system

Abstract

We provide the system. [Solution] The server has a means of monitoring for anomalies and collecting log data and metrics data. An artificial intelligence means for analyzing the collected data to identify the cause of the anomaly, A means for generating a recovery procedure based on the cause of the anomaly identified by the artificial intelligence means and presenting it to the terminal, A means for the user to approve the aforementioned recovery procedure, A means for automatically executing the aforementioned approved recovery procedure, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 server operation, fault detection, recovery work, and regular software updates are important tasks. However, conventionally, these tasks are often performed manually, leading to problems such as consumption of human resources and delays in work. There is also a risk of human errors, which has hindered stable server operation. Therefore, there is a need for a technology that can automate these tasks quickly and efficiently.

Means for Solving the Problems

[0005] To solve the aforementioned problems, the present invention provides a system comprising: means for a server to monitor anomalies and collect log data and metric data; artificial intelligence means for analyzing the collected data to identify the cause of the anomaly; means for generating recovery procedures based on the identified cause and presenting them to the terminal; means for the user to approve the presented recovery procedures; and means for automatically executing the approved recovery procedures. Furthermore, by including a function for the server to periodically check the system's software status and create and present update procedures as needed, and a function for the server to automatically apply updates after user approval, the system achieves efficient and rapid server operation.

[0006] A "server" is a computer system used to process and distribute data over a network.

[0007] An "abnormality" refers to an operation or state that deviates from normal operating conditions and may disrupt the normal functioning of the system.

[0008] "Log data" is a collection of information that records system operations and events, and is used for troubleshooting and performance monitoring.

[0009] "Metrics data" refers to data that shows numerical indicators related to the operation and performance of a system, and is used to evaluate its operational status.

[0010] "Artificial intelligence tools" refer to algorithms and programs used to analyze collected data and identify the cause of anomalies.

[0011] A "recovery procedure" is a set of instructions outlining the specific operations and steps necessary to restore a server to a normal operating state.

[0012] A "terminal" is a device that functions as an interface connecting a server and a user, and is used for inputting and outputting information.

[0013] A "user" refers to a person who is responsible for the operation and management of the system and makes decisions based on information from the server.

[0014] "Approval" means acknowledging that the presented procedures or operations are appropriate. [Brief explanation of the drawing]

[0015] [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] It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a processor with a reference numeral (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.

[0019] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include a flash memory (SSD (Solid State Drive)), a magnetic disk (e.g., a hard disk), or a magnetic tape, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is a system for rapidly responding to failures related to server operation and streamlining regular update work. The server constantly monitors for system anomalies and strives to detect abnormal events early by collecting log data and metric data. The collected data is analyzed by built-in artificial intelligence to identify the cause of the anomaly.

[0037] After the server identifies the cause, a recovery procedure is automatically generated and presented to the user via the terminal. The user reviews the displayed recovery procedure and approves it if they deem it appropriate. After receiving this user approval, the server automatically executes the recovery procedure. This process enables rapid server recovery while reducing the risk of human error.

[0038] Furthermore, the server periodically checks the status of the system software and, if it determines that an update is necessary, creates an update procedure. This update procedure is presented to the terminal, and the server awaits user approval. Once approval is received, the server automatically applies the update and performs subsequent actions such as restarting as needed. This ensures that the system's security and functionality are always up-to-date.

[0039] For example, if a server's operating speed drops abnormally, the server collects relevant metric data, and artificial intelligence detects an abnormal increase in CPU usage. Based on this result, the server generates recovery procedures, such as stopping unnecessary processes or readjusting the load balancer, and presents each procedure to the user. After the user confirms and approves, these procedures are executed, and the server's normal operation is restored. In this way, the present invention contributes to improving the efficiency and reliability of server operations.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server constantly monitors log data and metrics data to check for any anomalies. If an anomaly is detected, it collects detailed data to prepare for the next steps.

[0043] Step 2:

[0044] The artificial intelligence system within the server analyzes the collected log data and metrics data to identify the cause of the anomaly. The analysis includes comparing the data with historical data and known anomaly patterns.

[0045] Step 3:

[0046] Based on the identified cause of the server failure, appropriate recovery procedures are generated. These generated recovery procedures are sent to the terminal, providing the user with specific steps to take.

[0047] Step 4:

[0048] The user reviews the recovery procedure displayed on their device, and if they determine it is appropriate, they click a button to approve the procedure. The approval information is sent to the server.

[0049] Step 5:

[0050] After the server confirms user authorization, it automatically executes the instructed recovery procedures. These include freeing memory, restarting processes, and resetting settings.

[0051] Step 6:

[0052] The server periodically checks the software version and security patch status, and when it determines that an update is necessary, it automatically creates and displays the update procedure on the terminal.

[0053] Step 7:

[0054] Once the user reviews and approves the update procedure provided, the server automatically applies the update based on the approval information from the device. The system will restart if necessary.

[0055] (Example 1)

[0056] 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."

[0057] Modern information systems require rapid and accurate responses to server failures and problems. However, traditional methods often involve manual intervention in identifying the cause of failures and implementing countermeasures, leading to human error and delays. Furthermore, timely updates to system programs are difficult, resulting in security risks and performance degradation. To address these issues, automated processes are needed to improve efficiency and reliability.

[0058] 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.

[0059] In this invention, the server includes means for monitoring failures of the information processing device and collecting status data and performance indicator data; machine learning means for analyzing the collected data to identify the cause of the failure; and means for generating a recovery procedure based on the cause of the failure identified by the machine learning means and presenting it on the information display device. This enables automatic failure detection, rapid identification of the cause, and efficient presentation of recovery procedures. Furthermore, the system's up-to-dateness and safety can be maintained by automatically generating and applying update procedures based on periodic evaluation of the program state.

[0060] A "server" is a central information processing device that provides information processing services to clients over a network.

[0061] "Information processing device malfunction" refers to any defect or event that disrupts the normal operation of an information processing device.

[0062] "Status data" refers to collected data that indicates the operating status and health of an information processing device.

[0063] "Performance indicator data" refers to data that quantifies and shows the performance of an information processing device.

[0064] "Means of collection" refers to the functions and mechanisms for monitoring and acquiring data within a server.

[0065] "Machine learning methods" refer to artificial intelligence technologies that automatically identify the cause of a problem through data analysis.

[0066] A "recovery procedure" is a series of operations and processes that should be performed to resolve an identified problem.

[0067] An "information display device" is an output device that visually presents data and procedures to the user.

[0068] "User" refers to an individual or organization that operates, monitors, or manages information processing equipment or its services.

[0069] "Program status" refers to the current state and version information of the software running in an information processing system.

[0070] An "update procedure" refers to the specific steps taken to bring existing software and system configurations up to date.

[0071] "Restarting" refers to the operation of stopping an information processing device or its components and then starting them up again.

[0072] The embodiments for carrying out this invention will be described below.

[0073] The server, as the central hub of the entire information processing system, provides various services via the network. The server is equipped with dedicated monitoring software to continuously monitor and collect status and performance metrics. This monitoring software is implemented using, for example, open-source monitoring platforms or proprietary commercial products. This software constantly monitors CPU usage, memory usage, and disk I / O performance metrics.

[0074] The server is equipped with a generative AI model that analyzes collected data and identifies the cause of failures. This generative AI model learns from past failure data and can immediately identify the cause of newly occurring anomalies. This AI model uses machine learning libraries and has the ability to automatically detect anomaly patterns and perform correlation analysis.

[0075] For example, when a server experiences a slowdown, the server itself analyzes performance metrics data, and a generated AI model identifies that excessive CPU usage by a specific process is the cause. Based on this information, the server automatically generates recovery steps, such as stopping unnecessary processes or readjusting settings.

[0076] The generated recovery procedure is translated into natural language and presented to the terminal as a prompt. The terminal provides information to the user through a visual interface. For example, the prompt might appear as: "CPU overload by a specific process has been detected. Do you wish to stop the process?"

[0077] The user can proceed to the next step by reviewing the prompt displayed on the terminal and approving the procedure. Based on the approved procedure, the server automatically executes the process to resolve the problem, enabling a rapid recovery from the failure.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server continuously collects status and performance indicator data using monitoring software. Input data includes real-time CPU usage, memory usage, and disk I / O values. This data is stored in log files within the server, and the system is configured to automatically detect signs of anomalies. If abnormal data is found, it is identified along with its occurrence time and passed on to the next processing step.

[0081] Step 2:

[0082] The server inputs the collected data into a generative AI model and performs analysis to identify the cause of the anomaly. The input data is the anomaly dataset identified in step 1. The generative AI model uses the results of learning past anomaly patterns to identify the cause using methods such as correlation analysis and clustering. As output, detailed information on the identified cause is generated, and recommended recovery steps are created based on it.

[0083] Step 3:

[0084] The server converts the generated recovery procedure into natural language and presents it to the terminal as a prompt. The input data is the technical content of the recovery procedure obtained in step 2. A natural language processing software library is used for this conversion. The output is a human-readable prompt for the user. Specifically, the terminal displays a message such as, "CPU overload by a specific process has been detected. Do you want to stop the process?"

[0085] Step 4:

[0086] The user reviews the prompt displayed on the terminal and performs an action to approve or reject it. The input is the recovery procedure prompt displayed on the terminal. The user indicates approval by pressing the confirmation button on the screen. The approval result is returned to the server as output, and the process proceeds to the next step.

[0087] Step 5:

[0088] The server automatically executes recovery procedures upon user approval. The input is the recovery procedure approved by the user. The server stops necessary processes and makes configuration changes to eliminate the cause of the anomaly. The output is that the system returns to a normal operating state, and the execution history of this operation is recorded in the server's operation log.

[0089] (Application Example 1)

[0090] 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."

[0091] In modern information systems, while efficiency and security in server operations are paramount, rapid response to anomalies and regular software updates are crucial challenges. However, these processes often rely on manual methods, leading to problems such as human error and a lack of rapid response procedures. In addition, the increasing number of potential threats necessitates real-time threat detection and countermeasures. The development of effective and efficient systems to address these challenges is essential.

[0092] 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.

[0093] In this invention, the server includes means for monitoring anomalies and collecting recorded information and indicator information, intelligent function means for analyzing the collected information and identifying the cause of the anomaly, and means for generating a recovery procedure based on the identified cause of the anomaly and presenting it on a display device. This enables a rapid and accurate response when an anomaly occurs, and by immediately notifying potential threats, it is possible to improve system reliability and optimize operational efficiency.

[0094] A "server" is an information processing device that performs data processing and calculations and provides services to other computer systems via a network.

[0095] "Anomaly" refers to a state or operation in an information system that deviates from the normal operating range, including cases where the system's performance or security falls outside the recognized standards.

[0096] "Record information" refers to data that shows details of the system's operation and status, and includes historical information such as logs and transactions.

[0097] "Metric information" refers to data that serves as a standard for evaluating system performance and status, and includes metrics such as CPU usage and memory consumption.

[0098] "Intelligent function means" refers to artificial intelligence technology used to analyze collected data and identify the cause of anomalies, and is a device or program equipped with the function of performing data analysis and pattern recognition.

[0099] A "recovery procedure" is a series of processing steps performed to resolve an identified anomaly and restore the system to a normal state.

[0100] A "display device" is a hardware device that visually communicates the status and operating instructions of a computer system to the user.

[0101] "User" refers to an individual or organization that operates and manages a computer system, and is the entity that performs approvals and operations based on the system's instructions.

[0102] This invention is a system that achieves efficient server management and security monitoring through collaboration between servers, terminals, and users. The servers use advanced monitoring functions to detect anomalies and collect and analyze recorded information and indicator information in real time. Intelligent functional means, specifically AI-based data analysis algorithms, are used for this analysis to identify anomalies.

[0103] The terminal receives information transmitted from the server and presents it to the user via a display device. This includes recovery procedures and notifications of potential threats generated by the server. Based on this information, the user can make appropriate decisions or give instructions.

[0104] For example, if a server detects high CPU usage during a weekend night, the AI ​​will determine this to be an anomaly and generate recovery steps to reduce the CPU load. These steps include stopping unnecessary processes and distributing tasks. The terminal visually presents this to the user, and after user approval, the server automatically executes these steps to optimize the system load.

[0105] An example of a prompt message is: "Generate steps to optimize the server's security status and create a prompt to notify the user. Start with CPU usage data and include recommended actions when anomalies are detected." In this way, the entire system works together to achieve efficient server operation and enhanced security.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server monitors the system status and collects recorded and metric information in real time. This information includes metrics such as CPU usage and memory usage. For data processing, this information is stored in a database and used as foundational data for anomaly detection.

[0109] Step 2:

[0110] The server analyzes the collected metrics data using intelligent functions to determine whether or not an anomaly is present. The input data is the metrics data collected in step 1, and the output identifies whether or not an anomaly is present and its details (e.g., high CPU usage, excessive memory consumption). Machine learning algorithms are used for the analysis.

