system

Abstract

We provide the system. [Solution] An artificial intelligence module that analyzes information stored in a database and detects signs of problems in real time, A correction module means that automatically performs corrective actions for detected problems, A notification module means that notifies if a problem is unfixable or critical and escalates it to a human agent, A support delivery module means that provides personalized support to users, A resource optimization module means for predicting future resource demand based on past history, A self-healing module that automatically repairs minor problems, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern information technology systems are becoming increasingly complex, and it is difficult to comprehensively monitor various log information and performance information and detect potential problems in advance. As a result, the reliability of the system decreases, and the risk of unexpected downtime increases. Also, while the need for technical support from users is increasing, the optimal allocation of support resources cannot be achieved, and efficient operation may be hindered. Therefore, there is a need for new solutions to efficiently manage such complex systems and reduce potential problems.

Means for Solving the Problems

[0005] To address this challenge, the present invention provides an artificial intelligence module that analyzes information stored in a database to detect signs of problems in real time. Furthermore, it includes a correction module that automatically performs corrective actions on detected problems, and a notification module that escalates problems to human personnel if they are serious or cannot be automatically corrected. It also includes a support provision module to provide users with support tailored to their needs, and a resource optimization module that optimizes future resource allocation based on historical data. In addition, a self-healing module that automatically repairs minor problems can improve the operability and efficiency of the system.

[0006] A "database" is a system for systematically storing information in a format that is easily accessible and manageable.

[0007] An "artificial intelligence module" is a program or component that uses machine learning and data analysis technologies to analyze information in real time and detect potential problems.

[0008] A "correction module" is a program or component that has the function of automatically performing appropriate corrective actions for detected problems.

[0009] A "notification module" is a program or component that has the function of quickly notifying a human operator if a problem detected in the system is serious or cannot be fixed automatically.

[0010] A "support module" is a program or component that provides technical support tailored to user needs and enhances the user experience.

[0011] A "resource optimization module" is a program or component that analyzes historical data, predicts future resource demand, and efficiently allocates support resources.

[0012] A "self-healing module" is a program or component that has the function of automatically detecting and correcting minor problems within a system and maintaining the system's stability. [Brief explanation of the drawing]

[0013] [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 a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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.

[0017] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] The system for implementing this invention involves a server, terminals, and users each playing a specific role and working together. The server has the function of accessing a database and continuously collecting log data and performance data from various sources within the system. An artificial intelligence module is used to analyze the collected data and detect potential problems in real time. This module leverages machine learning algorithms to identify patterns that deviate from normal operation and detect early signs of problems.

[0035] Detected issues are automatically addressed by a remediation module. This module performs predefined actions, such as restarting services or changing configurations, depending on the nature of the problem. If the problem is serious or difficult to resolve automatically, a notification module escalates the issue to human IT personnel via email or other means of communication, providing them with detailed information.

[0036] Users can access the system through their device and receive personalized support. The support module analyzes the user's past behavior history and skill level, and based on that, provides appropriate IT training programs and support information. The device displays this information in various ways, including text, audio, images, and videos, enabling intuitive support for the user.

[0037] Furthermore, the server uses a resource optimization module to predict future resource demands based on historical data. This allows the support team to allocate resources efficiently, improving the quality and effectiveness of support delivery.

[0038] The self-healing module, as part of the server's functionality, automatically repairs minor problems. This allows users to utilize a seamless IT environment without even noticing the issues. For example, it automatically performs periodic cache clearing and memory optimization to maintain system performance. In this way, in the embodiment for carrying out the invention, each module works together to form a system that provides high reliability and efficiency.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server collects log and performance data in real time from sensors and applications within the system. This data is stored in a database in a structured format.

[0042] Step 2:

[0043] The server constantly analyzes data stored in the database via an artificial intelligence module. The AI ​​algorithm identifies anomalous patterns from this data and detects potential problems.

[0044] Step 3:

[0045] When a problem is detected, the server launches a remediation module and performs default remediation actions for issues that can be automatically fixed, such as restarting a specific service or adjusting configuration files.

[0046] Step 4:

[0047] If the issue is difficult to resolve or is critical, the server will escalate the problem to a human IT representative via a notification module. The notification will include details of the problem and recommended actions.

[0048] Step 5:

[0049] Users access support information from the server using their device. The server analyzes the user's skill level and past operation history to provide personalized support. Support is displayed in text and video formats.

[0050] Step 6:

[0051] The server uses a resource optimization module to analyze past support history and predict future support demand. This allows for efficient resource allocation to the support team.

[0052] Step 7:

[0053] A self-healing module installed in the server periodically checks the entire system and automatically repairs minor problems. This allows users to maintain a comfortable IT environment.

[0054] (Example 1)

[0055] 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."

[0056] In modern information systems, it has become increasingly difficult to quickly detect potential problems from vast amounts of data and respond appropriately. Furthermore, there is a need to efficiently meet a wide range of demands, including rapid problem correction, notification of unresolved issues, and the provision of personalized user support. However, systems that comprehensively provide these functions remain scarce. This invention aims to solve these challenges by providing a comprehensive information support system that includes information analysis, automated problem response, and optimization.

[0057] 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.

[0058] In this invention, the server includes a machine learning module means for analyzing information stored on an information medium and detecting signs of problems in real time, an automation module means for automatically executing corrective commands for detected problems, and a warning module means for notifying and escalating to a human operator if the problem is uncorrectable or critical. This enables real-time problem detection and rapid response.

[0059] "Information media" refers to the infrastructure for storing and managing digital data, and this includes databases and storage systems.

[0060] A "machine learning module" refers to a software component that uses machine learning algorithms to analyze data and detect patterns and anomalies.

[0061] "Automation module means" refers to software components that automatically execute specific predefined commands or processes.

[0062] A "warning module means" refers to a software component that notifies human operators of anomalies or problems that cannot be resolved.

[0063] "Support provision module means" refers to software components that provide personalized support and assistance information to users.

[0064] A "resource optimization module" refers to a software component that efficiently manages the resources of the entire system by analyzing past data and predicting future resource demands.

[0065] A "self-healing module" refers to a software component that automatically corrects minor problems.

[0066] "Connecting device means" refers to a device that displays various forms of information to the user and enables interactive operation.

[0067] In an embodiment of this invention, the system consists of three main elements: a server, a terminal, and a user. The role of each element and the associated hardware and software are described below.

[0068] The server manages data stored on information media and is responsible for data analysis using machine learning modules. Specifically, the server collects log information and performance information from databases and storage systems. Data management technologies such as SQL databases are used for this purpose. The machine learning modules employ machine learning frameworks such as TENSORFLOW® and PyTorch, which are used to perform real-time anomaly detection.

[0069] The terminal is responsible for providing information to the user and offers personalized support through support modules. The terminal uses a web-based dashboard as its user interface, and programming languages ​​such as Python and JavaScript (registered trademark) are used for data visualization. The terminal enables intuitive operation for the user using text, voice, images, and video.

[0070] Users access the system through their terminals to gain access to personalized support and problem-solving. The support provided to users is optimized based on their past usage history and skill level. For example, a specific prompt that a user might enter into the system could be, "Analyze the current system performance data and report any potential problems." In this way, users can give clear instructions through the system and receive the support they need.

[0071] The server also leverages resource optimization modules to predict future resource demands. This process utilizes Azure® Machine Learning and other artificial intelligence tools to enable efficient resource allocation. In this way, each element works together to create an efficient and reliable information support system.

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

[0073] Step 1:

[0074] The server collects log and performance information from the information medium. As input, the server executes SQL queries to extract logs from the database for a specific period and monitors the system's log files. The resulting output is a collection of raw data to be analyzed. At this stage, the data is still unprocessed, so the necessary preprocessing for the next step is performed.

[0075] Step 2:

[0076] The server inputs the collected data into a machine learning module to perform anomaly detection. As input, the server converts the raw data into a data frame and preprocesses it using a specific algorithm. Specifically, data normalization and missing value imputation are performed. The output generates warnings indicating invalid patterns or anomalies, making it possible to identify potential problems.

[0077] Step 3:

[0078] The server attempts to automatically correct detected problems. The input is the data that was deemed abnormal in the previous step. The server uses an automation module to launch a script, which may, for example, restart the relevant service or change its configuration. The output is the result of the correction, and the history is recorded in the log. This allows for quick resolution of minor issues.

[0079] Step 4:

[0080] The terminal notifies the user of the problem details and provides personalized support. As input, the terminal receives warning messages and support information sent from the server. The terminal displays this information on the user interface, using audio and visual indicators for explanation. The output consists of solutions and further action instructions provided to the user.

[0081] Step 5:

[0082] Users provide feedback to the system through their terminal and request further support. As input, users enter direct prompts to trigger further actions. For example, they might request, "I would like to see detailed system logs." The output is additional support based on that feedback.

[0083] Step 6:

[0084] The server uses a resource optimization module to predict future demand. This is inputted from previously collected data and usage history. The server runs analytical algorithms, for example, to analyze usage trends by time of day. The output is a resource allocation proposal and forecast report to address future loads. This result is shared with the support team and used to develop strategic action plans.

[0085] (Application Example 1)

[0086] 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."

[0087] In autonomous vehicles, preventing vehicle malfunctions and performance degradation is crucial. However, conventional methods have made it difficult to effectively utilize vehicle sensor information and detect and correct anomalies in real time. Furthermore, there is a lack of means to provide appropriate driving support and resource optimization to vehicle users in a unified manner.

[0088] 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.

[0089] In this invention, the server includes an information analysis module means for collecting sensor information in real time and analyzing the data to detect potential problems; a correction module means for automatically performing corrective actions for detected problems; and a notification module means for notifying and escalating to an administrator if the problem is uncorrectable or critical. This enables real-time anomaly detection and correction in autonomous vehicles, and efficiently provides driving support information and optimizes resources.

[0090] "Sensor information" refers to data detected by various sensors installed in the vehicle, and includes information about the operating status and vehicle's operational status.

[0091] An "information analysis module means" is a means that has the function of analyzing collected sensor information in real time and detecting anomalies or patterns.

[0092] A "correction module means" is a means that has the function of automatically responding to detected anomalies and correcting the problems.

[0093] A "notification module means" is a means that has the function of notifying the administrator of an anomaly when it is impossible to correct or a serious anomaly occurs, and prompting them to take action.

[0094] "Driving assistance information" refers to advice and navigation information related to driving a vehicle, provided to optimize the driver's operation.

[0095] "Eco-driving advice" refers to information designed to guide drivers on driving methods that improve fuel efficiency and reduce environmental impact.

[0096] A "resource optimization module means" is a means that analyzes past usage history to predict future demand for vehicle resources and enables efficient allocation.

[0097] A "self-healing module" is a means that has the function of automatically repairing minor problems within the vehicle and maintaining system performance.

[0098] This invention is a system for real-time monitoring, anomaly detection, and correction in autonomous vehicles. This system operates collaboratively, with the server, terminals, and users each fulfilling their respective roles.

[0099] The server collects information in real time from various sensors installed in the vehicle. This includes GPS, speed sensors, engine temperature sensors, etc., and this data is stored in the backend using Firebase. An information analysis module analyzes this sensor information using machine learning algorithms with TensorFlow to determine signs of abnormalities. For example, if the engine temperature is higher than normal, it detects an overheating anomaly.

[0100] The device provides a means of notifying the user when an anomaly is detected. The notification module has the functionality to send SMS or email to escalate detected anomalies to the administrator. In addition, the device can use the Google® Maps API to display driving assistance information and eco-driving advice.

[0101] Users can obtain information provided by the system through their terminals and take appropriate action. For example, they may receive advice from their terminals such as, "The engine temperature is high, please slow down," which promotes safer driving.

[0102] For example, if the engine load increases and the temperature rises rapidly during long-distance driving, the system will immediately detect this and send a notification to the user's terminal. It will also integrate with the Google Maps API to provide directions to the nearest service station. Through this entire process, real-time anomaly management and user support are achieved.

