system

Abstract

We provide the system. [Solution] Means of receiving customer inquiries, A means of analyzing received inquiries and determining the operational tasks that should be performed, Means for performing system operations to carry out the determined operational tasks, A means of reporting the execution results to the user, 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 persona chatbot control method 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the operation of an enterprise system, it is difficult to secure the necessary personnel to maintain a 24 / 7 / 365 operation. Especially in a cloud environment, immediate response and high judgment are required for diverse operation systems. Under such circumstances, automation technology for efficient operation with limited personnel needs to solve both the problem of insufficient manpower and the requirement for quick and accurate response.

Means for Solving the Problems

[0005] This invention provides a means for receiving customer inquiries, analyzing the content of those inquiries, and automatically determining the operational tasks that should be performed. It also includes means for performing necessary system operations based on the analysis results, thereby creating a system that handles inquiries without requiring human intervention. Furthermore, by providing means for autonomously determining necessary response operations through monitoring system alerts and means for performing periodic maintenance, the invention achieves efficient and continuous system operation.

[0006] "Means for receiving customer inquiries" refers to a function that provides an interface for receiving communications from users and transmitting their content to the system.

[0007] "Means for analyzing received inquiries and determining the operational tasks to be performed" refers to a function that interprets received information and executes a process to determine the necessary system operations.

[0008] "Means for performing system operations to execute determined operational tasks" refers to functions that perform procedures on the system to execute actions requested based on the analysis results.

[0009] "Means of reporting execution results to the user" refers to the means of organizing the results of operations performed by the system and communicating them to the user.

[0010] "A means of monitoring system alerts and autonomously determining the appropriate operational tasks to be performed" refers to a function that detects changes in the system's state and selects the appropriate response based on those changes.

[0011] "Means for determining and performing maintenance work that is required on a regular basis" refers to a function that schedules and executes maintenance work necessary to maintain the normal operation of the system. [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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

[0015] In the following embodiments, the labeled 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 labeled 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 labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[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 automates corporate system operation tasks using an AI agent. Specifically, it is a system in which an AI agent receives customer inquiries, analyzes them, selects appropriate operational tasks, and executes them. The system aims to automate routine tasks when complex operations are required in cloud environments, etc.

[0034] Users can submit inquiries to the system via chatbot or email. The server receives these inquiries and sends them to an AI agent. The agent analyzes the inquiry and determines the necessary action. In this process, natural language processing is used to understand the inquiry and identify specific tasks.

[0035] Based on the analysis results, the terminal performs the necessary system operations. For example, if there is an inquiry to check the server load status, the AI ​​agent uses that information to retrieve data from the monitoring system and reports the results to the user. The report is then delivered to the user again via the server.

[0036] Furthermore, the system has a continuous monitoring function, and if an alert is detected, the AI ​​agent can immediately determine a response and issue instructions to the terminal. In addition, regular maintenance work is also scheduled and carried out autonomously.

[0037] This invention aims to improve the efficiency of operational tasks, reduce the burden on system administrators, and enable quick and accurate responses. Specific examples include automatically restarting servers upon detecting an anomaly, or checking and reporting database backup status upon inquiry. This supports 24 / 7 operation and can address labor shortages.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users enter and submit their specific inquiries through chatbots or email systems. For example, they might ask, "I want to check the latest database backup."

[0041] Step 2:

[0042] The server receives inquiries from users and forwards them to the AI ​​agent. At this time, it formats the data appropriately to ensure the inquiries are processed correctly.

[0043] Step 3:

[0044] The AI ​​agent running on the server analyzes the received inquiry using a natural language processing engine. This analysis identifies the intent of the inquiry and the necessary operational actions.

[0045] Step 4:

[0046] The AI ​​agent determines the specific tasks to be performed based on the analysis results. For example, it might decide that it should retrieve information from the latest backup file of the database.

[0047] Step 5:

[0048] The terminal receives instructions from the AI ​​agent and performs the necessary operations on the target system. In this case, it accesses the database server and retrieves the latest backup information.

[0049] Step 6:

[0050] The device sends the acquired execution results back to the AI ​​agent. The AI ​​agent then reformats them into a format that is easy for the user to understand.

[0051] Step 7:

[0052] The server reports the formatted results received from the AI ​​agent to the user. The user can then view the backup date and time, as well as details of its status.

[0053] (Example 1)

[0054] 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."

[0055] In the operation of modern, complex information systems, manual information processing tasks require considerable effort, and labor shortages and human errors become particularly problematic when 24 / 7 / 365 support is necessary. Therefore, there is a need to automate information processing tasks to ensure rapid and accurate responses. Furthermore, automation of appropriate decision-making based on system status and maintenance tasks is also required.

[0056] 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.

[0057] In this invention, the server includes information processing means for receiving queries, information processing means for analyzing the received queries using a generation AI model and determining the information processing content to be performed, and information processing means for operating information devices based on the determined information processing content. This automates the operation of information systems, enabling rapid and accurate information processing while mitigating problems such as labor shortages and human errors.

[0058] An "inquiry" is a message or request sent by a user of an information system to request information or a task.

[0059] "Information processing means" refers to processes and devices for analyzing information, determining the content of work based on that analysis, and then executing that work.

[0060] A "generative AI model" is an algorithm that uses natural language processing to analyze text and understand its meaning.

[0061] "Information equipment" refers to devices that perform data processing and communication, including various devices such as computers and network equipment.

[0062] "Communication means" refers to protocols and devices used to send and receive information, i.e., networks and communication devices.

[0063] A "user" refers to a person or organization that uses the system or makes inquiries.

[0064] "Information systems" refers to all systems that collect, process, store, and distribute data, and include hardware and software.

[0065] This invention automates the operation of information systems by utilizing a generative AI model. Specific embodiments are described below.

[0066] Hardware and software configuration

[0067] The server is equipped with information processing means such as a chatbot or mail server for receiving inquiries. The server analyzes the received inquiries using a generative AI model and determines the appropriate information processing content. The generative AI model used here is expected to be an algorithm with natural language processing capabilities (for example, GPT-4®).

[0068] The terminal possesses the processing power and connectivity to operate information equipment in accordance with instructions from the server. Specifically, this includes application software for operating database management software and server monitoring systems.

[0069] Data processing and calculations

[0070] When the server analyzes the query, it uses natural language processing to process the text data and understand the user's intent. Based on this analysis, the terminal retrieves the necessary information and reprocesses the data to report it to the user. This entire process relies on the efficient generation of prompt messages.

[0071] Specific example

[0072] For example, a user might send a request to a chatbot saying, "Please report the current server load status." In this case, the server uses a generative AI model to analyze the request and instructs the terminal to retrieve the latest load information from the server monitoring system. The retrieved data is then reported to the user via the server.

[0073] Examples of prompts include "Please tell me the schedule for the next database backup" and "Please provide detailed instructions on how to respond when an anomaly is detected." The system can automatically provide appropriate information in response to such prompts.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user submits a query to the information system. This query is sent to the server in text format using a chatbot or email client. The input is the user's query, and the output is the text data received by the server. For example, the user might input "Please tell me the next maintenance schedule."

[0077] Step 2:

[0078] The server analyzes the received query using a generative AI model. The input is the text data received by the server in step 1, and natural language processing is performed based on this data to understand the intent of the query. The output is informational data indicating the analyzed intent. Specifically, the generative AI model extracts "maintenance scheduled" from the query sentence to obtain guidance for accessing related information.

[0079] Step 3:

[0080] The terminal performs the necessary processing based on the analysis results received from the server. The input is the analysis result data sent from the server, and the terminal uses this data to generate specific instructions for the information devices and systems to be operated. The output is the information data obtained as a result of the operation. As a specific example, the terminal accesses the schedule management system to obtain the date and time of the next maintenance.

[0081] Step 4:

[0082] The server reports information to the user based on the execution results received from the terminal. The input is the execution result data from the terminal, and the server generates the information to be returned to the user based on this data. The output is the report content sent to the user. Specifically, the server displays the message "The next maintenance is scheduled for 10:00 AM on May 10th" to the user's chatbot.

[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] With the advancement of modern information technology, corporate system management requires the processing of vast amounts of information and rapid response. However, traditional methods require significant human resources for handling inquiries, monitoring systems, and maintenance, leading to increased burdens on administrators. Security management, in particular, demands immediacy and accuracy, making manual processes impractical. To address this challenge, a method for efficient and autonomous system operation is necessary.

[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 from customers, means for analyzing the received information to determine what activities should be performed, and means for performing device operations to carry out the determined activities. This allows for autonomous system monitoring and the rapid execution of appropriate operational activities, thereby reducing the burden on administrators and enabling efficient system operation. Furthermore, by using a natural language processing model, the content of inquiries can be analyzed at a high level, and the situation can be confirmed in cooperation with the monitoring system. This makes it possible to further automate the management of the security system and expedite responses.

[0088] "Customer information" refers to the content of inquiries and requests provided by users or customers.

[0089] "Means for analyzing information and determining what activities should be taken" refers to elements that have the function of understanding received information and identifying the necessary responses and processes.

[0090] "Means for performing device operation" refers to elements that have the function of controlling the devices or systems necessary to actually carry out the determined activity.

[0091] A "natural language processing model" is a set of technical methods and algorithms designed to understand and analyze human language.

[0092] A "monitoring system" is a device or program used to monitor the status of a system or network and detect anomalies or changes.

[0093] "Activities" refer to specific tasks and actions performed in system operation and management.

[0094] "Administrator" refers to an individual or group responsible for the operation and management of a system or network.

[0095] "Inquiry details" refers to the specific information and issues contained in questions or requests provided by customers.

[0096] In this embodiment of the invention, the process begins with a server receiving and analyzing information from a customer. A natural language processing model is used for the analysis to gain a high level of understanding of the inquiry. The natural language processing model used here is preferably based on widely available AI technology in the market. Based on the analysis results, the server determines the actions to be taken and confirms the situation in cooperation with a monitoring system. The monitoring system utilizes multiple sensors and software to constantly monitor the system status and detect anomalies.

[0097] Next, the server performs device operations to carry out the determined activity. These operations are performed via a system management terminal and include software updates and security setting adjustments as needed. This ensures that the system operates in an optimal state.

[0098] As a concrete example, in corporate security management, a server may receive information from a customer such as "I want to check the latest security status," and automatically check and update the relevant security settings. The terminal then reports the results obtained from these operations to the user in real time.

[0099] An example of a prompt message used during implementation would be, "Please check the latest security status of the corporate network and update security patches." Based on this, the server can identify the specific actions that need to be taken and proceed with execution.

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The server receives an inquiry from a customer. The user then sends information via chatbot or email. The server stores this data in storage and performs preprocessing to feed it into a natural language processing model.