[0111] Step 3:

[0112] If the server detects an anomaly, it generates a recovery procedure. The analysis results from step 2 are used as input, and the output is a specific recovery procedure. This procedure can be implemented, for example, as a plan to stop unnecessary processes or perform load balancing.

[0113] Step 4:

[0114] The terminal receives the recovery procedure sent from the server and displays it on the display device. The input data is the recovery procedure generated in step 3, and the output includes the presentation of visualized information to the user.

[0115] Step 5:

[0116] The user reviews the presented recovery procedure and provides appropriate instructions. The input is the recovery procedure displayed on the terminal, and the output is instructions for approval or correction. Specific user actions include reviewing the information on the screen and clicking the approval button.

[0117] Step 6:

[0118] The server executes a recovery procedure approved by the user. User approval is required as input, and the output is the result of the execution. This step involves actions such as stopping a specified process or adjusting load balancing.

[0119] 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.

[0120] This invention combines a system in which a server monitors for anomalies and automates efficient recovery and periodic system updates with an emotion engine that recognizes user emotions. When an anomaly occurs, the server collects log data and metric data, and artificial intelligence measures identify the cause based on this data. The server then generates a recovery procedure and presents it to the user via a terminal.

[0121] Furthermore, the emotion engine analyzes emotional information in real time from the user's facial expressions, tone of voice, and input content via the terminal. Based on the results of this emotion analysis, the server can adjust the way and content of the recovery procedure is presented, taking care to reduce the user's psychological burden. For example, if the user is feeling stressed, the procedure will be presented concisely, and guidance messages will be displayed if necessary, allowing for flexible responses tailored to the user's situation.

[0122] For example, if a user's facial expression indicates anxiety when recovery procedures are presented on the terminal, the emotion engine recognizes this. The server receives this information and helps the user understand by providing additional explanations or options. In this way, the information retrieved can be customized according to the user's emotions, providing a more intuitive and frictionless user experience. This system effectively supports the stable operation of the server while maintaining a user-friendly interface.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The server monitors for anomalies and collects log data and metrics data. When an anomaly is detected, the collected data is sent to an artificial intelligence system.

[0126] Step 2:

[0127] The artificial intelligence system on the server analyzes the collected data in real time to identify the cause of the anomaly. At this stage, it compares the data against known patterns to clarify the type of malfunction.

[0128] Step 3:

[0129] Once the cause is identified, the server automatically generates recovery steps based on that cause. The generated recovery steps are organized in a user-friendly format and sent to the terminal.

[0130] Step 4:

[0131] The device presents recovery procedures to the user while simultaneously activating an emotion engine to analyze the user's emotional state from their facial expressions and tone of voice. Based on the results of the emotion analysis, the method and content of the procedure presentation are dynamically adjusted.

[0132] Step 5:

[0133] The user reviews the presented recovery procedure and approves it if they agree. The user's approval is sent to the server via the device.

[0134] Step 6:

[0135] The server automatically executes the recovery procedure based on the received authorization information. During execution, the progress is displayed on the terminal in real time. Each step is performed sequentially, continuing until the service is restored to normal.

[0136] Step 7:

[0137] The server periodically checks the system's software status, and if it determines that an update is necessary, it creates an update procedure and presents it to the terminal. The emotion engine takes the user's emotional state into consideration and adjusts how the update is presented.

[0138] Step 8:

[0139] Once the user approves the update procedure, the server will automatically apply the software update and perform restarts or process optimizations as needed.

[0140] (Example 2)

[0141] 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".

[0142] In modern information systems, it is crucial to respond quickly and appropriately when an anomaly occurs. However, traditional systems have difficulty responding flexibly to user emotions, sometimes placing an excessive burden on users. Furthermore, the lack of automation in system update and recovery procedures has resulted in operational complexity.

[0143] 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.

[0144] In this invention, the server includes means for monitoring anomalies and collecting data; intelligent means for analyzing the collected data and identifying the cause; means for generating procedures based on the cause and presenting them to an information processing device; means for acquiring and analyzing the user's emotions; and means for adjusting the procedures and changing the presented content based on the analyzed emotions. This enables appropriate recovery without burdening the user, automates update procedures, and allows for more flexible and effective system management.

[0145] A "server" is a device in a computer network that receives requests from clients, processes them, and provides data and services.

[0146] An "abnormality" refers to a phenomenon or state that deviates from the expected behavior of a system and is a factor that disrupts normal operation.

[0147] "Data" refers to information represented by symbols, numbers, or combinations thereof, in a format that can be processed by a computer.

[0148] An "intelligent tool" is a tool that has the ability to analyze data and perform a series of processes based on artificial intelligence to extract useful information from it.

[0149] A "procedure" is a set of steps or tasks that must be followed to achieve a specific objective.

[0150] An "information processing device" is a computer or terminal used to handle data, and is a device that enables data exchange between humans and machines.

[0151] A "user" is the entity that operates and utilizes a system or information device.

[0152] "Emotion" is a state of mind in humans, an intuitive and reactive psychological experience in response to a particular event or perception.

[0153] "Analysis" is the act of investigating data and phenomena in detail to reveal their structure and relationships.

[0154] "Adjustment" refers to the act of changing settings or configurations to suit specific conditions or circumstances.

[0155] In this invention, a server acts as the central point for monitoring anomalies in the information system and automatically generates efficient recovery procedures using the collected data. Common hardware capable of data stream processing is used for anomaly monitoring and data collection, and a data analysis platform such as Apache® Kafka can be used. The server receives log data and metrics data, collects this data, and records anomaly events.

[0156] The collected data is analyzed using artificial intelligence. Machine learning frameworks such as TENSORFLOW® are used to identify the cause of the anomaly. Based on the identified cause, the server generates appropriate recovery procedures. These procedures are presented to the user via a terminal on an information processing device. The terminal provides an interface to allow the user to review and execute the procedures.

[0157] In addition, the device incorporates an emotion engine that acquires emotional information through the user's facial expressions, tone of voice, and input content. This acquired data is analyzed in real time, enabling the presentation of information tailored to the user's emotional state. IBM Watson® tone analyzer and general facial recognition APIs are used for emotion analysis.

[0158] For example, if a user's facial expression indicates anxiety while reviewing the presented recovery procedure, the device sends this information to the emotion engine, and the server adjusts the procedure accordingly. Specifically, the procedure can be explained in more detail, or additional support options can be offered, thereby reducing the user's psychological burden.

[0159] The following is an example of a prompt message:

[0160] "When users follow the system recovery procedure, we want to provide appropriate additional information based on facial expression and tone data recognized by the emotion engine. Please explain how this system provides information in accordance with emotions."

[0161] This system will improve the user experience while supporting the efficient and stable operation of the servers.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The server monitors for anomalies and collects log and metric data. It retrieves data from the monitored system in real time and stores it using Apache Kafka. Data inputs include log files and performance metrics sent from the system, and output generates a formatted dataset for processing. Specifically, the server periodically scans the data stream and records any anomaly patterns it detects.

[0165] Step 2:

[0166] The server analyzes the collected data to identify the cause of the anomaly. It runs a generative AI model using a machine learning framework such as TensorFlow to analyze data patterns. The input is a formatted dataset, and the output is a list of factors that are likely to be anomalies. Specifically, the server calculates the similarity to past anomaly patterns and lists the most likely causes in order of priority.

[0167] Step 3:

[0168] The server generates recovery procedures based on the identified cause of the anomaly. It uses a template-based automated generation system to create the procedure document. The input consists of a list of anomaly causes and corresponding templates, and the output is a constructed recovery procedure document. Specifically, the server searches for a template for a particular anomaly, fills in the details, and prepares it for user presentation.

[0169] Step 4:

[0170] The terminal presents the user with recovery procedures provided by the server. The user can review the procedures on the screen and proceed through them sequentially. Specifically, the terminal displays the procedures in an easy-to-read format and allows the user to manage their progress using checkboxes or similar methods.

[0171] Step 5:

[0172] The device analyzes the user's emotions in real time. It acquires emotional data using the user's facial expressions and tone of voice, and inputs this into an emotion engine. The output is the user's current emotional state. Specifically, the device supplies data from the camera and microphone to an analysis tool and sends the resulting emotional parameters to a server.

[0173] Step 6:

[0174] The server adjusts the recovery procedure based on the user's emotions. Based on the emotion analysis results, it modifies the content and presentation of the procedure manual. The input is the user's emotional state, and the output is the adjusted procedure manual. Specifically, if the server determines that the user is highly anxious, it will offer more detailed explanations or additional support options.

[0175] (Application Example 2)

[0176] 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".

[0177] Conventional systems have problems such as significant psychological burden on users when an anomaly occurs, and a lack of uniform recovery procedures, which degrades the quality of the user experience. Furthermore, in the electronic payment process, there is a challenge in that it is not possible to respond flexibly to the user's emotional state, and anxiety and stress during payment cannot be adequately reduced.

[0178] 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.

[0179] In this invention, the server includes means for monitoring anomalies and collecting status data; means for generating a computational model that analyzes the collected data to identify the cause of the anomaly; means for generating a recovery procedure based on the cause of the anomaly identified by the computational model and presenting it to a communication device; means for collecting and analyzing user emotion information; and means for adjusting the method of presenting the recovery procedure based on the analyzed emotion information. This enables real-time and flexible responses in response to user emotions, improving the user experience in various processes, including electronic payments.

[0180] A "server" is an information processing device that monitors for anomalies and collects and manages related data.

[0181] "Status data" refers to information about the system's operating status and environmental conditions.

[0182] A "computational model means" is an algorithm or program that analyzes collected data and performs processing to identify the cause of an anomaly.

[0183] A "communication device" is a terminal device that allows a user to receive information.

[0184] "Emotional information" refers to data that indicates the user's psychological state, and includes information extracted from facial expressions, tone of voice, input content, etc.

[0185] "Means of analysis" refers to the process performed to derive specific results or conclusions based on data.

[0186] A "recovery procedure" refers to a set of steps or procedures used to restore an abnormal state to normal.

[0187] "Presentation method" refers to the techniques and styles used to communicate information and procedures to users.

[0188] "User experience" is a concept that refers to the overall impression and feelings that system users gain from using a service.

[0189] The system for realizing this invention includes a server for monitoring anomalies, a communication device operated by the user, and an emotion engine for analyzing the user's emotions. The server first detects an anomaly and collects state data. This state data concerns various operating conditions and environment variables of the system and is analyzed by a computational model on the server to identify the cause of the anomaly.

[0190] Based on the identified cause of the anomaly, the server generates a recovery procedure and presents it to the communication device. The communication device is a terminal that accepts user input, such as a smartphone or tablet. During this presentation process, the emotion engine collects and analyzes the user's emotional information. Using a camera and microphone, it monitors the user's facial expressions, tone of voice, and input content to determine their psychological state in real time.

[0191] Based on the analyzed emotional information, the server adjusts how it presents recovery procedures. For example, if the user is experiencing stress, it simplifies the recovery process and displays reassuring messages. It also reduces the user's psychological burden by offering flexible options.

[0192] For example, if a user shows anxiety while making a payment on an e-commerce site, the emotion engine can detect this and provide guidance messages to simplify the payment process. In this way, the system can provide a customized user experience that responds to emotions.

[0193] A concrete example of a prompt message for a generative AI model is, "Please provide the best feedback message for a user who is feeling stressed."

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The server monitors for anomalies and collects status data when one is detected. Specifically, it collects log data and system performance metrics, and integrates information that may indicate the cause of the anomaly. The input is real-time data from the system, and the output is foundational data for the next analysis step.

[0197] Step 2:

[0198] The server analyzes the collected state data using computational models to identify the cause of the anomaly. Specifically, it uses data mining techniques to identify anomaly patterns and diagnoses the cause by comparing them with past cases. The input is the state data acquired in step 1, and the output is cause information for generating recovery procedures.

[0199] Step 3:

[0200] The server generates recovery procedures based on the identified cause of the anomaly and presents them to the communication device. It utilizes a generation AI model to formulate the optimal recovery sequence. The input is the cause information obtained in step 2, and the output is the recovery procedure displayed on the user terminal.

[0201] Step 4:

[0202] The device collects and analyzes the user's emotional information. The emotion engine uses the camera and microphone to analyze the user's facial expressions and tone of voice in real time. The input is visual and auditory data indicating the user's current emotional state, and the output is the analyzed emotional information.

[0203] Step 5:

[0204] The server adjusts how the recovery procedure is presented based on the analyzed emotional information. If the user indicates anxiety or stress, the instructions are simplified, and reassuring messages are added. The input is the emotional information obtained in step 4, and the output is the adjusted display of the recovery procedure.