[0103] An example of a prompt for the generated AI model could be: "Design a user interface for an application that collects real-time data from vehicle sensors and detects and notifies of anomalies." This prompt allows system developers to efficiently design the necessary interface.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server collects information in real time from sensors mounted on the vehicle. It receives data from various sensors (GPS, speed sensor, engine temperature sensor, etc.) as input and sends this information to Firebase. The output is sensor information stored in the backend database, which is then used for subsequent analysis.

[0107] Step 2:

[0108] The server uses an information analysis module to analyze the collected sensor data. It receives sensor data obtained from Firebase as input and applies machine learning algorithms using TensorFlow to detect signs of anomalies. For example, it identifies overheating anomalies when engine temperature data exceeds the normal range. The output is the detection of anomalies and their detailed information.

[0109] Step 3:

[0110] The server uses a notification module to send details of the anomaly to the terminal. The input is anomaly information provided by the analysis module, and the anomaly is escalated to the administrator via SMS or email. The output is a notification message containing the details of the anomaly.

[0111] Step 4:

[0112] The terminal displays received notifications to the user. It receives messages sent from the notification module as input and displays them on the terminal screen. Simultaneously, it uses the Google Maps API to identify the nearest service station and provides that information to the user. The output consists of driving assistance information and alerts regarding anomalies displayed to the user.

[0113] Step 5:

[0114] The user takes appropriate action based on the information displayed on the terminal. Inputs are driving assistance information and abnormality notifications from the terminal, and outputs are the execution of corresponding actions. For example, this might involve slowing down the vehicle or heading to a service station as instructed.

[0115] Through these steps, real-time vehicle monitoring, anomaly detection, and response are achieved.

[0116] 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.

[0117] The system for implementing this invention operates primarily based on the interaction between a server, a terminal, and a user. The server forms a complex platform incorporating an emotion engine, with each module working in coordination. The emotion engine analyzes voice and facial expression data collected from the user to recognize the user's emotional state in real time. The server utilizes this emotional information to adjust support provision and optimize the user experience.

[0118] Specifically, the server uses an artificial intelligence module to analyze log and performance information collected from the entire system. This module monitors all operational patterns within the system, and if an anomaly is detected, it is passed on to a correction module. The correction module performs automated corrective actions to respond quickly, and a notification module reports the problem to IT personnel as needed.

[0119] Users can interact with this system at any time via their device. On the device, the emotion engine acquires the user's voice and facial expressions through the camera and microphone, and processes them in real time to understand the user's emotional state. For example, if the user shows signs of anxiety or frustration, the emotion engine immediately feeds that information back to the server, which then provides emergency support options or encouraging messages based on this information.

[0120] Furthermore, the system utilizes a resource optimization module to analyze past support history and sentiment data to predict future support needs. Based on this predictive data, resources are allocated optimally, resulting in a more efficient and effective support system.

[0121] Finally, a self-healing module implemented on the server periodically diagnoses the system and automatically repairs any minor underlying problems. This ensures that users can always use their devices in a stable environment. In this way, a system that incorporates an emotion engine can provide exceptional IT support while responding to the user's emotions.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The server continuously collects log data and performance data from the entire system. This data is stored in a database in real time and analyzed by an artificial intelligence module.

[0125] Step 2:

[0126] The artificial intelligence module analyzes log data to detect early signs of problems such as abnormal behavior or performance degradation. The detected problems are then passed on to the correction module.

[0127] Step 3:

[0128] The correction module automatically performs corrective actions for detected problems. These include predefined actions such as modifying server configurations or restarting specific processes.

[0129] Step 4:

[0130] If the fix is ​​unsuccessful or the problem is serious, the server will escalate the issue to a human IT representative via a notification module. The notification will include detailed information about the problem and suggested solutions.

[0131] Step 5:

[0132] Users interact with the system and receive support through their devices. The devices utilize an emotion engine that collects emotional data through the user's voice input and webcam footage.

[0133] Step 6:

[0134] The emotion engine analyzes collected data to recognize the user's emotional state in real time. For example, if it determines that the user is feeling stressed, it sends that information to the server.

[0135] Step 7:

[0136] The server receives emotion information, and the support module provides personalized support tailored to the user's emotions. This includes immediate answers to questions, provision of more detailed support guides, or presentation of relaxing content.

[0137] Step 8:

[0138] The server uses a resource optimization module to analyze past support and sentiment data to predict future needs. Based on this, appropriate resource allocation is performed.

[0139] Step 9:

[0140] The self-healing module periodically checks the entire system and proactively fixes minor issues. This ensures a consistently comfortable IT environment for the user.

[0141] (Example 2)

[0142] 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".

[0143] Traditional systems lacked support that took into account the user's emotional state, making it difficult to provide a personalized experience. Furthermore, there was no established method for predicting and quickly correcting potential problems that might arise during operation. This has resulted in challenges in improving user satisfaction and optimizing system uptime.

[0144] 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.

[0145] In this invention, the server includes an emotion analysis module means for acquiring voice and image data from the user and analyzing their emotional state; an artificial intelligence module means for continuously analyzing information stored in a database to predict operational problems; and a correction module means for quickly taking corrective actions based on the detected predictions. This enables the provision of personalized support that responds to the user's emotions and the early detection and correction of potential problems.

[0146] The "emotion analysis module means" is a means for analyzing voice and image data acquired from the user to identify the user's emotional state.

[0147] An "artificial intelligence module" is a means of analyzing information stored in a system to predict problems during operation.

[0148] A "correction module means" is a means for quickly taking corrective action in response to detected problems.

[0149] A "notification module means" is a means of escalating a problem to the responsible person and notifying them when the problem is serious or irreparable.

[0150] A "support provision module means" is a means for providing users with personalized support tailored to their emotional state.

[0151] A "resource optimization module" is a means of predicting future resource demand by utilizing past performance data.

[0152] A "self-healing module" is a means for automatically repairing minor operational problems within a system.

[0153] The system for implementing this invention is designed through interaction between a server, a terminal, and a user. The server forms a complex platform that includes multiple modules, such as an emotion analysis module, an artificial intelligence module, a correction module, a notification module, a support provision module, a resource optimization module, and a self-healing module. The server operates through the seamless cooperation of each module.

[0154] The user interacts with the system via a terminal. The terminal is equipped with a camera and microphone, which collect the user's voice and image data. An emotion analysis module uses this data to analyze the user's emotional state in real time and feeds it back to the server. This process uses specific AI algorithms and includes speech recognition and image analysis technologies.

[0155] On the server, an artificial intelligence module analyzes log and performance information collected from the entire system. This allows it to predict potential problems, and a corrective module automatically takes action to fix them as needed. This collaboration ensures that the system remains in optimal working order.

[0156] The support module provides personalized assistance and messages based on the user's emotional state, thereby improving the user experience.

[0157] For example, if a user experiences confusion while operating a device, the system immediately detects this using an emotion analysis module and provides feedback to the server. The server then provides appropriate help to assist the user in resolving the issue.

[0158] An example of an input prompt for a generative AI model might be, "How does the system provide support when the user is experiencing difficulties?"

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The user provides audio and image data through the device's camera and microphone. This serves as input to the system. The device transmits this audio and image data to an emotion analysis module in real time. The data processing performed here involves waveform analysis of the audio and facial expression extraction from the images. This generates initial data about the user's current emotional state.

[0162] Step 2:

[0163] The emotion analysis module installed in the device analyzes the user's emotions using input voice and image data. Specifically, it uses an AI algorithm to analyze changes in voice tone and facial features, generating emotion labels such as "reassured," "anxious," and "irritated" as output. These emotion labels represent the analyzed emotional state.

[0164] Step 3:

[0165] The server uses the emotion label received from the terminal to activate the support module. Specifically, it determines and presents appropriate support options based on the emotion label. For example, if the user is identified as "anxious," the server can provide a troubleshooting guide or send an encouraging message. The output of this process is a presentation of specific support actions for the user.

[0166] Step 4:

[0167] The server uses an artificial intelligence module to analyze system-wide log and performance information. The input data includes various activity logs and performance metrics within the system. Based on this data, it performs analysis to detect system anomaly patterns. The output obtained from this process is a prediction of potential problems and operational anomalies.

[0168] Step 5:

[0169] The server uses a self-healing module to perform corrective actions for predicted problems. Specific actions include running automated correction scripts and applying patches. The input data is anomaly detection information from the artificial intelligence module. The output of this process is a stabilized system state.

[0170] Step 6:

[0171] In some cases, if the server detects an unresolved or critical problem, the notification module is used to escalate the issue to a human contact. Specifically, a notification containing details of the problem is sent to the IT person, who is required to take action. At this stage, the input is unresolved problem information from the self-healing module, and the output is a notification to the person in charge.

[0172] Step 7:

[0173] The resource optimization module predicts future resource demands by analyzing historical performance data to identify trends. This is done by inputting past support history and system load trend data, resulting in an output of predicted future resource requirements. Based on this prediction, the server efficiently allocates resources.

[0174] (Application Example 2)

[0175] 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".

[0176] Conventional systems have struggled to accurately recognize users' emotional states and provide prompt and appropriate support based on those states. In particular, in caregiving settings, it is crucial to understand users' emotions in real time and respond accordingly, but there has been a lack of efficient means to achieve this.

[0177] 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.

[0178] In this invention, the server includes an intelligent computing module means that analyzes information stored in data storage and detects signs of problems in real time; a notification module means that notifies the user if the problem is irreparable or important and escalates it to a human representative; and an emotion recognition module means that analyzes the user's emotional state in real time and provides support based on emotion data. This makes it possible to provide appropriate support that is in line with the user's emotions.

[0179] An "intelligent computing module" is a program that analyzes information stored in data storage and has the function of detecting early signs of problems in real time.

[0180] A "correction module" is a program that has the function of automatically performing corrective operations on detected issues.

[0181] A "notification module" is a program that has the function of notifying human personnel and escalating issues when they are unfixable or critical.

[0182] A "support provision module" is a program that has functions to provide personalized support to users.

[0183] The "resource optimization module" is a program that predicts future resource demand based on past history and has the function of allocating resources optimally.

[0184] A "self-healing module" is a program that automatically repairs minor issues to maintain system stability.

[0185] An "emotion recognition module" is a program that analyzes a user's emotional state in real time and provides support based on that data.

[0186] The system for carrying out this invention mainly consists of an intelligent computing module, a correction module, a notification module, a support provision module, a resource optimization module, a self-repair module, and an emotion recognition module. The system operates appropriately based on the collected data through user interaction via a terminal.

[0187] The server utilizes intelligent computing modules to analyze information stored in data storage and detect early signs of problems in real time. This data analysis uses cloud-based AI platforms (e.g., Google Cloud AI and Amazon SageMaker) to enable the processing of large amounts of data.

[0188] The device collects the user's facial expressions and voice through its camera and microphone, and an emotion recognition module analyzes this data in real time. By employing Emotion AI and Microsoft® Azure Cognitive Services as its emotion engine, the device precisely analyzes emotional data and provides support tailored to the user's state.

[0189] When a user experiences anxiety, the support module automatically provides personalized support, and in some cases, the notification module sends an alert to the caregiver, enabling a quick response. This ensures prompt and accurate care for the elderly in care settings.

[0190] As a concrete example, by using the prompt "Please suggest appropriate responses when an elderly person feels anxious," the generative AI model can provide the most suitable response for the situation.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The user uses a device to collect voice and facial expression data using the camera and microphone. The input is real-time voice and video data, which is temporarily stored on the device. This data is then prepared as raw data for analysis by the emotion recognition module.

[0194] Step 2:

[0195] An emotion recognition module within the device analyzes collected voice and facial expression data in real time. The input data is converted into emotional features using Emotion AI or a similar model, and the user's emotional state (e.g., joy, sadness, anxiety) is output.

[0196] Step 3:

[0197] The server receives emotion data from the emotion recognition module and processes it further in the support module. The input is information about the user's emotional state, and the server determines appropriate support options and generates a response to the emotion based on this information.

[0198] Step 4:

[0199] The support module presents the user with support options it has generated. Based on the user's emotional state, the support options are either executed on the device or guided through the process. The output is support content that is reflected to the user in a visual or auditory form.

[0200] Step 5:

[0201] If a problem is detected that cannot be resolved by the server, the notification module will escalate it to the responsible party. The input is information related to an uncorrectable and critical emotional state, and the output is an alert message to the responsible party.