[0103] Step 2:

[0104] The server analyzes the input inquiry using a natural language processing model. Here, the server receives the customer's inquiry text as input data, and the natural language model analyzes this information to determine the actions and responses that should be taken. This analysis determines whether actions such as security checks or configuration changes are necessary.

[0105] Step 3:

[0106] The server identifies the activities to be performed based on the analysis results and accesses the monitoring system to check the current status. The analysis results are provided as input, and monitoring data is obtained as output. This data is used to determine the specific processing content and targets.

[0107] Step 4:

[0108] The server performs the necessary device operations to carry out the determined activities. Specifically, it adjusts security settings and updates the system based on information obtained from the monitoring system. Here, it receives instructions from the monitoring system as input and obtains updated settings and new system status as output.

[0109] Step 5:

[0110] The terminal reports these execution results to the user. The user can receive the report from the terminal and review its contents. The input is the execution result data received from the server, and the output is notification information to the user. This notification includes the latest security status check results and details of the configuration changes made.

[0111] 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.

[0112] This invention combines a system that automates system operation tasks using an AI agent with an emotion engine that recognizes user emotions. This system aims to improve the user experience by enabling responses that take into account the user's emotional state, in addition to normal inquiry processing.

[0113] Users make inquiries through the digital platform, while an emotion engine performs emotion recognition in the background. Specifically, along with the inquiry, the server invokes the emotion engine to analyze the emotional nuances contained in the user's text. This engine utilizes natural language processing technology to evaluate the emotional tone of the text.

[0114] The server transmits the inquiry content and sentiment analysis results to the AI ​​agent. In addition to analyzing the normal inquiry content, the AI ​​agent processes the inquiry by combining it with responses that take into account the user's emotional state. For example, if the user is feeling dissatisfied or stressed, the AI ​​agent will choose a polite and reassuring response.

[0115] The emotion data evaluated by the emotion engine helps generate appropriate responses. Emotion-based customization of responses is applied to system operations performed by the terminal. As a result, the terminal performs flexible, user-centric operations, returns the results to the server, and the server notifies the user of the results.

[0116] For example, when a user expresses concern about server performance, providing not only technical information but also encouraging and reassuring messages tailored to the user's feelings can reduce their stress.

[0117] This system aims to improve customer satisfaction by streamlining basic system operations while simultaneously providing more personalized interactions through emotion recognition.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] Users submit inquiries to the system using a chat interface or email. At this point, the user's message may contain emotional elements.

[0121] Step 2:

[0122] The server passes the user's query to the sentiment engine, which performs sentiment analysis. This process uses natural language processing algorithms to identify the emotional tone of the text.

[0123] Step 3:

[0124] The server transfers the emotion data obtained from the analysis results and the inquiry data to the AI ​​agent. This data includes the types and intensity of emotions the user may be feeling.

[0125] Step 4:

[0126] The AI ​​agent processes the received inquiry content by combining it with sentiment data. The agent determines the user's needs and decides on the optimal system action to address them.

[0127] Step 5:

[0128] The terminal performs the determined system operations. These operations include not only normal technical responses but also the preparation of emotion-based, personalized responses.

[0129] Step 6:

[0130] The terminal generates an emotionally sensitive response along with the execution result and sends it to the server. The response includes a polite and appropriate message that takes the user's emotions into consideration.

[0131] Step 7:

[0132] The server communicates this response to the user. The user can have a better experience by receiving an emotionally resonant message along with specific operational results.

[0133] (Example 2)

[0134] 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 will be referred to as the "terminal."

[0135] In modern system operations, there is a demand not only for responding to technical inquiries but also for providing personalized interactions that take customer emotions into consideration. However, traditional methods have struggled to accurately recognize emotions and generate appropriate responses based on them. As a result, the customer experience has been limited, and there have been limitations in improving satisfaction.

[0136] 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.

[0137] In this invention, the server includes means for receiving inquiries from customers, means for analyzing the received inquiries and recognizing emotions, and means for using a generative AI model that generates an appropriate response based on the analysis results and the content of the inquiry. This makes it possible to generate a response that accurately reflects the customer's emotions.

[0138] "Customer" refers to the user who utilizes this system as the recipient of goods or services.

[0139] An "inquiry" refers to information or questions that a customer sends to resolve doubts or problems they have with the system.

[0140] "Analysis" refers to the process of thoroughly analyzing the content of an inquiry received in order to understand its meaning and intent.

[0141] "Emotion recognition" refers to the technology that identifies a customer's emotional state from their inquiry text and evaluates its tone and nuances.

[0142] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate an appropriate response from input information.

[0143] "Customization" refers to modifying or adjusting the generated response to suit the individual customer's emotions and circumstances.

[0144] "Output device" refers to equipment or interfaces used to provide the generated response to the customer.

[0145] A "system warning" refers to an alert that notifies you of anomalies or significant changes in the system's state.

[0146] "Maintenance work" refers to conservative activities that need to be carried out periodically to ensure that a system continues to function properly.

[0147] This invention is a system for effectively handling customer inquiries and generating emotionally sensitive responses. The implementation of this invention primarily involves servers, terminals, and users. The details of each are specifically outlined below.

[0148] When the server receives a query, it uses an emotion engine to perform sentiment analysis on the text. This emotion engine utilizes "natural language processing technology," and can include, for example, "general sentiment recognition systems." This engine understands the customer's emotional tone and generates analysis results.

[0149] Subsequently, the server uses a generative AI model to generate the optimal response based on the analysis of the inquiry content and sentiment. This generation process utilizes generative AI tools such as "OpenAI® GPT-3®". The server inputs prompts into this model and requests it to generate a response.

[0150] The terminal further customizes the response received from the server and provides it to the user in its final form. For example, if the user states, "I'm worried about the server's slow response," the following prompt might be entered into the generative AI model:

[0151] "User inquiry: 'I'm worried about the slow server response.' The AI ​​agent should generate a message that provides a technical explanation and reassures the user in response to this inquiry."

[0152] In this step, the terminal appropriately customizes the results and generates an emotionally sensitive response. Finally, the terminal returns the generated response to the server, which then communicates its contents to the user. This entire process ensures that user inquiries are handled quickly and effectively, leading to improved customer satisfaction.

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] Users send inquiries to the server via a digital platform. These inquiries are in text format, and users enter their problems or questions into the server by pressing the submit button. The server receives them and proceeds to the next process.

[0156] Step 2:

[0157] The server inputs the received text data into the emotion engine. Here, "natural language processing technology" is used to analyze the emotional nuances within the user's text. The emotion engine analyzes the text and outputs an emotion score and emotion category (e.g., "anxiety" or "satisfaction"). This output serves as the basis for customizing the response.

[0158] Step 3:

[0159] The server invokes a generative AI model based on the analyzed sentiment data and the query content. Specifically, it uses the "OpenAI GPT-3" generative AI model to receive prompt text and generate an appropriate response. The prompt text reflects the query content and the results of the sentiment analysis. The generative AI model outputs direct responses to the query, as well as messages that provide reassurance.

[0160] Step 4:

[0161] The terminal receives response data from the server and generates the final output for the user. In this process, the output response is further customized and adjusted to suit the individual user's emotional state. The terminal transforms the response into a format that is easier to understand and more sensitive to the user's emotions.

[0162] Step 5:

[0163] The customized response from the device is returned to the server. The server sends the final result to the user. The user receives this and can confirm the response to their inquiry. This improves the quality of the user experience by resolving the problem and providing reassurance.

[0164] (Application Example 2)

[0165] 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".

[0166] In physical stores, employees are required to respond flexibly to customers' emotions when interacting with them face-to-face. However, it is difficult for employees to instantly and accurately grasp the emotions of every customer and provide service accordingly. There is a need to provide a solution to this problem and improve customer satisfaction.

[0167] 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.

[0168] In this invention, the server includes means for receiving customer inquiries, means for analyzing the received inquiries and determining the operational tasks to be performed, and means for analyzing the customer's emotions using emotion recognition technology and generating a response based on those emotions. This enables employees to understand customer emotions in real time and provide appropriate services.

[0169] An "inquiry" is a means of communication that allows customers to seek information about products or services or report problems.

[0170] "Operational work" is a general term for operations and processes performed for the purpose of maintaining and efficiently running a system.

[0171] "Emotion recognition technology" is a technology that analyzes a customer's facial expressions, voice, and behavior to identify their emotional state.

[0172] "Real-time evaluation" is a processing method that immediately analyzes the ongoing situation and reflects the results as soon as possible.

[0173] "Visual display" refers to showing information in a visible form on a display or device.

[0174] "Employee interpersonal skills" refers to the communication and service provision activities that staff engaged in customer service perform for customers.

[0175] The system implementing this invention exchanges information between a server, a terminal, and a user, and realizes interaction utilizing emotion recognition technology. The server receives a query sent by the user and performs content analysis using natural language processing technology. Next, emotion recognition technology is applied to evaluate the user's emotional state. For this evaluation, it is preferable to use software such as Google® Cloud Natural Language API or Microsoft® Azure® Text Analytics.

[0176] Based on the analyzed information, the server uses a generative AI model to construct appropriate responses to inquiries. This includes flexible replies that take into account the user's emotions. The terminal device visually displays the responses received from the server to the employee handling customer service. Specific examples of such devices include smart glasses. These glasses capture the customer's facial expressions and voice in real time and present suggestions to support the employee's actions during customer service.

[0177] For example, if the system detects that a customer is feeling anxious, it might display a message such as, "Please let us know if you need any assistance." Another example of prompt text to input into the generation AI model might be, "What emotions is this customer showing, and what is the appropriate response?"

[0178] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0179] Step 1:

[0180] The user enters their inquiry and sends it to the server via their device. This inquiry is the input and is structured as text data. The server receives this data and obtains the necessary information for the next processing step.

[0181] Step 2:

[0182] The server sends the text data of the query to a natural language processing engine (e.g., Google Cloud Natural Language API). The engine analyzes the input text data and outputs information to understand its content and intent. The output includes the subject and key entities of the text.

[0183] Step 3:

[0184] Based on the analysis results, the server uses emotion recognition technology to evaluate the user's emotional state. In this process, the previously obtained text data and associated emotion recognition algorithms are applied to quantify the emotional tone, which is then output as the evaluation result.

[0185] Step 4:

[0186] The server combines the analysis results and sentiment evaluation data and inputs them into a generative AI model. Using the prompt, "What emotion is this customer showing, and what is the appropriate response?", the model generates the optimal response accordingly. The output is a customized reply message designed to provide the user with appropriate service.

[0187] Step 5:

[0188] The terminal visually displays customized messages received from the server to the employee. When smart glasses are used, they support the employee's interaction with others by displaying response suggestions tailored to the user's state. Input is messages from the server, and output is visually displayed information.

