System

Abstract

An object of a system according to an embodiment is to improve the efficiency of network maintenance plan creation work.SOLUTION: A system includes a collection part, an analysis part, a planning part, and a procedure part. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The planning unit creates a maintenance plan based on the analysis result obtained by the analysis unit. The procedure unit generates an operation procedure based on the maintenance plan created by the planning 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, the task of creating a network maintenance plan is complicated, making it difficult to carry out efficient planned maintenance.

[0005] The system according to the embodiment aims to improve the efficiency of network maintenance plan creation work. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a planning unit, and a procedure unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The planning unit creates a maintenance plan based on the analysis results obtained by the analysis unit. The procedure unit generates a work procedure based on the maintenance plan created by the planning unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of network maintenance plan creation work. [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) A maintenance plan creation system according to an embodiment of the present invention uses a generation AI to create optimal maintenance plans and work procedures. The maintenance plan creation system collects data such as past failure records, recovery methods, and maintenance response details, and the generation AI analyzes this data to create an optimal maintenance plan and further generates specific work procedures. For example, the maintenance plan creation system extracts necessary information from various databases and log files and stores it in a database. Next, the generation AI analyzes the collected data and creates an optimal maintenance plan for performing planned maintenance before a failure occurs. Furthermore, specific work procedures are generated based on the maintenance plan created by the generation AI. This allows the maintenance plan creation system to improve the efficiency of time and costs associated with planned maintenance. For example, by planning maintenance in advance, the need to respond to unexpected failures is reduced, improving the efficiency of maintenance work. Furthermore, by providing appropriate work procedures, work errors are reduced and the quality of maintenance work is improved.

[0029] A maintenance plan creation system according to an embodiment includes a collection unit, an analysis unit, a planning unit, and a procedure unit. The collection unit collects data. The collection unit can extract necessary information from, for example, various databases and log files. The collection unit can collect information such as the type and frequency of past failures and the time required for recovery. The collection unit can also acquire information stored in a database. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses a generation AI to perform analysis based on data such as past failure records, recovery methods, and maintenance response details. For example, if a specific piece of equipment is likely to fail within a certain period of time, the analysis unit can plan maintenance of that equipment in advance. The planning unit creates a maintenance plan based on the analysis results obtained by the analysis unit. The planning unit, for example, uses a generation AI to create an optimal maintenance plan. The planning unit can create a maintenance schedule for a specific piece of equipment. The procedure unit generates a work procedure based on the maintenance plan created by the planning unit. The procedure unit uses, for example, a generation AI to create a work procedure manual that details the procedure for each task, the necessary materials, the work time, etc. The procedure unit can, for example, list the procedures, tools, replacement parts, etc. required for the replacement work of a specific device. This allows the maintenance plan creation system according to the embodiment to efficiently collect data, analyze it, create plans, and generate work procedures.

[0030] The collection unit includes an extraction unit that extracts information from various databases or log files. The extraction unit can extract information from, for example, SQL databases or NoSQL databases. The extraction unit can also extract information from, for example, system logs or application logs. The extraction unit can extract information such as the type and frequency of past failures, and the time required for recovery. This allows for efficient extraction of necessary information.

[0031] The analysis unit includes an algorithm unit that performs analysis based on data on past failure records, recovery methods, and maintenance response details. The algorithm unit performs analysis based on, for example, data on past failure records. The algorithm unit can analyze data such as the type of failure, the date and time of occurrence, and the extent of impact. The algorithm unit can also perform analysis based on data on recovery methods, for example. The algorithm unit can analyze manual recovery methods and automatic recovery methods, for example. The algorithm unit can also perform analysis based on data on maintenance response details, for example. The algorithm unit can analyze data such as response procedures and response times, for example. This makes it possible to perform analysis based on past data.

[0032] The planning unit includes an optimization unit that creates a maintenance plan. The optimization unit creates an optimal maintenance plan using, for example, a generation AI. The optimization unit can, for example, create a maintenance schedule for a specific device. The optimization unit can, for example, perform optimization based on evaluation criteria for the maintenance plan. The optimization unit can, for example, evaluate the efficiency and cost-effectiveness of the maintenance plan. This allows the creation of an optimal maintenance plan.

[0033] The procedure unit includes a creation unit that creates a work procedure manual that details the procedures, materials, and work time for each task. The creation unit creates the work procedure manual, for example, using a generation AI. The creation unit can, for example, list the procedures, tools, replacement parts, etc. required for the replacement work of a specific device. The creation unit can, for example, describe the details of the work procedure. The creation unit can, for example, describe the work time and required materials. The creation unit can, for example, customize the format and content of the work procedure manual. This allows for the creation of detailed work procedure manuals.

[0034] The collection unit can analyze past data collection history and select a collection method. The collection unit, for example, analyzes past data collection history. The collection unit can select, for example, the most efficient collection method. The collection unit can customize the collection method based on the past data collection history. The collection unit can analyze, for example, past data collection history and identify areas for improvement in the collection method. This makes it possible to select the optimal collection method based on the past data collection history.

[0035] The collection unit can filter data based on the current state or load status of the network when collecting data. The collection unit, for example, evaluates the current state of the network. The collection unit can evaluate, for example, the network bandwidth, delay, packet loss rate, etc. The collection unit can evaluate, for example, the network load status. The collection unit can evaluate, for example, the CPU usage rate, memory usage rate, etc. The collection unit can temporarily stop data collection, for example, when the network load is high. The collection unit can resume data collection, for example, when the network status is stable. The collection unit can adjust the type of data to be collected, for example, depending on the network load status. This allows data collection to be adjusted according to the network status.

