system

Abstract

The system according to this embodiment aims to quickly and accurately identify the cause of malfunctions and propose countermeasures during periodic inspections. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data from periodic inspections. The analysis unit analyzes the data collected by the data collection unit and identifies the cause of the malfunction. The proposal unit proposes countermeasures based on the cause identified by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the cause identification of defects and the proposal of countermeasures in regular inspections are not carried out quickly and accurately.

[0005] The system according to the embodiment aims to quickly and accurately identify the cause of defects and propose countermeasures in regular inspections.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data of regular inspections. The analysis unit analyzes the data collected by the collection unit and identifies the cause of the defect. The proposal unit proposes countermeasures based on the cause identified by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can quickly and accurately identify the cause of malfunctions and propose countermeasures during periodic inspections. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI-assisted system according to an embodiment of the present invention is a system that uses a generating AI to streamline the troubleshooting process for periodic inspections. The AI-assisted system collects data from periodic inspections and inputs it into the generating AI. The generating AI analyzes the collected data and identifies the cause of the malfunction. Based on the identified cause, the generating AI proposes countermeasures. These proposals include specific repair procedures and lists of necessary parts. This streamlines the troubleshooting process for periodic inspections and enables quick and accurate responses. For example, the AI-assisted system collects data from periodic inspections. This data includes inspection results, past malfunction history, and usage status. For example, machine operation logs, sensor data, and past repair history are collected. This data is input into the generating AI. Next, the generating AI analyzes the collected data. The generating AI analyzes the data and identifies the cause of the malfunction. For example, deterioration of a specific part or an abnormal operating pattern may be identified as the cause. The generating AI identifies the cause by referring to past data and similar cases. Furthermore, the generating AI proposes countermeasures based on the identified cause. These proposals include specific repair procedures and lists of necessary parts. For example, the system may suggest replacing or adjusting specific parts, or adding inspection items. Based on past repair history and expertise, the generating AI proposes the optimal solution. This streamlines the troubleshooting process during periodic inspections. By quickly and accurately identifying the cause of a malfunction and proposing the best solution, the generating AI reduces response time and improves repair accuracy. This increases machine uptime and reduces maintenance costs. For example, in manufacturing, using the generating AI during periodic inspections allows for the rapid identification of machine malfunctions and the implementation of appropriate countermeasures. This reduces production line downtime and improves production efficiency. Furthermore, in periodic inspections of power plants and infrastructure facilities, the use of the generating AI enables early detection and rapid response to malfunctions, improving equipment reliability. In short, AI-assisted systems streamline the troubleshooting process during periodic inspections, enabling quick and accurate responses.

[0029] The AI ​​assist system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data from periodic inspections. The data collection unit collects data such as inspection results, past malfunction history, and usage status. The data collection unit can collect, for example, machine operation logs, sensor data, and past repair history. The data collection unit can set, for example, the frequency of data collection, the type of data to collect, and the collection method. The analysis unit analyzes the data collected by the data collection unit and identifies the cause of the malfunction. The analysis unit identifies, for example, the deterioration of a specific part or an abnormal operating pattern as the cause. The analysis unit can identify the cause using, for example, an anomaly detection algorithm or failure mode analysis. The analysis unit can set, for example, the analysis algorithm to be used, the accuracy of the analysis, and the type of data to be analyzed. The proposal unit proposes countermeasures based on the cause identified by the analysis unit. The proposal unit proposes, for example, specific repair procedures and a list of necessary parts. The proposal unit can propose, for example, the replacement or adjustment of a specific part, or additional inspection items. The proposal unit can, for example, set details of the repair procedure, the tools to be used, and the time required for the repair. Based on past repair history and expertise, the proposal unit proposes the optimal solution. The proposal unit can, for example, refer to past repair history, expert opinions, and past research results. The proposal unit can, for example, evaluate the effectiveness, cost, and feasibility of the solution. As a result, the AI-assisted system according to this embodiment streamlines the troubleshooting process during periodic inspections, enabling quick and accurate responses.

[0030] The data collection unit collects data from periodic inspections. Specifically, it collects data such as inspection results, past malfunction history, and usage status. For example, it can collect machine operation logs, sensor data, and past repair history. This allows the data collection unit to gather diverse data for a detailed understanding of the machine's condition. The data collection unit can configure the frequency of data collection, the types of data to collect, and the collection method. For example, data from sensors is collected in real time, and operation logs are uploaded to the server periodically. Furthermore, the data collection unit can filter and preprocess data to ensure data quality. For example, it can remove noisy data or fill in missing data. This allows the data collection unit to provide high-quality data to the analysis unit. The data collection unit can also have a trigger function that immediately collects data when an anomaly is detected. For example, if a sensor detects an abnormal value, it collects detailed data at that moment and sends it to the analysis unit. This allows the data collection unit to support early detection of anomalies and rapid response.

