System

Abstract

An object of a system according to an embodiment is to efficiently and accurately perform a checking operation when an alarm is generated.SOLUTION: A system includes an alarm data collection part, a pattern learning part, a confirmation work proposal part, a confirmation work execution part, a report part, and a history storage part. The alarm data collection unit collects past alarm data. The pattern learning unit learns a pattern based on the data collected by the alarm data collection unit. The confirmation work proposal unit proposes confirmation work when an alarm is generated. The checking work execution unit executes the checking work proposed by the checking work proposal unit. The reporting unit reports a result of the checking work executed by the checking work execution unit. The history storage unit stores a history of the confirmation work executed by the confirmation work execution unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional technology, when an alarm occurs, confirmation work is performed manually, which is inefficient and prone to errors.

[0005] The system according to the embodiment aims to efficiently and accurately carry out confirmation work when an alarm occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes an alarm data collection unit, a pattern learning unit, a confirmation work proposal unit, a confirmation work execution unit, a reporting unit, and a history storage unit. The alarm data collection unit collects past alarm data. The pattern learning unit learns patterns based on the data collected by the alarm data collection unit. The confirmation work proposal unit proposes confirmation work when an alarm occurs. The confirmation work execution unit executes the confirmation work proposed by the confirmation work proposal unit. The reporting unit reports the results of the confirmation work executed by the confirmation work execution unit. The history storage unit stores the history of the confirmation work executed by the confirmation work execution unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently and accurately perform confirmation work when an alarm occurs. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The maintenance operation system according to the embodiment of the present invention is a system that learns patterns that have occurred in the past and uses a generation AI to assist in the series of confirmation tasks that are carried out during operation when an alarm occurs. This allows the maintenance operation system to streamline confirmation tasks when an alarm occurs, enabling a quick and accurate response.

[0029] The maintenance operation system according to the embodiment includes an alarm data collection unit, a pattern learning unit, a confirmation work proposal unit, a confirmation work execution unit, a reporting unit, and a history storage unit. The alarm data collection unit collects past alarm data. For example, it collects data such as system errors and security alerts. The pattern learning unit learns patterns based on the data collected by the alarm data collection unit. For example, it uses a machine learning algorithm to learn patterns when an alarm occurs. The confirmation work proposal unit proposes confirmation work when an alarm occurs. For example, it proposes items and procedures to be checked based on past response methods. The confirmation work execution unit executes the confirmation work proposed by the confirmation work proposal unit. For example, it checks system logs and identifies abnormalities. The reporting unit reports the results of the confirmation work performed by the confirmation work execution unit. For example, it summarizes the results of the confirmation work in text and sends them to an operation staff member by email. The history storage unit stores the history of the confirmation work performed by the confirmation work execution unit. For example, it stores the results in a database so that it can be referenced in the future. This enables the maintenance operation system according to the embodiment to streamline confirmation work when an alarm occurs and respond quickly and accurately.

[0030] The pattern learning unit simultaneously learns not only alarm data but also system operating status and external environmental data, enabling more accurate pattern recognition. For example, when the generation AI is learning alarm data, the pattern learning unit collects system operating status data and performs pattern recognition. For example, it analyzes CPU usage and memory usage to identify the cause of an alarm. The pattern learning unit also collects external environmental data (e.g., weather and network traffic) in addition to alarm data and performs pattern recognition. For example, it analyzes weather data to learn alarm occurrence patterns under specific weather conditions. The pattern learning unit also collects system operating status and external environmental data and performs pattern recognition when the generation AI is learning alarm data. For example, it analyzes network traffic data to identify the impact of increased traffic on alarm occurrence. This enables more accurate pattern recognition by taking system operating status and external environmental data into account.

[0031] The pattern learning unit can use an anomaly detection algorithm to automatically identify undiscovered anomalous patterns and add them to the learning data. For example, when the generation AI learns alarm data, the pattern learning unit uses the anomaly detection algorithm to identify undiscovered anomalous patterns. For example, it automatically detects anomalous log entries and system events and adds them to the learning data. The pattern learning unit also uses the anomaly detection algorithm to identify undiscovered anomalous patterns when learning alarm data. For example, it detects anomalous network traffic and anomalous usage of system resources and adds them to the learning data. The pattern learning unit also uses the anomaly detection algorithm to identify undiscovered anomalous patterns when the generation AI learns alarm data. For example, it detects anomalous user behavior and system errors and adds them to the learning data. In this way, by automatically identifying undiscovered anomalous patterns and adding them to the learning data, the accuracy of anomaly detection is improved.

