system

Abstract

We provide the system. [Solution] The information gathering device includes means for extracting data from system logs and error reports, The generating artificial intelligence agent analyzes the data collected by the information collection device and has means for determining the cause of the problem, The user device provides the user with a solution determined by the generating artificial intelligence agent and executes the solution based on the user's consent. 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 system operation services, inquiries and incident responses are complex and time-consuming tasks, and 24 / 7 / 365 availability is required. Therefore, there is a problem that many operations are personnel-intensive and inefficient. For this reason, it is difficult to solve problems quickly and accurately, and an effective method for reducing the human load is needed.

Means for Solving the Problems

[0005] This invention provides a system combining an information gathering device, a generating artificial intelligence agent, and a user device. The information gathering device predicts signs of problems from system logs and error reports and extracts data. The generating artificial intelligence agent analyzes the collected data, identifies the causes of the problems, and derives solutions. The user device presents the solutions to the user and automatically executes the solutions after obtaining the user's consent. This series of means makes it possible to automate and streamline operational tasks and significantly reduce the burden on human resources.

[0006] An "information gathering device" is a device used to extract necessary data from system logs and error reports.

[0007] A "generative artificial intelligence agent" is an artificial intelligence system that has the ability to analyze collected data, identify the causes of a problem, and derive solutions.

[0008] A "user device" is a terminal device that presents a solution to a problem to the user and, based on their consent, causes the system to execute that solution.

[0009] An "orchestration system" is a system in which multiple agents work together to perform automated tasks and coordinate towards a common goal.

[0010] A "solution" is a specific action plan to solve the problem identified by the generating artificial intelligence agent. [Brief explanation of the drawing]

[0011] [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]

[0012] 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.

[0013] First, let's explain the terminology used in the following explanation.

[0014] 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.

[0015] 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.

[0016] 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, etc.

[0017] 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), etc.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention automates and streamlines operational tasks by implementing a system that combines an information gathering device, a generating artificial intelligence agent, and a user device. The overall operation of the system is described below in natural language.

[0033] First, the server functions as an information gathering device, periodically collecting system logs and error reports to predict signs of anomalies. This enables early detection of problems.

[0034] Next, the device receives the information and uses a generative artificial intelligence agent to analyze the data in detail and identify the root cause of the problem. The AI ​​refers to a database of past data to find similar problems and their solutions.

[0035] Next, the user device presents the user with a recommended solution. The user reviews the displayed solution and gives their consent, which initiates a process to automatically implement the solution.

[0036] For example, if a user reports a login failure, the server immediately collects and analyzes the relevant error report, and the device's AI identifies the cause. If a password reset is necessary as a solution, the user's device will present this solution to the user. After obtaining the user's consent, the system will automatically perform the password reset and resolve the issue.

[0037] Thus, the system of the present invention automatically collects and analyzes information, proposes and implements solutions, and achieves a high level of efficiency while reducing the burden on human resources.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user enters a problem with the system into the inquiry form. The user provides details of the problem through the inquiry form and submits it to the system.

[0041] Step 2:

[0042] The terminal receives the entered inquiry and saves it to the database. It also notifies the user via an automated email that the inquiry has been received.

[0043] Step 3:

[0044] The server collects relevant system logs and error reports and analyzes the data to detect signs of anomalies. Predictive algorithms are used to identify potential problems.

[0045] Step 4:

[0046] The device analyzes the data using an artificial intelligence agent based on the collected information, searches for similar problems in a historical knowledge base, and identifies potential solutions.

[0047] Step 5:

[0048] The user device presents the identified solution to the user. The user reviews the presented solution and chooses to agree to it by following the instructions.

[0049] Step 6:

[0050] The server, with the user's consent, executes an automated script to make necessary configuration changes and system modifications. This completes the resolution of the problem.

[0051] Step 7:

[0052] The terminal verifies that all steps have been completed and records the processing results. It then sends a completion notification to the user, reporting that the problem has been resolved.

[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] Modern information systems are complex, and manual monitoring and management present challenges in quickly and efficiently detecting and resolving anomalies. This is especially true in environments where large amounts of data are generated, where rapidly identifying the root cause of problems and providing appropriate solutions is crucial. However, traditional methods are time-consuming and cumbersome in terms of information gathering, analysis, and solution implementation, hindering operational efficiency.

[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 means for an information processing device to extract information from electronic data, means for an artificial intelligence model to analyze the information acquired by the information processing device and identify the cause of a problem, and means for a user device to present the solution identified by the artificial intelligence model to the user and execute the solution based on the user's approval. This makes it possible to efficiently detect anomalies from large amounts of data and to quickly present and execute solutions.

[0058] An "information processing device" is a device used to extract necessary information from electronic data, and it plays a role in data collection and organization.

[0059] An "artificial intelligence model" is software or algorithms used to analyze collected data and identify problems or generate solutions.

[0060] A "user device" is a device that presents solutions generated by an artificial intelligence model to the user, receives input from the user, and prompts the user to execute the solution.

[0061] A "processing terminal" is a device that further processes information sent from an information processing device and searches for similar situations or solutions by comparing it with past databases.

[0062] A "database" is a collection of structured digital data designed for efficient storage and retrieval of information.

[0063] A "control device" is a device that monitors and manages the entire system in order to detect early signs of problems and recognize abnormal operational indicators.

[0064] This invention aims to build a system that automates information processing and solves problems efficiently and quickly. The specific forms for implementing this system are described below.

[0065] As an information processing device, the server collects electronic data. Specifically, the server uses log analysis software to extract necessary data from sources such as system logs and error reports. For this purpose, tools such as Splunk and ELK Stack can be used to efficiently organize and store the data.

[0066] Next, the terminal processes the data received from the server in a more advanced manner. The terminal analyzes the data using a generative AI model to identify the root cause of the problem. Specifically, the AI ​​model can utilize OpenAI's GPT or Google's BERT, which allows for comparison with past databases to find similar situations and solutions. The terminal also accesses database systems (such as MySQL or PostgreSQL) to efficiently search for data.

[0067] Subsequently, the user device presents the solution derived by the device to the user. The user device uses a front-end framework such as React or Vue.js to build a user interface and display the solution in a user-friendly format. Upon user approval, the user device automatically executes the solution.

[0068] As a concrete example, when this system receives a login failure report, the server instantly analyzes the relevant data, and the terminal's AI identifies the cause. If a password reset is suggested as a solution, the user device automatically performs the procedure. An example of a prompt message for the generated AI model would be, "Identify the cause of the login error and propose a solution."

[0069] The configuration of this invention makes it possible to automate a series of processes from information gathering to solution implementation, thereby significantly improving operational efficiency.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The server functions as an information processing device, periodically collecting electronic data. Inputs include system logs and error reports, which are processed through log analysis software (e.g., Splunk, ELK Stack). Specifically, the server scans these logs at regular intervals, identifying data entries that indicate anomalies. The output of this process is a set of collected data, which is stored in a database for subsequent analysis.

[0073] Step 2:

[0074] The terminal receives data from the server as input and uses a generative AI model to analyze the data in detail. For data processing, an AI model (e.g., OpenAI GPT, Google BERT) is used to analyze the input data, identify problems, and analyze their causes. Specifically, the terminal refers to past databases (e.g., MySQL, PostgreSQL) to search for similar problems and their solutions. This process outputs a summary of the problem and recommended solutions.

[0075] Step 3:

[0076] The user device receives output from the terminal and presents a solution to the user. The input includes the solution identified by the AI. The user device processes this information to present it through a user-friendly interface (e.g., using React or Vue.js). Specifically, the user device generates alerts, pop-up messages, etc., to clearly explain the solution to the user. The output at this stage is the user's choice or approval.

[0077] Step 4:

[0078] The user proceeds through the process by reviewing and agreeing to or selecting the presented solutions. The input is information from the user's device, and the output is the user's consent. Obtaining this consent triggers automated actions within the system, such as initiating a password reset. Specifically, after user approval, the user's device executes an automated script, which then calls a backend process to perform the configuration change.

[0079] (Application Example 1)

[0080] 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."

[0081] In data management facilities, infrastructure operation and monitoring involve a great deal of manual work, requiring high efficiency. In particular, rapidly detecting system malfunctions and environmental anomalies and implementing appropriate countermeasures is difficult, making the optimization of human resources a key challenge.

[0082] 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.

[0083] In this invention, the server includes means for an information monitoring device to collect information from sensing devices and operation records, means for a generating artificial intelligence agent to analyze the information collected by the information monitoring device and determine the cause of the anomaly, and means for a user device to present the solution determined by the generating artificial intelligence agent to the user and to execute the solution based on the user's agreement. This makes it possible to automatically and efficiently detect problems within a data management facility and to quickly implement countermeasures.

[0084] An "information monitoring device" is a device that has the function of collecting information from sensing devices and operation records.

[0085] A "generative artificial intelligence agent" is an artificial intelligence system that has the ability to analyze collected information and determine the cause of anomalies.

[0086] A "user device" is a device that presents a solution to the user and implements that solution based on the user's agreement.

[0087] A "data management facility" is a facility equipped with infrastructure for storing, managing, and processing large amounts of data.

[0088] "Infrastructure operation" refers to activities aimed at maintaining and managing the efficient operation of systems and equipment.

[0089] A "sensing device" is a device that utilizes sensor technology to detect the state of the environment or equipment and acquire data.

[0090] The server works in conjunction with sensing devices to collect operational information in order to monitor the infrastructure within the data management facility. Specifically, the server acquires real-time data from hardware such as IoT devices and surveillance cameras, and efficiently transfers and manages the data using a data streaming platform such as Apache® Kafka.

[0091] The terminal is equipped with a generative artificial intelligence agent that analyzes the cause of anomalies using collected data. This includes a function that utilizes a machine learning model using TENSORFLOW® to detect anomalies by comparing them with past data. Based on the analysis results, it identifies the problem that needs to be solved and the recommended countermeasures.

[0092] Furthermore, users can view analysis results and recommended solutions in real time through their devices. A mobile application built with Flutter® pushes notifications to the user's device. Once the user gives their consent, automated countermeasures are executed via the application, such as restarting the cooling system.

[0093] As a concrete example, if the temperature of a data management facility exceeds a certain threshold, the system immediately proposes adjusting the cooling system. A prompt such as, "Based on real-time monitoring data of the data center environment, detect abnormal temperature increases and propose the optimal cooling system operation method," is used to generate the best course of action for the AI ​​model. This prompt allows the generated AI model to take appropriate action, supporting the efficient operation of the facility.

[0094] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0095] Step 1:

[0096] The server collects environmental data such as temperature and humidity from sensing devices in real time. The input data is transmitted from various IoT devices and efficiently transferred via Apache Kafka. This data is received and stored in a time-series database to prepare for future analysis.

[0097] Step 2:

[0098] The device sends collected environmental data to a generating artificial intelligence agent. This data is used as input for AI analysis. A machine learning model using TensorFlow analyzes this data and detects anomalous data patterns. This analysis also references historical data, and data calculations are performed to identify the cause of the anomalies.

[0099] Step 3:

[0100] The device generates specific solutions based on the AI ​​analysis results. These solutions may include, for example, restarting or adjusting the cooling system. This solution is reflected in the prompt message, utilizing the generated AI model to convey messages such as, "We have detected an abnormal temperature rise and are proposing the optimal way to operate the cooling system."