[0031] The analysis unit analyzes the data collected by the data collection unit to identify the cause of the malfunction. Specifically, the analysis unit identifies causes such as deterioration of specific parts or abnormal operating patterns. The analysis unit can identify the cause using anomaly detection algorithms and failure mode analysis. For example, it can analyze machine operation logs to detect unusual operating patterns. It can also analyze sensor data to determine whether a specific part is deteriorating. The analysis unit can set the analysis algorithm to be used, the accuracy of the analysis, and the type of data to be analyzed. For example, it can use machine learning algorithms to learn abnormal patterns from past data and detect anomalies in new data. It can also use failure mode analysis to evaluate the likelihood of a specific part failing. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term risk assessment and trend analysis. For example, based on past failure history, it can predict how often a specific part will fail and plan preventive maintenance. As a result, the analysis unit can not only quickly and accurately analyze the collected data and identify the cause of the malfunction, but also handle long-term risk management and preventive maintenance.

[0032] The proposal department proposes countermeasures based on the causes identified by the analysis department. Specifically, the proposal department proposes specific repair procedures and lists of necessary parts. For example, it can propose the replacement or adjustment of specific parts, or additional inspection items. The proposal department can specify the details of the repair procedure, the tools to be used, and the time required for the repair. For example, it can describe the procedure for replacing parts in detail, clearly indicating the necessary tools and the time required for replacement. The proposal department proposes the optimal countermeasures based on past repair history and expertise. For example, it can refer to past repair history, expert opinions, and past research results. This allows the proposal department to propose the most effective and efficient countermeasures. Furthermore, the proposal department can evaluate the effectiveness, cost, and feasibility of the countermeasures. For example, it evaluates how effective the proposed countermeasures are, how much they cost, and how easy they are to implement, and selects the optimal countermeasure. This allows the proposal department to propose quick and accurate countermeasures based on the causes identified by the analysis department, and streamline the troubleshooting process during periodic inspections. The proposal department can provide users with specific action instructions and support a rapid response. For example, the department can provide a detailed manual outlining repair procedures and listing the necessary parts and tools. Furthermore, the proposal department can evaluate the effectiveness of the implemented measures and propose additional measures as needed. This allows the proposal department to streamline its regular inspection and defect response processes, enabling quick and accurate responses.

[0033] The data collection unit can collect data such as inspection results, past malfunction history, and usage status. For example, the data collection unit can collect inspection results. For example, the data collection unit can set inspection items, inspection methods, and the format for recording inspection results. For example, the data collection unit can collect past malfunction history. For example, the data collection unit can record the date and time of malfunction, the type of malfunction, and the cause of the malfunction. For example, the data collection unit can collect usage status. For example, the data collection unit can record the machine's operating time, operating environment, and operation history. By collecting data such as inspection results, past malfunction history, and usage status, more accurate analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as inspection results, past malfunction history, and usage status into AI and have the AI ​​perform data collection.

[0034] The analysis unit can analyze the collected data and identify causes such as deterioration of specific parts or abnormal operating patterns. For example, the analysis unit can identify deterioration of specific parts. For example, the analysis unit can evaluate signs of deterioration, the progression of deterioration, and the cause of deterioration. For example, the analysis unit can identify abnormal operating patterns. For example, the analysis unit can set up comparisons with normal operation, anomaly detection algorithms, and methods for recording operating patterns. This enables rapid and accurate cause identification by identifying deterioration of specific parts or abnormal operating patterns as the cause. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI perform the data analysis.

[0035] The suggestion unit can propose specific repair procedures and lists of necessary parts based on the identified cause. For example, the suggestion unit can propose specific repair procedures. For example, the suggestion unit can set the repair steps, the tools to be used, and the time required for the repair. For example, the suggestion unit can propose a list of necessary parts. For example, the suggestion unit can record the name of the part, the part number, and the quantity of the part. This enables quick and accurate countermeasures by proposing specific repair procedures and lists of necessary parts. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the suggestion unit can input the identified cause into a generation AI and have the generation AI propose repair procedures and lists of necessary parts.