[0189] Step 6:

[0190] Employees provide services based on the information displayed on their devices and request feedback from users as needed. The information output from the devices leads to the employees' actual actions.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Second Embodiment]

[0195] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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".

[0207] This invention automates corporate system operation tasks using an AI agent. Specifically, it is a system in which an AI agent receives customer inquiries, analyzes them, selects appropriate operational tasks, and executes them. The system aims to automate routine tasks when complex operations are required in cloud environments, etc.

[0208] Users can submit inquiries to the system via chatbot or email. The server receives these inquiries and sends them to an AI agent. The agent analyzes the inquiry and determines the necessary action. In this process, natural language processing is used to understand the inquiry and identify specific tasks.

[0209] Based on the analysis results, the terminal performs the necessary system operations. For example, if there is an inquiry to check the server load status, the AI ​​agent uses that information to retrieve data from the monitoring system and reports the results to the user. The report is then delivered to the user again via the server.

[0210] Furthermore, the system has a continuous monitoring function, and if an alert is detected, the AI ​​agent can immediately determine a course of action and issue instructions to the terminal. In addition, regular maintenance work is also scheduled and carried out autonomously.

[0211] This invention aims to improve the efficiency of operational tasks, reduce the burden on system administrators, and enable quick and accurate responses. Specific examples include automatically restarting servers upon detecting an anomaly, or checking and reporting database backup status upon inquiry. This supports 24 / 7 operation and can address labor shortages.

[0212] The following describes the processing flow.

[0213] Step 1:

[0214] Users enter and submit their specific inquiries through chatbots or email systems. For example, they might ask, "I want to check the latest database backup."

[0215] Step 2:

[0216] The server receives inquiries from users and forwards them to the AI ​​agent. At this time, it formats the data appropriately to ensure the inquiries are processed correctly.

[0217] Step 3:

[0218] The AI ​​agent running on the server analyzes the received inquiry using a natural language processing engine. This analysis identifies the intent of the inquiry and the necessary operational actions.

[0219] Step 4:

[0220] The AI ​​agent determines the specific tasks to be performed based on the analysis results. For example, it might decide that it should retrieve information from the latest backup file of the database.

[0221] Step 5:

[0222] The terminal receives instructions from the AI ​​agent and performs the necessary operations on the target system. In this case, it accesses the database server and retrieves the latest backup information.

[0223] Step 6:

[0224] The device sends the acquired execution results back to the AI ​​agent. The AI ​​agent then reformats them into a format that is easy for the user to understand.

[0225] Step 7:

[0226] The server reports the formatted results received from the AI ​​agent to the user. The user can then view the backup date and time, as well as details of its status.

[0227] (Example 1)

[0228] 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."

[0229] In the operation of modern, complex information systems, manual information processing tasks require considerable effort, and labor shortages and human errors become particularly problematic when 24 / 7 / 365 support is necessary. Therefore, there is a need to automate information processing tasks to ensure rapid and accurate responses. Furthermore, automation of appropriate decision-making based on system status and maintenance tasks is also required.

[0230] 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.

[0231] In this invention, the server includes information processing means for receiving queries, information processing means for analyzing the received queries using a generation AI model and determining the information processing content to be performed, and information processing means for operating information devices based on the determined information processing content. This automates the operation of information systems, enabling rapid and accurate information processing while mitigating problems such as labor shortages and human errors.

[0232] An "inquiry" is a message or request sent by a user of an information system to request information or a task.

[0233] "Information processing means" refers to processes and devices for analyzing information, determining the content of work based on that analysis, and then executing that work.

[0234] A "generative AI model" is an algorithm that uses natural language processing to analyze text and understand its meaning.

[0235] "Information equipment" refers to devices that perform data processing and communication, including various devices such as computers and network equipment.

[0236] "Communication means" refers to protocols and devices used to send and receive information, i.e., networks and communication devices.

[0237] A "user" refers to a person or organization that uses the system or makes inquiries.

[0238] "Information systems" refers to all systems that collect, process, store, and distribute data, and include hardware and software.

[0239] This invention automates the operation of information systems by utilizing a generative AI model. Specific embodiments are described below.

[0240] Hardware and software configuration

[0241] The server is equipped with information processing means such as a chatbot or mail server for receiving inquiries. The server analyzes the received inquiries using a generative AI model and determines the appropriate information processing content. The generative AI model used here is expected to be an algorithm with natural language processing capabilities (e.g., GPT-4).

[0242] The terminal possesses the processing power and connectivity to operate information equipment in accordance with instructions from the server. Specifically, this includes application software for operating database management software and server monitoring systems.

[0243] Data processing and calculations

[0244] When the server analyzes the query, it uses natural language processing to process the text data and understand the user's intent. Based on this analysis, the terminal retrieves the necessary information and reprocesses the data to report it to the user. This entire process relies on the efficient generation of prompt messages.

[0245] Specific example

[0246] For example, a user might send a request to a chatbot saying, "Please report the current server load status." In this case, the server uses a generative AI model to analyze the request and instructs the terminal to retrieve the latest load information from the server monitoring system. The retrieved data is then reported to the user via the server.

[0247] Examples of prompts include "Please tell me the schedule for the next database backup" and "Please provide detailed instructions on how to respond when an anomaly is detected." The system can automatically provide appropriate information in response to such prompts.

[0248] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0249] Step 1:

[0250] The user submits a query to the information system. This query is sent to the server in text format using a chatbot or email client. The input is the user's query, and the output is the text data received by the server. For example, the user might input "Please tell me the next maintenance schedule."

[0251] Step 2:

[0252] The server analyzes the received query using a generative AI model. The input is the text data received by the server in step 1, and natural language processing is performed based on this data to understand the intent of the query. The output is informational data indicating the analyzed intent. Specifically, the generative AI model extracts "maintenance scheduled" from the query sentence to obtain guidance for accessing related information.

[0253] Step 3:

[0254] The terminal performs the necessary processing based on the analysis results received from the server. The input is the analysis result data sent from the server, and the terminal uses this data to generate specific instructions for the information devices and systems to be operated. The output is the information data obtained as a result of the operation. As a specific example, the terminal accesses the schedule management system to obtain the date and time of the next maintenance.

[0255] Step 4:

[0256] The server reports information to the user based on the execution results received from the terminal. The input is the execution result data from the terminal, and the server generates the information to be returned to the user based on this data. The output is the report content sent to the user. Specifically, the server displays the message "The next maintenance is scheduled for 10:00 AM on May 10th" to the user's chatbot.

[0257] (Application Example 1)

[0258] 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."

[0259] With the advancement of modern information technology, corporate system management requires the processing of vast amounts of information and rapid response. However, traditional methods require significant human resources for handling inquiries, monitoring systems, and maintenance, leading to increased burdens on administrators. Security management, in particular, demands immediacy and accuracy, making manual processes impractical. To address this challenge, a method for efficient and autonomous system operation is necessary.

[0260] 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.

[0261] In this invention, the server includes means for receiving information from customers, means for analyzing the received information to determine what activities should be performed, and means for performing device operations to carry out the determined activities. This allows for autonomous system monitoring and the rapid execution of appropriate operational activities, thereby reducing the burden on administrators and enabling efficient system operation. Furthermore, by using a natural language processing model, the content of inquiries can be analyzed at a high level, and the situation can be confirmed in cooperation with the monitoring system. This makes it possible to further automate the management of the security system and expedite responses.

[0262] "Customer information" refers to the content of inquiries and requests provided by users or customers.

[0263] "Means for analyzing information and determining what activities should be taken" refers to elements that have the function of understanding received information and identifying the necessary responses and processes.

[0264] "Means for performing device operation" refers to elements that have the function of controlling the devices or systems necessary to actually carry out the determined activity.

[0265] A "natural language processing model" is a set of technical methods and algorithms designed to understand and analyze human language.

[0266] A "monitoring system" is a device or program used to monitor the status of a system or network and detect anomalies or changes.

[0267] "Activities" refer to specific tasks and actions performed in system operation and management.

[0268] "Administrator" refers to an individual or group responsible for the operation and management of a system or network.

[0269] "Inquiry details" refers to the specific information and issues contained in questions or requests provided by customers.

[0270] In this embodiment of the invention, the process begins with a server receiving and analyzing information from a customer. A natural language processing model is used for the analysis to gain a high level of understanding of the inquiry. The natural language processing model used here is preferably based on widely available AI technology in the market. Based on the analysis results, the server determines the actions to be taken and confirms the situation in cooperation with a monitoring system. The monitoring system utilizes multiple sensors and software to constantly monitor the system status and detect anomalies.

[0271] Next, the server performs device operations to carry out the determined activity. These operations are performed via a system management terminal and include software updates and security setting adjustments as needed. This ensures that the system operates in an optimal state.

[0272] As a concrete example, in corporate security management, a server may receive information from a customer such as "I want to check the latest security status," and automatically check and update the relevant security settings. The terminal then reports the results obtained from these operations to the user in real time.

[0273] An example of a prompt message used during implementation would be, "Please check the latest security status of the corporate network and update security patches." Based on this, the server can identify the specific actions that need to be taken and proceed with execution.

[0274] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0275] Step 1:

[0276] The server receives an inquiry from a customer. At this time, the user sends information via a chatbot or email. The server saves the data in storage and performs preprocessing for input into the natural language processing model.

[0277] Step 2:

[0278] The server analyzes the received inquiry using a natural language processing model. Here, it receives the customer's inquiry text as input data, and the natural language model analyzes the information and determines the activities or responses to be executed. Through this analysis, it is determined whether security confirmation or setting changes are required as the activity content.

[0279] Step 3:

[0280] The server identifies the activities to be executed based on the analysis results and accesses the monitoring system to check the current situation. The analysis results are provided as input, and monitoring data is obtained as output. This data is used to determine the specific processing content and targets.

[0281] Step 4:

[0282] The server performs the device operations necessary to execute the determined activities. Specifically, based on the information obtained from the monitoring system, it adjusts security settings or updates the system. Here, it receives instructions from the monitoring system as input and obtains updated settings or new system status as output.

[0283] Step 5:

[0284] The terminal reports these execution results to the user. The user can receive the report from the terminal and check the content. The input is the execution result data received from the server, and the output is the notification information to the user. This notification includes the confirmation result of the latest security status and the details of the performed setting changes.

[0285] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0286] The present invention combines an emotion engine that recognizes the user's emotions with a system for automating system operation work using an AI agent. This system enables responses that take into account the user's emotional state in addition to normal inquiry processing, aiming to improve the user experience.

[0287] The user makes an inquiry through a digital platform, and at that time, the emotion engine performs emotion recognition in the background. Specifically, the server calls the emotion engine to analyze the emotional nuances contained in the user's text along with the inquiry. This engine utilizes natural language processing technology to evaluate the emotional tone of the text.