[0101] Step 4:

[0102] The user reviews the solution through their device. A mobile application built with Flutter pushes the analysis results and recommendations in real time. At this stage, user input is required to obtain consent to the solution.

[0103] Step 5:

[0104] Once the user gives their consent, the user's device sends instructions to the server, and the recommended solution is implemented. Specific actions include restarting the cooling system via API. This process automatically resolves the issue and ensures stable operation of the data management facility.

[0105] 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.

[0106] This invention streamlines system operation tasks by combining an information gathering device, a generating artificial intelligence agent, a user device, and an emotion engine. This system is designed to provide quick and appropriate responses to user inquiries.

[0107] First, the server acts as an information gathering device, collecting data from system logs and error reports. This data is used to predict signs of problems occurring.

[0108] Next, the device utilizes a generative artificial intelligence agent to analyze the root causes of the problem based on the collected data. At this stage, it identifies similar problems from a historical knowledge base and generates solutions.

[0109] Furthermore, the user device utilizes an emotion engine to analyze the user's emotions when presenting solutions. The emotion engine evaluates the user's emotional state using voice and facial expression data and adapts the interface's response accordingly. For example, if the user is experiencing high levels of stress, the solution will be presented in a more user-friendly manner.

[0110] As a concrete example, when a user reports a system failure, the server first quickly collects system logs, which are then analyzed by the terminal. The problem is identified, and a standard solution is devised. Subsequently, when the solution is presented on the user's device, the user's emotional state is evaluated. Based on this evaluation, the solution is communicated in a way that matches the user's emotions. If the user is emotionally agitated, additional support options are offered to optimize the user experience.

[0111] Thus, by incorporating an emotion engine, the system of the present invention enables more personalized responses, leading to further efficiency improvements in operational tasks and enhanced customer satisfaction.

[0112] The following describes the processing flow.

[0113] Step 1:

[0114] A user enters and submits an inquiry about a system failure. The user describes the details in the form and selects options to specifically report the problem.

[0115] Step 2:

[0116] The terminal records the received inquiry in the database and notifies the user that the inquiry has been received. At this point, the basic content of the inquiry is recorded.

[0117] Step 3:

[0118] The server collects relevant data from system logs and error reports and analyzes for signs of anomalies. Data collection is performed in real time, and speed is essential.

[0119] Step 4:

[0120] The on-device artificial intelligence agent analyzes the root causes of a problem based on collected data and generates solutions. It then compares these solutions with a historical knowledge base to select the best solution.

[0121] Step 5:

[0122] The user device presents a solution to the problem. This is where the emotion engine comes into play, analyzing the user's emotional state using voice and visual data.

[0123] Step 6:

[0124] The user device adjusts its interface based on the user's emotions and presents solutions in a way that suits those emotions. If the user is feeling stressed, it will choose more approachable language.

[0125] Step 7:

[0126] The user agrees to the proposed solution and authorizes its implementation. The user can select additional support options as needed.

[0127] Step 8:

[0128] After confirming user consent, the server will launch an automated script to perform the necessary system modifications to resolve the issue.

[0129] Step 9:

[0130] The terminal confirms that all processes are complete and records the results. The user receives a completion notification and is informed that the problem has been resolved.

[0131] (Example 2)

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

[0133] In modern, highly sophisticated computing systems, there is a need to quickly detect signs of failure and resolve problems rapidly and accurately. Furthermore, providing appropriate support for technical issues faced by users and reducing their stress and frustration is also crucial. However, conventional systems often only report error messages without considering the user's emotional state, limiting their ability to improve customer satisfaction.

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

[0135] In this invention, the server includes means for an information integration device to extract data from the operation records and failure reports of the computing system, means for an intelligent agent to analyze the data extracted by the information integration device and determine the root cause of a problem, and means for an emotion analysis device to analyze the user's emotional state and optimize the response of the user interface device based on the results. This enables early detection of failures and efficient problem solving, reduces user stress, and improves customer satisfaction.

[0136] An "information integration device" is a device that has the function of extracting data from the operation records and failure reports of a computing system and evaluating the status of the system.

[0137] An "intelligent agent" is artificial intelligence that has the ability to analyze extracted data and identify the root cause of a problem.

[0138] A "user interface device" is a device that appropriately presents solutions to problems to the user and implements those solutions based on the user's consent.

[0139] An "emotion analysis device" is a device that evaluates the emotional state of a user and adjusts the operation of other system components based on the evaluation results.

[0140] A "control device" is a device that has the function of detecting signs of problems or abnormal performance indicators and issuing alarms as needed.

[0141] "Automated tasks" refer to a series of tasks performed by multiple agents according to specific rules or instructions, with the aim of improving operational efficiency.

[0142] This invention streamlines system operation tasks by combining an information integration device, an intelligent agent, a user interface device, and an emotion analysis device. Based on the system's design philosophy, specific embodiments are shown below.

[0143] The server acts as an information integration device and operates on a computer network. System operation logs and failure reports are centrally managed, and monitoring software (e.g., commonly used monitoring tools) is executed for appropriate data collection. The server extracts data in real time and provides foundational data for detecting early signs of problems.

[0144] The terminal utilizes an intelligent agent to analyze data sent from the server. Statistical analysis and machine learning are used to identify the root cause of problems. For example, data may be analyzed using Python libraries, and anomaly detection algorithms may be executed to identify problems. This process enables efficient troubleshooting.

[0145] The user device incorporates both a user interface device and an emotion analysis device. When presenting solutions to problems the user faces in an appropriate manner, the emotion analysis device identifies the user's voice and facial expressions, optimizing feedback to reduce stress and anxiety. For example, it analyzes emotions using voice APIs and image processing software, and adjusts the interface's response based on the results.

[0146] A concrete example is when a user reports a system failure, and the server promptly collects log data. The terminal then analyzes the problem and provides a quick solution. In this case, the user device presents the solution in a more user-friendly manner, responding to the user's prompt: "Please provide specific steps for suggesting the best solution when a system failure occurs. Please also explain how to handle situations where the user is feeling anxious."

[0147] In this way, the system can reduce the burden on users and aim to improve customer satisfaction.

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] The server collects operation logs and failure reports from the system in real time. This data includes event logs and error messages. This data is given as input, and an information integration device is used to filter out unnecessary noise and extract relevant information. As a result, a clean dataset is output, ready for analysis in the next step.

[0151] Step 2:

[0152] The terminal receives a clean dataset from the server as input. The intelligent agent uses a machine learning model to analyze this data. Specifically, it utilizes libraries such as TensorFlow and scikit-learn to identify anomalous patterns in the data. Through this analysis, it identifies the root cause of the problem and generates candidate solutions. The output is a list of details of the identified problem and its solutions.

[0153] Step 3:

[0154] The user device receives problem details and solutions sent from the terminal as input. Before presenting this information to the user in an easily understandable format, the user interface device uses an emotion analysis device to analyze the user's voice and facial expressions. Specifically, voice data is acquired from a microphone as input, and image data is collected from a camera. This data is analyzed to determine the user's emotional state. The output of this step is a method of presenting solutions optimized for that emotional state. For example, the solution might be displayed with a calm voice, or communication might be conducted in a friendly tone.

[0155] In this way, the entire system functions smoothly, providing efficient and effective support to users.

[0156] (Application Example 2)

[0157] 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".

[0158] There is a need to improve the efficiency of system operations and optimize the user experience by presenting information in a way that responds to the user's emotional state. In particular, when a system failure occurs, it is necessary to provide appropriate solutions quickly while reducing the stress the user experiences. However, current technology makes it difficult to provide solutions that take user emotions into consideration.

[0159] 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.

[0160] In this invention, the server includes means for extracting numerical values ​​from system logs and error reports; an artificial intelligence model as means for analyzing the numerical values ​​collected by the device and identifying the cause of a problem; means for presenting the solution determined by the artificial intelligence model to the user using an information presentation device and executing the solution based on the user's consent; and means for analyzing emotional data and adjusting the user experience based on the emotional state. This enables personalized responses that respond to emotions.

[0161] A "system log" is data that records the operation history of an information processing device.

[0162] An "error report" is a report that contains detailed information about errors that occurred within the system.

[0163] "Numerical values" refer to specific data extracted from logs and reports.

[0164] An "artificial intelligence model" is a collection of algorithms used to analyze data and identify the factors behind a problem.

[0165] An "information display device" is a device that displays information to the user and enables them to perform actions.

[0166] "Emotional data" refers to information related to emotions obtained from the user's facial expressions, tone of voice, and other similar data.

[0167] "Personalized responses" refer to responses that are tailored to the individual user's emotions and circumstances.

[0168] The system implementing this invention consists of a server for information processing, a terminal for analyzing data, and a display device for user use. The server extracts numerical data from system logs and error reports and transmits them to the terminal. The terminal uses a generative artificial intelligence model to analyze the data in detail and identify the root cause of the problem. Furthermore, a solution is generated based on the analysis results. When displaying the generated solution to the user, the user device uses a device that analyzes emotional data to determine the user's emotional state.

[0169] As a concrete example, a server collects sensor and log data and processes it using the Google Cloud Vision API or Amazon Polly system. The device then uses this data to analyze the user's emotional state in real time using a generative AI model, and presents appropriate content and countermeasures based on the user's level of interest and stress. For example, if the AI ​​model determines that the user is losing interest while watching a video, it instructs the AI ​​model to recommend more engaging content and provides gentle navigation using Amazon Polly's voice guidance function.

[0170] An example of a prompt message is: "Design a real-time system that analyzes the user's facial expressions and tone of voice to detect decreased interest or increased stress in real time, suggest personalized content, and improve the viewing experience." By considering the user's emotions and providing appropriate information in this way, it is possible to achieve efficient operations and high customer satisfaction.

[0171] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0172] Step 1:

[0173] The server extracts numerical data from system logs and error reports. The input consists of log data and error reports acquired in real time from the system in operation. The server filters and analyzes this data to extract specific metrics and error codes. The output is a dataset of these extracted data points, which is then sent to the terminal.

[0174] Step 2:

[0175] The terminal receives numerical data transmitted from the server and analyzes it using a generative artificial intelligence model. The input is numerical data from the server. The terminal uses an AI model (e.g., a machine learning algorithm) to analyze these numbers, identify the root causes of a problem, and generate solutions. The output, including the identified problem causes and recommended solutions, is sent to an information display device.

[0176] Step 3:

[0177] The user device receives a solution from the terminal and analyzes emotional data. Inputs include the solution and emotional data obtained from the user (e.g., facial expressions, voice tone). The user device uses emotional analysis software (e.g., facial recognition API or voice analysis API) to evaluate the user's emotional state and adjust how the solution is presented. Outputs include displaying or playing the adjusted solution and additional information (e.g., voice guidance).

[0178] Step 4:

[0179] The user reviews the presented solution and, if necessary, requests agreement or additional assistance. The input consists of the solution and its options displayed on the user's device. The user interacts with the system, approving the solution or selecting further support based on their understanding and feelings. The output determines the next action for the entire system, following the user's final instructions.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] [Second Embodiment]

[0184] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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".

[0196] This invention automates and streamlines operational tasks by implementing a system that combines an information gathering device, a generating artificial intelligence agent, and a user device. The overall operation of the system is described below in natural language.