[0036] The proposal unit can propose the optimal solution based on past repair history and expertise. For example, the proposal unit can refer to past repair history. For example, the proposal unit can record the date and time of repair, the details of the repair, and the results of the repair. For example, the proposal unit can refer to expertise. For example, the proposal unit can refer to technical manuals, expert opinions, and past research results. This improves the accuracy of repairs by proposing the optimal solution based on past repair history and expertise. Some or all of the above processes in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input past repair history and expertise into a generative AI and have the generative AI propose the optimal solution.

[0037] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past data collection history and reflect it in the next collection. For example, the data collection unit can select the optimal collection method under specific conditions based on past data collection history. For example, the data collection unit can analyze past data collection history, identify areas for improvement in the collection method, and reflect them in the next collection. In this way, by analyzing past data collection history, an efficient data collection method can be selected. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI and have the AI ​​select the optimal collection method.

[0038] The data collection unit can filter data based on the machine's operating status and environmental conditions during data collection. For example, the data collection unit can monitor the machine's operating status in real time and collect data only when an abnormality is detected. For example, the data collection unit can consider environmental conditions (temperature, humidity, etc.) and collect data only under specific conditions. For example, the data collection unit can combine the machine's operating status and environmental conditions to determine the optimal data collection timing. This allows for efficient collection of only the necessary data by filtering the data based on the machine's operating status and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the machine's operating status and environmental conditions into the AI ​​and have the AI ​​perform the data filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize data collection in a specific region and acquire data based on the characteristics of that region. For example, the data collection unit prioritizes the collection of highly relevant data based on geographical location information. For example, the data collection unit prioritizes data collection under specific conditions by considering geographical location information. This allows for the efficient collection of highly relevant data by considering geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0040] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze trends on social media and collect relevant data. For example, the data collection unit can collect relevant data based on user feedback on social media. For example, the data collection unit can monitor social media activity in real time and collect relevant data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit dynamically adjusts the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific analysis algorithm to machine motion data. For example, the analysis unit applies a different analysis algorithm to sensor data. For example, the analysis unit selects and applies the optimal analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the optimal analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the latest data to enable a quick response. For example, the analysis unit may prioritize the analysis of data from a specific period based on past data. For example, the analysis unit may dynamically adjust the priority of analysis according to the data collection period. This enables a quick response by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data collection period into the generative AI and have the generative AI determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data to provide a quick response. For example, the analysis unit may postpone the analysis of less relevant data to perform efficient analysis. For example, the analysis unit may dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0045] The proposal unit can adjust the level of detail of its proposals based on the importance of the causes. For example, the proposal unit will provide detailed proposals for causes with high importance. For example, the proposal unit will provide simplified proposals for causes with low importance. The proposal unit can dynamically adjust the level of detail of its proposals according to the importance of the causes. This allows for efficient proposals by adjusting the level of detail of the proposals according to the importance of the causes. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the importance of the causes into the generative AI and have the generative AI adjust the level of detail of the proposals.

[0046] The proposal unit can apply different proposal algorithms depending on the category of the cause when making a proposal. For example, the proposal unit applies a specific proposal algorithm to machine malfunctions. For example, the proposal unit applies a different proposal algorithm to sensor data anomalies. For example, the proposal unit selects and applies the optimal proposal algorithm depending on the category of the cause. This improves the accuracy of the proposal by applying the optimal proposal algorithm according to the category of the cause. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the category of the cause into a generative AI and have the generative AI execute the application of the optimal proposal algorithm.

[0047] The proposal unit can determine the priority of proposals based on when the cause was identified. For example, the proposal unit may prioritize the most recent cause to enable a quick response. For example, the proposal unit may prioritize the cause at a specific time based on past causes. For example, the proposal unit may dynamically adjust the priority of proposals according to when the cause was identified. This enables a quick response by determining the priority of proposals based on when the cause was identified. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit may input the time of cause identification into the generative AI and have the generative AI determine the priority of proposals.

[0048] The proposal unit can adjust the order of proposals based on the relevance of the causes when making a proposal. For example, the proposal unit may prioritize proposing highly relevant causes to ensure a quick response. For example, the proposal unit may postpone less relevant causes to ensure efficient proposals. For example, the proposal unit may dynamically adjust the order of proposals according to the relevance of the causes. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the causes. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the relevance of the causes into a generative AI and have the generative AI adjust the order of proposals.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can analyze past data collection history and select the optimal collection method. For example, it can identify the most efficient collection method from past data collection history and reflect it in the next collection. Based on past data collection history, it can select the optimal collection method under specific conditions. It can analyze past data collection history, identify areas for improvement in the collection method, and reflect them in the next collection. In this way, by analyzing past data collection history, an efficient data collection method can be selected. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI and have the AI ​​select the optimal collection method.