[0036] When collecting data, the collection unit can adjust the collection frequency depending on the importance of the data to be collected. The collection unit, for example, evaluates the importance of the data. The collection unit can evaluate, for example, the frequency of data use and the impact on business. The collection unit, for example, can collect data with high importance more frequently. The collection unit, for example, can reduce the collection frequency of data with low importance. The collection unit, for example, can dynamically adjust the collection frequency depending on the importance of the data. This makes it possible to adjust the collection frequency depending on the importance of the data.

[0037] During data collection, the collection unit can prioritize collection of highly relevant data based on geographical network distribution. The collection unit, for example, evaluates the geographical network distribution. The collection unit can evaluate, for example, network topology and geographical location information. The collection unit, for example, can prioritize collection of data from geographically important areas. The collection unit, for example, can prioritize collection of data with high geographical relevance. The collection unit, for example, can determine the priority of data to be collected taking into account the geographical network distribution. This allows data to be collected taking into account the geographical network distribution.

[0038] During data collection, the collection unit can analyze social media or external data sources to collect relevant data. For example, the collection unit can analyze social media posts and collect relevant data. For example, the collection unit can collect data from social media such as Twitter (registered trademark), Facebook (registered trademark), and Instagram (registered trademark). For example, the collection unit can collect relevant data from external data sources. For example, the collection unit can collect data from an API or a public database. For example, the collection unit can analyze social media or external data sources and determine the priority of data to be collected. This allows the collection of relevant data by analyzing social media or external data sources.

[0039] The collection unit can customize the collection method based on past feedback when collecting data. The collection unit can, for example, improve the collection method based on past feedback. The collection unit can customize the collection method by reflecting, for example, user evaluations and improvement suggestions. The collection unit can, for example, analyze past feedback and identify areas for improvement in the collection method. This allows the collection method to be customized by reflecting past feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis depending on the importance of the data. The analysis unit, for example, evaluates the importance of the data. The analysis unit can evaluate, for example, the frequency of data use and the impact on business. The analysis unit, for example, can analyze data with high importance in detail. The analysis unit, for example, can analyze data with low importance in a simplified manner. The analysis unit, for example, can dynamically adjust the level of detail of the analysis depending on the importance of the data. This makes it possible to adjust the level of detail of the analysis depending on the importance of the data.

[0041] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit, for example, evaluates the category of data. The analysis unit can evaluate categories such as text data, numerical data, and image data. The analysis unit can select an optimal analysis algorithm depending on the category of data. The analysis unit can customize an analysis algorithm based on the category of data. The analysis unit can perform analysis by combining multiple analysis algorithms depending on the category of data. This makes it possible to apply an optimal analysis algorithm depending on the category of data.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on past analysis results. The analysis unit can, for example, improve the analysis algorithm based on past analysis results. The analysis unit can, for example, improve the accuracy of the analysis by referring to past analysis results. The analysis unit can, for example, analyze past analysis results and identify areas for improvement in the analysis. This makes it possible to improve the accuracy of the analysis by referring to past analysis results.

[0043] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, evaluates the time when the data was collected. The analysis unit can evaluate, for example, the collection date and time or the collection frequency. The analysis unit, for example, can prioritize the analysis of the most recent data. The analysis unit, for example, can lower the analysis priority of older data. The analysis unit, for example, can dynamically adjust the analysis priority based on the time when the data was collected. This allows the analysis priority to be determined based on the time when the data was collected.

[0044] During analysis, the analysis unit can adjust the order of analysis depending on the relevance of the data. The analysis unit, for example, evaluates the relevance of the data. The analysis unit can evaluate the relevance of the data using, for example, correlation analysis or co-occurrence networks. The analysis unit, for example, can prioritize the analysis of highly relevant data. The analysis unit, for example, can postpone the order of analysis of less relevant data. The analysis unit, for example, can dynamically adjust the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis depending on the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. The analysis unit can evaluate the user's level of expertise based on, for example, a questionnaire survey or past usage history. For example, the analysis unit can use a lot of technical terms if the user's level of expertise is high. For example, the analysis unit can avoid using technical terms if the user's level of expertise is low. The analysis unit can adjust the way the analysis results are expressed depending on, for example, the user's level of expertise. This makes it possible to adjust the way the analysis results are expressed depending on the user's level of expertise.

[0046] When creating a plan, the planning unit can adjust the level of detail of the plan depending on the importance of the equipment. The planning unit, for example, evaluates the importance of the equipment. The planning unit can evaluate, for example, the frequency of use of the equipment and the impact on the business. The planning unit, for example, can create a detailed maintenance plan for equipment with high importance. The planning unit, for example, can create a simple maintenance plan for equipment with low importance. The planning unit, for example, can dynamically adjust the level of detail of the plan depending on the importance of the equipment. This makes it possible to adjust the level of detail of the plan depending on the importance of the equipment.

[0047] The planning unit can apply different planning algorithms depending on the category of equipment when creating a plan. The planning unit, for example, evaluates the category of equipment. The planning unit can evaluate categories such as servers, network equipment, and storage devices. The planning unit can select an optimal planning algorithm depending on the category of equipment. The planning unit can customize a planning algorithm based on the category of equipment. The planning unit can create a plan by combining multiple planning algorithms depending on the category of equipment. This makes it possible to apply an optimal planning algorithm depending on the category of equipment.