[0197] First, the server functions as an information gathering device, periodically collecting system logs and error reports to predict signs of anomalies. This enables early detection of problems.

[0198] Next, the device receives the information and uses a generative artificial intelligence agent to analyze the data in detail and identify the root cause of the problem. The AI ​​refers to a database of past data to find similar problems and their solutions.

[0199] Next, the user device presents the user with a recommended solution. The user reviews the displayed solution and gives their consent, which initiates a process to automatically implement the solution.

[0200] For example, if a user reports a login failure, the server immediately collects and analyzes the relevant error report, and the device's AI identifies the cause. If a password reset is necessary as a solution, the user's device will present this solution to the user. After obtaining the user's consent, the system will automatically perform the password reset and resolve the issue.

[0201] Thus, the system of the present invention automatically collects and analyzes information, proposes and implements solutions, and achieves a high level of efficiency while reducing the burden on human resources.

[0202] The following describes the processing flow.

[0203] Step 1:

[0204] The user enters a problem with the system into the inquiry form. The user provides details of the problem through the inquiry form and submits it to the system.

[0205] Step 2:

[0206] The terminal receives the entered inquiry and saves it to the database. It also notifies the user via an automated email that the inquiry has been received.

[0207] Step 3:

[0208] The server collects relevant system logs and error reports and analyzes the data to detect signs of anomalies. Predictive algorithms are used to identify potential problems.

[0209] Step 4:

[0210] The device analyzes the data using an artificial intelligence agent based on the collected information, searches for similar problems in a historical knowledge base, and identifies potential solutions.

[0211] Step 5:

[0212] The user device presents the identified solution to the user. The user reviews the presented solution and chooses to agree to it by following the instructions.

[0213] Step 6:

[0214] The server, with the user's consent, executes an automated script to make necessary configuration changes and system modifications. This completes the resolution of the problem.

[0215] Step 7:

[0216] The terminal verifies that all steps have been completed and records the processing results. It then sends a completion notification to the user, reporting that the problem has been resolved.

[0217] (Example 1)

[0218] 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."

[0219] Modern information systems are complex, and manual monitoring and management present challenges in quickly and efficiently detecting and resolving anomalies. This is especially true in environments where large amounts of data are generated, where rapidly identifying the root cause of problems and providing appropriate solutions is crucial. However, traditional methods are time-consuming and cumbersome in terms of information gathering, analysis, and solution implementation, hindering operational efficiency.

[0220] 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.

[0221] In this invention, the server includes means for an information processing device to extract information from electronic data, means for an artificial intelligence model to analyze the information acquired by the information processing device and identify the cause of a problem, and means for a user device to present the solution identified by the artificial intelligence model to the user and execute the solution based on the user's approval. This makes it possible to efficiently detect anomalies from large amounts of data and to quickly present and execute solutions.

[0222] An "information processing device" is a device used to extract necessary information from electronic data, and it plays a role in data collection and organization.

[0223] An "artificial intelligence model" is software or algorithms used to analyze collected data and identify problems or generate solutions.

[0224] A "user device" is a device that presents solutions generated by an artificial intelligence model to the user, receives input from the user, and prompts the user to execute the solution.

[0225] A "processing terminal" is a device that further processes information sent from an information processing device and searches for similar situations or solutions by comparing it with past databases.

[0226] A "database" is a collection of structured digital data designed for efficient storage and retrieval of information.

[0227] A "control device" is a device that monitors and manages the entire system in order to detect early signs of problems and recognize abnormal operational indicators.

[0228] This invention aims to build a system that automates information processing and solves problems efficiently and quickly. The specific forms for implementing this system are described below.

[0229] As an information processing device, the server collects electronic data. Specifically, the server uses log analysis software to extract necessary data from sources such as system logs and error reports. For this purpose, tools such as Splunk and ELK Stack can be used to efficiently organize and store the data.

[0230] Next, the terminal processes the data received from the server in a more advanced manner. The terminal analyzes the data using a generative AI model to identify the root cause of the problem. Specifically, AI models such as OpenAI's GPT or Google's BERT can be used, which compare and contrast with past databases to find similar situations and solutions. The terminal also accesses database systems (such as MySQL or PostgreSQL) to efficiently search for data.

[0231] Subsequently, the user device presents the solution derived by the device to the user. The user device uses a front-end framework such as React or Vue.js to build a user interface and display the solution in a user-friendly format. Upon user approval, the user device automatically executes the solution.

[0232] As a concrete example, when this system receives a login failure report, the server instantly analyzes the relevant data, and the terminal's AI identifies the cause. If a password reset is suggested as a solution, the user device automatically performs the procedure. An example of a prompt message for the generated AI model would be, "Identify the cause of the login error and propose a solution."

[0233] The configuration of this invention makes it possible to automate a series of processes from information gathering to solution implementation, thereby significantly improving operational efficiency.

[0234] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0235] Step 1:

[0236] The server functions as an information processing device, periodically collecting electronic data. Inputs include system logs and error reports, which are processed through log analysis software (e.g., Splunk, ELK Stack). Specifically, the server scans these logs at regular intervals, identifying data entries that indicate anomalies. The output of this process is a set of collected data, which is stored in a database for subsequent analysis.

[0237] Step 2:

[0238] The terminal receives data from the server as input and uses a generative AI model to analyze the data in detail. For data processing, an AI model (e.g., OpenAI GPT, Google BERT) is used to analyze the input data, identify problems, and analyze their causes. Specifically, the terminal refers to past databases (e.g., MySQL, PostgreSQL) to search for similar problems and their solutions. This process outputs a summary of the problem and recommended solutions.

[0239] Step 3:

[0240] The user device receives output from the terminal and presents a solution to the user. The input includes the solution identified by the AI. The user device processes this information to present it through a user-friendly interface (e.g., using React or Vue.js). Specifically, the user device generates alerts, pop-up messages, etc., to clearly explain the solution to the user. The output at this stage is the user's choice or approval.

[0241] Step 4:

[0242] The user proceeds through the process by reviewing and agreeing to or selecting the presented solutions. The input is information from the user's device, and the output is the user's consent. Obtaining this consent triggers automated actions within the system, such as initiating a password reset. Specifically, after user approval, the user's device executes an automated script, which then calls a backend process to perform the configuration change.

[0243] (Application Example 1)

[0244] 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."

[0245] In data management facilities, infrastructure operation and monitoring involve a great deal of manual work, requiring high efficiency. In particular, rapidly detecting system malfunctions and environmental anomalies and implementing appropriate countermeasures is difficult, making the optimization of human resources a key challenge.

[0246] 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.

[0247] In this invention, the server includes means for an information monitoring device to collect information from sensing devices and operation records, means for a generating artificial intelligence agent to analyze the information collected by the information monitoring device and determine the cause of the anomaly, and means for a user device to present the solution determined by the generating artificial intelligence agent to the user and to execute the solution based on the user's agreement. This makes it possible to automatically and efficiently detect problems within a data management facility and to quickly implement countermeasures.

[0248] An "information monitoring device" is a device that has the function of collecting information from sensing devices and operation records.

[0249] A "generative artificial intelligence agent" is an artificial intelligence system that has the ability to analyze collected information and determine the cause of anomalies.

[0250] A "user device" is a device that presents a solution to the user and implements that solution based on the user's agreement.

[0251] A "data management facility" is a facility equipped with infrastructure for storing, managing, and processing large amounts of data.

[0252] "Infrastructure operation" refers to activities aimed at maintaining and managing the efficient operation of systems and equipment.

[0253] A "sensing device" is a device that utilizes sensor technology to detect the state of the environment or equipment and acquire data.

[0254] The server works in conjunction with sensing devices to collect operational information in order to monitor the infrastructure within the data management facility. Specifically, the server acquires real-time data from hardware such as IoT devices and surveillance cameras, and efficiently transfers and manages the data using a data streaming platform such as Apache Kafka.

[0255] The device is equipped with a generative artificial intelligence agent that analyzes the cause of anomalies using collected data. This includes a function that utilizes machine learning models based on TensorFlow to detect anomalies by comparing them with historical data. Based on the analysis results, it identifies the problem that needs to be solved and recommends the appropriate countermeasures.

[0256] Furthermore, users can view analysis results and recommended solutions in real time through their devices. A mobile application built with Flutter pushes notifications to the user's device. Once the user gives their consent, automated countermeasures are executed through the application, such as restarting the cooling system.

[0257] As a concrete example, if the temperature of a data management facility exceeds a certain threshold, the system immediately proposes adjusting the cooling system. A prompt such as, "Based on real-time monitoring data of the data center environment, detect abnormal temperature increases and propose the optimal cooling system operation method," is used to generate the best course of action for the AI ​​model. This prompt allows the generated AI model to take appropriate action, supporting the efficient operation of the facility.

[0258] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0259] Step 1:

[0260] The server collects environmental data such as temperature and humidity from sensing devices in real time. The input data is transmitted from various IoT devices and efficiently transferred via Apache Kafka. This data is received and stored in a time-series database to prepare for future analysis.

[0261] Step 2:

[0262] The device sends collected environmental data to a generating artificial intelligence agent. This data is used as input for AI analysis. A machine learning model using TensorFlow analyzes this data and detects anomalous data patterns. This analysis also references historical data, and data calculations are performed to identify the cause of the anomalies.

[0263] Step 3:

[0264] The device generates specific solutions based on the AI ​​analysis results. These solutions may include, for example, restarting or adjusting the cooling system. This solution is reflected in the prompt message, utilizing the generated AI model to convey messages such as, "We have detected an abnormal temperature rise and are proposing the optimal way to operate the cooling system."

[0265] Step 4:

[0266] The user reviews the solution through their device. A mobile application built with Flutter pushes the analysis results and recommendations in real time. At this stage, user input is required to obtain consent to the solution.

[0267] Step 5:

[0268] Once the user gives their consent, the user's device sends instructions to the server, and the recommended solution is implemented. Specific actions include restarting the cooling system via API. This process automatically resolves the issue and ensures stable operation of the data management facility.

[0269] 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.

[0270] This invention streamlines system operation tasks by combining an information gathering device, a generating artificial intelligence agent, a user device, and an emotion engine. This system is designed to provide quick and appropriate responses to user inquiries.

[0271] First, the server acts as an information gathering device, collecting data from system logs and error reports. This data is used to predict signs of problems occurring.

[0272] Next, the device utilizes a generative artificial intelligence agent to analyze the root causes of the problem based on the collected data. At this stage, it identifies similar problems from a historical knowledge base and generates solutions.

[0273] Furthermore, the user device utilizes an emotion engine to analyze the user's emotions when presenting solutions. The emotion engine evaluates the user's emotional state using voice and facial expression data and adapts the interface's response accordingly. For example, if the user is experiencing high levels of stress, the solution will be presented in a more user-friendly manner.

[0274] As a concrete example, when a user reports a system failure, the server first quickly collects system logs, which are then analyzed by the terminal. The problem is identified, and a standard solution is devised. Subsequently, when the solution is presented on the user's device, the user's emotional state is evaluated. Based on this evaluation, the solution is communicated in a way that matches the user's emotions. If the user is emotionally agitated, additional support options are offered to optimize the user experience.