[0051] The data collection unit can filter data based on the machine's operating status and environmental conditions during data collection. For example, it can monitor the machine's operating status in real time and collect data only when an abnormality is detected. It can also consider environmental conditions (temperature, humidity, etc.) and collect data only under specific conditions. By combining the machine's operating status and environmental conditions, it can determine the optimal data collection timing. This allows for efficient collection of only the necessary data by filtering it based on the machine's operating status and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the machine's operating status and environmental conditions into the AI ​​and have the AI ​​perform the data filtering.

[0052] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on highly important data and a simplified analysis on less important data. The level of detail of the analysis is dynamically adjusted according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0053] The proposal unit can adjust the level of detail of its proposals based on the importance of the causes. For example, it can provide detailed proposals for high-importance causes and simplified proposals for low-importance causes. The level of detail of the proposal is dynamically adjusted according to the importance of the causes. This allows for efficient proposals by adjusting the level of detail according to the importance of the causes. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the importance of the causes into the generative AI and have the generative AI adjust the level of detail of the proposals.

[0054] The proposal unit can apply different proposal algorithms depending on the category of the cause when making a proposal. For example, a specific proposal algorithm may be applied to machine malfunctions. A different proposal algorithm may be applied to sensor data anomalies. The optimal proposal algorithm is selected and applied according to the category of the cause. This improves the accuracy of the proposal by applying the optimal proposal algorithm according to the category of the cause. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the category of the cause into a generative AI and have the generative AI execute the application of the optimal proposal algorithm.

[0055] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, it can prioritize the analysis of the latest data to enable a quick response. It can also prioritize the analysis of data from a specific period based on past data. It can dynamically adjust the analysis priority according to the data collection period. This enables a quick response by determining the analysis priority based on the data collection period. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data collection period into the generative AI and have the generative AI determine the analysis priority.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects periodic inspection data. The data collection unit collects data such as inspection results, past malfunction history, and usage status. The data collection unit can also collect machine operation logs, sensor data, and past repair history. The data collection unit can set the frequency of data collection, the type of data to collect, and the collection method. Step 2: The analysis unit analyzes the data collected by the data acquisition unit to identify the cause of the malfunction. The analysis unit may identify the cause as, for example, the deterioration of a specific component or an abnormal operating pattern. The analysis unit can identify the cause using, for example, an anomaly detection algorithm or failure mode analysis. The analysis unit can set, for example, the analysis algorithm to be used, the accuracy of the analysis, and the type of data to be analyzed. Step 3: The proposal department proposes countermeasures based on the causes identified by the analysis department. The proposal department proposes, for example, specific repair procedures and a list of necessary parts. The proposal department can propose, for example, replacement or adjustment of specific parts, or additional inspection items. The proposal department can set, for example, details of the repair procedure, the tools to be used, and the time required for the repair. The proposal department proposes the optimal countermeasures based on past repair history and expertise. The proposal department can refer, for example, past repair history, expert opinions, and past research results. The proposal department can evaluate, for example, the effectiveness of the countermeasures, the cost of the countermeasures, and the feasibility of the countermeasures.

[0058] (Example of form 2) An AI-assisted system according to an embodiment of the present invention is a system that uses a generating AI to streamline the troubleshooting process for periodic inspections. The AI-assisted system collects data from periodic inspections and inputs it into the generating AI. The generating AI analyzes the collected data and identifies the cause of the malfunction. Based on the identified cause, the generating AI proposes countermeasures. These proposals include specific repair procedures and lists of necessary parts. This streamlines the troubleshooting process for periodic inspections and enables quick and accurate responses. For example, the AI-assisted system collects data from periodic inspections. This data includes inspection results, past malfunction history, and usage status. For example, machine operation logs, sensor data, and past repair history are collected. This data is input into the generating AI. Next, the generating AI analyzes the collected data. The generating AI analyzes the data and identifies the cause of the malfunction. For example, deterioration of a specific part or an abnormal operating pattern may be identified as the cause. The generating AI identifies the cause by referring to past data and similar cases. Furthermore, the generating AI proposes countermeasures based on the identified cause. These proposals include specific repair procedures and lists of necessary parts. For example, the system may suggest replacing or adjusting specific parts, or adding inspection items. Based on past repair history and expertise, the generating AI proposes the optimal solution. This streamlines the troubleshooting process during periodic inspections. By quickly and accurately identifying the cause of a malfunction and proposing the best solution, the generating AI reduces response time and improves repair accuracy. This increases machine uptime and reduces maintenance costs. For example, in manufacturing, using the generating AI during periodic inspections allows for the rapid identification of machine malfunctions and the implementation of appropriate countermeasures. This reduces production line downtime and improves production efficiency. Furthermore, in periodic inspections of power plants and infrastructure facilities, the use of the generating AI enables early detection and rapid response to malfunctions, improving equipment reliability. In short, AI-assisted systems streamline the troubleshooting process during periodic inspections, enabling quick and accurate responses.

