system

Abstract

The system according to this embodiment aims to predict abnormalities in equipment and products and propose the optimal timing for preventive maintenance. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a quality control unit. The data collection unit collects sensor data and product data. The analysis unit analyzes the data collected by the data collection unit and predicts abnormalities. The proposal unit proposes the optimal timing for preventive maintenance based on the abnormalities predicted by the analysis unit. The quality control unit proposes an optimal quality control schedule based on the preventive maintenance schedule proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document No. 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 it is easy to delay the detection of abnormalities in equipment and products, and it is difficult to appropriately determine the timing of preventive maintenance.

[0005] The system according to the embodiment aims to predict abnormalities in equipment and products and propose the optimal timing for preventive maintenance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a quality control unit. The data collection unit collects sensor data and product data. The analysis unit analyzes the data collected by the data collection unit and predicts anomalies. The proposal unit proposes the optimal timing for preventive maintenance based on the anomalies predicted by the analysis unit. The quality control unit proposes an optimal quality control schedule based on the preventive maintenance schedule proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict abnormalities in equipment and products and propose the optimal timing for preventive maintenance. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) The AI ​​agent system according to an embodiment of the present invention is a system that enables rapid detection of anomalies in equipment and products in the manufacturing industry, thereby realizing preventive maintenance and quality assurance. This AI agent system solves these problems by analyzing sensor data and product data in real time and predicting anomalies. For example, the AI ​​agent system collects data from sensors attached to equipment and products in the manufacturing industry. This includes various sensor data such as temperature, vibration, and pressure. Next, the AI ​​analyzes the collected data and detects signs of anomalies. For example, by detecting abnormal patterns in vibration data, machine failure can be predicted. Furthermore, when an anomaly is predicted, the AI ​​agent system proposes the optimal timing for preventive maintenance. This helps to avoid production line shutdowns. For example, by replacing machine parts before they fail, production line interruptions can be prevented. The AI ​​agent system also proposes an optimal schedule for quality control. This helps to improve product quality. For example, by identifying factors that affect quality in the product manufacturing process and performing quality control at the optimal timing, product quality can be maintained. In this way, the AI ​​agent system realizes preventive maintenance and quality assurance in the manufacturing industry and supports the efficient operation of production lines. This is expected to improve production efficiency and quality for manufacturing companies and heavy industry equipment managers. The AI ​​agent system can enable preventative maintenance and quality assurance in manufacturing, supporting the efficient operation of production lines.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a quality control unit. The data collection unit collects sensor data and product data. The data collection unit can collect various sensor data, such as temperature, vibration, and pressure. For example, the data collection unit can collect temperature data using a temperature sensor. For example, the data collection unit can collect vibration data using a vibration sensor. For example, the data collection unit can collect pressure data using a pressure sensor. The analysis unit analyzes the data collected by the data collection unit and predicts abnormalities. For example, the analysis unit can analyze the collected temperature data and detect abnormal temperature changes. For example, the analysis unit can analyze the collected vibration data and detect abnormal vibration patterns. For example, the analysis unit can analyze the collected pressure data and detect abnormal pressure changes. The proposal unit proposes the optimal timing for preventive maintenance based on the abnormalities predicted by the analysis unit. For example, the proposal unit can propose the optimal timing for replacing machine parts when an abnormality is predicted. For example, the proposal unit can propose the optimal timing for performing machine maintenance when an abnormality is predicted. The proposal department can, for example, suggest the optimal timing for machine inspection when an anomaly is predicted. The quality control department can propose an optimal quality control schedule based on the preventive maintenance schedule proposed by the proposal department. The quality control department can, for example, suggest an optimal quality control schedule to improve product quality. The quality control department can, for example, identify factors affecting quality in the product manufacturing process and perform quality control at the optimal timing. The quality control department can, for example, suggest a schedule to optimize the frequency of product quality inspections. As a result, the AI ​​agent system according to this embodiment can realize preventive maintenance and quality assurance in the manufacturing industry and support the efficient operation of the production line.

[0030] The data collection unit collects sensor data and product data. Specifically, it can collect various sensor data such as temperature, vibration, and pressure. For example, when collecting temperature data using temperature sensors, sensors are placed at various points on the manufacturing line to monitor temperature changes in real time. This ensures thorough temperature control during the manufacturing process and maintains product quality. When collecting vibration data using vibration sensors, the operating status of the machinery is monitored in detail, and abnormal vibrations can be detected immediately. This prevents machine failures and maintains stable production. When collecting pressure data using pressure sensors, pressure fluctuations during the manufacturing process are monitored in real time, and abnormal pressure changes can be responded to immediately. This ensures product quality and improves the stability of the manufacturing process. The data collection unit centrally manages this sensor data and transmits it to a central database. The database updates the collected data in real time, making it accessible to the analysis and proposal departments. Furthermore, the data collection unit also collects product data. Product data includes various parameters during the manufacturing process and product characteristic information. This improves product quality control and traceability, and increases the overall efficiency of the manufacturing process. The data collection unit collects this data with high accuracy, providing a foundation for preventive maintenance and quality assurance in the manufacturing industry.