[0288] The server transfers the result of the emotion analysis to the AI agent simultaneously with the inquiry content. The AI agent combines responses that take into account the user's emotional state in addition to analyzing the normal inquiry content. For example, when the user is feeling dissatisfied or stressed, the AI agent selects a response that is polite and gives a sense of security.

[0289] The emotion data evaluated by the emotion engine helps to generate appropriate responses. Customization of responses based on emotions is applied to the system operations executed by the terminal. As a result, the terminal performs flexible and user-centered operation work, returns the result to the server, and the server notifies the user of the result.

[0290] As a specific example, for an inquiry from a user who is feeling anxious about the server's performance, not only provide technical information but also attach a message that gives encouragement or a sense of security that matches the user's emotions, thereby reducing the user's stress.

[0291] This system aims to improve customer satisfaction by streamlining basic system operations while simultaneously providing more personalized interactions through emotion recognition.

[0292] The following describes the processing flow.

[0293] Step 1:

[0294] Users submit inquiries to the system using a chat interface or email. At this point, the user's message may contain emotional elements.

[0295] Step 2:

[0296] The server passes the user's query to the sentiment engine, which performs sentiment analysis. This process uses natural language processing algorithms to identify the emotional tone of the text.

[0297] Step 3:

[0298] The server transfers the emotion data obtained from the analysis results and the inquiry data to the AI ​​agent. This data includes the types and intensity of emotions the user may be feeling.

[0299] Step 4:

[0300] The AI ​​agent processes the received inquiry content by combining it with sentiment data. The agent determines the user's needs and decides on the optimal system action to address them.

[0301] Step 5:

[0302] The terminal performs the determined system operations. These operations include not only normal technical responses but also the preparation of emotion-based, personalized responses.

[0303] Step 6:

[0304] The terminal generates a response considering the emotion along with the execution result and sends it to the server. The response content includes a polite and appropriate message considering the user's emotion.

[0305] Step 7:

[0306] The server conveys this response to the user. The user can obtain a better experience by receiving a message that accommodates the emotion along with the specific operation result.

[0307] (Example 2)

[0308] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0309] In modern system operation, it is required not only to respond to technical inquiries but also to provide personalized interactions considering the emotions of customers. However, with conventional methods, it has been difficult to accurately recognize emotions and generate appropriate responses based on them. As a result, the customer experience has been limited, and there has been a limit to improving satisfaction.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0311] In this invention, the server includes means for receiving an inquiry from a customer, means for analyzing the received inquiry and recognizing the emotion, and means for using a generation AI model that generates an appropriate response based on the analysis result and the inquiry content. Thereby, it becomes possible to generate a response that accurately reflects the emotion of the customer.

[0312] "Customer" refers to a user who uses this system as the target for providing goods or services.

[0313] An "inquiry" refers to information or questions that a customer sends to resolve doubts or problems they have with the system.

[0314] "Analysis" refers to the process of thoroughly analyzing the content of an inquiry received in order to understand its meaning and intent.

[0315] "Emotion recognition" refers to the technology that identifies a customer's emotional state from their inquiry text and evaluates its tone and nuances.

[0316] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate an appropriate response from input information.

[0317] "Customization" refers to modifying or adjusting the generated response to suit the individual customer's emotions and circumstances.

[0318] "Output device" refers to equipment or interfaces used to provide the generated response to the customer.

[0319] A "system warning" refers to an alert that notifies you of anomalies or significant changes in the system's state.

[0320] "Maintenance work" refers to conservative activities that need to be carried out periodically to ensure that a system continues to function properly.

[0321] This invention is a system for effectively handling customer inquiries and generating emotionally sensitive responses. The implementation of this invention primarily involves servers, terminals, and users. The details of each are specifically outlined below.

[0322] When the server receives a query, it uses an emotion engine to perform sentiment analysis on the text. This emotion engine utilizes "natural language processing technology," and can include, for example, "general sentiment recognition systems." This engine understands the customer's emotional tone and generates analysis results.

[0323] Subsequently, the server uses a generative AI model to generate the optimal response based on the analysis of the inquiry content and sentiment. This generation process utilizes generative AI tools such as "OpenAI GPT-3." The server inputs prompts into this model and requests it to generate a response.

[0324] The terminal further customizes the response received from the server and provides it to the user in its final form. For example, if the user states, "I'm worried about the server's slow response," the following prompt might be entered into the generative AI model:

[0325] "User inquiry: 'I'm worried about the slow server response.' The AI ​​agent should generate a message that provides a technical explanation and reassures the user in response to this inquiry."

[0326] In this step, the terminal appropriately customizes the results and generates an emotionally sensitive response. Finally, the terminal returns the generated response to the server, which then communicates its contents to the user. This entire process ensures that user inquiries are handled quickly and effectively, leading to improved customer satisfaction.

[0327] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0328] Step 1:

[0329] Users send inquiries to the server via a digital platform. These inquiries are in text format, and users enter their problems or questions into the server by pressing the submit button. The server receives them and proceeds to the next process.

[0330] Step 2:

[0331] The server inputs the received text data into the emotion engine. Here, "natural language processing technology" is used to analyze the emotional nuances within the user's text. The emotion engine analyzes the text and outputs an emotion score and emotion category (e.g., "anxiety" or "satisfaction"). This output serves as the basis for customizing the response.

[0332] Step 3:

[0333] The server invokes a generative AI model based on the analyzed sentiment data and the query content. Specifically, it uses the "OpenAI GPT-3" generative AI model to receive prompt text and generate an appropriate response. The prompt text reflects the query content and the results of the sentiment analysis. The generative AI model outputs direct responses to the query, as well as messages that provide reassurance.

[0334] Step 4:

[0335] The terminal receives response data from the server and generates the final output for the user. In this process, the output response is further customized and adjusted to suit the individual user's emotional state. The terminal transforms the response into a format that is easier to understand and more sensitive to the user's emotions.

[0336] Step 5:

[0337] The customized response from the device is returned to the server. The server sends the final result to the user. The user receives this and can confirm the response to their inquiry. This improves the quality of the user experience by resolving the problem and providing reassurance.

[0338] (Application Example 2)

[0339] 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."

[0340] In physical stores, employees are required to respond flexibly to customers' emotions when interacting with them face-to-face. However, it is difficult for employees to instantly and accurately grasp the emotions of every customer and provide service accordingly. There is a need to provide a solution to this problem and improve customer satisfaction.

[0341] 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.

[0342] In this invention, the server includes means for receiving customer inquiries, means for analyzing the received inquiries and determining the operational tasks to be performed, and means for analyzing the customer's emotions using emotion recognition technology and generating a response based on those emotions. This enables employees to understand customer emotions in real time and provide appropriate services.

[0343] An "inquiry" is a means of communication that allows customers to seek information about products or services or report problems.

[0344] "Operational work" is a general term for operations and processes performed for the purpose of maintaining and efficiently running a system.

[0345] "Emotion recognition technology" is a technology that analyzes a customer's facial expressions, voice, and behavior to identify their emotional state.

[0346] "Real-time evaluation" is a processing method that immediately analyzes the ongoing situation and reflects the results as soon as possible.

[0347] "Visual display" refers to showing information in a visible form on a display or device.

[0348] "Employee interpersonal skills" refers to the communication and service provision activities that staff engaged in customer service perform for customers.

[0349] The system implementing this invention exchanges information between a server, a terminal, and a user, and realizes interaction utilizing emotion recognition technology. The server receives a query sent by the user and performs content analysis using natural language processing technology. Next, emotion recognition technology is applied to evaluate the user's emotional state. For this evaluation, it is preferable to use software such as Google Cloud Natural Language API or Microsoft Azure Text Analytics.

[0350] Based on the analyzed information, the server uses a generative AI model to construct appropriate responses to inquiries. This includes flexible replies that take into account the user's emotions. The terminal device visually displays the responses received from the server to the employee handling customer service. Specific examples of such devices include smart glasses. These glasses capture the customer's facial expressions and voice in real time and present suggestions to support the employee's actions during customer service.

[0351] For example, if the system detects that a customer is feeling anxious, it might display a message such as, "Please let us know if you need any assistance." Another example of prompt text to input into the generation AI model might be, "What emotions is this customer showing, and what is the appropriate response?"

[0352] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0353] Step 1:

[0354] The user enters their inquiry and sends it to the server via their device. This inquiry is the input and is structured as text data. The server receives this data and obtains the necessary information for the next processing step.

[0355] Step 2:

[0356] The server sends the text data of the query to a natural language processing engine (e.g., Google Cloud Natural Language API). The engine analyzes the input text data and outputs information to understand its content and intent. The output includes the subject and key entities of the text.

[0357] Step 3:

[0358] Based on the analysis results, the server uses emotion recognition technology to evaluate the user's emotional state. In this process, the previously obtained text data and associated emotion recognition algorithms are applied to quantify the emotional tone, which is then output as the evaluation result.

[0359] Step 4:

[0360] The server combines the analysis results and sentiment evaluation data and inputs them into a generative AI model. Using the prompt, "What emotion is this customer showing, and what is the appropriate response?", the model generates the optimal response accordingly. The output is a customized reply message designed to provide the user with appropriate service.

[0361] Step 5:

[0362] The terminal visually displays customized messages received from the server to the employee. When smart glasses are used, they support the employee's interaction with others by displaying response suggestions tailored to the user's state. Input is messages from the server, and output is visually displayed information.

[0363] Step 6:

[0364] Employees provide services based on the information displayed on their devices and request feedback from users as needed. The information output from the devices leads to the employees' actual actions.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] [Third Embodiment]

[0369] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0370] 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.

[0371] 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).

[0372] 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.

[0373] 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.

[0374] 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).

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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".

[0381] This invention automates corporate system operation tasks using an AI agent. Specifically, it is a system in which an AI agent receives customer inquiries, analyzes them, selects appropriate operational tasks, and executes them. The system aims to automate routine tasks when complex operations are required in cloud environments, etc.

[0382] Users can submit inquiries to the system via chatbot or email. The server receives these inquiries and sends them to an AI agent. The agent analyzes the inquiry and determines the necessary action. In this process, natural language processing is used to understand the inquiry and identify specific tasks.

[0383] Based on the analysis results, the terminal performs the necessary system operations. For example, if there is an inquiry to check the server load status, the AI ​​agent uses that information to retrieve data from the monitoring system and reports the results to the user. The report is then delivered to the user again via the server.

[0384] Furthermore, the system has a continuous monitoring function, and if an alert is detected, the AI ​​agent can immediately determine a course of action and issue instructions to the terminal. In addition, regular maintenance work is also scheduled and carried out autonomously.

[0385] This invention aims to improve the efficiency of operational tasks, reduce the burden on system administrators, and enable quick and accurate responses. Specific examples include automatically restarting servers upon detecting an anomaly, or checking and reporting database backup status upon inquiry. This supports 24 / 7 operation and can address labor shortages.