[0205] Step 6:

[0206] The user reviews and approves the recovery procedure presented on the communication device. The user's action sends the approval information to the server. The input is the coordinated recovery procedure, and the output is the approval signal to initiate the recovery procedure.

[0207] Step 7:

[0208] The server, upon user approval, automatically executes recovery procedures. These procedures include system resets, relocation, and application of necessary updates. The input is the approval signal obtained in step 6, and the output is the stable system state after the anomaly has been corrected.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] This invention is a system for rapidly responding to failures related to server operation and streamlining regular update work. The server constantly monitors for system anomalies and strives to detect abnormal events early by collecting log data and metric data. The collected data is analyzed by built-in artificial intelligence to identify the cause of the anomaly.

[0226] After the server identifies the cause, a recovery procedure is automatically generated and presented to the user via the terminal. The user reviews the displayed recovery procedure and approves it if they deem it appropriate. After receiving this user approval, the server automatically executes the recovery procedure. This process enables rapid server recovery while reducing the risk of human error.

[0227] Furthermore, the server periodically checks the status of the system software and, if it determines that an update is necessary, creates an update procedure. This update procedure is presented to the terminal, and the server awaits user approval. Once approval is received, the server automatically applies the update and performs subsequent actions such as restarting as needed. This ensures that the system's security and functionality are always up-to-date.

[0228] For example, if a server's operating speed drops abnormally, the server collects relevant metric data, and artificial intelligence detects an abnormal increase in CPU usage. Based on this result, the server generates recovery procedures, such as stopping unnecessary processes or readjusting the load balancer, and presents each procedure to the user. After the user confirms and approves, these procedures are executed, and the server's normal operation is restored. In this way, the present invention contributes to improving the efficiency and reliability of server operations.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The server constantly monitors log data and metrics data to check for any anomalies. If an anomaly is detected, it collects detailed data to prepare for the next steps.

[0232] Step 2:

[0233] The artificial intelligence system within the server analyzes the collected log data and metrics data to identify the cause of the anomaly. The analysis includes comparing the data with historical data and known anomaly patterns.

[0234] Step 3:

[0235] Based on the identified cause of the server failure, appropriate recovery procedures are generated. These generated recovery procedures are sent to the terminal, providing the user with specific steps to take.

[0236] Step 4:

[0237] The user reviews the recovery procedure displayed on their device, and if they determine it is appropriate, they click a button to approve the procedure. The approval information is sent to the server.

[0238] Step 5:

[0239] After the server confirms user authorization, it automatically executes the instructed recovery procedures. These include freeing memory, restarting processes, and resetting settings.

[0240] Step 6:

[0241] The server periodically checks the software version and security patch status, and when it determines that an update is necessary, it automatically creates and displays the update procedure on the terminal.

[0242] Step 7:

[0243] Once the user reviews and approves the update procedure provided, the server automatically applies the update based on the approval information from the device. The system will restart if necessary.

[0244] (Example 1)

[0245] 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."

[0246] Modern information systems require rapid and accurate responses to server failures and problems. However, traditional methods often involve manual intervention in identifying the cause of failures and implementing countermeasures, leading to human error and delays. Furthermore, timely updates to system programs are difficult, resulting in security risks and performance degradation. To address these issues, automated processes are needed to improve efficiency and reliability.

[0247] 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.

[0248] In this invention, the server includes means for monitoring failures of the information processing device and collecting status data and performance indicator data; machine learning means for analyzing the collected data to identify the cause of the failure; and means for generating a recovery procedure based on the cause of the failure identified by the machine learning means and presenting it on the information display device. This enables automatic failure detection, rapid identification of the cause, and efficient presentation of recovery procedures. Furthermore, the system's up-to-dateness and safety can be maintained by automatically generating and applying update procedures based on periodic evaluation of the program state.

[0249] A "server" is a central information processing device that provides information processing services to clients over a network.

[0250] "Information processing device malfunction" refers to any defect or event that disrupts the normal operation of an information processing device.

[0251] "Status data" refers to collected data that indicates the operating status and health of an information processing device.

[0252] "Performance indicator data" refers to data that quantifies and shows the performance of an information processing device.

[0253] "Means of collection" refers to the functions and mechanisms for monitoring and acquiring data within a server.

[0254] "Machine learning methods" refer to artificial intelligence technologies that automatically identify the cause of a problem through data analysis.

[0255] A "recovery procedure" is a series of operations and processes that should be performed to resolve an identified problem.

[0256] An "information display device" is an output device that visually presents data and procedures to the user.

[0257] "User" refers to an individual or organization that operates, monitors, or manages information processing equipment or its services.

[0258] "Program status" refers to the current state and version information of the software running in an information processing system.

[0259] An "update procedure" refers to the specific steps taken to bring existing software and system configurations up to date.

[0260] "Restarting" refers to the operation of stopping an information processing device or its components and then starting them up again.

[0261] The embodiments for carrying out this invention will be described below.

[0262] The server, as the central hub of the entire information processing system, provides various services via the network. The server is equipped with dedicated monitoring software to continuously monitor and collect status and performance metrics. This monitoring software is implemented using, for example, open-source monitoring platforms or proprietary commercial products. This software constantly monitors CPU usage, memory usage, and disk I / O performance metrics.

[0263] The server is equipped with a generative AI model that analyzes collected data and identifies the cause of failures. This generative AI model learns from past failure data and can immediately identify the cause of newly occurring anomalies. This AI model uses machine learning libraries and has the ability to automatically detect anomaly patterns and perform correlation analysis.

[0264] For example, when a server experiences a slowdown, the server itself analyzes performance metrics data, and a generated AI model identifies that excessive CPU usage by a specific process is the cause. Based on this information, the server automatically generates recovery steps, such as stopping unnecessary processes or readjusting settings.

[0265] The generated recovery procedure is translated into natural language and presented to the terminal as a prompt. The terminal provides information to the user through a visual interface. For example, the prompt might appear as: "CPU overload by a specific process has been detected. Do you wish to stop the process?"

[0266] The user can proceed to the next step by reviewing the prompt displayed on the terminal and approving the procedure. Based on the approved procedure, the server automatically executes the process to resolve the problem, enabling a rapid recovery from the failure.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The server continuously collects status and performance indicator data using monitoring software. Input data includes real-time CPU usage, memory usage, and disk I / O values. This data is stored in log files within the server, and the system is configured to automatically detect signs of anomalies. If abnormal data is found, it is identified along with its occurrence time and passed on to the next processing step.

[0270] Step 2:

[0271] The server inputs the collected data into a generative AI model and performs analysis to identify the cause of the anomaly. The input data is the anomaly dataset identified in step 1. The generative AI model uses the results of learning past anomaly patterns to identify the cause using methods such as correlation analysis and clustering. As output, detailed information on the identified cause is generated, and recommended recovery steps are created based on it.

[0272] Step 3:

[0273] The server converts the generated recovery procedure into natural language and presents it to the terminal as a prompt. The input data is the technical content of the recovery procedure obtained in step 2. A natural language processing software library is used for this conversion. The output is a human-readable prompt for the user. Specifically, the terminal displays a message such as, "CPU overload by a specific process has been detected. Do you want to stop the process?"

[0274] Step 4:

[0275] The user reviews the prompt displayed on the terminal and performs an action to approve or reject it. The input is the recovery procedure prompt displayed on the terminal. The user indicates approval by pressing the confirmation button on the screen. The approval result is returned to the server as output, and the process proceeds to the next step.

[0276] Step 5:

[0277] The server automatically executes recovery procedures upon user approval. The input is the recovery procedure approved by the user. The server stops necessary processes and makes configuration changes to eliminate the cause of the anomaly. The output is that the system returns to a normal operating state, and the execution history of this operation is recorded in the server's operation log.

[0278] (Application Example 1)

[0279] 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."

[0280] In modern information systems, while efficiency and security in server operations are paramount, rapid response to anomalies and regular software updates are crucial challenges. However, these processes often rely on manual methods, leading to problems such as human error and a lack of rapid response procedures. In addition, the increasing number of potential threats necessitates real-time threat detection and countermeasures. The development of effective and efficient systems to address these challenges is essential.

[0281] 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.

[0282] In this invention, the server includes means for monitoring anomalies and collecting recorded information and indicator information, intelligent function means for analyzing the collected information and identifying the cause of the anomaly, and means for generating a recovery procedure based on the identified cause of the anomaly and presenting it on a display device. This enables a rapid and accurate response when an anomaly occurs, and by immediately notifying potential threats, it is possible to improve system reliability and optimize operational efficiency.

[0283] A "server" is an information processing device that performs data processing and calculations and provides services to other computer systems via a network.

[0284] "Abnormality" refers to a state or operation that deviates from the normal operating range in an information system, including cases where the performance and security of the system fall outside the recognized standards.

[0285] "Recorded information" is data that indicates the details of the operation and state of the system, including data such as logs and transaction history information.

[0286] "Indicator information" is data that serves as a criterion for evaluating system performance and state, including information such as CPU usage rate and memory consumption metrics.

[0287] "Intelligent function means" refers to artificial intelligence technology used to analyze the collected data and identify the cause of abnormalities, and is a device or program equipped with functions for data analysis and pattern recognition.

[0288] "Recovery procedure" is a series of processing steps executed to eliminate the identified abnormality and restore the system to a normal state.

[0289] "Display device" is a hardware device for visually communicating the state and operation instructions of a computer system to the user.

[0290] "User" refers to an individual or organization that operates and manages a computer system, and is the entity that performs approvals and operations based on the instructions of the system.

[0291] This invention is a system that realizes efficient server management and security monitoring through the cooperation of servers, terminals, and users. The server uses advanced monitoring functions to monitor abnormalities, and collects and analyzes recorded information and indicator information in real time. This analysis uses intelligent function means, specifically AI-based data analysis algorithms, to identify abnormalities.

[0292] The terminal receives information transmitted from the server and presents it to the user via a display device. This includes recovery procedures and notifications of potential threats generated by the server. Based on this information, the user can make appropriate decisions or give instructions.

[0293] For example, if a server detects high CPU usage during a weekend night, the AI ​​will determine this to be an anomaly and generate recovery steps to reduce the CPU load. These steps include stopping unnecessary processes and distributing tasks. The terminal visually presents this to the user, and after user approval, the server automatically executes these steps to optimize the system load.

[0294] An example of a prompt message is: "Generate steps to optimize the server's security status and create a prompt to notify the user. Start with CPU usage data and include recommended actions when anomalies are detected." In this way, the entire system works together to achieve efficient server operation and enhanced security.

[0295] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0296] Step 1:

[0297] The server monitors the system status and collects recorded and metric information in real time. This information includes metrics such as CPU usage and memory usage. For data processing, this information is stored in a database and used as foundational data for anomaly detection.

[0298] Step 2:

[0299] The server analyzes the metric data collected by leveraging intelligent functions, and determines whether there are any anomalies. The input data is the metric data collected in Step 1, and the output is to identify the presence or absence of anomalies and their details (e.g., high CPU usage, excessive memory consumption). Machine learning algorithms are used in the analysis operation.

[0300] Step 3:

[0301] When the server identifies an anomaly, it generates a recovery procedure. As input, the analysis results from Step 2 are used, and the output is a specific recovery procedure. This procedure is concretized as, for example, stopping unnecessary processes or an execution plan for load distribution.

[0302] Step 4:

[0303] The terminal receives the recovery procedure sent from the server and displays it on the display device. The input data is the recovery procedure generated in Step 3, and the output includes the operation of presenting the visualized information to the user.

[0304] Step 5:

[0305] The user reviews the presented recovery procedure and gives appropriate instructions. The input is the recovery procedure displayed from the terminal, and the output is an instruction for approval or modification. Specific operations of the user include confirmation work on the screen and operation of the approval button.

[0306] Step 6:

[0307] The server executes the recovery procedure approved by the user. User approval is required as input, and the output is the execution result. Operations such as stopping the specified process and adjusting load distribution are performed in this step.

[0308] Furthermore, it may be combined with an emotion engine that estimates the user's emotion. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0309] This invention combines a system in which a server monitors for anomalies and automates efficient recovery and periodic system updates with an emotion engine that recognizes user emotions. When an anomaly occurs, the server collects log data and metric data, and artificial intelligence measures identify the cause based on this data. The server then generates a recovery procedure and presents it to the user via a terminal.

[0310] Furthermore, the emotion engine analyzes emotional information in real time from the user's facial expressions, tone of voice, and input content via the terminal. Based on the results of this emotion analysis, the server can adjust the way and content of the recovery procedure is presented, taking care to reduce the user's psychological burden. For example, if the user is feeling stressed, the procedure will be presented concisely, and guidance messages will be displayed if necessary, allowing for flexible responses tailored to the user's situation.