[0202] Step 6:

[0203] The generative AI model proposes long-term countermeasures for the user's emotional state based on prompt text. The prompt text "Please suggest appropriate countermeasures when an elderly person feels anxious" is input to this model, and it generates specific countermeasures as output.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] The system for implementing this invention involves a server, terminals, and users each playing a specific role and working together. The server has the function of accessing a database and continuously collecting log data and performance data from various sources within the system. An artificial intelligence module is used to analyze the collected data and detect potential problems in real time. This module leverages machine learning algorithms to identify patterns that deviate from normal operation and detect early signs of problems.

[0221] Detected issues are automatically addressed by a remediation module. This module performs predefined actions, such as restarting services or changing configurations, depending on the nature of the problem. If the problem is serious or difficult to resolve automatically, a notification module escalates the issue to human IT personnel via email or other means of communication, providing them with detailed information.

[0222] Users can access the system through their device and receive personalized support. The support module analyzes the user's past behavior history and skill level, and based on that, provides appropriate IT training programs and support information. The device displays this information in various ways, including text, audio, images, and videos, enabling intuitive support for the user.

[0223] Furthermore, the server uses a resource optimization module to predict future resource demands based on historical data. This allows the support team to allocate resources efficiently, improving the quality and effectiveness of support delivery.

[0224] The self-healing module, as part of the server's functionality, automatically repairs minor problems. This allows users to utilize a seamless IT environment without even noticing the issues. For example, it automatically performs periodic cache clearing and memory optimization to maintain system performance. In this way, in the embodiment for carrying out the invention, each module works together to form a system that provides high reliability and efficiency.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The server collects log and performance data in real time from sensors and applications within the system. This data is stored in a database in a structured format.

[0228] Step 2:

[0229] The server constantly analyzes data stored in the database via an artificial intelligence module. The AI ​​algorithm identifies anomalous patterns from this data and detects potential problems.

[0230] Step 3:

[0231] When a problem is detected, the server launches a remediation module and performs default remediation actions for issues that can be automatically fixed, such as restarting a specific service or adjusting configuration files.

[0232] Step 4:

[0233] If the issue is difficult to resolve or is critical, the server will escalate the problem to a human IT representative via a notification module. The notification will include details of the problem and recommended actions.

[0234] Step 5:

[0235] Users access support information from the server using their device. The server analyzes the user's skill level and past operation history to provide personalized support. Support is displayed in text and video formats.

[0236] Step 6:

[0237] The server uses a resource optimization module to analyze past support history and predict future support demand. This allows for efficient resource allocation to the support team.

[0238] Step 7:

[0239] A self-healing module installed in the server periodically checks the entire system and automatically repairs minor problems. This allows users to maintain a comfortable IT environment.

[0240] (Example 1)

[0241] 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."

[0242] In modern information systems, it has become increasingly difficult to quickly detect potential problems from vast amounts of data and respond appropriately. Furthermore, there is a need to efficiently meet a wide range of demands, including rapid problem correction, notification of unresolved issues, and the provision of personalized user support. However, systems that comprehensively provide these functions remain scarce. This invention aims to solve these challenges by providing a comprehensive information support system that includes information analysis, automated problem response, and optimization.

[0243] 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.

[0244] In this invention, the server includes a machine learning module means for analyzing information stored on an information medium and detecting signs of problems in real time, an automation module means for automatically executing corrective commands for detected problems, and a warning module means for notifying and escalating to a human operator if the problem is uncorrectable or critical. This enables real-time problem detection and rapid response.

[0245] "Information media" refers to the infrastructure for storing and managing digital data, and this includes databases and storage systems.

[0246] A "machine learning module" refers to a software component that uses machine learning algorithms to analyze data and detect patterns and anomalies.

[0247] "Automation module means" refers to software components that automatically execute specific predefined commands or processes.

[0248] A "warning module means" refers to a software component that notifies human operators of anomalies or problems that cannot be resolved.

[0249] "Support provision module means" refers to software components that provide personalized support and assistance information to users.

[0250] A "resource optimization module" refers to a software component that efficiently manages the resources of the entire system by analyzing past data and predicting future resource demands.

[0251] A "self-healing module" refers to a software component that automatically corrects minor problems.

[0252] "Connecting device means" refers to a device that displays various forms of information to the user and enables interactive operation.

[0253] In an embodiment of this invention, the system consists of three main elements: a server, a terminal, and a user. The role of each element and the associated hardware and software are described below.

[0254] The server manages data stored on information media and is responsible for data analysis using machine learning modules. Specifically, the server collects log information and performance information from databases and storage systems. Data management technologies such as SQL databases are used for this purpose. Machine learning modules employ machine learning frameworks such as TensorFlow and PyTorch, which are used to perform real-time anomaly detection.

[0255] The terminal is responsible for providing information to the user and offers personalized support through support modules. The terminal uses a web-based dashboard as its user interface, and programming languages ​​such as Python and JavaScript are used for data visualization. The terminal enables intuitive operation for the user using text, voice, images, and video.

[0256] Users access the system through their terminals to gain access to personalized support and problem-solving. The support provided to users is optimized based on their past usage history and skill level. For example, a specific prompt that a user might enter into the system could be, "Analyze the current system performance data and report any potential problems." In this way, users can give clear instructions through the system and receive the support they need.

[0257] The server also leverages resource optimization modules to predict future resource demands. This process utilizes Azure Machine Learning and other artificial intelligence tools to enable efficient resource allocation. In this way, each element works together to create an efficient and reliable information support system.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The server collects log and performance information from the information medium. As input, the server executes SQL queries to extract logs from the database for a specific period and monitors the system's log files. The resulting output is a collection of raw data to be analyzed. At this stage, the data is still unprocessed, so the necessary preprocessing for the next step is performed.

[0261] Step 2:

[0262] The server inputs the collected data into a machine learning module to perform anomaly detection. As input, the server converts the raw data into a data frame and preprocesses it using a specific algorithm. Specifically, data normalization and missing value imputation are performed. The output generates warnings indicating invalid patterns or anomalies, making it possible to identify potential problems.

[0263] Step 3:

[0264] The server attempts to automatically correct detected problems. The input is the data that was deemed abnormal in the previous step. The server uses an automation module to launch a script, which may, for example, restart the relevant service or change its configuration. The output is the result of the correction, and the history is recorded in the log. This allows for quick resolution of minor issues.

[0265] Step 4:

[0266] The terminal notifies the user of the problem details and provides personalized support. As input, the terminal receives warning messages and support information sent from the server. The terminal displays this information on the user interface, using audio and visual indicators for explanation. The output consists of solutions and further action instructions provided to the user.

[0267] Step 5:

[0268] Users provide feedback to the system through their terminal and request further support. As input, users enter direct prompts to trigger further actions. For example, they might request, "I would like to see detailed system logs." The output is additional support based on that feedback.

[0269] Step 6:

[0270] The server uses a resource optimization module to predict future demand. This is inputted from previously collected data and usage history. The server runs analytical algorithms, for example, to analyze usage trends by time of day. The output is a resource allocation proposal and forecast report to address future loads. This result is shared with the support team and used to develop strategic action plans.

[0271] (Application Example 1)

[0272] 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."

[0273] In autonomous vehicles, preventing vehicle malfunctions and performance degradation is crucial. However, conventional methods have made it difficult to effectively utilize vehicle sensor information and detect and correct anomalies in real time. Furthermore, there is a lack of means to provide appropriate driving support and resource optimization to vehicle users in a unified manner.

[0274] 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.

[0275] In this invention, the server includes an information analysis module means for collecting sensor information in real time and analyzing the data to detect potential problems; a correction module means for automatically performing corrective actions for detected problems; and a notification module means for notifying and escalating to an administrator if the problem is uncorrectable or critical. This enables real-time anomaly detection and correction in autonomous vehicles, and efficiently provides driving support information and optimizes resources.

[0276] "Sensor information" refers to data detected by various sensors installed in the vehicle, and includes information about the operating status and vehicle's operational status.

[0277] An "information analysis module means" is a means that has the function of analyzing collected sensor information in real time and detecting anomalies or patterns.

[0278] A "correction module means" is a means that has the function of automatically responding to detected anomalies and correcting the problems.

[0279] A "notification module means" is a means that has the function of notifying the administrator of an anomaly when it is impossible to correct or a serious anomaly occurs, and prompting them to take action.

[0280] "Driving assistance information" refers to advice and navigation information related to driving a vehicle, provided to optimize the driver's operation.

[0281] "Eco-driving advice" refers to information designed to guide drivers on driving methods that improve fuel efficiency and reduce environmental impact.

[0282] A "resource optimization module means" is a means that analyzes past usage history to predict future demand for vehicle resources and enables efficient allocation.

[0283] The "self-healing module means" is a means having a function to automatically repair minor problems in a vehicle and maintain system performance.

[0284] This invention is a system aimed at real-time monitoring, anomaly detection, and correction in autonomous vehicles. In this system, the server, terminal, and user each play their respective roles and cooperate to operate.

[0285] The server collects information in real time from various sensors installed in the vehicle. This includes GPS, speed sensors, engine temperature sensors, etc., and this data is saved to the backend using Firebase. An information analysis module analyzes this sensor information using a machine learning algorithm with TensorFlow to determine signs of anomalies. For example, when the engine temperature is higher than normal, an anomaly in heat generation is detected.

[0286] The terminal provides a means for notifying the user when an anomaly is detected. The notification module has a function to send SMS or emails to escalate the detected anomaly to the administrator. In addition, the terminal can utilize the Google Maps API to display driving assistance information and advice on eco-driving.

[0287] The user can obtain information provided by the system through the terminal and take appropriate actions. For example, the user can receive advice such as "The temperature of the engine is high, so please reduce the speed" from the terminal, which promotes safe driving.

[0288] As a specific example, when the engine load increases and the temperature rises rapidly during a long-distance drive, the system immediately detects this, sends a notification to the user terminal, and also provides guidance to the nearest service station in cooperation with the Google Maps API. Through this series of processes, real-time anomaly management and user support are realized.

[0289] An example of a prompt for the generated AI model could be: "Design a user interface for an application that collects real-time data from vehicle sensors and detects and notifies of anomalies." This prompt allows system developers to efficiently design the necessary interface.

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] The server collects information in real time from sensors mounted on the vehicle. It receives data from various sensors (GPS, speed sensor, engine temperature sensor, etc.) as input and sends this information to Firebase. The output is sensor information stored in the backend database, which is then used for subsequent analysis.

[0293] Step 2:

[0294] The server uses an information analysis module to analyze the collected sensor data. It receives sensor data obtained from Firebase as input and applies machine learning algorithms using TensorFlow to detect signs of anomalies. For example, it identifies overheating anomalies when engine temperature data exceeds the normal range. The output is the detection of anomalies and their detailed information.

[0295] Step 3:

[0296] The server uses a notification module to send details of the anomaly to the terminal. The input is anomaly information provided by the analysis module, and the anomaly is escalated to the administrator via SMS or email. The output is a notification message containing the details of the anomaly.

[0297] Step 4:

[0298] The terminal displays received notifications to the user. It receives messages sent from the notification module as input and displays them on the terminal screen. Simultaneously, it uses the Google Maps API to identify the nearest service station and provides that information to the user. The output consists of driving assistance information and alerts regarding anomalies displayed to the user.

[0299] Step 5:

[0300] The user takes appropriate action based on the information displayed on the terminal. Inputs are driving assistance information and abnormality notifications from the terminal, and outputs are the execution of corresponding actions. For example, this might involve slowing down the vehicle or heading to a service station as instructed.

[0301] Through these steps, real-time vehicle monitoring, anomaly detection, and response are achieved.

[0302] 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.

[0303] The system for implementing this invention operates primarily based on the interaction between a server, a terminal, and a user. The server forms a complex platform incorporating an emotion engine, with each module working in coordination. The emotion engine analyzes voice and facial expression data collected from the user to recognize the user's emotional state in real time. The server utilizes this emotional information to adjust support provision and optimize the user experience.