[0059] The AI ​​assist system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data from periodic inspections. The data collection unit collects data such as inspection results, past malfunction history, and usage status. The data collection unit can collect, for example, machine operation logs, sensor data, and past repair history. The data collection unit can set, for example, the frequency of data collection, the type of data to collect, and the collection method. The analysis unit analyzes the data collected by the data collection unit and identifies the cause of the malfunction. The analysis unit identifies, for example, the deterioration of a specific part or an abnormal operating pattern as the cause. The analysis unit can identify the cause using, for example, an anomaly detection algorithm or failure mode analysis. The analysis unit can set, for example, the analysis algorithm to be used, the accuracy of the analysis, and the type of data to be analyzed. The proposal unit proposes countermeasures based on the cause identified by the analysis unit. The proposal unit proposes, for example, specific repair procedures and a list of necessary parts. The proposal unit can propose, for example, the replacement or adjustment of a specific part, or additional inspection items. The proposal unit can, for example, set details of the repair procedure, the tools to be used, and the time required for the repair. Based on past repair history and expertise, the proposal unit proposes the optimal solution. The proposal unit can, for example, refer to past repair history, expert opinions, and past research results. The proposal unit can, for example, evaluate the effectiveness, cost, and feasibility of the solution. As a result, the AI-assisted system according to this embodiment streamlines the troubleshooting process during periodic inspections, enabling quick and accurate responses.

[0060] The data collection unit collects data from periodic inspections. Specifically, it collects data such as inspection results, past malfunction history, and usage status. For example, it can collect machine operation logs, sensor data, and past repair history. This allows the data collection unit to gather diverse data for a detailed understanding of the machine's condition. The data collection unit can configure the frequency of data collection, the types of data to collect, and the collection method. For example, data from sensors is collected in real time, and operation logs are uploaded to the server periodically. Furthermore, the data collection unit can filter and preprocess data to ensure data quality. For example, it can remove noisy data or fill in missing data. This allows the data collection unit to provide high-quality data to the analysis unit. The data collection unit can also have a trigger function that immediately collects data when an anomaly is detected. For example, if a sensor detects an abnormal value, it collects detailed data at that moment and sends it to the analysis unit. This allows the data collection unit to support early detection of anomalies and rapid response.

[0061] The analysis unit analyzes the data collected by the data collection unit to identify the cause of the malfunction. Specifically, the analysis unit identifies causes such as deterioration of specific parts or abnormal operating patterns. The analysis unit can identify the cause using anomaly detection algorithms and failure mode analysis. For example, it can analyze machine operation logs to detect unusual operating patterns. It can also analyze sensor data to determine whether a specific part is deteriorating. The analysis unit can set the analysis algorithm to be used, the accuracy of the analysis, and the type of data to be analyzed. For example, it can use machine learning algorithms to learn abnormal patterns from past data and detect anomalies in new data. It can also use failure mode analysis to evaluate the likelihood of a specific part failing. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term risk assessment and trend analysis. For example, based on past failure history, it can predict how often a specific part will fail and plan preventive maintenance. As a result, the analysis unit can not only quickly and accurately analyze the collected data and identify the cause of the malfunction, but also handle long-term risk management and preventive maintenance.

