzes the prompt and uses natural language processing techniques to summarize the information, which is short and to the point.

[0756] Example: A generative AI summarizes the details "An error occurs on the login screen" as "Request to fix the error on the login screen."

[0757] 5. The generative AI sends the generated summary back to the server

[0758] The generative AI sends the generated summary back to the server as an API response, which the server receives and proceeds to the next step.

[0759] Example: The generative AI sends back to the server a summary titled "Request to fix login screen error."

[0760] 6. The server stores the generated summary in the information management system and displays it as a summary of the information.

[0761] The server adds the generated summary to the original information using the information management system's API. The user can view the generated summary on the information management system's interface.

[0762] Example: The server stores a summary "Request to fix login screen error" in the information management system and allows the user to view this summary on the information details screen.

[0763] Prompt Sentence Examples

[0764] An example of a prompt to be sent to the generative artificial intelligence is as follows:

[0765] Please summarize the information content of "Request for system bug fix."

[0766] By inputting this prompt into a generative artificial intelligence, the AI ​​analyzes the information content and generates an appropriate summary.

[0767] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0768] Step 1:

[0769] A user registers new information in the information management system.

[0770] The user registers new information using the information management system interface, entering information such as a title, detailed description, and priority, and the new information is then saved in the information management system.

[0771] Specifically, the user enters details such as "An error occurs on the login screen" under the title "Request to fix a system bug" and registers it in the information management system.

[0772] Step 2:

[0773] The server retrieves new information from the information management system.

[0774] The server periodically calls the information management system's API to retrieve new information data. As input, it receives the API response from the information management system. As output, the retrieved data is stored on the server in JSON format.

[0775] Specifically, the server retrieves information from the information management system, including the title "Request to fix a system bug" and the details "An error occurs on the login screen."

[0776] Step 3:

[0777] The server sends the acquired information to the generative artificial intelligence.

[0778] The server generates an appropriate prompt to send the acquired information to the generative AI. As input, it creates a prompt based on the acquired information. As output, the prompt is sent to the generative AI as an API request.

[0779] Specifically, the server sends a request to the generative artificial intelligence to summarize the information content, "a request to fix a system bug."

[0780] Step 4:

[0781] Generative AI analyzes the content of information and generates summaries

[0782] The generative AI analyzes the received prompt and summarizes the information content using natural language processing techniques. The prompt is received as input, and the generated summary is generated as output.

[0783] As a specific operation, the generative artificial intelligence summarizes the details of "an error occurs on the login screen" as "a request to fix the error on the login screen."

[0784] Step 5:

[0785] The generative AI sends the generated summary back to the server

[0786] The generative AI sends the generated summary back to the server as an API response. The generated summary is received as input. The summary is sent back to the server as output.

[0787] Specifically, the generative artificial intelligence sends a summary to the server titled "Request to fix an error on the login screen."

[0788] Step 6:

[0789] The server stores the generated summary in the information management system and displays it as summary information of the information.

[0790] The server adds the generated summary to the original information using the information management system's API. As input, it receives the generated summary. As output, it stores the summary in the information management system and makes it available for the user to review.

[0791] Specifically, the server stores a summary titled "Request to correct login screen error" in the information management system, and the user can check this summary on the information details screen.

[0792] (Application example 2)

[0793] Next, a description will be given of Application Example 2 of Form 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."

[0794] When managing tickets for maintenance and repairs that occur within a factory, it is necessary to efficiently understand the contents of the tickets and respond quickly. However, with conventional systems, ticket contents must be manually analyzed and summarized, which is time-consuming and labor-intensive. Furthermore, if the ticket contents are complex, the quality of the summary may decline. This can reduce work efficiency and disrupt factory operations.

[0795] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0796] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating summaries for each ticket, a means for creating a summary of the contents of a new ticket when it is created and updating the original summary information, a means for managing maintenance and repair tickets generated in the factory, a means for analyzing the contents of the ticket and generating summaries using the generative AI, a means for saving the generated summaries in the ticket management system, and a means for being installed in the factory robot. This makes it possible to efficiently analyze the contents of tickets and generate summaries.

[0797] A "ticket management system" is a system that manages maintenance and repair tickets that occur within a factory, and creates, updates, and views tickets.

[0798] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze the contents of tickets and generate summaries.

[0799] A "means for creating a summary" is a means that has the function of analyzing the contents of a ticket, extracting important information, and summarizing it concisely.

[0800] "A means for creating a summary of the contents of a new ticket when it is created and updating the original summary information" is a means that has the function of analyzing the contents of a newly created ticket, generating a summary, and updating the existing summary information.

[0801] "A means for managing tickets for maintenance and repairs generated within a factory" refers to a means that has the function of centrally managing tickets related to maintenance and repairs generated within a factory.

[0802] "Means for saving the generated summary in the ticket management system" refers to means that has the function of saving the summary generated by the generative AI in the ticket management system.

[0803] "Means to be installed on factory robots" refers to a means to install an application on a robot used in a factory and have the function of linking the ticket management system with generative AI.

[0804] As an embodiment of the present invention, a system is constructed that efficiently manages maintenance and repair tickets generated in a factory and generates summaries. A specific embodiment of this system is shown below.

[0805] System configuration

[0806] The system consists of the following major components:

[0807] 1. Ticket Management System: A system for managing maintenance and repair tickets that occur within the factory. Tickets can be created, updated, and viewed.

[0808] 2. Generative AI: This is artificial intelligence that uses natural language processing technology to analyze the contents of tickets and generate summaries.

[0809] 3. Factory robot: A robot used in a factory, on which an application that connects the ticket management system with generative AI is installed.

[0810] Program processing

[0811] The server first retrieves maintenance or repair tickets from the ticket management system. Next, it sends the contents of the retrieved tickets to a generative AI system, which generates summaries using natural language processing technology. The generated summaries are then saved back into the ticket management system.

[0812] Hardware and software used

[0813] Hardware: Factory robots

[0814] Software: Ticket management system, generative AI (e.g., GPT-3), Python

[0815] Data processing and calculation

[0816] 1. Data Acquisition: Acquire ticket contents from the ticket management system.

[0817] 2. Data analysis: The ticket contents are sent to a generative AI, which generates a summary using natural language processing technology.

[0818] 3. Data storage: The generated summary is stored in the ticket management system.

[0819] Specific examples

[0820] For example, when a machine malfunctions in a factory, a maintenance ticket is registered in the ticket management system. A factory robot retrieves the ticket and sends it to a generative AI to generate a summary. The summary is then stored in the ticket management system, allowing workers to efficiently understand the contents of the ticket.

[0821] Prompt Sentence Examples

[0822] "Please summarize the contents of the maintenance ticket obtained from the ticket management system. The contents are as follows: {Ticket content}"

[0823] In this way, a system can be realized that can significantly improve the efficiency of maintenance and repair within a factory.

[0824] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0825] Step 1:

[0826] The server retrieves maintenance and repair tickets from the ticket management system.

[0827] Input: Ticket information registered in the ticket management system

[0828] Data processing: Use the ticket management system's API to obtain the ticket contents.

[0829] Output: The retrieved ticket details

[0830] Step 2:

[0831] The server sends the contents of the acquired ticket to the generation AI.

[0832] Input: The content of the ticket obtained

[0833] Data processing: Send the ticket contents using a generative AI API.

[0834] Output: The ticket content sent to the generative AI

[0835] Step 3:

[0836] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[0837] Input: Ticket content sent to the generative AI

[0838] Data Computing: Using natural language processing techniques, the content of tickets is analyzed, key information is extracted, and a summary is generated.

[0839] Output: The generated summary

[0840] Step 4:

[0841] The server stores the generated summary in the ticket management system.

[0842] Input: Generated summary

[0843] Data processing: Use the ticket management system's API to store the generated summary.

[0844] Output: Summary stored in ticket management system

[0845] Step 5:

[0846] The user views the generated summary through the ticket management system.

[0847] Input: Summary stored in ticket management system

[0848] Data processing: Display summaries through the ticket management system interface.

[0849] Output: A summary that the user sees

[0850] In this way, maintenance and repair tickets within the factory can be managed efficiently and workers can respond quickly.

[0851] 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.

[0852] "Example 1"

[0853] The present invention is a system that links a ticket management system with a generative AI to create summaries of each ticket, and when a new ticket is created, creates a summary of the ticket's contents and updates the original summary information. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, the generative AI uses natural language processing technology to analyze the ticket contents and generate a summary. The emotion engine also analyzes the user's emotions from the ticket contents and provides the results to the generative AI. The generative AI generates a summary that takes the user's emotions into account based on the emotion analysis results provided by the emotion engine.

[0854] "Example 2"

[0855] As a concrete example, consider a system that uses a ticket management system, the generative AI "GPT-3," and the emotion engine "IBM Watson Tone Analyzer." The ticket contents are obtained from the ticket management system and sent to GPT-3 and IBM Watson Tone Analyzer. GPT-3 uses natural language processing technology to analyze the ticket contents and generate a summary. Meanwhile, IBM Watson Tone Analyzer analyzes the user's emotions from the ticket contents and provides the results to GPT-3. GPT-3 generates a summary that takes the user's emotions into account based on the emotion analysis results provided by IBM Watson Tone Analyzer. The generated summary is sent back to the ticket management system and saved as the ticket summary information. When a new ticket is created, the contents are also sent to GPT-3 and IBM Watson Tone Analyzer, and a summary is generated.

[0856] The processing flow of each embodiment will be described below.

[0857] "Example 1"

[0858] Step 1: Get the ticket contents from the ticket management system.

[0859] Step 2: The acquired ticket content is sent to the generative AI, which analyzes the content and generates a summary.

[0860] Step 3: At the same time, the content of the acquired ticket is sent to the emotion engine to analyze the user's emotions.

[0861] Step 4: Provide the emotion analysis results provided by the emotion engine to the generative AI.

[0862] Step 5: The generative AI generates a summary that takes the user's emotions into account based on the provided emotion analysis results.

[0863] "Example 2"

[0864] Step 1: Get the ticket contents from the ticket management system.

[0865] Step 2: The contents of the acquired ticket are sent to the generative AI "GPT-3", which analyzes the contents and generates a summary.

[0866] Step 3: At the same time, the contents of the acquired ticket are sent to the emotion engine "IBM Watson Tone Analyzer" to analyze the user's emotions.

[0867] Step 4: Provide the sentiment analysis results provided by IBM Watson Tone Analyzer to GPT-3.

[0868] Step 5: GPT-3 generates a summary that takes into account the user's emotions based on the sentiment analysis results provided by IBM Watson Tone Analyzer.

[0869] Step 6: The generated summary is sent back to the ticket management system and stored as the ticket summary information.

[0870] Step 7: When a new ticket is created, its contents are also sent to GPT-3 and IBM Watson Tone Analyzer to generate a summary.

[0871] Example 1

[0872] Next, a description will be given of Example 1 of Form 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."

[0873] Conventional ticket management systems have the problem that ticket contents must be summarized manually, which is time-consuming and labor-intensive. In addition, they are unable to generate summaries that take into account the user's feelings, making it difficult to properly reflect the user's complaints and requests. This can lead to reduced ticket management efficiency and lower user satisfaction.

[0874] 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.

[0875] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating a summary of each ticket, a means for creating a summary of the contents of a new ticket when it is opened and updating the original summary information, a means including an emotion engine for analyzing user emotions from the ticket contents, a means for generating a summary based on the emotion analysis results provided by the emotion engine, and a means for saving the generated summary in the ticket management system. This makes it possible to automatically summarize the contents of tickets and generate summaries that take user emotions into consideration.

[0876] A "ticket management system" is a system for managing problems and requests reported by users and recording and tracking them as tickets.

[0877] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze input data and generate summaries and other information.

[0878] The "means for creating a summary" is a function for automatically generating a summary that concisely summarizes the contents of the ticket.

[0879] "A means for creating a summary of the contents of a new ticket when it is created and updating the original summary information" is a function for summarizing the contents of a newly created ticket and adding or updating the summary to existing summary information.

[0880] The "emotion engine" is a technology that analyzes user emotions from the contents of tickets and provides the results.

[0881] The "means for generating a summary based on the emotion analysis result" is a function for generating a summary that reflects the user's emotions, taking into account the emotion analysis result provided by the emotion engine.

[0882] The "means for saving the generated summary in the ticket management system" is a function for sending the generated summary to the ticket management system and saving it therein.

[0883] This invention is a system that links a ticket management system with a generative AI, automatically generating summaries for each ticket and providing summaries that take user emotions into consideration. Specific embodiments of this system are described below.

[0884] System configuration

[0885] The system consists of the following main components:

[0886] 1. Ticket Management System: A system for managing problems and requests reported by users and recording and tracking them as tickets.

[0887] 2. Generative AI: This is artificial intelligence that uses natural language processing techniques to analyze input data and generate summaries and other information.

[0888] 3. Emotion Engine: A technology that analyzes user emotions from the contents of tickets and provides the results.

[0889] Data flow

[0890] 1. Get Ticket: The server retrieves the newly created ticket from the ticket management system, including the details entered by the user.

[0891] 2. Data transmission: The server sends the acquired ticket contents to the generative AI, which then analyzes the data using natural language processing technology.

[0892] 3. Ticket content analysis: The generative AI analyzes the ticket content and generates a summary. At the same time, the emotion engine works to analyze the user's emotions from the ticket content.

[0893] 4. Providing emotion analysis results: The emotion engine identifies the user's emotions and provides the results to the generative AI.

[0894] 5. Summary generation: The generative AI generates a summary that takes into account the user's emotions based on the emotion analysis results provided by the emotion engine.

[0895] 6. Save Abstract: The generated abstract is sent back to the ticket management system via the server, which saves it along with the original ticket information.

[0896] Specific examples

[0897] For example, if a user submits a ticket stating that "the system is slow," the server sends this information to the generative AI. The generative AI uses natural language processing technology to analyze the content, "The system is slow," and the emotion engine analyzes that the user is dissatisfied. The generative AI takes this emotion into account and generates a summary that reads, "The user is dissatisfied with the system's slowness." This summary is then saved in the ticket management system.