[0275] Thus, by incorporating an emotion engine, the system of the present invention enables more personalized responses, leading to further efficiency improvements in operational tasks and enhanced customer satisfaction.

[0276] The following describes the processing flow.

[0277] Step 1:

[0278] The user inputs and sends an inquiry regarding system failures. The user describes details in a form and selects options for specifically reporting the problem.

[0279] Step 2:

[0280] The terminal records the received inquiry in a database and notifies the user that the inquiry has been received. At this point, the basic inquiry content is recorded.

[0281] Step 3:

[0282] The server collects relevant data from system logs and error reports and analyzes signs of abnormalities. The data collection is performed in real time and rapidity is required.

[0283] Step 4:

[0284] The artificial intelligence agent on the terminal analyzes the cause of the problem based on the collected data and generates a solution. It is compared with the past knowledge base to select the best solution.

[0285] Step 5:

[0286] The user device presents a solution to the problem. Here, the emotion engine becomes active and analyzes the emotional state using the user's voice and visual data.

[0287] Step 6:

[0288] The user device adjusts the interface based on the user's emotion and presents the solution in a form suitable for the emotion. If the user is feeling stressed, it selects a friendly way of speaking.

[0289] Step 7:

[0290] The user agrees to the proposed solution and authorizes its implementation. The user can select additional support options as needed.

[0291] Step 8:

[0292] After confirming user consent, the server will launch an automated script to perform the necessary system modifications to resolve the issue.

[0293] Step 9:

[0294] The terminal confirms that all processes are complete and records the results. The user receives a completion notification and is informed that the problem has been resolved.

[0295] (Example 2)

[0296] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0297] In modern, highly sophisticated computing systems, there is a need to quickly detect signs of failure and resolve problems rapidly and accurately. Furthermore, providing appropriate support for technical issues faced by users and reducing their stress and frustration is also crucial. However, conventional systems often only report error messages without considering the user's emotional state, limiting their ability to improve customer satisfaction.

[0298] 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.

[0299] In this invention, the server includes means for an information integration device to extract data from the operation records and failure reports of the computing system, means for an intelligent agent to analyze the data extracted by the information integration device and determine the root cause of a problem, and means for an emotion analysis device to analyze the user's emotional state and optimize the response of the user interface device based on the results. This enables early detection of failures and efficient problem solving, reduces user stress, and improves customer satisfaction.

[0300] An "information integration device" is a device that has the function of extracting data from the operation records and failure reports of a computing system and evaluating the status of the system.

[0301] An "intelligent agent" is artificial intelligence that has the ability to analyze extracted data and identify the root cause of a problem.

[0302] A "user interface device" is a device that appropriately presents solutions to problems to the user and implements those solutions based on the user's consent.

[0303] An "emotion analysis device" is a device that evaluates the emotional state of a user and adjusts the operation of other system components based on the evaluation results.

[0304] A "control device" is a device that has the function of detecting signs of problems or abnormal performance indicators and issuing alarms as needed.

[0305] "Automated tasks" refer to a series of tasks performed by multiple agents according to specific rules or instructions, with the aim of improving operational efficiency.

[0306] This invention streamlines system operation tasks by combining an information integration device, an intelligent agent, a user interface device, and an emotion analysis device. Based on the system's design philosophy, specific embodiments are shown below.

[0307] The server acts as an information integration device and operates on a computer network. The operation records and trouble reports of the system are centrally managed, and monitoring software (e.g., commonly used monitoring tools) is executed for appropriate data collection. The server extracts data in real time and provides basic data for detecting signs of problems.

[0308] The terminal utilizes intelligent agents to analyze the data sent from the server. Here, statistical analysis and machine learning are used to identify the root cause of problems. For example, data may be analyzed using Python libraries, and problem identification may be performed by executing an anomaly detection algorithm. This process enables efficient troubleshooting.

[0309] The user device has both a user interface device and an emotion analysis device. When presenting solutions to the problems faced by the user in an appropriate form, the emotion analysis device discriminates the user's voice and facial expressions, and optimizes the feedback to reduce stress and anxiety. For example, emotions are analyzed using voice APIs or image processing software, and the response of the interface is adjusted based on the results.

[0310] As a specific example, when the user reports a system failure, there is a case where the server quickly collects log data. Then, the terminal analyzes the problem and presents a prompt response. At this time, the user device presents the solution in a more friendly expression in response to the user's prompt sentence "Please teach me the specific steps for proposing the optimal solution when a system failure occurs. Also, explain how to respond when the user feels anxious."

[0311] In this way, the system can reduce the burden on the user and aim to improve customer satisfaction.

[0312] The flow of the specific process in Example 2 will be described using FIG. 13.

[0313] Step 1:

[0314] The server collects operation logs and failure reports from the system in real time. This data includes event logs and error messages. This data is given as input, and an information integration device is used to filter out unnecessary noise and extract relevant information. As a result, a clean dataset is output, ready for analysis in the next step.

[0315] Step 2:

[0316] The terminal receives a clean dataset from the server as input. The intelligent agent uses a machine learning model to analyze this data. Specifically, it utilizes libraries such as TensorFlow and scikit-learn to identify anomalous patterns in the data. Through this analysis, it identifies the root cause of the problem and generates candidate solutions. The output is a list of details of the identified problem and its solutions.

[0317] Step 3:

[0318] The user device receives problem details and solutions sent from the terminal as input. Before presenting this information to the user in an easily understandable format, the user interface device uses an emotion analysis device to analyze the user's voice and facial expressions. Specifically, voice data is acquired from a microphone as input, and image data is collected from a camera. This data is analyzed to determine the user's emotional state. The output of this step is a method of presenting solutions optimized for that emotional state. For example, the solution might be displayed with a calm voice, or communication might be conducted in a friendly tone.

[0319] In this way, the entire system functions smoothly, providing efficient and effective support to users.

[0320] (Application Example 2)

[0321] 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."

[0322] There is a need to improve the efficiency of system operations and optimize the user experience by presenting information in a way that responds to the user's emotional state. In particular, when a system failure occurs, it is necessary to provide appropriate solutions quickly while reducing the stress the user experiences. However, current technology makes it difficult to provide solutions that take user emotions into consideration.

[0323] 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.

[0324] In this invention, the server includes means for extracting numerical values ​​from system logs and error reports; an artificial intelligence model as means for analyzing the numerical values ​​collected by the device and identifying the cause of a problem; means for presenting the solution determined by the artificial intelligence model to the user using an information presentation device and executing the solution based on the user's consent; and means for analyzing emotional data and adjusting the user experience based on the emotional state. This enables personalized responses that respond to emotions.

[0325] A "system log" is data that records the operation history of an information processing device.

[0326] An "error report" is a report that contains detailed information about errors that occurred within the system.

[0327] "Numerical values" refer to specific data extracted from logs and reports.

[0328] An "artificial intelligence model" is a collection of algorithms used to analyze data and identify the factors behind a problem.

[0329] An "information display device" is a device that displays information to the user and enables them to perform actions.

[0330] "Emotional data" refers to information related to emotions obtained from the user's facial expressions, tone of voice, and other similar data.

[0331] "Personalized responses" refer to responses that are tailored to the individual user's emotions and circumstances.

[0332] The system implementing this invention consists of a server for information processing, a terminal for analyzing data, and a display device for user use. The server extracts numerical data from system logs and error reports and transmits them to the terminal. The terminal uses a generative artificial intelligence model to analyze the data in detail and identify the root cause of the problem. Furthermore, a solution is generated based on the analysis results. When displaying the generated solution to the user, the user device uses a device that analyzes emotional data to determine the user's emotional state.

[0333] As a concrete example, a server collects sensor and log data and processes it using the Google Cloud Vision API or Amazon Polly system. The device then uses this data to analyze the user's emotional state in real time using a generative AI model, and presents appropriate content and countermeasures based on the user's level of interest and stress. For example, if the AI ​​model determines that the user is losing interest while watching a video, it instructs the AI ​​model to recommend more engaging content and provides gentle navigation using Amazon Polly's voice guidance function.

[0334] An example of a prompt message is: "Design a real-time system that analyzes the user's facial expressions and tone of voice to detect decreased interest or increased stress in real time, suggest personalized content, and improve the viewing experience." By considering the user's emotions and providing appropriate information in this way, it is possible to achieve efficient operations and high customer satisfaction.

[0335] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0336] Step 1:

[0337] The server extracts numerical data from system logs and error reports. The input consists of log data and error reports acquired in real time from the system in operation. The server filters and analyzes this data to extract specific metrics and error codes. The output is a dataset of these extracted data points, which is then sent to the terminal.

[0338] Step 2:

[0339] The terminal receives numerical data transmitted from the server and analyzes it using a generative artificial intelligence model. The input is numerical data from the server. The terminal uses an AI model (e.g., a machine learning algorithm) to analyze these numbers, identify the root causes of a problem, and generate solutions. The output, including the identified problem causes and recommended solutions, is sent to an information display device.

[0340] Step 3:

[0341] The user device receives a solution from the terminal and analyzes emotional data. Inputs include the solution and emotional data obtained from the user (e.g., facial expressions, voice tone). The user device uses emotional analysis software (e.g., facial recognition API or voice analysis API) to evaluate the user's emotional state and adjust how the solution is presented. Outputs include displaying or playing the adjusted solution and additional information (e.g., voice guidance).

[0342] Step 4:

[0343] The user reviews the presented solution and, if necessary, requests agreement or additional assistance. The input consists of the solution and its options displayed on the user's device. The user interacts with the system, approving the solution or selecting further support based on their understanding and feelings. The output determines the next action for the entire system, following the user's final instructions.

[0344] 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.

[0345] 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.

[0346] 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.

[0347] [Third Embodiment]

[0348] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0349] 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.

[0350] 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).

[0351] 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.

[0352] 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.

[0353] 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).

[0354] 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.

[0355] 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.

[0356] 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.

[0357] 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.

[0358] 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.

[0359] 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".

[0360] This invention automates and streamlines operational tasks by implementing a system that combines an information gathering device, a generating artificial intelligence agent, and a user device. The overall operation of the system is described below in natural language.

[0361] First, the server functions as an information gathering device, periodically collecting system logs and error reports to predict signs of anomalies. This enables early detection of problems.

[0362] Next, the device receives the information and uses a generative artificial intelligence agent to analyze the data in detail and identify the root cause of the problem. The AI ​​refers to a database of past data to find similar problems and their solutions.

[0363] Next, the user device presents the user with a recommended solution. The user reviews the displayed solution and gives their consent, which initiates a process to automatically implement the solution.

[0364] For example, if a user reports a login failure, the server immediately collects and analyzes the relevant error report, and the device's AI identifies the cause. If a password reset is necessary as a solution, the user's device will present this solution to the user. After obtaining the user's consent, the system will automatically perform the password reset and resolve the issue.

[0365] Thus, the system of the present invention automatically collects and analyzes information, proposes and implements solutions, and achieves a high level of efficiency while reducing the burden on human resources.

[0366] The following describes the processing flow.

[0367] Step 1:

[0368] The user enters a problem with the system into the inquiry form. The user provides details of the problem through the inquiry form and submits it to the system.

[0369] Step 2:

[0370] The terminal receives the entered inquiry and saves it to the database. It also notifies the user via an automated email that the inquiry has been received.