[0311] For example, if a user's facial expression indicates anxiety when recovery procedures are presented on the terminal, the emotion engine recognizes this. The server receives this information and helps the user understand by providing additional explanations or options. In this way, the information retrieved can be customized according to the user's emotions, providing a more intuitive and frictionless user experience. This system effectively supports the stable operation of the server while maintaining a user-friendly interface.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The server monitors for anomalies and collects log data and metrics data. When an anomaly is detected, the collected data is sent to an artificial intelligence system.

[0315] Step 2:

[0316] The artificial intelligence system on the server analyzes the collected data in real time to identify the cause of the anomaly. At this stage, it compares the data against known patterns to clarify the type of malfunction.

[0317] Step 3:

[0318] Once the cause is identified, the server automatically generates recovery steps based on that cause. The generated recovery steps are organized in a user-friendly format and sent to the terminal.

[0319] Step 4:

[0320] The device presents recovery procedures to the user while simultaneously activating an emotion engine to analyze the user's emotional state from their facial expressions and tone of voice. Based on the results of the emotion analysis, the method and content of the procedure presentation are dynamically adjusted.

[0321] Step 5:

[0322] The user reviews the presented recovery procedure and approves it if they agree. The user's approval is sent to the server via the device.

[0323] Step 6:

[0324] The server automatically executes the recovery procedure based on the received authorization information. During execution, the progress is displayed on the terminal in real time. Each step is performed sequentially, continuing until the service is restored to normal.

[0325] Step 7:

[0326] The server periodically checks the system's software status, and if it determines that an update is necessary, it creates an update procedure and presents it to the terminal. The emotion engine takes the user's emotional state into consideration and adjusts how the update is presented.

[0327] Step 8:

[0328] Once the user approves the update procedure, the server will automatically apply the software update and perform restarts or process optimizations as needed.

[0329] (Example 2)

[0330] 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".

[0331] In modern information systems, it is crucial to respond quickly and appropriately when an anomaly occurs. However, traditional systems have difficulty responding flexibly to user emotions, sometimes placing an excessive burden on users. Furthermore, the lack of automation in system update and recovery procedures has resulted in operational complexity.

[0332] 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.

[0333] In this invention, the server includes means for monitoring anomalies and collecting data; intelligent means for analyzing the collected data and identifying the cause; means for generating procedures based on the cause and presenting them to an information processing device; means for acquiring and analyzing the user's emotions; and means for adjusting the procedures and changing the presented content based on the analyzed emotions. This enables appropriate recovery without burdening the user, automates update procedures, and allows for more flexible and effective system management.

[0334] A "server" is a device in a computer network that receives requests from clients, processes them, and provides data and services.

[0335] An "abnormality" refers to a phenomenon or state that deviates from the expected behavior of a system and is a factor that disrupts normal operation.

[0336] "Data" refers to information represented by symbols, numbers, or combinations thereof, in a format that can be processed by a computer.

[0337] An "intelligent tool" is a tool that has the ability to analyze data and perform a series of processes based on artificial intelligence to extract useful information from it.

[0338] A "procedure" is a set of steps or tasks that must be followed to achieve a specific objective.

[0339] An "information processing device" is a computer or terminal used to handle data, and is a device that enables data exchange between humans and machines.

[0340] A "user" is the entity that operates and utilizes a system or information device.

[0341] "Emotion" is a state of mind in humans, an intuitive and reactive psychological experience in response to a particular event or perception.

[0342] "Analysis" is the act of investigating data and phenomena in detail to reveal their structure and relationships.

[0343] "Adjustment" refers to the act of changing settings or configurations to suit specific conditions or circumstances.

[0344] In this invention, a server acts as the central point for monitoring anomalies in the information system and automatically generates efficient recovery procedures using the collected data. Common hardware capable of data stream processing is used for anomaly monitoring and data collection, and a data analysis platform such as Apache Kafka can be used. The server receives log data and metrics data, collects this data, and records anomaly events.

[0345] The collected data is analyzed using artificial intelligence. Machine learning frameworks such as TensorFlow are used to identify the cause of the anomaly. Based on the identified cause, the server generates appropriate recovery procedures. These procedures are presented to the user via a terminal on an information processing device. The terminal provides an interface that allows the user to review and execute the procedures.

[0346] In addition, the device incorporates an emotion engine that acquires emotional information through the user's facial expressions, tone of voice, and input content. This acquired data is analyzed in real time, enabling the presentation of information tailored to the user's emotional state. IBM Watson's tone analyzer and general facial recognition APIs are used for emotion analysis.

[0347] For example, if a user's facial expression indicates anxiety while reviewing the presented recovery procedure, the device sends this information to the emotion engine, and the server adjusts the procedure accordingly. Specifically, the procedure can be explained in more detail, or additional support options can be offered, thereby reducing the user's psychological burden.

[0348] The following is an example of a prompt message:

[0349] "When users follow the system recovery procedure, we want to provide appropriate additional information based on facial expression and tone data recognized by the emotion engine. Please explain how this system provides information in accordance with emotions."

[0350] This system will improve the user experience while supporting the efficient and stable operation of the servers.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] The server monitors for anomalies and collects log and metric data. It retrieves data from the monitored system in real time and stores it using Apache Kafka. Data inputs include log files and performance metrics sent from the system, and output generates a formatted dataset for processing. Specifically, the server periodically scans the data stream and records any anomaly patterns it detects.

[0354] Step 2:

[0355] The server analyzes the collected data to identify the cause of the anomaly. It runs a generative AI model using a machine learning framework such as TensorFlow to analyze data patterns. The input is a formatted dataset, and the output is a list of factors that are likely to be anomalies. Specifically, the server calculates the similarity to past anomaly patterns and lists the most likely causes in order of priority.

[0356] Step 3:

[0357] The server generates recovery procedures based on the identified cause of the anomaly. It uses a template-based automated generation system to create the procedure document. The input consists of a list of anomaly causes and corresponding templates, and the output is a constructed recovery procedure document. Specifically, the server searches for a template for a particular anomaly, fills in the details, and prepares it for user presentation.

[0358] Step 4:

[0359] The terminal presents the user with recovery procedures provided by the server. The user can review the procedures on the screen and proceed through them sequentially. Specifically, the terminal displays the procedures in an easy-to-read format and allows the user to manage their progress using checkboxes or similar methods.

[0360] Step 5:

[0361] The device analyzes the user's emotions in real time. It acquires emotional data using the user's facial expressions and tone of voice, and inputs this into an emotion engine. The output is the user's current emotional state. Specifically, the device supplies data from the camera and microphone to an analysis tool and sends the resulting emotional parameters to a server.

[0362] Step 6:

[0363] The server adjusts the recovery procedure based on the user's emotions. Based on the emotion analysis results, it modifies the content and presentation of the procedure manual. The input is the user's emotional state, and the output is the adjusted procedure manual. Specifically, if the server determines that the user is highly anxious, it will offer more detailed explanations or additional support options.

[0364] (Application Example 2)

[0365] 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."

[0366] Conventional systems have problems such as significant psychological burden on users when an anomaly occurs, and a lack of uniform recovery procedures, which degrades the quality of the user experience. Furthermore, in the electronic payment process, there is a challenge in that it is not possible to respond flexibly to the user's emotional state, and anxiety and stress during payment cannot be adequately reduced.

[0367] 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.

[0368] In this invention, the server includes means for monitoring anomalies and collecting status data; means for generating a computational model that analyzes the collected data to identify the cause of the anomaly; means for generating a recovery procedure based on the cause of the anomaly identified by the computational model and presenting it to a communication device; means for collecting and analyzing user emotion information; and means for adjusting the method of presenting the recovery procedure based on the analyzed emotion information. This enables real-time and flexible responses in response to user emotions, improving the user experience in various processes, including electronic payments.

[0369] A "server" is an information processing device that monitors for anomalies and collects and manages related data.

[0370] "Status data" refers to information about the system's operating status and environmental conditions.

[0371] A "computational model means" is an algorithm or program that analyzes collected data and performs processing to identify the cause of an anomaly.

[0372] A "communication device" is a terminal device that allows a user to receive information.

[0373] "Emotional information" refers to data that indicates the user's psychological state, and includes information extracted from facial expressions, tone of voice, input content, etc.

[0374] "Means of analysis" refers to the process performed to derive specific results or conclusions based on data.

[0375] A "recovery procedure" refers to a set of steps or procedures used to restore an abnormal state to normal.

[0376] "Presentation method" refers to the techniques and styles used to communicate information and procedures to users.

[0377] "User experience" is a concept that refers to the overall impression and feelings that system users gain from using a service.

[0378] The system for realizing this invention includes a server for monitoring anomalies, a communication device operated by the user, and an emotion engine for analyzing the user's emotions. The server first detects an anomaly and collects state data. This state data concerns various operating conditions and environment variables of the system and is analyzed by a computational model on the server to identify the cause of the anomaly.

[0379] Based on the identified cause of the anomaly, the server generates a recovery procedure and presents it to the communication device. The communication device is a terminal that accepts user input, such as a smartphone or tablet. During this presentation process, the emotion engine collects and analyzes the user's emotional information. Using a camera and microphone, it monitors the user's facial expressions, tone of voice, and input content to determine their psychological state in real time.

[0380] Based on the analyzed emotional information, the server adjusts how it presents recovery procedures. For example, if the user is experiencing stress, it simplifies the recovery process and displays reassuring messages. It also reduces the user's psychological burden by offering flexible options.

[0381] For example, if a user shows anxiety while making a payment on an e-commerce site, the emotion engine can detect this and provide guidance messages to simplify the payment process. In this way, the system can provide a customized user experience that responds to emotions.

[0382] A concrete example of a prompt message for a generative AI model is, "Please provide the best feedback message for a user who is feeling stressed."

[0383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0384] Step 1:

[0385] The server monitors for anomalies and collects status data when one is detected. Specifically, it collects log data and system performance metrics, and integrates information that may indicate the cause of the anomaly. The input is real-time data from the system, and the output is foundational data for the next analysis step.

[0386] Step 2:

[0387] The server analyzes the collected state data using computational models to identify the cause of the anomaly. Specifically, it uses data mining techniques to identify anomaly patterns and diagnoses the cause by comparing them with past cases. The input is the state data acquired in step 1, and the output is cause information for generating recovery procedures.

[0388] Step 3:

[0389] The server generates recovery procedures based on the identified cause of the anomaly and presents them to the communication device. It utilizes a generation AI model to formulate the optimal recovery sequence. The input is the cause information obtained in step 2, and the output is the recovery procedure displayed on the user terminal.

[0390] Step 4:

[0391] The device collects and analyzes the user's emotional information. The emotion engine uses the camera and microphone to analyze the user's facial expressions and tone of voice in real time. The input is visual and auditory data indicating the user's current emotional state, and the output is the analyzed emotional information.

[0392] Step 5:

[0393] The server adjusts how the recovery procedure is presented based on the analyzed emotional information. If the user indicates anxiety or stress, the instructions are simplified, and reassuring messages are added. The input is the emotional information obtained in step 4, and the output is the adjusted display of the recovery procedure.

[0394] Step 6:

[0395] The user reviews and approves the recovery procedure presented on the communication device. The user's action sends the approval information to the server. The input is the coordinated recovery procedure, and the output is the approval signal to initiate the recovery procedure.

[0396] Step 7:

[0397] The server, upon user approval, automatically executes recovery procedures. These procedures include system resets, relocation, and application of necessary updates. The input is the approval signal obtained in step 6, and the output is the stable system state after the anomaly has been corrected.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] [Third Embodiment]

[0402] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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".

[0414] This invention is a system for rapidly responding to failures related to server operation and streamlining regular update work. The server constantly monitors for system anomalies and strives to detect abnormal events early by collecting log data and metric data. The collected data is analyzed by built-in artificial intelligence to identify the cause of the anomaly.

[0415] After the server identifies the cause, a recovery procedure is automatically generated and presented to the user via the terminal. The user reviews the displayed recovery procedure and approves it if they deem it appropriate. After receiving this user approval, the server automatically executes the recovery procedure. This process enables rapid server recovery while reducing the risk of human error.

[0416] Furthermore, the server periodically checks the status of the system software and, if it determines that an update is necessary, creates an update procedure. This update procedure is presented to the terminal, and the server awaits user approval. Once approval is received, the server automatically applies the update and performs subsequent actions such as restarting as needed. This ensures that the system's security and functionality are always up-to-date.

[0417] For example, if a server's operating speed drops abnormally, the server collects relevant metric data, and artificial intelligence detects an abnormal increase in CPU usage. Based on this result, the server generates recovery procedures, such as stopping unnecessary processes or readjusting the load balancer, and presents each procedure to the user. After the user confirms and approves, these procedures are executed, and the server's normal operation is restored. In this way, the present invention contributes to improving the efficiency and reliability of server operations.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The server constantly monitors log data and metrics data to check for any anomalies. If an anomaly is detected, it collects detailed data to prepare for the next steps.