[0898] Prompt Sentence Examples

[0899] Below are some example prompts to input to a generative AI model:

[0900] Generate summaries for tickets where users report that the system is running slowly. Consider the user's feelings and create appropriate summaries.

[0901] By using this prompt, the generative AI can generate a summary that reflects the user's emotions.

[0902] In this way, by linking a ticket management system with generative AI, it becomes possible to automatically summarize the contents of tickets and generate summaries that take user emotions into account. This is expected to improve ticket management efficiency and user satisfaction.

[0903] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0904] Step 1:

[0905] The server retrieves the details of newly created tickets from the ticket management system. As input, it receives new ticket information from the ticket management system database. Specifically, when a new ticket is created in response to a user reporting that the system is running slowly, it retrieves the details. As output, it passes the retrieved ticket details to the next processing step.

[0906] Step 2:

[0907] The server sends the acquired ticket contents to the generative AI. As input, it receives the ticket contents acquired in step 1. Specifically, it sends the ticket contents to the generative AI in a standard data format such as JSON. For example, the following JSON data is sent to the generative AI:

[0908] json

[0909] {

[0910] "ticket_id": "12345",

[0911] "content": "System is running slow"

[0912] }

[0913] As an output, the data sent to the generative AI is passed on to the next processing step.

[0914] Step 3:

[0915] The generative AI analyzes the content of the received ticket. As input, it receives the content of the ticket sent in step 2. Specifically, it uses natural language processing technology to understand the content of the ticket and prepares to generate a summary. As output, the analysis results are passed to the next processing step.

[0916] Step 4:

[0917] The emotion engine analyzes the user's emotions from the ticket content. As input, it receives the ticket content analyzed by the generative AI in step 3. Specifically, it analyzes that the user is dissatisfied based on the content that "the system is running slowly." As output, the emotion analysis results are provided to the generative AI.

[0918] Step 5:

[0919] The generative AI generates a summary that takes into account the user's emotions based on the emotion analysis results provided by the emotion engine. As input, it receives the emotion analysis results provided in step 4. Specifically, it generates the summary "The user is frustrated by the system's slow performance." As output, the generated summary is passed to the next processing step.

[0920] Step 6:

[0921] The server sends the generated summary back to the ticket management system. As input, it receives the summary generated in step 5. Specifically, it sends the generated summary to the ticket management system and stores it. For example, the generated summary for ticket ID "12345" is stored. As output, the summary is stored in the ticket management system.

[0922] Step 7:

[0923] When a new ticket is created, the contents of the ticket are sent to the generative AI in a similar procedure, and a summary is generated. The contents of the newly created ticket are received as input. Specifically, steps 1 to 6 are repeated. As output, the generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[0924] (Application example 1)

[0925] Next, a description will be given of Application Example 1 of Form 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."

[0926] Conventional ticket management systems have problems in that it takes a lot of time and effort to summarize the contents of tickets, and it is difficult to respond in a way that takes the user's feelings into account. In particular, solving these problems is important for online shopping sites, where customers make a wide variety of inquiries and require quick and appropriate responses.

[0927] 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.

[0928] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating a summary of each ticket, a means for creating a summary of the contents of a new ticket when it is opened and updating the original summary information, a means for analyzing user emotions from the ticket contents using a sentiment analysis engine, a means for the generative AI to generate a summary that takes user emotions into consideration based on the sentiment analysis result, and a means for saving the generated summary and the sentiment analysis result in the ticket management system. This makes it possible to quickly summarize the contents of tickets and respond to them in a way that takes user emotions into consideration.

[0929] A "ticket management system" is a system for managing customer inquiries and problem reports, and recording and tracking them as tickets.

[0930] "Generative AI" is artificial intelligence that uses natural language processing techniques to analyze text data and perform summarization and other generative tasks.

[0931] A "summary" is information that concisely summarizes the contents of a ticket, extracting and expressing the important points in a short form.

[0932] An "emotion analysis engine" is software or algorithms that analyze a user's emotions from text data and identify their emotional state.

[0933] "User's emotions" refers to the user's psychological state and emotions that can be inferred from the text included in the ticket content.

[0934] "Storage" refers to recording the generated summary and sentiment analysis results in a storage device such as a database or file system.

[0935] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[0936] "Raising a ticket" means creating a new ticket and registering it in the system.

[0937] "Ticket update" means changing or adding content to an existing ticket.

[0938] "Viewing a ticket" means viewing and checking the contents of a ticket registered in the system.

[0939] A system for implementing the present invention includes a ticket management system, a generative AI, and an emotion analysis engine. Specific embodiments of the system will be described below.

[0940] System configuration

[0941] The server has a means to link the ticket management system with the generative AI. The ticket management system is a system for recording and tracking customer inquiries and problem reports as tickets. The generative AI is an artificial intelligence that uses natural language processing technology to analyze the contents of the ticket and generate a summary. The sentiment analysis engine is software or an algorithm that analyzes user emotions from text data and identifies their emotional state.

[0942] Program processing

[0943] The server first retrieves a new ticket from the ticket management system. The contents of the retrieved ticket are sent to a generative AI, which generates a summary using natural language processing technology. The generated summary is then sent to a sentiment analysis engine, which analyzes the user's emotions. The results of the sentiment analysis are provided to the generative AI, which then regenerates a summary that takes the user's emotions into account. This summary and the results of the sentiment analysis are stored in the ticket management system.

[0944] Hardware and software used

[0945] Hardware: Servers, smartphones, PCs

[0946] Software: ticket management systems, generative AI (e.g., OpenAI API), sentiment analysis engines (e.g., SentimentAnalyzer)

[0947] Specific examples

[0948] For example, if a customer inquires that "the product has not arrived," the following prompt sentence is input into the generative AI model.

[0949] Example prompt sentence:

[0950] Please summarize the following:

[0951] I haven't received my item. My order number is 12345. Please deal with this as soon as possible.

[0952] When this prompt is input into the generative AI model, the summary generated is "Inquiry regarding non-delivery of product. Order number 12345." Sentiment analysis also detects emotions such as "anger" and "dissatisfaction." This allows customer support representatives to respond quickly and appropriately.

[0953] This system allows for quick summarization of ticket contents and responses that take into account the user's feelings, which is expected to improve customer satisfaction.

[0954] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0955] Step 1:

[0956] The server obtains a new ticket from the ticket management system.

[0957] Input: A new ticket entered into the ticket management system.

[0958] Output: New ticket content.

[0959] Specific behavior: Calls the ticket management system API to retrieve new ticket data, including information such as ticket ID, customer inquiry, and order number.

[0960] Step 2:

[0961] The server sends the contents of the acquired ticket to the generative AI and generates a summary.

[0962] Input: New ticket content.

[0963] Output: The generated summary.

[0964] Specific operation: The ticket contents are sent as a prompt to the generative AI (e.g., OpenAI API). The generative AI uses natural language processing technology to generate a summary and return it to the server.

[0965] Step 3:

[0966] The server sends the generated summary to a sentiment analysis engine to analyze the user's sentiment.

[0967] Input: The generated summary.

[0968] Output: Emotion analysis results.

[0969] Specific operation: Send the summary to a sentiment analysis engine (e.g., SentimentAnalyzer) to analyze the user's sentiment from the text data. The type of sentiment (e.g., anger, frustration, joy, etc.) is obtained as the analysis result.

[0970] Step 4:

[0971] The server provides the emotion analysis results to the generative AI, which then regenerates a summary that takes the user's emotions into account.

[0972] Input: Sentiment analysis results, original summary.

[0973] Output: A new sentiment-aware summary.

[0974] Specific operation: A prompt containing the results of emotion analysis is sent to the generative AI, which then generates a summary that reflects the user's emotions. For example, it generates a summary that includes information such as "The customer is angry, so a quick response is required."

[0975] Step 5:

[0976] The server stores the generated summary and sentiment analysis results in the ticket management system.

[0977] Input: New emotion-aware summarization, sentiment analysis results.

[0978] Output: Summary and sentiment analysis results stored in the ticket management system.

[0979] What it does: It calls the API of the ticket management system and saves the generated summary and sentiment analysis results to the corresponding ticket, so that support agents can view the summary and sentiment information when viewing the ticket.

[0980] Example 2

[0981] Next, a description will be given of Example 2 of Form 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."

[0982] Conventional ticket management systems were unable to take the user's emotions into account when summarizing the contents of a ticket, making it difficult to respond appropriately based on the user's emotions. Furthermore, when a new ticket was created, there was a lack of a way to quickly and accurately summarize its contents and update the existing summary information. This could lead to a decrease in ticket management efficiency and user satisfaction.

[0983] 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.

[0984] In this invention, the server includes means for linking the ticket management system with the generative artificial intelligence, means for creating a summary of each ticket, means for creating a summary of the contents of a new ticket when it is opened and updating the original summary information, means for analyzing user emotions from the contents of the ticket using an emotion analysis engine and providing the results to the generative artificial intelligence, means for the generative artificial intelligence to generate a summary that takes user emotions into consideration based on the emotion analysis results, and means for saving the generated summary in the ticket management system. This enables the generation of summaries that take user emotions into consideration and the rapid and accurate updating of summary information.

[0985] "Ticket Management System" means software or a platform for managing the opening, updating, and viewing of tickets.

[0986] "Generative AI" refers to an AI system that uses natural language processing techniques to analyze text data and generate summaries and other information.

[0987] An "emotion analysis engine" is software or a system that analyzes user emotions from text data and provides the results.

[0988] A "summary" is a text that concisely summarizes the contents of a ticket, extracting and shortening important information.

[0989] "New tickets" are tickets that have been newly added to the system and have not yet been processed.

[0990] "Natural language processing technology" is a technology that allows computers to understand, interpret, and generate human language.

[0991] "User emotion" refers to the user's psychological state or emotion analyzed from text data, and examples include dissatisfaction, joy, anger, etc.

[0992] "API" stands for Application Programming Interface, an interface that allows different software systems to communicate with each other.

[0993] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for structuring and representing data.

[0994] This invention is a system that summarizes the contents of tickets and generates summaries that take into account the emotions of users by linking a ticket management system with generative artificial intelligence and an emotion analysis engine. Specific embodiments of this system are described below.

[0995] Hardware and software used

[0996] Ticket Management System: Software or platform for managing ticket creation, updates, and viewing.

[0997] Generative AI: An AI system that uses natural language processing techniques to analyze text data and generate summaries and other information.

[0998] Sentiment analysis engine: Software or system for analyzing user sentiment from text data and providing the results.

[0999] System Operation Overview

[1000] The server obtains the ticket contents from the ticket management system and sends them to the generative artificial intelligence and sentiment analysis engine. The generative artificial intelligence uses natural language processing technology to analyze the ticket contents and generate a summary. Meanwhile, the sentiment analysis engine analyzes the user's sentiment from the ticket contents and provides the results to the generative artificial intelligence. The generative artificial intelligence generates a summary that takes the user's sentiment into consideration based on the sentiment analysis results. The generated summary is saved in the ticket management system via the server.

[1001] Specific examples

[1002] Ticket contents

[1003] Ticket ID: 12345

[1004] What it says: "We're having trouble with users logging in. The error message is 'Invalid credentials'."

[1005] Prompt Sentence Examples

[1006] "Please summarize the ticket below: 'We are experiencing issues with users being unable to log in. The error message is 'Invalid credentials'.'"

[1007] Example

[1008] 1. Obtaining a ticket

[1009] The server retrieves the details of ticket ID 12345 from the ticket management system.

[1010] Specifically, the server utilizes the API of the ticket management system to request ticket information corresponding to a specific ticket ID.

[1011] 2. Submitting ticket details

[1012] The server sends the contents of the acquired ticket to the generative artificial intelligence and sentiment analysis engine.

[1013] Specifically, the server sends the ticket contents in JSON format as a POST request to the API of the generative artificial intelligence and sentiment analysis engine.

[1014] 3. Summary Generation

[1015] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1016] Example: Produces the summary "A problem occurred where the user was unable to log in. The error message was 'Invalid credentials'."

[1017] 4. Emotion analysis

[1018] The emotion analysis engine analyzes user emotions from the contents of the ticket and provides the results to generative artificial intelligence.

[1019] Example: Detect the emotion of "dissatisfaction" from the ticket content and send the results to generative artificial intelligence.

[1020] 5. Emotion-Aware Summary Generation

[1021] Generative AI generates summaries that take into account the user's emotions based on the results of emotion analysis.

[1022] Example: Produces the summary "Users are having trouble logging in. The error message is 'Invalid credentials'. Users are frustrated."

[1023] 6. Save the summary

[1024] The server sends the generated summary back to the ticket management system and stores it as summary information for ticket ID 12345.

[1025] In this way, it is possible to generate summaries that take into account the user's feelings and to update the summary information quickly and accurately.

[1026] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1027] Step 1:

[1028] Obtaining a ticket

[1029] The server retrieves the ticket contents from the ticket management system.

[1030] Input: Ticket ID (e.g. 12345)

[1031] Specific operation: The server uses the ticket management system's API to send the request "GET / k / v1 / record.json?app=APP_ID&id=12345".

[1032] Output: Ticket details (e.g. "We're having trouble with users logging in. The error message is 'Invalid credentials'.")

[1033] Step 2:

[1034] Submitting ticket details

[1035] The server sends the contents of the acquired ticket to the generative artificial intelligence and sentiment analysis engine.

[1036] Input: Ticket details (e.g. "We are experiencing issues with users being unable to log in. The error message is 'Invalid credentials'.")

[1037] Specific operation: The server sends the ticket contents in JSON format as a POST request to the generative AI and sentiment analysis engine APIs.

[1038] Output: Ticket content is sent to the generative artificial intelligence and sentiment analysis engine.

[1039] Step 3:

[1040] Generate a summary

[1041] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1042] Input: Ticket details (e.g. "We are experiencing issues with users being unable to log in. The error message is 'Invalid credentials'.")

[1043] Specific operation: The generative AI analyzes the content of the received ticket, extracts important information, and generates a short summary.

[1044] Output: Summary (e.g. "There was a problem with the user being unable to log in. The error message was 'Invalid credentials'")

[1045] Step 4:

[1046] Emotion analysis

[1047] The emotion analysis engine analyzes user emotions from the contents of the ticket and provides the results to generative artificial intelligence.