[0371] Step 3:

[0372] The server collects relevant system logs and error reports and analyzes the data to detect signs of anomalies. Predictive algorithms are used to identify potential problems.

[0373] Step 4:

[0374] The device analyzes the data using an artificial intelligence agent based on the collected information, searches for similar problems in a historical knowledge base, and identifies potential solutions.

[0375] Step 5:

[0376] The user device presents the identified solution to the user. The user reviews the presented solution and chooses to agree to it by following the instructions.

[0377] Step 6:

[0378] The server, with the user's consent, executes an automated script to make necessary configuration changes and system modifications. This completes the resolution of the problem.

[0379] Step 7:

[0380] The terminal verifies that all steps have been completed and records the processing results. It then sends a completion notification to the user, reporting that the problem has been resolved.

[0381] (Example 1)

[0382] 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."

[0383] Modern information systems are complex, and manual monitoring and management present challenges in quickly and efficiently detecting and resolving anomalies. This is especially true in environments where large amounts of data are generated, where rapidly identifying the root cause of problems and providing appropriate solutions is crucial. However, traditional methods are time-consuming and cumbersome in terms of information gathering, analysis, and solution implementation, hindering operational efficiency.

[0384] 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.

[0385] In this invention, the server includes means for an information processing device to extract information from electronic data, means for an artificial intelligence model to analyze the information acquired by the information processing device and identify the cause of a problem, and means for a user device to present the solution identified by the artificial intelligence model to the user and execute the solution based on the user's approval. This makes it possible to efficiently detect anomalies from large amounts of data and to quickly present and execute solutions.

[0386] An "information processing device" is a device used to extract necessary information from electronic data, and it plays a role in data collection and organization.

[0387] An "artificial intelligence model" is software or algorithms used to analyze collected data and identify problems or generate solutions.

[0388] A "user device" is a device that presents solutions generated by an artificial intelligence model to the user, receives input from the user, and prompts the user to execute the solution.

[0389] A "processing terminal" is a device that further processes information sent from an information processing device and searches for similar situations or solutions by comparing it with past databases.

[0390] A "database" is a collection of structured digital data designed for efficient storage and retrieval of information.

[0391] A "control device" is a device that monitors and manages the entire system in order to detect early signs of problems and recognize abnormal operational indicators.

[0392] This invention aims to build a system that automates information processing and solves problems efficiently and quickly. The specific forms for implementing this system are described below.

[0393] As an information processing device, the server collects electronic data. Specifically, the server uses log analysis software to extract necessary data from sources such as system logs and error reports. For this purpose, tools such as Splunk and ELK Stack can be used to efficiently organize and store the data.

[0394] Next, the terminal processes the data received from the server in a more advanced manner. The terminal analyzes the data using a generative AI model to identify the root cause of the problem. Specifically, AI models such as OpenAI's GPT or Google's BERT can be used, which compare and contrast with past databases to find similar situations and solutions. The terminal also accesses database systems (such as MySQL or PostgreSQL) to efficiently search for data.

[0395] Subsequently, the user device presents the solution derived by the device to the user. The user device uses a front-end framework such as React or Vue.js to build a user interface and display the solution in a user-friendly format. Upon user approval, the user device automatically executes the solution.

[0396] As a concrete example, when this system receives a login failure report, the server instantly analyzes the relevant data, and the terminal's AI identifies the cause. If a password reset is suggested as a solution, the user device automatically performs the procedure. An example of a prompt message for the generated AI model would be, "Identify the cause of the login error and propose a solution."

[0397] The configuration of this invention makes it possible to automate a series of processes from information gathering to solution implementation, thereby significantly improving operational efficiency.

[0398] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0399] Step 1:

[0400] The server functions as an information processing device, periodically collecting electronic data. Inputs include system logs and error reports, which are processed through log analysis software (e.g., Splunk, ELK Stack). Specifically, the server scans these logs at regular intervals, identifying data entries that indicate anomalies. The output of this process is a set of collected data, which is stored in a database for subsequent analysis.

[0401] Step 2:

[0402] The terminal receives data from the server as input and uses a generative AI model to analyze the data in detail. For data processing, an AI model (e.g., OpenAI GPT, Google BERT) is used to analyze the input data, identify problems, and analyze their causes. Specifically, the terminal refers to past databases (e.g., MySQL, PostgreSQL) to search for similar problems and their solutions. This process outputs a summary of the problem and recommended solutions.

[0403] Step 3:

[0404] The user device receives output from the terminal and presents a solution to the user. The input includes the solution identified by the AI. The user device processes this information to present it through a user-friendly interface (e.g., using React or Vue.js). Specifically, the user device generates alerts, pop-up messages, etc., to clearly explain the solution to the user. The output at this stage is the user's choice or approval.

[0405] Step 4:

[0406] The user proceeds through the process by reviewing and agreeing to or selecting the presented solutions. The input is information from the user's device, and the output is the user's consent. Obtaining this consent triggers automated actions within the system, such as initiating a password reset. Specifically, after user approval, the user's device executes an automated script, which then calls a backend process to perform the configuration change.

[0407] (Application Example 1)

[0408] 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."

[0409] In data management facilities, infrastructure operation and monitoring involve a great deal of manual work, requiring high efficiency. In particular, rapidly detecting system malfunctions and environmental anomalies and implementing appropriate countermeasures is difficult, making the optimization of human resources a key challenge.

[0410] 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.

[0411] In this invention, the server includes means for an information monitoring device to collect information from sensing devices and operation records, means for a generating artificial intelligence agent to analyze the information collected by the information monitoring device and determine the cause of the anomaly, and means for a user device to present the solution determined by the generating artificial intelligence agent to the user and to execute the solution based on the user's agreement. This makes it possible to automatically and efficiently detect problems within a data management facility and to quickly implement countermeasures.

[0412] An "information monitoring device" is a device that has the function of collecting information from sensing devices and operation records.

[0413] A "generative artificial intelligence agent" is an artificial intelligence system that has the ability to analyze collected information and determine the cause of anomalies.

[0414] A "user device" is a device that presents a solution to the user and implements that solution based on the user's agreement.

[0415] A "data management facility" is a facility equipped with infrastructure for storing, managing, and processing large amounts of data.

[0416] "Infrastructure operation" refers to activities aimed at maintaining and managing the efficient operation of systems and equipment.

[0417] A "sensing device" is a device that utilizes sensor technology to detect the state of the environment or equipment and acquire data.

[0418] The server works in conjunction with sensing devices to collect operational information in order to monitor the infrastructure within the data management facility. Specifically, the server acquires real-time data from hardware such as IoT devices and surveillance cameras, and efficiently transfers and manages the data using a data streaming platform such as Apache Kafka.

[0419] The device is equipped with a generative artificial intelligence agent that analyzes the cause of anomalies using collected data. This includes a function that utilizes machine learning models based on TensorFlow to detect anomalies by comparing them with historical data. Based on the analysis results, it identifies the problem that needs to be solved and recommends the appropriate countermeasures.

[0420] Furthermore, users can view analysis results and recommended solutions in real time through their devices. A mobile application built with Flutter pushes notifications to the user's device. Once the user gives their consent, automated countermeasures are executed through the application, such as restarting the cooling system.

[0421] As a concrete example, if the temperature of a data management facility exceeds a certain threshold, the system immediately proposes adjusting the cooling system. A prompt such as, "Based on real-time monitoring data of the data center environment, detect abnormal temperature increases and propose the optimal cooling system operation method," is used to generate the best course of action for the AI ​​model. This prompt allows the generated AI model to take appropriate action, supporting the efficient operation of the facility.

[0422] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0423] Step 1:

[0424] The server collects environmental data such as temperature and humidity from sensing devices in real time. The input data is transmitted from various IoT devices and efficiently transferred via Apache Kafka. This data is received and stored in a time-series database to prepare for future analysis.

[0425] Step 2:

[0426] The device sends collected environmental data to a generating artificial intelligence agent. This data is used as input for AI analysis. A machine learning model using TensorFlow analyzes this data and detects anomalous data patterns. This analysis also references historical data, and data calculations are performed to identify the cause of the anomalies.

[0427] Step 3:

[0428] The device generates specific solutions based on the AI ​​analysis results. These solutions may include, for example, restarting or adjusting the cooling system. This solution is reflected in the prompt message, utilizing the generated AI model to convey messages such as, "We have detected an abnormal temperature rise and are proposing the optimal way to operate the cooling system."

[0429] Step 4:

[0430] The user reviews the solution through their device. A mobile application built with Flutter pushes the analysis results and recommendations in real time. At this stage, user input is required to obtain consent to the solution.

[0431] Step 5:

[0432] Once the user gives their consent, the user's device sends instructions to the server, and the recommended solution is implemented. Specific actions include restarting the cooling system via API. This process automatically resolves the issue and ensures stable operation of the data management facility.

[0433] 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.

[0434] This invention streamlines system operation tasks by combining an information gathering device, a generating artificial intelligence agent, a user device, and an emotion engine. This system is designed to provide quick and appropriate responses to user inquiries.

[0435] First, the server acts as an information gathering device, collecting data from system logs and error reports. This data is used to predict signs of problems occurring.

[0436] Next, the device utilizes a generative artificial intelligence agent to analyze the root causes of the problem based on the collected data. At this stage, it identifies similar problems from a historical knowledge base and generates solutions.

[0437] Furthermore, the user device utilizes an emotion engine to analyze the user's emotions when presenting solutions. The emotion engine evaluates the user's emotional state using voice and facial expression data and adapts the interface's response accordingly. For example, if the user is experiencing high levels of stress, the solution will be presented in a more user-friendly manner.

[0438] As a concrete example, when a user reports a system failure, the server first quickly collects system logs, which are then analyzed by the terminal. The problem is identified, and a standard solution is devised. Subsequently, when the solution is presented on the user's device, the user's emotional state is evaluated. Based on this evaluation, the solution is communicated in a way that matches the user's emotions. If the user is emotionally agitated, additional support options are offered to optimize the user experience.

[0439] Thus, by incorporating an emotion engine, the system of the present invention enables more personalized responses, leading to further efficiency improvements in operational tasks and enhanced customer satisfaction.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] A user enters and submits an inquiry about a system failure. The user describes the details in the form and selects options to specifically report the problem.

[0443] Step 2:

[0444] The terminal records the received inquiry in the database and notifies the user that the inquiry has been received. At this point, the basic content of the inquiry is recorded.

[0445] Step 3:

[0446] The server collects relevant data from system logs and error reports and analyzes for signs of anomalies. Data collection is performed in real time, and speed is essential.

[0447] Step 4:

[0448] The on-device artificial intelligence agent analyzes the root causes of a problem based on collected data and generates solutions. It then compares these solutions with a historical knowledge base to select the best solution.

[0449] Step 5:

[0450] The user device presents a solution to the problem. This is where the emotion engine comes into play, analyzing the user's emotional state using voice and visual data.

[0451] Step 6:

[0452] The user device adjusts its interface based on the user's emotions and presents solutions in a way that suits those emotions. If the user is feeling stressed, it will choose more approachable language.

[0453] Step 7:

[0454] The user agrees to the proposed solution and authorizes its implementation. The user can select additional support options as needed.

[0455] Step 8:

[0456] After confirming user consent, the server will launch an automated script to perform the necessary system modifications to resolve the issue.