[0031] The analysis unit analyzes data collected by the data collection unit to predict anomalies. Specifically, it can analyze collected temperature data and detect abnormal temperature changes. For example, in temperature data analysis using AI, it can detect abnormal temperature increases or decreases by comparing them with past data and identify their causes. In vibration data analysis, it learns the operating patterns of machinery and detects abnormal vibration patterns. For example, the AI ​​can distinguish between normal and abnormal vibration patterns and issue an immediate warning when an anomaly occurs. In pressure data analysis, it monitors pressure fluctuations during the manufacturing process and detects abnormal pressure changes. For example, the AI ​​learns the fluctuation patterns of pressure data and responds immediately when abnormal fluctuations occur. The analysis unit analyzes this data in real time to detect and predict anomalies early. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past anomaly data, it can predict fluctuations in risk in specific machinery or manufacturing processes and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The proposal department suggests the optimal timing for preventive maintenance based on anomalies predicted by the analysis department. Specifically, it can suggest the optimal time to replace machine parts when an anomaly is predicted. For example, the AI ​​compares past data with the current situation, evaluates the lifespan and deterioration status of parts, and calculates the optimal replacement time. When suggesting the optimal time for machine maintenance, the AI ​​creates an optimal maintenance schedule based on the anomaly prediction results and maintenance history. This maximizes machine utilization and minimizes the risk of failure. When suggesting the optimal time for machine inspection, the AI ​​creates an optimal inspection schedule based on the anomaly prediction results and inspection history. This keeps the machine in optimal condition at all times and prevents unexpected failures. The proposal department makes these suggestions in real time, contributing to increased efficiency and reliability in the manufacturing process. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can evaluate the results of maintenance and inspections carried out based on the suggested preventive maintenance schedule and reflect them in future suggestions. This allows the proposal department to consistently provide highly accurate proposals based on the latest information, enabling preventive maintenance and quality assurance in the manufacturing industry.

[0033] The Quality Control Department proposes an optimal quality control schedule based on the preventive maintenance schedule proposed by the Proposal Department. Specifically, it can propose an optimal quality control schedule to improve product quality. For example, it can identify factors that affect quality at each stage of the manufacturing process and perform quality control at the optimal timing. This ensures that product quality remains consistently high. The Quality Control Department can identify factors that affect quality in the product manufacturing process and perform quality control at the optimal timing. For example, it can conduct quality inspections at each manufacturing stage and respond immediately if an abnormality is detected. This ensures product quality and minimizes the occurrence of defective products. The Quality Control Department can propose a schedule that optimizes the frequency of product quality inspections. For example, it can adjust the frequency of quality inspections according to the product characteristics and the status of the manufacturing process to achieve efficient quality control. This allows the Quality Control Department to improve the efficiency of the manufacturing process while maintaining product quality. Furthermore, the Quality Control Department can collaborate with the Proposal Department and the Analysis Department to update the quality control schedule in real time. For example, if an abnormality is predicted, it can immediately review the quality control schedule and take appropriate action. This allows the quality control department to provide highly accurate quality control based on the latest information at all times, enabling preventive maintenance and quality assurance in the manufacturing industry.

[0034] The data collection unit can collect various sensor data such as temperature, vibration, and pressure. For example, the data collection unit can collect temperature data using a temperature sensor. The data collection unit can also collect vibration data using a vibration sensor. The data collection unit can also collect pressure data using a pressure sensor. By collecting various sensor data, it becomes easier to detect signs of abnormalities. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input temperature data obtained from a temperature sensor into a generating AI and have the generating AI perform analysis of the temperature data.

[0035] The analysis unit can analyze the collected data and detect signs of anomalies. For example, the analysis unit can analyze the collected temperature data and detect abnormal temperature changes. For example, the analysis unit can also analyze the collected vibration data and detect abnormal vibration patterns. For example, the analysis unit can analyze the collected pressure data and detect abnormal pressure changes. In this way, signs of anomalies can be detected early by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the detection of signs of anomalies.

[0036] The suggestion unit can propose the optimal timing for preventive maintenance when an anomaly is predicted. For example, the suggestion unit can propose the optimal timing for replacing machine parts when an anomaly is predicted. The suggestion unit can also propose the optimal timing for performing machine maintenance when an anomaly is predicted. The suggestion unit can also propose the optimal timing for inspecting the machine when an anomaly is predicted. This makes it possible to avoid production line shutdowns by proposing the optimal timing for preventive maintenance when an anomaly is predicted. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, when an anomaly is predicted, the suggestion unit can input the optimal timing for preventive maintenance into a generating AI and have the generating AI execute a proposal for the optimal timing.

[0037] The Quality Control Department can propose an optimal schedule for quality control. For example, the Quality Control Department can propose an optimal quality control schedule to improve product quality. For example, the Quality Control Department can identify factors that affect quality in the product manufacturing process and perform quality control at the optimal timing. For example, the Quality Control Department can propose a schedule to optimize the frequency of product quality inspections. In this way, product quality can be improved by proposing an optimal quality control schedule. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or not using AI. For example, the Quality Control Department can input an optimal quality control schedule into a generating AI and have the generating AI execute a proposal for the optimal schedule.