[0062] The proposal department proposes countermeasures based on the causes identified by the analysis department. Specifically, the proposal department proposes specific repair procedures and lists of necessary parts. For example, it can propose the replacement or adjustment of specific parts, or additional inspection items. The proposal department can specify the details of the repair procedure, the tools to be used, and the time required for the repair. For example, it can describe the procedure for replacing parts in detail, clearly indicating the necessary tools and the time required for replacement. The proposal department proposes the optimal countermeasures based on past repair history and expertise. For example, it can refer to past repair history, expert opinions, and past research results. This allows the proposal department to propose the most effective and efficient countermeasures. Furthermore, the proposal department can evaluate the effectiveness, cost, and feasibility of the countermeasures. For example, it evaluates how effective the proposed countermeasures are, how much they cost, and how easy they are to implement, and selects the optimal countermeasure. This allows the proposal department to propose quick and accurate countermeasures based on the causes identified by the analysis department, and streamline the troubleshooting process during periodic inspections. The proposal department can provide users with specific action instructions and support a rapid response. For example, the department can provide a detailed manual outlining repair procedures and listing the necessary parts and tools. Furthermore, the proposal department can evaluate the effectiveness of the implemented measures and propose additional measures as needed. This allows the proposal department to streamline its regular inspection and defect response processes, enabling quick and accurate responses.

[0063] The data collection unit can collect data such as inspection results, past malfunction history, and usage status. For example, the data collection unit can collect inspection results. For example, the data collection unit can set inspection items, inspection methods, and the format for recording inspection results. For example, the data collection unit can collect past malfunction history. For example, the data collection unit can record the date and time of malfunction, the type of malfunction, and the cause of the malfunction. For example, the data collection unit can collect usage status. For example, the data collection unit can record the machine's operating time, operating environment, and operation history. By collecting data such as inspection results, past malfunction history, and usage status, more accurate analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as inspection results, past malfunction history, and usage status into AI and have the AI ​​perform data collection.

[0064] The analysis unit can analyze the collected data and identify causes such as deterioration of specific parts or abnormal operating patterns. For example, the analysis unit can identify deterioration of specific parts. For example, the analysis unit can evaluate signs of deterioration, the progression of deterioration, and the cause of deterioration. For example, the analysis unit can identify abnormal operating patterns. For example, the analysis unit can set up comparisons with normal operation, anomaly detection algorithms, and methods for recording operating patterns. This enables rapid and accurate cause identification by identifying deterioration of specific parts or abnormal operating patterns as the cause. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI perform the data analysis.

[0065] The suggestion unit can propose specific repair procedures and lists of necessary parts based on the identified cause. For example, the suggestion unit can propose specific repair procedures. For example, the suggestion unit can set the repair steps, the tools to be used, and the time required for the repair. For example, the suggestion unit can propose a list of necessary parts. For example, the suggestion unit can record the name of the part, the part number, and the quantity of the part. This enables quick and accurate countermeasures by proposing specific repair procedures and lists of necessary parts. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the suggestion unit can input the identified cause into a generation AI and have the generation AI propose repair procedures and lists of necessary parts.

[0066] The proposal unit can propose the optimal solution based on past repair history and expertise. For example, the proposal unit can refer to past repair history. For example, the proposal unit can record the date and time of repair, the details of the repair, and the results of the repair. For example, the proposal unit can refer to expertise. For example, the proposal unit can refer to technical manuals, expert opinions, and past research results. This improves the accuracy of repairs by proposing the optimal solution based on past repair history and expertise. Some or all of the above processes in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input past repair history and expertise into a generative AI and have the generative AI propose the optimal solution.

[0067] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit collects more detailed data to obtain more information. For example, if the user is in a hurry, the data collection unit collects only the minimum necessary data and quickly moves on to the next step. This reduces the user's burden and enables efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0068] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past data collection history and reflect it in the next collection. For example, the data collection unit can select the optimal collection method under specific conditions based on past data collection history. For example, the data collection unit can analyze past data collection history, identify areas for improvement in the collection method, and reflect them in the next collection. In this way, by analyzing past data collection history, an efficient data collection method can be selected. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI and have the AI ​​select the optimal collection method.

[0069] The data collection unit can filter data based on the machine's operating status and environmental conditions during data collection. For example, the data collection unit can monitor the machine's operating status in real time and collect data only when an abnormality is detected. For example, the data collection unit can consider environmental conditions (temperature, humidity, etc.) and collect data only under specific conditions. For example, the data collection unit can combine the machine's operating status and environmental conditions to determine the optimal data collection timing. This allows for efficient collection of only the necessary data by filtering the data based on the machine's operating status and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the machine's operating status and environmental conditions into the AI ​​and have the AI ​​perform the data filtering.