[0421] Step 2:

[0422] The artificial intelligence system within the server analyzes the collected log data and metrics data to identify the cause of the anomaly. The analysis includes comparing the data with historical data and known anomaly patterns.

[0423] Step 3:

[0424] Based on the identified cause of the server failure, appropriate recovery procedures are generated. These generated recovery procedures are sent to the terminal, providing the user with specific steps to take.

[0425] Step 4:

[0426] The user reviews the recovery procedure displayed on their device, and if they determine it is appropriate, they click a button to approve the procedure. The approval information is sent to the server.

[0427] Step 5:

[0428] After the server confirms user authorization, it automatically executes the instructed recovery procedures. These include freeing memory, restarting processes, and resetting settings.

[0429] Step 6:

[0430] The server periodically checks the software version and security patch status, and when it determines that an update is necessary, it automatically creates and displays the update procedure on the terminal.

[0431] Step 7:

[0432] Once the user reviews and approves the update procedure provided, the server automatically applies the update based on the approval information from the device. The system will restart if necessary.

[0433] (Example 1)

[0434] 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."

[0435] Modern information systems require rapid and accurate responses to server failures and problems. However, traditional methods often involve manual intervention in identifying the cause of failures and implementing countermeasures, leading to human error and delays. Furthermore, timely updates to system programs are difficult, resulting in security risks and performance degradation. To address these issues, automated processes are needed to improve efficiency and reliability.

[0436] 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.

[0437] In this invention, the server includes means for monitoring failures of the information processing device and collecting status data and performance indicator data; machine learning means for analyzing the collected data to identify the cause of the failure; and means for generating a recovery procedure based on the cause of the failure identified by the machine learning means and presenting it on the information display device. This enables automatic failure detection, rapid identification of the cause, and efficient presentation of recovery procedures. Furthermore, the system's up-to-dateness and safety can be maintained by automatically generating and applying update procedures based on periodic evaluation of the program state.

[0438] A "server" is a central information processing device that provides information processing services to clients over a network.

[0439] "Information processing device malfunction" refers to any defect or event that disrupts the normal operation of an information processing device.

[0440] "Status data" refers to collected data that indicates the operating status and health of an information processing device.

[0441] "Performance indicator data" refers to data that quantifies and shows the performance of an information processing device.

[0442] "Means of collection" refers to the functions and mechanisms for monitoring and acquiring data within a server.

[0443] "Machine learning methods" refer to artificial intelligence technologies that automatically identify the cause of a problem through data analysis.

[0444] A "recovery procedure" is a series of operations and processes that should be performed to resolve an identified problem.

[0445] An "information display device" is an output device that visually presents data and procedures to the user.

[0446] "User" refers to an individual or organization that operates, monitors, or manages information processing equipment or its services.

[0447] "Program status" refers to the current state and version information of the software running in an information processing system.

[0448] An "update procedure" refers to the specific steps taken to bring existing software and system configurations up to date.

[0449] "Restarting" refers to the operation of stopping an information processing device or its components and then starting them up again.

[0450] The embodiments for carrying out this invention will be described below.

[0451] The server, as the central hub of the entire information processing system, provides various services via the network. The server is equipped with dedicated monitoring software to continuously monitor and collect status and performance metrics. This monitoring software is implemented using, for example, open-source monitoring platforms or proprietary commercial products. This software constantly monitors CPU usage, memory usage, and disk I / O performance metrics.

[0452] The server is equipped with a generative AI model that analyzes collected data and identifies the cause of failures. This generative AI model learns from past failure data and can immediately identify the cause of newly occurring anomalies. This AI model uses machine learning libraries and has the ability to automatically detect anomaly patterns and perform correlation analysis.

[0453] For example, when a server experiences a slowdown, the server itself analyzes performance metrics data, and a generated AI model identifies that excessive CPU usage by a specific process is the cause. Based on this information, the server automatically generates recovery steps, such as stopping unnecessary processes or readjusting settings.

[0454] The generated recovery procedure is translated into natural language and presented to the terminal as a prompt. The terminal provides information to the user through a visual interface. For example, the prompt might appear as: "CPU overload by a specific process has been detected. Do you wish to stop the process?"

[0455] The user can proceed to the next step by reviewing the prompt displayed on the terminal and approving the procedure. Based on the approved procedure, the server automatically executes the process to resolve the problem, enabling a rapid recovery from the failure.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The server continuously collects status and performance indicator data using monitoring software. Input data includes real-time CPU usage, memory usage, and disk I / O values. This data is stored in log files within the server, and the system is configured to automatically detect signs of anomalies. If abnormal data is found, it is identified along with its occurrence time and passed on to the next processing step.

[0459] Step 2:

[0460] The server inputs the collected data into a generative AI model and performs analysis to identify the cause of the anomaly. The input data is the anomaly dataset identified in step 1. The generative AI model uses the results of learning past anomaly patterns to identify the cause using methods such as correlation analysis and clustering. As output, detailed information on the identified cause is generated, and recommended recovery steps are created based on it.

[0461] Step 3:

[0462] The server converts the generated recovery procedure into natural language and presents it to the terminal as a prompt. The input data is the technical content of the recovery procedure obtained in step 2. A natural language processing software library is used for this conversion. The output is a human-readable prompt for the user. Specifically, the terminal displays a message such as, "CPU overload by a specific process has been detected. Do you want to stop the process?"

[0463] Step 4:

[0464] The user reviews the prompt displayed on the terminal and performs an action to approve or reject it. The input is the recovery procedure prompt displayed on the terminal. The user indicates approval by pressing the confirmation button on the screen. The approval result is returned to the server as output, and the process proceeds to the next step.

[0465] Step 5:

[0466] The server automatically executes recovery procedures upon user approval. The input is the recovery procedure approved by the user. The server stops necessary processes and makes configuration changes to eliminate the cause of the anomaly. The output is that the system returns to a normal operating state, and the execution history of this operation is recorded in the server's operation log.

[0467] (Application Example 1)

[0468] 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."

[0469] In modern information systems, while efficiency and security in server operations are paramount, rapid response to anomalies and regular software updates are crucial challenges. However, these processes often rely on manual methods, leading to problems such as human error and a lack of rapid response procedures. In addition, the increasing number of potential threats necessitates real-time threat detection and countermeasures. The development of effective and efficient systems to address these challenges is essential.

[0470] 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.

[0471] In this invention, the server includes means for monitoring anomalies and collecting recorded information and indicator information, intelligent function means for analyzing the collected information and identifying the cause of the anomaly, and means for generating a recovery procedure based on the identified cause of the anomaly and presenting it on a display device. This enables a rapid and accurate response when an anomaly occurs, and by immediately notifying potential threats, it is possible to improve system reliability and optimize operational efficiency.

[0472] A "server" is an information processing device that performs data processing and calculations and provides services to other computer systems via a network.

[0473] "Anomaly" refers to a state or operation in an information system that deviates from the normal operating range, including cases where the system's performance or safety falls outside the recognized standards.

[0474] "Record information" refers to data that shows details of the system's operation and status, and includes historical information such as logs and transactions.

[0475] "Metric information" refers to data that serves as a standard for evaluating system performance and status, and includes metrics such as CPU usage and memory consumption.

[0476] "Intelligent function means" refers to artificial intelligence technology used to analyze collected data and identify the cause of anomalies, and is a device or program equipped with the function of performing data analysis and pattern recognition.

[0477] A "recovery procedure" is a series of processing steps performed to resolve an identified anomaly and restore the system to a normal state.

[0478] A "display device" is a hardware device that visually communicates the status and operating instructions of a computer system to the user.

[0479] "User" refers to an individual or organization that operates and manages a computer system, and is the entity that performs approvals and operations based on the system's instructions.

[0480] This invention is a system that achieves efficient server management and security monitoring through collaboration between servers, terminals, and users. The servers use advanced monitoring functions to detect anomalies and collect and analyze recorded information and indicator information in real time. Intelligent functional means, specifically AI-based data analysis algorithms, are used for this analysis to identify anomalies.

[0481] The terminal receives information transmitted from the server and presents it to the user via a display device. This includes recovery procedures and notifications of potential threats generated by the server. Based on this information, the user can make appropriate decisions or give instructions.

[0482] For example, if a server detects high CPU usage during a weekend night, the AI ​​will determine this to be an anomaly and generate recovery steps to reduce the CPU load. These steps include stopping unnecessary processes and distributing tasks. The terminal visually presents this to the user, and after user approval, the server automatically executes these steps to optimize the system load.

[0483] An example of a prompt message is: "Generate steps to optimize the server's security status and create a prompt to notify the user. Start with CPU usage data and include recommended actions when anomalies are detected." In this way, the entire system works together to achieve efficient server operation and enhanced security.

[0484] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0485] Step 1:

[0486] The server monitors the system status and collects recorded and metric information in real time. This information includes metrics such as CPU usage and memory usage. For data processing, this information is stored in a database and used as foundational data for anomaly detection.

[0487] Step 2:

[0488] The server analyzes the collected metrics data using intelligent functions to determine whether or not an anomaly is present. The input data is the metrics data collected in step 1, and the output identifies whether or not an anomaly is present and its details (e.g., high CPU usage, excessive memory consumption). Machine learning algorithms are used for the analysis.

[0489] Step 3:

[0490] If the server detects an anomaly, it generates a recovery procedure. The analysis results from step 2 are used as input, and the output is a specific recovery procedure. This procedure can be implemented, for example, as a plan to stop unnecessary processes or perform load balancing.

[0491] Step 4:

[0492] The terminal receives the recovery procedure sent from the server and displays it on the display device. The input data is the recovery procedure generated in step 3, and the output includes the presentation of visualized information to the user.

[0493] Step 5:

[0494] The user reviews the presented recovery procedure and provides appropriate instructions. The input is the recovery procedure displayed on the terminal, and the output is instructions for approval or correction. Specific user actions include reviewing the information on the screen and clicking the approval button.

[0495] Step 6:

[0496] The server executes a recovery procedure approved by the user. User approval is required as input, and the output is the result of the execution. This step involves actions such as stopping a specified process or adjusting load balancing.

[0497] 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.

[0498] This invention combines a system in which a server monitors for anomalies and automates efficient recovery and periodic system updates with an emotion engine that recognizes user emotions. When an anomaly occurs, the server collects log data and metric data, and artificial intelligence measures identify the cause based on this data. The server then generates a recovery procedure and presents it to the user via a terminal.

[0499] Furthermore, the emotion engine analyzes emotional information in real time from the user's facial expressions, tone of voice, and input content via the terminal. Based on the results of this emotion analysis, the server can adjust the way and content of the recovery procedure is presented, taking care to reduce the user's psychological burden. For example, if the user is feeling stressed, the procedure will be presented concisely, and guidance messages will be displayed if necessary, allowing for flexible responses tailored to the user's situation.

[0500] For example, if a user's facial expression indicates anxiety when recovery procedures are presented on the terminal, the emotion engine recognizes this. The server receives this information and helps the user understand by providing additional explanations or options. In this way, the information retrieved can be customized according to the user's emotions, providing a more intuitive and frictionless user experience. This system effectively supports the stable operation of the server while maintaining a user-friendly interface.

[0501] The following describes the processing flow.

[0502] Step 1:

[0503] The server monitors for anomalies and collects log data and metrics data. When an anomaly is detected, the collected data is sent to an artificial intelligence system.

[0504] Step 2:

[0505] The artificial intelligence system on the server analyzes the collected data in real time to identify the cause of the anomaly. At this stage, it compares the data against known patterns to clarify the type of malfunction.

[0506] Step 3:

[0507] Once the cause is identified, the server automatically generates recovery steps based on that cause. The generated recovery steps are organized in a user-friendly format and sent to the terminal.

[0508] Step 4:

[0509] The device presents recovery procedures to the user while simultaneously activating an emotion engine to analyze the user's emotional state from their facial expressions and tone of voice. Based on the results of the emotion analysis, the method and content of the procedure presentation are dynamically adjusted.

[0510] Step 5:

[0511] The user reviews the presented recovery procedure and approves it if they agree. The user's approval is sent to the server via the device.

[0512] Step 6:

[0513] The server automatically executes the recovery procedure based on the received authorization information. During execution, the progress is displayed on the terminal in real time. Each step is performed sequentially, continuing until the service is restored to normal.

[0514] Step 7:

[0515] The server periodically checks the system's software status, and if it determines that an update is necessary, it creates an update procedure and presents it to the terminal. The emotion engine takes the user's emotional state into consideration and adjusts how the update is presented.

[0516] Step 8:

[0517] Once the user approves the update procedure, the server will automatically apply the software update and perform restarts or process optimizations as needed.

[0518] (Example 2)

[0519] 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."

