system

Abstract

We provide the system. [Solution] A means of receiving information regarding operational changes, A means for identifying the parts of the relevant documents that need to be modified based on the aforementioned operational changes, A means for automatically correcting the aforementioned corrections, A means of automatically creating change management tickets and requesting approvals, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional operation change management, document correction accompanying changes requires a great deal of man-hours, and manual correction is likely to result in mistakes. In addition, since the invoicing and approval processes related to change management are complicated, work efficiency is required. As a result, there are problems that time and human resources are consumed and concentration on important tasks is hindered.

Means for Solving the Problems

[0005] This invention relates to a system that receives information on operational changes and automatically identifies and corrects the relevant documents based on that information. Specifically, it uses artificial intelligence models and natural language processing technology to analyze operational change information. By automating the creation of change management documents and approval requests, it becomes possible to significantly reduce man-hours and improve the accuracy of work. In this way, it provides a means to improve the efficiency and quality of business operations.

[0006] "Operational change" refers to changing the current operating method in a system or business process to a new method.

[0007] "Document revision" refers to the process of updating or correcting the content of related documents in response to operational changes.

[0008] An "artificial intelligence model" refers to machine learning and deep learning algorithms that learn from data, recognize patterns, and autonomously solve problems.

[0009] "Natural language processing technology" refers to a set of technologies that enable computers to understand, interpret, and generate human language.

[0010] "Initiating a document" refers to the act of creating and submitting an official document or electronic form in order to initiate necessary procedures or processes in business operations.

[0011] An "approval request" is the process of seeking permission from an authorized person or group to formally implement a decision or change. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a system that automates document modification and change management processes associated with operational changes. This system functions by having a server perform a series of processes based on information about operational changes entered by users via a terminal.

[0034] First, the user inputs operational change information from their terminal and sends it to the server. The server analyzes the received information and uses artificial intelligence models and natural language processing techniques to understand the changes. This analysis identifies which documents and processes the operational changes will affect.

[0035] Next, the server automatically corrects documents within the identified scope of impact. Specifically, it uses AI technology to find the necessary sections within the documents and rewrite them with content based on the latest information. This enables rapid document updates while preventing errors.

[0036] Furthermore, the server automates the change management process. Specifically, it has a function that automatically creates change management tickets based on operational change information and sends approval requests to the relevant personnel. Once approval is complete, a notification is sent to the user, and the change is officially confirmed.

[0037] For example, if a user enters "Change from version 2.0 to 2.1 of software B," the server will use this information to search for relevant documents and update the version information to the latest version. In addition, it will quickly obtain necessary approvals and ensure a smooth transition of operations.

[0038] This system significantly reduces manual document revisions and approval processes, allowing users to focus on more important tasks. The goal of this automated process is to improve work efficiency and increase overall company productivity.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user uses a terminal to input information about the changes before and after the operation change management system interface and sends it to the server. The information includes the item to be changed, its state before the change, and its new state after the change.

[0042] Step 2:

[0043] The server analyzes the received operational change information. Here, it utilizes generative AI models and natural language processing techniques to break down the input information and understand the purpose and scope of the changes.

[0044] Step 3:

[0045] The server uses the analysis results to search the system for potentially affected documents and identify the changes. This includes identifying specific keywords and phrases within the documents.

[0046] Step 4:

[0047] For identified changes, the server uses natural language generation technology to automatically correct the document content. Specifically, it performs a process of rewriting outdated information to reflect the latest state.

[0048] Step 5:

[0049] The server automates the change management process. It generates a ticket corresponding to the operational change and sends an approval request to the designated approver.

[0050] Step 6:

[0051] After the approver completes the approval process through the system, the server confirms that the approval is complete. Upon approval, the server automatically sends a notification to the user, and the entire process of the operational change is completed.

[0052] (Example 1)

[0053] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0054] Responding to operational changes requires the rapid and accurate revision of relevant documents and the streamlining of the change management process. However, performing these tasks manually is time-consuming, labor-intensive, and carries the risk of errors. Furthermore, the approval process must be executed quickly, necessitating a system to address these challenges.

[0055] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0056] In this invention, the server includes means for receiving information on operational changes using an information processing device, means for identifying the parts of relevant documents that need to be modified based on the operational changes using generated artificial intelligence technology, and means for automatically modifying the identified parts and verifying the results. This enables accurate automatic modification of documents based on operational change information and efficient change management.

[0057] An "information processing device" is a device used to receive, analyze, and process data, and includes devices such as servers and computers.

[0058] "Operational changes" refer to changes in settings and procedures within a system or process, and are operations performed to adapt to new conditions or environments.

[0059] "Generated artificial intelligence technology" refers to artificial intelligence algorithms and models used to automate data analysis and processing.

[0060] "Document revisions" refer to specific locations or parts within a document that need to be updated or revised based on changes in operations.

[0061] "Means of identification" refer to methods and processes for analyzing information, identifying necessary parts, and determining appropriate actions.

[0062] An "automatic correction mechanism" is a mechanism that autonomously executes the necessary changes to detected areas and updates the content.

[0063] "Change management" refers to the entire process of managing the coordination, approval, and implementation of changes related to operational modifications.

[0064] An "approval request" refers to the act of making a request to seek approval from stakeholders regarding changes or processes.

[0065] "Means of notification" refers to methods or systems for conveying specific information to users or relevant departments.

[0066] This invention is an information processing system that automates document modification and change management processes associated with operational changes. The system starts operating when a user inputs information about operational changes using a terminal and sends it to the server.

[0067] Specifically, when the server receives information, it uses generative AI models and natural language processing techniques to analyze the operational change information. This analysis identifies which documents and processes are affected and determines where the documents need to be modified. While general AI and NLP techniques are used for the analysis, the ability to quickly and accurately access information in the database is particularly required.

[0068] The server then automatically corrects the identified documents. Leveraging AI technology, the server updates necessary sections based on specific rules and patterns, rewriting the documents to reflect the latest information. Because this process occurs without user intervention, corrections are made quickly, reducing human error.

[0069] Furthermore, the server automatically executes the change management process. This includes creating change management tickets based on operational change information, and then automatically sending approval requests to the relevant departments. Once approval is complete, the server sends a notification to the user.

[0070] For example, if a user enters "System X will be updated to add a new feature Y," the server will search for relevant documents, automatically incorporate information about the new feature, and, if necessary, send approval requests to relevant parties. Furthermore, the prompt text to be entered into the generating AI model could be a specific instruction such as, "Please reflect the update details in the document and start the approval process."

[0071] This system allows companies to respond quickly to operational changes and significantly improve process efficiency.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The user uses a terminal to input information about operational changes. The user uses a form to enter specific details of the changes, and the information is sent to the server via the terminal as input for the next step.

[0075] Step 2:

[0076] The server receives operational change information sent from the terminal. The server analyzes this information using a generative AI model and natural language processing technology to identify which documents or processes the operational change affects. In this step, the input text data is converted into string data for analysis, and the AI ​​model outputs the areas that need to be modified.

[0077] Step 3:

[0078] The server automatically modifies the relevant documents based on the identified changes. Using AI technology, the document content is accurately updated to reflect operational changes and output as a new version. In this step, the identified modification information is processed as input, and document data based on the latest information is output.

[0079] Step 4:

[0080] The server creates a change management ticket and automatically sends an approval request to the relevant department. Here, the modified document information is entered, and a notification prompting the start of the approval process is output. A prompt message is generated, and the system automatically sends the approval request.

[0081] Step 5:

[0082] The server monitors the approval process and sends a notification to the user once approval is complete. The approved status is processed as input, and a notification is output to the user's device. This allows the user to confirm that the operational changes have been officially reflected.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] Managing operational change information within factories often involves manually updating work orders and maintenance schedules, which is inefficient in environments where quick responses are required. Furthermore, manual updates carry the risk of human error. It is necessary to improve this situation and enhance work efficiency.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes means for receiving information on operational changes, means for identifying the parts of related information assets that need to be modified based on the operational changes, means for automatically modifying the parts that need to be modified, means for automatically creating change management slips and requesting approvals, and means for automatically updating physical work instructions and maintenance plans based on operational change information. This enables the rapid and accurate updating of documents and field instructions in response to operational changes.

[0088] "Operational changes" refer to revisions to procedures or settings in business processes or systems, and are changes made to adapt to new instructions or conditions.

[0089] "Information assets" refer to tangible or intangible information held by a company, including work instructions and maintenance plans, and are resources necessary for business operations.

[0090] "A section requiring modification" refers to a specific part of an information asset that needs to be updated due to a change in operations.

[0091] "Change management" is a set of processes for proposing, approving, implementing, and tracking changes.

[0092] "Automatic updating means" refers to a technology that updates information assets to the latest state as needed, without manual intervention, based on operational change information.

[0093] A "generative artificial intelligence model" is a system that incorporates learning algorithms used for information analysis and process automation.

[0094] The system implementing this invention enables the reception and analysis of operational change information, and the automatic updating of modified sections of specific information assets. The server first receives information regarding operational changes from users. This information is obtained from the factory's operational management system and various sensors. The received data is analyzed using the Google Cloud Natural Language API. As a result of the analysis, information assets that require modification are identified.

[0095] The server then automatically updates the system based on the identified corrections using a generative artificial intelligence model. This process leverages AWS® AI services to rewrite work orders and maintenance plans in real time. Furthermore, change management is automated through Microsoft® Power Automate, notifying responsible personnel if approval is required, and confirming the update once approval is complete. This entire process is directly reflected in field robots or terminals using Raspberry Pi, significantly improving work efficiency.

[0096] For example, if a factory machine requires an earlier scheduled maintenance, the system immediately receives this information and automatically updates the maintenance schedule. It also generates a prompt message such as, "Please update the relevant work orders based on the latest operational change information," to clarify instructions for the responsible personnel. This prompt helps stakeholders intuitively understand the necessary changes and respond quickly.

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The user inputs operational change information from a terminal and sends that data to the server. The input data includes details of the operational change and information about the affected equipment and processes. The server receives this input and prepares the data for the next analysis step.

[0100] Step 2:

[0101] The server analyzes the received operational change information using the Google Cloud Natural Language API. The input is the operational change information sent by the user, and the output is a semantic understanding of the analyzed changes. Here, the AI ​​model performs natural language processing on the data to identify which parts of the information assets are affected.

[0102] Step 3:

[0103] The server prepares to correct the affected information assets based on the analysis results. Specifically, it utilizes AWS AI services to automatically extract the parts of documents and data that need correction. The input is the affected areas identified by the analysis, and the output is a list of the areas that need correction.

[0104] Step 4:

[0105] The server automatically updates the identified areas that need correction. For example, it rewrites work orders and maintenance schedules with the latest information. AWS AI services are used here to execute the specific correction tasks. The input is a list of areas that need correction, and the output is the updated information assets.