[0038] The data collection unit can analyze past anomaly data and select the optimal sensor placement. For example, the data collection unit can add sensors to locations where anomalies are likely to occur based on past anomaly data. The data collection unit can also optimize sensor placement based on the frequency of anomaly data occurrence. For example, the data collection unit can place different types of sensors depending on the type of anomaly data. This allows for the addition of sensors to locations where anomalies are likely to occur by analyzing past anomaly data. 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 past anomaly data into a generating AI and have the generating AI select the optimal sensor placement.

[0039] The data collection unit can filter sensor data based on the operating status of the equipment. For example, when the equipment is in operation, the data collection unit can collect important sensor data in real time. For example, when the equipment is stopped, the data collection unit can also perform periodic data collection. For example, the data collection unit can change the type of data to collect depending on the operating status of the equipment. This allows for efficient data collection by changing the type of data to collect depending on the operating status of the equipment. 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 equipment operating status data into a generating AI and have the generating AI perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the equipment when collecting sensor data. For example, the data collection unit can prioritize the collection of data from areas prone to anomalies based on the geographical location information of the equipment. The data collection unit can also prioritize the collection of data showing signs of anomalies by considering the geographical location information. The data collection unit can also prioritize the collection of important sensor data based on the geographical location information. This allows for the priority collection of data from areas prone to anomalies based on the geographical location information of the equipment. 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 the geographical location information of the equipment into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0041] The data collection unit can analyze the equipment's maintenance history and collect relevant data when collecting sensor data. For example, the data collection unit can prioritize collecting data from areas prone to malfunctions based on the equipment's maintenance history. The data collection unit can also prioritize collecting important sensor data by analyzing the maintenance history. For example, the data collection unit can prioritize collecting data showing signs of malfunctions based on the maintenance history. This allows for the priority collection of data from areas prone to malfunctions based on the equipment's maintenance history. 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 the equipment's maintenance history into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the anomaly during the analysis. For example, the analysis unit can perform a detailed analysis for anomalies of high importance. For example, the analysis unit can also perform a simplified analysis for anomalies of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the anomaly. This enables efficient anomaly analysis by performing detailed analysis according to the importance of the anomaly. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input anomaly importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the anomaly during analysis. For example, for mechanical anomalies, the analysis unit can apply an algorithm that analyzes vibration data. For example, for temperature anomalies, the analysis unit can also apply an algorithm that analyzes temperature data. For example, for pressure anomalies, the analysis unit can also apply an algorithm that analyzes pressure data. This makes it easier to identify the cause of an anomaly by applying the appropriate analysis algorithm according to the category of the anomaly. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input anomaly category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the timing of the anomaly's occurrence. For example, the analysis unit may prioritize the analysis of data immediately after the anomaly occurred. The analysis unit may also determine the priority of analysis based on the time period in which the anomaly occurred. The analysis unit may also adjust the level of detail of the analysis according to the timing of the anomaly's occurrence. This enables rapid anomaly analysis by determining the priority of analysis based on the timing of the anomaly's occurrence. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the anomaly occurrence timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the correlation of anomalies during the analysis. For example, the analysis unit prioritizes the analysis of data with a high correlation to anomalies. The analysis unit can also determine the order of analysis based on the correlation of anomalies. The analysis unit can also adjust the level of detail of the analysis according to the correlation of anomalies. This allows for efficient anomaly analysis by adjusting the order of analysis based on the correlation of anomalies. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input anomaly correlation data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The proposal unit can adjust the level of detail of its proposals based on the severity of the anomaly. For example, the proposal unit can provide detailed proposals for high-severity anomalies. For example, it can provide simpler proposals for low-severity anomalies. The proposal unit can also determine the priority of proposals according to the severity of the anomaly. This enables efficient preventive maintenance by providing detailed proposals according to the severity of the anomaly. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input anomaly severity data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the category of the anomaly during the proposal process. For example, for mechanical anomalies, the proposal unit can make proposals based on vibration data. For example, for temperature anomalies, the proposal unit can also make proposals based on temperature data. For example, for pressure anomalies, the proposal unit can also make proposals based on pressure data. This makes it easier to identify the cause of an anomaly by applying the appropriate proposal algorithm according to the category of the anomaly. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input anomaly category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0048] The proposal unit can determine the priority of proposals based on when the anomaly occurred. For example, the proposal unit may prioritize proposals made immediately after the anomaly occurred. The proposal unit may also determine the priority of proposals based on the time period in which the anomaly occurred. The proposal unit may also adjust the level of detail of proposals according to when the anomaly occurred. This enables a rapid response by determining the priority of proposals based on when the anomaly occurred. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input anomaly occurrence time data into a generating AI and have the generating AI perform the determination of proposal priority.

[0049] The proposal unit can adjust the order of proposals based on the relevance of anomalies when making proposals. For example, the proposal unit can prioritize proposals with a high relevance to anomalies. The proposal unit can also determine the order of proposals based on the relevance of anomalies. The proposal unit can also adjust the level of detail of proposals according to the relevance of anomalies. This allows for efficient preventive maintenance by adjusting the order of proposals based on the relevance of anomalies. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input anomaly relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0050] The Quality Control Department can adjust the level of detail in its quality control proposals based on the importance of the product. For example, the Quality Control Department can provide detailed quality control proposals for high-importance products, and simplified quality control proposals for low-importance products. The Quality Control Department can also prioritize quality control measures based on product importance. This allows for efficient quality control by providing detailed quality control proposals according to product importance. Some or all of the above processes in the Quality Control Department may be performed using AI, or not. For example, the Quality Control Department can input product importance data into a generating AI and have the generating AI adjust the level of detail in its proposals.