[0520] In modern information systems, it is crucial to respond quickly and appropriately when an anomaly occurs. However, traditional systems have difficulty responding flexibly to user emotions, sometimes placing an excessive burden on users. Furthermore, the lack of automation in system update and recovery procedures has resulted in operational complexity.

[0521] 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.

[0522] In this invention, the server includes means for monitoring anomalies and collecting data; intelligent means for analyzing the collected data and identifying the cause; means for generating procedures based on the cause and presenting them to an information processing device; means for acquiring and analyzing the user's emotions; and means for adjusting the procedures and changing the presented content based on the analyzed emotions. This enables appropriate recovery without burdening the user, automates update procedures, and allows for more flexible and effective system management.

[0523] A "server" is a device in a computer network that receives requests from clients, processes them, and provides data and services.

[0524] An "abnormality" refers to a phenomenon or state that deviates from the expected behavior of a system and is a factor that disrupts normal operation.

[0525] "Data" refers to information represented by symbols, numbers, or combinations thereof, in a format that can be processed by a computer.

[0526] An "intelligent tool" is a tool that has the ability to analyze data and perform a series of processes based on artificial intelligence to extract useful information from it.

[0527] A "procedure" is a set of steps or tasks that must be followed to achieve a specific objective.

[0528] An "information processing device" is a computer or terminal used to handle data, and is a device that enables data exchange between humans and machines.

[0529] A "user" is the entity that operates and utilizes a system or information device.

[0530] "Emotion" is a state of mind in humans, an intuitive and reactive psychological experience in response to a particular event or perception.

[0531] "Analysis" is the act of investigating data and phenomena in detail to reveal their structure and relationships.

[0532] "Adjustment" refers to the act of changing settings or configurations to suit specific conditions or circumstances.

[0533] In this invention, a server acts as the central point for monitoring anomalies in the information system and automatically generates efficient recovery procedures using the collected data. Common hardware capable of data stream processing is used for anomaly monitoring and data collection, and a data analysis platform such as Apache Kafka can be used. The server receives log data and metrics data, collects this data, and records anomaly events.

[0534] The collected data is analyzed using artificial intelligence. Machine learning frameworks such as TensorFlow are used to identify the cause of the anomaly. Based on the identified cause, the server generates appropriate recovery procedures. These procedures are presented to the user via a terminal on an information processing device. The terminal provides an interface that allows the user to review and execute the procedures.

[0535] In addition, the device incorporates an emotion engine that acquires emotional information through the user's facial expressions, tone of voice, and input content. This acquired data is analyzed in real time, enabling the presentation of information tailored to the user's emotional state. IBM Watson's tone analyzer and general facial recognition APIs are used for emotion analysis.

[0536] For example, if a user's facial expression indicates anxiety while reviewing the presented recovery procedure, the device sends this information to the emotion engine, and the server adjusts the procedure accordingly. Specifically, the procedure can be explained in more detail, or additional support options can be offered, thereby reducing the user's psychological burden.

[0537] The following is an example of a prompt message:

[0538] "When users follow the system recovery procedure, we want to provide appropriate additional information based on facial expression and tone data recognized by the emotion engine. Please explain how this system provides emotion-sensitive information."

[0539] This system will improve the user experience while supporting the efficient and stable operation of the servers.

[0540] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0541] Step 1:

[0542] The server monitors for anomalies and collects log and metric data. It retrieves data from the monitored system in real time and stores it using Apache Kafka. Data inputs include log files and performance metrics sent from the system, and output generates a formatted dataset for processing. Specifically, the server periodically scans the data stream and records any anomaly patterns it detects.

[0543] Step 2:

[0544] The server analyzes the collected data to identify the cause of the anomaly. It runs a generative AI model using a machine learning framework such as TensorFlow to analyze data patterns. The input is a formatted dataset, and the output is a list of factors that are likely to be anomalies. Specifically, the server calculates the similarity to past anomaly patterns and lists the most likely causes in order of priority.

[0545] Step 3:

[0546] The server generates recovery procedures based on the identified cause of the anomaly. It uses a template-based automated generation system to create the procedure document. The input consists of a list of anomaly causes and corresponding templates, and the output is a constructed recovery procedure document. Specifically, the server searches for a template for a particular anomaly, fills in the details, and prepares it for user presentation.

[0547] Step 4:

[0548] The terminal presents the user with recovery procedures provided by the server. The user can review the procedures on the screen and proceed through them sequentially. Specifically, the terminal displays the procedures in an easy-to-read format and allows the user to manage their progress using checkboxes or similar methods.

[0549] Step 5:

[0550] The device analyzes the user's emotions in real time. It acquires emotional data using the user's facial expressions and tone of voice, and inputs this into an emotion engine. The output is the user's current emotional state. Specifically, the device supplies data from the camera and microphone to an analysis tool and sends the resulting emotional parameters to a server.

[0551] Step 6:

[0552] The server adjusts the recovery procedure based on the user's emotions. Based on the emotion analysis results, it modifies the content and presentation of the procedure manual. The input is the user's emotional state, and the output is the adjusted procedure manual. Specifically, if the server determines that the user is highly anxious, it will offer more detailed explanations or additional support options.

[0553] (Application Example 2)

[0554] 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."

[0555] Conventional systems have problems such as significant psychological burden on users when an anomaly occurs, and a lack of uniform recovery procedures, which degrades the quality of the user experience. Furthermore, in the electronic payment process, there is a challenge in that it is not possible to respond flexibly to the user's emotional state, and anxiety and stress during payment cannot be adequately reduced.

[0556] 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.

[0557] In this invention, the server includes means for monitoring anomalies and collecting status data; means for generating a computational model that analyzes the collected data to identify the cause of the anomaly; means for generating a recovery procedure based on the cause of the anomaly identified by the computational model and presenting it to a communication device; means for collecting and analyzing user emotion information; and means for adjusting the method of presenting the recovery procedure based on the analyzed emotion information. This enables real-time and flexible responses in response to user emotions, improving the user experience in various processes, including electronic payments.

[0558] A "server" is an information processing device that monitors for anomalies and collects and manages related data.

[0559] "Status data" refers to information about the system's operating status and environmental conditions.

[0560] A "computational model means" is an algorithm or program that analyzes collected data and performs processing to identify the cause of an anomaly.

[0561] A "communication device" is a terminal device that allows a user to receive information.

[0562] "Emotional information" refers to data that indicates the user's psychological state, and includes information extracted from facial expressions, tone of voice, input content, etc.

[0563] "Means of analysis" refers to the process performed to derive specific results or conclusions based on data.

[0564] A "recovery procedure" refers to a set of steps or procedures used to restore an abnormal state to normal.

[0565] "Presentation method" refers to the techniques and styles used to communicate information and procedures to users.

[0566] "User experience" is a concept that refers to the overall impression and feelings that system users gain from using a service.

[0567] The system for realizing this invention includes a server for monitoring anomalies, a communication device operated by the user, and an emotion engine for analyzing the user's emotions. The server first detects an anomaly and collects state data. This state data concerns various operating conditions and environment variables of the system and is analyzed by a computational model on the server to identify the cause of the anomaly.

[0568] Based on the identified cause of the anomaly, the server generates a recovery procedure and presents it to the communication device. The communication device is a terminal that accepts user input, such as a smartphone or tablet. During this presentation process, the emotion engine collects and analyzes the user's emotional information. Using a camera and microphone, it monitors the user's facial expressions, tone of voice, and input content to determine their psychological state in real time.

[0569] Based on the analyzed emotional information, the server adjusts how it presents recovery procedures. For example, if the user is experiencing stress, it simplifies the recovery process and displays reassuring messages. It also reduces the user's psychological burden by offering flexible options.

[0570] For example, if a user shows anxiety while making a payment on an e-commerce site, the emotion engine can detect this and provide guidance messages to simplify the payment process. In this way, the system can provide a customized user experience that responds to emotions.

[0571] A concrete example of a prompt message for a generative AI model is, "Please provide the best feedback message for a user who is feeling stressed."

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The server monitors for anomalies and collects status data when one is detected. Specifically, it collects log data and system performance metrics, and integrates information that may indicate the cause of the anomaly. The input is real-time data from the system, and the output is foundational data for the next analysis step.

[0575] Step 2:

[0576] The server analyzes the collected state data using computational models to identify the cause of the anomaly. Specifically, it uses data mining techniques to identify anomaly patterns and diagnoses the cause by comparing them with past cases. The input is the state data acquired in step 1, and the output is cause information for generating recovery procedures.

[0577] Step 3:

[0578] The server generates recovery procedures based on the identified cause of the anomaly and presents them to the communication device. It utilizes a generation AI model to formulate the optimal recovery sequence. The input is the cause information obtained in step 2, and the output is the recovery procedure displayed on the user terminal.

[0579] Step 4:

[0580] The device collects and analyzes the user's emotional information. The emotion engine uses the camera and microphone to analyze the user's facial expressions and tone of voice in real time. The input is visual and auditory data indicating the user's current emotional state, and the output is the analyzed emotional information.

[0581] Step 5:

[0582] The server adjusts how the recovery procedure is presented based on the analyzed emotional information. If the user indicates anxiety or stress, the instructions are simplified, and reassuring messages are added. The input is the emotional information obtained in step 4, and the output is the adjusted display of the recovery procedure.

[0583] Step 6:

[0584] The user reviews and approves the recovery procedure presented on the communication device. The user's action sends the approval information to the server. The input is the coordinated recovery procedure, and the output is the approval signal to initiate the recovery procedure.

[0585] Step 7:

[0586] The server, upon user approval, automatically executes recovery procedures. These procedures include system resets, relocation, and application of necessary updates. The input is the approval signal obtained in step 6, and the output is the stable system state after the anomaly has been corrected.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] [Fourth Embodiment]

[0591] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0592] 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.

[0593] 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).

[0594] 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.

[0595] 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.

[0596] 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).

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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".

[0604] This invention is a system for rapidly responding to failures related to server operation and streamlining regular update work. The server constantly monitors for system anomalies and strives to detect abnormal events early by collecting log data and metric data. The collected data is analyzed by built-in artificial intelligence to identify the cause of the anomaly.

[0605] After the server identifies the cause, a recovery procedure is automatically generated and presented to the user via the terminal. The user reviews the displayed recovery procedure and approves it if they deem it appropriate. After receiving this user approval, the server automatically executes the recovery procedure. This process enables rapid server recovery while reducing the risk of human error.

[0606] Furthermore, the server periodically checks the status of the system software and, if it determines that an update is necessary, creates an update procedure. This update procedure is presented to the terminal, and the server awaits user approval. Once approval is received, the server automatically applies the update and performs subsequent actions such as restarting as needed. This ensures that the system's security and functionality are always up-to-date.

[0607] For example, if a server's operating speed drops abnormally, the server collects relevant metric data, and artificial intelligence detects an abnormal increase in CPU usage. Based on this result, the server generates recovery procedures, such as stopping unnecessary processes or readjusting the load balancer, and presents each procedure to the user. After the user confirms and approves, these procedures are executed, and the server's normal operation is restored. In this way, the present invention contributes to improving the efficiency and reliability of server operations.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The server constantly monitors log data and metrics data to check for any anomalies. If an anomaly is detected, it collects detailed data to prepare for the next steps.

[0611] Step 2:

[0612] The artificial intelligence system within the server analyzes the collected log data and metrics data to identify the cause of the anomaly. The analysis includes comparing the data with historical data and known anomaly patterns.

[0613] Step 3:

[0614] Based on the identified cause of the server failure, appropriate recovery procedures are generated. These generated recovery procedures are sent to the terminal, providing the user with specific steps to take.

[0615] Step 4:

[0616] The user reviews the recovery procedure displayed on their device, and if they determine it is appropriate, they click a button to approve the procedure. The approval information is sent to the server.

[0617] Step 5:

[0618] After the server confirms user authorization, it automatically executes the instructed recovery procedures. These include freeing memory, restarting processes, and resetting settings.

[0619] Step 6:

[0620] The server periodically checks the software version and security patch status, and when it determines that an update is necessary, it automatically creates and displays the update procedure on the terminal.

[0621] Step 7:

[0622] Once the user reviews and approves the update procedure provided, the server automatically applies the update based on the approval information from the device. The system will restart if necessary.

[0623] (Example 1)

[0624] 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".

[0625] Modern information systems require rapid and accurate responses to server failures and problems. However, traditional methods often involve manual intervention in identifying the cause of failures and implementing countermeasures, leading to human error and delays. Furthermore, timely updates to system programs are difficult, resulting in security risks and performance degradation. To address these issues, automated processes are needed to improve efficiency and reliability.

[0626] 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.