[0032] The pattern learning unit can integrate alarm data from different systems and networks to perform cross-system pattern learning. In the pattern learning unit, for example, the generation AI collects alarm data from different systems and networks, integrates it, and performs pattern learning. For example, it analyzes data from multiple servers and network devices and identifies common abnormal patterns. The pattern learning unit also integrates alarm data from different systems and networks to perform cross-system pattern learning. For example, it collects alarm data from multiple systems within a data center and learns the overall pattern. The pattern learning unit also collects alarm data from different systems and networks, integrates it, and performs pattern learning. For example, it integrates data from cloud environments and on-premises environments and identifies abnormal patterns. This makes cross-system pattern learning possible by integrating data from different systems and networks.

[0033] The pattern learning unit can share the learning results of the alarm data with other maintenance operation systems, accumulating knowledge across the industry. For example, the generation AI in the pattern learning unit shares the learning results of the alarm data with other maintenance operation systems, accumulating knowledge across the industry. For example, the learning results can be shared using a common database, making them available to other systems. The pattern learning unit also shares the learning results of the alarm data with other maintenance operation systems, accumulating knowledge across the industry. For example, the learning results can be shared in an industry-standard format, making them available to other systems. The pattern learning unit also shares the learning results of the alarm data with other maintenance operation systems, accumulating knowledge across the industry. For example, best practices can be shared across the industry, improving responses in each system. In this way, by sharing learning results, knowledge can be accumulated across the industry, and maintenance operations can be made more efficient.

[0034] The confirmation work proposal unit can propose the optimal confirmation procedure by taking into consideration the operating status of the system and external environmental data at the time an alarm occurs. For example, the generation AI proposes the optimal confirmation procedure by taking into consideration the operating status of the system at the time an alarm occurs. For example, the confirmation work proposal unit adjusts the confirmation procedure based on CPU usage and memory usage. The confirmation work proposal unit also proposes the optimal confirmation procedure by taking into consideration external environmental data (for example, weather and network traffic) when proposing confirmation work. For example, the confirmation work proposal unit adjusts the confirmation procedure based on weather data. The confirmation work proposal unit also proposes the optimal confirmation procedure by taking into consideration the operating status of the system and external environmental data at the time an alarm occurs. For example, the confirmation work proposal unit adjusts the confirmation procedure based on network traffic data. This makes it possible to propose the optimal confirmation procedure by taking into consideration the operating status of the system and external environmental data.

[0035] The confirmation work proposal unit evaluates the effectiveness of the proposed confirmation work, and the generation AI can continuously improve the proposal based on the feedback. For example, the confirmation work proposal unit evaluates the effectiveness of the proposed confirmation work by the generation AI and continuously improves the proposal based on the feedback. For example, it analyzes the success rate and time of the confirmation work and adjusts the proposal. The confirmation work proposal unit also evaluates the effectiveness of the proposed confirmation work, and the generation AI improves the proposal based on the feedback. For example, it collects operator feedback and optimizes the proposal. The confirmation work proposal unit also evaluates the effectiveness of the proposed confirmation work by the generation AI and continuously improves the proposal based on the feedback. For example, it stores the results of the confirmation work in a database and reflects them in the next proposal. In this way, by evaluating the effectiveness of the proposed confirmation work and continuously improving the proposal based on the feedback, it becomes possible to propose more appropriate confirmation work.

[0036] The confirmation work proposal unit can integrate confirmation work when an alarm occurs in different systems or networks, and propose the optimal cross-system confirmation procedure. For example, the generation AI in the confirmation work proposal unit integrates confirmation work when an alarm occurs in different systems or networks, and proposes the optimal confirmation procedure. For example, it analyzes data from multiple servers and network devices and proposes a common confirmation procedure. The confirmation work proposal unit also integrates confirmation work when an alarm occurs in different systems or networks, and proposes the optimal cross-system confirmation procedure. For example, it integrates confirmation work for multiple systems within a data center. The confirmation work proposal unit also integrates confirmation work when an alarm occurs in different systems or networks, and proposes the optimal cross-system confirmation procedure. For example, it integrates confirmation work for cloud environments and on-premise environments. This makes it possible to propose the optimal cross-system confirmation procedure by integrating confirmation work in different systems and networks.

[0037] The confirmation work proposal unit can share the confirmation work proposals with other maintenance and operation systems, accumulating knowledge across the industry. For example, the confirmation work proposal unit shares the confirmation work proposed by the generation AI with other maintenance and operation systems, accumulating knowledge across the industry. For example, the proposal content can be shared using a common database, making it available to other systems. The confirmation work proposal unit also shares the confirmation work proposals with other maintenance and operation systems, accumulating knowledge across the industry. For example, the proposal content can be shared in an industry-standard format, making it available to other systems. The confirmation work proposal unit also shares the confirmation work proposed by the generation AI with other maintenance and operation systems, accumulating knowledge across the industry. For example, best practices can be shared across the industry, improving responses in each system. In this way, by sharing confirmation work proposals, knowledge can be accumulated across the industry, and maintenance and operation can be made more efficient.