[0048] When creating a plan, the planning unit can improve the accuracy of the plan based on past planning results. The planning unit can, for example, improve a planning algorithm based on past planning results. The planning unit can, for example, improve the accuracy of the plan by referring to past planning results. The planning unit can, for example, analyze past planning results and identify areas for improvement in the plan. In this way, the accuracy of the plan can be improved by referring to past planning results.

[0049] When creating a plan, the planning unit can determine the priority of the plan based on the time when the equipment is used. The planning unit, for example, evaluates the time when the equipment is used. The planning unit can evaluate, for example, the start date of use and the frequency of use. The planning unit, for example, can create a maintenance plan giving priority to the newest equipment. The planning unit, for example, can lower the priority of the plan for older equipment. The planning unit, for example, can dynamically adjust the priority of the plan based on the time when the equipment is used. This makes it possible to determine the priority of the plan based on the time when the equipment is used.

[0050] The planning unit can adjust the order of the plan based on the relevance of the equipment when creating the plan. The planning unit, for example, evaluates the relevance of the equipment. The planning unit can evaluate the relevance of the equipment using, for example, correlation analysis or co-occurrence networks. The planning unit, for example, can prioritize incorporating highly relevant equipment into the maintenance plan. The planning unit, for example, can postpone the order of the plan for less relevant equipment. The planning unit, for example, can dynamically adjust the order of the plan based on the relevance of the equipment. This makes it possible to adjust the order of the plan based on the relevance of the equipment.

[0051] When creating a plan, the planning unit can adjust the use of technical terms in the plan depending on the user's level of expertise. The planning unit, for example, evaluates the user's level of expertise. The planning unit can evaluate the user's level of expertise based on, for example, a questionnaire survey or past usage history. For example, the planning unit can use a lot of technical terms if the user's level of expertise is high. For example, the planning unit can avoid technical terms if the user's level of expertise is low. The planning unit can adjust the way the plan is expressed depending on, for example, the user's level of expertise. This makes it possible to adjust the way the plan is expressed depending on the user's level of expertise.

[0052] When generating a work procedure, the procedure section can adjust the level of detail of the procedure depending on the importance of the work. The procedure section, for example, evaluates the importance of the work. The procedure section, for example, can evaluate the scope of impact of the work and the impact on business. The procedure section, for example, can create detailed procedures for work of high importance. The procedure section, for example, can create simplified procedures for work of low importance. The procedure section, for example, can dynamically adjust the level of detail of the procedure depending on the importance of the work. This makes it possible to adjust the level of detail of the procedure depending on the importance of the work.

[0053] When generating a work procedure, the procedure unit can apply different procedure generation algorithms depending on the work category. The procedure unit, for example, evaluates the work category. The procedure unit can evaluate categories such as maintenance work, repair work, and upgrade work. The procedure unit can select an optimal procedure generation algorithm depending on the work category. The procedure unit can customize the procedure generation algorithm based on the work category. The procedure unit can create a procedure by combining multiple procedure generation algorithms depending on the work category. This makes it possible to apply an optimal procedure generation algorithm depending on the work category.

[0054] When generating a work procedure, the procedure unit can improve the accuracy of the procedure based on past procedure results. For example, the procedure unit can improve the procedure generation algorithm based on past procedure results. For example, the procedure unit can improve the accuracy of the procedure by referring to past procedure results. For example, the procedure unit can analyze past procedure results and identify areas for improvement in the procedure. In this way, the accuracy of the procedure can be improved by referring to past procedure results.

[0055] When generating work procedures, the procedure section can determine the priority of procedures based on the timing of the work. The procedure section, for example, evaluates the timing of the work. The procedure section can evaluate, for example, the start date of the work and the frequency of the work. The procedure section, for example, can prioritize the creation of procedures for work to be performed soon. The procedure section, for example, can postpone procedures for work that are not scheduled for execution for a long time. The procedure section, for example, can dynamically adjust the priority of procedures based on the timing of the work. This makes it possible to determine the priority of procedures based on the timing of the work.

[0056] When generating a work procedure, the procedure unit can adjust the order of procedures based on the relevance of the tasks. The procedure unit, for example, evaluates the relevance of the tasks. The procedure unit can evaluate the relevance of the tasks using, for example, correlation analysis or co-occurrence networks. The procedure unit, for example, can prioritize incorporating highly relevant tasks into the procedure. The procedure unit, for example, can postpone the order of less relevant tasks. The procedure unit, for example, can dynamically adjust the order of procedures based on the relevance of the tasks. This makes it possible to adjust the order of procedures based on the relevance of the tasks.

[0057] When generating a work procedure, the procedure unit can adjust the use of technical terms in the procedure depending on the user's level of expertise. The procedure unit, for example, evaluates the user's level of expertise. The procedure unit can evaluate the user's level of expertise based on, for example, a questionnaire survey or past usage history. The procedure unit can use a lot of technical terms if the user's level of expertise is high. The procedure unit can avoid using technical terms if the user's level of expertise is low. The procedure unit can adjust the way the procedure is expressed depending on, for example, the user's level of expertise. This makes it possible to adjust the way the procedure is expressed depending on the user's level of expertise.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, it can evaluate categories such as text data, numerical data, and image data and select the optimal analysis algorithm. It can also customize the analysis algorithm based on the data category. Furthermore, it can analyze by combining multiple analysis algorithms depending on the data category. This allows it to apply the optimal analysis algorithm depending on the data category.