[0051] The Quality Control Department can apply different suggestion algorithms depending on the product category when proposing quality control. For example, the Quality Control Department can propose electrical quality control for electronic products, mechanical quality control for mechanical products, and chemical quality control for chemical products. By applying the appropriate suggestion algorithm according to the product category, the accuracy of quality control is improved. Some or all of the above processing in the Quality Control Department may be performed using AI, for example, or without AI. For example, the Quality Control Department can input product category data into a generating AI and have the generating AI apply different suggestion algorithms.

[0052] The Quality Control Department can determine the priority of quality control proposals based on the product's manufacturing date. For example, the Quality Control Department can determine the priority of quality control based on the product's manufacturing date. The Quality Control Department can also adjust the level of detail of quality control according to the product's manufacturing date. The Quality Control Department can also determine the order of quality control based on the product's manufacturing date. This enables rapid quality control by determining the priority of quality control based on the product's manufacturing date. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or without AI. For example, the Quality Control Department can input product manufacturing date data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0053] The Quality Control Department can adjust the order of quality control proposals based on product relevance when making proposals. For example, the Quality Control Department can prioritize quality control measures that are highly relevant to the products. The Quality Control Department can also determine the order of quality control measures based on product relevance. The Quality Control Department can also adjust the level of detail of quality control measures according to product relevance. This allows for efficient quality control by adjusting the order of proposals based on product relevance. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or without AI. For example, the Quality Control Department can input product relevance data into a generating AI and have the generating AI adjust the order of proposals.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The analysis unit can determine the priority of analysis based on the frequency of anomaly occurrence. For example, the analysis unit will prioritize the analysis of frequently occurring anomalies. For example, the analysis unit may postpone the analysis of anomalies that occur infrequently. The analysis unit may also adjust the level of detail of the analysis according to the frequency of anomaly occurrence. This enables efficient anomaly analysis by determining the priority of analysis based on the frequency of anomaly occurrence. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input anomaly occurrence frequency data into a generating AI and have the generating AI perform the determination of analysis priority.

[0056] The proposal unit can determine the priority of proposals based on the scope of the anomaly's impact. For example, the proposal unit will prioritize proposals for anomalies with a wide scope of impact. For example, the proposal unit may postpone proposals for anomalies with a narrow scope of impact. The proposal unit may also adjust the level of detail of proposals according to the scope of the anomaly's impact. This enables efficient preventive maintenance by determining the priority of proposals based on the scope of the anomaly's impact. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input anomaly impact scope data into a generating AI and have the generating AI determine the priority of proposals.

[0057] The Quality Control Department can make quality control suggestions based on market feedback on the product. For example, the Quality Control Department can analyze market feedback and identify areas for improvement in quality control. The Quality Control Department can also make quality control suggestions based on customer complaint data. The Quality Control Department can also make quality control suggestions considering market trends. This makes it possible to improve product quality by making quality control suggestions based on market feedback. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or not. For example, the Quality Control Department can input market feedback data into a generating AI and have the generating AI execute quality control suggestions.

[0058] The data collection unit can adjust the frequency of data collection based on the frequency of equipment use when collecting sensor data. For example, the data collection unit can increase the frequency of data collection for equipment that is used frequently. For example, the data collection unit can also decrease the frequency of data collection for equipment that is used infrequently. The data collection unit can also change the type of data to be collected according to the frequency of equipment use. This allows for efficient data collection by adjusting the frequency of data collection based on the frequency of equipment use. 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 equipment usage frequency data into a generating AI and have the generating AI adjust the frequency of data collection.

[0059] The analysis unit can determine the priority of analysis based on the cause of the anomaly. For example, the analysis unit will prioritize the analysis of anomalies with clear causes. For example, the analysis unit may postpone the analysis of anomalies with unknown causes. The analysis unit may also adjust the level of detail of the analysis according to the cause of the anomaly. This enables efficient anomaly analysis by determining the priority of analysis based on the cause of the anomaly. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input anomaly cause data into a generating AI and have the generating AI determine the priority of analysis.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data acquisition unit collects sensor data and product data. For example, it collects temperature data using a temperature sensor, vibration data using a vibration sensor, and pressure data using a pressure sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts anomalies. For example, it analyzes the collected temperature data to detect abnormal temperature changes, analyzes the vibration data to detect abnormal vibration patterns, and analyzes the pressure data to detect abnormal pressure changes. Step 3: The proposal unit suggests the optimal timing for preventive maintenance based on the anomalies predicted by the analysis unit. For example, if an anomaly is predicted, it suggests the optimal time to replace machine parts, perform machine maintenance, or inspect the machine. Step 4: The Quality Control Department proposes an optimal quality control schedule based on the preventive maintenance schedule proposed by the Proposal Department. For example, they propose an optimal quality control schedule to improve product quality, a schedule to identify factors that affect quality in the product manufacturing process and perform quality control at the optimal time, and a schedule to optimize the frequency of product quality inspections.