[0106] Step 5:

[0107] The server uses Microsoft Power Automate to automatically create change tickets and request approval from relevant parties for change management. Inputs include details of the operational change and information about the relevant parties, while output is an approval request notification. After approval is complete, the user is notified that the change has been finalized.

[0108] Step 6:

[0109] The terminal or robot performs on-site work based on the received update information. Specifically, it operates or maintains equipment according to the new work instructions. The input is the updated work instructions, and the output is the completion of the actual work.

[0110] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0111] This invention is a system that combines automation of document revision and change management processes associated with operational changes with an emotion engine that recognizes user emotions and adjusts the system's response based on those emotions.

[0112] Users input information about operational changes into the system interface using a terminal. The input information is sent to a server, which analyzes the operational changes. Generative artificial intelligence models and natural language processing techniques are used in the analysis to identify the parts of the affected related documents that need to be modified.

[0113] Furthermore, this system recognizes the user's emotions during input using an emotion engine. This engine analyzes the user's language patterns, input speed, and past interaction data to determine their emotional state. The results are recorded on the server side and used to enhance the user experience.

[0114] Once the document's automatic correction is complete, the server initiates a change management process and automatically requests the necessary approvals. Based on the user's emotions as recognized by the emotion engine, the system dynamically adjusts the notification content and interface design to deliver information in a way that is most acceptable to the user.

[0115] For example, if a user enters information such as "change the security policy for system C," the server will scan the existing policy document and automatically rewrite it with the latest policy. On the other hand, if the emotion engine determines that the user is feeling anxious, the server will provide a message in a gentler tone and additional support information.

[0116] This system improves the user experience and enhances the efficiency and accuracy of the entire operational change process. In this way, it makes change management in business operations smoother and more effective.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The user uses a terminal to access the operational change management system and input information before and after the change. During this process, the terminal records input speed, word choice, and other details, and sends this information to the emotion engine.

[0120] Step 2:

[0121] The server receives operational change information sent from the terminal. The emotion engine analyzes the received information and recognizes the user's emotional state based on language patterns and input characteristics.

[0122] Step 3:

[0123] The server uses generative artificial intelligence models and natural language processing techniques to analyze operational change information. Based on the analysis, it searches for relevant documents and identifies areas that require correction.

[0124] Step 4:

[0125] The server uses natural language processing technology to automatically correct the document based on the identified areas that need correction. This prevents manual errors and allows for quick document updates.

[0126] Step 5:

[0127] The server automatically creates change management tickets and sends approval requests to the necessary approvers. Based on the emotional state determined by the emotion engine, the notification text and interface display are modified to be more user-friendly.

[0128] Step 6:

[0129] Once the approver completes the approval process using the system, the server records the result and sends a notification to the user confirming approval. The notification uses the most acceptable language, based on the results detected by the emotion engine.

[0130] Through this series of processes, the system can improve the user experience while efficiently managing operational changes.

[0131] (Example 2)

[0132] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0133] Traditional operational change management processes suffered from inefficiency and accuracy in document revisions associated with changes, resulting in an inadequate user experience. Furthermore, providing information without considering user sentiment often led to a lack of a proper user experience.

[0134] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0135] In this invention, the server includes means for receiving information about operational changes, means for identifying the parts of relevant documents that need to be modified based on the operational changes, means for automatically modifying the parts that need to be modified, means for automatically creating change management tickets and requesting approval, means for recognizing the user's emotions, and means for adjusting notification content and the interface based on the emotions. This makes it possible to improve the user experience while increasing the efficiency and accuracy of the operational change process.

[0136] "Operational changes" refer to altering procedures or policies related to the operation of a system or process.

[0137] "Means of receiving information" refers to a system equipped with the functionality to capture user input and instructions as data.

[0138] "Means for identifying the parts of related documents that need to be modified" refers to a function that automatically finds the affected parts in existing documents based on operational changes.

[0139] "Means of automatic correction" refers to a system that allows identified areas for correction to be fixed using automated processes and technologies.

[0140] "Methods for automatically creating change management slips and requesting approvals" refers to a function that, after an operational change has been identified, records that change and automatically requests necessary approvals from relevant parties.

[0141] "Means of recognizing emotions" refers to a function that analyzes and determines a user's emotional state based on their input information and behavioral patterns.

[0142] "Means for adjusting notification content and interface" refers to a function that enables the dynamic modification of the content and display format of information provided according to the user's emotional state.

[0143] This invention is a system that automates efficient document revision and change management related to operational change management, and in addition, it provides a function that recognizes user emotions and provides appropriate feedback.

[0144] Users use a terminal to input information about operational changes into the system interface. This information is transmitted to a server via the internet. The server analyzes the operational change information using generative AI models and natural language processing technology to identify sections in the relevant documents that require modification. Possible generative AI models include technologies such as OpenAI®, a common natural language processing model.

[0145] Furthermore, the server uses an emotion engine to recognize the emotions a user is feeling when entering information. This engine analyzes the user's language patterns, input speed, and past interaction data. For example, if a user inputs "Change the security policy of system C," the server identifies the document related to the operational change and modifies that document. During this analysis process, if the user's input is rushed or brief, the server recognizes emotions such as anxiety or impatience.

[0146] Once the modifications are complete, the server automatically initiates a change management process and requests necessary approvals from relevant parties. At this stage, information tailored to the user's emotional state is provided. For example, if the server determines that the user is feeling anxious, it will provide reassuring messages and supplementary explanations in a gentle tone.

[0147] As a concrete example, let's look at an example of a prompt to a generative AI model. For instance, a prompt could be designed to say, "Based on information about operational changes and user sentiment, please suggest appropriate document changes and notification methods."

[0148] This system efficiently manages the operational change process while simultaneously improving the quality of the user experience. It reduces the workload associated with operational changes and enables smoother and more accurate change management.

[0149] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0150] Step 1:

[0151] The user uses a terminal to input information about the operational change into the interface. Specifically, they type "The security policy for System C needs to be changed" using the keyboard and click the submit button. This action sends the user's input data to the server.

[0152] Step 2:

[0153] The server provides operational change information received from the user as a prompt to the generating AI model. The input data includes text information entered by the user. The server uses the generating AI model to perform natural language processing, search for relevant documents, and identify areas that need correction. The output is information about the identified areas that need correction. Specifically, it searches for relevant security policy documents and displays the areas that need correction.

[0154] Step 3:

[0155] The server automatically updates the identified modifications. This involves retrieving existing documents from the database and rewriting them with the latest policy information. The input is information about the modifications and the new policy information, and the output is the updated document file. Specifically, the system automatically creates and saves a new document reflecting the changes.

[0156] Step 4:

[0157] Simultaneously, the device collects emotional data based on user input. This includes analyzing the user's input speed and text tone. Input data includes the user's input history and current interaction data. The server uses an emotional engine to analyze this data and identify the user's emotional state. The output is metadata indicating the user's emotions.

[0158] Step 5:

[0159] The server adjusts the notification content used when creating a change management process based on the user's emotional state obtained from the emotion engine. Specifically, if it determines that the user is anxious, it generates a reassuring message. This process uses the user's emotional metadata as input and outputs the adjusted notification message. The server sends these messages to relevant parties and automatically requests the necessary approvals.

[0160] (Application Example 2)

[0161] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0162] In modern information systems, frequent operational changes necessitate efficient documentation revisions and change management processes. Furthermore, providing user-friendly interfaces is crucial for improving the user experience. However, performing these tasks manually is time-consuming and labor-intensive, and increases the likelihood of errors. Therefore, the challenge lies in automating operational change management and documentation revisions, while also enabling adaptive responses that consider user emotions.

[0163] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0164] In this invention, the server includes means for receiving information on operational changes, means for identifying the parts of the relevant documents that need to be modified, means for automatically making the modifications, means for automatically creating change management slips and requesting approvals, and means for recognizing the emotional state of the user and adjusting the response accordingly. This makes it possible to efficiently and accurately manage operational changes and modify documents, and to provide an optimized interface for the user.

[0165] "Information regarding operational changes" refers to data and instructions related to changes in how systems and processes are operated.

[0166] "Related document revisions" refers to specific parts of documents that require revision due to operational changes.

[0167] "Automatically correcting" means that the system correctly edits the parts that need correction without requiring human intervention.

[0168] "Automating change management process creation and approval requests" means that the system automatically initiates the change management process and performs the necessary approval procedures.

[0169] "Recognizing the user's emotional state and adjusting responses" means determining the user's emotions from their input and actions, and adapting the system's responses and interface to that emotional state.

[0170] To implement this invention, a terminal is first required to receive information regarding operational changes. Information entered from this terminal is transmitted to a server. Based on the received information, the server uses a generative AI model and natural language processing technology to identify the parts of the document that need to be corrected and automatically makes the corrections. As a result, the creation of change management documents and approval requests are also processed automatically.

[0171] Furthermore, the server activates an emotion engine to recognize the user's emotional state. This engine determines emotions by analyzing the user's language patterns, input speed, and past interaction data. Based on the results, the server dynamically adjusts notification content and system interface design to improve the user experience.

[0172] As a concrete example, in an autonomous vehicle, if a user requests a change to the vehicle's operating schedule, the vehicle's computer sends this information to a server, which automatically modifies the necessary documents to match the new schedule. Simultaneously, if the emotion engine detects the user's anxiety, relaxing music is provided, enabling a more comfortable journey.

[0173] Example of a prompt message input to the generating AI model: "The user wants to change the vehicle's operating schedule. Please use the emotion engine to analyze the user's emotions and provide a safe and comfortable driving environment."

[0174] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0175] Step 1:

[0176] Users input information about operational changes using a terminal. This input includes the changes themselves and the reasons for them. This information is sent from the terminal to the server. The input information is processed as text data.

[0177] Step 2:

[0178] The server analyzes the received operational change information using natural language processing technology. It tokenizes the input text data and performs semantic analysis to identify specific sections of the relevant documents. Based on this, it generates a list of parts that require modification.

[0179] Step 3:

[0180] The server automatically corrects the listed errors using a generative AI model. The generative AI model generates appropriate wording for the document and uses that wording to make the corrections. The corrected document is saved to the server, and the updated document data is provided as output.

[0181] Step 4:

[0182] The server automatically initiates change management requests and approval requests. Based on the modified document data, it generates the appropriate request to the change management system and starts the approval routine. The input to this process is the updated document data, and the output is the approval status.

[0183] Step 5:

[0184] The server uses an emotion engine to recognize the user's emotions during input. It identifies emotions by analyzing the user's input speed and terminology choices, and comparing them with past interaction data. Based on the identified emotion data, the system adjusts the tone of notifications to the user and dynamically customizes the interface. As a result, an optimized interface is provided to the user.

[0185] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0186] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0187] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0188] [Second Embodiment]

[0189] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0190] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0191] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0192] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0193] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0194] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0195] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0196] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0197] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0198] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0199] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0200] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0201] This invention provides a system that automates document modification and change management processes associated with operational changes. This system functions by having a server perform a series of processes based on information about operational changes entered by users via a terminal.