[0060] When generating work procedures, the procedure unit can apply different procedure generation algorithms depending on the work category. For example, it can evaluate categories such as maintenance work, repair work, and upgrade work and select the optimal procedure generation algorithm. It can also customize the procedure generation algorithm based on the work category. Furthermore, it can create procedures by combining multiple procedure generation algorithms depending on the work category. This makes it possible to apply the optimal procedure generation algorithm depending on the work category.

[0061] The collection unit can filter data during data collection based on the current network status or load status. For example, it can evaluate network bandwidth, latency, packet loss rate, etc., and temporarily stop data collection if the network load is high. It can also resume data collection when the network status is stable. Furthermore, it can adjust the type of data to be collected depending on the network load status. This allows data collection to be adjusted according to the network status.

[0062] When creating a plan, the planning unit can apply different planning algorithms depending on the category of equipment. For example, it can evaluate categories such as servers, network equipment, and storage devices and select the optimal planning algorithm. It can also customize the planning algorithm based on the category of equipment. Furthermore, it can create a plan by combining multiple planning algorithms depending on the category of equipment. This makes it possible to apply the optimal planning algorithm depending on the category of equipment.

[0063] During data collection, the collection unit can analyze social media or external data sources to collect relevant data. For example, it can analyze social media posts and collect relevant data. It can also collect data from social media such as Twitter, Facebook, and Instagram. It can also collect relevant data from external data sources. This allows it to analyze social media and external data sources to collect relevant data.

[0064] During analysis, the analysis unit can determine the analysis priority based on the time of data collection. For example, the analysis unit can evaluate the collection date and time and collection frequency and prioritize analysis of the most recent data. Also, the analysis priority of older data can be lowered. Furthermore, the analysis priority can be dynamically adjusted based on the time of data collection. This allows the analysis priority to be determined based on the time of data collection.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects data. The collection unit can extract necessary information from various databases and log files, for example. The collection unit can collect information such as the type of failures that have occurred in the past, their frequency, and the time required for recovery. The collection unit can also acquire information stored in a database. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses a generation AI to perform analysis based on data such as past failure records, recovery methods, and maintenance response details. For example, if a specific device is likely to fail within a certain period of time, the analysis unit can plan maintenance for that device in advance. Step 3: The planning unit creates a maintenance plan based on the analysis results obtained by the analysis unit. The planning unit creates an optimal maintenance plan, for example, using a generation AI. The planning unit can create, for example, a maintenance schedule for a specific piece of equipment. Step 4: The procedure section generates work procedures based on the maintenance plan created by the planning section. For example, the procedure section uses a generation AI to create a work procedure manual that details the steps, required materials, and work time for each task. For example, the procedure section can list the procedures, tools, and replacement parts required for replacing a specific piece of equipment.

[0067] (Example 2) A maintenance plan creation system according to an embodiment of the present invention uses a generation AI to create optimal maintenance plans and work procedures. The maintenance plan creation system collects data such as past failure records, recovery methods, and maintenance response details, and the generation AI analyzes this data to create an optimal maintenance plan and further generates specific work procedures. For example, the maintenance plan creation system extracts necessary information from various databases and log files and stores it in a database. Next, the generation AI analyzes the collected data and creates an optimal maintenance plan for performing planned maintenance before a failure occurs. Furthermore, specific work procedures are generated based on the maintenance plan created by the generation AI. This allows the maintenance plan creation system to improve the efficiency of time and costs associated with planned maintenance. For example, by planning maintenance in advance, the need to respond to unexpected failures is reduced, improving the efficiency of maintenance work. Furthermore, by providing appropriate work procedures, work errors are reduced and the quality of maintenance work is improved.

[0068] A maintenance plan creation system according to an embodiment includes a collection unit, an analysis unit, a planning unit, and a procedure unit. The collection unit collects data. The collection unit can extract necessary information from, for example, various databases and log files. The collection unit can collect information such as the type and frequency of past failures and the time required for recovery. The collection unit can also acquire information stored in a database. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses a generation AI to perform analysis based on data such as past failure records, recovery methods, and maintenance response details. For example, if a specific piece of equipment is likely to fail within a certain period of time, the analysis unit can plan maintenance of that equipment in advance. The planning unit creates a maintenance plan based on the analysis results obtained by the analysis unit. The planning unit, for example, uses a generation AI to create an optimal maintenance plan. The planning unit can create a maintenance schedule for a specific piece of equipment. The procedure unit generates a work procedure based on the maintenance plan created by the planning unit. The procedure unit uses, for example, a generation AI to create a work procedure manual that details the procedure for each task, the necessary materials, the work time, etc. The procedure unit can, for example, list the procedures, tools, replacement parts, etc. required for the replacement work of a specific device. This allows the maintenance plan creation system according to the embodiment to efficiently collect data, analyze it, create plans, and generate work procedures.

[0069] The collection unit includes an extraction unit that extracts information from various databases or log files. The extraction unit can extract information from, for example, SQL databases or NoSQL databases. The extraction unit can also extract information from, for example, system logs or application logs. The extraction unit can extract information such as the type and frequency of past failures, and the time required for recovery. This allows for efficient extraction of necessary information.