[0062] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that enables rapid detection of anomalies in equipment and products in the manufacturing industry, thereby realizing preventive maintenance and quality assurance. This AI agent system solves these problems by analyzing sensor data and product data in real time and predicting anomalies. For example, the AI ​​agent system collects data from sensors attached to equipment and products in the manufacturing industry. This includes various sensor data such as temperature, vibration, and pressure. Next, the AI ​​analyzes the collected data and detects signs of anomalies. For example, by detecting abnormal patterns in vibration data, machine failure can be predicted. Furthermore, when an anomaly is predicted, the AI ​​agent system proposes the optimal timing for preventive maintenance. This helps to avoid production line shutdowns. For example, by replacing machine parts before they fail, production line interruptions can be prevented. The AI ​​agent system also proposes an optimal schedule for quality control. This helps to improve product quality. For example, by identifying factors that affect quality in the product manufacturing process and performing quality control at the optimal timing, product quality can be maintained. In this way, the AI ​​agent system realizes preventive maintenance and quality assurance in the manufacturing industry and supports the efficient operation of production lines. This is expected to improve production efficiency and quality for manufacturing companies and heavy industry equipment managers. The AI ​​agent system can enable preventative maintenance and quality assurance in manufacturing, supporting the efficient operation of production lines.

[0063] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a quality control unit. The data collection unit collects sensor data and product data. The data collection unit can collect various sensor data, such as temperature, vibration, and pressure. For example, the data collection unit can collect temperature data using a temperature sensor. For example, the data collection unit can collect vibration data using a vibration sensor. For example, the data collection unit can collect pressure data using a pressure sensor. The analysis unit analyzes the data collected by the data collection unit and predicts abnormalities. For example, the analysis unit can analyze the collected temperature data and detect abnormal temperature changes. For example, the analysis unit can analyze the collected vibration data and detect abnormal vibration patterns. For example, the analysis unit can analyze the collected pressure data and detect abnormal pressure changes. The proposal unit proposes the optimal timing for preventive maintenance based on the abnormalities predicted by the analysis unit. For example, the proposal unit can propose the optimal timing for replacing machine parts when an abnormality is predicted. For example, the proposal unit can propose the optimal timing for performing machine maintenance when an abnormality is predicted. The proposal department can, for example, suggest the optimal timing for machine inspection when an anomaly is predicted. The quality control department can propose an optimal quality control schedule based on the preventive maintenance schedule proposed by the proposal department. The quality control department can, for example, suggest an optimal quality control schedule to improve product quality. The quality control department can, for example, identify factors affecting quality in the product manufacturing process and perform quality control at the optimal timing. The quality control department can, for example, suggest a schedule to optimize the frequency of product quality inspections. As a result, the AI ​​agent system according to this embodiment can realize preventive maintenance and quality assurance in the manufacturing industry and support the efficient operation of the production line.

[0064] The data collection unit collects sensor data and product data. Specifically, it can collect various sensor data such as temperature, vibration, and pressure. For example, when collecting temperature data using temperature sensors, sensors are placed at various points on the manufacturing line to monitor temperature changes in real time. This ensures thorough temperature control during the manufacturing process and maintains product quality. When collecting vibration data using vibration sensors, the operating status of the machinery is monitored in detail, and abnormal vibrations can be detected immediately. This prevents machine failures and maintains stable production. When collecting pressure data using pressure sensors, pressure fluctuations during the manufacturing process are monitored in real time, and abnormal pressure changes can be responded to immediately. This ensures product quality and improves the stability of the manufacturing process. The data collection unit centrally manages this sensor data and transmits it to a central database. The database updates the collected data in real time, making it accessible to the analysis and proposal departments. Furthermore, the data collection unit also collects product data. Product data includes various parameters during the manufacturing process and product characteristic information. This improves product quality control and traceability, and increases the overall efficiency of the manufacturing process. The data collection unit collects this data with high accuracy, providing a foundation for preventive maintenance and quality assurance in the manufacturing industry.

[0065] The analysis unit analyzes data collected by the data collection unit to predict anomalies. Specifically, it can analyze collected temperature data and detect abnormal temperature changes. For example, in temperature data analysis using AI, it can detect abnormal temperature increases or decreases by comparing them with past data and identify their causes. In vibration data analysis, it learns the operating patterns of machinery and detects abnormal vibration patterns. For example, the AI ​​can distinguish between normal and abnormal vibration patterns and issue an immediate warning when an anomaly occurs. In pressure data analysis, it monitors pressure fluctuations during the manufacturing process and detects abnormal pressure changes. For example, the AI ​​learns the fluctuation patterns of pressure data and responds immediately when abnormal fluctuations occur. The analysis unit analyzes this data in real time to detect and predict anomalies early. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past anomaly data, it can predict fluctuations in risk in specific machinery or manufacturing processes and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0066] The proposal department suggests the optimal timing for preventive maintenance based on anomalies predicted by the analysis department. Specifically, it can suggest the optimal time to replace machine parts when an anomaly is predicted. For example, the AI ​​compares past data with the current situation, evaluates the lifespan and deterioration status of parts, and calculates the optimal replacement time. When suggesting the optimal time for machine maintenance, the AI ​​creates an optimal maintenance schedule based on the anomaly prediction results and maintenance history. This maximizes machine utilization and minimizes the risk of failure. When suggesting the optimal time for machine inspection, the AI ​​creates an optimal inspection schedule based on the anomaly prediction results and inspection history. This keeps the machine in optimal condition at all times and prevents unexpected failures. The proposal department makes these suggestions in real time, contributing to increased efficiency and reliability in the manufacturing process. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can evaluate the results of maintenance and inspections carried out based on the suggested preventive maintenance schedule and reflect them in future suggestions. This allows the proposal department to consistently provide highly accurate proposals based on the latest information, enabling preventive maintenance and quality assurance in the manufacturing industry.