[0202] First, the user inputs operational change information from their terminal and sends it to the server. The server analyzes the received information and uses artificial intelligence models and natural language processing techniques to understand the changes. This analysis identifies which documents and processes the operational changes will affect.

[0203] Next, the server automatically corrects documents within the identified scope of impact. Specifically, it uses AI technology to find the necessary sections within the documents and rewrite them with content based on the latest information. This enables rapid document updates while preventing errors.

[0204] Furthermore, the server automates the change management process. Specifically, it has a function that automatically creates change management tickets based on operational change information and sends approval requests to the relevant personnel. Once approval is complete, a notification is sent to the user, and the change is officially confirmed.

[0205] For example, if a user enters "Change from version 2.0 to 2.1 of software B," the server will use this information to search for relevant documents and update the version information to the latest version. In addition, it will quickly obtain necessary approvals and ensure a smooth transition of operations.

[0206] This system significantly reduces manual document revisions and approval processes, allowing users to focus on more important tasks. The goal of this automated process is to improve work efficiency and increase overall company productivity.

[0207] The following describes the processing flow.

[0208] Step 1:

[0209] The user uses a terminal to input information about the changes before and after the operation change management system interface and sends it to the server. The information includes the item to be changed, its state before the change, and its new state after the change.

[0210] Step 2:

[0211] The server analyzes the received operational change information. Here, it utilizes generative AI models and natural language processing techniques to break down the input information and understand the purpose and scope of the changes.

[0212] Step 3:

[0213] The server uses the analysis results to search the system for potentially affected documents and identify the changes. This includes identifying specific keywords and phrases within the documents.

[0214] Step 4:

[0215] For identified changes, the server uses natural language generation technology to automatically correct the document content. Specifically, it performs a process of rewriting outdated information to reflect the latest state.

[0216] Step 5:

[0217] The server automates the change management process. It generates a ticket corresponding to the operational change and sends an approval request to the designated approver.

[0218] Step 6:

[0219] After the approver completes the approval process through the system, the server confirms that the approval is complete. Upon approval, the server automatically sends a notification to the user, and the entire process of the operational change is completed.

[0220] (Example 1)

[0221] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0222] Responding to operational changes requires the rapid and accurate revision of relevant documents and the streamlining of the change management process. However, performing these tasks manually is time-consuming, labor-intensive, and carries the risk of errors. Furthermore, the approval process must be executed quickly, necessitating a system to address these challenges.

[0223] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0224] In this invention, the server includes means for receiving information on operational changes using an information processing device, means for identifying the parts of relevant documents that need to be modified based on the operational changes using generated artificial intelligence technology, and means for automatically modifying the identified parts and verifying the results. This enables accurate automatic modification of documents based on operational change information and efficient change management.

[0225] An "information processing device" is a device used to receive, analyze, and process data, and includes devices such as servers and computers.

[0226] "Operational changes" refer to changes in settings and procedures within a system or process, and are operations performed to adapt to new conditions or environments.

[0227] "Generated artificial intelligence technology" refers to artificial intelligence algorithms and models used to automate data analysis and processing.

[0228] "Document revisions" refer to specific locations or parts within a document that need to be updated or revised based on changes in operations.

[0229] "Means of identification" refer to methods and processes for analyzing information, identifying necessary parts, and determining appropriate actions.

[0230] An "automatic correction mechanism" is a mechanism that autonomously executes the necessary changes to detected areas and updates the content.

[0231] "Change management" refers to the entire process of managing the coordination, approval, and implementation of changes related to operational modifications.

[0232] An "approval request" refers to the act of making a request to seek approval from stakeholders regarding changes or processes.

[0233] "Means of notification" refers to methods or systems for conveying specific information to users or relevant departments.

[0234] This invention is an information processing system that automates document modification and change management processes associated with operational changes. The system starts operating when a user inputs information about operational changes using a terminal and sends it to the server.

[0235] Specifically, when the server receives information, it uses generative AI models and natural language processing techniques to analyze the operational change information. This analysis identifies which documents and processes are affected and determines where the documents need to be modified. While general AI and NLP techniques are used for the analysis, the ability to quickly and accurately access information in the database is particularly required.

[0236] The server then automatically corrects the identified documents. Leveraging AI technology, the server updates necessary sections based on specific rules and patterns, rewriting the documents to reflect the latest information. Because this process occurs without user intervention, corrections are made quickly, reducing human error.

[0237] Furthermore, the server automatically executes the change management process. This includes creating change management tickets based on operational change information, and then automatically sending approval requests to the relevant departments. Once approval is complete, the server sends a notification to the user.

[0238] For example, if a user enters "System X will be updated to add a new feature Y," the server will search for relevant documents, automatically incorporate information about the new feature, and, if necessary, send approval requests to relevant parties. Furthermore, the prompt text to be entered into the generating AI model could be a specific instruction such as, "Please reflect the update details in the document and start the approval process."

[0239] This system allows companies to respond quickly to operational changes and significantly improve process efficiency.

[0240] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0241] Step 1:

[0242] The user uses a terminal to input information about operational changes. The user uses a form to enter specific details of the changes, and the information is sent to the server via the terminal as input for the next step.

[0243] Step 2:

[0244] The server receives operational change information sent from the terminal. The server analyzes this information using a generative AI model and natural language processing technology to identify which documents or processes the operational change affects. In this step, the input text data is converted into string data for analysis, and the AI ​​model outputs the areas that need to be modified.

[0245] Step 3:

[0246] The server automatically modifies the relevant documents based on the identified changes. Using AI technology, the document content is accurately updated to reflect operational changes and output as a new version. In this step, the identified modification information is processed as input, and document data based on the latest information is output.

[0247] Step 4:

[0248] The server creates a change management ticket and automatically sends an approval request to the relevant department. Here, the modified document information is entered, and a notification prompting the start of the approval process is output. A prompt message is generated, and the system automatically sends the approval request.

[0249] Step 5:

[0250] The server monitors the approval process and sends a notification to the user once approval is complete. The approved status is processed as input, and a notification is output to the user's device. This allows the user to confirm that the operational changes have been officially reflected.

[0251] (Application Example 1)

[0252] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0253] Managing operational change information within factories often involves manually updating work orders and maintenance schedules, which is inefficient in environments where quick responses are required. Furthermore, manual updates carry the risk of human error. It is necessary to improve this situation and enhance work efficiency.

[0254] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0255] In this invention, the server includes means for receiving information on operational changes, means for identifying the parts of related information assets that need to be modified based on the operational changes, means for automatically modifying the parts that need to be modified, means for automatically creating change management slips and requesting approvals, and means for automatically updating physical work instructions and maintenance plans based on operational change information. This enables the rapid and accurate updating of documents and field instructions in response to operational changes.

[0256] "Operational changes" refer to revisions to procedures or settings in business processes or systems, and are changes made to adapt to new instructions or conditions.

[0257] "Information assets" refer to tangible or intangible information held by a company, including work instructions and maintenance plans, and are resources necessary for business operations.

[0258] "A section requiring modification" refers to a specific part of an information asset that needs to be updated due to a change in operations.

[0259] "Change management" is a set of processes for proposing, approving, implementing, and tracking changes.

[0260] "Automatic updating means" refers to a technology that updates information assets to the latest state as needed, without manual intervention, based on operational change information.

[0261] A "generative artificial intelligence model" is a system that incorporates learning algorithms used for information analysis and process automation.

[0262] The system implementing this invention enables the reception and analysis of operational change information, and the automatic updating of modified sections of specific information assets. The server first receives information about operational changes from users. This information is obtained from the factory's operational management system and various sensors. The received data is analyzed using the Google Cloud Natural Language API. As a result of the analysis, information assets that require modification are identified.

[0263] The server then uses a generative artificial intelligence model to automatically identify and update the corrections. This process leverages AWS AI services to rewrite work orders and maintenance plans in real time. Furthermore, change management is automated through Microsoft Power Automate, notifying responsible personnel if approval is required, and confirming the update once approval is complete. This entire process is directly reflected in field robots or terminals using Raspberry Pi, significantly improving work efficiency.

[0264] For example, if a factory machine requires an earlier scheduled maintenance, the system immediately receives this information and automatically updates the maintenance schedule. It also generates a prompt message such as, "Please update the relevant work orders based on the latest operational change information," to clarify instructions for the responsible personnel. This prompt helps stakeholders intuitively understand the necessary changes and respond quickly.

[0265] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0266] Step 1:

[0267] The user inputs operational change information from a terminal and sends that data to the server. The input data includes details of the operational change and information about the affected equipment and processes. The server receives this input and prepares the data for the next analysis step.

[0268] Step 2:

[0269] The server analyzes the received operational change information using the Google Cloud Natural Language API. The input is the operational change information sent by the user, and the output is a semantic understanding of the analyzed changes. Here, the AI ​​model performs natural language processing on the data to identify which parts of the information assets are affected.

[0270] Step 3:

[0271] The server prepares to correct the affected information assets based on the analysis results. Specifically, it utilizes AWS AI services to automatically extract the parts of documents and data that need correction. The input is the affected areas identified by the analysis, and the output is a list of the areas that need correction.

[0272] Step 4:

[0273] The server automatically updates the identified areas that need correction. For example, it rewrites work orders and maintenance schedules with the latest information. AWS AI services are used here to execute the specific correction tasks. The input is a list of areas that need correction, and the output is the updated information assets.

[0274] Step 5:

[0275] The server uses Microsoft Power Automate to automatically create change tickets and request approval from relevant parties for change management. Inputs include details of the operational change and information about the relevant parties, while output is an approval request notification. After approval is complete, the user is notified that the change has been finalized.

[0276] Step 6:

[0277] The terminal or robot performs on-site work based on the received update information. Specifically, it operates or maintains equipment according to the new work instructions. The input is the updated work instructions, and the output is the completion of the actual work.

[0278] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0279] This invention is a system that combines automation of document revision and change management processes associated with operational changes with an emotion engine that recognizes user emotions and adjusts the system's response based on those emotions.

[0280] Users input information about operational changes into the system interface using a terminal. The input information is sent to a server, which analyzes the operational changes. Generative artificial intelligence models and natural language processing techniques are used in the analysis to identify the parts of the affected related documents that need to be modified.

[0281] Furthermore, this system recognizes the user's emotions at the time of input by means of an emotion engine. This engine analyzes the user's language patterns, input speed, and past interaction data to determine the emotional state. The results are recorded on the server side and utilized to enhance the user experience.

[0282] When the automatic correction of the document is completed, the server initiates a change management process and automatically requests the necessary approvals. Here, based on the user's emotions recognized by the emotion engine, the system dynamically adjusts the content of the notification and the design of the interface to provide information in the most acceptable form for the user.

[0283] As a specific example, when the user inputs information such as "Change the security policy of System C", the server scans the existing policy document and automatically rewrites it to the latest policy. On the other hand, if the emotion engine determines that the user is anxious, the server provides a message in a gentle tone and additional support information.