[0070] The analysis unit includes an algorithm unit that performs analysis based on data on past failure records, recovery methods, and maintenance response details. The algorithm unit performs analysis based on, for example, data on past failure records. The algorithm unit can analyze data such as the type of failure, the date and time of occurrence, and the extent of impact. The algorithm unit can also perform analysis based on data on recovery methods, for example. The algorithm unit can analyze manual recovery methods and automatic recovery methods, for example. The algorithm unit can also perform analysis based on data on maintenance response details, for example. The algorithm unit can analyze data such as response procedures and response times, for example. This makes it possible to perform analysis based on past data.

[0071] The planning unit includes an optimization unit that creates a maintenance plan. The optimization unit creates an optimal maintenance plan using, for example, a generation AI. The optimization unit can, for example, create a maintenance schedule for a specific device. The optimization unit can, for example, perform optimization based on evaluation criteria for the maintenance plan. The optimization unit can, for example, evaluate the efficiency and cost-effectiveness of the maintenance plan. This allows the creation of an optimal maintenance plan.

[0072] The procedure unit includes a creation unit that creates a work procedure manual that details the procedures, materials, and work time for each task. The creation unit creates the work procedure manual, for example, using a generation AI. The creation unit can, for example, list the procedures, tools, replacement parts, etc. required for the replacement work of a specific device. The creation unit can, for example, describe the details of the work procedure. The creation unit can, for example, describe the work time and required materials. The creation unit can, for example, customize the format and content of the work procedure manual. This allows for the creation of detailed work procedure manuals.

[0073] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, uses an emotion recognition algorithm to estimate the user's emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden. For example, if the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. For example, if the user is in a hurry, the collection unit can quickly collect data to collect the minimum amount of data necessary. This makes it possible to adjust the timing of data collection based on the user's emotions.

[0074] The collection unit can analyze past data collection history and select a collection method. The collection unit, for example, analyzes past data collection history. The collection unit can select, for example, the most efficient collection method. The collection unit can customize the collection method based on the past data collection history. The collection unit can analyze, for example, past data collection history and identify areas for improvement in the collection method. This makes it possible to select the optimal collection method based on the past data collection history.

[0075] The collection unit can filter data based on the current state or load status of the network when collecting data. The collection unit, for example, evaluates the current state of the network. The collection unit can evaluate, for example, the network bandwidth, delay, packet loss rate, etc. The collection unit can evaluate, for example, the network load status. The collection unit can evaluate, for example, the CPU usage rate, memory usage rate, etc. The collection unit can temporarily stop data collection, for example, when the network load is high. The collection unit can resume data collection, for example, when the network status is stable. The collection unit can adjust the type of data to be collected, for example, depending on the network load status. This allows data collection to be adjusted according to the network status.

[0076] When collecting data, the collection unit can adjust the collection frequency depending on the importance of the data to be collected. The collection unit, for example, evaluates the importance of the data. The collection unit can evaluate, for example, the frequency of data use and the impact on business. The collection unit, for example, can collect data with high importance more frequently. The collection unit, for example, can reduce the collection frequency of data with low importance. The collection unit, for example, can dynamically adjust the collection frequency depending on the importance of the data. This makes it possible to adjust the collection frequency depending on the importance of the data.

[0077] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion using, for example, an emotion recognition algorithm. For example, when the user is feeling stressed, the collection unit can prioritize collecting data of high importance. For example, when the user is relaxed, the collection unit can prioritize collecting detailed data. For example, when the user is in a hurry, the collection unit can prioritize collecting the minimum amount of data necessary. This makes it possible to determine the priority of data to be collected based on the user's emotion.

[0078] During data collection, the collection unit can prioritize collection of highly relevant data based on geographical network distribution. The collection unit, for example, evaluates the geographical network distribution. The collection unit can evaluate, for example, network topology and geographical location information. The collection unit, for example, can prioritize collection of data from geographically important areas. The collection unit, for example, can prioritize collection of data with high geographical relevance. The collection unit, for example, can determine the priority of data to be collected taking into account the geographical network distribution. This allows data to be collected taking into account the geographical network distribution.

[0079] During data collection, the collection unit may analyze social media or external data sources to collect relevant data. For example, the collection unit may analyze social media posts and collect relevant data. For example, the collection unit may collect data from social media such as Twitter, Facebook, and Instagram. For example, the collection unit may collect relevant data from external data sources. For example, the collection unit may collect data from an API or a public database. For example, the collection unit may analyze social media or external data sources and determine the priority of data to be collected. This allows the collection of relevant data by analyzing social media or external data sources.

[0080] The collection unit can customize the collection method based on past feedback when collecting data. The collection unit can, for example, improve the collection method based on past feedback. The collection unit can customize the collection method by reflecting, for example, user evaluations and improvement suggestions. The collection unit can, for example, analyze past feedback and identify areas for improvement in the collection method. This allows the collection method to be customized by reflecting past feedback.

[0081] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented depending on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, an emotion recognition algorithm. For example, if the user is nervous, the analysis unit can provide a simple and highly visible analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a summary analysis result. This makes it possible to adjust the way the analysis is presented depending on the user's emotion.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis depending on the importance of the data. The analysis unit, for example, evaluates the importance of the data. The analysis unit can evaluate, for example, the frequency of data use and the impact on business. The analysis unit, for example, can analyze data with high importance in detail. The analysis unit, for example, can analyze data with low importance in a simplified manner. The analysis unit, for example, can dynamically adjust the level of detail of the analysis depending on the importance of the data. This makes it possible to adjust the level of detail of the analysis depending on the importance of the data.