[0304] Specifically, the server uses an artificial intelligence module to analyze the log information and performance information obtained from the entire system. This module monitors all operation patterns within the system and, when an anomaly is detected, passes it on to the correction module. The correction module executes automatic correction actions for prompt response, and the notification module reports problems to IT personnel as necessary.

[0305] Users can always interact with this system via a terminal. In the terminal, the emotion engine acquires the user's voice and expressions through a camera and a microphone and processes them in real time to understand what emotional state the user is in. For example, when the user shows uneasiness or irritation, the emotion engine immediately feeds back this information to the server, and the server provides emergency support options or encouraging messages based on this.

[0306] Also, the system utilizes a resource optimization module to analyze past support histories and emotion data to predict future support needs. Based on this predicted data, optimized resource allocation is performed, and a more efficient and effective support system is established.

[0307] Finally, the self-healing module implemented on the server periodically diagnoses the system and automatically repairs potential minor problems. As a result, users can always use the terminal in a stable environment. In this way, the system combined with the emotion engine can provide excellent IT support while reacting to the user's emotions.

[0308] The following describes the processing flow.

[0309] Step 1:

[0310] The server continuously collects log data and performance data from the entire system. This data is stored in a database in real time and analyzed by an artificial intelligence module.

[0311] Step 2:

[0312] The artificial intelligence module analyzes log data to detect early signs of problems such as abnormal behavior or performance degradation. The detected problems are then passed on to the correction module.

[0313] Step 3:

[0314] The correction module automatically performs corrective actions for detected problems. These include predefined actions such as modifying server configurations or restarting specific processes.

[0315] Step 4:

[0316] If the fix is ​​unsuccessful or the problem is serious, the server will escalate the issue to a human IT representative via a notification module. The notification will include detailed information about the problem and suggested solutions.

[0317] Step 5:

[0318] Users interact with the system and receive support through their devices. The devices utilize an emotion engine that collects emotional data through the user's voice input and webcam footage.

[0319] Step 6:

[0320] The emotion engine analyzes collected data to recognize the user's emotional state in real time. For example, if it determines that the user is feeling stressed, it sends that information to the server.

[0321] Step 7:

[0322] The server receives emotion information, and the support module provides personalized support tailored to the user's emotions. This includes immediate answers to questions, provision of more detailed support guides, or presentation of relaxing content.

[0323] Step 8:

[0324] The server uses a resource optimization module to analyze past support and sentiment data to predict future needs. Based on this, appropriate resource allocation is performed.

[0325] Step 9:

[0326] The self-healing module periodically checks the entire system and proactively fixes minor issues. This ensures a consistently comfortable IT environment for the user.

[0327] (Example 2)

[0328] 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".

[0329] Traditional systems lacked support that took into account the user's emotional state, making it difficult to provide a personalized experience. Furthermore, there was no established method for predicting and quickly correcting potential problems that might arise during operation. This has resulted in challenges in improving user satisfaction and optimizing system uptime.

[0330] 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.

[0331] In this invention, the server includes an emotion analysis module means for acquiring voice and image data from the user and analyzing their emotional state; an artificial intelligence module means for continuously analyzing information stored in a database to predict operational problems; and a correction module means for quickly taking corrective actions based on the detected predictions. This enables the provision of personalized support that responds to the user's emotions and the early detection and correction of potential problems.

[0332] The "emotion analysis module means" is a means for analyzing voice and image data acquired from the user to identify the user's emotional state.

[0333] An "artificial intelligence module" is a means of analyzing information stored in a system to predict problems during operation.

[0334] A "correction module means" is a means for quickly taking corrective action in response to detected problems.

[0335] A "notification module means" is a means of escalating a problem to the responsible person and notifying them when the problem is serious or irreparable.

[0336] A "support provision module means" is a means for providing users with personalized support tailored to their emotional state.

[0337] A "resource optimization module" is a means of predicting future resource demand by utilizing past performance data.

[0338] A "self-healing module" is a means for automatically repairing minor operational problems within a system.

[0339] The system for implementing this invention is designed through interaction between a server, a terminal, and a user. The server forms a complex platform that includes multiple modules, such as an emotion analysis module, an artificial intelligence module, a correction module, a notification module, a support provision module, a resource optimization module, and a self-healing module. The server operates through the seamless cooperation of each module.

[0340] The user interacts with the system via a terminal. The terminal is equipped with a camera and microphone, which collect the user's voice and image data. An emotion analysis module uses this data to analyze the user's emotional state in real time and feeds it back to the server. This process uses specific AI algorithms and includes speech recognition and image analysis technologies.

[0341] On the server, an artificial intelligence module analyzes log and performance information collected from the entire system. This allows it to predict potential problems, and a corrective module automatically takes action to fix them as needed. This collaboration ensures that the system remains in optimal working order.

[0342] The support module provides personalized assistance and messages based on the user's emotional state, thereby improving the user experience.

[0343] For example, if a user experiences confusion while operating a device, the system immediately detects this using an emotion analysis module and provides feedback to the server. The server then provides appropriate help to assist the user in resolving the issue.

[0344] An example of an input prompt for a generative AI model might be, "How does the system provide support when the user is experiencing difficulties?"

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The user provides audio and image data through the device's camera and microphone. This serves as input to the system. The device transmits this audio and image data to an emotion analysis module in real time. The data processing performed here involves waveform analysis of the audio and facial expression extraction from the images. This generates initial data about the user's current emotional state.

[0348] Step 2:

[0349] The emotion analysis module installed in the device analyzes the user's emotions using input voice and image data. Specifically, it uses an AI algorithm to analyze changes in voice tone and facial features, generating emotion labels such as "reassured," "anxious," and "irritated" as output. These emotion labels represent the analyzed emotional state.

[0350] Step 3:

[0351] The server uses the emotion label received from the terminal to activate the support module. Specifically, it determines and presents appropriate support options based on the emotion label. For example, if the user is identified as "anxious," the server can provide a troubleshooting guide or send an encouraging message. The output of this process is a presentation of specific support actions for the user.

[0352] Step 4:

[0353] The server uses an artificial intelligence module to analyze system-wide log and performance information. The input data includes various activity logs and performance metrics within the system. Based on this data, it performs analysis to detect system anomaly patterns. The output obtained from this process is a prediction of potential problems and operational anomalies.

[0354] Step 5:

[0355] The server uses a self-healing module to perform corrective actions for predicted problems. Specific actions include running automated correction scripts and applying patches. The input data is anomaly detection information from the artificial intelligence module. The output of this process is a stabilized system state.

[0356] Step 6:

[0357] In some cases, if the server detects an unresolved or critical problem, the notification module is used to escalate the issue to a human contact. Specifically, a notification containing details of the problem is sent to the IT person, who is required to take action. At this stage, the input is unresolved problem information from the self-healing module, and the output is a notification to the person in charge.

[0358] Step 7:

[0359] The resource optimization module predicts future resource demands by analyzing historical performance data to identify trends. This is done by inputting past support history and system load trend data, resulting in an output of predicted future resource requirements. Based on this prediction, the server efficiently allocates resources.

[0360] (Application Example 2)

[0361] 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."

[0362] Conventional systems have struggled to accurately recognize users' emotional states and provide prompt and appropriate support based on those states. In particular, in caregiving settings, it is crucial to understand users' emotions in real time and respond accordingly, but there has been a lack of efficient means to achieve this.

[0363] 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.

[0364] In this invention, the server includes an intelligent computing module means that analyzes information stored in data storage and detects signs of problems in real time; a notification module means that notifies the user if the problem is irreparable or important and escalates it to a human representative; and an emotion recognition module means that analyzes the user's emotional state in real time and provides support based on emotion data. This makes it possible to provide appropriate support that is in line with the user's emotions.

[0365] An "intelligent computing module" is a program that analyzes information stored in data storage and has the function of detecting early signs of problems in real time.

[0366] A "correction module" is a program that has the function of automatically performing corrective operations on detected issues.

[0367] A "notification module" is a program that has the function of notifying human personnel and escalating issues when they are unfixable or critical.

[0368] A "support provision module" is a program that has functions to provide personalized support to users.

[0369] The "resource optimization module" is a program that predicts future resource demand based on past history and has the function of allocating resources optimally.

[0370] A "self-healing module" is a program that automatically repairs minor issues to maintain system stability.

[0371] An "emotion recognition module" is a program that analyzes a user's emotional state in real time and provides support based on that data.

[0372] The system for carrying out this invention mainly consists of an intelligent computing module, a correction module, a notification module, a support provision module, a resource optimization module, a self-repair module, and an emotion recognition module. The system operates appropriately based on the collected data through user interaction via a terminal.

[0373] The server utilizes intelligent computing modules to analyze information stored in data storage and detect early signs of problems in real time. This data analysis uses cloud-based AI platforms (e.g., Google Cloud AI and Amazon SageMaker) to enable the processing of large amounts of data.

[0374] The device collects the user's facial expressions and voice through its camera and microphone, and an emotion recognition module analyzes this data in real time. By employing Emotion AI and Microsoft Azure Cognitive Services as the emotion engine, it precisely analyzes emotional data and provides support tailored to the user's state.

[0375] When a user experiences anxiety, the support module automatically provides personalized support, and in some cases, the notification module sends an alert to the caregiver, enabling a quick response. This ensures prompt and accurate care for the elderly in care settings.

[0376] As a concrete example, by using the prompt "Please suggest appropriate responses when an elderly person feels anxious," the generative AI model can provide the most suitable response for the situation.

[0377] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0378] Step 1:

[0379] The user uses a device to collect voice and facial expression data using the camera and microphone. The input is real-time voice and video data, which is temporarily stored on the device. This data is then prepared as raw data for analysis by the emotion recognition module.

[0380] Step 2:

[0381] An emotion recognition module within the device analyzes collected voice and facial expression data in real time. The input data is converted into emotional features using Emotion AI or a similar model, and the user's emotional state (e.g., joy, sadness, anxiety) is output.

[0382] Step 3:

[0383] The server receives emotion data from the emotion recognition module and processes it further in the support module. The input is information about the user's emotional state, and the server determines appropriate support options and generates a response to the emotion based on this information.

[0384] Step 4:

[0385] The support module presents the user with support options it has generated. Based on the user's emotional state, the support options are either executed on the device or guided through the process. The output is support content that is reflected to the user in a visual or auditory form.

[0386] Step 5:

[0387] If a problem is detected that cannot be resolved by the server, the notification module will escalate it to the responsible party. The input is information related to an uncorrectable and critical emotional state, and the output is an alert message to the responsible party.

[0388] Step 6:

[0389] The generative AI model proposes long-term countermeasures for the user's emotional state based on prompt text. The prompt text "Please suggest appropriate countermeasures when an elderly person feels anxious" is input to this model, and it generates specific countermeasures as output.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] [Third Embodiment]

[0394] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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".

[0406] The system for implementing this invention involves a server, terminals, and users each playing a specific role and working together. The server has the function of accessing a database and continuously collecting log data and performance data from various sources within the system. An artificial intelligence module is used to analyze the collected data and detect potential problems in real time. This module leverages machine learning algorithms to identify patterns that deviate from normal operation and detect early signs of problems.

[0407] Detected issues are automatically addressed by a remediation module. This module performs predefined actions, such as restarting services or changing configurations, depending on the nature of the problem. If the problem is serious or difficult to resolve automatically, a notification module escalates the issue to human IT personnel via email or other means of communication, providing them with detailed information.

[0408] Users can access the system through their device and receive personalized support. The support module analyzes the user's past behavior history and skill level, and based on that, provides appropriate IT training programs and support information. The device displays this information in various ways, including text, audio, images, and videos, enabling intuitive support for the user.

[0409] Furthermore, the server uses a resource optimization module to predict future resource demands based on historical data. This allows the support team to allocate resources efficiently, improving the quality and effectiveness of support delivery.

[0410] The self-healing module, as part of the server's functionality, automatically repairs minor problems. This allows users to utilize a seamless IT environment without even noticing the issues. For example, it automatically performs periodic cache clearing and memory optimization to maintain system performance. In this way, in the embodiment for carrying out the invention, each module works together to form a system that provides high reliability and efficiency.

[0411] The following describes the processing flow.