[0457] Step 9:

[0458] The terminal confirms that all processes are complete and records the results. The user receives a completion notification and is informed that the problem has been resolved.

[0459] (Example 2)

[0460] 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."

[0461] In modern, highly sophisticated computing systems, there is a need to quickly detect signs of failure and resolve problems rapidly and accurately. Furthermore, providing appropriate support for technical issues faced by users and reducing their stress and frustration is also crucial. However, conventional systems often only report error messages without considering the user's emotional state, limiting their ability to improve customer satisfaction.

[0462] 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.

[0463] In this invention, the server includes means for an information integration device to extract data from the operation records and failure reports of the computing system, means for an intelligent agent to analyze the data extracted by the information integration device and determine the root cause of a problem, and means for an emotion analysis device to analyze the user's emotional state and optimize the response of the user interface device based on the results. This enables early detection of failures and efficient problem solving, reduces user stress, and improves customer satisfaction.

[0464] An "information integration device" is a device that has the function of extracting data from the operation records and failure reports of a computing system and evaluating the status of the system.

[0465] An "intelligent agent" is artificial intelligence that has the ability to analyze extracted data and identify the root cause of a problem.

[0466] A "user interface device" is a device that appropriately presents solutions to problems to the user and implements those solutions based on the user's consent.

[0467] An "emotion analysis device" is a device that evaluates the emotional state of a user and adjusts the operation of other system components based on the evaluation results.

[0468] A "control device" is a device that has the function of detecting signs of problems or abnormal performance indicators and issuing alarms as needed.

[0469] "Automated tasks" refer to a series of tasks performed by multiple agents according to specific rules or instructions, with the aim of improving operational efficiency.

[0470] This invention streamlines system operation tasks by combining an information integration device, an intelligent agent, a user interface device, and an emotion analysis device. Based on the system's design philosophy, specific embodiments are shown below.

[0471] The server acts as an information integration device and operates on a computer network. System operation logs and failure reports are centrally managed, and monitoring software (e.g., commonly used monitoring tools) is executed for appropriate data collection. The server extracts data in real time and provides foundational data for detecting early signs of problems.

[0472] The terminal utilizes an intelligent agent to analyze data sent from the server. Statistical analysis and machine learning are used to identify the root cause of problems. For example, data may be analyzed using Python libraries, and anomaly detection algorithms may be executed to identify problems. This process enables efficient troubleshooting.

[0473] The user device incorporates both a user interface device and an emotion analysis device. When presenting solutions to problems the user faces in an appropriate manner, the emotion analysis device identifies the user's voice and facial expressions, optimizing feedback to reduce stress and anxiety. For example, it analyzes emotions using voice APIs and image processing software, and adjusts the interface's response based on the results.

[0474] A concrete example is when a user reports a system failure, and the server promptly collects log data. The terminal then analyzes the problem and provides a quick solution. In this case, the user device presents the solution in a more user-friendly manner, responding to the user's prompt: "Please provide specific steps for suggesting the best solution when a system failure occurs. Please also explain how to handle situations where the user is feeling anxious."

[0475] In this way, the system can reduce the burden on users and aim to improve customer satisfaction.

[0476] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0477] Step 1:

[0478] The server collects operation logs and failure reports from the system in real time. This data includes event logs and error messages. This data is given as input, and an information integration device is used to filter out unnecessary noise and extract relevant information. As a result, a clean dataset is output, ready for analysis in the next step.

[0479] Step 2:

[0480] The terminal receives a clean dataset from the server as input. The intelligent agent uses a machine learning model to analyze this data. Specifically, it utilizes libraries such as TensorFlow and scikit-learn to identify anomalous patterns in the data. Through this analysis, it identifies the root cause of the problem and generates candidate solutions. The output is a list of details of the identified problem and its solutions.

[0481] Step 3:

[0482] The user device receives problem details and solutions sent from the terminal as input. Before presenting this information to the user in an easily understandable format, the user interface device uses an emotion analysis device to analyze the user's voice and facial expressions. Specifically, voice data is acquired from a microphone as input, and image data is collected from a camera. This data is analyzed to determine the user's emotional state. The output of this step is a method of presenting solutions optimized for that emotional state. For example, the solution might be displayed with a calm voice, or communication might be conducted in a friendly tone.

[0483] In this way, the entire system functions smoothly, providing efficient and effective support to users.

[0484] (Application Example 2)

[0485] 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."

[0486] There is a need to improve the efficiency of system operations and optimize the user experience by presenting information in a way that responds to the user's emotional state. In particular, when a system failure occurs, it is necessary to provide appropriate solutions quickly while reducing the stress the user experiences. However, current technology makes it difficult to provide solutions that take user emotions into consideration.

[0487] 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.

[0488] In this invention, the server includes means for extracting numerical values ​​from system logs and error reports; an artificial intelligence model as means for analyzing the numerical values ​​collected by the device and identifying the cause of a problem; means for presenting the solution determined by the artificial intelligence model to the user using an information presentation device and executing the solution based on the user's consent; and means for analyzing emotional data and adjusting the user experience based on the emotional state. This enables personalized responses that respond to emotions.

[0489] A "system log" is data that records the operation history of an information processing device.

[0490] An "error report" is a report that contains detailed information about errors that occurred within the system.

[0491] "Numerical values" refer to specific data extracted from logs and reports.

[0492] An "artificial intelligence model" is a collection of algorithms used to analyze data and identify the factors behind a problem.

[0493] An "information display device" is a device that displays information to the user and enables them to perform actions.

[0494] "Emotional data" refers to information related to emotions obtained from the user's facial expressions, tone of voice, and other similar data.

[0495] "Personalized responses" refer to responses that are tailored to the individual user's emotions and circumstances.

[0496] The system implementing this invention consists of a server for information processing, a terminal for analyzing data, and a display device for user use. The server extracts numerical data from system logs and error reports and transmits them to the terminal. The terminal uses a generative artificial intelligence model to analyze the data in detail and identify the root cause of the problem. Furthermore, a solution is generated based on the analysis results. When displaying the generated solution to the user, the user device uses a device that analyzes emotional data to determine the user's emotional state.

[0497] As a concrete example, a server collects sensor and log data and processes it using the Google Cloud Vision API or Amazon Polly system. The device then uses this data to analyze the user's emotional state in real time using a generative AI model, and presents appropriate content and countermeasures based on the user's level of interest and stress. For example, if the AI ​​model determines that the user is losing interest while watching a video, it instructs the AI ​​model to recommend more engaging content and provides gentle navigation using Amazon Polly's voice guidance function.

[0498] An example of a prompt message is: "Design a real-time system that analyzes the user's facial expressions and tone of voice to detect decreased interest or increased stress in real time, suggest personalized content, and improve the viewing experience." By considering the user's emotions and providing appropriate information in this way, it is possible to achieve efficient operations and high customer satisfaction.

[0499] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0500] Step 1:

[0501] The server extracts numerical data from system logs and error reports. The input consists of log data and error reports acquired in real time from the system in operation. The server filters and analyzes this data to extract specific metrics and error codes. The output is a dataset of these extracted data points, which is then sent to the terminal.

[0502] Step 2:

[0503] The terminal receives numerical data transmitted from the server and analyzes it using a generative artificial intelligence model. The input is numerical data from the server. The terminal uses an AI model (e.g., a machine learning algorithm) to analyze these numbers, identify the root causes of a problem, and generate solutions. The output, including the identified problem causes and recommended solutions, is sent to an information display device.

[0504] Step 3:

[0505] The user device receives a solution from the terminal and analyzes emotional data. Inputs include the solution and emotional data obtained from the user (e.g., facial expressions, voice tone). The user device uses emotional analysis software (e.g., facial recognition API or voice analysis API) to evaluate the user's emotional state and adjust how the solution is presented. Outputs include displaying or playing the adjusted solution and additional information (e.g., voice guidance).

[0506] Step 4:

[0507] The user reviews the presented solution and, if necessary, requests agreement or additional assistance. The input consists of the solution and its options displayed on the user's device. The user interacts with the system, approving the solution or selecting further support based on their understanding and feelings. The output determines the next action for the entire system, following the user's final instructions.

[0508] 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.

[0509] 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.

[0510] 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.

[0511] [Fourth Embodiment]

[0512] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0513] 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.

[0514] 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).

[0515] 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.

[0516] 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.

[0517] 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).

[0518] 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.

[0519] 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.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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.

[0524] 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".

[0525] This invention automates and streamlines operational tasks by implementing a system that combines an information gathering device, a generating artificial intelligence agent, and a user device. The overall operation of the system is described below in natural language.

[0526] First, the server functions as an information gathering device, periodically collecting system logs and error reports to predict signs of anomalies. This enables early detection of problems.

[0527] Next, the device receives the information and uses a generative artificial intelligence agent to analyze the data in detail and identify the root cause of the problem. The AI ​​refers to a database of past data to find similar problems and their solutions.

[0528] Next, the user device presents the user with a recommended solution. The user reviews the displayed solution and gives their consent, which initiates a process to automatically implement the solution.

[0529] For example, if a user reports a login failure, the server immediately collects and analyzes the relevant error report, and the device's AI identifies the cause. If a password reset is necessary as a solution, the user's device will present this solution to the user. After obtaining the user's consent, the system will automatically perform the password reset and resolve the issue.

[0530] Thus, the system of the present invention automatically collects and analyzes information, proposes and implements solutions, and achieves a high level of efficiency while reducing the burden on human resources.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] The user enters a problem with the system into the inquiry form. The user provides details of the problem through the inquiry form and submits it to the system.

[0534] Step 2:

[0535] The terminal receives the entered inquiry and saves it to the database. It also notifies the user via an automated email that the inquiry has been received.

[0536] Step 3:

[0537] The server collects relevant system logs and error reports and analyzes the data to detect signs of anomalies. Predictive algorithms are used to identify potential problems.

[0538] Step 4:

[0539] The device analyzes the data using an artificial intelligence agent based on the collected information, searches for similar problems in a historical knowledge base, and identifies potential solutions.

[0540] Step 5:

[0541] The user device presents the identified solution to the user. The user reviews the presented solution and chooses to agree to it by following the instructions.

[0542] Step 6:

[0543] The server, with the user's consent, executes an automated script to make necessary configuration changes and system modifications. This completes the resolution of the problem.

[0544] Step 7:

[0545] The terminal verifies that all steps have been completed and records the processing results. It then sends a completion notification to the user, reporting that the problem has been resolved.

[0546] (Example 1)

[0547] 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".

[0548] Modern information systems are complex, and manual monitoring and management present challenges in quickly and efficiently detecting and resolving anomalies. This is especially true in environments where large amounts of data are generated, where rapidly identifying the root cause of problems and providing appropriate solutions is crucial. However, traditional methods are time-consuming and cumbersome in terms of information gathering, analysis, and solution implementation, hindering operational efficiency.

[0549] 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.

[0550] In this invention, the server includes means for an information processing device to extract information from electronic data, means for an artificial intelligence model to analyze the information acquired by the information processing device and identify the cause of a problem, and means for a user device to present the solution identified by the artificial intelligence model to the user and execute the solution based on the user's approval. This makes it possible to efficiently detect anomalies from large amounts of data and to quickly present and execute solutions.

[0551] An "information processing device" is a device used to extract necessary information from electronic data, and it plays a role in data collection and organization.