[0284] This system improves the user experience and enhances the efficiency and accuracy of the entire operation change process. In this way, it becomes possible to make change management in business smoother and more effective.

[0285] The following describes the processing flow.

[0286] Step 1:

[0287] The user uses the terminal to access the operation change management system and inputs information before and after the change. At this time, the terminal records the input speed and the choice of words, etc., and transmits them to the emotion engine.

[0288] Step 2:

[0289] The server receives the operation change information transmitted from the terminal. The emotion engine analyzes the received information and recognizes the user's emotional state from the language patterns and input characteristics.

[0290] Step 3:

[0291] The server uses generative artificial intelligence models and natural language processing techniques to analyze operational change information. Based on the analysis, it searches for relevant documents and identifies areas that require correction.

[0292] Step 4:

[0293] The server uses natural language processing technology to automatically correct the document based on the identified areas that need correction. This prevents manual errors and allows for quick document updates.

[0294] Step 5:

[0295] The server automatically creates change management tickets and sends approval requests to the necessary approvers. Based on the emotional state determined by the emotion engine, the notification text and interface display are modified to be more user-friendly.

[0296] Step 6:

[0297] Once the approver completes the approval process using the system, the server records the result and sends a notification to the user confirming approval. The notification uses the most acceptable language, based on the results detected by the emotion engine.

[0298] Through this series of processes, the system can improve the user experience while efficiently managing operational changes.

[0299] (Example 2)

[0300] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0301] In the conventional operation change management process, there were problems such as low efficiency and accuracy of document correction associated with changes, and insufficient provision of the user experience. In addition, information provision that did not consider the user's feelings often lacked an appropriate user experience.

[0302] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in the second embodiment is realized by the following respective means.

[0303] In this invention, the server includes means for receiving information related to an operation change, means for specifying correction locations of related documents based on the operation change, means for automatically correcting the correction locations, means for automatically issuing and requesting approval for change management, means for recognizing the user's feelings, and means for adjusting the notification content and interface based on the feelings. Thereby, it becomes possible to improve the efficiency and accuracy of the operation change process while improving the user experience.

[0304] "Operation change" refers to changing procedures and policies related to the operation of a system or process.

[0305] "Means for receiving information" refers to a mechanism having a function capable of taking in user input and instructions as data.

[0306] "Means for specifying correction locations of related documents" refers to a function for automatically finding affected parts in existing documents based on an operation change.

[0307] "Means for automatically correcting" refers to a mechanism capable of correcting the specified correction locations using automated processes and technologies.

[0308] "Means for automatically issuing and requesting approval for change management" refers to a function for recording the change and automatically requesting necessary approval from relevant parties after the operation change is specified.

[0309] "Means of recognizing emotions" refers to a function that analyzes and determines a user's emotional state based on their input information and behavioral patterns.

[0310] "Means for adjusting notification content and interface" refers to a function that enables the dynamic modification of the content and display format of information provided according to the user's emotional state.

[0311] This invention is a system that automates efficient document revision and change management related to operational change management, and in addition, it provides a function that recognizes user emotions and provides appropriate feedback.

[0312] Users use a terminal to input information about operational changes into the system interface. This information is transmitted to a server via the internet. The server analyzes the operational change information using generative AI models and natural language processing techniques to identify sections in the relevant documents that require modification. Possible generative AI models include technologies such as OpenAI, a common natural language processing model.

[0313] Furthermore, the server uses an emotion engine to recognize the emotions a user is feeling when entering information. This engine analyzes the user's language patterns, input speed, and past interaction data. For example, if a user inputs "Change the security policy of system C," the server identifies the document related to the operational change and modifies that document. During this analysis process, if the user's input is rushed or brief, the server recognizes emotions such as anxiety or impatience.

[0314] Once the modifications are complete, the server automatically initiates a change management process and requests necessary approvals from relevant parties. At this stage, information tailored to the user's emotional state is provided. For example, if the server determines that the user is feeling anxious, it will provide reassuring messages and supplementary explanations in a gentle tone.

[0315] As a concrete example, let's look at an example of a prompt to a generative AI model. For instance, a prompt could be designed to say, "Based on information about operational changes and user sentiment, please suggest appropriate document changes and notification methods."

[0316] This system efficiently manages the operational change process while simultaneously improving the quality of the user experience. It reduces the workload associated with operational changes and enables smoother and more accurate change management.

[0317] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0318] Step 1:

[0319] The user uses a terminal to input information about the operational change into the interface. Specifically, they type "The security policy for System C needs to be changed" using the keyboard and click the submit button. This action sends the user's input data to the server.

[0320] Step 2:

[0321] The server provides operational change information received from the user as a prompt to the generating AI model. The input data includes text information entered by the user. The server uses the generating AI model to perform natural language processing, search for relevant documents, and identify areas that need correction. The output is information about the identified areas that need correction. Specifically, it searches for relevant security policy documents and displays the areas that need correction.

[0322] Step 3:

[0323] The server automatically updates the identified modifications. This involves retrieving existing documents from the database and rewriting them with the latest policy information. The input is information about the modifications and the new policy information, and the output is the updated document file. Specifically, the system automatically creates and saves a new document reflecting the changes.

[0324] Step 4:

[0325] Simultaneously, the device collects emotional data based on user input. This includes analyzing the user's input speed and text tone. Input data includes the user's input history and current interaction data. The server uses an emotional engine to analyze this data and identify the user's emotional state. The output is metadata indicating the user's emotions.

[0326] Step 5:

[0327] The server adjusts the notification content used when creating a change management process based on the user's emotional state obtained from the emotion engine. Specifically, if it determines that the user is anxious, it generates a reassuring message. This process uses the user's emotional metadata as input and outputs the adjusted notification message. The server sends these messages to relevant parties and automatically requests the necessary approvals.

[0328] (Application Example 2)

[0329] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0330] In modern information systems, frequent operational changes necessitate efficient documentation revisions and change management processes. Furthermore, providing user-friendly interfaces is crucial for improving the user experience. However, performing these tasks manually is time-consuming and labor-intensive, and increases the likelihood of errors. Therefore, the challenge lies in automating operational change management and documentation revisions, while also enabling adaptive responses that consider user emotions.

[0331] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0332] In this invention, the server includes means for receiving information on operational changes, means for identifying the parts of the relevant documents that need to be modified, means for automatically making the modifications, means for automatically creating change management slips and requesting approvals, and means for recognizing the emotional state of the user and adjusting the response accordingly. This makes it possible to efficiently and accurately manage operational changes and modify documents, and to provide an optimized interface for the user.

[0333] "Information regarding operational changes" refers to data and instructions related to changes in how systems and processes are operated.

[0334] "Related document revisions" refers to specific parts of documents that require revision due to operational changes.

[0335] "Automatically correcting" means that the system correctly edits the parts that need correction without requiring human intervention.

[0336] "Automating change management process creation and approval requests" means that the system automatically initiates the change management process and performs the necessary approval procedures.

[0337] "Recognizing the user's emotional state and adjusting responses" means determining the user's emotions from their input and actions, and adapting the system's responses and interface to that emotional state.

[0338] To implement this invention, a terminal is first required to receive information regarding operational changes. Information entered from this terminal is transmitted to a server. Based on the received information, the server uses a generative AI model and natural language processing technology to identify the parts of the document that need to be corrected and automatically makes the corrections. As a result, the creation of change management documents and approval requests are also processed automatically.

[0339] Furthermore, the server activates an emotion engine to recognize the user's emotional state. This engine determines emotions by analyzing the user's language patterns, input speed, and past interaction data. Based on the results, the server dynamically adjusts notification content and system interface design to improve the user experience.

[0340] As a concrete example, in an autonomous vehicle, if a user requests a change to the vehicle's operating schedule, the vehicle's computer sends this information to a server, which automatically modifies the necessary documents to match the new schedule. Simultaneously, if the emotion engine detects the user's anxiety, relaxing music is provided, enabling a more comfortable journey.

[0341] Example of a prompt message input to the generating AI model: "The user wants to change the vehicle's operating schedule. Please use the emotion engine to analyze the user's emotions and provide a safe and comfortable driving environment."

[0342] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0343] Step 1:

[0344] Users input information about operational changes using a terminal. This input includes the changes themselves and the reasons for them. This information is sent from the terminal to the server. The input information is processed as text data.

[0345] Step 2:

[0346] The server analyzes the received operational change information using natural language processing technology. It tokenizes the input text data and performs semantic analysis to identify specific sections of the relevant documents. Based on this, it generates a list of parts that require modification.

[0347] Step 3:

[0348] The server automatically corrects the listed errors using a generative AI model. The generative AI model generates appropriate wording for the document and uses that wording to make the corrections. The corrected document is saved to the server, and the updated document data is provided as output.

[0349] Step 4:

[0350] The server automatically initiates change management requests and approval requests. Based on the modified document data, it generates the appropriate request to the change management system and starts the approval routine. The input to this process is the updated document data, and the output is the approval status.

[0351] Step 5:

[0352] The server uses an emotion engine to recognize the user's emotions during input. It identifies emotions by analyzing the user's input speed and terminology choices, and comparing them with past interaction data. Based on the identified emotion data, the system adjusts the tone of notifications to the user and dynamically customizes the interface. As a result, an optimized interface is provided to the user.

[0353] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0354] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0355] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0356] [Third Embodiment]

[0357] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0358] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0359] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0360] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0361] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0362] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0363] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0364] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0365] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0366] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0367] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0368] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0369] This invention provides a system that automates document modification and change management processes associated with operational changes. This system functions by having a server perform a series of processes based on information about operational changes entered by users via a terminal.

[0370] First, the user inputs operational change information from their terminal and sends it to the server. The server analyzes the received information and uses artificial intelligence models and natural language processing techniques to understand the changes. This analysis identifies which documents and processes the operational changes will affect.

[0371] Next, the server automatically corrects documents within the identified scope of impact. Specifically, it uses AI technology to find the necessary sections within the documents and rewrite them with content based on the latest information. This enables rapid document updates while preventing errors.

[0372] Furthermore, the server automates the change management process. Specifically, it has a function that automatically creates change management tickets based on operational change information and sends approval requests to the relevant personnel. Once approval is complete, a notification is sent to the user, and the change is officially confirmed.

[0373] For example, if a user enters "Change from version 2.0 to 2.1 of software B," the server will use this information to search for relevant documents and update the version information to the latest version. In addition, it will quickly obtain necessary approvals and ensure a smooth transition of operations.

[0374] This system significantly reduces manual document revisions and approval processes, allowing users to focus on more important tasks. The goal of this automated process is to improve work efficiency and increase overall company productivity.

[0375] The following describes the processing flow.

[0376] Step 1:

[0377] The user uses a terminal to input information about the changes before and after the operation change management system interface and sends it to the server. The information includes the item to be changed, its state before the change, and its new state after the change.