[1048] Input: Ticket details (e.g. "We are experiencing issues with users being unable to log in. The error message is 'Invalid credentials'.")

[1049] What it does: The sentiment analysis engine analyzes ticket content and detects emotions (e.g., dissatisfaction).

[1050] Output: Sentiment analysis result (e.g., "dissatisfied")

[1051] Step 5:

[1052] Emotion-aware summary generation

[1053] Generative AI generates summaries that take into account the user's emotions based on the results of emotion analysis.

[1054] Input: Summary (e.g., "A user is having trouble logging in. The error message is 'Invalid credentials'"), Sentiment analysis results (e.g., "Dissatisfied")

[1055] Specific operation: The generative AI takes into account the results of sentiment analysis and adds emotional information to the summary text.

[1056] Output: A summary that takes sentiment into account (e.g., "The user is having trouble logging in. The error message is 'Invalid credentials'. The user is frustrated.")

[1057] Step 6:

[1058] Save Summary

[1059] The server sends the generated summary back to the ticket management system and stores it as the ticket summary information.

[1060] Input: A sentiment-based summary (e.g., "Users are having trouble logging in. The error message is 'Invalid credentials'. Users are frustrated.")

[1061] Specific operation: The server sends the generated summary in JSON format to the ticket management system API as a POST request to update the ticket information.

[1062] Output: Summary information is saved in the ticket management system.

[1063] (Application example 2)

[1064] Next, a description will be given of Application Example 2 of Form 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."

[1065] With conventional ticket management systems, it was difficult to efficiently analyze and summarize the content of user inquiries and complaints. Furthermore, they were unable to respond in a way that took user feelings into consideration, which led to a decline in the quality of customer support. This resulted in issues such as a decline in user satisfaction and delayed responses.

[1066] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1067] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating a summary of each ticket, a means for creating a summary of the contents of a new ticket when it is opened and updating the original summary information, a means for analyzing user emotions, a means for generating a summary based on the results of the emotion analysis, and a means for saving the generated summary in the ticket management system. This makes it possible to generate summaries that take user emotions into consideration, improving the quality of customer support and enabling quick and appropriate responses.

[1068] A "ticket management system" is a system that manages tickets such as user inquiries and complaints, and creates, updates, and views tickets.

[1069] "Generative AI" is artificial intelligence that uses natural language processing techniques to analyze text data and perform summarization and other generative tasks.

[1070] A "means for creating a summary" is a means that has the function of analyzing the contents of a ticket, extracting important information, and summarizing it concisely.

[1071] "A means for creating a summary of the contents of a new ticket when it is created and updating the original summary information" is a means that has the function of analyzing the contents of a newly created ticket, generating a summary of it, and keeping the existing summary information up to date.

[1072] The "means for analyzing user emotions" refers to a means having the function of identifying a user's emotions from text data and analyzing that emotional state.

[1073] The "means for generating a summary based on the result of sentiment analysis" refers to a means that has the function of generating a more appropriate summary by taking into account the result of sentiment analysis of the user.

[1074] The "means for saving the generated summary in the ticket management system" is a means having a function for saving the generated summary in the ticket management system and making it available for later reference.

[1075] The system for implementing this invention includes a ticket management system, a generative AI, an emotion analysis engine, and a server for linking these. Specifically, it has the following configuration and performs the following processes.

[1076] System Configuration

[1077] 1. Ticket Management System:

[1078] This is a system for managing inquiries and complaints from users, and has the ability to create, update, and view tickets.

[1079] As a concrete example, we will use a common ticket management system.

[1080] 2. Generative AI:

[1081] It is an artificial intelligence that uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1082] As a concrete example, we will use a generative AI model.

[1083] 3. Sentiment Analysis Engine:

[1084] It is an engine that analyzes user emotions from text data and identifies their emotional state.

[1085] As a concrete example, we use a sentiment analysis engine.

[1086] 4. Server:

[1087] It is a central processing unit that links the ticket management system, generative AI, and sentiment analysis engine.

[1088] The server acquires data from each system, performs the necessary processing, and stores the results.

[1089] Processing flow

[1090] 1. Getting a ticket:

[1091] A user submits an inquiry or complaint to the ticket management system.

[1092] The server obtains the new ticket contents from the ticket management system.

[1093] 2. Emotion analysis:

[1094] The server sends the acquired ticket contents to a sentiment analysis engine to analyze the user's sentiment.

[1095] The emotion analysis engine identifies emotions from the text data and returns the results to the server.

[1096] 3. Summary generation:

[1097] The server sends the ticket content and the results of emotion analysis to the generative AI, which then generates a summary.

[1098] Generative AI uses natural language processing technology to analyze ticket content and generate summaries that take emotions into account.

[1099] 4. Save the summary:

[1100] The server stores the generated summary in the ticket management system.

[1101] The ticket management system stores the summary information along with the original ticket information for future reference.

[1102] Specific examples

[1103] For example, if a user sends an inquiry saying "the product has not arrived," the following processing will be performed.

[1104] 1. An inquiry is posted in the ticket management system saying, "My product hasn't arrived yet. What's going on? I'm very unhappy."

[1105] 2. The server receives the query and sends it to the emotion analysis engine.

[1106] 3. The emotion analysis engine analyzes the emotion of "dissatisfaction" and returns the results to the server.

[1107] 4. The server sends the inquiry content and the results of the sentiment analysis to the generative AI, which generates a summary such as, "There was an inquiry about the product not arriving, and the user is dissatisfied."

[1108] 5. The generated summary is stored in the ticket management system for quick response by customer support representatives.

[1109] Prompt Sentence Examples

[1110] Summarize the user's query and generate a sentiment-based summary. Analyze the following:

[1111] Inquiry: "I haven't received my item yet. What's going on? I'm very unhappy."

[1112] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1113] Step 1:

[1114] A user submits an inquiry or complaint to the ticket management system.

[1115] Input: The query entered by the user.

[1116] Output: A new ticket is created in the ticket management system.

[1117] Specific operation: The user uses a smartphone or PC to enter the inquiry details into the ticket management system interface and presses the send button.

[1118] Step 2:

[1119] The server retrieves the new ticket contents from the ticket management system.

[1120] Input: A new ticket saved in the ticket management system.

[1121] Output: The contents of the new ticket are forwarded to the server.

[1122] Specific operation: The server periodically polls the ticket management system to check whether a new ticket exists. If a new ticket exists, it retrieves its contents.

[1123] Step 3:

[1124] The server sends the acquired ticket contents to the emotion analysis engine to analyze the user's emotions.

[1125] Input: New ticket content.

[1126] Output: Sentiment analysis results from the sentiment analysis engine.

[1127] Specific operation: The server sends the ticket contents in text format to the sentiment analysis engine, which analyzes the text data to identify the emotional state, and returns the analysis result to the server.

[1128] Step 4:

[1129] The server sends the ticket content and sentiment analysis results to the generative AI, which then generates a summary.

[1130] Input: Ticket content and sentiment analysis results.

[1131] Output: Generative AI summary.

[1132] Specific operation: The server sends the ticket content and the results of sentiment analysis as a prompt to the generative AI, which then uses natural language processing technology to generate a summary, which is then returned to the server.

[1133] Step 5:

[1134] The server stores the generated summary in the ticket management system.

[1135] Input: Generative AI summary.

[1136] Output: A summary stored in the ticket management system.

[1137] What happens: The server adds the generated summary to the corresponding ticket in the ticket management system and saves it, making the summary information available to customer support agents.

[1138] 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.

[1139] 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> ) 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.

[1140] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1141] 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.

[1142] [Third embodiment]

[1143] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1144] 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.

[1145] 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).

[1146] 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.

[1147] 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.

[1148] 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).

[1149] 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.

[1150] 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.

[1151] 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.

[1152] 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.

[1153] 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.

[1154] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1155] "Example 1"

[1156] One embodiment of the present invention is a system that links a ticket management system with a generative AI. This system sends the contents of tickets acquired from the ticket management system to the generative AI, which then uses natural language processing technology to analyze the ticket contents and generate a summary. The generated summary is sent back to the ticket management system and saved as the ticket's summary information. When a new ticket is created, the contents of that ticket are also sent to the generative AI, which then generates a summary. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[1157] "Example 2"

[1158] As a specific example, GPT-3 can be used as a generative AI. The contents of tickets obtained from a ticket management system are sent to GPT-3, which then uses natural language processing technology to analyze the contents of the ticket and generate a summary. The generated summary is sent back to the ticket management system and saved as the ticket's summary information. When a new ticket is created, the contents of that ticket are also sent to GPT-3, and a summary is generated. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[1159] The processing flow of each embodiment will be described below.

[1160] "Example 1"

[1161] Step 1: Get the ticket contents from the ticket management system.

[1162] Step 2: Send the acquired ticket contents to the generation AI.

[1163] Step 3: The generative AI uses natural language processing technology to analyze the ticket content and generate a summary.

[1164] Step 4: The generated summary is sent back to the ticket management system and stored as the ticket summary information.

[1165] Step 5: When a new ticket is created, its contents are also sent to the generative AI, which generates a summary.

[1166] Step 6: The generated new ticket summary is saved in the ticket management system along with the original summary information.

[1167] "Example 2"

[1168] Step 1: Obtain the ticket details from the ticket management system. Step 2: Send the obtained ticket details to the generative AI "GPT-3."

[1169] Step 3: GPT-3 uses natural language processing techniques to analyze the ticket content and generate a summary.

[1170] Step 4: The generated summary is sent back to the ticket management system and stored as the ticket summary information.

[1171] Step 5: When a new ticket is created, its contents are also sent to GPT-3, and a summary is generated.

[1172] Step 6: The generated new ticket summary is saved in the ticket management system along with the original summary information.

[1173] Example 1

[1174] Next, a description will be given of Example 1 of Form 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."

[1175] In traditional ticket management systems, ticket content had to be summarized manually, which was time-consuming and labor-intensive. Furthermore, when a new ticket was created, it was difficult to quickly summarize its contents and update the original summary information. This reduced the efficiency of ticket management and could lead to important information being overlooked.

[1176] 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.

[1177] In this invention, the server includes means for linking the ticket management system with the generative artificial intelligence, means for creating a summary of each ticket, means for creating a summary of the contents of a new ticket when it is created and updating the original summary information, means for acquiring the contents of the newly created ticket from the ticket management system, means for sending the acquired ticket contents to the generative artificial intelligence, means for receiving the summary generated from the generative artificial intelligence, and means for saving the received summary in the ticket management system. This makes it possible to automatically summarize the contents of tickets and manage them quickly.

[1178] A "ticket management system" is a software or hardware system for managing the creation, updating, and viewing of tickets.

[1179] "Generative AI" refers to an AI system that uses natural language processing techniques to analyze input text data and perform summarization and other generative tasks.

[1180] The "summary" is a concise summary of the ticket's contents, briefly expressing the main points and issues of the ticket.

[1181] A "new ticket" is a ticket that has been newly created by a user and for which no summary has yet been created.

[1182] A "server" is a computer system that connects the ticket management system with generative artificial intelligence and acquires, sends, receives, and stores data.

[1183] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language, and is used to analyze text data and generate summaries.

[1184] "API" stands for Application Programming Interface, an interface for exchanging data and functions between different software systems.

[1185] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for structuring and representing data.

[1186] This invention is a system that links a ticket management system with generative artificial intelligence, automatically creating summaries for each ticket, generating summaries of the contents of new tickets when they are created, and updating the original summary information.

[1187] The server first retrieves the details of newly created tickets from the ticket management system. A ticket management system is a software or hardware system that manages the creation, updating, and viewing of tickets. The server periodically calls the ticket management system's API to check whether new tickets exist.

[1188] The server then sends the retrieved ticket contents to a generative AI. The generative AI uses a system that uses natural language processing technology to analyze text data and generate a summary. Specifically, a generative AI model such as "OpenAI GPT-4" is used. The server converts the ticket contents into JSON format and sends a POST request to the generative AI's API endpoint.

[1189] The generative AI analyzes the content of the ticket received and generates a summary. For example, in response to a ticket that states "High server memory usage," it generates a summary such as "A problem has occurred with high server memory usage."

[1190] The generated summary is sent back to the server, which analyzes the response from the generative AI and extracts the summary text. The server then saves the received summary in the ticket management system. Specifically, it calls the ticket management system's API and sends a request to update the summary information.

[1191] When a new ticket is created, the server sends the ticket contents to the generative AI in a similar manner to generate a summary. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[1192] As a concrete example, if a user submits a ticket stating that "server memory usage is high," the server retrieves the contents of this ticket and sends it to the generative AI. The generative AI analyzes the content of "server memory usage is high" and generates a summary such as "a problem has occurred with high server memory usage." The generated summary is sent back to the server and stored in the ticket management system.

[1193] An example of a prompt sentence might be:

[1194] "Please summarize the ticket below:

[1195] Ticket details: The server's memory usage is high. It often peaks especially at night. We are considering increasing the memory as a solution.

[1196] By sending this prompt to the generative AI, the AI ​​will analyze the ticket contents and generate a summary.

[1197] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1198] Step 1:

[1199] The server obtains the contents of the newly created ticket from the ticket management system.

[1200] Input: Ticket management system API endpoint

[1201] Output: New ticket content (text data)

[1202] Specific operation: The server periodically calls the API of the ticket management system to check whether a new ticket exists. If a new ticket exists, it retrieves its contents.

[1203] Step 2:

[1204] The server sends the contents of the acquired ticket to the generative artificial intelligence.

[1205] Input: New ticket content (text data)

[1206] Output: Request to the generative AI (JSON format)

[1207] Specific operation: The server converts the acquired ticket contents into JSON format and sends a POST request to the API endpoint of the generative artificial intelligence.

[1208] Step 3:

[1209] Generative AI analyzes the ticket contents and generates a summary.

[1210] Input: Ticket content (JSON format)

[1211] Output: Summary (text data)

[1212] How it works: The generative AI uses natural language processing technology to analyze the content of the ticket it receives and generate a summary. For example, in response to a ticket that says "Server memory usage is high," it generates a summary such as "A problem with high server memory usage has occurred."