[0038] The confirmation task execution unit can select the optimal execution procedure for automatically executed confirmation tasks by taking into account the system's operating status and external environmental data. For example, in confirmation tasks automatically executed by the generation AI, the confirmation task execution unit selects the optimal execution procedure by taking into account the system's operating status. For example, the execution procedure is adjusted based on CPU usage and memory usage. Furthermore, in confirmation tasks automatically executed by the generation AI, the confirmation task execution unit selects the optimal execution procedure by taking into account external environmental data (for example, weather and network traffic). For example, the execution procedure is adjusted based on weather data. Furthermore, in confirmation tasks automatically executed by the generation AI, the confirmation task execution unit selects the optimal execution procedure by taking into account the system's operating status and external environmental data. For example, the execution procedure is adjusted based on network traffic data. This makes it possible to select the optimal execution procedure by taking into account the system's operating status and external environmental data.

[0039] The confirmation task execution unit evaluates the effectiveness of the automatically executed confirmation task, and the generation AI can continuously improve the execution content based on feedback. The confirmation task execution unit, for example, evaluates the effectiveness of the automatically executed confirmation task by the generation AI, and continuously improves the execution content based on feedback. For example, it analyzes the success rate and time of the confirmation task and adjusts the execution content. The confirmation task execution unit also evaluates the effectiveness of the automatically executed confirmation task, and the generation AI improves the execution content based on feedback. For example, it collects operator feedback and optimizes the execution content. The confirmation task execution unit also evaluates the effectiveness of the automatically executed confirmation task by the generation AI, and continuously improves the execution content based on feedback. For example, it stores the results of the confirmation task in a database and reflects them in the next execution. In this way, by evaluating the effectiveness of the automatically executed confirmation task and continuously improving the execution content based on feedback, it becomes possible to execute more appropriate confirmation tasks.

[0040] The verification task execution unit can integrate verification tasks across different systems and networks and propose the optimal cross-system automated execution procedure. In the verification task execution unit, for example, the generation AI integrates verification tasks across different systems and networks and proposes the optimal automated execution procedure. For example, it analyzes data from multiple servers and network devices and proposes a common automated execution procedure. The verification task execution unit also integrates verification tasks across different systems and networks and proposes the optimal cross-system automated execution procedure. For example, it integrates verification tasks across multiple systems within a data center. The verification task execution unit also integrates verification tasks across different systems and networks and proposes the optimal automated execution procedure. For example, it integrates verification tasks for cloud environments and on-premise environments. In this way, by integrating verification tasks across different systems and networks, it becomes possible to propose the optimal cross-system automated execution procedure.

[0041] The confirmation task execution unit can share the results of the automatically performed confirmation tasks with other maintenance and operation systems, accumulating knowledge across the industry. For example, the confirmation task execution unit shares the results of confirmation tasks automatically performed by the generation AI with other maintenance and operation systems, accumulating knowledge across the industry. For example, the results can be shared using a common database, making them available to other systems. The confirmation task execution unit also shares the results of the automatically performed confirmation tasks with other maintenance and operation systems, accumulating knowledge across the industry. For example, the results can be shared in an industry-standard format, making them available to other systems. The confirmation task execution unit also shares the results of confirmation tasks automatically performed by the generation AI with other maintenance and operation systems, accumulating knowledge across the industry. For example, best practices can be shared across the industry, improving responses in each system. In this way, by sharing the results of automatically performed confirmation tasks, knowledge can be accumulated across the industry, and maintenance and operation can be made more efficient.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The maintenance operation system may further include a prediction unit. The prediction unit can predict future alarm occurrences based on past alarm data and system operation status data. For example, it calculates the probability of an alarm occurring during a specific time period or under specific system load conditions, and notifies the operations staff in advance. The prediction unit can also take external environmental data (e.g., weather and network traffic) into consideration to evaluate the risk of alarm occurrence. For example, it can predict the risk of alarm occurrence under specific weather conditions based on weather data. In this way, the prediction unit can predict future alarm occurrences in advance, providing the operations staff with an opportunity to take countermeasures.

[0044] The maintenance operation system can further include a training department. The training department can provide alarm response training to operators. For example, it can conduct simulations based on past alarm data to train operators so that they can respond quickly and accurately when an actual alarm occurs. The training department can also provide customized training programs according to the skill level of the operators. For example, it can teach basic response procedures to new operators and advanced troubleshooting techniques to experienced operators. In this way, the training department can improve the skills of operators and improve the quality of alarm response.

[0045] The maintenance operation system can further include a resource optimization unit. The resource optimization unit can monitor the system's resource usage and optimally allocate resources. For example, it can monitor CPU and memory usage in real time and reallocate resources to avoid overload. The resource optimization unit can also secure the resources required in advance when a specific alarm occurs. For example, when an alarm occurs that requires large-scale data processing, the necessary resources can be allocated with priority. This allows the resource optimization unit to maintain stable system operation and improve the efficiency of alarm response.