[0412] Step 1:

[0413] The server collects log and performance data in real time from sensors and applications within the system. This data is stored in a database in a structured format.

[0414] Step 2:

[0415] The server constantly analyzes data stored in the database via an artificial intelligence module. The AI ​​algorithm identifies anomalous patterns from this data and detects potential problems.

[0416] Step 3:

[0417] When a problem is detected, the server launches a remediation module and performs default remediation actions for issues that can be automatically fixed, such as restarting a specific service or adjusting configuration files.

[0418] Step 4:

[0419] If the issue is difficult to resolve or is critical, the server will escalate the problem to a human IT representative through a notification module. The notification will include details of the problem and recommended actions.

[0420] Step 5:

[0421] Users access support information from the server using their device. The server analyzes the user's skill level and past operation history to provide personalized support. Support is displayed in text and video formats.

[0422] Step 6:

[0423] The server uses a resource optimization module to analyze past support history and predict future support demand. This allows for efficient resource allocation to the support team.

[0424] Step 7:

[0425] A self-healing module installed in the server periodically checks the entire system and automatically repairs minor problems. This allows users to maintain a comfortable IT environment.

[0426] (Example 1)

[0427] 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."

[0428] In modern information systems, it has become increasingly difficult to quickly detect potential problems from vast amounts of data and respond appropriately. Furthermore, there is a need to efficiently meet a wide range of demands, including rapid problem correction, notification of unresolved issues, and the provision of personalized user support. However, systems that comprehensively provide these functions remain scarce. This invention aims to solve these challenges by providing a comprehensive information support system that includes information analysis, automated problem response, and optimization.

[0429] 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.

[0430] In this invention, the server includes a machine learning module means for analyzing information stored on an information medium and detecting signs of problems in real time, an automation module means for automatically executing corrective commands for detected problems, and a warning module means for notifying and escalating to a human operator if the problem is uncorrectable or critical. This enables real-time problem detection and rapid response.

[0431] "Information media" refers to the infrastructure for storing and managing digital data, and this includes databases and storage systems.

[0432] A "machine learning module" refers to a software component that uses machine learning algorithms to analyze data and detect patterns and anomalies.

[0433] "Automation module means" refers to software components that automatically execute specific predefined commands or processes.

[0434] A "warning module means" refers to a software component that notifies human operators of anomalies or problems that cannot be resolved.

[0435] "Support provision module means" refers to software components that provide personalized support and assistance information to users.

[0436] A "resource optimization module" refers to a software component that efficiently manages the resources of the entire system by analyzing past data and predicting future resource demands.

[0437] A "self-healing module" refers to a software component that automatically corrects minor problems.

[0438] "Connecting device means" refers to a device that displays various forms of information to the user and enables interactive operation.

[0439] In an embodiment of this invention, the system consists of three main elements: a server, a terminal, and a user. The role of each element and the associated hardware and software are described below.

[0440] The server manages data stored on information media and is responsible for data analysis using machine learning modules. Specifically, the server collects log information and performance information from databases and storage systems. Data management technologies such as SQL databases are used for this purpose. Machine learning modules employ machine learning frameworks such as TensorFlow and PyTorch, which are used to perform real-time anomaly detection.

[0441] The terminal is responsible for providing information to the user and offers personalized support through support modules. The terminal uses a web-based dashboard as its user interface, and programming languages ​​such as Python and JavaScript are used for data visualization. The terminal enables intuitive operation for the user using text, voice, images, and video.

[0442] Users access the system through their terminals to gain access to personalized support and problem-solving. The support provided to users is optimized based on their past usage history and skill level. For example, a specific prompt that a user might enter into the system could be, "Analyze the current system performance data and report any potential problems." In this way, users can give clear instructions through the system and receive the support they need.

[0443] The server also leverages resource optimization modules to predict future resource demands. This process utilizes Azure Machine Learning and other artificial intelligence tools to enable efficient resource allocation. In this way, each element works together to create an efficient and reliable information support system.

[0444] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0445] Step 1:

[0446] The server collects log and performance information from the information medium. As input, the server executes SQL queries to extract logs from the database for a specific period and monitors the system's log files. The resulting output is a collection of raw data to be analyzed. At this stage, the data is still unprocessed, so the necessary preprocessing for the next step is performed.

[0447] Step 2:

[0448] The server inputs the collected data into a machine learning module to perform anomaly detection. As input, the server converts the raw data into a data frame and preprocesses it using a specific algorithm. Specifically, data normalization and missing value imputation are performed. The output generates warnings indicating invalid patterns or anomalies, making it possible to identify potential problems.

[0449] Step 3:

[0450] The server attempts to automatically correct detected problems. The input is the data that was deemed abnormal in the previous step. The server uses an automation module to launch a script, which may, for example, restart the relevant service or change its configuration. The output is the result of the correction, and the history is recorded in the log. This allows for quick resolution of minor issues.

[0451] Step 4:

[0452] The terminal notifies the user of the problem details and provides personalized support. As input, the terminal receives warning messages and support information sent from the server. The terminal displays this information on the user interface, using audio and visual indicators for explanation. The output consists of solutions and further action instructions provided to the user.

[0453] Step 5:

[0454] Users provide feedback to the system through their terminal and request further support. As input, users enter direct prompts to trigger further actions. For example, they might request, "I would like to see detailed system logs." The output is additional support based on that feedback.

[0455] Step 6:

[0456] The server uses a resource optimization module to predict future demand. This is inputted from previously collected data and usage history. The server runs analytical algorithms, for example, to analyze usage trends by time of day. The output is a resource allocation proposal and forecast report to address future loads. This result is shared with the support team and used to develop strategic action plans.

[0457] (Application Example 1)

[0458] 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."

[0459] In autonomous vehicles, preventing vehicle malfunctions and performance degradation is crucial. However, conventional methods have made it difficult to effectively utilize vehicle sensor information and detect and correct anomalies in real time. Furthermore, there is a lack of means to provide appropriate driving support and resource optimization to vehicle users in a unified manner.

[0460] 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.

[0461] In this invention, the server includes an information analysis module means for collecting sensor information in real time and analyzing the data to detect potential problems; a correction module means for automatically performing corrective actions for detected problems; and a notification module means for notifying and escalating to an administrator if the problem is uncorrectable or critical. This enables real-time anomaly detection and correction in autonomous vehicles, and efficiently provides driving support information and optimizes resources.

[0462] "Sensor information" refers to data detected by various sensors installed in the vehicle, and includes information about the operating status and vehicle's operational status.

[0463] An "information analysis module means" is a means that has the function of analyzing collected sensor information in real time and detecting anomalies or patterns.

[0464] A "correction module means" is a means that has the function of automatically responding to detected anomalies and correcting the problems.

[0465] A "notification module means" is a means that has the function of notifying the administrator of an anomaly when it is impossible to correct or a serious anomaly occurs, and prompting them to take action.

[0466] "Driving assistance information" refers to advice and navigation information related to driving a vehicle, provided to optimize the driver's operation.

[0467] "Eco-driving advice" refers to information designed to guide drivers on driving methods that improve fuel efficiency and reduce environmental impact.

[0468] A "resource optimization module means" is a means that analyzes past usage history to predict future demand for vehicle resources and enables efficient allocation.

[0469] A "self-healing module" is a means that has the function of automatically repairing minor problems within the vehicle and maintaining system performance.

[0470] This invention is a system for real-time monitoring, anomaly detection, and correction in autonomous vehicles. This system operates collaboratively, with the server, terminals, and users each fulfilling their respective roles.

[0471] The server collects information in real time from various sensors installed in the vehicle. This includes GPS, speed sensors, engine temperature sensors, etc., and this data is stored in the backend using Firebase. An information analysis module analyzes this sensor information using machine learning algorithms with TensorFlow to determine signs of abnormalities. For example, if the engine temperature is higher than normal, it detects an overheating anomaly.

[0472] The device provides a means of notifying the user when an anomaly is detected. The notification module has the functionality to send SMS or email to escalate detected anomalies to the administrator. In addition, the device can use the Google Maps API to display driving assistance information and eco-driving advice.

[0473] Users can obtain information provided by the system through their terminals and take appropriate action. For example, they may receive advice from their terminals such as, "The engine temperature is high, please slow down," which promotes safer driving.

[0474] For example, if the engine load increases and the temperature rises rapidly during long-distance driving, the system will immediately detect this and send a notification to the user's terminal. It will also integrate with the Google Maps API to provide directions to the nearest service station. Through this entire process, real-time anomaly management and user support are achieved.

[0475] An example of a prompt for the generated AI model could be: "Design a user interface for an application that collects real-time data from vehicle sensors and detects and notifies of anomalies." This prompt allows system developers to efficiently design the necessary interface.

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] The server collects information in real time from sensors mounted on the vehicle. It receives data from various sensors (GPS, speed sensor, engine temperature sensor, etc.) as input and sends this information to Firebase. The output is sensor information stored in the backend database, which is then used for subsequent analysis.

[0479] Step 2:

[0480] The server uses an information analysis module to analyze the collected sensor data. It receives sensor data obtained from Firebase as input and applies machine learning algorithms using TensorFlow to detect signs of anomalies. For example, it identifies overheating anomalies when engine temperature data exceeds the normal range. The output is the detection of anomalies and their detailed information.

[0481] Step 3:

[0482] The server uses a notification module to send details of the anomaly to the terminal. The input is anomaly information provided by the analysis module, and the anomaly is escalated to the administrator via SMS or email. The output is a notification message containing the details of the anomaly.

[0483] Step 4:

[0484] The terminal displays received notifications to the user. It receives messages sent from the notification module as input and displays them on the terminal screen. Simultaneously, it uses the Google Maps API to identify the nearest service station and provides that information to the user. The output consists of driving assistance information and alerts regarding anomalies displayed to the user.

[0485] Step 5:

[0486] The user takes appropriate action based on the information displayed on the terminal. Inputs are driving assistance information and abnormality notifications from the terminal, and outputs are the execution of corresponding actions. For example, this might involve slowing down the vehicle or heading to a service station as instructed.

[0487] Through these steps, real-time vehicle monitoring, anomaly detection, and response are achieved.

[0488] 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.

[0489] The system for implementing this invention operates primarily based on the interaction between a server, a terminal, and a user. The server forms a complex platform incorporating an emotion engine, with each module working in coordination. The emotion engine analyzes voice and facial expression data collected from the user to recognize the user's emotional state in real time. The server utilizes this emotional information to adjust support provision and optimize the user experience.

[0490] Specifically, the server uses an artificial intelligence module to analyze log and performance information collected from the entire system. This module monitors all operational patterns within the system, and if an anomaly is detected, it is passed on to a correction module. The correction module performs automated corrective actions to respond quickly, and a notification module reports the problem to IT personnel as needed.

[0491] Users can interact with this system at any time via their device. On the device, the emotion engine acquires the user's voice and facial expressions through the camera and microphone, and processes them in real time to understand the user's emotional state. For example, if the user shows signs of anxiety or frustration, the emotion engine immediately feeds that information back to the server, which then provides emergency support options or encouraging messages based on this information.

[0492] Furthermore, the system utilizes a resource optimization module to analyze past support history and sentiment data to predict future support needs. Based on this predictive data, resources are allocated optimally, resulting in a more efficient and effective support system.

[0493] Finally, a self-healing module implemented on the server periodically diagnoses the system and automatically repairs any minor underlying problems. This ensures that users can always use their devices in a stable environment. In this way, a system that incorporates an emotion engine can provide exceptional IT support while responding to the user's emotions.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] The server continuously collects log data and performance data from the entire system. This data is stored in a database in real time and analyzed by an artificial intelligence module.

[0497] Step 2:

[0498] The artificial intelligence module analyzes log data to detect early signs of problems such as abnormal behavior or performance degradation. The detected problems are then passed on to the correction module.

[0499] Step 3:

[0500] The correction module automatically performs corrective actions for detected problems. These include predefined actions such as modifying server configurations or restarting specific processes.

[0501] Step 4:

[0502] If the fix is ​​unsuccessful or the problem is serious, the server will escalate the issue to a human IT representative via a notification module. The notification will include detailed information about the problem and suggested solutions.