[0627] In this invention, the server includes means for monitoring failures of the information processing device and collecting status data and performance indicator data; machine learning means for analyzing the collected data to identify the cause of the failure; and means for generating a recovery procedure based on the cause of the failure identified by the machine learning means and presenting it on the information display device. This enables automatic failure detection, rapid identification of the cause, and efficient presentation of recovery procedures. Furthermore, the system's up-to-dateness and safety can be maintained by automatically generating and applying update procedures based on periodic evaluation of the program state.

[0628] A "server" is a central information processing device that provides information processing services to clients over a network.

[0629] "Information processing device malfunction" refers to any defect or event that disrupts the normal operation of an information processing device.

[0630] "Status data" refers to collected data that indicates the operating status and health of an information processing device.

[0631] "Performance indicator data" refers to data that quantifies and shows the performance of an information processing device.

[0632] "Means of collection" refers to the functions and mechanisms for monitoring and acquiring data within a server.

[0633] "Machine learning methods" refer to artificial intelligence technologies that automatically identify the cause of a problem through data analysis.

[0634] A "recovery procedure" is a series of operations and processes that should be performed to resolve an identified problem.

[0635] An "information display device" is an output device that visually presents data and procedures to the user.

[0636] "User" refers to an individual or organization that operates, monitors, or manages information processing equipment or its services.

[0637] "Program status" refers to the current state and version information of the software running in an information processing system.

[0638] An "update procedure" refers to the specific steps taken to bring existing software and system configurations up to date.

[0639] "Restarting" refers to the operation of stopping an information processing device or its components and then starting them up again.

[0640] The embodiments for carrying out this invention will be described below.

[0641] The server, as the central hub of the entire information processing system, provides various services via the network. The server is equipped with dedicated monitoring software to continuously monitor and collect status and performance metrics. This monitoring software is implemented using, for example, open-source monitoring platforms or proprietary commercial products. This software constantly monitors CPU usage, memory usage, and disk I / O performance metrics.

[0642] The server is equipped with a generative AI model that analyzes collected data and identifies the cause of failures. This generative AI model learns from past failure data and can immediately identify the cause of newly occurring anomalies. This AI model uses machine learning libraries and has the ability to automatically detect anomaly patterns and perform correlation analysis.

[0643] For example, when a server experiences a slowdown, the server itself analyzes performance metrics data, and a generated AI model identifies that excessive CPU usage by a specific process is the cause. Based on this information, the server automatically generates recovery steps, such as stopping unnecessary processes or readjusting settings.

[0644] The generated recovery procedure is translated into natural language and presented to the terminal as a prompt. The terminal provides information to the user through a visual interface. For example, the prompt might appear as: "CPU overload by a specific process has been detected. Do you wish to stop the process?"

[0645] The user can proceed to the next step by reviewing the prompt displayed on the terminal and approving the procedure. Based on the approved procedure, the server automatically executes the process to resolve the problem, enabling a rapid recovery from the failure.

[0646] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0647] Step 1:

[0648] The server continuously collects status and performance indicator data using monitoring software. Input data includes real-time CPU usage, memory usage, and disk I / O values. This data is stored in log files within the server, and the system is configured to automatically detect signs of anomalies. If abnormal data is found, it is identified along with its occurrence time and passed on to the next processing step.

[0649] Step 2:

[0650] The server inputs the collected data into a generative AI model and performs analysis to identify the cause of the anomaly. The input data is the anomaly dataset identified in step 1. The generative AI model uses the results of learning past anomaly patterns to identify the cause using methods such as correlation analysis and clustering. As output, detailed information on the identified cause is generated, and recommended recovery steps are created based on it.

[0651] Step 3:

[0652] The server converts the generated recovery procedure into natural language and presents it to the terminal as a prompt. The input data is the technical content of the recovery procedure obtained in step 2. A natural language processing software library is used for this conversion. The output is a human-readable prompt for the user. Specifically, the terminal displays a message such as, "CPU overload by a specific process has been detected. Do you want to stop the process?"

[0653] Step 4:

[0654] The user reviews the prompt displayed on the terminal and performs an action to approve or reject it. The input is the recovery procedure prompt displayed on the terminal. The user indicates approval by pressing the confirmation button on the screen. The approval result is returned to the server as output, and the process proceeds to the next step.

[0655] Step 5:

[0656] The server automatically executes recovery procedures upon user approval. The input is the recovery procedure approved by the user. The server stops necessary processes and makes configuration changes to eliminate the cause of the anomaly. The output is that the system returns to a normal operating state, and the execution history of this operation is recorded in the server's operation log.

[0657] (Application Example 1)

[0658] 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".

[0659] In modern information systems, while efficiency and security in server operations are paramount, rapid response to anomalies and regular software updates are crucial challenges. However, these processes often rely on manual methods, leading to problems such as human error and a lack of rapid response procedures. In addition, the increasing number of potential threats necessitates real-time threat detection and countermeasures. The development of effective and efficient systems to address these challenges is essential.

[0660] 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.

[0661] In this invention, the server includes means for monitoring anomalies and collecting recorded information and indicator information, intelligent function means for analyzing the collected information and identifying the cause of the anomaly, and means for generating a recovery procedure based on the identified cause of the anomaly and presenting it on a display device. This enables a rapid and accurate response when an anomaly occurs, and by immediately notifying potential threats, it is possible to improve system reliability and optimize operational efficiency.

[0662] A "server" is an information processing device that performs data processing and calculations and provides services to other computer systems via a network.

[0663] "Anomaly" refers to a state or operation in an information system that deviates from the normal operating range, including cases where the system's performance or safety falls outside the recognized standards.

[0664] "Record information" refers to data that shows details of the system's operation and status, and includes historical information such as logs and transactions.

[0665] "Metric information" refers to data that serves as a standard for evaluating system performance and status, and includes metrics such as CPU usage and memory consumption.

[0666] "Intelligent function means" refers to artificial intelligence technology used to analyze collected data and identify the cause of anomalies, and is a device or program equipped with the function of performing data analysis and pattern recognition.

[0667] A "recovery procedure" is a series of processing steps performed to resolve an identified anomaly and restore the system to a normal state.

[0668] A "display device" is a hardware device that visually communicates the status and operating instructions of a computer system to the user.

[0669] "User" refers to an individual or organization that operates and manages a computer system, and is the entity that performs approvals and operations based on the system's instructions.

[0670] This invention is a system that achieves efficient server management and security monitoring through collaboration between servers, terminals, and users. The servers use advanced monitoring functions to detect anomalies and collect and analyze recorded information and indicator information in real time. Intelligent functional means, specifically AI-based data analysis algorithms, are used for this analysis to identify anomalies.

[0671] The terminal receives information transmitted from the server and presents it to the user via a display device. This includes recovery procedures and notifications of potential threats generated by the server. Based on this information, the user can make appropriate decisions or give instructions.

[0672] For example, if a server detects high CPU usage during a weekend night, the AI ​​will determine this to be an anomaly and generate recovery steps to reduce the CPU load. These steps include stopping unnecessary processes and distributing tasks. The terminal visually presents this to the user, and after user approval, the server automatically executes these steps to optimize the system load.

[0673] An example of a prompt message is: "Generate steps to optimize the server's security status and create a prompt to notify the user. Start with CPU usage data and include recommended actions when anomalies are detected." In this way, the entire system works together to achieve efficient server operation and enhanced security.

[0674] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0675] Step 1:

[0676] The server monitors the system status and collects recorded and metric information in real time. This information includes metrics such as CPU usage and memory usage. For data processing, this information is stored in a database and used as foundational data for anomaly detection.

[0677] Step 2:

[0678] The server analyzes the collected metrics data using intelligent functions to determine whether or not an anomaly is present. The input data is the metrics data collected in step 1, and the output identifies whether or not an anomaly is present and its details (e.g., high CPU usage, excessive memory consumption). Machine learning algorithms are used for the analysis.

[0679] Step 3:

[0680] If the server detects an anomaly, it generates a recovery procedure. The analysis results from step 2 are used as input, and the output is a specific recovery procedure. This procedure can be implemented, for example, as a plan to stop unnecessary processes or perform load balancing.

[0681] Step 4:

[0682] The terminal receives the recovery procedure sent from the server and displays it on the display device. The input data is the recovery procedure generated in step 3, and the output includes the presentation of visualized information to the user.

[0683] Step 5:

[0684] The user reviews the presented recovery procedure and provides appropriate instructions. The input is the recovery procedure displayed on the terminal, and the output is instructions for approval or correction. Specific user actions include reviewing the information on the screen and clicking the approval button.

[0685] Step 6:

[0686] The server executes a recovery procedure approved by the user. User approval is required as input, and the output is the result of the execution. This step involves actions such as stopping a specified process or adjusting load balancing.

[0687] 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.

[0688] This invention combines a system in which a server monitors for anomalies and automates efficient recovery and periodic system updates with an emotion engine that recognizes user emotions. When an anomaly occurs, the server collects log data and metric data, and artificial intelligence measures identify the cause based on this data. The server then generates a recovery procedure and presents it to the user via a terminal.

[0689] Furthermore, the emotion engine analyzes emotional information in real time from the user's facial expressions, tone of voice, and input content via the terminal. Based on the results of this emotion analysis, the server can adjust the way and content of the recovery procedure is presented, taking care to reduce the user's psychological burden. For example, if the user is feeling stressed, the procedure will be presented concisely, and guidance messages will be displayed if necessary, allowing for flexible responses tailored to the user's situation.

[0690] For example, if a user's facial expression indicates anxiety when recovery procedures are presented on the terminal, the emotion engine recognizes this. The server receives this information and helps the user understand by providing additional explanations or options. In this way, the information retrieved can be customized according to the user's emotions, providing a more intuitive and frictionless user experience. This system effectively supports the stable operation of the server while maintaining a user-friendly interface.

[0691] The following describes the processing flow.

[0692] Step 1:

[0693] The server monitors for anomalies and collects log data and metrics data. When an anomaly is detected, the collected data is sent to an artificial intelligence system.

[0694] Step 2:

[0695] The artificial intelligence system on the server analyzes the collected data in real time to identify the cause of the anomaly. At this stage, it compares the data against known patterns to clarify the type of malfunction.

[0696] Step 3:

[0697] Once the cause is identified, the server automatically generates recovery steps based on that cause. The generated recovery steps are organized in a user-friendly format and sent to the terminal.

[0698] Step 4:

[0699] The device presents recovery procedures to the user while simultaneously activating an emotion engine to analyze the user's emotional state from their facial expressions and tone of voice. Based on the results of the emotion analysis, the method and content of the procedure presentation are dynamically adjusted.

[0700] Step 5:

[0701] The user reviews the presented recovery procedure and approves it if they agree. The user's approval is sent to the server via the device.

[0702] Step 6:

[0703] The server automatically executes the recovery procedure based on the received authorization information. During execution, the progress is displayed on the terminal in real time. Each step is performed sequentially, continuing until the service is restored to normal.

[0704] Step 7:

[0705] The server periodically checks the system's software status, and if it determines that an update is necessary, it creates an update procedure and presents it to the terminal. The emotion engine takes the user's emotional state into consideration and adjusts how the update is presented.

[0706] Step 8:

[0707] Once the user approves the update procedure, the server will automatically apply the software update and perform restarts or process optimizations as needed.

[0708] (Example 2)

[0709] 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".

[0710] In modern information systems, it is crucial to respond quickly and appropriately when an anomaly occurs. However, traditional systems have difficulty responding flexibly to user emotions, sometimes placing an excessive burden on users. Furthermore, the lack of automation in system update and recovery procedures has resulted in operational complexity.

[0711] 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.

[0712] In this invention, the server includes means for monitoring anomalies and collecting data; intelligent means for analyzing the collected data and identifying the cause; means for generating procedures based on the cause and presenting them to an information processing device; means for acquiring and analyzing the user's emotions; and means for adjusting the procedures and changing the presented content based on the analyzed emotions. This enables appropriate recovery without burdening the user, automates update procedures, and allows for more flexible and effective system management.

[0713] A "server" is a device in a computer network that receives requests from clients, processes them, and provides data and services.

[0714] An "abnormality" refers to a phenomenon or state that deviates from the expected behavior of a system and is a factor that disrupts normal operation.

[0715] "Data" refers to information represented by symbols, numbers, or combinations thereof, in a format that can be processed by a computer.

[0716] An "intelligent tool" is a tool that has the ability to analyze data and perform a series of processes based on artificial intelligence to extract useful information from it.

[0717] A "procedure" is a set of steps or tasks that must be followed to achieve a specific objective.

[0718] An "information processing device" is a computer or terminal used to handle data, and is a device that enables data exchange between humans and machines.

[0719] A "user" is the entity that operates and utilizes a system or information device.

[0720] "Emotion" is a state of mind in humans, an intuitive and reactive psychological experience in response to a particular event or perception.

[0721] "Analysis" is the act of investigating data and phenomena in detail to reveal their structure and relationships.