[0083] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit, for example, evaluates the category of data. The analysis unit can evaluate categories such as text data, numerical data, and image data. The analysis unit can select an optimal analysis algorithm depending on the category of data. The analysis unit can customize an analysis algorithm based on the category of data. The analysis unit can perform analysis by combining multiple analysis algorithms depending on the category of data. This makes it possible to apply an optimal analysis algorithm depending on the category of data.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis based on past analysis results. The analysis unit can, for example, improve the analysis algorithm based on past analysis results. The analysis unit can, for example, improve the accuracy of the analysis by referring to past analysis results. The analysis unit can, for example, analyze past analysis results and identify areas for improvement in the analysis. This makes it possible to improve the accuracy of the analysis by referring to past analysis results.

[0085] The analysis unit can estimate the user's emotion and adjust the length of the analysis depending on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, an emotion recognition algorithm. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows the length of the analysis to be adjusted depending on the user's emotion.

[0086] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, evaluates the time when the data was collected. The analysis unit can evaluate, for example, the collection date and time or the collection frequency. The analysis unit, for example, can prioritize the analysis of the most recent data. The analysis unit, for example, can lower the analysis priority of older data. The analysis unit, for example, can dynamically adjust the analysis priority based on the time when the data was collected. This allows the analysis priority to be determined based on the time when the data was collected.

[0087] During analysis, the analysis unit can adjust the order of analysis depending on the relevance of the data. The analysis unit, for example, evaluates the relevance of the data. The analysis unit can evaluate the relevance of the data using, for example, correlation analysis or co-occurrence networks. The analysis unit, for example, can prioritize the analysis of highly relevant data. The analysis unit, for example, can postpone the order of analysis of less relevant data. The analysis unit, for example, can dynamically adjust the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data.

[0088] During analysis, the analysis unit can adjust the use of technical terms in the analysis depending on the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. The analysis unit can evaluate the user's level of expertise based on, for example, a questionnaire survey or past usage history. For example, the analysis unit can use a lot of technical terms if the user's level of expertise is high. For example, the analysis unit can avoid using technical terms if the user's level of expertise is low. The analysis unit can adjust the way the analysis results are expressed depending on, for example, the user's level of expertise. This makes it possible to adjust the way the analysis results are expressed depending on the user's level of expertise.

[0089] The planning unit can estimate the user's emotions and adjust the way the maintenance plan is presented based on the estimated user's emotions. The planning unit estimates the user's emotions using, for example, an emotion recognition algorithm. For example, if the user is nervous, the planning unit can provide a simple, highly visible maintenance plan. For example, if the user is relaxed, the planning unit can provide a detailed maintenance plan. For example, if the user is in a hurry, the planning unit can provide a maintenance plan that focuses on the main points. This makes it possible to adjust the way the maintenance plan is presented based on the user's emotions.

[0090] When creating a plan, the planning unit can adjust the level of detail of the plan depending on the importance of the equipment. The planning unit, for example, evaluates the importance of the equipment. The planning unit can evaluate, for example, the frequency of use of the equipment and the impact on the business. The planning unit, for example, can create a detailed maintenance plan for equipment with high importance. The planning unit, for example, can create a simple maintenance plan for equipment with low importance. The planning unit, for example, can dynamically adjust the level of detail of the plan depending on the importance of the equipment. This makes it possible to adjust the level of detail of the plan depending on the importance of the equipment.

[0091] The planning unit can apply different planning algorithms depending on the category of equipment when creating a plan. The planning unit, for example, evaluates the category of equipment. The planning unit can evaluate categories such as servers, network equipment, and storage devices. The planning unit can select an optimal planning algorithm depending on the category of equipment. The planning unit can customize a planning algorithm based on the category of equipment. The planning unit can create a plan by combining multiple planning algorithms depending on the category of equipment. This makes it possible to apply an optimal planning algorithm depending on the category of equipment.

[0092] When creating a plan, the planning unit can improve the accuracy of the plan based on past planning results. The planning unit can, for example, improve a planning algorithm based on past planning results. The planning unit can, for example, improve the accuracy of the plan by referring to past planning results. The planning unit can, for example, analyze past planning results and identify areas for improvement in the plan. In this way, the accuracy of the plan can be improved by referring to past planning results.

[0093] The planning unit can estimate the user's emotion and adjust the length of the plan depending on the estimated user's emotion. The planning unit estimates the user's emotion using, for example, an emotion recognition algorithm. For example, if the user is in a hurry, the planning unit can provide a short and concise plan. For example, if the user is relaxed, the planning unit can provide a detailed plan. For example, if the user is excited, the planning unit can provide a visually stimulating plan. This allows the length of the plan to be adjusted depending on the user's emotion.

[0094] When creating a plan, the planning unit can determine the priority of the plan based on the time when the equipment is used. The planning unit, for example, evaluates the time when the equipment is used. The planning unit can evaluate, for example, the start date of use and the frequency of use. The planning unit, for example, can create a maintenance plan giving priority to the newest equipment. The planning unit, for example, can lower the priority of the plan for older equipment. The planning unit, for example, can dynamically adjust the priority of the plan based on the time when the equipment is used. This makes it possible to determine the priority of the plan based on the time when the equipment is used.

[0095] The planning unit can adjust the order of the plan based on the relevance of the equipment when creating the plan. The planning unit, for example, evaluates the relevance of the equipment. The planning unit can evaluate the relevance of the equipment using, for example, correlation analysis or co-occurrence networks. The planning unit, for example, can prioritize incorporating highly relevant equipment into the maintenance plan. The planning unit, for example, can postpone the order of the plan for less relevant equipment. The planning unit, for example, can dynamically adjust the order of the plan based on the relevance of the equipment. This makes it possible to adjust the order of the plan based on the relevance of the equipment.