[0067] The Quality Control Department proposes an optimal quality control schedule based on the preventive maintenance schedule proposed by the Proposal Department. Specifically, it can propose an optimal quality control schedule to improve product quality. For example, it can identify factors that affect quality at each stage of the manufacturing process and perform quality control at the optimal timing. This ensures that product quality remains consistently high. The Quality Control Department can identify factors that affect quality in the product manufacturing process and perform quality control at the optimal timing. For example, it can conduct quality inspections at each manufacturing stage and respond immediately if an abnormality is detected. This ensures product quality and minimizes the occurrence of defective products. The Quality Control Department can propose a schedule that optimizes the frequency of product quality inspections. For example, it can adjust the frequency of quality inspections according to the product characteristics and the status of the manufacturing process to achieve efficient quality control. This allows the Quality Control Department to improve the efficiency of the manufacturing process while maintaining product quality. Furthermore, the Quality Control Department can collaborate with the Proposal Department and the Analysis Department to update the quality control schedule in real time. For example, if an abnormality is predicted, it can immediately review the quality control schedule and take appropriate action. This allows the quality control department to provide highly accurate quality control based on the latest information at all times, enabling preventive maintenance and quality assurance in the manufacturing industry.

[0068] The data collection unit can collect various sensor data such as temperature, vibration, and pressure. For example, the data collection unit can collect temperature data using a temperature sensor. The data collection unit can also collect vibration data using a vibration sensor. The data collection unit can also collect pressure data using a pressure sensor. By collecting various sensor data, it becomes easier to detect signs of abnormalities. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input temperature data obtained from a temperature sensor into a generating AI and have the generating AI perform analysis of the temperature data.

[0069] The analysis unit can analyze the collected data and detect signs of anomalies. For example, the analysis unit can analyze the collected temperature data and detect abnormal temperature changes. For example, the analysis unit can also analyze the collected vibration data and detect abnormal vibration patterns. For example, the analysis unit can analyze the collected pressure data and detect abnormal pressure changes. In this way, signs of anomalies can be detected early by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the detection of signs of anomalies.

[0070] The suggestion unit can propose the optimal timing for preventive maintenance when an anomaly is predicted. For example, the suggestion unit can propose the optimal timing for replacing machine parts when an anomaly is predicted. The suggestion unit can also propose the optimal timing for performing machine maintenance when an anomaly is predicted. The suggestion unit can also propose the optimal timing for inspecting the machine when an anomaly is predicted. This makes it possible to avoid production line shutdowns by proposing the optimal timing for preventive maintenance when an anomaly is predicted. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, when an anomaly is predicted, the suggestion unit can input the optimal timing for preventive maintenance into a generating AI and have the generating AI execute a proposal for the optimal timing.

[0071] The Quality Control Department can propose an optimal schedule for quality control. For example, the Quality Control Department can propose an optimal quality control schedule to improve product quality. For example, the Quality Control Department can identify factors that affect quality in the product manufacturing process and perform quality control at the optimal timing. For example, the Quality Control Department can propose a schedule to optimize the frequency of product quality inspections. In this way, product quality can be improved by proposing an optimal quality control schedule. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or not using AI. For example, the Quality Control Department can input an optimal quality control schedule into a generating AI and have the generating AI execute a proposal for the optimal schedule.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of sensor data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to detect anomalies early. For example, if the user is relaxed, the data collection unit can decrease the collection frequency to reduce the system load. For example, if the user is in a hurry, the data collection unit can prioritize the collection of only important sensor data. By adjusting the timing of sensor data collection according to the user's emotions, early detection of anomalies and reduction of system load become possible. 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 without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The data collection unit can analyze past anomaly data and select the optimal sensor placement. For example, the data collection unit can add sensors to locations where anomalies are likely to occur based on past anomaly data. The data collection unit can also optimize sensor placement based on the frequency of anomaly data occurrence. For example, the data collection unit can place different types of sensors depending on the type of anomaly data. This allows for the addition of sensors to locations where anomalies are likely to occur by analyzing past anomaly data. 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 past anomaly data into a generating AI and have the generating AI select the optimal sensor placement.