[0503] Step 5:

[0504] Users interact with the system and receive support through their devices. The devices utilize an emotion engine that collects emotional data through the user's voice input and webcam footage.

[0505] Step 6:

[0506] The emotion engine analyzes collected data to recognize the user's emotional state in real time. For example, if it determines that the user is feeling stressed, it sends that information to the server.

[0507] Step 7:

[0508] The server receives emotion information, and the support module provides personalized support tailored to the user's emotions. This includes immediate answers to questions, provision of more detailed support guides, or presentation of relaxing content.

[0509] Step 8:

[0510] The server uses a resource optimization module to analyze past support and sentiment data to predict future needs. Based on this, appropriate resource allocation is performed.

[0511] Step 9:

[0512] The self-healing module periodically checks the entire system and proactively fixes minor issues. This ensures a consistently comfortable IT environment for the user.

[0513] (Example 2)

[0514] 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."

[0515] Traditional systems lacked support that took into account the user's emotional state, making it difficult to provide a personalized experience. Furthermore, there was no established method for predicting and quickly correcting potential problems that might arise during operation. This has resulted in challenges in improving user satisfaction and optimizing system uptime.

[0516] 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.

[0517] In this invention, the server includes an emotion analysis module means for acquiring voice and image data from the user and analyzing their emotional state; an artificial intelligence module means for continuously analyzing information stored in a database to predict operational problems; and a correction module means for quickly taking corrective actions based on the detected predictions. This enables the provision of personalized support that responds to the user's emotions and the early detection and correction of potential problems.

[0518] The "emotion analysis module means" is a means for analyzing voice and image data acquired from the user to identify the user's emotional state.

[0519] An "artificial intelligence module" is a means of analyzing information stored in a system to predict problems during operation.

[0520] A "correction module means" is a means for quickly taking corrective action in response to detected problems.

[0521] A "notification module means" is a means of escalating a problem to the responsible person and notifying them when the problem is serious or irreparable.

[0522] A "support provision module means" is a means for providing users with personalized support tailored to their emotional state.

[0523] A "resource optimization module" is a means of predicting future resource demand by utilizing past performance data.

[0524] A "self-healing module" is a means for automatically repairing minor operational problems within a system.

[0525] The system for implementing this invention is designed through interaction between a server, a terminal, and a user. The server forms a complex platform that includes multiple modules, such as an emotion analysis module, an artificial intelligence module, a correction module, a notification module, a support provision module, a resource optimization module, and a self-healing module. The server operates through the seamless cooperation of each module.

[0526] The user interacts with the system via a terminal. The terminal is equipped with a camera and microphone, which collect the user's voice and image data. An emotion analysis module uses this data to analyze the user's emotional state in real time and feeds it back to the server. This process uses specific AI algorithms and includes speech recognition and image analysis technologies.

[0527] On the server, an artificial intelligence module analyzes log and performance information collected from the entire system. This allows it to predict potential problems, and a corrective module automatically takes action to fix them as needed. This collaboration ensures that the system remains in optimal working order.

[0528] The support module provides personalized assistance and messages based on the user's emotional state, thereby improving the user experience.

[0529] For example, if a user experiences confusion while operating a device, the system immediately detects this using an emotion analysis module and provides feedback to the server. The server then provides appropriate help to assist the user in resolving the issue.

[0530] An example of an input prompt for a generative AI model might be, "How does the system provide support when the user is experiencing difficulties?"

[0531] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0532] Step 1:

[0533] The user provides audio and image data through the device's camera and microphone. This serves as input to the system. The device transmits this audio and image data to an emotion analysis module in real time. The data processing performed here involves waveform analysis of the audio and facial expression extraction from the images. This generates initial data about the user's current emotional state.

[0534] Step 2:

[0535] The emotion analysis module installed in the device analyzes the user's emotions using input voice and image data. Specifically, it uses an AI algorithm to analyze changes in voice tone and facial features, generating emotion labels such as "reassured," "anxious," and "irritated" as output. These emotion labels represent the analyzed emotional state.

[0536] Step 3:

[0537] The server uses the emotion label received from the terminal to activate the support module. Specifically, it determines and presents appropriate support options based on the emotion label. For example, if the user is identified as "anxious," the server can provide a troubleshooting guide or send an encouraging message. The output of this process is a presentation of specific support actions for the user.

[0538] Step 4:

[0539] The server uses an artificial intelligence module to analyze system-wide log and performance information. The input data includes various activity logs and performance metrics within the system. Based on this data, it performs analysis to detect system anomaly patterns. The output obtained from this process is a prediction of potential problems and operational anomalies.

[0540] Step 5:

[0541] The server uses a self-healing module to perform corrective actions for predicted problems. Specific actions include running automated correction scripts and applying patches. The input data is anomaly detection information from the artificial intelligence module. The output of this process is a stabilized system state.

[0542] Step 6:

[0543] In some cases, if the server detects an unresolved or critical problem, the notification module is used to escalate the issue to a human contact. Specifically, a notification containing details of the problem is sent to the IT person, who is required to take action. At this stage, the input is unresolved problem information from the self-healing module, and the output is a notification to the person in charge.

[0544] Step 7:

[0545] The resource optimization module predicts future resource demands by analyzing historical performance data to identify trends. This is done by inputting past support history and system load trend data, resulting in an output of predicted future resource requirements. Based on this prediction, the server efficiently allocates resources.

[0546] (Application Example 2)

[0547] 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."

[0548] Conventional systems have struggled to accurately recognize users' emotional states and provide prompt and appropriate support based on those states. In particular, in caregiving settings, it is crucial to understand users' emotions in real time and respond accordingly, but there has been a lack of efficient means to achieve this.

[0549] 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.

[0550] In this invention, the server includes an intelligent computing module means that analyzes information stored in data storage and detects signs of problems in real time; a notification module means that notifies the user if the problem is irreparable or important and escalates it to a human representative; and an emotion recognition module means that analyzes the user's emotional state in real time and provides support based on emotion data. This makes it possible to provide appropriate support that is in line with the user's emotions.

[0551] An "intelligent computing module" is a program that analyzes information stored in data storage and has the function of detecting early signs of problems in real time.

[0552] A "correction module" is a program that has the function of automatically performing corrective operations on detected issues.

[0553] A "notification module" is a program that has the function of notifying human personnel and escalating issues when they are unfixable or critical.

[0554] A "support provision module" is a program that has functions to provide personalized support to users.

[0555] The "resource optimization module" is a program that predicts future resource demand based on past history and has the function of allocating resources optimally.

[0556] A "self-healing module" is a program that automatically repairs minor issues to maintain system stability.

[0557] An "emotion recognition module" is a program that analyzes a user's emotional state in real time and provides support based on that data.

[0558] The system for carrying out this invention mainly consists of an intelligent computing module, a correction module, a notification module, a support provision module, a resource optimization module, a self-repair module, and an emotion recognition module. The system operates appropriately based on the collected data through user interaction via a terminal.

[0559] The server utilizes intelligent computing modules to analyze information stored in data storage and detect early signs of problems in real time. This data analysis uses cloud-based AI platforms (e.g., Google Cloud AI and Amazon SageMaker) to enable the processing of large amounts of data.

[0560] The device collects the user's facial expressions and voice through its camera and microphone, and an emotion recognition module analyzes this data in real time. By employing Emotion AI and Microsoft Azure Cognitive Services as the emotion engine, it precisely analyzes emotional data and provides support tailored to the user's state.

[0561] When a user experiences anxiety, the support module automatically provides personalized support, and in some cases, the notification module sends an alert to the caregiver, enabling a quick response. This ensures prompt and accurate care for the elderly in care settings.

[0562] As a concrete example, by using the prompt "Please suggest appropriate responses when an elderly person feels anxious," the generative AI model can provide the most suitable response for the situation.

[0563] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0564] Step 1:

[0565] The user uses a device to collect voice and facial expression data using the camera and microphone. The input is real-time voice and video data, which is temporarily stored on the device. This data is then prepared as raw data for analysis by the emotion recognition module.

[0566] Step 2:

[0567] An emotion recognition module within the device analyzes collected voice and facial expression data in real time. The input data is converted into emotional features using Emotion AI or a similar model, and the user's emotional state (e.g., joy, sadness, anxiety) is output.

[0568] Step 3:

[0569] The server receives emotion data from the emotion recognition module and processes it further in the support module. The input is information about the user's emotional state, and the server determines appropriate support options and generates a response to the emotion based on this information.

[0570] Step 4:

[0571] The support module presents the user with support options it has generated. Based on the user's emotional state, the support options are either executed on the device or guided through the process. The output is support content that is reflected to the user in a visual or auditory form.

[0572] Step 5:

[0573] If a problem is detected that cannot be resolved by the server, the notification module will escalate it to the responsible party. The input is information related to an uncorrectable and critical emotional state, and the output is an alert message to the responsible party.

[0574] Step 6:

[0575] The generative AI model proposes long-term countermeasures for the user's emotional state based on prompt text. The prompt text "Please suggest appropriate countermeasures when an elderly person feels anxious" is input to this model, and it generates specific countermeasures as output.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] [Fourth Embodiment]

[0580] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0581] 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.

[0582] 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).

[0583] 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.

[0584] 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.

[0585] 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).

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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".

[0593] The system for implementing this invention involves a server, terminals, and users each playing a specific role and working together. The server has the function of accessing a database and continuously collecting log data and performance data from various sources within the system. An artificial intelligence module is used to analyze the collected data and detect potential problems in real time. This module leverages machine learning algorithms to identify patterns that deviate from normal operation and detect early signs of problems.

[0594] Detected issues are automatically addressed by a remediation module. This module performs predefined actions, such as restarting services or changing configurations, depending on the nature of the problem. If the problem is serious or difficult to resolve automatically, a notification module escalates the issue to human IT personnel via email or other means of communication, providing them with detailed information.

[0595] Users can access the system through their device and receive personalized support. The support module analyzes the user's past behavior history and skill level, and based on that, provides appropriate IT training programs and support information. The device displays this information in various ways, including text, audio, images, and videos, enabling intuitive support for the user.

[0596] Furthermore, the server uses a resource optimization module to predict future resource demands based on historical data. This allows the support team to allocate resources efficiently, improving the quality and effectiveness of support delivery.

[0597] The self-healing module, as part of the server's functionality, automatically repairs minor problems. This allows users to utilize a seamless IT environment without even noticing the issues. For example, it automatically performs periodic cache clearing and memory optimization to maintain system performance. In this way, in the embodiment for carrying out the invention, each module works together to form a system that provides high reliability and efficiency.

[0598] The following describes the processing flow.

[0599] Step 1:

[0600] The server collects log and performance data in real time from sensors and applications within the system. This data is stored in a database in a structured format.

[0601] Step 2:

[0602] The server constantly analyzes data stored in the database via an artificial intelligence module. The AI ​​algorithm identifies anomalous patterns from this data and detects potential problems.

[0603] Step 3:

[0604] When a problem is detected, the server launches a remediation module and performs default remediation actions for issues that can be automatically fixed, such as restarting a specific service or adjusting configuration files.

[0605] Step 4:

[0606] If the issue is difficult to resolve or is critical, the server will escalate the problem to a human IT representative via a notification module. The notification will include details of the problem and recommended actions.

[0607] Step 5:

[0608] Users access support information from the server using their device. The server analyzes the user's skill level and past operation history to provide personalized support. Support is displayed in text and video formats.

[0609] Step 6:

[0610] The server uses a resource optimization module to analyze past support history and predict future support demand. This allows for efficient resource allocation to the support team.

[0611] Step 7:

[0612] A self-healing module installed in the server periodically checks the entire system and automatically repairs minor problems. This allows users to maintain a comfortable IT environment.

[0613] (Example 1)

[0614] 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".

[0615] In modern information systems, it has become increasingly difficult to quickly detect potential problems from vast amounts of data and respond appropriately. Furthermore, there is a need to efficiently meet a wide range of demands, including rapid problem correction, notification of unresolved issues, and the provision of personalized user support. However, systems that comprehensively provide these functions remain scarce. This invention aims to solve these challenges by providing a comprehensive information support system that includes information analysis, automated problem response, and optimization.

[0616] 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.