[1213] Step 4:

[1214] The server receives the summary generated from the generative artificial intelligence.

[1215] Input: Response from generative AI (JSON format)

[1216] Output: Summary (text data)

[1217] Specific operation: The server analyzes the response from the generative artificial intelligence API and extracts summary text.

[1218] Step 5:

[1219] The server stores the received summary in the ticket management system.

[1220] Input: Summary (text data)

[1221] Output: Update request to ticket management system (API call)

[1222] Specific operation: The server calls the API of the ticket management system and sends a request to update the summary information.

[1223] Step 6:

[1224] When a user submits a new ticket, the server again obtains the contents of the new ticket from the ticket management system and sends them to the generative artificial intelligence in the same manner, causing a summary to be generated.

[1225] Input: New ticket content (text data)

[1226] Output: Summary (text data)

[1227] Specific operation: When a user submits a new ticket, the server retrieves the content from the ticket management system and sends it to the generative AI. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[1228] (Application example 1)

[1229] Next, a description will be given of Application Example 1 of Form 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."

[1230] In factory maintenance work, the detailed content of tickets makes it difficult for workers to quickly understand and respond to them. Furthermore, because the content of tickets is so diverse, they need to be summarized, but manual summarization takes time and effort. This reduces the efficiency of maintenance work and reduces productivity.

[1231] 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.

[1232] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating summaries of each ticket, a means for creating a summary of the contents of a new ticket when it is created and updating the original summary information, and a means for acquiring the contents of maintenance tickets within the factory, generating summaries using the generative AI, and saving them. This allows the contents of maintenance tickets to be summarized quickly and accurately, enabling workers to perform maintenance work efficiently.

[1233] A "ticket management system" is a system that manages the creation, updating, and viewing of tickets.

[1234] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze text data and perform summarization and generation.

[1235] The "summary" is a concise summary of the ticket contents.

[1236] A "maintenance ticket" is a ticket that contains information about maintenance work that occurs within a factory.

[1237] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[1238] "In-plant" means the interior of a facility where manufacturing operations take place.

[1239] A "server" is a computer system that processes and stores data on a network.

[1240] As an embodiment of the present invention, a system is constructed that automatically generates ticket summaries using generative AI in a maintenance ticket management system within a factory. A specific embodiment of this system is shown below.

[1241] System configuration

[1242] 1. Hardware

[1243] Server: A computer system that processes and stores data. It houses the ticket management system and generative AI.

[1244] Factory Robot: A robot that performs maintenance work in a factory. It communicates with the server to obtain maintenance ticket information and receive a summary.

[1245] 2. Software

[1246] Ticket management system: A system that manages ticket creation, updates, and viewing. Ticket content is provided to the generative AI via API.

[1247] Generative AI: This is an AI that uses natural language processing technology to analyze text data and generate summaries. It receives ticket content via API and generates summaries.

[1248] Data processing and calculation

[1249] 1. Obtaining ticket details

[1250] The server obtains the contents of maintenance tickets generated within the factory through the ticket management system's API.

[1251] 2. Summary Generation Using Generative AI

[1252] The server sends the acquired ticket contents to the generative AI API, which uses natural language processing technology to analyze the ticket contents and generate a summary.

[1253] 3. Save the summary

[1254] The server saves the generated summary through the API of the ticket management system, so that the summary information of the maintenance ticket is saved in the ticket management system.

[1255] Specific examples

[1256] Ticket details: "The belt on machine A is loose and needs to be replaced."

[1257] Produced summary: "The belt on machine A needs to be replaced."

[1258] Prompt Sentence Examples

[1259] Summarize the following text:

[1260] "The belt on machine A is loose and needs to be replaced."

[1261] In this way, a system can be realized that supports factory robots in efficiently performing maintenance work.

[1262] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1263] Step 1:

[1264] The server obtains the contents of maintenance tickets generated within the factory through the ticket management system's API.

[1265] Input: Maintenance ticket information from your ticket management system

[1266] Output: The contents of the retrieved maintenance ticket

[1267] What happens: The server sends an HTTP request to retrieve ticket data from the ticket management system's API. The retrieved data is received in JSON format.

[1268] Step 2:

[1269] The server sends the acquired ticket contents to the API of the generation AI.

[1270] Input: The content of the acquired maintenance ticket

[1271] Output: Ticket content sent to the generative AI

[1272] Specific operation: The server sends an HTTP POST request to the generative AI API and sends the ticket contents in JSON format.

[1273] Step 3:

[1274] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1275] Input: Ticket content sent to the generative AI

[1276] Output: A summary of the generated tickets

[1277] How it works: The generative AI analyzes the content of the received ticket, extracts important information, and generates a summary, which is then returned to the server in JSON format.

[1278] Step 4:

[1279] The server stores the generated summary through the API of the ticket management system.

[1280] Input: Summary of generated ticket

[1281] Output: Summary information stored in the ticket management system

[1282] What happens: The server sends an HTTP POST request to the ticket management system's API and saves the generated summary in JSON format.

[1283] Step 5:

[1284] A user views summarized maintenance ticket information through a ticket management system.

[1285] Input: Summary information stored in the ticket management system

[1286] Output: Summarized maintenance ticket information for user viewing

[1287] Specific operation: A user views summarized maintenance ticket information through the ticket management system interface. The system displays the saved summary information.

[1288] Example 2

[1289] Next, a description will be given of Example 2 of Form 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."

[1290] Conventional information management systems have the problem that information summaries must be created manually, which takes time and effort. In addition, when new information is registered, it is difficult to quickly and accurately summarize the content and update existing summary information. This makes information management cumbersome and hinders efficient operation.

[1291] 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.

[1292] In this invention, the server includes means for acquiring new information from the information management system, means for transmitting the acquired information to the generative AI, means for receiving summaries generated by the generative AI, and means for storing the generated summaries in the information management system, thereby enabling automatic generation of summaries of new information and rapid and accurate updating of existing summary information.

[1293] An "information management system" is a software or hardware system for managing the registration, updating, and viewing of information.

[1294] "Generative AI" refers to an AI system that uses natural language processing techniques to analyze input information and perform summarization and other generative tasks.

[1295] A "summary" is a short summary of the main points of information, intended to convey the content of the original information concisely.

[1296] "New information" refers to information that has been newly registered in the information management system and is to be distinguished from existing information.

[1297] A "server" is a computer system that sends and receives data between an information management system and generative artificial intelligence.

[1298] "Means of acquisition" refers to the methods and processes for acquiring new information from the information management system.

[1299] "Means for transmitting" refers to the method or process for transmitting acquired information to generative artificial intelligence.

[1300] "Means for receiving" refers to a method or process for receiving a summary generated by a generative artificial intelligence.

[1301] "Storage means" refers to the method or process for storing the generated summary in an information management system.

[1302] This invention is a system that automatically generates summaries of new information and updates existing summaries quickly and accurately by linking an information management system with generative artificial intelligence. Specific embodiments of this system are described below.

[1303] Hardware and software used

[1304] Information management systems: These are software or hardware systems used to manage the registration, updating, and viewing of information. Examples include database management systems and cloud-based information management platforms.

[1305] Generative AI: This is an AI system that uses natural language processing techniques to analyze input information and perform summarization and other generative tasks. A specific example is an AI service equipped with a natural language processing model.

[1306] System Operation

[1307] 1. A user registers new information in the information management system.

[1308] A user uses the interface of the information management system to register new information, including a title, a detailed description, and a priority.

[1309] Example: User enters the subject "System bug fix request" with the details "Error occurs on login screen."

[1310] 2. The server retrieves new information from the information management system.

[1311] The server periodically calls the information management system's API to obtain new information. The obtained data is received in JSON format.

[1312] Example: A server retrieves information from an information management system with the title "System bug fix request" and the details "Error occurs on login screen."

[1313] 3. The server sends the acquired information to the generative AI

[1314] The server generates an appropriate prompt to send the acquired information to the generative AI, which then sends the prompt as an API request to the generative AI.

[1315] Example: The server sends a request to the generative artificial intelligence to summarize the information content "Request to fix a system bug."

[1316] 4. Generative AI analyzes the content of information and generates summaries

[1317] The generative AI analyzes the prompt and uses natural language processing techniques to summarize the information, which is short and to the point.

[1318] Example: A generative AI summarizes the details "An error occurs on the login screen" as "Request to fix the error on the login screen."

[1319] 5. The generative AI sends the generated summary back to the server

[1320] The generative AI sends the generated summary back to the server as an API response, which the server receives and proceeds to the next step.

[1321] Example: The generative AI sends back to the server a summary titled "Request to fix login screen error."

[1322] 6. The server stores the generated summary in the information management system and displays it as a summary of the information.

[1323] The server adds the generated summary to the original information using the information management system's API. The user can view the generated summary on the information management system's interface.

[1324] Example: The server stores a summary "Request to fix login screen error" in the information management system and allows the user to view this summary on the information details screen.

[1325] Prompt Sentence Examples

[1326] An example of a prompt to be sent to the generative artificial intelligence is as follows:

[1327] Please summarize the information content of "Request for system bug fix."

[1328] By inputting this prompt into a generative artificial intelligence, the AI ​​analyzes the information content and generates an appropriate summary.

[1329] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1330] Step 1:

[1331] A user registers new information in the information management system.

[1332] The user registers new information using the information management system interface, entering information such as a title, detailed description, and priority, and the new information is then saved in the information management system.

[1333] Specifically, the user enters details such as "An error occurs on the login screen" under the title "Request to fix a system bug" and registers it in the information management system.

[1334] Step 2:

[1335] The server retrieves new information from the information management system.

[1336] The server periodically calls the information management system's API to retrieve new information data. As input, it receives the API response from the information management system. As output, the retrieved data is stored on the server in JSON format.

[1337] Specifically, the server retrieves information from the information management system, including the title "Request to fix a system bug" and the details "An error occurs on the login screen."

[1338] Step 3:

[1339] The server sends the acquired information to the generative artificial intelligence.

[1340] The server generates an appropriate prompt to send the acquired information to the generative AI. As input, it creates a prompt based on the acquired information. As output, the prompt is sent to the generative AI as an API request.

[1341] Specifically, the server sends a request to the generative artificial intelligence to summarize the information content, "a request to fix a system bug."

[1342] Step 4:

[1343] Generative AI analyzes the content of information and generates summaries

[1344] The generative AI analyzes the received prompt and summarizes the information content using natural language processing techniques. The prompt is received as input, and the generated summary is generated as output.

[1345] As a specific operation, the generative artificial intelligence summarizes the details of "an error occurs on the login screen" as "a request to fix the error on the login screen."

[1346] Step 5:

[1347] The generative AI sends the generated summary back to the server

[1348] The generative AI sends the generated summary back to the server as an API response. The generated summary is received as input. The summary is sent back to the server as output.

[1349] Specifically, the generative artificial intelligence sends a summary to the server titled "Request to fix an error on the login screen."

[1350] Step 6:

[1351] The server stores the generated summary in the information management system and displays it as summary information of the information.

[1352] The server adds the generated summary to the original information using the information management system's API. As input, it receives the generated summary. As output, it stores the summary in the information management system and makes it available for the user to review.

[1353] Specifically, the server stores a summary titled "Request to correct login screen error" in the information management system, and the user can check this summary on the information details screen.

[1354] (Application example 2)

[1355] Next, a description will be given of Application Example 2 of Form 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."

[1356] When managing tickets for maintenance and repairs that occur within a factory, it is necessary to efficiently understand the contents of the tickets and respond quickly. However, with conventional systems, ticket contents must be manually analyzed and summarized, which is time-consuming and labor-intensive. Furthermore, if the ticket contents are complex, the quality of the summary may decline. This can reduce work efficiency and disrupt factory operations.

[1357] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1358] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating summaries for each ticket, a means for creating a summary of the contents of a new ticket when it is created and updating the original summary information, a means for managing maintenance and repair tickets generated in the factory, a means for analyzing the contents of the ticket and generating summaries using the generative AI, a means for saving the generated summaries in the ticket management system, and a means for being installed in the factory robot. This makes it possible to efficiently analyze the contents of tickets and generate summaries.

[1359] A "ticket management system" is a system that manages maintenance and repair tickets that occur within a factory, and creates, updates, and views tickets.

[1360] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1361] A "means for creating a summary" is a means that has the function of analyzing the contents of a ticket, extracting important information, and summarizing it concisely.

[1362] "A means for creating a summary of the contents of a new ticket when it is created and updating the original summary information" is a means that has the function of analyzing the contents of a newly created ticket, generating a summary, and updating the existing summary information.

[1363] "A means for managing tickets for maintenance and repairs generated within a factory" refers to a means that has the function of centrally managing tickets related to maintenance and repairs generated within a factory.

[1364] "Means for saving the generated summary in the ticket management system" refers to means that has the function of saving the summary generated by the generative AI in the ticket management system.

[1365] "Means to be installed on factory robots" refers to a means to install an application on a robot used in a factory and have the function of linking the ticket management system with generative AI.

[1366] As an embodiment of the present invention, a system is constructed that efficiently manages maintenance and repair tickets generated in a factory and generates summaries. A specific embodiment of this system is shown below.

[1367] System configuration

[1368] The system consists of the following major components:

[1369] 1. Ticket Management System: A system for managing maintenance and repair tickets that occur within the factory. Tickets can be created, updated, and viewed.

[1370] 2. Generative AI: This is artificial intelligence that uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1371] 3. Factory robot: A robot used in a factory, on which an application that connects the ticket management system with generative AI is installed.

[1372] Program processing

[1373] The server first retrieves maintenance or repair tickets from the ticket management system. Next, it sends the contents of the retrieved tickets to a generative AI system, which generates summaries using natural language processing technology. The generated summaries are then saved back into the ticket management system.

[1374] Hardware and software used

[1375] Hardware: Factory robots

[1376] Software: Ticket management system, generative AI (e.g., GPT-3), Python

[1377] Data processing and calculation

[1378] 1. Data Acquisition: Acquire ticket contents from the ticket management system.

[1379] 2. Data analysis: The ticket contents are sent to a generative AI, which generates a summary using natural language processing technology.

[1380] 3. Data storage: The generated summary is stored in the ticket management system.