[0046] The maintenance operation system can further include a communication support unit. The communication support unit supports information sharing between operators and can improve the efficiency of alarm responses. For example, it provides a chat function that allows operators to share information in real time when an alarm occurs. The communication support unit can also provide advice based on past alarm response history. For example, it can present operators with how to respond when a similar alarm occurs. In this way, the communication support unit strengthens cooperation between operators and enables quick and effective alarm responses.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The alarm data collection unit collects past alarm data, such as data on system errors and security alerts. Step 2: The pattern learning unit learns patterns based on the data collected by the alarm data collection unit. For example, the pattern at the time of alarm occurrence is learned using a machine learning algorithm. Step 3: The confirmation work suggestion unit suggests confirmation work when an alarm occurs. For example, it suggests items and procedures to be checked based on past response methods. Step 4: The verification work execution unit executes the verification work proposed by the verification work proposal unit, for example, by checking the system log and identifying any abnormalities. Step 5: The reporting unit reports the results of the verification work performed by the verification work execution unit. For example, the reporting unit may summarize the results of the verification work in text form and send it to the operations staff by email. Step 6: The history storage unit stores the history of the verification work performed by the verification work execution unit, for example, in a database, so that it can be referenced in the future.

[0049] (Example 2) The maintenance operation system according to the embodiment of the present invention is a system that learns patterns that have occurred in the past and uses a generation AI to assist in the series of confirmation tasks that are carried out during operation when an alarm occurs. This allows the maintenance operation system to streamline confirmation tasks when an alarm occurs, enabling a quick and accurate response.

[0050] The maintenance operation system according to the embodiment includes an alarm data collection unit, a pattern learning unit, a confirmation work proposal unit, a confirmation work execution unit, a reporting unit, and a history storage unit. The alarm data collection unit collects past alarm data. For example, it collects data such as system errors and security alerts. The pattern learning unit learns patterns based on the data collected by the alarm data collection unit. For example, it uses a machine learning algorithm to learn patterns when an alarm occurs. The confirmation work proposal unit proposes confirmation work when an alarm occurs. For example, it proposes items and procedures to be checked based on past response methods. The confirmation work execution unit executes the confirmation work proposed by the confirmation work proposal unit. For example, it checks system logs and identifies abnormalities. The reporting unit reports the results of the confirmation work performed by the confirmation work execution unit. For example, it summarizes the results of the confirmation work in text and sends them to an operation staff member by email. The history storage unit stores the history of the confirmation work performed by the confirmation work execution unit. For example, it stores the results in a database so that it can be referenced in the future. This enables the maintenance operation system according to the embodiment to streamline confirmation work when an alarm occurs and respond quickly and accurately.

[0051] The pattern learning unit can collect emotional data of the operator when an alarm occurs and learn patterns of emotional fluctuations. For example, the pattern learning unit collects emotional data of the operator when the generation AI learns alarm data and analyzes patterns of emotional fluctuations. For example, it quantifies stress levels and anxiety at the time of an alarm and stores them in a database. The pattern learning unit also collects emotional data of the operator when an alarm occurs and learns patterns of emotional fluctuations. For example, it analyzes heart rate and facial expression changes at the time of an alarm and records emotional fluctuations. The pattern learning unit also collects emotional data of the operator when the generation AI learns alarm data and analyzes patterns of emotional fluctuations. For example, it analyzes voice tone and wording at the time of an alarm to identify emotional fluctuations. This allows for more appropriate responses by learning patterns of emotional fluctuations based on the operator's emotional data.

[0052] The pattern learning unit simultaneously learns not only alarm data but also system operating status and external environmental data, enabling more accurate pattern recognition. For example, when the generation AI is learning alarm data, the pattern learning unit collects system operating status data and performs pattern recognition. For example, it analyzes CPU usage and memory usage to identify the cause of an alarm. The pattern learning unit also collects external environmental data (e.g., weather and network traffic) in addition to alarm data and performs pattern recognition. For example, it analyzes weather data to learn alarm occurrence patterns under specific weather conditions. The pattern learning unit also collects system operating status and external environmental data and performs pattern recognition when the generation AI is learning alarm data. For example, it analyzes network traffic data to identify the impact of increased traffic on alarm occurrence. This enables more accurate pattern recognition by taking system operating status and external environmental data into account.