[0386] The following describes the processing flow.

[0387] Step 1:

[0388] Users enter and submit their specific inquiries through chatbots or email systems. For example, they might ask, "I want to check the latest database backup."

[0389] Step 2:

[0390] The server receives inquiries from users and forwards them to the AI ​​agent. At this time, it formats the data appropriately to ensure the inquiries are processed correctly.

[0391] Step 3:

[0392] The AI ​​agent running on the server analyzes the received inquiry using a natural language processing engine. This analysis identifies the intent of the inquiry and the necessary operational actions.

[0393] Step 4:

[0394] The AI ​​agent determines the specific tasks to be performed based on the analysis results. For example, it might decide that it should retrieve information from the latest backup file of the database.

[0395] Step 5:

[0396] The terminal receives instructions from the AI ​​agent and performs the necessary operations on the target system. In this case, it accesses the database server and retrieves the latest backup information.

[0397] Step 6:

[0398] The device sends the acquired execution results back to the AI ​​agent. The AI ​​agent then reformats them into a format that is easy for the user to understand.

[0399] Step 7:

[0400] The server reports the formatted results received from the AI ​​agent to the user. The user can then view the backup date and time, as well as details of its status.

[0401] (Example 1)

[0402] 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."

[0403] In the operation of modern, complex information systems, manual information processing tasks require considerable effort, and labor shortages and human errors become particularly problematic when 24 / 7 / 365 support is necessary. Therefore, there is a need to automate information processing tasks to ensure rapid and accurate responses. Furthermore, automation of appropriate decision-making based on system status and maintenance tasks is also required.

[0404] 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.

[0405] In this invention, the server includes information processing means for receiving queries, information processing means for analyzing the received queries using a generation AI model and determining the information processing content to be performed, and information processing means for operating information devices based on the determined information processing content. This automates the operation of information systems, enabling rapid and accurate information processing while mitigating problems such as labor shortages and human errors.

[0406] An "inquiry" is a message or request sent by a user of an information system to request information or a task.

[0407] "Information processing means" refers to processes and devices for analyzing information, determining the content of work based on that analysis, and then executing that work.

[0408] A "generative AI model" is an algorithm that uses natural language processing to analyze text and understand its meaning.

[0409] "Information equipment" refers to devices that perform data processing and communication, including various devices such as computers and network equipment.

[0410] "Communication means" refers to protocols and devices used to send and receive information, i.e., networks and communication devices.

[0411] A "user" refers to a person or organization that uses the system or makes inquiries.

[0412] "Information systems" refers to all systems that collect, process, store, and distribute data, and include hardware and software.

[0413] This invention automates the operation of information systems by utilizing a generative AI model. Specific embodiments are described below.

[0414] Hardware and software configuration

[0415] The server is equipped with information processing means such as a chatbot or mail server for receiving inquiries. The server analyzes the received inquiries using a generative AI model and determines the appropriate information processing content. The generative AI model used here is expected to be an algorithm with natural language processing capabilities (e.g., GPT-4).

[0416] The terminal possesses the processing power and connectivity to operate information equipment in accordance with instructions from the server. Specifically, this includes application software for operating database management software and server monitoring systems.

[0417] Data processing and calculations

[0418] When the server analyzes the query, it uses natural language processing to process the text data and understand the user's intent. Based on this analysis, the terminal retrieves the necessary information and reprocesses the data to report it to the user. This entire process relies on the efficient generation of prompt messages.

[0419] Specific example

[0420] For example, a user might send a request to a chatbot saying, "Please report the current server load status." In this case, the server uses a generative AI model to analyze the request and instructs the terminal to retrieve the latest load information from the server monitoring system. The retrieved data is then reported to the user via the server.

[0421] Examples of prompts include "Please tell me the schedule for the next database backup" and "Please provide detailed instructions on how to respond when an anomaly is detected." The system can automatically provide appropriate information in response to such prompts.

[0422] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0423] Step 1:

[0424] The user submits a query to the information system. This query is sent to the server in text format using a chatbot or email client. The input is the user's query, and the output is the text data received by the server. For example, the user might input "Please tell me the next maintenance schedule."

[0425] Step 2:

[0426] The server analyzes the received query using a generative AI model. The input is the text data received by the server in step 1, and natural language processing is performed based on this data to understand the intent of the query. The output is informational data indicating the analyzed intent. Specifically, the generative AI model extracts "maintenance scheduled" from the query sentence to obtain guidance for accessing related information.

[0427] Step 3:

[0428] The terminal performs the necessary processing based on the analysis results received from the server. The input is the analysis result data sent from the server, and the terminal uses this data to generate specific instructions for the information devices and systems to be operated. The output is the information data obtained as a result of the operation. As a specific example, the terminal accesses the schedule management system to obtain the date and time of the next maintenance.

[0429] Step 4:

[0430] The server reports information to the user based on the execution results received from the terminal. The input is the execution result data from the terminal, and the server generates the information to be returned to the user based on this data. The output is the report content sent to the user. Specifically, the server displays the message "The next maintenance is scheduled for 10:00 AM on May 10th" to the user's chatbot.

[0431] (Application Example 1)

[0432] 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."

[0433] With the advancement of modern information technology, corporate system management requires the processing of vast amounts of information and rapid response. However, traditional methods require significant human resources for handling inquiries, monitoring systems, and maintenance, leading to increased burdens on administrators. Security management, in particular, demands immediacy and accuracy, making manual processes impractical. To address this challenge, a method for efficient and autonomous system operation is necessary.

[0434] 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.

[0435] In this invention, the server includes means for receiving information from customers, means for analyzing the received information to determine what activities should be performed, and means for performing device operations to carry out the determined activities. This allows for autonomous system monitoring and the rapid execution of appropriate operational activities, thereby reducing the burden on administrators and enabling efficient system operation. Furthermore, by using a natural language processing model, the content of inquiries can be analyzed at a high level, and the situation can be confirmed in cooperation with the monitoring system. This makes it possible to further automate the management of the security system and expedite responses.

[0436] "Customer information" refers to the content of inquiries and requests provided by users or customers.

[0437] "Means for analyzing information and determining what activities should be taken" refers to elements that have the function of understanding received information and identifying the necessary responses and processes.

[0438] "Means for performing device operation" refers to elements that have the function of controlling the devices or systems necessary to actually carry out the determined activity.

[0439] A "natural language processing model" is a set of technical methods and algorithms designed to understand and analyze human language.

[0440] A "monitoring system" is a device or program used to monitor the status of a system or network and detect anomalies or changes.

[0441] "Activities" refer to specific tasks and actions performed in system operation and management.

[0442] "Administrator" refers to an individual or group responsible for the operation and management of a system or network.

[0443] "Inquiry details" refers to the specific information and issues contained in questions or requests provided by customers.

[0444] In this embodiment of the invention, the process begins with a server receiving and analyzing information from a customer. A natural language processing model is used for the analysis to gain a high level of understanding of the inquiry. The natural language processing model used here is preferably based on widely available AI technology in the market. Based on the analysis results, the server determines the actions to be taken and confirms the situation in cooperation with a monitoring system. The monitoring system utilizes multiple sensors and software to constantly monitor the system status and detect anomalies.

[0445] Next, the server performs device operations to carry out the determined activity. These operations are performed via a system management terminal and include software updates and security setting adjustments as needed. This ensures that the system operates in an optimal state.

[0446] As a concrete example, in corporate security management, a server may receive information from a customer such as "I want to check the latest security status," and automatically check and update the relevant security settings. The terminal then reports the results obtained from these operations to the user in real time.

[0447] An example of a prompt message used during implementation would be, "Please check the latest security status of the corporate network and update security patches." Based on this, the server can identify the specific actions that need to be taken and proceed with execution.

[0448] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0449] Step 1:

[0450] The server receives an inquiry from a customer. The user then sends information via chatbot or email. The server stores this data in storage and performs preprocessing to feed it into a natural language processing model.

[0451] Step 2:

[0452] The server analyzes the input inquiry using a natural language processing model. Here, the server receives the customer's inquiry text as input data, and the natural language model analyzes this information to determine the actions and responses that should be taken. This analysis determines whether actions such as security checks or configuration changes are necessary.

[0453] Step 3:

[0454] The server identifies the activities to be performed based on the analysis results and accesses the monitoring system to check the current status. The analysis results are provided as input, and monitoring data is obtained as output. This data is used to determine the specific processing content and targets.

[0455] Step 4:

[0456] The server performs the necessary device operations to carry out the determined activities. Specifically, it adjusts security settings and updates the system based on information obtained from the monitoring system. Here, it receives instructions from the monitoring system as input and obtains updated settings and new system status as output.

[0457] Step 5:

[0458] The terminal reports these execution results to the user. The user can receive the report from the terminal and review its contents. The input is the execution result data received from the server, and the output is notification information to the user. This notification includes the latest security status check results and details of the configuration changes made.

[0459] 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.

[0460] This invention combines a system that automates system operation tasks using an AI agent with an emotion engine that recognizes user emotions. This system aims to improve the user experience by enabling responses that take into account the user's emotional state, in addition to normal inquiry processing.

[0461] Users make inquiries through the digital platform, while an emotion engine performs emotion recognition in the background. Specifically, along with the inquiry, the server invokes the emotion engine to analyze the emotional nuances contained in the user's text. This engine utilizes natural language processing technology to evaluate the emotional tone of the text.

[0462] The server transmits the inquiry content and the sentiment analysis results to the AI ​​agent. In addition to analyzing the normal inquiry content, the AI ​​agent processes the inquiry by combining it with responses that take into account the user's emotional state. For example, if the user is feeling dissatisfied or stressed, the AI ​​agent will choose a polite and reassuring response.

[0463] The emotion data evaluated by the emotion engine helps generate appropriate responses. Emotion-based customization of responses is applied to system operations performed by the terminal. As a result, the terminal performs flexible, user-centric operations, returns the results to the server, and the server notifies the user of the results.

[0464] For example, when a user expresses concern about server performance, providing not only technical information but also encouraging and reassuring messages tailored to the user's feelings can reduce their stress.

[0465] This system aims to improve customer satisfaction by streamlining basic system operations while simultaneously providing more personalized interactions through emotion recognition.

[0466] The following describes the processing flow.

[0467] Step 1:

[0468] Users submit inquiries to the system using a chat interface or email. At this point, the user's message may contain emotional elements.

[0469] Step 2:

[0470] The server passes the user's query to the sentiment engine, which performs sentiment analysis. This process uses natural language processing algorithms to identify the emotional tone of the text.

[0471] Step 3:

[0472] The server transfers the emotion data obtained from the analysis results and the inquiry data to the AI ​​agent. This data includes the types and intensity of emotions the user may be feeling.