[0074] The data collection unit can filter sensor data based on the operating status of the equipment. For example, when the equipment is in operation, the data collection unit can collect important sensor data in real time. For example, when the equipment is stopped, the data collection unit can also perform periodic data collection. For example, the data collection unit can change the type of data to collect depending on the operating status of the equipment. This allows for efficient data collection by changing the type of data to collect depending on the operating status of the equipment. 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 equipment operating status data into a generating AI and have the generating AI perform the filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of sensor data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important sensor data. For example, if the user is relaxed, the data collection unit can also perform overall data collection. For example, if the user is in a hurry, the data collection unit can also prioritize collecting data that shows signs of anomalies. This allows for the priority collection of important data by determining the priority of sensor 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 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 emotion estimation.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the equipment when collecting sensor data. For example, the data collection unit can prioritize the collection of data from areas prone to anomalies based on the geographical location information of the equipment. The data collection unit can also prioritize the collection of data showing signs of anomalies by considering the geographical location information. The data collection unit can also prioritize the collection of important sensor data based on the geographical location information. This allows for the priority collection of data from areas prone to anomalies based on the geographical location information of the equipment. 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 the geographical location information of the equipment into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0077] The data collection unit can analyze the equipment's maintenance history and collect relevant data when collecting sensor data. For example, the data collection unit can prioritize collecting data from areas prone to malfunctions based on the equipment's maintenance history. The data collection unit can also prioritize collecting important sensor data by analyzing the maintenance history. For example, the data collection unit can prioritize collecting data showing signs of malfunctions based on the maintenance history. This allows for the priority collection of data from areas prone to malfunctions based on the equipment's maintenance history. 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 the equipment's maintenance history into a generating AI and have the generating AI perform the collection of relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the anomaly analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can perform a rapid analysis to detect anomalies early. For example, if the user is relaxed, the analysis unit can also perform a detailed analysis to identify the cause of the anomaly. For example, if the user is in a hurry, the analysis unit can prioritize the analysis of only important anomalies. This allows for rapid and detailed analysis by adjusting the anomaly analysis method 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-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the anomaly analysis method.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the anomaly during the analysis. For example, the analysis unit can perform a detailed analysis for anomalies of high importance. For example, the analysis unit can also perform a simplified analysis for anomalies of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the anomaly. This enables efficient anomaly analysis by performing detailed analysis according to the importance of the anomaly. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input anomaly importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of the anomaly during analysis. For example, for mechanical anomalies, the analysis unit can apply an algorithm that analyzes vibration data. For example, for temperature anomalies, the analysis unit can also apply an algorithm that analyzes temperature data. For example, for pressure anomalies, the analysis unit can also apply an algorithm that analyzes pressure data. This makes it easier to identify the cause of an anomaly by applying the appropriate analysis algorithm according to the category of the anomaly. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input anomaly category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a highly visible display is possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a 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 AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0082] The analysis unit can determine the priority of analysis based on the timing of the anomaly's occurrence. For example, the analysis unit may prioritize the analysis of data immediately after the anomaly occurred. The analysis unit may also determine the priority of analysis based on the time period in which the anomaly occurred. The analysis unit may also adjust the level of detail of the analysis according to the timing of the anomaly's occurrence. This enables rapid anomaly analysis by determining the priority of analysis based on the timing of the anomaly's occurrence. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the anomaly occurrence timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the correlation of anomalies during the analysis. For example, the analysis unit prioritizes the analysis of data with a high correlation to anomalies. The analysis unit can also determine the order of analysis based on the correlation of anomalies. The analysis unit can also adjust the level of detail of the analysis according to the correlation of anomalies. This allows for efficient anomaly analysis by adjusting the order of analysis based on the correlation of anomalies. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input anomaly correlation data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0084] The suggestion unit can estimate the user's emotions and adjust the preventive maintenance suggestion method based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can provide quick suggestions to address anomalies early. If the user is relaxed, the suggestion unit can also provide detailed suggestions to identify the cause of anomalies. If the user is in a hurry, the suggestion unit can prioritize suggesting only important anomalies. This allows for quick and detailed suggestions by adjusting the preventive maintenance suggestion method 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the preventive maintenance suggestion method.

[0085] The proposal unit can adjust the level of detail of its proposals based on the severity of the anomaly. For example, the proposal unit can provide detailed proposals for high-severity anomalies. For example, it can provide simpler proposals for low-severity anomalies. The proposal unit can also determine the priority of proposals according to the severity of the anomaly. This enables efficient preventive maintenance by providing detailed proposals according to the severity of the anomaly. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input anomaly severity data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0086] The proposal unit can apply different proposal algorithms depending on the category of the anomaly during the proposal process. For example, for mechanical anomalies, the proposal unit can make proposals based on vibration data. For example, for temperature anomalies, the proposal unit can also make proposals based on temperature data. For example, for pressure anomalies, the proposal unit can also make proposals based on pressure data. This makes it easier to identify the cause of an anomaly by applying the appropriate proposal algorithm according to the category of the anomaly. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input anomaly category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0087] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize important suggestions. If the user is relaxed, the suggestion unit may also provide general suggestions. If the user is in a hurry, the suggestion unit may also prioritize suggestions that show signs of anomaly. This allows for prioritizing important suggestions by determining the priority of suggestions 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0088] The proposal unit can determine the priority of proposals based on when the anomaly occurred. For example, the proposal unit may prioritize proposals made immediately after the anomaly occurred. The proposal unit may also determine the priority of proposals based on the time period in which the anomaly occurred. The proposal unit may also adjust the level of detail of proposals according to when the anomaly occurred. This enables a rapid response by determining the priority of proposals based on when the anomaly occurred. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input anomaly occurrence time data into a generating AI and have the generating AI perform the determination of proposal priority.