[0617] In this invention, the server includes a machine learning module means for analyzing information stored on an information medium and detecting signs of problems in real time, an automation module means for automatically executing corrective commands for detected problems, and a warning module means for notifying and escalating to a human operator if the problem is uncorrectable or critical. This enables real-time problem detection and rapid response.

[0618] "Information media" refers to the infrastructure for storing and managing digital data, and this includes databases and storage systems.

[0619] A "machine learning module" refers to a software component that uses machine learning algorithms to analyze data and detect patterns and anomalies.

[0620] "Automation module means" refers to software components that automatically execute specific predefined commands or processes.

[0621] A "warning module means" refers to a software component that notifies human operators of anomalies or problems that cannot be resolved.

[0622] "Support provision module means" refers to software components that provide personalized support and assistance information to users.

[0623] A "resource optimization module" refers to a software component that efficiently manages the resources of the entire system by analyzing past data and predicting future resource demands.

[0624] A "self-healing module" refers to a software component that automatically corrects minor problems.

[0625] "Connecting device means" refers to a device that displays various forms of information to the user and enables interactive operation.

[0626] In an embodiment of this invention, the system consists of three main elements: a server, a terminal, and a user. The role of each element and the associated hardware and software are described below.

[0627] The server manages data stored on information media and is responsible for data analysis using machine learning modules. Specifically, the server collects log information and performance information from databases and storage systems. Data management technologies such as SQL databases are used for this purpose. Machine learning modules employ machine learning frameworks such as TensorFlow and PyTorch, which are used to perform real-time anomaly detection.

[0628] The terminal is responsible for providing information to the user and offers personalized support through support modules. The terminal uses a web-based dashboard as its user interface, and programming languages ​​such as Python and JavaScript are used for data visualization. The terminal enables intuitive operation for the user using text, voice, images, and video.

[0629] Users access the system through their terminals to gain access to personalized support and problem-solving. The support provided to users is optimized based on their past usage history and skill level. For example, a specific prompt that a user might enter into the system could be, "Analyze the current system performance data and report any potential problems." In this way, users can give clear instructions through the system and receive the support they need.

[0630] The server also leverages resource optimization modules to predict future resource demands. This process utilizes Azure Machine Learning and other artificial intelligence tools to enable efficient resource allocation. In this way, each element works together to create an efficient and reliable information support system.

[0631] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0632] Step 1:

[0633] The server collects log and performance information from the information medium. As input, the server executes SQL queries to extract logs from the database for a specific period and monitors the system's log files. The resulting output is a collection of raw data to be analyzed. At this stage, the data is still unprocessed, so the necessary preprocessing for the next step is performed.

[0634] Step 2:

[0635] The server inputs the collected data into a machine learning module to perform anomaly detection. As input, the server converts the raw data into a data frame and preprocesses it using a specific algorithm. Specifically, data normalization and missing value imputation are performed. The output generates warnings indicating invalid patterns or anomalies, making it possible to identify potential problems.

[0636] Step 3:

[0637] The server attempts to automatically correct detected problems. The input is the data that was deemed abnormal in the previous step. The server uses an automation module to launch a script, which may, for example, restart the relevant service or change its configuration. The output is the result of the correction, and the history is recorded in the log. This allows for quick resolution of minor issues.

[0638] Step 4:

[0639] The terminal notifies the user of the problem details and provides personalized support. As input, the terminal receives warning messages and support information sent from the server. The terminal displays this information on the user interface, using audio and visual indicators for explanation. The output consists of solutions and further action instructions provided to the user.

[0640] Step 5:

[0641] Users provide feedback to the system through their terminal and request further support. As input, users enter direct prompts to trigger further actions. For example, they might request, "I would like to see detailed system logs." The output is additional support based on that feedback.

[0642] Step 6:

[0643] The server uses a resource optimization module to predict future demand. This is inputted from previously collected data and usage history. The server runs analytical algorithms, for example, to analyze usage trends by time of day. The output is a resource allocation proposal and forecast report to address future loads. This result is shared with the support team and used to develop strategic action plans.

[0644] (Application Example 1)

[0645] 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".

[0646] In autonomous vehicles, preventing vehicle malfunctions and performance degradation is crucial. However, conventional methods have made it difficult to effectively utilize vehicle sensor information and detect and correct anomalies in real time. Furthermore, there is a lack of means to provide appropriate driving support and resource optimization to vehicle users in a unified manner.

[0647] 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.

[0648] In this invention, the server includes an information analysis module means for collecting sensor information in real time and analyzing the data to detect potential problems; a correction module means for automatically performing corrective actions for detected problems; and a notification module means for notifying and escalating to an administrator if the problem is uncorrectable or critical. This enables real-time anomaly detection and correction in autonomous vehicles, and efficiently provides driving support information and optimizes resources.

[0649] "Sensor information" refers to data detected by various sensors installed in the vehicle, and includes information about the operating status and vehicle's operational status.

[0650] An "information analysis module means" is a means that has the function of analyzing collected sensor information in real time and detecting anomalies or patterns.

[0651] A "correction module means" is a means that has the function of automatically responding to detected anomalies and correcting the problems.

[0652] A "notification module means" is a means that has the function of notifying the administrator of an anomaly when it is impossible to correct or a serious anomaly occurs, and prompting them to take action.

[0653] "Driving assistance information" refers to advice and navigation information related to driving a vehicle, provided to optimize the driver's operation.

[0654] "Eco-driving advice" refers to information designed to guide drivers on driving methods that improve fuel efficiency and reduce environmental impact.

[0655] A "resource optimization module means" is a means that analyzes past usage history to predict future demand for vehicle resources and enables efficient allocation.

[0656] A "self-healing module" is a means that has the function of automatically repairing minor problems within the vehicle and maintaining system performance.

[0657] This invention is a system for real-time monitoring, anomaly detection, and correction in autonomous vehicles. This system operates collaboratively, with the server, terminals, and users each fulfilling their respective roles.

[0658] The server collects information in real time from various sensors installed in the vehicle. This includes GPS, speed sensors, engine temperature sensors, etc., and this data is stored in the backend using Firebase. An information analysis module analyzes this sensor information using machine learning algorithms with TensorFlow to determine signs of abnormalities. For example, if the engine temperature is higher than normal, it detects an overheating anomaly.

[0659] The device provides a means of notifying the user when an anomaly is detected. The notification module has the functionality to send SMS or email to escalate detected anomalies to the administrator. In addition, the device can use the Google Maps API to display driving assistance information and eco-driving advice.

[0660] Users can obtain information provided by the system through their terminals and take appropriate action. For example, they may receive advice from their terminals such as, "The engine temperature is high, please slow down," which promotes safer driving.

[0661] For example, if the engine load increases and the temperature rises rapidly during long-distance driving, the system will immediately detect this and send a notification to the user's terminal. It will also integrate with the Google Maps API to provide directions to the nearest service station. Through this entire process, real-time anomaly management and user support are achieved.

[0662] An example of a prompt for the generated AI model could be: "Design a user interface for an application that collects real-time data from vehicle sensors and detects and notifies of anomalies." This prompt allows system developers to efficiently design the necessary interface.

[0663] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0664] Step 1:

[0665] The server collects information in real time from sensors mounted on the vehicle. It receives data from various sensors (GPS, speed sensor, engine temperature sensor, etc.) as input and sends this information to Firebase. The output is sensor information stored in the backend database, which is then used for subsequent analysis.

[0666] Step 2:

[0667] The server uses an information analysis module to analyze the collected sensor data. It receives sensor data obtained from Firebase as input and applies machine learning algorithms using TensorFlow to detect signs of anomalies. For example, it identifies overheating anomalies when engine temperature data exceeds the normal range. The output is the detection of anomalies and their detailed information.

[0668] Step 3:

[0669] The server uses a notification module to send details of the anomaly to the terminal. The input is anomaly information provided by the analysis module, and the anomaly is escalated to the administrator via SMS or email. The output is a notification message containing the details of the anomaly.

[0670] Step 4:

[0671] The terminal displays received notifications to the user. It receives messages sent from the notification module as input and displays them on the terminal screen. Simultaneously, it uses the Google Maps API to identify the nearest service station and provides that information to the user. The output consists of driving assistance information and alerts regarding anomalies displayed to the user.

[0672] Step 5:

[0673] The user takes appropriate action based on the information displayed on the terminal. Inputs are driving assistance information and abnormality notifications from the terminal, and outputs are the execution of corresponding actions. For example, this might involve slowing down the vehicle or heading to a service station as instructed.

[0674] Through these steps, real-time vehicle monitoring, anomaly detection, and response are achieved.

[0675] 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.

[0676] The system for implementing this invention operates primarily based on the interaction between a server, a terminal, and a user. The server forms a complex platform incorporating an emotion engine, with each module working in coordination. The emotion engine analyzes voice and facial expression data collected from the user to recognize the user's emotional state in real time. The server utilizes this emotional information to adjust support provision and optimize the user experience.

[0677] Specifically, the server uses an artificial intelligence module to analyze log and performance information collected from the entire system. This module monitors all operational patterns within the system, and if an anomaly is detected, it is passed on to a correction module. The correction module performs automated corrective actions to respond quickly, and a notification module reports the problem to IT personnel as needed.

[0678] Users can interact with this system at any time via their device. On the device, the emotion engine acquires the user's voice and facial expressions through the camera and microphone, and processes them in real time to understand the user's emotional state. For example, if the user shows signs of anxiety or frustration, the emotion engine immediately feeds that information back to the server, which then provides emergency support options or encouraging messages based on this information.

[0679] Furthermore, the system utilizes a resource optimization module to analyze past support history and sentiment data to predict future support needs. Based on this predictive data, resources are allocated optimally, resulting in a more efficient and effective support system.

[0680] Finally, a self-healing module implemented on the server periodically diagnoses the system and automatically repairs any minor underlying problems. This ensures that users can always use their devices in a stable environment. In this way, a system that incorporates an emotion engine can provide exceptional IT support while responding to the user's emotions.

[0681] The following describes the processing flow.

[0682] Step 1:

[0683] The server continuously collects log data and performance data from the entire system. This data is stored in a database in real time and analyzed by an artificial intelligence module.

[0684] Step 2:

[0685] The artificial intelligence module analyzes log data to detect early signs of problems such as abnormal behavior or performance degradation. The detected problems are then passed on to the correction module.

[0686] Step 3:

[0687] The correction module automatically performs corrective actions for detected problems. These include predefined actions such as modifying server configurations or restarting specific processes.

[0688] Step 4:

[0689] If the fix is ​​unsuccessful or the problem is serious, the server will escalate the issue to a human IT representative via a notification module. The notification will include detailed information about the problem and suggested solutions.

[0690] Step 5:

[0691] Users interact with the system and receive support through their devices. The devices utilize an emotion engine that collects emotional data through the user's voice input and webcam footage.

[0692] Step 6:

[0693] The emotion engine analyzes collected data to recognize the user's emotional state in real time. For example, if it determines that the user is feeling stressed, it sends that information to the server.

[0694] Step 7:

[0695] The server receives emotion information, and the support module provides personalized support tailored to the user's emotions. This includes immediate answers to questions, provision of more detailed support guides, or presentation of relaxing content.

[0696] Step 8:

[0697] The server uses a resource optimization module to analyze past support and sentiment data to predict future needs. Based on this, appropriate resource allocation is performed.

[0698] Step 9:

[0699] The self-healing module periodically checks the entire system and proactively fixes minor issues. This ensures a consistently comfortable IT environment for the user.

[0700] (Example 2)

[0701] 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".

[0702] Traditional systems lacked support that took into account the user's emotional state, making it difficult to provide a personalized experience. Furthermore, there was no established method for predicting and quickly correcting potential problems that might arise during operation. This has resulted in challenges in improving user satisfaction and optimizing system uptime.

[0703] 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.

[0704] In this invention, the server includes an emotion analysis module means for acquiring voice and image data from the user and analyzing their emotional state; an artificial intelligence module means for continuously analyzing information stored in a database to predict operational problems; and a correction module means for quickly taking corrective actions based on the detected predictions. This enables the provision of personalized support that responds to the user's emotions and the early detection and correction of potential problems.