[1381] Specific examples

[1382] For example, when a machine malfunctions in a factory, a maintenance ticket is registered in the ticket management system. A factory robot retrieves the ticket and sends it to a generative AI to generate a summary. The summary is then stored in the ticket management system, allowing workers to efficiently understand the contents of the ticket.

[1383] Prompt Sentence Examples

[1384] "Please summarize the contents of the maintenance ticket obtained from the ticket management system. The contents are as follows: {Ticket content}"

[1385] In this way, a system can be realized that can significantly improve the efficiency of maintenance and repair within a factory.

[1386] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1387] Step 1:

[1388] The server retrieves maintenance and repair tickets from the ticket management system.

[1389] Input: Ticket information registered in the ticket management system

[1390] Data processing: Use the ticket management system's API to obtain the ticket contents.

[1391] Output: The retrieved ticket details

[1392] Step 2:

[1393] The server sends the contents of the acquired ticket to the generation AI.

[1394] Input: The content of the ticket obtained

[1395] Data processing: Send the ticket contents using a generative AI API.

[1396] Output: The ticket content sent to the generative AI

[1397] Step 3:

[1398] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1399] Input: Ticket content sent to the generative AI

[1400] Data Computing: Using natural language processing techniques, the content of tickets is analyzed, key information is extracted, and a summary is generated.

[1401] Output: The generated summary

[1402] Step 4:

[1403] The server stores the generated summary in the ticket management system.

[1404] Input: Generated summary

[1405] Data processing: Use the ticket management system's API to store the generated summary.

[1406] Output: Summary stored in ticket management system

[1407] Step 5:

[1408] The user views the generated summary through the ticket management system.

[1409] Input: Summary stored in ticket management system

[1410] Data processing: Display summaries through the ticket management system interface.

[1411] Output: A summary that the user sees

[1412] In this way, maintenance and repair tickets within the factory can be managed efficiently and workers can respond quickly.

[1413] 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.

[1414] "Example 1"

[1415] The present invention is a system that links a ticket management system with a generative AI to create summaries of each ticket, and when a new ticket is created, creates a summary of the ticket's contents and updates the original summary information. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, the generative AI uses natural language processing technology to analyze the ticket contents and generate a summary. The emotion engine also analyzes the user's emotions from the ticket contents and provides the results to the generative AI. The generative AI generates a summary that takes the user's emotions into account based on the emotion analysis results provided by the emotion engine.

[1416] "Example 2"

[1417] As a concrete example, consider a system that uses a ticket management system, the generative AI "GPT-3," and the emotion engine "IBM Watson Tone Analyzer." The ticket contents are obtained from the ticket management system and sent to GPT-3 and IBM Watson Tone Analyzer. GPT-3 uses natural language processing technology to analyze the ticket contents and generate a summary. Meanwhile, IBM Watson Tone Analyzer analyzes the user's emotions from the ticket contents and provides the results to GPT-3. GPT-3 generates a summary that takes the user's emotions into account based on the emotion analysis results provided by IBM Watson Tone Analyzer. The generated summary is sent back to the ticket management system and saved as the ticket summary information. When a new ticket is created, the contents are also sent to GPT-3 and IBM Watson Tone Analyzer, and a summary is generated.

[1418] The processing flow of each embodiment will be described below.

[1419] "Example 1"

[1420] Step 1: Get the ticket contents from the ticket management system.

[1421] Step 2: The acquired ticket content is sent to the generative AI, which analyzes the content and generates a summary.

[1422] Step 3: At the same time, the content of the acquired ticket is sent to the emotion engine to analyze the user's emotions.

[1423] Step 4: Provide the emotion analysis results provided by the emotion engine to the generative AI.

[1424] Step 5: The generative AI generates a summary that takes the user's emotions into account based on the provided emotion analysis results.

[1425] "Example 2"

[1426] Step 1: Obtain the ticket contents from the ticket management system. Step 2: Send the obtained ticket contents to the generative AI "GPT-3", which analyzes the contents and generates a summary.

[1427] Step 3: At the same time, the contents of the acquired ticket are sent to the emotion engine "IBM Watson Tone Analyzer" to analyze the user's emotions.

[1428] Step 4: Provide the sentiment analysis results provided by IBM Watson Tone Analyzer to GPT-3.

[1429] Step 5: GPT-3 generates a summary that takes into account the user's emotions based on the sentiment analysis results provided by IBM Watson Tone Analyzer.

[1430] Step 6: The generated summary is sent back to the ticket management system and stored as the ticket summary information.

[1431] Step 7: When a new ticket is created, its contents are also sent to GPT-3 and IBM Watson Tone Analyzer to generate a summary.

[1432] Example 1

[1433] Next, a description will be given of Example 1 of Form 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."

[1434] Conventional ticket management systems have the problem that ticket contents must be summarized manually, which is time-consuming and labor-intensive. In addition, they are unable to generate summaries that take into account the user's feelings, making it difficult to properly reflect the user's complaints and requests. This can lead to reduced ticket management efficiency and lower user satisfaction.

[1435] 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.

[1436] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating a summary of each ticket, a means for creating a summary of the contents of a new ticket when it is opened and updating the original summary information, a means including an emotion engine for analyzing user emotions from the ticket contents, a means for generating a summary based on the emotion analysis results provided by the emotion engine, and a means for saving the generated summary in the ticket management system. This makes it possible to automatically summarize the contents of tickets and generate summaries that take user emotions into consideration.

[1437] A "ticket management system" is a system for managing problems and requests reported by users and recording and tracking them as tickets.

[1438] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze input data and generate summaries and other information.

[1439] The "means for creating a summary" is a function for automatically generating a summary that concisely summarizes the contents of the ticket.

[1440] "A means for creating a summary of the contents of a new ticket when it is created and updating the original summary information" is a function for summarizing the contents of a newly created ticket and adding or updating the summary to existing summary information.

[1441] The "emotion engine" is a technology that analyzes user emotions from the contents of tickets and provides the results.

[1442] The "means for generating a summary based on the emotion analysis result" is a function for generating a summary that reflects the user's emotions, taking into account the emotion analysis result provided by the emotion engine.

[1443] The "means for saving the generated summary in the ticket management system" is a function for sending the generated summary to the ticket management system and saving it therein.

[1444] This invention is a system that links a ticket management system with a generative AI, automatically generating summaries for each ticket and providing summaries that take user emotions into consideration. Specific embodiments of this system are described below.

[1445] System configuration

[1446] The system consists of the following main components:

[1447] 1. Ticket Management System: A system for managing problems and requests reported by users and recording and tracking them as tickets.

[1448] 2. Generative AI: This is artificial intelligence that uses natural language processing techniques to analyze input data and generate summaries and other information.

[1449] 3. Emotion Engine: A technology that analyzes user emotions from the contents of tickets and provides the results.

[1450] Data flow

[1451] 1. Get Ticket: The server retrieves the newly created ticket from the ticket management system, including the details entered by the user.

[1452] 2. Data transmission: The server sends the acquired ticket contents to the generative AI, which then analyzes the data using natural language processing technology.

[1453] 3. Ticket content analysis: The generative AI analyzes the ticket content and generates a summary. At the same time, the emotion engine works to analyze the user's emotions from the ticket content.

[1454] 4. Providing emotion analysis results: The emotion engine identifies the user's emotions and provides the results to the generative AI.

[1455] 5. Summary generation: The generative AI generates a summary that takes into account the user's emotions based on the emotion analysis results provided by the emotion engine.

[1456] 6. Save Abstract: The generated abstract is sent back to the ticket management system via the server, which saves it along with the original ticket information.

[1457] Specific examples

[1458] For example, if a user submits a ticket stating that "the system is slow," the server sends this information to the generative AI. The generative AI uses natural language processing technology to analyze the content, "The system is slow," and the emotion engine analyzes that the user is dissatisfied. The generative AI takes this emotion into account and generates a summary that reads, "The user is dissatisfied with the system's slowness." This summary is then saved in the ticket management system.

[1459] Prompt Sentence Examples

[1460] Below are some example prompts to input to a generative AI model:

[1461] Generate summaries for tickets where users report that the system is running slowly. Consider the user's feelings and create appropriate summaries.

[1462] By using this prompt, the generative AI can generate a summary that reflects the user's emotions.

[1463] In this way, by linking a ticket management system with generative AI, it becomes possible to automatically summarize the contents of tickets and generate summaries that take user emotions into account. This is expected to improve ticket management efficiency and user satisfaction.

[1464] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1465] Step 1:

[1466] The server retrieves the details of newly created tickets from the ticket management system. As input, it receives new ticket information from the ticket management system database. Specifically, when a new ticket is created in response to a user reporting that the system is running slowly, it retrieves the details. As output, it passes the retrieved ticket details to the next processing step.

[1467] Step 2:

[1468] The server sends the acquired ticket contents to the generative AI. As input, it receives the ticket contents acquired in step 1. Specifically, it sends the ticket contents to the generative AI in a standard data format such as JSON. For example, the following JSON data is sent to the generative AI:

[1469] json

[1470] {

[1471] "ticket_id": "12345",

[1472] "content": "System is running slow"

[1473] }

[1474] As an output, the data sent to the generative AI is passed on to the next processing step.

[1475] Step 3:

[1476] The generative AI analyzes the content of the received ticket. As input, it receives the content of the ticket sent in step 2. Specifically, it uses natural language processing technology to understand the content of the ticket and prepares to generate a summary. As output, the analysis results are passed to the next processing step.

[1477] Step 4:

[1478] The emotion engine analyzes the user's emotions from the ticket content. As input, it receives the ticket content analyzed by the generative AI in step 3. Specifically, it analyzes that the user is dissatisfied based on the content that "the system is running slowly." As output, the emotion analysis results are provided to the generative AI.

[1479] Step 5:

[1480] The generative AI generates a summary that takes into account the user's emotions based on the emotion analysis results provided by the emotion engine. As input, it receives the emotion analysis results provided in step 4. Specifically, it generates the summary "The user is frustrated by the system's slow performance." As output, the generated summary is passed to the next processing step.

[1481] Step 6:

[1482] The server sends the generated summary back to the ticket management system. As input, it receives the summary generated in step 5. Specifically, it sends the generated summary to the ticket management system and stores it. For example, the generated summary for ticket ID "12345" is stored. As output, the summary is stored in the ticket management system.

[1483] Step 7:

[1484] When a new ticket is created, the contents of the ticket are sent to the generative AI in a similar procedure, and a summary is generated. The contents of the newly created ticket are received as input. Specifically, steps 1 to 6 are repeated. As output, the generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[1485] (Application example 1)

[1486] Next, a description will be given of Application Example 1 of Form 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."

[1487] Conventional ticket management systems have problems in that it takes a lot of time and effort to summarize the contents of tickets, and it is difficult to respond in a way that takes the user's feelings into account. In particular, solving these problems is important for online shopping sites, where customers make a wide variety of inquiries and require quick and appropriate responses.

[1488] 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.

[1489] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating a summary of each ticket, a means for creating a summary of the contents of a new ticket when it is opened and updating the original summary information, a means for analyzing user emotions from the ticket contents using a sentiment analysis engine, a means for the generative AI to generate a summary that takes user emotions into consideration based on the sentiment analysis result, and a means for saving the generated summary and the sentiment analysis result in the ticket management system. This makes it possible to quickly summarize the contents of tickets and respond to them in a way that takes user emotions into consideration.

[1490] A "ticket management system" is a system for managing customer inquiries and problem reports, and recording and tracking them as tickets.

[1491] "Generative AI" is artificial intelligence that uses natural language processing techniques to analyze text data and perform summarization and other generative tasks.

[1492] A "summary" is information that concisely summarizes the contents of a ticket, extracting and expressing the important points in a short form.

[1493] An "emotion analysis engine" is software or algorithms that analyze a user's emotions from text data and identify their emotional state.

[1494] "User's emotions" refers to the user's psychological state and emotions that can be inferred from the text included in the ticket content.

[1495] "Storage" refers to recording the generated summary and sentiment analysis results in a storage device such as a database or file system.

[1496] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[1497] "Raising a ticket" means creating a new ticket and registering it in the system.

[1498] "Ticket update" means changing or adding content to an existing ticket.

[1499] "Viewing a ticket" means viewing and checking the contents of a ticket registered in the system.

[1500] A system for implementing the present invention includes a ticket management system, a generative AI, and an emotion analysis engine. Specific embodiments of the system will be described below.

[1501] System configuration

[1502] The server has a means to link the ticket management system with the generative AI. The ticket management system is a system for recording and tracking customer inquiries and problem reports as tickets. The generative AI is an artificial intelligence that uses natural language processing technology to analyze the contents of the ticket and generate a summary. The sentiment analysis engine is software or an algorithm that analyzes user emotions from text data and identifies their emotional state.

[1503] Program processing

[1504] The server first retrieves a new ticket from the ticket management system. The contents of the retrieved ticket are sent to a generative AI, which generates a summary using natural language processing technology. The generated summary is then sent to a sentiment analysis engine, which analyzes the user's emotions. The results of the sentiment analysis are provided to the generative AI, which then regenerates a summary that takes the user's emotions into account. This summary and the results of the sentiment analysis are stored in the ticket management system.

[1505] Hardware and software used

[1506] Hardware: Servers, smartphones, PCs

[1507] Software: ticket management systems, generative AI (e.g., OpenAI API), sentiment analysis engines (e.g., SentimentAnalyzer)

[1508] Specific examples

[1509] For example, if a customer inquires that "the product has not arrived," the following prompt sentence is input into the generative AI model.

[1510] Example prompt sentence:

[1511] Please summarize the following:

[1512] I haven't received my item. My order number is 12345. Please deal with this as soon as possible.

[1513] When this prompt is input into the generative AI model, the summary generated is "Inquiry regarding non-delivery of product. Order number 12345." Sentiment analysis also detects emotions such as "anger" and "dissatisfaction." This allows customer support representatives to respond quickly and appropriately.

[1514] This system allows for quick summarization of ticket contents and responses that take into account the user's feelings, which is expected to improve customer satisfaction.

[1515] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1516] Step 1:

[1517] The server obtains a new ticket from the ticket management system.

[1518] Input: A new ticket entered into the ticket management system.