[0378] Step 2:

[0379] The server analyzes the received operational change information. Here, it utilizes generative AI models and natural language processing techniques to break down the input information and understand the purpose and scope of the changes.

[0380] Step 3:

[0381] The server uses the analysis results to search the system for potentially affected documents and identify the changes. This includes identifying specific keywords and phrases within the documents.

[0382] Step 4:

[0383] For identified changes, the server uses natural language generation technology to automatically correct the document content. Specifically, it performs a process of rewriting outdated information to reflect the latest state.

[0384] Step 5:

[0385] The server automates the change management process. It generates a ticket corresponding to the operational change and sends an approval request to the designated approver.

[0386] Step 6:

[0387] After the approver completes the approval process through the system, the server confirms that the approval is complete. Upon approval, the server automatically sends a notification to the user, and the entire process of the operational change is completed.

[0388] (Example 1)

[0389] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0390] Responding to operational changes requires the rapid and accurate revision of relevant documents and the streamlining of the change management process. However, performing these tasks manually is time-consuming, labor-intensive, and carries the risk of errors. Furthermore, the approval process must be executed quickly, necessitating a system to address these challenges.

[0391] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0392] In this invention, the server includes means for receiving information on operational changes using an information processing device, means for identifying the parts of relevant documents that need to be modified based on the operational changes using generated artificial intelligence technology, and means for automatically modifying the identified parts and verifying the results. This enables accurate automatic modification of documents based on operational change information and efficient change management.

[0393] An "information processing device" is a device used to receive, analyze, and process data, and includes devices such as servers and computers.

[0394] "Operational changes" refer to changes in settings and procedures within a system or process, and are operations performed to adapt to new conditions or environments.

[0395] "Generated artificial intelligence technology" refers to artificial intelligence algorithms and models used to automate data analysis and processing.

[0396] "Document revisions" refer to specific locations or parts within a document that need to be updated or revised based on changes in operations.

[0397] "Means of identification" refer to methods and processes for analyzing information, identifying necessary parts, and determining appropriate actions.

[0398] An "automatic correction mechanism" is a mechanism that autonomously executes the necessary changes to detected areas and updates the content.

[0399] "Change management" refers to the entire process of managing the coordination, approval, and implementation of changes related to operational modifications.

[0400] An "approval request" refers to the act of making a request to seek approval from stakeholders regarding changes or processes.

[0401] "Means of notification" refers to methods or systems for conveying specific information to users or relevant departments.

[0402] This invention is an information processing system that automates document modification and change management processes associated with operational changes. The system starts operating when a user inputs information about operational changes using a terminal and sends it to the server.

[0403] Specifically, when the server receives information, it uses generative AI models and natural language processing techniques to analyze the operational change information. This analysis identifies which documents and processes are affected and determines where the documents need to be modified. While general AI and NLP techniques are used for the analysis, the ability to quickly and accurately access information in the database is particularly required.

[0404] The server then automatically corrects the identified documents. Leveraging AI technology, the server updates necessary sections based on specific rules and patterns, rewriting the documents to reflect the latest information. Because this process occurs without user intervention, corrections are made quickly, reducing human error.

[0405] Furthermore, the server automatically executes the change management process. This includes creating change management tickets based on operational change information, and then automatically sending approval requests to the relevant departments. Once approval is complete, the server sends a notification to the user.

[0406] For example, if a user enters "System X will be updated to add a new feature Y," the server will search for relevant documents, automatically incorporate information about the new feature, and, if necessary, send approval requests to relevant parties. Furthermore, the prompt text to be entered into the generating AI model could be a specific instruction such as, "Please reflect the update details in the document and start the approval process."

[0407] This system allows companies to respond quickly to operational changes and significantly improve process efficiency.

[0408] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0409] Step 1:

[0410] The user uses a terminal to input information about operational changes. The user uses a form to enter specific details of the changes, and the information is sent to the server via the terminal as input for the next step.

[0411] Step 2:

[0412] The server receives operational change information sent from the terminal. The server analyzes this information using a generative AI model and natural language processing technology to identify which documents or processes the operational change affects. In this step, the input text data is converted into string data for analysis, and the AI ​​model outputs the areas that need to be modified.

[0413] Step 3:

[0414] The server automatically modifies the relevant documents based on the identified changes. Using AI technology, the document content is accurately updated to reflect operational changes and output as a new version. In this step, the identified modification information is processed as input, and document data based on the latest information is output.

[0415] Step 4:

[0416] The server creates a change management ticket and automatically sends an approval request to the relevant department. Here, the modified document information is entered, and a notification prompting the start of the approval process is output. A prompt message is generated, and the system automatically sends the approval request.

[0417] Step 5:

[0418] The server monitors the approval process and sends a notification to the user once approval is complete. The approved status is processed as input, and a notification is output to the user's device. This allows the user to confirm that the operational changes have been officially reflected.

[0419] (Application Example 1)

[0420] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0421] Managing operational change information within factories often involves manually updating work orders and maintenance schedules, which is inefficient in environments where quick responses are required. Furthermore, manual updates carry the risk of human error. It is necessary to improve this situation and enhance work efficiency.

[0422] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0423] In this invention, the server includes means for receiving information on operational changes, means for identifying the parts of related information assets that need to be modified based on the operational changes, means for automatically modifying the parts that need to be modified, means for automatically creating change management slips and requesting approvals, and means for automatically updating physical work instructions and maintenance plans based on operational change information. This enables the rapid and accurate updating of documents and field instructions in response to operational changes.

[0424] "Operational changes" refer to revisions to procedures or settings in business processes or systems, and are changes made to adapt to new instructions or conditions.

[0425] "Information assets" refer to tangible or intangible information held by a company, including work instructions and maintenance plans, and are resources necessary for business operations.

[0426] "A section requiring modification" refers to a specific part of an information asset that needs to be updated due to a change in operations.

[0427] "Change management" is a set of processes for proposing, approving, implementing, and tracking changes.

[0428] "Automatic updating means" refers to a technology that updates information assets to the latest state as needed, without manual intervention, based on operational change information.

[0429] A "generative artificial intelligence model" is a system that incorporates learning algorithms used for information analysis and process automation.

[0430] The system implementing this invention enables the reception and analysis of operational change information, and the automatic updating of modified sections of specific information assets. The server first receives information about operational changes from users. This information is obtained from the factory's operational management system and various sensors. The received data is analyzed using the Google Cloud Natural Language API. As a result of the analysis, information assets that require modification are identified.

[0431] The server then uses a generative artificial intelligence model to automatically identify and update the corrections. This process leverages AWS AI services to rewrite work orders and maintenance plans in real time. Furthermore, change management is automated through Microsoft Power Automate, notifying responsible personnel if approval is required, and confirming the update once approval is complete. This entire process is directly reflected in field robots or terminals using Raspberry Pi, significantly improving work efficiency.

[0432] For example, if a factory machine requires an earlier scheduled maintenance, the system immediately receives this information and automatically updates the maintenance schedule. It also generates a prompt message such as, "Please update the relevant work orders based on the latest operational change information," to clarify instructions for the responsible personnel. This prompt helps stakeholders intuitively understand the necessary changes and respond quickly.

[0433] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0434] Step 1:

[0435] The user inputs operational change information from a terminal and sends that data to the server. The input data includes details of the operational change and information about the affected equipment and processes. The server receives this input and prepares the data for the next analysis step.

[0436] Step 2:

[0437] The server analyzes the received operational change information using the Google Cloud Natural Language API. The input is the operational change information sent by the user, and the output is a semantic understanding of the analyzed changes. Here, the AI ​​model performs natural language processing on the data to identify which parts of the information assets are affected.

[0438] Step 3:

[0439] The server prepares to correct the affected information assets based on the analysis results. Specifically, it utilizes AWS AI services to automatically extract the parts of documents and data that need correction. The input is the affected areas identified by the analysis, and the output is a list of the areas that need correction.

[0440] Step 4:

[0441] The server automatically updates the identified areas that need correction. For example, it rewrites work orders and maintenance schedules with the latest information. AWS AI services are used here to execute the specific correction tasks. The input is a list of areas that need correction, and the output is the updated information assets.

[0442] Step 5:

[0443] The server uses Microsoft Power Automate to automatically create change tickets and request approval from relevant parties for change management. Inputs include details of the operational change and information about the relevant parties, while output is an approval request notification. After approval is complete, the user is notified that the change has been finalized.

[0444] Step 6:

[0445] The terminal or robot performs on-site work based on the received update information. Specifically, it operates or maintains equipment according to the new work instructions. The input is the updated work instructions, and the output is the completion of the actual work.

[0446] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0447] This invention is a system that combines automation of document revision and change management processes associated with operational changes with an emotion engine that recognizes user emotions and adjusts the system's response based on those emotions.

[0448] Users input information about operational changes into the system interface using a terminal. The input information is sent to a server, which analyzes the operational changes. Generative artificial intelligence models and natural language processing techniques are used in the analysis to identify the parts of the affected related documents that need to be modified.

[0449] Furthermore, this system recognizes the user's emotions during input using an emotion engine. This engine analyzes the user's language patterns, input speed, and past interaction data to determine their emotional state. The results are recorded on the server side and used to enhance the user experience.

[0450] Once the document's automatic correction is complete, the server initiates a change management process and automatically requests the necessary approvals. Based on the user's emotions as recognized by the emotion engine, the system dynamically adjusts the notification content and interface design to deliver information in a way that is most acceptable to the user.

[0451] For example, if a user enters information such as "change the security policy for system C," the server will scan the existing policy document and automatically rewrite it with the latest policy. On the other hand, if the emotion engine determines that the user is feeling anxious, the server will provide a message in a gentler tone and additional support information.

[0452] This system improves the user experience and enhances the efficiency and accuracy of the entire operational change process. In this way, it makes change management in business operations smoother and more effective.

[0453] The following describes the processing flow.

[0454] Step 1:

[0455] The user uses a terminal to access the operational change management system and input information before and after the change. During this process, the terminal records input speed, word choice, and other details, and sends this information to the emotion engine.

[0456] Step 2:

[0457] The server receives operational change information sent from the terminal. The emotion engine analyzes the received information and recognizes the user's emotional state based on language patterns and input characteristics.

[0458] Step 3:

[0459] The server uses generative artificial intelligence models and natural language processing techniques to analyze operational change information. Based on the analysis, it searches for relevant documents and identifies areas that require correction.

[0460] Step 4:

[0461] The server uses natural language processing technology to automatically correct the document based on the identified areas that need correction. This prevents manual errors and allows for quick document updates.

[0462] Step 5:

[0463] The server automatically creates change management tickets and sends approval requests to the necessary approvers. Based on the emotional state determined by the emotion engine, the notification text and interface display are modified to be more user-friendly.

[0464] Step 6:

[0465] Once the approver completes the approval process using the system, the server records the result and sends a notification to the user confirming approval. The notification uses the most acceptable language, based on the results detected by the emotion engine.