[0705] The "emotion analysis module means" is a means for analyzing voice and image data acquired from the user to identify the user's emotional state.

[0706] An "artificial intelligence module" is a means of analyzing information stored in a system to predict problems during operation.

[0707] A "correction module means" is a means for quickly taking corrective action in response to detected problems.

[0708] A "notification module means" is a means of escalating a problem to the responsible person and notifying them when the problem is serious or irreparable.

[0709] A "support provision module means" is a means for providing users with personalized support tailored to their emotional state.

[0710] A "resource optimization module" is a means of predicting future resource demand by utilizing past performance data.

[0711] A "self-healing module" is a means for automatically repairing minor operational problems within a system.

[0712] The system for implementing this invention is designed through interaction between a server, a terminal, and a user. The server forms a complex platform that includes multiple modules, such as an emotion analysis module, an artificial intelligence module, a correction module, a notification module, a support provision module, a resource optimization module, and a self-healing module. The server operates through the seamless cooperation of each module.

[0713] The user interacts with the system via a terminal. The terminal is equipped with a camera and microphone, which collect the user's voice and image data. An emotion analysis module uses this data to analyze the user's emotional state in real time and feeds it back to the server. This process uses specific AI algorithms and includes speech recognition and image analysis technologies.

[0714] On the server, an artificial intelligence module analyzes log and performance information collected from the entire system. This allows it to predict potential problems, and a corrective module automatically takes action to fix them as needed. This collaboration ensures that the system remains in optimal working order.

[0715] The support module provides personalized assistance and messages based on the user's emotional state, thereby improving the user experience.

[0716] For example, if a user experiences confusion while operating a device, the system immediately detects this using an emotion analysis module and provides feedback to the server. The server then provides appropriate help to assist the user in resolving the issue.

[0717] An example of an input prompt for a generative AI model might be, "How does the system provide support when the user is experiencing difficulties?"

[0718] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0719] Step 1:

[0720] The user provides audio and image data through the device's camera and microphone. This serves as input to the system. The device transmits this audio and image data to an emotion analysis module in real time. The data processing performed here involves waveform analysis of the audio and facial expression extraction from the images. This generates initial data about the user's current emotional state.

[0721] Step 2:

[0722] The emotion analysis module installed in the device analyzes the user's emotions using input voice and image data. Specifically, it uses an AI algorithm to analyze changes in voice tone and facial features, generating emotion labels such as "reassured," "anxious," and "irritated" as output. These emotion labels represent the analyzed emotional state.

[0723] Step 3:

[0724] The server uses the emotion label received from the terminal to activate the support module. Specifically, it determines and presents appropriate support options based on the emotion label. For example, if the user is identified as "anxious," the server can provide a troubleshooting guide or send an encouraging message. The output of this process is a presentation of specific support actions for the user.

[0725] Step 4:

[0726] The server uses an artificial intelligence module to analyze system-wide log and performance information. The input data includes various activity logs and performance metrics within the system. Based on this data, it performs analysis to detect system anomaly patterns. The output obtained from this process is a prediction of potential problems and operational anomalies.

[0727] Step 5:

[0728] The server uses a self-healing module to perform corrective actions for predicted problems. Specific actions include running automated correction scripts and applying patches. The input data is anomaly detection information from the artificial intelligence module. The output of this process is a stabilized system state.

[0729] Step 6:

[0730] In some cases, if the server detects an unresolved or critical problem, the notification module is used to escalate the issue to a human contact. Specifically, a notification containing details of the problem is sent to the IT person, who is required to take action. At this stage, the input is unresolved problem information from the self-healing module, and the output is a notification to the person in charge.

[0731] Step 7:

[0732] The resource optimization module predicts future resource demands by analyzing historical performance data to identify trends. This is done by inputting past support history and system load trend data, resulting in an output of predicted future resource requirements. Based on this prediction, the server efficiently allocates resources.

[0733] (Application Example 2)

[0734] 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".

[0735] Conventional systems have struggled to accurately recognize users' emotional states and provide prompt and appropriate support based on those states. In particular, in caregiving settings, it is crucial to understand users' emotions in real time and respond accordingly, but there has been a lack of efficient means to achieve this.

[0736] 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.

[0737] In this invention, the server includes an intelligent computing module means that analyzes information stored in data storage and detects signs of problems in real time; a notification module means that notifies the user if the problem is irreparable or important and escalates it to a human representative; and an emotion recognition module means that analyzes the user's emotional state in real time and provides support based on emotion data. This makes it possible to provide appropriate support that is in line with the user's emotions.

[0738] An "intelligent computing module" is a program that analyzes information stored in data storage and has the function of detecting early signs of problems in real time.

[0739] A "correction module" is a program that has the function of automatically performing corrective operations on detected issues.

[0740] A "notification module" is a program that has the function of notifying human personnel and escalating issues when they are unfixable or critical.

[0741] A "support provision module" is a program that has functions to provide personalized support to users.

[0742] The "resource optimization module" is a program that predicts future resource demand based on past history and has the function of allocating resources optimally.

[0743] A "self-healing module" is a program that automatically repairs minor issues to maintain system stability.

[0744] An "emotion recognition module" is a program that analyzes a user's emotional state in real time and provides support based on that data.

[0745] The system for carrying out this invention mainly consists of an intelligent computing module, a correction module, a notification module, a support provision module, a resource optimization module, a self-repair module, and an emotion recognition module. The system operates appropriately based on the collected data through user interaction via a terminal.

[0746] The server utilizes intelligent computing modules to analyze information stored in data storage and detect early signs of problems in real time. This data analysis uses cloud-based AI platforms (e.g., Google Cloud AI and Amazon SageMaker) to enable the processing of large amounts of data.

[0747] The device collects the user's facial expressions and voice through its camera and microphone, and an emotion recognition module analyzes this data in real time. By employing Emotion AI and Microsoft Azure Cognitive Services as the emotion engine, it precisely analyzes emotional data and provides support tailored to the user's state.

[0748] When a user experiences anxiety, the support module automatically provides personalized support, and in some cases, the notification module sends an alert to the caregiver, enabling a quick response. This ensures prompt and accurate care for the elderly in care settings.

[0749] As a concrete example, by using the prompt "Please suggest appropriate responses when an elderly person feels anxious," the generative AI model can provide the most suitable response for the situation.

[0750] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0751] Step 1:

[0752] The user uses a device to collect voice and facial expression data using the camera and microphone. The input is real-time voice and video data, which is temporarily stored on the device. This data is then prepared as raw data for analysis by the emotion recognition module.

[0753] Step 2:

[0754] An emotion recognition module within the device analyzes collected voice and facial expression data in real time. The input data is converted into emotional features using Emotion AI or a similar model, and the user's emotional state (e.g., joy, sadness, anxiety) is output.

[0755] Step 3:

[0756] The server receives emotion data from the emotion recognition module and processes it further in the support module. The input is information about the user's emotional state, and the server determines appropriate support options and generates a response to the emotion based on this information.

[0757] Step 4:

[0758] The support module presents the user with support options it has generated. Based on the user's emotional state, the support options are either executed on the device or guided through the process. The output is support content that is reflected to the user in a visual or auditory form.

[0759] Step 5:

[0760] If a problem is detected that cannot be resolved by the server, the notification module will escalate it to the responsible party. The input is information related to an uncorrectable and critical emotional state, and the output is an alert message to the responsible party.

[0761] Step 6:

[0762] The generative AI model proposes long-term countermeasures for the user's emotional state based on prompt text. The prompt text "Please suggest appropriate countermeasures when an elderly person feels anxious" is input to this model, and it generates specific countermeasures as output.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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."

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] The following is further disclosed regarding the embodiments described above.

[0785] (Claim 1)

[0786] An artificial intelligence module that analyzes information stored in a database and detects signs of problems in real time,

[0787] A correction module means that automatically performs corrective actions for detected problems,

[0788] A notification module means that notifies if a problem is unfixable or critical and escalates it to a human agent,

[0789] A support delivery module means that provides personalized support to users,

[0790] A resource optimization module means for predicting future resource demand based on past history,

[0791] A self-healing module that automatically repairs minor problems,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, characterized in that the artificial intelligence module means has the function of collecting and analyzing log information and performance information.

[0795] (Claim 3)

[0796] The system according to claim 1, characterized in that the self-healing module means has a function to periodically optimize the environment.

[0797] "Example 1"

[0798] (Claim 1)

[0799] A machine learning module that analyzes information stored on an information medium and detects signs of a problem in real time,

[0800] An automated module means that automatically executes corrective commands for detected problems,

[0801] A warning module means that notifies and escalates to a human operator if the problem is uncorrectable or critical,

[0802] A support delivery module means that provides individualized support to the user,

[0803] A resource optimization module means for predicting future resource demand based on past records,

[0804] A self-healing module means that automatically repairs minor problems,

[0805] Connecting device means including a display device that transmits information to the user in various formats,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, characterized in that the machine learning module means has the function of collecting and analyzing recorded information and performance information.

[0809] (Claim 3)

[0810] The system according to claim 1, characterized in that the self-healing module means has a function to periodically optimize the environment.

[0811] "Application Example 1"

[0812] (Claim 1)

[0813] An information analysis module means that collects sensor information in real time, analyzes the data, and detects potential problems,

[0814] A correction module means that automatically performs corrective actions for detected problems,

[0815] A notification module means that notifies and escalates to an administrator if the problem is unfixable or critical,

[0816] A support module means that provides users with driving assistance information and advice on eco-driving,

[0817] A resource optimization module means that predicts future resource demand based on past usage history,

[0818] A self-healing module that automatically repairs minor problems and continuously optimizes the environment,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, characterized in that the artificial intelligence module means has the function of collecting and analyzing operational information and operational information.

[0822] (Claim 3)

[0823] The system according to claim 1, characterized in that the self-healing module means has a function to continuously optimize performance.

[0824] "Example 2 of combining an emotion engine"

[0825] (Claim 1)

[0826] An emotion analysis module means that acquires voice and image data from the user and analyzes their emotional state,

[0827] An artificial intelligence module that continuously analyzes information stored in a database to predict operational problems,

[0828] Correction module means for rapidly taking corrective actions based on detected predictions,

[0829] A notification module means for escalating and notifying the responsible person in the event of a serious or uncorrectable problem,

[0830] A support provision module means that provides personalized support to users according to their emotional state,

[0831] A resource optimization module that utilizes past performance data to forecast future resource demand,

[0832] A self-healing module means that automatically repairs minor operational problems,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, characterized in that the artificial intelligence module means has the function of acquiring and analyzing recorded information and processed information.

[0836] (Claim 3)

[0837] The system according to claim 1, characterized in that the self-healing module means has a function to periodically optimize the operating environment.

[0838] "Application example 2 when combining with an emotional engine"

[0839] (Claim 1)

[0840] An intelligent computing module means that analyzes information stored in data storage and detects signs of problems in real time,

[0841] A correction module means that automatically performs corrective operations on detected issues,

[0842] A notification module means that notifies when an issue is unfixable or critical and escalates it to a human agent,

[0843] A support delivery module means that provides individualized support to users,

[0844] A resource optimization module means for predicting future resource demand based on past history,

[0845] A self-healing module means that automatically repairs minor issues,

[0846] An emotion recognition module means that analyzes the user's emotional state in real time and provides support based on emotional data,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, characterized in that the intelligent computing module means has a function to collect and analyze recorded information and performance information.

[0850] (Claim 3)

[0851] The system according to claim 1, characterized in that the self-healing module means has a function to periodically optimize the environment. [Explanation of symbols]

[0852] 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. An artificial intelligence module that analyzes information stored in a database and detects signs of problems in real time, A correction module means that automatically performs corrective actions for detected problems, A notification module means that notifies if a problem is unfixable or critical and escalates it to a human agent, A support delivery module means that provides personalized support to users, A resource optimization module means for predicting future resource demand based on past history, A self-healing module that automatically repairs minor problems, A system that includes this.

2. The system according to claim 1, characterized in that the artificial intelligence module means has the function of collecting and analyzing log information and performance information.

3. The system according to claim 1, characterized in that the self-healing module means has a function to periodically optimize the environment.

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