[0053] The pattern learning unit can use an anomaly detection algorithm to automatically identify undiscovered anomalous patterns and add them to the learning data. For example, when the generation AI learns alarm data, the pattern learning unit uses the anomaly detection algorithm to identify undiscovered anomalous patterns. For example, it automatically detects anomalous log entries and system events and adds them to the learning data. The pattern learning unit also uses the anomaly detection algorithm to identify undiscovered anomalous patterns when learning alarm data. For example, it detects anomalous network traffic and anomalous usage of system resources and adds them to the learning data. The pattern learning unit also uses the anomaly detection algorithm to identify undiscovered anomalous patterns when the generation AI learns alarm data. For example, it detects anomalous user behavior and system errors and adds them to the learning data. In this way, by automatically identifying undiscovered anomalous patterns and adding them to the learning data, the accuracy of anomaly detection is improved.

[0054] The pattern learning unit can integrate alarm data from different systems and networks to perform cross-system pattern learning. In the pattern learning unit, for example, the generation AI collects alarm data from different systems and networks, integrates it, and performs pattern learning. For example, it analyzes data from multiple servers and network devices and identifies common abnormal patterns. The pattern learning unit also integrates alarm data from different systems and networks to perform cross-system pattern learning. For example, it collects alarm data from multiple systems within a data center and learns the overall pattern. The pattern learning unit also collects alarm data from different systems and networks, integrates it, and performs pattern learning. For example, it integrates data from cloud environments and on-premises environments and identifies abnormal patterns. This makes cross-system pattern learning possible by integrating data from different systems and networks.

[0055] The pattern learning unit can share the learning results of the alarm data with other maintenance operation systems, accumulating knowledge across the industry. For example, the generation AI in the pattern learning unit shares the learning results of the alarm data with other maintenance operation systems, accumulating knowledge across the industry. For example, the learning results can be shared using a common database, making them available to other systems. The pattern learning unit also shares the learning results of the alarm data with other maintenance operation systems, accumulating knowledge across the industry. For example, the learning results can be shared in an industry-standard format, making them available to other systems. The pattern learning unit also shares the learning results of the alarm data with other maintenance operation systems, accumulating knowledge across the industry. For example, best practices can be shared across the industry, improving responses in each system. In this way, by sharing learning results, knowledge can be accumulated across the industry, and maintenance operations can be made more efficient.

[0056] The pattern learning unit uses the emotion estimation function to collect emotional data of operators when alarms occur in real time, and can optimize alarm responses based on their emotions. In the pattern learning unit, for example, the generation AI uses the emotion estimation function to collect emotional data of operators when alarms occur in real time. For example, the pattern learning unit analyzes the operator's facial expressions and voice and calculates an emotion score. The pattern learning unit also uses the emotion estimation function to collect emotional data of operators when alarms occur in real time, and optimizes alarm responses based on their emotions. For example, it suggests relaxing if the stress level is high. The pattern learning unit also uses the emotion estimation function to collect emotional data of operators when alarms occur in real time, and optimizes alarm responses based on their emotions. For example, it adjusts response procedures based on the emotional data. This makes it possible to collect emotional data of operators in real time and optimize alarm responses based on their emotions.

[0057] The confirmation task suggestion unit can include stress reduction measures based on the operator's past emotional data. For example, the confirmation task suggestion unit includes stress reduction measures based on the operator's past emotional data in the confirmation tasks suggested by the generation AI. For example, it makes a suggestion to avoid tasks that have been highly stressful in the past. The confirmation task suggestion unit also suggests confirmation tasks that include stress reduction measures based on the operator's past emotional data. For example, it suggests taking a break to relax. The confirmation task suggestion unit also includes stress reduction measures based on the operator's past emotional data in the confirmation tasks suggested by the generation AI. For example, it makes a suggestion to perform the task during a time when stress is low. This makes it possible to suggest confirmation tasks that include stress reduction measures based on the operator's past emotional data.

[0058] The confirmation work proposal unit can propose the optimal confirmation procedure by taking into consideration the operating status of the system and external environmental data at the time an alarm occurs. For example, the generation AI proposes the optimal confirmation procedure by taking into consideration the operating status of the system at the time an alarm occurs. For example, the confirmation work proposal unit adjusts the confirmation procedure based on CPU usage and memory usage. The confirmation work proposal unit also proposes the optimal confirmation procedure by taking into consideration external environmental data (for example, weather and network traffic) when proposing confirmation work. For example, the confirmation work proposal unit adjusts the confirmation procedure based on weather data. The confirmation work proposal unit also proposes the optimal confirmation procedure by taking into consideration the operating status of the system and external environmental data at the time an alarm occurs. For example, the confirmation work proposal unit adjusts the confirmation procedure based on network traffic data. This makes it possible to propose the optimal confirmation procedure by taking into consideration the operating status of the system and external environmental data.