[0466] Through this series of processes, the system can improve the user experience while efficiently managing operational changes.

[0467] (Example 2)

[0468] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0469] Traditional operational change management processes suffered from inefficiency and accuracy in document revisions associated with changes, resulting in an inadequate user experience. Furthermore, providing information without considering user sentiment often led to a lack of a proper user experience.

[0470] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0471] In this invention, the server includes means for receiving information about operational changes, means for identifying the parts of relevant documents that need to be modified based on the operational changes, means for automatically modifying the parts that need to be modified, means for automatically creating change management tickets and requesting approval, means for recognizing the user's emotions, and means for adjusting notification content and the interface based on the emotions. This makes it possible to improve the user experience while increasing the efficiency and accuracy of the operational change process.

[0472] "Operational changes" refer to altering procedures or policies related to the operation of a system or process.

[0473] "Means of receiving information" refers to a system equipped with the functionality to capture user input and instructions as data.

[0474] "Means for identifying the parts of related documents that need to be modified" refers to a function that automatically finds the affected parts in existing documents based on operational changes.

[0475] "Means of automatic correction" refers to a system that allows identified areas for correction to be fixed using automated processes and technologies.

[0476] "Methods for automatically creating change management slips and requesting approvals" refers to a function that, after an operational change has been identified, records that change and automatically requests necessary approvals from relevant parties.

[0477] "Means of recognizing emotions" refers to a function that analyzes and determines a user's emotional state based on their input information and behavioral patterns.

[0478] "Means for adjusting notification content and interface" refers to a function that enables the dynamic modification of the content and display format of information provided according to the user's emotional state.

[0479] This invention is a system that automates efficient document revision and change management related to operational change management, and in addition, it provides a function that recognizes user emotions and provides appropriate feedback.

[0480] Users use a terminal to input information about operational changes into the system interface. This information is transmitted to a server via the internet. The server analyzes the operational change information using generative AI models and natural language processing techniques to identify sections in the relevant documents that require modification. Possible generative AI models include technologies such as OpenAI, a common natural language processing model.

[0481] Furthermore, the server uses an emotion engine to recognize the emotions a user is feeling when entering information. This engine analyzes the user's language patterns, input speed, and past interaction data. For example, if a user inputs "Change the security policy of system C," the server identifies the document related to the operational change and modifies that document. During this analysis process, if the user's input is rushed or brief, the server recognizes emotions such as anxiety or impatience.

[0482] Once the modifications are complete, the server automatically initiates a change management process and requests necessary approvals from relevant parties. At this stage, information tailored to the user's emotional state is provided. For example, if the server determines that the user is feeling anxious, it will provide reassuring messages and supplementary explanations in a gentle tone.

[0483] As a concrete example, let's look at an example of a prompt to a generative AI model. For instance, a prompt could be designed to say, "Based on information about operational changes and user sentiment, please suggest appropriate document changes and notification methods."

[0484] This system efficiently manages the operational change process while simultaneously improving the quality of the user experience. It reduces the workload associated with operational changes and enables smoother and more accurate change management.

[0485] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0486] Step 1:

[0487] The user uses a terminal to input information about the operational change into the interface. Specifically, they type "The security policy for System C needs to be changed" using the keyboard and click the submit button. This action sends the user's input data to the server.

[0488] Step 2:

[0489] The server provides operational change information received from the user as a prompt to the generating AI model. The input data includes text information entered by the user. The server uses the generating AI model to perform natural language processing, search for relevant documents, and identify areas that need correction. The output is information about the identified areas that need correction. Specifically, it searches for relevant security policy documents and displays the areas that need correction.

[0490] Step 3:

[0491] The server automatically updates the identified modifications. This involves retrieving existing documents from the database and rewriting them with the latest policy information. The input is information about the modifications and the new policy information, and the output is the updated document file. Specifically, the system automatically creates and saves a new document reflecting the changes.

[0492] Step 4:

[0493] Simultaneously, the device collects emotional data based on user input. This includes analyzing the user's input speed and text tone. Input data includes the user's input history and current interaction data. The server uses an emotional engine to analyze this data and identify the user's emotional state. The output is metadata indicating the user's emotions.

[0494] Step 5:

[0495] The server adjusts the notification content used when creating a change management process based on the user's emotional state obtained from the emotion engine. Specifically, if it determines that the user is anxious, it generates a reassuring message. This process uses the user's emotional metadata as input and outputs the adjusted notification message. The server sends these messages to relevant parties and automatically requests the necessary approvals.

[0496] (Application Example 2)

[0497] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0498] In modern information systems, frequent operational changes necessitate efficient documentation revisions and change management processes. Furthermore, providing user-friendly interfaces is crucial for improving the user experience. However, performing these tasks manually is time-consuming and labor-intensive, and increases the likelihood of errors. Therefore, the challenge lies in automating operational change management and documentation revisions, while also enabling adaptive responses that consider user emotions.

[0499] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0500] In this invention, the server includes means for receiving information on operational changes, means for identifying the parts of the relevant documents that need to be modified, means for automatically making the modifications, means for automatically creating change management slips and requesting approvals, and means for recognizing the emotional state of the user and adjusting the response accordingly. This makes it possible to efficiently and accurately manage operational changes and modify documents, and to provide an optimized interface for the user.

[0501] "Information regarding operational changes" refers to data and instructions related to changes in how systems and processes are operated.

[0502] "Related document revisions" refers to specific parts of documents that require revision due to operational changes.

[0503] "Automatically correcting" means that the system correctly edits the parts that need correction without requiring human intervention.

[0504] "Automating change management process creation and approval requests" means that the system automatically initiates the change management process and performs the necessary approval procedures.

[0505] "Recognizing the user's emotional state and adjusting responses" means determining the user's emotions from their input and actions, and adapting the system's responses and interface to that emotional state.

[0506] To implement this invention, a terminal is first required to receive information regarding operational changes. Information entered from this terminal is transmitted to a server. Based on the received information, the server uses a generative AI model and natural language processing technology to identify the parts of the document that need to be corrected and automatically makes the corrections. As a result, the creation of change management documents and approval requests are also processed automatically.

[0507] Furthermore, the server activates an emotion engine to recognize the user's emotional state. This engine determines emotions by analyzing the user's language patterns, input speed, and past interaction data. Based on the results, the server dynamically adjusts notification content and system interface design to improve the user experience.

[0508] As a concrete example, in an autonomous vehicle, if a user requests a change to the vehicle's operating schedule, the vehicle's computer sends this information to a server, which automatically modifies the necessary documents to match the new schedule. Simultaneously, if the emotion engine detects the user's anxiety, relaxing music is provided, enabling a more comfortable journey.

[0509] Example of a prompt message input to the generating AI model: "The user wants to change the vehicle's operating schedule. Please use the emotion engine to analyze the user's emotions and provide a safe and comfortable driving environment."

[0510] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0511] Step 1:

[0512] Users input information about operational changes using a terminal. This input includes the changes themselves and the reasons for them. This information is sent from the terminal to the server. The input information is processed as text data.

[0513] Step 2:

[0514] The server analyzes the received operational change information using natural language processing technology. It tokenizes the input text data and performs semantic analysis to identify specific sections of the relevant documents. Based on this, it generates a list of parts that require modification.

[0515] Step 3:

[0516] The server automatically corrects the listed errors using a generative AI model. The generative AI model generates appropriate wording for the document and uses that wording to make the corrections. The corrected document is saved to the server, and the updated document data is provided as output.

[0517] Step 4:

[0518] The server automatically initiates change management requests and approval requests. Based on the modified document data, it generates the appropriate request to the change management system and starts the approval routine. The input to this process is the updated document data, and the output is the approval status.

[0519] Step 5:

[0520] The server uses an emotion engine to recognize the user's emotions during input. It identifies emotions by analyzing the user's input speed and terminology choices, and comparing them with past interaction data. Based on the identified emotion data, the system adjusts the tone of notifications to the user and dynamically customizes the interface. As a result, an optimized interface is provided to the user.

[0521] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0522] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0523] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0524] [Fourth Embodiment]

[0525] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0526] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0527] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0528] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0529] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0530] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0531] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0532] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0533] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0534] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0535] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0536] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0537] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0538] This invention provides a system that automates document modification and change management processes associated with operational changes. This system functions by having a server perform a series of processes based on information about operational changes entered by users via a terminal.

[0539] First, the user inputs operational change information from their terminal and sends it to the server. The server analyzes the received information and uses artificial intelligence models and natural language processing techniques to understand the changes. This analysis identifies which documents and processes the operational changes will affect.

[0540] Next, the server automatically corrects documents within the identified scope of impact. Specifically, it uses AI technology to find the necessary sections within the documents and rewrite them with content based on the latest information. This enables rapid document updates while preventing errors.

[0541] Furthermore, the server automates the change management process. Specifically, it has a function that automatically creates change management tickets based on operational change information and sends approval requests to the relevant personnel. Once approval is complete, a notification is sent to the user, and the change is officially confirmed.

[0542] For example, if a user enters "Change from version 2.0 to 2.1 of software B," the server will use this information to search for relevant documents and update the version information to the latest version. In addition, it will quickly obtain necessary approvals and ensure a smooth transition of operations.

[0543] This system significantly reduces manual document revisions and approval processes, allowing users to focus on more important tasks. The goal of this automated process is to improve work efficiency and increase overall company productivity.

[0544] The following describes the processing flow.

[0545] Step 1:

[0546] The user uses a terminal to input information about the changes before and after the operation change management system interface and sends it to the server. The information includes the item to be changed, its state before the change, and its new state after the change.

[0547] Step 2:

[0548] The server analyzes the received operational change information. Here, it utilizes generative AI models and natural language processing techniques to break down the input information and understand the purpose and scope of the changes.

[0549] Step 3:

[0550] The server uses the analysis results to search the system for potentially affected documents and identify the changes. This includes identifying specific keywords and phrases within the documents.

[0551] Step 4:

[0552] For identified changes, the server uses natural language generation technology to automatically correct the document content. Specifically, it performs a process of rewriting outdated information to reflect the latest state.

[0553] Step 5:

[0554] The server automates the change management process. It generates a ticket corresponding to the operational change and sends an approval request to the designated approver.

[0555] Step 6:

[0556] After the approver completes the approval process through the system, the server confirms that the approval is complete. Upon approval, the server automatically sends a notification to the user, and the entire process of the operational change is completed.

[0557] (Example 1)

[0558] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0559] Responding to operational changes requires the rapid and accurate revision of relevant documents and the streamlining of the change management process. However, performing these tasks manually is time-consuming, labor-intensive, and carries the risk of errors. Furthermore, the approval process must be executed quickly, necessitating a system to address these challenges.

[0560] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0561] In this invention, the server includes means for receiving information on operational changes using an information processing device, means for identifying the parts of relevant documents that need to be modified based on the operational changes using generated artificial intelligence technology, and means for automatically modifying the identified parts and verifying the results. This enables accurate automatic modification of documents based on operational change information and efficient change management.