[0096] When creating a plan, the planning unit can adjust the use of technical terms in the plan depending on the user's level of expertise. The planning unit, for example, evaluates the user's level of expertise. The planning unit can evaluate the user's level of expertise based on, for example, a questionnaire survey or past usage history. For example, the planning unit can use a lot of technical terms if the user's level of expertise is high. For example, the planning unit can avoid technical terms if the user's level of expertise is low. The planning unit can adjust the way the plan is expressed depending on, for example, the user's level of expertise. This makes it possible to adjust the way the plan is expressed depending on the user's level of expertise.

[0097] The procedure unit can estimate the user's emotion and adjust the way in which the work procedure is presented based on the estimated user's emotion. The procedure unit estimates the user's emotion using, for example, an emotion recognition algorithm. For example, if the user is nervous, the procedure unit can provide simple, highly visible work procedures. For example, if the user is relaxed, the procedure unit can provide detailed work procedures. For example, if the user is in a hurry, the procedure unit can provide work procedures that focus on the main points. This makes it possible to adjust the way in which the work procedure is presented based on the user's emotion.

[0098] When generating a work procedure, the procedure section can adjust the level of detail of the procedure depending on the importance of the work. The procedure section, for example, evaluates the importance of the work. The procedure section, for example, can evaluate the scope of impact of the work and the impact on business. The procedure section, for example, can create detailed procedures for work of high importance. The procedure section, for example, can create simplified procedures for work of low importance. The procedure section, for example, can dynamically adjust the level of detail of the procedure depending on the importance of the work. This makes it possible to adjust the level of detail of the procedure depending on the importance of the work.

[0099] When generating a work procedure, the procedure unit can apply different procedure generation algorithms depending on the work category. The procedure unit, for example, evaluates the work category. The procedure unit can evaluate categories such as maintenance work, repair work, and upgrade work. The procedure unit can select an optimal procedure generation algorithm depending on the work category. The procedure unit can customize the procedure generation algorithm based on the work category. The procedure unit can create a procedure by combining multiple procedure generation algorithms depending on the work category. This makes it possible to apply an optimal procedure generation algorithm depending on the work category.

[0100] When generating a work procedure, the procedure unit can improve the accuracy of the procedure based on past procedure results. For example, the procedure unit can improve the procedure generation algorithm based on past procedure results. For example, the procedure unit can improve the accuracy of the procedure by referring to past procedure results. For example, the procedure unit can analyze past procedure results and identify areas for improvement in the procedure. In this way, the accuracy of the procedure can be improved by referring to past procedure results.

[0101] The procedure unit can estimate the user's emotion and adjust the length of the procedure depending on the estimated user's emotion. The procedure unit estimates the user's emotion using, for example, an emotion recognition algorithm. For example, the procedure unit can provide short and to-the-point instructions when the user is in a hurry. For example, the procedure unit can provide detailed instructions when the user is relaxed. For example, the procedure unit can provide visually stimulating instructions when the user is excited. This allows the length of the procedure to be adjusted depending on the user's emotion.

[0102] When generating work procedures, the procedure section can determine the priority of procedures based on the timing of the work. The procedure section, for example, evaluates the timing of the work. The procedure section can evaluate, for example, the start date of the work and the frequency of the work. The procedure section, for example, can prioritize the creation of procedures for work to be performed soon. The procedure section, for example, can postpone procedures for work that are not scheduled for execution for a long time. The procedure section, for example, can dynamically adjust the priority of procedures based on the timing of the work. This makes it possible to determine the priority of procedures based on the timing of the work.

[0103] When generating a work procedure, the procedure unit can adjust the order of procedures based on the relevance of the tasks. The procedure unit, for example, evaluates the relevance of the tasks. The procedure unit can evaluate the relevance of the tasks using, for example, correlation analysis or co-occurrence networks. The procedure unit, for example, can prioritize incorporating highly relevant tasks into the procedure. The procedure unit, for example, can postpone the order of less relevant tasks. The procedure unit, for example, can dynamically adjust the order of procedures based on the relevance of the tasks. This makes it possible to adjust the order of procedures based on the relevance of the tasks.

[0104] When generating a work procedure, the procedure unit can adjust the use of technical terms in the procedure depending on the user's level of expertise. The procedure unit, for example, evaluates the user's level of expertise. The procedure unit can evaluate the user's level of expertise based on, for example, a questionnaire survey or past usage history. The procedure unit can use a lot of technical terms if the user's level of expertise is high. The procedure unit can avoid using technical terms if the user's level of expertise is low. The procedure unit can adjust the way the procedure is expressed depending on, for example, the user's level of expertise. This makes it possible to adjust the way the procedure is expressed depending on the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, planning unit, and procedure unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the computer 36 and camera 42 of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes past failure records and recovery methods using a generation AI. The planning unit creates an optimal maintenance plan, for example, by the specific processing unit 290 of the data processing device 12, and the procedure unit generates specific work procedures, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, planning unit, and procedure unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the computer 36 and camera 42 of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes past failure records and recovery methods using a generation AI. The planning unit creates an optimal maintenance plan, for example, by the specific processing unit 290 of the data processing device 12, and the procedure unit generates specific work procedures, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, planning unit, and procedural unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects data using the computer 36 and camera 42 of the headset terminal 314, and the data is analyzed by the identification processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes past failure records and recovery methods using a generation AI. The planning unit creates an optimal maintenance plan, for example, by the identification processing unit 290 of the data processing device 12, and the procedural unit generates specific work procedures, for example, by the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, planning unit, and procedural unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the computer 36 and camera 42 of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes past failure records and recovery methods using a generation AI. The planning unit creates an optimal maintenance plan, for example, by the specific processing unit 290 of the data processing device 12, and the procedural unit generates specific work procedures, for example, by the control unit 46A of the robot 414.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, data collection can be performed quickly to collect the minimum amount of data necessary. This makes it possible to adjust the timing of data collection according to the user's emotions.