[0059] The confirmation work proposal unit evaluates the effectiveness of the proposed confirmation work, and the generation AI can continuously improve the proposal based on the feedback. For example, the confirmation work proposal unit evaluates the effectiveness of the proposed confirmation work by the generation AI and continuously improves the proposal based on the feedback. For example, it analyzes the success rate and time of the confirmation work and adjusts the proposal. The confirmation work proposal unit also evaluates the effectiveness of the proposed confirmation work, and the generation AI improves the proposal based on the feedback. For example, it collects operator feedback and optimizes the proposal. The confirmation work proposal unit also evaluates the effectiveness of the proposed confirmation work by the generation AI and continuously improves the proposal based on the feedback. For example, it stores the results of the confirmation work in a database and reflects them in the next proposal. In this way, by evaluating the effectiveness of the proposed confirmation work and continuously improving the proposal based on the feedback, it becomes possible to propose more appropriate confirmation work.

[0060] The confirmation work proposal unit can integrate confirmation work when an alarm occurs in different systems or networks, and propose the optimal cross-system confirmation procedure. For example, the generation AI in the confirmation work proposal unit integrates confirmation work when an alarm occurs in different systems or networks, and proposes the optimal confirmation procedure. For example, it analyzes data from multiple servers and network devices and proposes a common confirmation procedure. The confirmation work proposal unit also integrates confirmation work when an alarm occurs in different systems or networks, and proposes the optimal cross-system confirmation procedure. For example, it integrates confirmation work for multiple systems within a data center. The confirmation work proposal unit also integrates confirmation work when an alarm occurs in different systems or networks, and proposes the optimal cross-system confirmation procedure. For example, it integrates confirmation work for cloud environments and on-premise environments. This makes it possible to propose the optimal cross-system confirmation procedure by integrating confirmation work in different systems and networks.

[0061] The confirmation work proposal unit can share the confirmation work proposals with other maintenance and operation systems, accumulating knowledge across the industry. For example, the confirmation work proposal unit shares the confirmation work proposed by the generation AI with other maintenance and operation systems, accumulating knowledge across the industry. For example, the proposal content can be shared using a common database, making it available to other systems. The confirmation work proposal unit also shares the confirmation work proposals with other maintenance and operation systems, accumulating knowledge across the industry. For example, the proposal content can be shared in an industry-standard format, making it available to other systems. The confirmation work proposal unit also shares the confirmation work proposed by the generation AI with other maintenance and operation systems, accumulating knowledge across the industry. For example, best practices can be shared across the industry, improving responses in each system. In this way, by sharing confirmation work proposals, knowledge can be accumulated across the industry, and maintenance and operation can be made more efficient.

[0062] The confirmation task suggestion unit uses the emotion estimation function to suggest confirmation tasks according to the emotional state of the operator, thereby reducing stress. In the confirmation task suggestion unit, for example, the generation AI uses the emotion estimation function to suggest confirmation tasks according to the emotional state of the operator. For example, if the stress level is high, it will prioritize suggesting easier tasks. In addition, the confirmation task suggestion unit uses the emotion estimation function to suggest confirmation tasks according to the emotional state of the operator, thereby reducing stress. For example, it will suggest break times for relaxation. In addition, the generation AI uses the emotion estimation function to suggest confirmation tasks according to the emotional state of the operator. For example, it will adjust work procedures based on emotion data to reduce stress. In this way, stress can be reduced by suggesting confirmation tasks according to the emotional state of the operator.

[0063] The confirmation task execution unit can include stress reduction measures based on the operator's emotional data. For example, the confirmation task execution unit includes stress reduction measures based on the operator's emotional data in the confirmation task that is automatically performed by the generation AI. For example, it adjusts the automatic execution procedure to avoid high-stress tasks. The confirmation task execution unit also automatically executes confirmation tasks that include stress reduction measures based on the operator's emotional data. For example, it automatically sets a break time for relaxation. The confirmation task execution unit also includes stress reduction measures based on the operator's emotional data in the confirmation task that is automatically performed by the generation AI. For example, it automatically executes the task during a time period when stress is low. This makes it possible to execute confirmation tasks that include stress reduction measures based on the operator's emotional data.

[0064] The confirmation task execution unit can select the optimal execution procedure for automatically executed confirmation tasks by taking into account the system's operating status and external environmental data. For example, in confirmation tasks automatically executed by the generation AI, the confirmation task execution unit selects the optimal execution procedure by taking into account the system's operating status. For example, the execution procedure is adjusted based on CPU usage and memory usage. Furthermore, in confirmation tasks automatically executed by the generation AI, the confirmation task execution unit selects the optimal execution procedure by taking into account external environmental data (for example, weather and network traffic). For example, the execution procedure is adjusted based on weather data. Furthermore, in confirmation tasks automatically executed by the generation AI, the confirmation task execution unit selects the optimal execution procedure by taking into account the system's operating status and external environmental data. For example, the execution procedure is adjusted based on network traffic data. This makes it possible to select the optimal execution procedure by taking into account the system's operating status and external environmental data.