[0473] Step 4:

[0474] The AI ​​agent processes the received inquiry content by combining it with sentiment data. The agent determines the user's needs and decides on the optimal system action to address them.

[0475] Step 5:

[0476] The terminal performs the determined system operations. These operations include not only normal technical responses but also the preparation of emotion-based, personalized responses.

[0477] Step 6:

[0478] The terminal generates an emotionally sensitive response along with the execution result and sends it to the server. The response includes a polite and appropriate message that takes the user's emotions into consideration.

[0479] Step 7:

[0480] The server communicates this response to the user. The user can have a better experience by receiving an emotionally resonant message along with specific operational results.

[0481] (Example 2)

[0482] 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."

[0483] In modern system operations, there is a demand not only for responding to technical inquiries but also for providing personalized interactions that take customer emotions into consideration. However, traditional methods have struggled to accurately recognize emotions and generate appropriate responses based on them. As a result, the customer experience has been limited, and there have been limitations in improving satisfaction.

[0484] 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.

[0485] In this invention, the server includes means for receiving inquiries from customers, means for analyzing the received inquiries and recognizing emotions, and means for using a generative AI model that generates an appropriate response based on the analysis results and the content of the inquiry. This makes it possible to generate a response that accurately reflects the customer's emotions.

[0486] "Customer" refers to the user who utilizes this system as the recipient of goods or services.

[0487] An "inquiry" refers to information or questions that a customer sends to resolve doubts or problems they have with the system.

[0488] "Analysis" refers to the process of thoroughly analyzing the content of an inquiry received in order to understand its meaning and intent.

[0489] "Emotion recognition" refers to the technology that identifies a customer's emotional state from their inquiry text and evaluates its tone and nuances.

[0490] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate an appropriate response from input information.

[0491] "Customization" refers to modifying or adjusting the generated response to suit the individual customer's emotions and circumstances.

[0492] "Output device" refers to equipment or interfaces used to provide the generated response to the customer.

[0493] A "system warning" refers to an alert that notifies you of anomalies or significant changes in the system's state.

[0494] "Maintenance work" refers to conservative activities that need to be carried out periodically to ensure that a system continues to function properly.

[0495] This invention is a system for effectively handling customer inquiries and generating emotionally sensitive responses. The implementation of this invention primarily involves servers, terminals, and users. The details of each are specifically outlined below.

[0496] When the server receives a query, it uses an emotion engine to perform sentiment analysis on the text. This emotion engine utilizes "natural language processing technology," and can include, for example, "general sentiment recognition systems." This engine understands the customer's emotional tone and generates analysis results.

[0497] Subsequently, the server uses a generative AI model to generate the optimal response based on the analysis of the inquiry content and sentiment. This generation process utilizes generative AI tools such as "OpenAI GPT-3." The server inputs prompts into this model, requesting it to generate a response.

[0498] The terminal further customizes the response received from the server and provides it to the user in its final form. For example, if the user states, "I'm worried about the server's slow response," the following prompt might be entered into the generative AI model:

[0499] "User inquiry: 'I'm worried about the slow server response.' The AI ​​agent should generate a message that provides a technical explanation and reassures the user in response to this inquiry."

[0500] In this step, the terminal appropriately customizes the results and generates an emotionally sensitive response. Finally, the terminal returns the generated response to the server, which then communicates its contents to the user. This entire process ensures that user inquiries are handled quickly and effectively, leading to improved customer satisfaction.

[0501] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0502] Step 1:

[0503] Users send inquiries to the server via a digital platform. These inquiries are in text format, and users enter their problems or questions into the server by pressing the submit button. The server receives them and proceeds to the next process.

[0504] Step 2:

[0505] The server inputs the received text data into the emotion engine. Here, "natural language processing technology" is used to analyze the emotional nuances within the user's text. The emotion engine analyzes the text and outputs an emotion score and emotion category (e.g., "anxiety" or "satisfaction"). This output serves as the basis for customizing the response.

[0506] Step 3:

[0507] The server invokes a generative AI model based on the analyzed sentiment data and the query content. Specifically, it uses the "OpenAI GPT-3" generative AI model to receive prompt text and generate an appropriate response. The prompt text reflects the query content and the results of the sentiment analysis. The generative AI model outputs direct responses to the query, as well as messages that provide reassurance.

[0508] Step 4:

[0509] The terminal receives response data from the server and generates the final output for the user. In this process, the output response is further customized and adjusted to suit the individual user's emotional state. The terminal transforms the response into a format that is easier to understand and more sensitive to the user's emotions.

[0510] Step 5:

[0511] The customized response from the device is returned to the server. The server sends the final result to the user. The user receives this and can confirm the response to their inquiry. This improves the quality of the user experience by resolving the problem and providing reassurance.

[0512] (Application Example 2)

[0513] 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."

[0514] In physical stores, employees are required to respond flexibly to customers' emotions when interacting with them face-to-face. However, it is difficult for employees to instantly and accurately grasp the emotions of every customer and provide service accordingly. There is a need to provide a solution to this problem and improve customer satisfaction.

[0515] 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.

[0516] In this invention, the server includes means for receiving customer inquiries, means for analyzing the received inquiries and determining the operational tasks to be performed, and means for analyzing the customer's emotions using emotion recognition technology and generating a response based on those emotions. This enables employees to understand customer emotions in real time and provide appropriate services.

[0517] An "inquiry" is a means of communication that allows customers to seek information about products or services or report problems.

[0518] "Operational work" is a general term for operations and processes performed for the purpose of maintaining and efficiently running a system.

[0519] "Emotion recognition technology" is a technology that analyzes a customer's facial expressions, voice, and behavior to identify their emotional state.

[0520] "Real-time evaluation" is a processing method that immediately analyzes the ongoing situation and reflects the results as soon as possible.

[0521] "Visual display" refers to showing information in a visible form on a display or device.

[0522] "Employee interpersonal skills" refers to the communication and service provision activities that staff engaged in customer service perform for customers.

[0523] The system implementing this invention exchanges information between a server, a terminal, and a user, and realizes interaction utilizing emotion recognition technology. The server receives a query sent by the user and performs content analysis using natural language processing technology. Next, emotion recognition technology is applied to evaluate the user's emotional state. For this evaluation, it is preferable to use software such as Google Cloud Natural Language API or Microsoft Azure Text Analytics.

[0524] Based on the analyzed information, the server uses a generative AI model to construct appropriate responses to inquiries. This includes flexible replies that take into account the user's emotions. The terminal device visually displays the responses received from the server to the employee handling customer service. Specific examples of such devices include smart glasses. These glasses capture the customer's facial expressions and voice in real time and present suggestions to support the employee's actions during customer service.

[0525] For example, if the system detects that a customer is feeling anxious, it might display a message such as, "Please let us know if you need any assistance." Another example of prompt text to input into the generation AI model might be, "What emotions is this customer showing, and what is the appropriate response?"

[0526] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0527] Step 1:

[0528] The user enters their inquiry and sends it to the server via their device. This inquiry is the input and is structured as text data. The server receives this data and obtains the necessary information for the next processing step.

[0529] Step 2:

[0530] The server sends the text data of the query to a natural language processing engine (e.g., Google Cloud Natural Language API). The engine analyzes the input text data and outputs information to understand its content and intent. The output includes the subject and key entities of the text.

[0531] Step 3:

[0532] Based on the analysis results, the server uses emotion recognition technology to evaluate the user's emotional state. In this process, the previously obtained text data and associated emotion recognition algorithms are applied to quantify the emotional tone, which is then output as the evaluation result.

[0533] Step 4:

[0534] The server combines the analysis results and sentiment evaluation data and inputs them into a generative AI model. Using the prompt, "What emotion is this customer showing, and what is the appropriate response?", the model generates the optimal response accordingly. The output is a customized reply message designed to provide the user with appropriate service.

[0535] Step 5:

[0536] The terminal visually displays customized messages received from the server to the employee. When smart glasses are used, they support the employee's interaction with others by displaying response suggestions tailored to the user's state. Input is messages from the server, and output is visually displayed information.

[0537] Step 6:

[0538] Employees provide services based on the information displayed on their devices and request feedback from users as needed. The information output from the devices leads to the employees' actual actions.

[0539] 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.

[0540] 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.

[0541] 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.

[0542] [Fourth Embodiment]

[0543] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0544] 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.

[0545] 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).

[0546] 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.

[0547] 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.

[0548] 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).

[0549] 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.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] 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.

[0554] 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.

[0555] 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".

[0556] This invention automates corporate system operation tasks using an AI agent. Specifically, it is a system in which an AI agent receives customer inquiries, analyzes them, selects appropriate operational tasks, and executes them. The system aims to automate routine tasks when complex operations are required in cloud environments, etc.

[0557] Users can submit inquiries to the system via chatbot or email. The server receives these inquiries and sends them to an AI agent. The agent analyzes the inquiry and determines the necessary action. In this process, natural language processing is used to understand the inquiry and identify specific tasks.

[0558] Based on the analysis results, the terminal performs the necessary system operations. For example, if there is an inquiry to check the server load status, the AI ​​agent uses that information to retrieve data from the monitoring system and reports the results to the user. The report is then delivered to the user again via the server.

[0559] Furthermore, the system has a continuous monitoring function, and if an alert is detected, the AI ​​agent can immediately determine a course of action and issue instructions to the terminal. In addition, regular maintenance work is also scheduled and carried out autonomously.

[0560] This invention aims to improve the efficiency of operational tasks, reduce the burden on system administrators, and enable quick and accurate responses. Specific examples include automatically restarting servers upon detecting an anomaly, or checking and reporting database backup status upon inquiry. This supports 24 / 7 operation and can address labor shortages.

[0561] The following describes the processing flow.

[0562] Step 1:

[0563] Users enter and submit their specific inquiries through chatbots or email systems. For example, they might ask, "I want to check the latest database backup."

[0564] Step 2:

[0565] The server receives inquiries from users and forwards them to the AI ​​agent. At this time, it formats the data appropriately to ensure the inquiries are processed correctly.

[0566] Step 3:

[0567] The AI ​​agent running on the server analyzes the received inquiry using a natural language processing engine. This analysis identifies the intent of the inquiry and the necessary operational actions.

[0568] Step 4:

[0569] The AI ​​agent determines the specific tasks to be performed based on the analysis results. For example, it might decide that it should retrieve information from the latest backup file of the database.

[0570] Step 5:

[0571] The terminal receives instructions from the AI ​​agent and performs the necessary operations on the target system. In this case, it accesses the database server and retrieves the latest backup information.

[0572] Step 6:

[0573] The device sends the acquired execution results back to the AI ​​agent. The AI ​​agent then reformats them into a format that is easy for the user to understand.