[0562] An "information processing device" is a device used to receive, analyze, and process data, and includes devices such as servers and computers.

[0563] "Operational changes" refer to changes in settings and procedures within a system or process, and are operations performed to adapt to new conditions or environments.

[0564] "Generated artificial intelligence technology" refers to artificial intelligence algorithms and models used to automate data analysis and processing.

[0565] "Document revisions" refer to specific locations or parts within a document that need to be updated or revised based on changes in operations.

[0566] "Means of identification" refer to methods and processes for analyzing information, identifying necessary parts, and determining appropriate actions.

[0567] An "automatic correction mechanism" is a mechanism that autonomously executes the necessary changes to detected areas and updates the content.

[0568] "Change management" refers to the entire process of managing the coordination, approval, and implementation of changes related to operational modifications.

[0569] An "approval request" refers to the act of making a request to seek approval from stakeholders regarding changes or processes.

[0570] "Means of notification" refers to methods or systems for conveying specific information to users or relevant departments.

[0571] This invention is an information processing system that automates document modification and change management processes associated with operational changes. The system starts operating when a user inputs information about operational changes using a terminal and sends it to the server.

[0572] Specifically, when the server receives information, it uses generative AI models and natural language processing techniques to analyze the operational change information. This analysis identifies which documents and processes are affected and determines where the documents need to be modified. While general AI and NLP techniques are used for the analysis, the ability to quickly and accurately access information in the database is particularly required.

[0573] The server then automatically corrects the identified documents. Leveraging AI technology, the server updates necessary sections based on specific rules and patterns, rewriting the documents to reflect the latest information. Because this process occurs without user intervention, corrections are made quickly, reducing human error.

[0574] Furthermore, the server automatically executes the change management process. This includes creating change management tickets based on operational change information, and then automatically sending approval requests to the relevant departments. Once approval is complete, the server sends a notification to the user.

[0575] For example, if a user enters "System X will be updated to add a new feature Y," the server will search for relevant documents, automatically incorporate information about the new feature, and, if necessary, send approval requests to relevant parties. Furthermore, the prompt text to be entered into the generating AI model could be a specific instruction such as, "Please reflect the update details in the document and start the approval process."

[0576] This system allows companies to respond quickly to operational changes and significantly improve process efficiency.

[0577] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0578] Step 1:

[0579] The user uses a terminal to input information about operational changes. The user uses a form to enter specific details of the changes, and the information is sent to the server via the terminal as input for the next step.

[0580] Step 2:

[0581] The server receives operational change information sent from the terminal. The server analyzes this information using a generative AI model and natural language processing technology to identify which documents or processes the operational change affects. In this step, the input text data is converted into string data for analysis, and the AI ​​model outputs the areas that need to be modified.

[0582] Step 3:

[0583] The server automatically modifies the relevant documents based on the identified changes. Using AI technology, the document content is accurately updated to reflect operational changes and output as a new version. In this step, the identified modification information is processed as input, and document data based on the latest information is output.

[0584] Step 4:

[0585] The server creates a change management ticket and automatically sends an approval request to the relevant department. Here, the modified document information is entered, and a notification prompting the start of the approval process is output. A prompt message is generated, and the system automatically sends the approval request.

[0586] Step 5:

[0587] The server monitors the approval process and sends a notification to the user once approval is complete. The approved status is processed as input, and a notification is output to the user's device. This allows the user to confirm that the operational changes have been officially reflected.

[0588] (Application Example 1)

[0589] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0590] Managing operational change information within factories often involves manually updating work orders and maintenance schedules, which is inefficient in environments where quick responses are required. Furthermore, manual updates carry the risk of human error. It is necessary to improve this situation and enhance work efficiency.

[0591] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0592] In this invention, the server includes means for receiving information on operational changes, means for identifying the parts of related information assets that need to be modified based on the operational changes, means for automatically modifying the parts that need to be modified, means for automatically creating change management slips and requesting approvals, and means for automatically updating physical work instructions and maintenance plans based on operational change information. This enables the rapid and accurate updating of documents and field instructions in response to operational changes.

[0593] "Operational changes" refer to revisions to procedures or settings in business processes or systems, and are changes made to adapt to new instructions or conditions.

[0594] "Information assets" refer to tangible or intangible information held by a company, including work instructions and maintenance plans, and are resources necessary for business operations.

[0595] "A section requiring modification" refers to a specific part of an information asset that needs to be updated due to a change in operations.

[0596] "Change management" is a set of processes for proposing, approving, implementing, and tracking changes.

[0597] "Automatic updating means" refers to a technology that updates information assets to the latest state as needed, without manual intervention, based on operational change information.

[0598] A "generative artificial intelligence model" is a system that incorporates learning algorithms used for information analysis and process automation.

[0599] The system implementing this invention enables the reception and analysis of operational change information, and the automatic updating of modified sections of specific information assets. The server first receives information about operational changes from users. This information is obtained from the factory's operational management system and various sensors. The received data is analyzed using the Google Cloud Natural Language API. As a result of the analysis, information assets that require modification are identified.

[0600] The server then uses a generative artificial intelligence model to automatically identify and update the corrections. This process leverages AWS AI services to rewrite work orders and maintenance plans in real time. Furthermore, change management is automated through Microsoft Power Automate, notifying responsible personnel if approval is required, and confirming the update once approval is complete. This entire process is directly reflected in field robots or terminals using Raspberry Pi, significantly improving work efficiency.

[0601] For example, if a factory machine requires an earlier scheduled maintenance, the system immediately receives this information and automatically updates the maintenance schedule. It also generates a prompt message such as, "Please update the relevant work orders based on the latest operational change information," to clarify instructions for the responsible personnel. This prompt helps stakeholders intuitively understand the necessary changes and respond quickly.

[0602] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0603] Step 1:

[0604] The user inputs operational change information from a terminal and sends that data to the server. The input data includes details of the operational change and information about the affected equipment and processes. The server receives this input and prepares the data for the next analysis step.

[0605] Step 2:

[0606] The server analyzes the received operational change information using the Google Cloud Natural Language API. The input is the operational change information sent by the user, and the output is a semantic understanding of the analyzed changes. Here, the AI ​​model performs natural language processing on the data to identify which parts of the information assets are affected.

[0607] Step 3:

[0608] The server prepares to correct the affected information assets based on the analysis results. Specifically, it utilizes AWS AI services to automatically extract the parts of documents and data that need correction. The input is the affected areas identified by the analysis, and the output is a list of the areas that need correction.

[0609] Step 4:

[0610] The server automatically updates the identified areas that need correction. For example, it rewrites work orders and maintenance schedules with the latest information. AWS AI services are used here to execute the specific correction tasks. The input is a list of areas that need correction, and the output is the updated information assets.

[0611] Step 5:

[0612] The server uses Microsoft Power Automate to automatically create change tickets and request approval from relevant parties for change management. Inputs include details of the operational change and information about the relevant parties, while output is an approval request notification. After approval is complete, the user is notified that the change has been finalized.

[0613] Step 6:

[0614] The terminal or robot performs on-site work based on the received update information. Specifically, it operates or maintains equipment according to the new work instructions. The input is the updated work instructions, and the output is the completion of the actual work.

[0615] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0616] This invention is a system that combines automation of document revision and change management processes associated with operational changes with an emotion engine that recognizes user emotions and adjusts the system's response based on those emotions.

[0617] Users input information about operational changes into the system interface using a terminal. The input information is sent to a server, which analyzes the operational changes. Generative artificial intelligence models and natural language processing techniques are used in the analysis to identify the parts of the affected related documents that need to be modified.

[0618] Furthermore, this system recognizes the user's emotions during input using an emotion engine. This engine analyzes the user's language patterns, input speed, and past interaction data to determine their emotional state. The results are recorded on the server side and used to enhance the user experience.

[0619] Once the document's automatic correction is complete, the server initiates a change management process and automatically requests the necessary approvals. Based on the user's emotions as recognized by the emotion engine, the system dynamically adjusts the notification content and interface design to deliver information in a way that is most acceptable to the user.

[0620] For example, if a user enters information such as "change the security policy for system C," the server will scan the existing policy document and automatically rewrite it with the latest policy. On the other hand, if the emotion engine determines that the user is feeling anxious, the server will provide a message in a gentler tone and additional support information.

[0621] This system improves the user experience and enhances the efficiency and accuracy of the entire operational change process. In this way, it makes change management in business operations smoother and more effective.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] The user uses a terminal to access the operational change management system and input information before and after the change. During this process, the terminal records input speed, word choice, and other details, and sends this information to the emotion engine.

[0625] Step 2:

[0626] The server receives operational change information sent from the terminal. The emotion engine analyzes the received information and recognizes the user's emotional state based on language patterns and input characteristics.

[0627] Step 3:

[0628] The server uses generative artificial intelligence models and natural language processing techniques to analyze operational change information. Based on the analysis, it searches for relevant documents and identifies areas that require correction.

[0629] Step 4:

[0630] The server uses natural language processing technology to automatically correct the document based on the identified areas that need correction. This prevents manual errors and allows for quick document updates.

[0631] Step 5:

[0632] The server automatically creates change management tickets and sends approval requests to the necessary approvers. Based on the emotional state determined by the emotion engine, the notification text and interface display are modified to be more user-friendly.

[0633] Step 6:

[0634] Once the approver completes the approval process using the system, the server records the result and sends a notification to the user confirming approval. The notification uses the most acceptable language, based on the results detected by the emotion engine.

[0635] Through this series of processes, the system can improve the user experience while efficiently managing operational changes.

[0636] (Example 2)

[0637] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0638] Traditional operational change management processes suffered from inefficiency and accuracy in document revisions associated with changes, resulting in an inadequate user experience. Furthermore, providing information without considering user sentiment often led to a lack of a proper user experience.

[0639] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0640] In this invention, the server includes means for receiving information about operational changes, means for identifying the parts of relevant documents that need to be modified based on the operational changes, means for automatically modifying the parts that need to be modified, means for automatically creating change management tickets and requesting approval, means for recognizing the user's emotions, and means for adjusting notification content and the interface based on the emotions. This makes it possible to improve the user experience while increasing the efficiency and accuracy of the operational change process.

[0641] "Operational changes" refer to altering procedures or policies related to the operation of a system or process.

[0642] "Means of receiving information" refers to a system equipped with the functionality to capture user input and instructions as data.

[0643] "Means for identifying the parts of related documents that need to be modified" refers to a function that automatically finds the affected parts in existing documents based on operational changes.

[0644] "Means of automatic correction" refers to a system that allows identified areas for correction to be fixed using automated processes and technologies.

[0645] "Methods for automatically creating change management slips and requesting approvals" refers to a function that, after an operational change has been identified, records that change and automatically requests necessary approvals from relevant parties.