[1519] Output: New ticket content.

[1520] Specific behavior: Calls the ticket management system API to retrieve new ticket data, including information such as ticket ID, customer inquiry, and order number.

[1521] Step 2:

[1522] The server sends the contents of the acquired ticket to the generative AI and generates a summary.

[1523] Input: New ticket content.

[1524] Output: The generated summary.

[1525] Specific operation: The ticket contents are sent as a prompt to the generative AI (e.g., OpenAI API). The generative AI uses natural language processing technology to generate a summary and return it to the server.

[1526] Step 3:

[1527] The server sends the generated summary to a sentiment analysis engine to analyze the user's sentiment.

[1528] Input: The generated summary.

[1529] Output: Emotion analysis results.

[1530] Specific operation: Send the summary to a sentiment analysis engine (e.g., SentimentAnalyzer) to analyze the user's sentiment from the text data. The type of sentiment (e.g., anger, frustration, joy, etc.) is obtained as the analysis result.

[1531] Step 4:

[1532] The server provides the emotion analysis results to the generative AI, which then regenerates a summary that takes the user's emotions into account.

[1533] Input: Sentiment analysis results, original summary.

[1534] Output: A new sentiment-aware summary.

[1535] Specific operation: A prompt containing the results of emotion analysis is sent to the generative AI, which then generates a summary that reflects the user's emotions. For example, it generates a summary that includes information such as "The customer is angry, so a quick response is required."

[1536] Step 5:

[1537] The server stores the generated summary and sentiment analysis results in the ticket management system.

[1538] Input: New emotion-aware summarization, sentiment analysis results.

[1539] Output: Summary and sentiment analysis results stored in the ticket management system.

[1540] What it does: It calls the API of the ticket management system and saves the generated summary and sentiment analysis results to the corresponding ticket, so that support agents can view the summary and sentiment information when viewing the ticket.

[1541] Example 2

[1542] Next, a description will be given of Example 2 of Form 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."

[1543] Conventional ticket management systems were unable to take the user's emotions into account when summarizing the contents of a ticket, making it difficult to respond appropriately based on the user's emotions. Furthermore, when a new ticket was created, there was a lack of a way to quickly and accurately summarize its contents and update the existing summary information. This could lead to a decrease in ticket management efficiency and user satisfaction.

[1544] 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.

[1545] In this invention, the server includes means for linking the ticket management system with the generative artificial intelligence, means for creating a summary of each ticket, means for creating a summary of the contents of a new ticket when it is opened and updating the original summary information, means for analyzing user emotions from the contents of the ticket using an emotion analysis engine and providing the results to the generative artificial intelligence, means for the generative artificial intelligence to generate a summary that takes user emotions into consideration based on the emotion analysis results, and means for saving the generated summary in the ticket management system. This enables the generation of summaries that take user emotions into consideration and the rapid and accurate updating of summary information.

[1546] "Ticket Management System" means software or a platform for managing the opening, updating, and viewing of tickets.

[1547] "Generative AI" refers to an AI system that uses natural language processing techniques to analyze text data and generate summaries and other information.

[1548] An "emotion analysis engine" is software or a system that analyzes user emotions from text data and provides the results.

[1549] A "summary" is a text that concisely summarizes the contents of a ticket, extracting and shortening important information.

[1550] "New tickets" are tickets that have been newly added to the system and have not yet been processed.

[1551] "Natural language processing technology" is a technology that allows computers to understand, interpret, and generate human language.

[1552] "User emotion" refers to the user's psychological state or emotion analyzed from text data, and examples include dissatisfaction, joy, anger, etc.

[1553] "API" stands for Application Programming Interface, an interface that allows different software systems to communicate with each other.

[1554] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for structuring and representing data.

[1555] This invention is a system that summarizes the contents of tickets and generates summaries that take into account the emotions of users by linking a ticket management system with generative artificial intelligence and an emotion analysis engine. Specific embodiments of this system are described below.

[1556] Hardware and software used

[1557] Ticket Management System: Software or platform for managing ticket creation, updates, and viewing.

[1558] Generative AI: An AI system that uses natural language processing techniques to analyze text data and generate summaries and other information.

[1559] Sentiment analysis engine: Software or system for analyzing user sentiment from text data and providing the results.

[1560] System Operation Overview

[1561] The server obtains the ticket contents from the ticket management system and sends them to the generative artificial intelligence and sentiment analysis engine. The generative artificial intelligence uses natural language processing technology to analyze the ticket contents and generate a summary. Meanwhile, the sentiment analysis engine analyzes the user's sentiment from the ticket contents and provides the results to the generative artificial intelligence. The generative artificial intelligence generates a summary that takes the user's sentiment into consideration based on the sentiment analysis results. The generated summary is saved in the ticket management system via the server.

[1562] Specific examples

[1563] Ticket contents

[1564] Ticket ID: 12345

[1565] What it says: "We're having trouble with users logging in. The error message is 'Invalid credentials'."

[1566] Prompt Sentence Examples

[1567] "Please summarize the ticket below: 'We are experiencing issues with users being unable to log in. The error message is 'Invalid credentials'.'"

[1568] Example

[1569] 1. Obtaining a ticket

[1570] The server retrieves the details of ticket ID 12345 from the ticket management system.

[1571] Specifically, the server utilizes the API of the ticket management system to request ticket information corresponding to a specific ticket ID.

[1572] 2. Submitting ticket details

[1573] The server sends the contents of the acquired ticket to the generative artificial intelligence and sentiment analysis engine.

[1574] Specifically, the server sends the ticket contents in JSON format as a POST request to the API of the generative artificial intelligence and sentiment analysis engine.

[1575] 3. Summary Generation

[1576] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1577] Example: Produces the summary "A problem occurred where the user was unable to log in. The error message was 'Invalid credentials'."

[1578] 4. Emotion analysis

[1579] The emotion analysis engine analyzes user emotions from the contents of the ticket and provides the results to generative artificial intelligence.

[1580] Example: Detect the emotion of "dissatisfaction" from the ticket content and send the results to generative artificial intelligence.

[1581] 5. Emotion-Aware Summary Generation

[1582] Generative AI generates summaries that take into account the user's emotions based on the results of emotion analysis.

[1583] Example: Produces the summary "Users are having trouble logging in. The error message is 'Invalid credentials'. Users are frustrated."

[1584] 6. Save the summary

[1585] The server sends the generated summary back to the ticket management system and stores it as summary information for ticket ID 12345.

[1586] In this way, it is possible to generate summaries that take into account the user's feelings and to update the summary information quickly and accurately.

[1587] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1588] Step 1:

[1589] Obtaining a ticket

[1590] The server retrieves the ticket contents from the ticket management system.

[1591] Input: Ticket ID (e.g. 12345)

[1592] Specific operation: The server uses the ticket management system's API to send the request "GET / k / v1 / record.json?app=APP_ID&id=12345".

[1593] Output: Ticket details (e.g. "We're having trouble with users logging in. The error message is 'Invalid credentials'.")

[1594] Step 2:

[1595] Submitting ticket details

[1596] The server sends the contents of the acquired ticket to the generative artificial intelligence and sentiment analysis engine.

[1597] Input: Ticket details (e.g. "We are experiencing issues with users being unable to log in. The error message is 'Invalid credentials'.")

[1598] Specific operation: The server sends the ticket contents in JSON format as a POST request to the generative AI and sentiment analysis engine APIs.

[1599] Output: Ticket content is sent to the generative artificial intelligence and sentiment analysis engine.

[1600] Step 3:

[1601] Generate a summary

[1602] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1603] Input: Ticket details (e.g. "We are experiencing issues with users being unable to log in. The error message is 'Invalid credentials'.")

[1604] Specific operation: The generative AI analyzes the content of the received ticket, extracts important information, and generates a short summary.

[1605] Output: Summary (e.g. "There was a problem with the user being unable to log in. The error message was 'Invalid credentials'")

[1606] Step 4:

[1607] Emotion analysis

[1608] The emotion analysis engine analyzes user emotions from the contents of the ticket and provides the results to generative artificial intelligence.

[1609] Input: Ticket details (e.g. "We are experiencing issues with users being unable to log in. The error message is 'Invalid credentials'.")

[1610] What it does: The sentiment analysis engine analyzes ticket content and detects emotions (e.g., dissatisfaction).

[1611] Output: Sentiment analysis result (e.g., "dissatisfied")

[1612] Step 5:

[1613] Emotion-aware summary generation

[1614] Generative AI generates summaries that take into account the user's emotions based on the results of emotion analysis.

[1615] Input: Summary (e.g., "A user is having trouble logging in. The error message is 'Invalid credentials'"), Sentiment analysis results (e.g., "Dissatisfied")

[1616] Specific operation: The generative AI takes into account the results of sentiment analysis and adds emotional information to the summary text.

[1617] Output: A summary that takes sentiment into account (e.g., "The user is having trouble logging in. The error message is 'Invalid credentials'. The user is frustrated.")

[1618] Step 6:

[1619] Save Summary

[1620] The server sends the generated summary back to the ticket management system and stores it as the ticket summary information.

[1621] Input: A sentiment-based summary (e.g., "Users are having trouble logging in. The error message is 'Invalid credentials'. Users are frustrated.")

[1622] Specific operation: The server sends the generated summary in JSON format to the ticket management system API as a POST request to update the ticket information.

[1623] Output: Summary information is saved in the ticket management system.

[1624] (Application example 2)

[1625] Next, a description will be given of Application Example 2 of Form 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."

[1626] With conventional ticket management systems, it was difficult to efficiently analyze and summarize the content of user inquiries and complaints. Furthermore, they were unable to respond in a way that took user feelings into consideration, which led to a decline in the quality of customer support. This resulted in issues such as a decline in user satisfaction and delayed responses.

[1627] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1628] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating a summary of each ticket, a means for creating a summary of the contents of a new ticket when it is opened and updating the original summary information, a means for analyzing user emotions, a means for generating a summary based on the results of the emotion analysis, and a means for saving the generated summary in the ticket management system. This makes it possible to generate summaries that take user emotions into consideration, improving the quality of customer support and enabling quick and appropriate responses.

[1629] A "ticket management system" is a system that manages tickets such as user inquiries and complaints, and creates, updates, and views tickets.

[1630] "Generative AI" is artificial intelligence that uses natural language processing techniques to analyze text data and perform summarization and other generative tasks.

[1631] A "means for creating a summary" is a means that has the function of analyzing the contents of a ticket, extracting important information, and summarizing it concisely.

[1632] "A means for creating a summary of the contents of a new ticket when it is created and updating the original summary information" is a means that has the function of analyzing the contents of a newly created ticket, generating a summary of it, and keeping the existing summary information up to date.

[1633] The "means for analyzing user emotions" refers to a means having the function of identifying a user's emotions from text data and analyzing that emotional state.

[1634] The "means for generating a summary based on the result of sentiment analysis" refers to a means that has the function of generating a more appropriate summary by taking into account the result of sentiment analysis of the user.

[1635] The "means for saving the generated summary in the ticket management system" is a means having a function for saving the generated summary in the ticket management system and making it available for later reference.

[1636] The system for implementing this invention includes a ticket management system, a generative AI, an emotion analysis engine, and a server for linking these. Specifically, it has the following configuration and performs the following processes.

[1637] System Configuration

[1638] 1. Ticket Management System:

[1639] This is a system for managing inquiries and complaints from users, and has the ability to create, update, and view tickets.

[1640] As a concrete example, we will use a common ticket management system.

[1641] 2. Generative AI:

[1642] It is an artificial intelligence that uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1643] As a concrete example, we will use a generative AI model.

[1644] 3. Sentiment Analysis Engine:

[1645] It is an engine that analyzes user emotions from text data and identifies their emotional state.

[1646] As a concrete example, we use a sentiment analysis engine.

[1647] 4. Server:

[1648] It is a central processing unit that links the ticket management system, generative AI, and sentiment analysis engine.

[1649] The server acquires data from each system, performs the necessary processing, and stores the results.

[1650] Processing flow

[1651] 1. Getting a ticket:

[1652] A user submits an inquiry or complaint to the ticket management system.

[1653] The server obtains the new ticket contents from the ticket management system.

[1654] 2. Emotion analysis:

[1655] The server sends the acquired ticket contents to a sentiment analysis engine to analyze the user's sentiment.

[1656] The emotion analysis engine identifies emotions from the text data and returns the results to the server.

[1657] 3. Summary generation:

[1658] The server sends the ticket content and the results of emotion analysis to the generative AI, which then generates a summary.

[1659] Generative AI uses natural language processing technology to analyze ticket content and generate summaries that take emotions into account.

[1660] 4. Save the summary:

[1661] The server stores the generated summary in the ticket management system.

[1662] The ticket management system stores the summary information along with the original ticket information for future reference.

[1663] Specific examples

[1664] For example, if a user sends an inquiry saying "the product has not arrived," the following processing will be performed.

[1665] 1. An inquiry is posted in the ticket management system saying, "My product hasn't arrived yet. What's going on? I'm very unhappy."

[1666] 2. The server receives the query and sends it to the emotion analysis engine.

[1667] 3. The emotion analysis engine analyzes the emotion of "dissatisfaction" and returns the results to the server.

[1668] 4. The server sends the inquiry content and the results of the sentiment analysis to the generative AI, which generates a summary such as, "There was an inquiry about the product not arriving, and the user is dissatisfied."

[1669] 5. The generated summary is stored in the ticket management system for quick response by customer support representatives.

[1670] Prompt Sentence Examples

[1671] Summarize the user's query and generate a sentiment-based summary. Analyze the following:

[1672] Inquiry: "I haven't received my item yet. What's going on? I'm very unhappy."

[1673] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1674] Step 1:

[1675] A user submits an inquiry or complaint to the ticket management system.

[1676] Input: The query entered by the user.

[1677] Output: A new ticket is created in the ticket management system.

[1678] Specific operation: The user uses a smartphone or PC to enter the inquiry details into the ticket management system interface and presses the send button.

[1679] Step 2:

[1680] The server retrieves the new ticket contents from the ticket management system.

[1681] Input: A new ticket saved in the ticket management system.

[1682] Output: The contents of the new ticket are forwarded to the server.