[0574] Step 7:

[0575] The server reports the formatted results received from the AI ​​agent to the user. The user can then view the backup date and time, as well as details of its status.

[0576] (Example 1)

[0577] 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".

[0578] In the operation of modern, complex information systems, manual information processing tasks require considerable effort, and labor shortages and human errors become particularly problematic when 24 / 7 / 365 support is necessary. Therefore, there is a need to automate information processing tasks to ensure rapid and accurate responses. Furthermore, automation of appropriate decision-making based on system status and maintenance tasks is also required.

[0579] 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.

[0580] In this invention, the server includes information processing means for receiving queries, information processing means for analyzing the received queries using a generation AI model and determining the information processing content to be performed, and information processing means for operating information devices based on the determined information processing content. This automates the operation of information systems, enabling rapid and accurate information processing while mitigating problems such as labor shortages and human errors.

[0581] An "inquiry" is a message or request sent by a user of an information system to request information or a task.

[0582] "Information processing means" refers to processes and devices for analyzing information, determining the content of work based on that analysis, and then executing that work.

[0583] A "generative AI model" is an algorithm that uses natural language processing to analyze text and understand its meaning.

[0584] "Information equipment" refers to devices that perform data processing and communication, including various devices such as computers and network equipment.

[0585] "Communication means" refers to protocols and devices used to send and receive information, i.e., networks and communication devices.

[0586] A "user" refers to a person or organization that uses the system or makes inquiries.

[0587] "Information systems" refers to all systems that collect, process, store, and distribute data, and include hardware and software.

[0588] This invention automates the operation of information systems by utilizing a generative AI model. Specific embodiments are described below.

[0589] Hardware and software configuration

[0590] The server is equipped with information processing means such as a chatbot or mail server for receiving inquiries. The server analyzes the received inquiries using a generative AI model and determines the appropriate information processing content. The generative AI model used here is expected to be an algorithm with natural language processing capabilities (e.g., GPT-4).

[0591] The terminal possesses the processing power and connectivity to operate information equipment in accordance with instructions from the server. Specifically, this includes application software for operating database management software and server monitoring systems.

[0592] Data processing and calculations

[0593] When the server analyzes the query, it uses natural language processing to process the text data and understand the user's intent. Based on this analysis, the terminal retrieves the necessary information and reprocesses the data to report it to the user. This entire process relies on the efficient generation of prompt messages.

[0594] Specific example

[0595] For example, a user might send a request to a chatbot saying, "Please report the current server load status." In this case, the server uses a generative AI model to analyze the request and instructs the terminal to retrieve the latest load information from the server monitoring system. The retrieved data is then reported to the user via the server.

[0596] Examples of prompts include "Please tell me the schedule for the next database backup" and "Please provide detailed instructions on how to respond when an anomaly is detected." The system can automatically provide appropriate information in response to such prompts.

[0597] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0598] Step 1:

[0599] The user submits a query to the information system. This query is sent to the server in text format using a chatbot or email client. The input is the user's query, and the output is the text data received by the server. For example, the user might input "Please tell me the next maintenance schedule."

[0600] Step 2:

[0601] The server analyzes the received query using a generative AI model. The input is the text data received by the server in step 1, and natural language processing is performed based on this data to understand the intent of the query. The output is informational data indicating the analyzed intent. Specifically, the generative AI model extracts "maintenance scheduled" from the query sentence to obtain guidance for accessing related information.

[0602] Step 3:

[0603] The terminal performs the necessary processing based on the analysis results received from the server. The input is the analysis result data sent from the server, and the terminal uses this data to generate specific instructions for the information devices and systems to be operated. The output is the information data obtained as a result of the operation. As a specific example, the terminal accesses the schedule management system to obtain the date and time of the next maintenance.

[0604] Step 4:

[0605] The server reports information to the user based on the execution results received from the terminal. The input is the execution result data from the terminal, and the server generates the information to be returned to the user based on this data. The output is the report content sent to the user. Specifically, the server displays the message "The next maintenance is scheduled for 10:00 AM on May 10th" to the user's chatbot.

[0606] (Application Example 1)

[0607] 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".

[0608] With the advancement of modern information technology, corporate system management requires the processing of vast amounts of information and rapid response. However, traditional methods require significant human resources for handling inquiries, monitoring systems, and maintenance, leading to increased burdens on administrators. Security management, in particular, demands immediacy and accuracy, making manual processes impractical. To address this challenge, a method for efficient and autonomous system operation is necessary.

[0609] 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.

[0610] In this invention, the server includes means for receiving information from customers, means for analyzing the received information to determine what activities should be performed, and means for performing device operations to carry out the determined activities. This allows for autonomous system monitoring and the rapid execution of appropriate operational activities, thereby reducing the burden on administrators and enabling efficient system operation. Furthermore, by using a natural language processing model, the content of inquiries can be analyzed at a high level, and the situation can be confirmed in cooperation with the monitoring system. This makes it possible to further automate the management of the security system and expedite responses.

[0611] "Customer information" refers to the content of inquiries and requests provided by users or customers.

[0612] "Means for analyzing information and determining what activities should be taken" refers to elements that have the function of understanding received information and identifying the necessary responses and processes.

[0613] "Means for performing device operation" refers to elements that have the function of controlling the devices or systems necessary to actually carry out the determined activity.

[0614] A "natural language processing model" is a set of technical methods and algorithms designed to understand and analyze human language.

[0615] A "monitoring system" is a device or program used to monitor the status of a system or network and detect anomalies or changes.

[0616] "Activities" refer to specific tasks and actions performed in system operation and management.

[0617] "Administrator" refers to an individual or group responsible for the operation and management of a system or network.

[0618] "Inquiry details" refers to the specific information and issues contained in questions or requests provided by customers.

[0619] In this embodiment of the invention, the process begins with a server receiving and analyzing information from a customer. A natural language processing model is used for the analysis to gain a high level of understanding of the inquiry. The natural language processing model used here is preferably based on widely available AI technology in the market. Based on the analysis results, the server determines the actions to be taken and confirms the situation in cooperation with a monitoring system. The monitoring system utilizes multiple sensors and software to constantly monitor the system status and detect anomalies.

[0620] Next, the server performs device operations to carry out the determined activity. These operations are performed via a system management terminal and include software updates and security setting adjustments as needed. This ensures that the system operates in an optimal state.

[0621] As a concrete example, in corporate security management, a server may receive information from a customer such as "I want to check the latest security status," and automatically check and update the relevant security settings. The terminal then reports the results obtained from these operations to the user in real time.

[0622] An example of a prompt message used during implementation would be, "Please check the latest security status of the corporate network and update security patches." Based on this, the server can identify the specific actions that need to be taken and proceed with execution.

[0623] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0624] Step 1:

[0625] The server receives an inquiry from a customer. The user then sends information via chatbot or email. The server stores this data in storage and performs preprocessing to feed it into a natural language processing model.

[0626] Step 2:

[0627] The server analyzes the input inquiry using a natural language processing model. Here, the server receives the customer's inquiry text as input data, and the natural language model analyzes this information to determine the actions and responses that should be taken. This analysis determines whether actions such as security checks or configuration changes are necessary.

[0628] Step 3:

[0629] The server identifies the activities to be performed based on the analysis results and accesses the monitoring system to check the current status. The analysis results are provided as input, and monitoring data is obtained as output. This data is used to determine the specific processing content and targets.

[0630] Step 4:

[0631] The server performs the necessary device operations to carry out the determined activities. Specifically, it adjusts security settings and updates the system based on information obtained from the monitoring system. Here, it receives instructions from the monitoring system as input and obtains updated settings and new system status as output.

[0632] Step 5:

[0633] The terminal reports these execution results to the user. The user can receive the report from the terminal and review its contents. The input is the execution result data received from the server, and the output is notification information to the user. This notification includes the latest security status check results and details of the configuration changes made.

[0634] 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.

[0635] This invention combines a system that automates system operation tasks using an AI agent with an emotion engine that recognizes user emotions. This system aims to improve the user experience by enabling responses that take into account the user's emotional state, in addition to normal inquiry processing.

[0636] Users make inquiries through the digital platform, while an emotion engine performs emotion recognition in the background. Specifically, along with the inquiry, the server invokes the emotion engine to analyze the emotional nuances contained in the user's text. This engine utilizes natural language processing technology to evaluate the emotional tone of the text.

[0637] The server transmits the inquiry content and the sentiment analysis results to the AI ​​agent. In addition to analyzing the normal inquiry content, the AI ​​agent processes the inquiry by combining it with responses that take into account the user's emotional state. For example, if the user is feeling dissatisfied or stressed, the AI ​​agent will choose a polite and reassuring response.

[0638] The emotion data evaluated by the emotion engine helps generate appropriate responses. Emotion-based customization of responses is applied to system operations performed by the terminal. As a result, the terminal performs flexible, user-centric operations, returns the results to the server, and the server notifies the user of the results.

[0639] For example, when a user expresses concern about server performance, providing not only technical information but also encouraging and reassuring messages tailored to the user's feelings can reduce their stress.

[0640] This system aims to improve customer satisfaction by streamlining basic system operations while simultaneously providing more personalized interactions through emotion recognition.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] Users submit inquiries to the system using a chat interface or email. At this point, the user's message may contain emotional elements.

[0644] Step 2:

[0645] The server passes the user's query to the sentiment engine, which performs sentiment analysis. This process uses natural language processing algorithms to identify the emotional tone of the text.

[0646] Step 3:

[0647] The server transfers the emotion data obtained from the analysis results and the inquiry data to the AI ​​agent. This data includes the types and intensity of emotions the user may be feeling.

[0648] Step 4:

[0649] The AI ​​agent processes the received inquiry content by combining it with sentiment data. The agent determines the user's needs and decides on the optimal system action to address them.

[0650] Step 5:

[0651] The terminal performs the determined system operations. These operations include not only normal technical responses but also the preparation of emotion-based, personalized responses.

[0652] Step 6:

[0653] The terminal generates an emotionally sensitive response along with the execution result and sends it to the server. The response includes a polite and appropriate message that takes the user's emotions into consideration.

[0654] Step 7:

[0655] The server communicates this response to the user. The user can have a better experience by receiving an emotionally resonant message along with specific operational results.

[0656] (Example 2)

[0657] 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".

[0658] In modern system operations, there is a demand not only for responding to technical inquiries but also for providing personalized interactions that take customer emotions into consideration. However, traditional methods have struggled to accurately recognize emotions and generate appropriate responses based on them. As a result, the customer experience has been limited, and there have been limitations in improving satisfaction.

[0659] 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.

[0660] In this invention, the server includes means for receiving inquiries from customers, means for analyzing the received inquiries and recognizing emotions, and means for using a generative AI model that generates an appropriate response based on the analysis results and the content of the inquiry. This makes it possible to generate a response that accurately reflects the customer's emotions.