[0646] "Means of recognizing emotions" refers to a function that analyzes and determines a user's emotional state based on their input information and behavioral patterns.

[0647] "Means for adjusting notification content and interface" refers to a function that enables the dynamic modification of the content and display format of information provided according to the user's emotional state.

[0648] This invention is a system that automates efficient document revision and change management related to operational change management, and in addition, it provides a function that recognizes user emotions and provides appropriate feedback.

[0649] Users use a terminal to input information about operational changes into the system interface. This information is transmitted to a server via the internet. The server analyzes the operational change information using generative AI models and natural language processing techniques to identify sections in the relevant documents that require modification. Possible generative AI models include technologies such as OpenAI, a common natural language processing model.

[0650] Furthermore, the server uses an emotion engine to recognize the emotions a user is feeling when entering information. This engine analyzes the user's language patterns, input speed, and past interaction data. For example, if a user inputs "Change the security policy of system C," the server identifies the document related to the operational change and modifies that document. During this analysis process, if the user's input is rushed or brief, the server recognizes emotions such as anxiety or impatience.

[0651] Once the modifications are complete, the server automatically initiates a change management process and requests necessary approvals from relevant parties. At this stage, information tailored to the user's emotional state is provided. For example, if the server determines that the user is feeling anxious, it will provide reassuring messages and supplementary explanations in a gentle tone.

[0652] As a concrete example, let's look at an example of a prompt to a generative AI model. For instance, a prompt could be designed to say, "Based on information about operational changes and user sentiment, please suggest appropriate document changes and notification methods."

[0653] This system efficiently manages the operational change process while simultaneously improving the quality of the user experience. It reduces the workload associated with operational changes and enables smoother and more accurate change management.

[0654] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0655] Step 1:

[0656] The user uses a terminal to input information about the operational change into the interface. Specifically, they type "The security policy for System C needs to be changed" using the keyboard and click the submit button. This action sends the user's input data to the server.

[0657] Step 2:

[0658] The server provides operational change information received from the user as a prompt to the generating AI model. The input data includes text information entered by the user. The server uses the generating AI model to perform natural language processing, search for relevant documents, and identify areas that need correction. The output is information about the identified areas that need correction. Specifically, it searches for relevant security policy documents and displays the areas that need correction.

[0659] Step 3:

[0660] The server automatically updates the identified modifications. This involves retrieving existing documents from the database and rewriting them with the latest policy information. The input is information about the modifications and the new policy information, and the output is the updated document file. Specifically, the system automatically creates and saves a new document reflecting the changes.

[0661] Step 4:

[0662] Simultaneously, the device collects emotional data based on user input. This includes analyzing the user's input speed and text tone. Input data includes the user's input history and current interaction data. The server uses an emotional engine to analyze this data and identify the user's emotional state. The output is metadata indicating the user's emotions.

[0663] Step 5:

[0664] The server adjusts the notification content used when creating a change management process based on the user's emotional state obtained from the emotion engine. Specifically, if it determines that the user is anxious, it generates a reassuring message. This process uses the user's emotional metadata as input and outputs the adjusted notification message. The server sends these messages to relevant parties and automatically requests the necessary approvals.

[0665] (Application Example 2)

[0666] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0667] In modern information systems, frequent operational changes necessitate efficient documentation revisions and change management processes. Furthermore, providing user-friendly interfaces is crucial for improving the user experience. However, performing these tasks manually is time-consuming and labor-intensive, and increases the likelihood of errors. Therefore, the challenge lies in automating operational change management and documentation revisions, while also enabling adaptive responses that consider user emotions.

[0668] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0669] In this invention, the server includes means for receiving information on operational changes, means for identifying the parts of the relevant documents that need to be modified, means for automatically making the modifications, means for automatically creating change management slips and requesting approvals, and means for recognizing the emotional state of the user and adjusting the response accordingly. This makes it possible to efficiently and accurately manage operational changes and modify documents, and to provide an optimized interface for the user.

[0670] "Information regarding operational changes" refers to data and instructions related to changes in how systems and processes are operated.

[0671] "Related document revisions" refers to specific parts of documents that require revision due to operational changes.

[0672] "Automatically correcting" means that the system correctly edits the parts that need correction without requiring human intervention.

[0673] "Automating change management process creation and approval requests" means that the system automatically initiates the change management process and performs the necessary approval procedures.

[0674] "Recognizing the user's emotional state and adjusting responses" means determining the user's emotions from their input and actions, and adapting the system's responses and interface to that emotional state.

[0675] To implement this invention, a terminal is first required to receive information regarding operational changes. Information entered from this terminal is transmitted to a server. Based on the received information, the server uses a generative AI model and natural language processing technology to identify the parts of the document that need to be corrected and automatically makes the corrections. As a result, the creation of change management documents and approval requests are also processed automatically.

[0676] Furthermore, the server activates an emotion engine to recognize the user's emotional state. This engine determines emotions by analyzing the user's language patterns, input speed, and past interaction data. Based on the results, the server dynamically adjusts notification content and system interface design to improve the user experience.

[0677] As a concrete example, in an autonomous vehicle, if a user requests a change to the vehicle's operating schedule, the vehicle's computer sends this information to a server, which automatically modifies the necessary documents to match the new schedule. Simultaneously, if the emotion engine detects the user's anxiety, relaxing music is provided, enabling a more comfortable journey.

[0678] Example of a prompt message input to the generating AI model: "The user wants to change the vehicle's operating schedule. Please use the emotion engine to analyze the user's emotions and provide a safe and comfortable driving environment."

[0679] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0680] Step 1:

[0681] Users input information about operational changes using a terminal. This input includes the changes themselves and the reasons for them. This information is sent from the terminal to the server. The input information is processed as text data.

[0682] Step 2:

[0683] The server analyzes the received operational change information using natural language processing technology. It tokenizes the input text data and performs semantic analysis to identify specific sections of the relevant documents. Based on this, it generates a list of parts that require modification.

[0684] Step 3:

[0685] The server automatically corrects the listed errors using a generative AI model. The generative AI model generates appropriate wording for the document and uses that wording to make the corrections. The corrected document is saved to the server, and the updated document data is provided as output.

[0686] Step 4:

[0687] The server automatically initiates change management requests and approval requests. Based on the modified document data, it generates the appropriate request to the change management system and starts the approval routine. The input to this process is the updated document data, and the output is the approval status.

[0688] Step 5:

[0689] The server uses an emotion engine to recognize the user's emotions during input. It identifies emotions by analyzing the user's input speed and terminology choices, and comparing them with past interaction data. Based on the identified emotion data, the system adjusts the tone of notifications to the user and dynamically customizes the interface. As a result, an optimized interface is provided to the user.

[0690] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0691] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0692] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0693] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0694] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0695] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0696] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0697] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0698] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0699] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0700] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0701] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0702] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0703] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0704] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0705] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0706] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0707] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0708] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0709] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0710] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0711] The following is further disclosed regarding the embodiments described above.

[0712] (Claim 1)

[0713] A means of receiving information regarding operational changes,

[0714] A means for identifying the parts of the relevant documents that need to be modified based on the aforementioned operational changes,

[0715] A means for automatically correcting the aforementioned corrections,

[0716] A means of automatically creating change management tickets and requesting approvals,

[0717] A system that includes this.

[0718] (Claim 2)

[0719] The system according to claim 1, which analyzes the operational change information using natural language processing technology and modifies the document.

[0720] (Claim 3)

[0721] The system according to claim 1, which uses a generative artificial intelligence model to analyze operational change information and identify areas for correction.

[0722] "Example 1"

[0723] (Claim 1)

[0724] A means for receiving information regarding changes in operation using an information processing device,

[0725] A means for identifying the parts of the relevant documents that need to be modified based on the changes in the operation, using the generated artificial intelligence technology,

[0726] A means to automatically identify and correct the problematic areas and verify the results,

[0727] A means to automatically create documents for change management and send approval requests to the relevant departments,

[0728] A means of sending a notification to the user after approval is complete,

[0729] A system that includes this.

[0730] (Claim 2)

[0731] The system according to claim 1, which uses natural language processing technology to analyze information on changes in the operation and modifies the document.

[0732] (Claim 3)

[0733] The system according to claim 1, which uses a generated data processing model to analyze operational change information and identify areas for correction.

[0734] "Application Example 1"

[0735] (Claim 1)

[0736] A means of receiving information regarding operational changes,

[0737] A means for identifying the parts of the relevant information assets that need to be modified based on the aforementioned operational changes,

[0738] A means for automatically correcting the aforementioned corrections,

[0739] A means of automatically creating change management tickets and requesting approvals,

[0740] A means to automatically update physical work instructions and maintenance plans based on operational change information,

[0741] A system that includes this.

[0742] (Claim 2)

[0743] The system according to claim 1, which analyzes the operational change information using natural language processing technology, modifies the information assets, and automatically updates physical work instructions and maintenance plans.

[0744] (Claim 3)

[0745] The system according to claim 1, which uses a generative artificial intelligence model to analyze operational change information, identify areas for correction, and further update physical work instructions and maintenance plans.

[0746] "Example 2 of combining an emotion engine"

[0747] (Claim 1)

[0748] A means of receiving information regarding operational changes,

[0749] A means for identifying the parts of the relevant documents that need to be modified based on the aforementioned operational changes,

[0750] A means for automatically correcting the aforementioned corrections,

[0751] A means of automatically creating change management tickets and requesting approvals,

[0752] Means of recognizing user emotions,

[0753] Means for adjusting notification content and interface based on the aforementioned emotions,

[0754] A system that includes this.

[0755] (Claim 2)

[0756] The system according to claim 1, which analyzes the operational change information using natural language processing technology and modifies the document.

[0757] (Claim 3)

[0758] The system according to claim 1, which uses a generative artificial intelligence model to analyze operational change information and identify areas for correction.

[0759] "Application example 2 when combining with an emotional engine"

[0760] (Claim 1)

[0761] A means of receiving information regarding operational changes,

[0762] A means for identifying the parts of the relevant documents that need to be modified based on the aforementioned operational changes,

[0763] A means for automatically correcting the aforementioned corrections,

[0764] A means of automatically creating change management tickets and requesting approvals,

[0765] A means of recognizing the user's emotional state and adjusting the response,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, which analyzes the operational change information using natural language processing technology and modifies the document.

[0769] (Claim 3)

[0770] The system according to claim 1, which uses a generative artificial intelligence model to analyze operational change information and identify areas for correction. [Explanation of symbols]

[0771] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving information regarding operational changes, A means for identifying the parts of the relevant documents that need to be modified based on the aforementioned operational changes, A means for automatically correcting the aforementioned corrections, A means of automatically creating change management tickets and requesting approvals, A system that includes this.

2. The system according to claim 1, which analyzes the operational change information using natural language processing technology and modifies the document.

3. The system according to claim 1, which uses a generative artificial intelligence model to analyze operational change information and identify areas for correction.

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