[1683] Specific operation: The server periodically polls the ticket management system to check whether a new ticket exists. If a new ticket exists, it retrieves its contents.

[1684] Step 3:

[1685] The server sends the acquired ticket contents to the emotion analysis engine to analyze the user's emotions.

[1686] Input: New ticket content.

[1687] Output: Sentiment analysis results from the sentiment analysis engine.

[1688] Specific operation: The server sends the ticket contents in text format to the sentiment analysis engine, which analyzes the text data to identify the emotional state, and returns the analysis result to the server.

[1689] Step 4:

[1690] The server sends the ticket content and sentiment analysis results to the generative AI, which then generates a summary.

[1691] Input: Ticket content and sentiment analysis results.

[1692] Output: Generative AI summary.

[1693] Specific operation: The server sends the ticket content and the results of sentiment analysis as a prompt to the generative AI, which then uses natural language processing technology to generate a summary, which is then returned to the server.

[1694] Step 5:

[1695] The server stores the generated summary in the ticket management system.

[1696] Input: Generative AI summary.

[1697] Output: A summary stored in the ticket management system.

[1698] What happens: The server adds the generated summary to the corresponding ticket in the ticket management system and saves it, making the summary information available to customer support agents.

[1699] 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.

[1700] 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> ) 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.

[1701] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1702] 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.

[1703] [Fourth embodiment]

[1704] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1705] 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.

[1706] 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).

[1707] 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.

[1708] 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.

[1709] 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).

[1710] 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.

[1711] 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.

[1712] 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.

[1713] 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.

[1714] 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.

[1715] 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.

[1716] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1717] "Example 1"

[1718] One embodiment of the present invention is a system that links a ticket management system with a generative AI. This system sends the contents of tickets acquired from the ticket management system to the generative AI, which then uses natural language processing technology to analyze the ticket contents and generate a summary. The generated summary is sent back to the ticket management system and saved as the ticket's summary information. When a new ticket is created, the contents of that ticket are also sent to the generative AI, which then generates a summary. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[1719] "Example 2"

[1720] As a specific example, GPT-3 can be used as a generative AI. The contents of tickets obtained from a ticket management system are sent to GPT-3, which then uses natural language processing technology to analyze the contents of the ticket and generate a summary. The generated summary is sent back to the ticket management system and saved as the ticket's summary information. When a new ticket is created, the contents of that ticket are also sent to GPT-3, and a summary is generated. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[1721] The processing flow of each embodiment will be described below.

[1722] "Example 1"

[1723] Step 1: Get the ticket contents from the ticket management system.

[1724] Step 2: Send the acquired ticket contents to the generation AI.

[1725] Step 3: The generative AI uses natural language processing technology to analyze the ticket content and generate a summary.

[1726] Step 4: The generated summary is sent back to the ticket management system and stored as the ticket summary information.

[1727] Step 5: When a new ticket is created, its contents are also sent to the generative AI, which generates a summary.

[1728] Step 6: The generated new ticket summary is saved in the ticket management system along with the original summary information.

[1729] "Example 2"

[1730] Step 1: Obtain the ticket details from the ticket management system. Step 2: Send the obtained ticket details to the generative AI "GPT-3."

[1731] Step 3: GPT-3 uses natural language processing techniques to analyze the ticket content and generate a summary.

[1732] Step 4: The generated summary is sent back to the ticket management system and stored as the ticket summary information.

[1733] Step 5: When a new ticket is created, its contents are also sent to GPT-3, and a summary is generated.

[1734] Step 6: The generated new ticket summary is saved in the ticket management system along with the original summary information.

[1735] Example 1

[1736] Next, a description will be given of Example 1 of Form 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."

[1737] In traditional ticket management systems, ticket content had to be summarized manually, which was time-consuming and labor-intensive. Furthermore, when a new ticket was created, it was difficult to quickly summarize its contents and update the original summary information. This reduced the efficiency of ticket management and could lead to important information being overlooked.

[1738] 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.

[1739] In this invention, the server includes means for linking the ticket management system with the generative artificial intelligence, means for creating a summary of each ticket, means for creating a summary of the contents of a new ticket when it is created and updating the original summary information, means for acquiring the contents of the newly created ticket from the ticket management system, means for sending the acquired ticket contents to the generative artificial intelligence, means for receiving the summary generated from the generative artificial intelligence, and means for saving the received summary in the ticket management system. This makes it possible to automatically summarize the contents of tickets and manage them quickly.

[1740] A "ticket management system" is a software or hardware system for managing the creation, updating, and viewing of tickets.

[1741] "Generative AI" refers to an AI system that uses natural language processing techniques to analyze input text data and perform summarization and other generative tasks.

[1742] The "summary" is a concise summary of the ticket's contents, briefly expressing the main points and issues of the ticket.

[1743] A "new ticket" is a ticket that has been newly created by a user and for which no summary has yet been created.

[1744] A "server" is a computer system that connects the ticket management system with generative artificial intelligence and acquires, sends, receives, and stores data.

[1745] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language, and is used to analyze text data and generate summaries.

[1746] "API" stands for Application Programming Interface, an interface for exchanging data and functions between different software systems.

[1747] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for structuring and representing data.

[1748] This invention is a system that links a ticket management system with generative artificial intelligence, automatically creating summaries for each ticket, generating summaries of the contents of new tickets when they are created, and updating the original summary information.

[1749] The server first retrieves the details of newly created tickets from the ticket management system. A ticket management system is a software or hardware system that manages the creation, updating, and viewing of tickets. The server periodically calls the ticket management system's API to check whether new tickets exist.

[1750] The server then sends the retrieved ticket contents to a generative AI. The generative AI uses a system that uses natural language processing technology to analyze text data and generate a summary. Specifically, a generative AI model such as "OpenAI GPT-4" is used. The server converts the ticket contents into JSON format and sends a POST request to the generative AI's API endpoint.

[1751] The generative AI analyzes the content of the ticket received and generates a summary. For example, in response to a ticket that states "High server memory usage," it generates a summary such as "A problem has occurred with high server memory usage."

[1752] The generated summary is sent back to the server, which analyzes the response from the generative AI and extracts the summary text. The server then saves the received summary in the ticket management system. Specifically, it calls the ticket management system's API and sends a request to update the summary information.

[1753] When a new ticket is created, the server sends the ticket contents to the generative AI in a similar manner to generate a summary. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[1754] As a concrete example, if a user submits a ticket stating that "server memory usage is high," the server retrieves the contents of this ticket and sends it to the generative AI. The generative AI analyzes the content of "server memory usage is high" and generates a summary such as "a problem has occurred with high server memory usage." The generated summary is sent back to the server and stored in the ticket management system.

[1755] An example of a prompt sentence might be:

[1756] "Please summarize the ticket below:

[1757] Ticket details: The server's memory usage is high. It often peaks especially at night. We are considering increasing the memory as a solution.

[1758] By sending this prompt to the generative AI, the AI ​​will analyze the ticket contents and generate a summary.

[1759] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1760] Step 1:

[1761] The server obtains the contents of the newly created ticket from the ticket management system.

[1762] Input: Ticket management system API endpoint

[1763] Output: New ticket content (text data)

[1764] Specific operation: The server periodically calls the API of the ticket management system to check whether a new ticket exists. If a new ticket exists, it retrieves its contents.

[1765] Step 2:

[1766] The server sends the contents of the acquired ticket to the generative artificial intelligence.

[1767] Input: New ticket content (text data)

[1768] Output: Request to the generative AI (JSON format)

[1769] Specific operation: The server converts the acquired ticket contents into JSON format and sends a POST request to the API endpoint of the generative artificial intelligence.

[1770] Step 3:

[1771] Generative AI analyzes the ticket contents and generates a summary.

[1772] Input: Ticket content (JSON format)

[1773] Output: Summary (text data)

[1774] How it works: The generative AI uses natural language processing technology to analyze the content of the ticket it receives and generate a summary. For example, in response to a ticket that says "Server memory usage is high," it generates a summary such as "A problem with high server memory usage has occurred."

[1775] Step 4:

[1776] The server receives the summary generated from the generative artificial intelligence.

[1777] Input: Response from generative AI (JSON format)

[1778] Output: Summary (text data)

[1779] Specific operation: The server analyzes the response from the generative artificial intelligence API and extracts summary text.

[1780] Step 5:

[1781] The server stores the received summary in the ticket management system.

[1782] Input: Summary (text data)

[1783] Output: Update request to ticket management system (API call)

[1784] Specific operation: The server calls the API of the ticket management system and sends a request to update the summary information.

[1785] Step 6:

[1786] When a user submits a new ticket, the server again obtains the contents of the new ticket from the ticket management system and sends them to the generative artificial intelligence in the same manner, causing a summary to be generated.

[1787] Input: New ticket content (text data)

[1788] Output: Summary (text data)

[1789] Specific operation: When a user submits a new ticket, the server retrieves the content from the ticket management system and sends it to the generative AI. The generated summary of the new ticket is saved in the ticket management system along with the original summary information.

[1790] (Application example 1)

[1791] Next, a description will be given of Application Example 1 of Embodiment 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."

[1792] In factory maintenance work, the detailed content of tickets makes it difficult for workers to quickly understand and respond to them. Furthermore, because the content of tickets is so diverse, they need to be summarized, but manual summarization takes time and effort. This reduces the efficiency of maintenance work and reduces productivity.

[1793] 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.

[1794] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating summaries of each ticket, a means for creating a summary of the contents of a new ticket when it is created and updating the original summary information, and a means for acquiring the contents of maintenance tickets within the factory, generating summaries using the generative AI, and saving them. This allows the contents of maintenance tickets to be summarized quickly and accurately, enabling workers to perform maintenance work efficiently.

[1795] A "ticket management system" is a system that manages the creation, updating, and viewing of tickets.

[1796] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze text data and perform summarization and generation.

[1797] The "summary" is a concise summary of the ticket contents.

[1798] A "maintenance ticket" is a ticket that contains information about maintenance work that occurs within a factory.

[1799] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[1800] "In-plant" means the interior of a facility where manufacturing operations take place.

[1801] A "server" is a computer system that processes and stores data on a network.

[1802] As an embodiment of the present invention, a system is constructed that automatically generates ticket summaries using generative AI in a maintenance ticket management system within a factory. A specific embodiment of this system is shown below.

[1803] System configuration

[1804] 1. Hardware

[1805] Server: A computer system that processes and stores data. It houses the ticket management system and generative AI.

[1806] Factory Robot: A robot that performs maintenance work in a factory. It communicates with the server to obtain maintenance ticket information and receive a summary.

[1807] 2. Software

[1808] Ticket management system: A system that manages ticket creation, updates, and viewing. Ticket content is provided to the generative AI via API.

[1809] Generative AI: This is an AI that uses natural language processing technology to analyze text data and generate summaries. It receives ticket content via API and generates summaries.

[1810] Data processing and calculation

[1811] 1. Obtaining ticket details

[1812] The server obtains the contents of maintenance tickets generated within the factory through the ticket management system's API.

[1813] 2. Summary Generation Using Generative AI

[1814] The server sends the acquired ticket contents to the generative AI API, which uses natural language processing technology to analyze the ticket contents and generate a summary.

[1815] 3. Save the summary

[1816] The server saves the generated summary through the API of the ticket management system, so that the summary information of the maintenance ticket is saved in the ticket management system.

[1817] Specific examples

[1818] Ticket details: "The belt on machine A is loose and needs to be replaced."

[1819] Produced summary: "The belt on machine A needs to be replaced."

[1820] Prompt Sentence Examples

[1821] Summarize the following text:

[1822] "The belt on machine A is loose and needs to be replaced."

[1823] In this way, a system can be realized that supports factory robots in efficiently performing maintenance work.

[1824] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1825] Step 1:

[1826] The server obtains the contents of maintenance tickets generated within the factory through the ticket management system's API.

[1827] Input: Maintenance ticket information from your ticket management system

[1828] Output: The contents of the retrieved maintenance ticket

[1829] What happens: The server sends an HTTP request to retrieve ticket data from the ticket management system's API. The retrieved data is received in JSON format.

[1830] Step 2:

[1831] The server sends the acquired ticket contents to the API of the generation AI.

[1832] Input: The content of the acquired maintenance ticket

[1833] Output: Ticket content sent to the generative AI

[1834] Specific operation: The server sends an HTTP POST request to the generative AI API and sends the ticket contents in JSON format.

[1835] Step 3:

[1836] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1837] Input: Ticket content sent to the generative AI

[1838] Output: A summary of the generated tickets

[1839] How it works: The generative AI analyzes the content of the received ticket, extracts important information, and generates a summary, which is then returned to the server in JSON format.

[1840] Step 4:

[1841] The server stores the generated summary through the API of the ticket management system.

[1842] Input: Summary of generated ticket

[1843] Output: Summary information stored in the ticket management system

[1844] What happens: The server sends an HTTP POST request to the ticket management system's API and saves the generated summary in JSON format.

[1845] Step 5:

[1846] A user views summarized maintenance ticket information through a ticket management system.

[1847] Input: Summary information stored in the ticket management system

[1848] Output: Summarized maintenance ticket information for user viewing

[1849] Specific operation: A user views summarized maintenance ticket information through the ticket management system interface. The system displays the saved summary information.

[1850] Example 2

[1851] Next, a description will be given of Example 2 of Form 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."

[1852] Conventional information management systems have the problem that information summaries must be created manually, which takes time and effort. In addition, when new information is registered, it is difficult to quickly and accurately summarize the content and update existing summary information. This makes information management cumbersome and hinders efficient operation.

[1853] 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.

[1854] In this invention, the server includes means for acquiring new information from the information management system, means for transmitting the acquired information to the generative AI, means for receiving summaries generated by the generative AI, and means for storing the generated summaries in the information management system, thereby enabling automatic generation of summaries of new information and rapid and accurate updating of existing summary information.

[1855] An "information management system" is a software or hardware system for managing the registration, updating, and viewing of information.

[1856] "Generative AI" refers to an AI system that uses natural language processing techniques to analyze input information and perform summarization and other generative tasks.

[1857] A "summary" is a short summary of the main points of information, intended to convey the content of the original information concisely.