[0722] "Adjustment" refers to the act of changing settings or configurations to suit specific conditions or circumstances.

[0723] In this invention, a server acts as the central point for monitoring anomalies in the information system and automatically generates efficient recovery procedures using the collected data. Common hardware capable of data stream processing is used for anomaly monitoring and data collection, and a data analysis platform such as Apache Kafka can be used. The server receives log data and metrics data, collects this data, and records anomaly events.

[0724] The collected data is analyzed using artificial intelligence. Machine learning frameworks such as TensorFlow are used to identify the cause of the anomaly. Based on the identified cause, the server generates appropriate recovery procedures. These procedures are presented to the user via a terminal on an information processing device. The terminal provides an interface that allows the user to review and execute the procedures.

[0725] In addition, the device incorporates an emotion engine that acquires emotional information through the user's facial expressions, tone of voice, and input content. This acquired data is analyzed in real time, enabling the presentation of information tailored to the user's emotional state. IBM Watson's tone analyzer and general facial recognition APIs are used for emotion analysis.

[0726] For example, if a user's facial expression indicates anxiety while reviewing the presented recovery procedure, the device sends this information to the emotion engine, and the server adjusts the procedure accordingly. Specifically, the procedure can be explained in more detail, or additional support options can be offered, thereby reducing the user's psychological burden.

[0727] The following is an example of a prompt message:

[0728] "When users follow the system recovery procedure, we want to provide appropriate additional information based on facial expression and tone data recognized by the emotion engine. Please explain how this system provides emotion-sensitive information."

[0729] This system will improve the user experience while supporting the efficient and stable operation of the servers.

[0730] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0731] Step 1:

[0732] The server monitors for anomalies and collects log and metric data. It retrieves data from the monitored system in real time and stores it using Apache Kafka. Data inputs include log files and performance metrics sent from the system, and output generates a formatted dataset for processing. Specifically, the server periodically scans the data stream and records any anomaly patterns it detects.

[0733] Step 2:

[0734] The server analyzes the collected data to identify the cause of the anomaly. It runs a generative AI model using a machine learning framework such as TensorFlow to analyze data patterns. The input is a formatted dataset, and the output is a list of factors that are likely to be anomalies. Specifically, the server calculates the similarity to past anomaly patterns and lists the most likely causes in order of priority.

[0735] Step 3:

[0736] The server generates recovery procedures based on the identified cause of the anomaly. It uses a template-based automated generation system to create the procedure document. The input consists of a list of anomaly causes and corresponding templates, and the output is a constructed recovery procedure document. Specifically, the server searches for a template for a particular anomaly, fills in the details, and prepares it for user presentation.

[0737] Step 4:

[0738] The terminal presents the user with recovery procedures provided by the server. The user can review the procedures on the screen and proceed through them sequentially. Specifically, the terminal displays the procedures in an easy-to-read format and allows the user to manage their progress using checkboxes or similar methods.

[0739] Step 5:

[0740] The device analyzes the user's emotions in real time. It acquires emotional data using the user's facial expressions and tone of voice, and inputs this into an emotion engine. The output is the user's current emotional state. Specifically, the device supplies data from the camera and microphone to an analysis tool and sends the resulting emotional parameters to a server.

[0741] Step 6:

[0742] The server adjusts the recovery procedure based on the user's emotions. Based on the emotion analysis results, it modifies the content and presentation of the procedure manual. The input is the user's emotional state, and the output is the adjusted procedure manual. Specifically, if the server determines that the user is highly anxious, it will offer more detailed explanations or additional support options.

[0743] (Application Example 2)

[0744] 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".

[0745] Conventional systems have problems such as significant psychological burden on users when an anomaly occurs, and a lack of uniform recovery procedures, which degrades the quality of the user experience. Furthermore, in the electronic payment process, there is a challenge in that it is not possible to respond flexibly to the user's emotional state, and anxiety and stress during payment cannot be adequately reduced.

[0746] 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.

[0747] In this invention, the server includes means for monitoring anomalies and collecting status data; means for generating a computational model that analyzes the collected data to identify the cause of the anomaly; means for generating a recovery procedure based on the cause of the anomaly identified by the computational model and presenting it to a communication device; means for collecting and analyzing user emotion information; and means for adjusting the method of presenting the recovery procedure based on the analyzed emotion information. This enables real-time and flexible responses in response to user emotions, improving the user experience in various processes, including electronic payments.

[0748] A "server" is an information processing device that monitors for anomalies and collects and manages related data.

[0749] "Status data" refers to information about the system's operating status and environmental conditions.

[0750] A "computational model means" is an algorithm or program that analyzes collected data and performs processing to identify the cause of an anomaly.

[0751] A "communication device" is a terminal device that allows a user to receive information.

[0752] "Emotional information" refers to data that indicates the user's psychological state, and includes information extracted from facial expressions, tone of voice, input content, etc.

[0753] "Means of analysis" refers to the process performed to derive specific results or conclusions based on data.

[0754] A "recovery procedure" refers to a set of steps or procedures used to restore an abnormal state to normal.

[0755] "Presentation method" refers to the techniques and styles used to communicate information and procedures to users.

[0756] "User experience" is a concept that refers to the overall impression and feelings that system users gain from using a service.

[0757] The system for realizing this invention includes a server for monitoring anomalies, a communication device operated by the user, and an emotion engine for analyzing the user's emotions. The server first detects an anomaly and collects state data. This state data concerns various operating conditions and environment variables of the system and is analyzed by a computational model on the server to identify the cause of the anomaly.

[0758] Based on the identified cause of the anomaly, the server generates a recovery procedure and presents it to the communication device. The communication device is a terminal that accepts user input, such as a smartphone or tablet. During this presentation process, the emotion engine collects and analyzes the user's emotional information. Using a camera and microphone, it monitors the user's facial expressions, tone of voice, and input content to determine their psychological state in real time.

[0759] Based on the analyzed emotional information, the server adjusts how it presents recovery procedures. For example, if the user is experiencing stress, it simplifies the recovery process and displays reassuring messages. It also reduces the user's psychological burden by offering flexible options.

[0760] For example, if a user shows anxiety while making a payment on an e-commerce site, the emotion engine can detect this and provide guidance messages to simplify the payment process. In this way, the system can provide a customized user experience that responds to emotions.

[0761] A concrete example of a prompt message for a generative AI model is, "Please provide the best feedback message for a user who is feeling stressed."

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0763] Step 1:

[0764] The server monitors for anomalies and collects status data when one is detected. Specifically, it collects log data and system performance metrics, and integrates information that may indicate the cause of the anomaly. The input is real-time data from the system, and the output is foundational data for the next analysis step.

[0765] Step 2:

[0766] The server analyzes the collected state data using computational models to identify the cause of the anomaly. Specifically, it uses data mining techniques to identify anomaly patterns and diagnoses the cause by comparing them with past cases. The input is the state data acquired in step 1, and the output is cause information for generating recovery procedures.

[0767] Step 3:

[0768] The server generates recovery procedures based on the identified cause of the anomaly and presents them to the communication device. It utilizes a generation AI model to formulate the optimal recovery sequence. The input is the cause information obtained in step 2, and the output is the recovery procedure displayed on the user terminal.

[0769] Step 4:

[0770] The device collects and analyzes the user's emotional information. The emotion engine uses the camera and microphone to analyze the user's facial expressions and tone of voice in real time. The input is visual and auditory data indicating the user's current emotional state, and the output is the analyzed emotional information.

[0771] Step 5:

[0772] The server adjusts how the recovery procedure is presented based on the analyzed emotional information. If the user indicates anxiety or stress, the instructions are simplified, and reassuring messages are added. The input is the emotional information obtained in step 4, and the output is the adjusted display of the recovery procedure.

[0773] Step 6:

[0774] The user reviews and approves the recovery procedure presented on the communication device. The user's action sends the approval information to the server. The input is the coordinated recovery procedure, and the output is the approval signal to initiate the recovery procedure.

[0775] Step 7:

[0776] The server, upon user approval, automatically executes recovery procedures. These procedures include system resets, relocation, and application of necessary updates. The input is the approval signal obtained in step 6, and the output is the stable system state after the anomaly has been corrected.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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."

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] The following is further disclosed regarding the embodiments described above.

[0799] (Claim 1)

[0800] The server has a means of monitoring for anomalies and collecting log data and metrics data.

[0801] An artificial intelligence means for analyzing the collected data to identify the cause of the anomaly,

[0802] A means for generating a recovery procedure based on the cause of the anomaly identified by the artificial intelligence means and presenting it to the terminal,

[0803] A means for the user to approve the aforementioned recovery procedure,

[0804] A means for automatically executing the aforementioned approved recovery procedure,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, wherein the server includes means for periodically checking the software status of the system, creating update procedures as necessary, and displaying them on a terminal.

[0808] (Claim 3)

[0809] The system according to claim 1, further comprising means for the server to automatically apply the update and perform any necessary restarts after the user has approved the update procedure.

[0810] "Example 1"

[0811] (Claim 1)

[0812] A means by which the server monitors for failures in the information processing device and collects status data and performance indicator data,

[0813] A machine learning method for analyzing the collected data to identify the cause of the failure,

[0814] A means for generating a recovery procedure based on the cause of the failure identified by the machine learning means and presenting it on an information display device,

[0815] A means for the user to approve the recovery procedure presented above,

[0816] A means for automatically executing the aforementioned approved recovery procedure,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein the server periodically evaluates the program state of the information processing system, creates an update procedure as necessary, and displays it on an information display device.

[0820] (Claim 3)

[0821] The system according to claim 1, further comprising means for the server to automatically apply the program update and perform any necessary restarts after the user has approved the update procedure.

[0822] "Application Example 1"

[0823] (Claim 1)

[0824] A server that monitors for anomalies and collects recorded information and indicator information,

[0825] An intelligent function means for analyzing the collected information to identify the cause of the anomaly,

[0826] A means for generating a recovery procedure based on the cause of the abnormality identified by the intelligent function means and presenting it on a display device,

[0827] A means for the user to approve the recovery procedure presented above,

[0828] A means for automatically executing the aforementioned approved recovery procedure,

[0829] Means for immediately notifying the user of a potential threat using the aforementioned display device,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, wherein the server includes means for periodically checking the software status of the system, creating an update procedure as necessary, and displaying it on a display device.

[0833] (Claim 3)

[0834] The system according to claim 1, further comprising means for the server to automatically apply the update and perform any necessary restarts after the user has approved the update procedure.

[0835] "Example 2 of combining an emotion engine"

[0836] (Claim 1)

[0837] The server monitors the phenomenon and collects data,

[0838] An intelligent means for analyzing the collected data to identify the factors causing the phenomenon,

[0839] A means for generating a procedure based on the factors of the phenomenon identified by the intelligent means and presenting it to an information processing device,

[0840] A means of acquiring and analyzing user emotions,

[0841] Means for adjusting the procedure and changing the presented content based on the analyzed user emotions,

[0842] A means for the user to accept the aforementioned procedure,

[0843] A means for automatically executing the accepted procedure,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, further comprising means for the server to periodically check its status, create an update procedure as necessary, and display it on an information processing device.

[0847] (Claim 3)

[0848] The system according to claim 1, further comprising means for the server to automatically apply the changes and perform any necessary restarts after the user accepts the update procedure.

[0849] "Application example 2 when combining with an emotional engine"

[0850] (Claim 1)

[0851] A means of monitoring the server for anomalies and collecting status data,

[0852] A computational model means for analyzing the collected data to identify the cause of the anomaly,

[0853] A means for generating a recovery procedure based on the cause of the anomaly identified by the calculation model means and presenting it to the communication device,

[0854] A means for the user to approve the aforementioned recovery procedure,

[0855] A means for automatically executing the aforementioned approved recovery procedure,

[0856] A means of collecting and analyzing user sentiment information,

[0857] A means for adjusting the method of presenting recovery procedures based on the analyzed emotional information,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, wherein the server periodically checks the operating status of the system, creates an update procedure as necessary, and displays it on a communication device.

[0861] (Claim 3)

[0862] The system according to claim 1, further comprising means for the server to automatically apply the update and perform any necessary restarts after the user has approved the update procedure. [Explanation of symbols]

[0863] 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 server has a means of monitoring for anomalies and collecting log data and metrics data. An artificial intelligence means for analyzing the collected data to identify the cause of the anomaly, A means for generating a recovery procedure based on the cause of the anomaly identified by the artificial intelligence means and presenting it to the terminal, A means for the user to approve the aforementioned recovery procedure, A means for automatically executing the aforementioned approved recovery procedure, A system that includes this.

2. The system according to claim 1, further comprising means for the server to periodically check the software status of the system, create an update procedure as necessary, and display it on a terminal.

3. The system according to claim 1, further comprising means for the server to automatically apply the update and perform any necessary restarts after the user has approved the update procedure.

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