[0661] "Customer" refers to the user who utilizes this system as the recipient of goods or services.

[0662] An "inquiry" refers to information or questions that a customer sends to resolve doubts or problems they have with the system.

[0663] "Analysis" refers to the process of thoroughly analyzing the content of an inquiry received in order to understand its meaning and intent.

[0664] "Emotion recognition" refers to the technology that identifies a customer's emotional state from their inquiry text and evaluates its tone and nuances.

[0665] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate an appropriate response from input information.

[0666] "Customization" refers to modifying or adjusting the generated response to suit the individual customer's emotions and circumstances.

[0667] "Output device" refers to equipment or interfaces used to provide the generated response to the customer.

[0668] A "system warning" refers to an alert that notifies you of anomalies or significant changes in the system's state.

[0669] "Maintenance work" refers to conservative activities that need to be carried out periodically to ensure that a system continues to function properly.

[0670] This invention is a system for effectively handling customer inquiries and generating emotionally sensitive responses. The implementation of this invention primarily involves servers, terminals, and users. The details of each are specifically outlined below.

[0671] When the server receives a query, it uses an emotion engine to perform sentiment analysis on the text. This emotion engine utilizes "natural language processing technology," and can include, for example, "general sentiment recognition systems." This engine understands the customer's emotional tone and generates analysis results.

[0672] Subsequently, the server uses a generative AI model to generate the optimal response based on the analysis of the inquiry content and sentiment. This generation process utilizes generative AI tools such as "OpenAI GPT-3." The server inputs prompts into this model, requesting it to generate a response.

[0673] The terminal further customizes the response received from the server and provides it to the user in its final form. For example, if the user states, "I'm worried about the server's slow response," the following prompt might be entered into the generative AI model:

[0674] "User inquiry: 'I'm worried about the slow server response.' The AI ​​agent should generate a message that provides a technical explanation and reassures the user in response to this inquiry."

[0675] In this step, the terminal appropriately customizes the results and generates an emotionally sensitive response. Finally, the terminal returns the generated response to the server, which then communicates its contents to the user. This entire process ensures that user inquiries are handled quickly and effectively, leading to improved customer satisfaction.

[0676] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0677] Step 1:

[0678] Users send inquiries to the server via a digital platform. These inquiries are in text format, and users enter their problems or questions into the server by pressing the submit button. The server receives them and proceeds to the next process.

[0679] Step 2:

[0680] The server inputs the received text data into the emotion engine. Here, "natural language processing technology" is used to analyze the emotional nuances within the user's text. The emotion engine analyzes the text and outputs an emotion score and emotion category (e.g., "anxiety" or "satisfaction"). This output serves as the basis for customizing the response.

[0681] Step 3:

[0682] The server invokes a generative AI model based on the analyzed sentiment data and the query content. Specifically, it uses the "OpenAI GPT-3" generative AI model to receive prompt text and generate an appropriate response. The prompt text reflects the query content and the results of the sentiment analysis. The generative AI model outputs direct responses to the query, as well as messages that provide reassurance.

[0683] Step 4:

[0684] The terminal receives response data from the server and generates the final output for the user. In this process, the output response is further customized and adjusted to suit the individual user's emotional state. The terminal transforms the response into a format that is easier to understand and more sensitive to the user's emotions.

[0685] Step 5:

[0686] The customized response from the device is returned to the server. The server sends the final result to the user. The user receives this and can confirm the response to their inquiry. This improves the quality of the user experience by resolving the problem and providing reassurance.

[0687] (Application Example 2)

[0688] 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".

[0689] In physical stores, employees are required to respond flexibly to customers' emotions when interacting with them face-to-face. However, it is difficult for employees to instantly and accurately grasp the emotions of every customer and provide service accordingly. There is a need to provide a solution to this problem and improve customer satisfaction.

[0690] 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.

[0691] In this invention, the server includes means for receiving customer inquiries, means for analyzing the received inquiries and determining the operational tasks to be performed, and means for analyzing the customer's emotions using emotion recognition technology and generating a response based on those emotions. This enables employees to understand customer emotions in real time and provide appropriate services.

[0692] An "inquiry" is a means of communication that allows customers to seek information about products or services or report problems.

[0693] "Operational work" is a general term for operations and processes performed for the purpose of maintaining and efficiently running a system.

[0694] "Emotion recognition technology" is a technology that analyzes a customer's facial expressions, voice, and behavior to identify their emotional state.

[0695] "Real-time evaluation" is a processing method that immediately analyzes the ongoing situation and reflects the results as soon as possible.

[0696] "Visual display" refers to showing information in a visible form on a display or device.

[0697] "Employee interpersonal skills" refers to the communication and service provision activities that staff engaged in customer service perform for customers.

[0698] The system implementing this invention exchanges information between a server, a terminal, and a user, and realizes interaction utilizing emotion recognition technology. The server receives a query sent by the user and performs content analysis using natural language processing technology. Next, emotion recognition technology is applied to evaluate the user's emotional state. For this evaluation, it is preferable to use software such as Google Cloud Natural Language API or Microsoft Azure Text Analytics.

[0699] Based on the analyzed information, the server uses a generative AI model to construct appropriate responses to inquiries. This includes flexible replies that take into account the user's emotions. The terminal device visually displays the responses received from the server to the employee handling customer service. Specific examples of such devices include smart glasses. These glasses capture the customer's facial expressions and voice in real time and present suggestions to support the employee's actions during customer service.

[0700] For example, if the system detects that a customer is feeling anxious, it might display a message such as, "Please let us know if you need any assistance." Another example of prompt text to input into the generation AI model might be, "What emotions is this customer showing, and what is the appropriate response?"

[0701] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0702] Step 1:

[0703] The user enters their inquiry and sends it to the server via their device. This inquiry is the input and is structured as text data. The server receives this data and obtains the necessary information for the next processing step.

[0704] Step 2:

[0705] The server sends the text data of the query to a natural language processing engine (e.g., Google Cloud Natural Language API). The engine analyzes the input text data and outputs information to understand its content and intent. The output includes the subject and key entities of the text.

[0706] Step 3:

[0707] Based on the analysis results, the server uses emotion recognition technology to evaluate the user's emotional state. In this process, the previously obtained text data and associated emotion recognition algorithms are applied to quantify the emotional tone, which is then output as the evaluation result.

[0708] Step 4:

[0709] The server combines the analysis results and sentiment evaluation data and inputs them into a generative AI model. Using the prompt, "What emotion is this customer showing, and what is the appropriate response?", the model generates the optimal response accordingly. The output is a customized reply message designed to provide the user with appropriate service.

[0710] Step 5:

[0711] The terminal visually displays customized messages received from the server to the employee. When smart glasses are used, they support the employee's interaction with others by displaying response suggestions tailored to the user's state. Input is messages from the server, and output is visually displayed information.

[0712] Step 6:

[0713] Employees provide services based on the information displayed on their devices and request feedback from users as needed. The information output from the devices leads to the employees' actual actions.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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."

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] The following is further disclosed regarding the embodiments described above.

[0736] (Claim 1)

[0737] Means of receiving customer inquiries,

[0738] A means of analyzing received inquiries and determining the operational tasks that should be performed,

[0739] Means for performing system operations to carry out the determined operational tasks,

[0740] A means of reporting the execution results to the user,

[0741] A system that includes this.

[0742] (Claim 2)

[0743] The system according to claim 1, comprising means for monitoring system alerts and autonomously determining corresponding operational tasks.

[0744] (Claim 3)

[0745] The system according to claim 1, comprising means for determining and performing maintenance work that is required on a regular basis.

[0746] "Example 1"

[0747] (Claim 1)

[0748] Information processing means for receiving inquiries,

[0749] Information processing means that analyzes the received inquiry using a generation AI model and determines the information processing content to be performed,

[0750] Information processing means for operating information equipment based on the determined information processing content,

[0751] Information processing means for reporting the results of the above operation to the user via communication means,

[0752] A system that includes this.

[0753] (Claim 2)

[0754] The system according to claim 1, comprising information processing means for monitoring the operation of an information system and autonomously determining the corresponding information processing content based on the monitoring results.

[0755] (Claim 3)

[0756] The system according to claim 1, comprising information processing means for determining and performing maintenance work that is required on a regular basis.

[0757] "Application Example 1"

[0758] (Claim 1)

[0759] Means of receiving information from customers,

[0760] A means of analyzing the received information and determining the activity to be performed,

[0761] Means for performing the device operation to carry out the determined activity,

[0762] A means of reporting the execution results to the user,

[0763] A means of performing analysis using a natural language processing model,

[0764] A means of checking the situation and deciding on actions in conjunction with a monitoring system,

[0765] A system that includes this.

[0766] (Claim 2)

[0767] The system according to claim 1, further comprising means for using an AI agent to monitor system notifications and autonomously determine and execute corresponding activities.

[0768] (Claim 3)

[0769] The system according to claim 1, comprising means for determining the maintenance work required on a regular basis, performing the work, and identifying and adjusting parts that require updating.

[0770] "Example 2 of combining an emotion engine"

[0771] (Claim 1)

[0772] Means of receiving customer inquiries,

[0773] A means of analyzing received inquiries and recognizing emotions,

[0774] A method using a generative AI model that generates an appropriate response based on the analysis results and the content of the inquiry,

[0775] A means of customizing the generated response based on sentiment analysis data,

[0776] A means for reporting the execution results through an output device,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, comprising means for monitoring system warnings, autonomously determining corresponding operational tasks, and generating emotionally sensitive responses.

[0780] (Claim 3)

[0781] The system according to claim 1, comprising means for determining periodic maintenance tasks and reporting those tasks with emotion-based, customized responses.

[0782] "Application example 2 when combining with an emotional engine"

[0783] (Claim 1)

[0784] Means of receiving customer inquiries,

[0785] A means of analyzing received inquiries and determining the operational tasks that should be performed,

[0786] Means for performing system operations to carry out the determined operational tasks,

[0787] A means of reporting the execution results to the user,

[0788] A means for analyzing customer emotions using emotion recognition technology and generating a response based on those emotions,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, comprising means for evaluating the emotions of customers in a physical store in real time using emotion recognition technology and providing services accordingly.

[0792] (Claim 3)

[0793] The system according to claim 1, comprising means for visually displaying appropriate service suggestions based on emotions and supporting employee interpersonal interactions. [Explanation of Symbols]

[0794] 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. Means of receiving customer inquiries, A means of analyzing received inquiries and determining the operational tasks that should be performed, Means for performing system operations to carry out the determined operational tasks, A means of reporting the execution results to the user, A system that includes this.

2. The system according to claim 1, comprising means for monitoring system alerts and autonomously determining corresponding operational tasks.

3. The system according to claim 1, comprising means for determining and performing maintenance work that is required on a regular basis.

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