[0070] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only high-priority data. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0071] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize data collection in a specific region and acquire data based on the characteristics of that region. For example, the data collection unit prioritizes the collection of highly relevant data based on geographical location information. For example, the data collection unit prioritizes data collection under specific conditions by considering geographical location information. This allows for the efficient collection of highly relevant data by considering geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0072] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze trends on social media and collect relevant data. For example, the data collection unit can collect relevant data based on user feedback on social media. For example, the data collection unit can monitor social media activity in real time and collect relevant data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit dynamically adjusts the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific analysis algorithm to machine motion data. For example, the analysis unit applies a different analysis algorithm to sensor data. For example, the analysis unit selects and applies the optimal analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the optimal analysis algorithm.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an analysis result of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0077] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the latest data to enable a quick response. For example, the analysis unit may prioritize the analysis of data from a specific period based on past data. For example, the analysis unit may dynamically adjust the priority of analysis according to the data collection period. This enables a quick response by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data collection period into the generative AI and have the generative AI determine the priority of analysis.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data to provide a quick response. For example, the analysis unit may postpone the analysis of less relevant data to perform efficient analysis. For example, the analysis unit may dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0079] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit will provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0080] The proposal unit can adjust the level of detail of its proposals based on the importance of the causes. For example, the proposal unit will provide detailed proposals for causes with high importance. For example, the proposal unit will provide simplified proposals for causes with low importance. The proposal unit can dynamically adjust the level of detail of its proposals according to the importance of the causes. This allows for efficient proposals by adjusting the level of detail of the proposals according to the importance of the causes. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the importance of the causes into the generative AI and have the generative AI adjust the level of detail of the proposals.

[0081] The proposal unit can apply different proposal algorithms depending on the category of the cause when making a proposal. For example, the proposal unit applies a specific proposal algorithm to machine malfunctions. For example, the proposal unit applies a different proposal algorithm to sensor data anomalies. For example, the proposal unit selects and applies the optimal proposal algorithm depending on the category of the cause. This improves the accuracy of the proposal by applying the optimal proposal algorithm according to the category of the cause. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the category of the cause into a generative AI and have the generative AI execute the application of the optimal proposal algorithm.

[0082] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is excited, the suggestion unit will provide visually stimulating suggestions. By adjusting the length of suggestions according to the user's emotions, the suggestion unit can provide suggestions of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0083] The proposal unit can determine the priority of proposals based on when the cause was identified. For example, the proposal unit may prioritize the most recent cause to enable a quick response. For example, the proposal unit may prioritize the cause at a specific time based on past causes. For example, the proposal unit may dynamically adjust the priority of proposals according to when the cause was identified. This enables a quick response by determining the priority of proposals based on when the cause was identified. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit may input the time of cause identification into the generative AI and have the generative AI determine the priority of proposals.

[0084] The proposal unit can adjust the order of proposals based on the relevance of the causes when making a proposal. For example, the proposal unit may prioritize proposing highly relevant causes to ensure a quick response. For example, the proposal unit may postpone less relevant causes to ensure efficient proposals. For example, the proposal unit may dynamically adjust the order of proposals according to the relevance of the causes. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the causes. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the relevance of the causes into a generative AI and have the generative AI adjust the order of proposals.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. If the user is relaxed, more detailed data collection can be performed to obtain more information. If the user is in a hurry, only the minimum necessary data can be collected to quickly move on to the next step. In this way, by adjusting the timing of data collection according to the user's emotions, the user's burden can be reduced and efficient data collection can be achieved. Emotion estimation can be achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it provides a simple and easy-to-understand analysis result. If the user is relaxed, it provides a detailed analysis result. If the user is in a hurry, it provides a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0088] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, it can provide simple and easily understandable suggestions. If the user is relaxed, it can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, the system can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0089] The data collection unit can analyze past data collection history and select the optimal collection method. For example, it can identify the most efficient collection method from past data collection history and reflect it in the next collection. Based on past data collection history, it can select the optimal collection method under specific conditions. It can analyze past data collection history, identify areas for improvement in the collection method, and reflect them in the next collection. In this way, by analyzing past data collection history, an efficient data collection method can be selected. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI and have the AI ​​select the optimal collection method.

[0090] The data collection unit can filter data based on the machine's operating status and environmental conditions during data collection. For example, it can monitor the machine's operating status in real time and collect data only when an abnormality is detected. It can also consider environmental conditions (temperature, humidity, etc.) and collect data only under specific conditions. By combining the machine's operating status and environmental conditions, it can determine the optimal data collection timing. This allows for efficient collection of only the necessary data by filtering it based on the machine's operating status and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the machine's operating status and environmental conditions into the AI ​​and have the AI ​​perform the data filtering.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on highly important data and a simplified analysis on less important data. The level of detail of the analysis is dynamically adjusted according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0092] The proposal unit can adjust the level of detail of its proposals based on the importance of the causes. For example, it can provide detailed proposals for high-importance causes and simplified proposals for low-importance causes. The level of detail of the proposal is dynamically adjusted according to the importance of the causes. This allows for efficient proposals by adjusting the level of detail according to the importance of the causes. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the importance of the causes into the generative AI and have the generative AI adjust the level of detail of the proposals.