[0552] An "artificial intelligence model" is software or algorithms used to analyze collected data and identify problems or generate solutions.

[0553] A "user device" is a device that presents solutions generated by an artificial intelligence model to the user, receives input from the user, and prompts the user to execute the solution.

[0554] A "processing terminal" is a device that further processes information sent from an information processing device and searches for similar situations or solutions by comparing it with past databases.

[0555] A "database" is a collection of structured digital data designed for efficient storage and retrieval of information.

[0556] A "control device" is a device that monitors and manages the entire system in order to detect early signs of problems and recognize abnormal operational indicators.

[0557] This invention aims to build a system that automates information processing and solves problems efficiently and quickly. The specific forms for implementing this system are described below.

[0558] As an information processing device, the server collects electronic data. Specifically, the server uses log analysis software to extract necessary data from sources such as system logs and error reports. For this purpose, tools such as Splunk and ELK Stack can be used to efficiently organize and store the data.

[0559] Next, the terminal processes the data received from the server in a more advanced manner. The terminal analyzes the data using a generative AI model to identify the root cause of the problem. Specifically, AI models such as OpenAI's GPT or Google's BERT can be used, which compare and contrast with past databases to find similar situations and solutions. The terminal also accesses database systems (such as MySQL or PostgreSQL) to efficiently search for data.

[0560] Subsequently, the user device presents the solution derived by the device to the user. The user device uses a front-end framework such as React or Vue.js to build a user interface and display the solution in a user-friendly format. Upon user approval, the user device automatically executes the solution.

[0561] As a concrete example, when this system receives a login failure report, the server instantly analyzes the relevant data, and the terminal's AI identifies the cause. If a password reset is suggested as a solution, the user device automatically performs the procedure. An example of a prompt message for the generated AI model would be, "Identify the cause of the login error and propose a solution."

[0562] The configuration of this invention makes it possible to automate a series of processes from information gathering to solution implementation, thereby significantly improving operational efficiency.

[0563] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0564] Step 1:

[0565] The server functions as an information processing device, periodically collecting electronic data. Inputs include system logs and error reports, which are processed through log analysis software (e.g., Splunk, ELK Stack). Specifically, the server scans these logs at regular intervals, identifying data entries that indicate anomalies. The output of this process is a set of collected data, which is stored in a database for subsequent analysis.

[0566] Step 2:

[0567] The terminal receives data from the server as input and uses a generative AI model to analyze the data in detail. For data processing, an AI model (e.g., OpenAI GPT, Google BERT) is used to analyze the input data, identify problems, and analyze their causes. Specifically, the terminal refers to past databases (e.g., MySQL, PostgreSQL) to search for similar problems and their solutions. This process outputs a summary of the problem and recommended solutions.

[0568] Step 3:

[0569] The user device receives output from the terminal and presents a solution to the user. The input includes the solution identified by the AI. The user device processes this information to present it through a user-friendly interface (e.g., using React or Vue.js). Specifically, the user device generates alerts, pop-up messages, etc., to clearly explain the solution to the user. The output at this stage is the user's choice or approval.

[0570] Step 4:

[0571] The user proceeds through the process by reviewing and agreeing to or selecting the presented solutions. The input is information from the user's device, and the output is the user's consent. Obtaining this consent triggers automated actions within the system, such as initiating a password reset. Specifically, after user approval, the user's device executes an automated script, which then calls a backend process to perform the configuration change.

[0572] (Application Example 1)

[0573] 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".

[0574] In data management facilities, infrastructure operation and monitoring involve a great deal of manual work, requiring high efficiency. In particular, rapidly detecting system malfunctions and environmental anomalies and implementing appropriate countermeasures is difficult, making the optimization of human resources a key challenge.

[0575] 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.

[0576] In this invention, the server includes means for an information monitoring device to collect information from sensing devices and operation records, means for a generating artificial intelligence agent to analyze the information collected by the information monitoring device and determine the cause of the anomaly, and means for a user device to present the solution determined by the generating artificial intelligence agent to the user and to execute the solution based on the user's agreement. This makes it possible to automatically and efficiently detect problems within a data management facility and to quickly implement countermeasures.

[0577] An "information monitoring device" is a device that has the function of collecting information from sensing devices and operation records.

[0578] A "generative artificial intelligence agent" is an artificial intelligence system that has the ability to analyze collected information and determine the cause of anomalies.

[0579] A "user device" is a device that presents a solution to the user and implements that solution based on the user's agreement.

[0580] A "data management facility" is a facility equipped with infrastructure for storing, managing, and processing large amounts of data.

[0581] "Infrastructure operation" refers to activities aimed at maintaining and managing the efficient operation of systems and equipment.

[0582] A "sensing device" is a device that utilizes sensor technology to detect the state of the environment or equipment and acquire data.

[0583] The server works in conjunction with sensing devices to collect operational information in order to monitor the infrastructure within the data management facility. Specifically, the server acquires real-time data from hardware such as IoT devices and surveillance cameras, and efficiently transfers and manages the data using a data streaming platform such as Apache Kafka.

[0584] The device is equipped with a generative artificial intelligence agent that analyzes the cause of anomalies using collected data. This includes a function that utilizes machine learning models based on TensorFlow to detect anomalies by comparing them with historical data. Based on the analysis results, it identifies the problem that needs to be solved and recommends the appropriate countermeasures.

[0585] Furthermore, users can view analysis results and recommended solutions in real time through their devices. A mobile application built with Flutter pushes notifications to the user's device. Once the user gives their consent, automated countermeasures are executed through the application, such as restarting the cooling system.

[0586] As a concrete example, if the temperature of a data management facility exceeds a certain threshold, the system immediately proposes adjusting the cooling system. A prompt such as, "Based on real-time monitoring data of the data center environment, detect abnormal temperature increases and propose the optimal cooling system operation method," is used to generate the best course of action for the AI ​​model. This prompt allows the generated AI model to take appropriate action, supporting the efficient operation of the facility.

[0587] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0588] Step 1:

[0589] The server collects environmental data such as temperature and humidity from sensing devices in real time. The input data is transmitted from various IoT devices and efficiently transferred via Apache Kafka. This data is received and stored in a time-series database to prepare for future analysis.

[0590] Step 2:

[0591] The device sends collected environmental data to a generating artificial intelligence agent. This data is used as input for AI analysis. A machine learning model using TensorFlow analyzes this data and detects anomalous data patterns. This analysis also references historical data, and data calculations are performed to identify the cause of the anomalies.

[0592] Step 3:

[0593] The device generates specific solutions based on the AI ​​analysis results. These solutions may include, for example, restarting or adjusting the cooling system. This solution is reflected in the prompt message, utilizing the generated AI model to convey messages such as, "We have detected an abnormal temperature rise and are proposing the optimal way to operate the cooling system."

[0594] Step 4:

[0595] The user reviews the solution through their device. A mobile application built with Flutter pushes the analysis results and recommendations in real time. At this stage, user input is required to obtain consent to the solution.

[0596] Step 5:

[0597] Once the user gives their consent, the user's device sends instructions to the server, and the recommended solution is implemented. Specific actions include restarting the cooling system via API. This process automatically resolves the issue and ensures stable operation of the data management facility.

[0598] 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.

[0599] This invention streamlines system operation tasks by combining an information gathering device, a generating artificial intelligence agent, a user device, and an emotion engine. This system is designed to provide quick and appropriate responses to user inquiries.

[0600] First, the server acts as an information gathering device, collecting data from system logs and error reports. This data is used to predict signs of problems occurring.

[0601] Next, the device utilizes a generative artificial intelligence agent to analyze the root causes of the problem based on the collected data. At this stage, it identifies similar problems from a historical knowledge base and generates solutions.

[0602] Furthermore, the user device utilizes an emotion engine to analyze the user's emotions when presenting solutions. The emotion engine evaluates the user's emotional state using voice and facial expression data and adapts the interface's response accordingly. For example, if the user is experiencing high levels of stress, the solution will be presented in a more user-friendly manner.

[0603] As a concrete example, when a user reports a system failure, the server first quickly collects system logs, which are then analyzed by the terminal. The problem is identified, and a standard solution is devised. Subsequently, when the solution is presented on the user's device, the user's emotional state is evaluated. Based on this evaluation, the solution is communicated in a way that matches the user's emotions. If the user is emotionally agitated, additional support options are offered to optimize the user experience.

[0604] Thus, by incorporating an emotion engine, the system of the present invention enables more personalized responses, leading to further efficiency improvements in operational tasks and enhanced customer satisfaction.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] A user enters and submits an inquiry about a system failure. The user describes the details in the form and selects options to specifically report the problem.

[0608] Step 2:

[0609] The terminal records the received inquiry in the database and notifies the user that the inquiry has been received. At this point, the basic content of the inquiry is recorded.

[0610] Step 3:

[0611] The server collects relevant data from system logs and error reports and analyzes for signs of anomalies. Data collection is performed in real time, and speed is essential.

[0612] Step 4:

[0613] The on-device artificial intelligence agent analyzes the root causes of a problem based on collected data and generates solutions. It then compares these solutions with a historical knowledge base to select the best solution.

[0614] Step 5:

[0615] The user device presents a solution to the problem. This is where the emotion engine comes into play, analyzing the user's emotional state using voice and visual data.

[0616] Step 6:

[0617] The user device adjusts its interface based on the user's emotions and presents solutions in a way that suits those emotions. If the user is feeling stressed, it will choose more approachable language.

[0618] Step 7:

[0619] The user agrees to the proposed solution and authorizes its implementation. The user can select additional support options as needed.

[0620] Step 8:

[0621] After confirming user consent, the server will launch an automated script to perform the necessary system modifications to resolve the issue.

[0622] Step 9:

[0623] The terminal confirms that all processes are complete and records the results. The user receives a completion notification and is informed that the problem has been resolved.

[0624] (Example 2)

[0625] 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".

[0626] In modern, highly sophisticated computing systems, there is a need to quickly detect signs of failure and resolve problems rapidly and accurately. Furthermore, providing appropriate support for technical issues faced by users and reducing their stress and frustration is also crucial. However, conventional systems often only report error messages without considering the user's emotional state, limiting their ability to improve customer satisfaction.

[0627] 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.

[0628] In this invention, the server includes means for an information integration device to extract data from the operation records and failure reports of the computing system, means for an intelligent agent to analyze the data extracted by the information integration device and determine the root cause of a problem, and means for an emotion analysis device to analyze the user's emotional state and optimize the response of the user interface device based on the results. This enables early detection of failures and efficient problem solving, reduces user stress, and improves customer satisfaction.

[0629] An "information integration device" is a device that has the function of extracting data from the operation records and failure reports of a computing system and evaluating the status of the system.

[0630] An "intelligent agent" is artificial intelligence that has the ability to analyze extracted data and identify the root cause of a problem.

[0631] A "user interface device" is a device that appropriately presents solutions to problems to the user and implements those solutions based on the user's consent.

[0632] An "emotion analysis device" is a device that evaluates the emotional state of a user and adjusts the operation of other system components based on the evaluation results.

[0633] A "control device" is a device that has the function of detecting signs of problems or abnormal performance indicators and issuing alarms as needed.

[0634] "Automated tasks" refer to a series of tasks performed by multiple agents according to specific rules or instructions, with the aim of improving operational efficiency.