[0065] The confirmation task execution unit evaluates the effectiveness of the automatically executed confirmation task, and the generation AI can continuously improve the execution content based on feedback. The confirmation task execution unit, for example, evaluates the effectiveness of the automatically executed confirmation task by the generation AI, and continuously improves the execution content based on feedback. For example, it analyzes the success rate and time of the confirmation task and adjusts the execution content. The confirmation task execution unit also evaluates the effectiveness of the automatically executed confirmation task, and the generation AI improves the execution content based on feedback. For example, it collects operator feedback and optimizes the execution content. The confirmation task execution unit also evaluates the effectiveness of the automatically executed confirmation task by the generation AI, and continuously improves the execution content based on feedback. For example, it stores the results of the confirmation task in a database and reflects them in the next execution. In this way, by evaluating the effectiveness of the automatically executed confirmation task and continuously improving the execution content based on feedback, it becomes possible to execute more appropriate confirmation tasks.

[0066] The verification task execution unit can integrate verification tasks across different systems and networks and propose the optimal cross-system automated execution procedure. In the verification task execution unit, for example, the generation AI integrates verification tasks across different systems and networks and proposes the optimal automated execution procedure. For example, it analyzes data from multiple servers and network devices and proposes a common automated execution procedure. The verification task execution unit also integrates verification tasks across different systems and networks and proposes the optimal cross-system automated execution procedure. For example, it integrates verification tasks across multiple systems within a data center. The verification task execution unit also integrates verification tasks across different systems and networks and proposes the optimal automated execution procedure. For example, it integrates verification tasks for cloud environments and on-premise environments. In this way, by integrating verification tasks across different systems and networks, it becomes possible to propose the optimal cross-system automated execution procedure.

[0067] The confirmation task execution unit can share the results of the automatically performed confirmation tasks with other maintenance and operation systems, accumulating knowledge across the industry. For example, the confirmation task execution unit shares the results of confirmation tasks automatically performed by the generation AI with other maintenance and operation systems, accumulating knowledge across the industry. For example, the results can be shared using a common database, making them available to other systems. The confirmation task execution unit also shares the results of the automatically performed confirmation tasks with other maintenance and operation systems, accumulating knowledge across the industry. For example, the results can be shared in an industry-standard format, making them available to other systems. The confirmation task execution unit also shares the results of confirmation tasks automatically performed by the generation AI with other maintenance and operation systems, accumulating knowledge across the industry. For example, best practices can be shared across the industry, improving responses in each system. In this way, by sharing the results of automatically performed confirmation tasks, knowledge can be accumulated across the industry, and maintenance and operation can be made more efficient.

[0068] The confirmation task execution unit uses the emotion estimation function to select an automatic execution procedure according to the emotional state of the operator, thereby reducing stress. In the confirmation task execution unit, for example, the generation AI uses the emotion estimation function to select an automatic execution procedure according to the emotional state of the operator. For example, when the stress level is high, easy tasks are automatically executed preferentially. The confirmation task execution unit also uses the emotion estimation function to select an automatic execution procedure according to the emotional state of the operator, thereby reducing stress. For example, a break time for relaxation is automatically set. In the confirmation task execution unit, the generation AI also uses the emotion estimation function to select an automatic execution procedure according to the emotional state of the operator. For example, the work procedure is adjusted based on the emotion data to reduce stress. In this way, stress can be reduced by selecting an automatic execution procedure according to the emotional state of the operator.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The maintenance operation system may further include a prediction unit. The prediction unit can predict future alarm occurrences based on past alarm data and system operation status data. For example, it calculates the probability of an alarm occurring during a specific time period or under specific system load conditions, and notifies the operations staff in advance. The prediction unit can also take external environmental data (e.g., weather and network traffic) into consideration to evaluate the risk of alarm occurrence. For example, it can predict the risk of alarm occurrence under specific weather conditions based on weather data. In this way, the prediction unit can predict future alarm occurrences in advance, providing the operations staff with an opportunity to take countermeasures.

[0071] The maintenance operation system can further include a user feedback collection unit. The user feedback collection unit can collect feedback from operators and use it to improve the system. For example, it can collect the operators' opinions and thoughts on the confirmation work procedures and proposal contents. The user feedback collection unit can also collect data on the operators' work efficiency and stress levels and use it to optimize the system. For example, if a particular work procedure is burdensome for the operator, it can make suggestions to improve that procedure. In this way, the user feedback collection unit can reflect the operators' opinions and improve the usability and efficiency of the system.

[0072] The maintenance operation system can further include a training department. The training department can provide alarm response training to operators. For example, it can conduct simulations based on past alarm data to train operators so that they can respond quickly and accurately when an actual alarm occurs. The training department can also provide customized training programs according to the skill level of the operators. For example, it can teach basic response procedures to new operators and advanced troubleshooting techniques to experienced operators. In this way, the training department can improve the skills of operators and improve the quality of alarm response.