[1858] "New information" refers to information that has been newly registered in the information management system and is to be distinguished from existing information.

[1859] A "server" is a computer system that sends and receives data between an information management system and generative artificial intelligence.

[1860] "Means of acquisition" refers to the methods and processes for acquiring new information from the information management system.

[1861] "Means for transmitting" refers to the method or process for transmitting acquired information to generative artificial intelligence.

[1862] "Means for receiving" refers to a method or process for receiving a summary generated by a generative artificial intelligence.

[1863] "Storage means" refers to the method or process for storing the generated summary in an information management system.

[1864] This invention is a system that automatically generates summaries of new information and updates existing summaries quickly and accurately by linking an information management system with generative artificial intelligence. Specific embodiments of this system are described below.

[1865] Hardware and software used

[1866] Information management systems: These are software or hardware systems used to manage the registration, updating, and viewing of information. Examples include database management systems and cloud-based information management platforms.

[1867] Generative AI: This is an AI system that uses natural language processing techniques to analyze input information and perform summarization and other generative tasks. A specific example is an AI service equipped with a natural language processing model.

[1868] System Operation

[1869] 1. A user registers new information in the information management system.

[1870] A user uses the interface of the information management system to register new information, including a title, a detailed description, and a priority.

[1871] Example: User enters the subject "System bug fix request" with the details "Error occurs on login screen."

[1872] 2. The server retrieves new information from the information management system.

[1873] The server periodically calls the information management system's API to obtain new information. The obtained data is received in JSON format.

[1874] Example: A server retrieves information from an information management system with the title "System bug fix request" and the details "Error occurs on login screen."

[1875] 3. The server sends the acquired information to the generative AI

[1876] The server generates an appropriate prompt to send the acquired information to the generative AI, which then sends the prompt as an API request to the generative AI.

[1877] Example: The server sends a request to the generative artificial intelligence to summarize the information content "Request to fix a system bug."

[1878] 4. Generative AI analyzes the content of information and generates summaries

[1879] The generative AI analyzes the prompt and uses natural language processing techniques to summarize the information, which is short and to the point.

[1880] Example: A generative AI summarizes the details "An error occurs on the login screen" as "Request to fix the error on the login screen."

[1881] 5. The generative AI sends the generated summary back to the server

[1882] The generative AI sends the generated summary back to the server as an API response, which the server receives and proceeds to the next step.

[1883] Example: The generative AI sends back to the server a summary titled "Request to fix login screen error."

[1884] 6. The server stores the generated summary in the information management system and displays it as a summary of the information.

[1885] The server adds the generated summary to the original information using the information management system's API. The user can view the generated summary on the information management system's interface.

[1886] Example: The server stores a summary "Request to fix login screen error" in the information management system and allows the user to view this summary on the information details screen.

[1887] Prompt Sentence Examples

[1888] An example of a prompt to be sent to the generative artificial intelligence is as follows:

[1889] Please summarize the information content of "Request for system bug fix."

[1890] By inputting this prompt into a generative artificial intelligence, the AI ​​analyzes the information content and generates an appropriate summary.

[1891] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1892] Step 1:

[1893] A user registers new information in the information management system.

[1894] The user registers new information using the information management system interface, entering information such as a title, detailed description, and priority, and the new information is then saved in the information management system.

[1895] Specifically, the user enters details such as "An error occurs on the login screen" under the title "Request to fix a system bug" and registers it in the information management system.

[1896] Step 2:

[1897] The server retrieves new information from the information management system.

[1898] The server periodically calls the information management system's API to retrieve new information data. As input, it receives the API response from the information management system. As output, the retrieved data is stored on the server in JSON format.

[1899] Specifically, the server retrieves information from the information management system, including the title "Request to fix a system bug" and the details "An error occurs on the login screen."

[1900] Step 3:

[1901] The server sends the acquired information to the generative artificial intelligence.

[1902] The server generates an appropriate prompt to send the acquired information to the generative AI. As input, it creates a prompt based on the acquired information. As output, the prompt is sent to the generative AI as an API request.

[1903] Specifically, the server sends a request to the generative artificial intelligence to summarize the information content, "a request to fix a system bug."

[1904] Step 4:

[1905] Generative AI analyzes the content of information and generates summaries

[1906] The generative AI analyzes the received prompt and summarizes the information content using natural language processing techniques. The prompt is received as input, and the generated summary is generated as output.

[1907] As a specific operation, the generative artificial intelligence summarizes the details of "an error occurs on the login screen" as "a request to fix the error on the login screen."

[1908] Step 5:

[1909] The generative AI sends the generated summary back to the server

[1910] The generative AI sends the generated summary back to the server as an API response. The generated summary is received as input. The summary is sent back to the server as output.

[1911] Specifically, the generative artificial intelligence sends a summary to the server titled "Request to fix an error on the login screen."

[1912] Step 6:

[1913] The server stores the generated summary in the information management system and displays it as summary information of the information.

[1914] The server adds the generated summary to the original information using the information management system's API. As input, it receives the generated summary. As output, it stores the summary in the information management system and makes it available for the user to review.

[1915] Specifically, the server stores a summary titled "Request to correct login screen error" in the information management system, and the user can check this summary on the information details screen.

[1916] (Application example 2)

[1917] Next, a description will be given of Application Example 2 of Form 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."

[1918] When managing tickets for maintenance and repairs that occur within a factory, it is necessary to efficiently understand the contents of the tickets and respond quickly. However, with conventional systems, ticket contents must be manually analyzed and summarized, which is time-consuming and labor-intensive. Furthermore, if the ticket contents are complex, the quality of the summary may decline. This can reduce work efficiency and disrupt factory operations.

[1919] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1920] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating summaries for each ticket, a means for creating a summary of the contents of a new ticket when it is created and updating the original summary information, a means for managing maintenance and repair tickets generated in the factory, a means for analyzing the contents of the ticket and generating summaries using the generative AI, a means for saving the generated summaries in the ticket management system, and a means for being installed in the factory robot. This makes it possible to efficiently analyze the contents of tickets and generate summaries.

[1921] A "ticket management system" is a system that manages maintenance and repair tickets that occur within a factory, and creates, updates, and views tickets.

[1922] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1923] A "means for creating a summary" is a means that has the function of analyzing the contents of a ticket, extracting important information, and summarizing it concisely.

[1924] "A means for creating a summary of the contents of a new ticket when it is created and updating the original summary information" is a means that has the function of analyzing the contents of a newly created ticket, generating a summary, and updating the existing summary information.

[1925] "A means for managing tickets for maintenance and repairs generated within a factory" refers to a means that has the function of centrally managing tickets related to maintenance and repairs generated within a factory.

[1926] "Means for saving the generated summary in the ticket management system" refers to means that has the function of saving the summary generated by the generative AI in the ticket management system.

[1927] "Means to be installed on factory robots" refers to a means to install an application on a robot used in a factory and have the function of linking the ticket management system with generative AI.

[1928] As an embodiment of the present invention, a system is constructed that efficiently manages maintenance and repair tickets generated in a factory and generates summaries. A specific embodiment of this system is shown below.

[1929] System configuration

[1930] The system consists of the following major components:

[1931] 1. Ticket Management System: A system for managing maintenance and repair tickets that occur within the factory. Tickets can be created, updated, and viewed.

[1932] 2. Generative AI: This is artificial intelligence that uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1933] 3. Factory robot: A robot used in a factory, on which an application that connects the ticket management system with generative AI is installed.

[1934] Program processing

[1935] The server first retrieves maintenance or repair tickets from the ticket management system. Next, it sends the contents of the retrieved tickets to a generative AI system, which generates summaries using natural language processing technology. The generated summaries are then saved back into the ticket management system.

[1936] Hardware and software used

[1937] Hardware: Factory robots

[1938] Software: Ticket management system, generative AI (e.g., GPT-3), Python

[1939] Data processing and calculation

[1940] 1. Data Acquisition: Acquire ticket contents from the ticket management system.

[1941] 2. Data analysis: The ticket contents are sent to a generative AI, which generates a summary using natural language processing technology.

[1942] 3. Data storage: The generated summary is stored in the ticket management system.

[1943] Specific examples

[1944] For example, when a machine malfunctions in a factory, a maintenance ticket is registered in the ticket management system. A factory robot retrieves the ticket and sends it to a generative AI to generate a summary. The summary is then stored in the ticket management system, allowing workers to efficiently understand the contents of the ticket.

[1945] Prompt Sentence Examples

[1946] "Please summarize the contents of the maintenance ticket obtained from the ticket management system. The contents are as follows: {Ticket content}"

[1947] In this way, a system can be realized that can significantly improve the efficiency of maintenance and repair within a factory.

[1948] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1949] Step 1:

[1950] The server retrieves maintenance and repair tickets from the ticket management system.

[1951] Input: Ticket information registered in the ticket management system

[1952] Data processing: Use the ticket management system's API to obtain the ticket contents.

[1953] Output: The retrieved ticket details

[1954] Step 2:

[1955] The server sends the contents of the acquired ticket to the generation AI.

[1956] Input: The content of the ticket obtained

[1957] Data processing: Send the ticket contents using a generative AI API.

[1958] Output: The ticket content sent to the generative AI

[1959] Step 3:

[1960] Generative AI uses natural language processing technology to analyze the contents of tickets and generate summaries.

[1961] Input: Ticket content sent to the generative AI

[1962] Data Computing: Using natural language processing techniques, the content of tickets is analyzed, key information is extracted, and a summary is generated.

[1963] Output: The generated summary

[1964] Step 4:

[1965] The server stores the generated summary in the ticket management system.

[1966] Input: Generated summary

[1967] Data processing: Use the ticket management system's API to store the generated summary.

[1968] Output: Summary stored in ticket management system

[1969] Step 5:

[1970] The user views the generated summary through the ticket management system.

[1971] Input: Summary stored in ticket management system

[1972] Data processing: Display summaries through the ticket management system interface.

[1973] Output: A summary that the user sees

[1974] In this way, maintenance and repair tickets within the factory can be managed efficiently and workers can respond quickly.

[1975] 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.

[1976] "Example 1"

[1977] The present invention is a system that links a ticket management system with a generative AI to create summaries of each ticket, and when a new ticket is created, creates a summary of the ticket's contents and updates the original summary information. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, the generative AI uses natural language processing technology to analyze the ticket contents and generate a summary. The emotion engine also analyzes the user's emotions from the ticket contents and provides the results to the generative AI. The generative AI generates a summary that takes the user's emotions into account based on the emotion analysis results provided by the emotion engine.

[1978] "Example 2"

[1979] As a concrete example, consider a system that uses a ticket management system, the generative AI "GPT-3," and the emotion engine "IBM Watson Tone Analyzer." The ticket contents are obtained from the ticket management system and sent to GPT-3 and IBM Watson Tone Analyzer. GPT-3 uses natural language processing technology to analyze the ticket contents and generate a summary. Meanwhile, IBM Watson Tone Analyzer analyzes the user's emotions from the ticket contents and provides the results to GPT-3. GPT-3 generates a summary that takes the user's emotions into account based on the emotion analysis results provided by IBM Watson Tone Analyzer. The generated summary is sent back to the ticket management system and saved as the ticket summary information. When a new ticket is created, the contents are also sent to GPT-3 and IBM Watson Tone Analyzer, and a summary is generated.

[1980] The processing flow of each embodiment will be described below.

[1981] "Example 1"

[1982] Step 1: Get the ticket contents from the ticket management system.

[1983] Step 2: The acquired ticket content is sent to the generative AI, which analyzes the content and generates a summary.

[1984] Step 3: At the same time, the content of the acquired ticket is sent to the emotion engine to analyze the user's emotions.

[1985] Step 4: Provide the emotion analysis results provided by the emotion engine to the generative AI.

[1986] Step 5: The generative AI generates a summary that takes the user's emotions into account based on the provided emotion analysis results.

[1987] "Example 2"

[1988] Step 1: Obtain the ticket contents from the ticket management system. Step 2: Send the obtained ticket contents to the generative AI "GPT-3", which analyzes the contents and generates a summary.

[1989] Step 3: At the same time, the contents of the acquired ticket are sent to the emotion engine "IBM Watson Tone Analyzer" to analyze the user's emotions.

[1990] Step 4: Sentiment analysis provided by IBM Watson Tone Analyzer

[1991] The results are provided to GPT-3.

[1992] Step 5: GPT-3 is provided by IBM Watson Tone Analyzer

[1993] Based on the emotion analysis results, a summary is generated that takes the user's emotions into consideration.

[1994] Step 6: The generated summary is sent back to the ticket management system and stored as the ticket summary information.

[1995] Step 7: When a new ticket is created, its contents are also sent to GPT-3 and IBM Watson Tone Analyzer to generate a summary.

[1996] Example 1

[1997] Next, a description will be given of Example 1 of Form 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."

[1998] Conventional ticket management systems have the problem that ticket contents must be summarized manually, which is time-consuming and labor-intensive. In addition, they are unable to generate summaries that take into account the user's feelings, making it difficult to properly reflect the user's complaints and requests. This can lead to reduced ticket management efficiency and lower user satisfaction.

[1999] 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.

[2000] In this invention, the server includes a means for linking the ticket management system with the generative AI, a means for creating a summary of each ticket, a means for creating a summary of the contents of a new ticket whe...

Claims

1. A means for linking a ticket management system with generative artificial intelligence; a means for analyzing a user's emotion from the content of a ticket using an emotion analysis engine, and providing the content of the ticket and information on the analyzed emotion of the user to the generative artificial intelligence; means for generating a summary of the ticket according to the user's emotional information based on the content of the ticket and the provided emotional information of the user, using the generative artificial intelligence; a means for creating a summary of the content of a new ticket by a means for generating a summary of the ticket based on the content of the new ticket and the user's emotion obtained from the content of the new ticket through analysis by the emotion analysis engine when the new ticket is created, and updating the original summary; means for storing the updated summary in said ticket management system; A system including:

2. the means for generating a summary analyzes the content of the ticket using natural language processing technology in the generative artificial intelligence and generates a summary; The system of claim 1 .

3. The ticket management system manages the creation, updating, and viewing of tickets, and provides the contents of the tickets to the generative artificial intelligence. The system of claim 1 .

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