[0107] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, it can evaluate categories such as text data, numerical data, and image data and select the optimal analysis algorithm. It can also customize the analysis algorithm based on the data category. Furthermore, it can analyze by combining multiple analysis algorithms depending on the data category. This allows it to apply the optimal analysis algorithm depending on the data category.

[0108] The planning unit can estimate the user's emotions and adjust the way the maintenance plan is presented based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible maintenance plan can be provided. If the user is relaxed, a detailed maintenance plan can be provided. Furthermore, if the user is in a hurry, a maintenance plan that focuses on the main points can be provided. This makes it possible to adjust the way the maintenance plan is presented depending on the user's emotions.

[0109] When generating work procedures, the procedure unit can apply different procedure generation algorithms depending on the work category. For example, it can evaluate categories such as maintenance work, repair work, and upgrade work and select the optimal procedure generation algorithm. It can also customize the procedure generation algorithm based on the work category. Furthermore, it can create procedures by combining multiple procedure generation algorithms depending on the work category. This makes it possible to apply the optimal procedure generation algorithm depending on the work category.

[0110] The collection unit can filter data during data collection based on the current network status or load status. For example, it can evaluate network bandwidth, latency, packet loss rate, etc., and temporarily stop data collection if the network load is high. It can also resume data collection when the network status is stable. Furthermore, it can adjust the type of data to be collected depending on the network load status. This allows data collection to be adjusted according to the network status.

[0111] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that focus on the main points. This makes it possible to adjust the way the analysis is presented depending on the user's emotions.

[0112] When creating a plan, the planning unit can apply different planning algorithms depending on the category of equipment. For example, it can evaluate categories such as servers, network equipment, and storage devices and select the optimal planning algorithm. It can also customize the planning algorithm based on the category of equipment. Furthermore, it can create a plan by combining multiple planning algorithms depending on the category of equipment. This makes it possible to apply the optimal planning algorithm depending on the category of equipment.

[0113] The procedure unit can estimate the user's emotions and adjust the way in which work procedures are presented based on the estimated user's emotions. For example, if the user is nervous, it can provide simple, highly visible work procedures. If the user is relaxed, it can provide detailed work procedures. If the user is in a hurry, it can provide work procedures that focus on the main points. This makes it possible to adjust the way in which work procedures are presented according to the user's emotions.

[0114] During data collection, the collection unit can analyze social media or external data sources to collect relevant data. For example, it can analyze social media posts and collect relevant data. It can also collect data from social media such as Twitter, Facebook, and Instagram. It can also collect relevant data from external data sources. This allows it to analyze social media and external data sources to collect relevant data.

[0115] During analysis, the analysis unit can determine the analysis priority based on the time of data collection. For example, the analysis unit can evaluate the collection date and time and collection frequency and prioritize analysis of the most recent data. Also, the analysis priority of older data can be lowered. Furthermore, the analysis priority can be dynamically adjusted based on the time of data collection. This allows the analysis priority to be determined based on the time of data collection.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects data. The collection unit can extract necessary information from various databases and log files, for example. The collection unit can collect information such as the type of failures that have occurred in the past, their frequency, and the time required for recovery. The collection unit can also acquire information stored in a database. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses a generation AI to perform analysis based on data such as past failure records, recovery methods, and maintenance response details. For example, if a specific device is likely to fail within a certain period of time, the analysis unit can plan maintenance for that device in advance. Step 3: The planning unit creates a maintenance plan based on the analysis results obtained by the analysis unit. The planning unit creates an optimal maintenance plan, for example, using a generation AI. The planning unit can create, for example, a maintenance schedule for a specific piece of equipment. Step 4: The procedure section generates work procedures based on the maintenance plan created by the planning section. For example, the procedure section uses a generation AI to create a work procedure manual that details the steps, required materials, and work time for each task. For example, the procedure section can list the procedures, tools, and replacement parts required for replacing a specific piece of equipment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a planning unit that creates a maintenance plan based on the analysis results obtained by the analysis unit; a procedure unit that generates a work procedure based on the maintenance plan created by the planning unit; Equipped with A system characterized by:

2. The collecting unit Equipped with an extraction unit that extracts information from various databases or log files 2. The system of claim 1.

3. The analysis unit Equipped with an algorithm section that performs analysis based on data on past failures, recovery methods, and maintenance response details 2. The system of claim 1.

4. The planning unit Equipped with an optimization department that creates maintenance plans 2. The system of claim 1.

5. The procedure section We have a department that creates work procedure manuals that detail the procedures, materials, and work times for each task.

2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Analyze past data collection history and select collection methods 2. The system of claim 1.

8. The collecting unit Filtering data collection based on the current state or load of the network 2. The system of claim 1.

9. The collecting unit When collecting data, adjust the frequency of collection depending on the importance of the data being collected.

2. The system of claim 1.

Images