[0073] The maintenance operation system can further include a resource optimization unit. The resource optimization unit can monitor the system's resource usage and optimally allocate resources. For example, it can monitor CPU and memory usage in real time and reallocate resources to avoid overload. The resource optimization unit can also secure the resources required in advance when a specific alarm occurs. For example, when an alarm occurs that requires large-scale data processing, the necessary resources can be allocated with priority. This allows the resource optimization unit to maintain stable system operation and improve the efficiency of alarm response.

[0074] The maintenance operation system can further include a communication support unit. The communication support unit supports information sharing between operators and can improve the efficiency of alarm responses. For example, it provides a chat function that allows operators to share information in real time when an alarm occurs. The communication support unit can also provide advice based on past alarm response history. For example, it can present operators with how to respond when a similar alarm occurs. In this way, the communication support unit strengthens cooperation between operators and enables quick and effective alarm responses.

[0075] The maintenance operation system can also use the emotion estimation function to evaluate the performance of operators based on their emotional data. For example, it can evaluate stress levels and concentration levels during alarm response and quantify the operator's performance. It can also use the emotional data to determine an operator's aptitude and the need for training. For example, it can adjust the placement of certain operators to avoid tasks that cause high stress. In this way, performance evaluation using the emotion estimation function can create optimal placement and training plans that take into account the aptitude of the operators.

[0076] The maintenance operation system can also use the emotion estimation function to conduct team building based on the emotion data of operators. For example, teams can be formed of operators who get along well with each other based on the emotion data. The emotion data can also be used to provide advice to smooth communication within the team. For example, suggestions can be made to operators who are under a lot of stress to help them relax. In this way, team building using the emotion estimation function can promote cooperation between operators and improve the efficiency of alarm response.

[0077] The maintenance operation system can further use the emotion estimation function to provide motivation improvement measures based on the operator's emotion data. For example, incentives to increase the operator's motivation can be proposed based on the emotion data. Also, based on the emotion data, a refreshment program to reduce operator stress can be provided. For example, regular breaks and relaxation can be proposed. In this way, motivation improvement measures using the emotion estimation function can improve the ease of work for operators and improve the quality of alarm response.

[0078] The maintenance operation system can also use the emotion estimation function to provide feedback based on the operator's emotional data. For example, based on the emotional data, feedback can be given according to the operator's stress level or concentration level. The emotional data can also be used to evaluate the operator's performance and suggest areas for improvement. For example, if a specific work procedure is causing stress, suggestions can be made to improve that procedure. In this way, feedback using the emotion estimation function can help improve operator performance and increase the efficiency of alarm response.

[0079] The maintenance operation system can also use the emotion estimation function to manage stress based on the operator's emotional data. For example, the emotional data can be used to monitor the operator's stress level in real time and suggest stress reduction measures as needed. The emotional data can also be used to take preventative measures before the operator's stress level increases. For example, the system can suggest regular refreshment and relaxation time. In this way, stress management using the emotion estimation function can maintain the health of operators and improve the quality of alarm response.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The alarm data collection unit collects past alarm data, such as data on system errors and security alerts. Step 2: The pattern learning unit learns patterns based on the data collected by the alarm data collection unit. For example, the pattern at the time of alarm occurrence is learned using a machine learning algorithm. Step 3: The confirmation work suggestion unit suggests confirmation work when an alarm occurs. For example, it suggests items and procedures to be checked based on past response methods. Step 4: The verification work execution unit executes the verification work proposed by the verification work proposal unit, for example, by checking the system log and identifying any abnormalities. Step 5: The reporting unit reports the results of the verification work performed by the verification work execution unit. For example, the reporting unit may summarize the results of the verification work in text form and send it to the operations staff by email. Step 6: The history storage unit stores the history of the verification work performed by the verification work execution unit, for example, in a database, so that it can be referenced in the future.

[0082] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an alarm data collection unit that collects past alarm data; a pattern learning unit that learns a pattern based on the data collected by the alarm data collecting unit; a confirmation work suggestion unit that suggests confirmation work when an alarm occurs; a confirmation work execution unit that executes the confirmation work proposed by the confirmation work proposal unit; a reporting unit that reports a result of the confirmation work performed by the confirmation work performing unit; a history storage unit that stores a history of the confirmation work performed by the confirmation work execution unit. A system characterized by:

2. The pattern learning unit Collect emotional data from operators when the alarm occurs and learn patterns of emotional fluctuations.

2. The system of claim 1.

3. The pattern learning unit In addition to the alarm data, the system also learns the operating status and external environmental data, enabling more accurate pattern recognition.

2. The system of claim 1.

4. The pattern learning unit Anomaly detection algorithms are used to automatically identify undiscovered abnormal patterns and add them to the training data.

2. The system of claim 1.

5. The pattern learning unit Integrate the alarm data from the different systems and networks to perform cross-system pattern learning 2. The system of claim 1.

Images