[0635] This invention streamlines system operation tasks by combining an information integration device, an intelligent agent, a user interface device, and an emotion analysis device. Based on the system's design philosophy, specific embodiments are shown below.

[0636] The server acts as an information integration device and operates on a computer network. System operation logs and failure reports are centrally managed, and monitoring software (e.g., commonly used monitoring tools) is executed for appropriate data collection. The server extracts data in real time and provides foundational data for detecting early signs of problems.

[0637] The terminal utilizes an intelligent agent to analyze data sent from the server. Statistical analysis and machine learning are used to identify the root cause of problems. For example, data may be analyzed using Python libraries, and anomaly detection algorithms may be executed to identify problems. This process enables efficient troubleshooting.

[0638] The user device incorporates both a user interface device and an emotion analysis device. When presenting solutions to problems the user faces in an appropriate manner, the emotion analysis device identifies the user's voice and facial expressions, optimizing feedback to reduce stress and anxiety. For example, it analyzes emotions using voice APIs and image processing software, and adjusts the interface's response based on the results.

[0639] A concrete example is when a user reports a system failure, and the server promptly collects log data. The terminal then analyzes the problem and provides a quick solution. In this case, the user device presents the solution in a more user-friendly manner, responding to the user's prompt: "Please provide specific steps for suggesting the best solution when a system failure occurs. Please also explain how to handle situations where the user is feeling anxious."

[0640] In this way, the system can reduce the burden on users and aim to improve customer satisfaction.

[0641] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0642] Step 1:

[0643] The server collects operation logs and failure reports from the system in real time. This data includes event logs and error messages. This data is given as input, and an information integration device is used to filter out unnecessary noise and extract relevant information. As a result, a clean dataset is output, ready for analysis in the next step.

[0644] Step 2:

[0645] The terminal receives a clean dataset from the server as input. The intelligent agent uses a machine learning model to analyze this data. Specifically, it utilizes libraries such as TensorFlow and scikit-learn to identify anomalous patterns in the data. Through this analysis, it identifies the root cause of the problem and generates candidate solutions. The output is a list of details of the identified problem and its solutions.

[0646] Step 3:

[0647] The user device receives problem details and solutions sent from the terminal as input. Before presenting this information to the user in an easily understandable format, the user interface device uses an emotion analysis device to analyze the user's voice and facial expressions. Specifically, voice data is acquired from a microphone as input, and image data is collected from a camera. This data is analyzed to determine the user's emotional state. The output of this step is a method of presenting solutions optimized for that emotional state. For example, the solution might be displayed with a calm voice, or communication might be conducted in a friendly tone.

[0648] In this way, the entire system functions smoothly, providing efficient and effective support to users.

[0649] (Application Example 2)

[0650] 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".

[0651] There is a need to improve the efficiency of system operations and optimize the user experience by presenting information in a way that responds to the user's emotional state. In particular, when a system failure occurs, it is necessary to provide appropriate solutions quickly while reducing the stress the user experiences. However, current technology makes it difficult to provide solutions that take user emotions into consideration.

[0652] 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.

[0653] In this invention, the server includes means for extracting numerical values ​​from system logs and error reports; an artificial intelligence model as means for analyzing the numerical values ​​collected by the device and identifying the cause of a problem; means for presenting the solution determined by the artificial intelligence model to the user using an information presentation device and executing the solution based on the user's consent; and means for analyzing emotional data and adjusting the user experience based on the emotional state. This enables personalized responses that respond to emotions.

[0654] A "system log" is data that records the operation history of an information processing device.

[0655] An "error report" is a report that contains detailed information about errors that occurred within the system.

[0656] "Numerical values" refer to specific data extracted from logs and reports.

[0657] An "artificial intelligence model" is a collection of algorithms used to analyze data and identify the factors behind a problem.

[0658] An "information display device" is a device that displays information to the user and enables them to perform actions.

[0659] "Emotional data" refers to information related to emotions obtained from the user's facial expressions, tone of voice, and other similar data.

[0660] "Personalized responses" refer to responses that are tailored to the individual user's emotions and circumstances.

[0661] The system implementing this invention consists of a server for information processing, a terminal for analyzing data, and a display device for user use. The server extracts numerical data from system logs and error reports and transmits them to the terminal. The terminal uses a generative artificial intelligence model to analyze the data in detail and identify the root cause of the problem. Furthermore, a solution is generated based on the analysis results. When displaying the generated solution to the user, the user device uses a device that analyzes emotional data to determine the user's emotional state.

[0662] As a concrete example, a server collects sensor and log data and processes it using the Google Cloud Vision API or Amazon Polly system. The device then uses this data to analyze the user's emotional state in real time using a generative AI model, and presents appropriate content and countermeasures based on the user's level of interest and stress. For example, if the AI ​​model determines that the user is losing interest while watching a video, it instructs the AI ​​model to recommend more engaging content and provides gentle navigation using Amazon Polly's voice guidance function.

[0663] An example of a prompt message is: "Design a real-time system that analyzes the user's facial expressions and tone of voice to detect decreased interest or increased stress in real time, suggest personalized content, and improve the viewing experience." By considering the user's emotions and providing appropriate information in this way, it is possible to achieve efficient operations and high customer satisfaction.

[0664] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0665] Step 1:

[0666] The server extracts numerical data from system logs and error reports. The input consists of log data and error reports acquired in real time from the system in operation. The server filters and analyzes this data to extract specific metrics and error codes. The output is a dataset of these extracted data points, which is then sent to the terminal.

[0667] Step 2:

[0668] The terminal receives numerical data transmitted from the server and analyzes it using a generative artificial intelligence model. The input is numerical data from the server. The terminal uses an AI model (e.g., a machine learning algorithm) to analyze these numbers, identify the root causes of a problem, and generate solutions. The output, including the identified problem causes and recommended solutions, is sent to an information display device.

[0669] Step 3:

[0670] The user device receives a solution from the terminal and analyzes emotional data. Inputs include the solution and emotional data obtained from the user (e.g., facial expressions, voice tone). The user device uses emotional analysis software (e.g., facial recognition API or voice analysis API) to evaluate the user's emotional state and adjust how the solution is presented. Outputs include displaying or playing the adjusted solution and additional information (e.g., voice guidance).

[0671] Step 4:

[0672] The user reviews the presented solution and, if necessary, requests agreement or additional assistance. The input consists of the solution and its options displayed on the user's device. The user interacts with the system, approving the solution or selecting further support based on their understanding and feelings. The output determines the next action for the entire system, following the user's final instructions.

[0673] 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.

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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."

[0682] 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.

[0683] 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.

[0684] 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.

[0685] 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.

[0686] 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.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] 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.

[0691] 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.

[0692] 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.

[0693] 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.

[0694] The following is further disclosed regarding the embodiments described above.

[0695] (Claim 1)

[0696] The information gathering device includes means for extracting data from system logs and error reports,

[0697] The generating artificial intelligence agent analyzes the data collected by the information collection device and has means for determining the cause of the problem,

[0698] The user device provides the user with a solution determined by the generating artificial intelligence agent and executes the solution based on the user's consent.

[0699] A system that includes this.

[0700] (Claim 2)

[0701] The system according to claim 1, further comprising means for multiple agents to perform different automated tasks and coordinate their actions toward a common goal.

[0702] (Claim 3)

[0703] The system according to claim 1, further comprising an orchestration device for predicting signs of a problem and detecting abnormal performance indicators to generate alerts.

[0704] "Example 1"

[0705] (Claim 1)

[0706] An information processing device is a means for extracting information from electronic data,

[0707] An artificial intelligence model analyzes information acquired by the information processing device and provides means for identifying the cause of the problem.

[0708] The user device includes means for presenting the solution identified by the artificial intelligence model to the user and executing the solution based on the user's approval,

[0709] The processing terminal further processes the information received from the information processing device and searches for similar situations and solutions by comparing it with past databases,

[0710] A system that includes this.

[0711] (Claim 2)

[0712] The system according to claim 1, wherein multiple artificial intelligence agents perform various automated processes and cooperate with each other to achieve a common objective.

[0713] (Claim 3)

[0714] The system according to claim 1, comprising a control device for detecting signs of a problem, recognizing abnormal operational indicators, and generating a warning.

[0715] "Application Example 1"

[0716] (Claim 1)

[0717] The information monitoring device includes means for collecting information from sensing devices and operation records,

[0718] The generating artificial intelligence agent analyzes the information collected by the information monitoring device and determines the cause of the anomaly,

[0719] The user device includes means for presenting the solution determined by the generating artificial intelligence agent to the user and executing the solution based on the user's agreement,

[0720] To automate the operation of infrastructure in data management facilities, a means of automatically executing recommended countermeasures,

[0721] A system that includes this.

[0722] (Claim 2)

[0723] The system according to claim 1, further comprising means for multiple agents to perform different automated tasks and to carry out coordinated activities toward a common goal.

[0724] (Claim 3)

[0725] The system according to claim 1, further comprising an integrated device for predicting signs of problems, detecting abnormal operational indicators and generating warnings, and further performing environmental monitoring of a data management facility.

[0726] "Example 2 of combining an emotion engine"

[0727] (Claim 1)

[0728] The information integration device includes means for extracting data from the operation records and failure reports of the computing system,

[0729] An intelligent agent analyzes the data extracted by the information integration device and has means to determine the root cause of the problem.

[0730] The user interface device includes means for presenting the solution determined by the intelligent agent to the user and evaluating the user's response,

[0731] The emotion analysis device includes means for analyzing the user's emotional state and optimizing the response of the user interface device based on the results,

[0732] A system that includes this.

[0733] (Claim 2)

[0734] The system according to claim 1, further comprising means for multiple intelligent agents to perform different automated tasks and to work together toward a common goal.

[0735] (Claim 3)

[0736] The system according to claim 1, further comprising a control device for predicting signs of a problem, detecting abnormal performance indicators, and generating an alarm.

[0737] "Application example 2 when combining with an emotional engine"

[0738] (Claim 1)

[0739] A device for extracting numerical values ​​from system logs and error reports,

[0740] An artificial intelligence model is used as a means to analyze the numerical data collected by the aforementioned device and identify the factors of the problem.

[0741] A means for presenting the solution identified by the artificial intelligence model to the user using an information presentation device, and for executing the solution based on the user's consent,

[0742] A means of analyzing emotional data and adjusting the user experience based on emotional state,

[0743] A system that includes this.

[0744] (Claim 2)

[0745] The system according to claim 1, further comprising means for performing different automated tasks and coordinated actions toward a common purpose.

[0746] (Claim 3)

[0747] The system according to claim 1, comprising a control device for detecting abnormal performance indicators and generating warnings, and means for measuring variables related to emotional state and generating personalized suggestions. [Explanation of symbols]

[0748] 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. The information gathering device includes means for extracting data from system logs and error reports, The generating artificial intelligence agent analyzes the data collected by the information collection device and has means for determining the cause of the problem, The user device provides the user with a solution determined by the generating artificial intelligence agent and executes the solution based on the user's consent. A system that includes this.

2. The system according to claim 1, further comprising means for multiple agents to perform different automated tasks and coordinate their actions toward a common goal.

3. The system according to claim 1, further comprising an orchestration device for predicting signs of a problem, detecting abnormal performance indicators, and generating alerts.

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