[0093] The proposal unit can apply different proposal algorithms depending on the category of the cause when making a proposal. For example, a specific proposal algorithm may be applied to machine malfunctions. A different proposal algorithm may be applied to sensor data anomalies. The optimal proposal algorithm is selected and applied according to the category of the cause. This improves the accuracy of the proposal by applying the optimal proposal algorithm according to the category of the cause. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the category of the cause into a generative AI and have the generative AI execute the application of the optimal proposal algorithm.

[0094] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, only high-priority data will be collected preferentially. If the user is relaxed, detailed data will be collected preferentially. If the user is in a hurry, data that can be collected quickly will be collected preferentially. In this way, important data can be collected preferentially by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0095] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, it can prioritize the analysis of the latest data to enable a quick response. It can also prioritize the analysis of data from a specific period based on past data. It can dynamically adjust the analysis priority according to the data collection period. This enables a quick response by determining the analysis priority based on the data collection period. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data collection period into the generative AI and have the generative AI determine the analysis priority.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The data collection unit collects periodic inspection data. The data collection unit collects data such as inspection results, past malfunction history, and usage status. The data collection unit can also collect machine operation logs, sensor data, and past repair history. The data collection unit can set the frequency of data collection, the type of data to collect, and the collection method. Step 2: The analysis unit analyzes the data collected by the data acquisition unit to identify the cause of the malfunction. The analysis unit may identify the cause as, for example, the deterioration of a specific component or an abnormal operating pattern. The analysis unit can identify the cause using, for example, an anomaly detection algorithm or failure mode analysis. The analysis unit can set, for example, the analysis algorithm to be used, the accuracy of the analysis, and the type of data to be analyzed. Step 3: The proposal department proposes countermeasures based on the causes identified by the analysis department. The proposal department proposes, for example, specific repair procedures and a list of necessary parts. The proposal department can propose, for example, replacement or adjustment of specific parts, or additional inspection items. The proposal department can set, for example, details of the repair procedure, the tools to be used, and the time required for the repair. The proposal department proposes the optimal countermeasures based on past repair history and expertise. The proposal department can refer, for example, past repair history, expert opinions, and past research results. The proposal department can evaluate, for example, the effectiveness of the countermeasures, the cost of the countermeasures, and the feasibility of the countermeasures.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0101] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects periodic inspection data using the camera 42 and sensors of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to identify the cause of the malfunction. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and proposes specific repair procedures and a list of necessary parts based on the identified cause. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects periodic inspection data using the camera 42 and sensors of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to identify the cause of the malfunction. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and proposes specific repair procedures and a list of necessary parts based on the identified cause. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects periodic inspection data using the camera 42 and sensors of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to identify the cause of the malfunction. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and proposes specific repair procedures and a list of necessary parts based on the identified cause. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects periodic inspection data using the camera 42 and sensors of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the cause of the malfunction. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes specific repair procedures and a list of necessary parts based on the identified cause. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) A data collection unit that collects data from periodic inspections, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the cause of the malfunction, The system includes a proposal unit that proposes countermeasures based on the causes identified by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as inspection results, past malfunction history, and usage status. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to identify the cause, such as the deterioration of specific components or abnormal operating patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the identified cause, we will propose specific repair procedures and a list of necessary parts. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on past repair history and expertise, we propose the most suitable solution. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the machine's operating status and environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the cause. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the category of the cause. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the cause was identified. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the causes. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from periodic inspections, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the cause of the malfunction, The system includes a proposal unit that proposes countermeasures based on the causes identified by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as inspection results, past malfunction history, and usage status. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to identify the cause, such as the deterioration of specific components or abnormal operating patterns. The system according to feature 1.

4. The aforementioned proposal section is, Based on the identified cause, we will propose specific repair procedures and a list of necessary parts. The system according to feature 1.

5. The aforementioned proposal section is, Based on past repair history and expertise, we propose the most suitable solution. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the machine's operating status and environmental conditions. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system according to feature 1.

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