[0089] The proposal unit can adjust the order of proposals based on the relevance of anomalies when making proposals. For example, the proposal unit can prioritize proposals with a high relevance to anomalies. The proposal unit can also determine the order of proposals based on the relevance of anomalies. The proposal unit can also adjust the level of detail of proposals according to the relevance of anomalies. This allows for efficient preventive maintenance by adjusting the order of proposals based on the relevance of anomalies. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input anomaly relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0090] The Quality Control Department can estimate the user's emotions and adjust its quality control suggestions based on those emotions. For example, if the user is stressed, the Quality Control Department can provide prompt suggestions to address quality issues early. If the user is relaxed, the Quality Control Department can provide detailed suggestions to identify the root cause of the quality issue. If the user is in a hurry, the Quality Control Department can prioritize suggesting only critical quality control issues. This allows for prompt and detailed suggestions by adjusting the quality control suggestions 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 may be, 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 Quality Control Department may be performed using AI or not. For example, the Quality Control Department can input user emotion data into a generative AI and have the generative AI adjust its quality control suggestions.

[0091] The Quality Control Department can adjust the level of detail in its quality control proposals based on the importance of the product. For example, the Quality Control Department can provide detailed quality control proposals for high-importance products, and simplified quality control proposals for low-importance products. The Quality Control Department can also prioritize quality control measures based on product importance. This allows for efficient quality control by providing detailed quality control proposals according to product importance. Some or all of the above processes in the Quality Control Department may be performed using AI, or not. For example, the Quality Control Department can input product importance data into a generating AI and have the generating AI adjust the level of detail in its proposals.

[0092] The Quality Control Department can apply different suggestion algorithms depending on the product category when proposing quality control. For example, the Quality Control Department can propose electrical quality control for electronic products, mechanical quality control for mechanical products, and chemical quality control for chemical products. By applying the appropriate suggestion algorithm according to the product category, the accuracy of quality control is improved. Some or all of the above processing in the Quality Control Department may be performed using AI, for example, or without AI. For example, the Quality Control Department can input product category data into a generating AI and have the generating AI apply different suggestion algorithms.

[0093] The quality control department can estimate the user's emotions and determine quality control priorities based on those estimated emotions. For example, if the user is stressed, the quality control department can prioritize critical quality control tasks. If the user is relaxed, the quality control department can also prioritize overall quality control tasks. If the user is in a hurry, the quality control department can also prioritize quality control tasks that show signs of anomalies. This allows for prioritizing critical quality control tasks according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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 quality control department may be performed using AI or not. For example, the quality control department can input user emotion data into a generative AI and have the generative AI determine quality control priorities.

[0094] The Quality Control Department can determine the priority of quality control proposals based on the product's manufacturing date. For example, the Quality Control Department can determine the priority of quality control based on the product's manufacturing date. The Quality Control Department can also adjust the level of detail of quality control according to the product's manufacturing date. The Quality Control Department can also determine the order of quality control based on the product's manufacturing date. This enables rapid quality control by determining the priority of quality control based on the product's manufacturing date. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or without AI. For example, the Quality Control Department can input product manufacturing date data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0095] The Quality Control Department can adjust the order of quality control proposals based on product relevance when making proposals. For example, the Quality Control Department can prioritize quality control measures that are highly relevant to the products. The Quality Control Department can also determine the order of quality control measures based on product relevance. The Quality Control Department can also adjust the level of detail of quality control measures according to product relevance. This allows for efficient quality control by adjusting the order of proposals based on product relevance. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or without AI. For example, the Quality Control Department can input product relevance data into a generating AI and have the generating AI adjust the order of proposals.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can determine the priority of analysis based on the frequency of anomaly occurrence. For example, the analysis unit will prioritize the analysis of frequently occurring anomalies. For example, the analysis unit may postpone the analysis of anomalies that occur infrequently. The analysis unit may also adjust the level of detail of the analysis according to the frequency of anomaly occurrence. This enables efficient anomaly analysis by determining the priority of analysis based on the frequency of anomaly occurrence. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input anomaly occurrence frequency data into a generating AI and have the generating AI perform the determination of analysis priority.

[0098] The proposal unit can determine the priority of proposals based on the scope of the anomaly's impact. For example, the proposal unit will prioritize proposals for anomalies with a wide scope of impact. For example, the proposal unit may postpone proposals for anomalies with a narrow scope of impact. The proposal unit may also adjust the level of detail of proposals according to the scope of the anomaly's impact. This enables efficient preventive maintenance by determining the priority of proposals based on the scope of the anomaly's impact. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input anomaly impact scope data into a generating AI and have the generating AI determine the priority of proposals.

[0099] The Quality Control Department can make quality control suggestions based on market feedback on the product. For example, the Quality Control Department can analyze market feedback and identify areas for improvement in quality control. The Quality Control Department can also make quality control suggestions based on customer complaint data. The Quality Control Department can also make quality control suggestions considering market trends. This makes it possible to improve product quality by making quality control suggestions based on market feedback. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or not. For example, the Quality Control Department can input market feedback data into a generating AI and have the generating AI execute quality control suggestions.

[0100] The data collection unit can adjust the frequency of data collection based on the frequency of equipment use when collecting sensor data. For example, the data collection unit can increase the frequency of data collection for equipment that is used frequently. For example, the data collection unit can also decrease the frequency of data collection for equipment that is used infrequently. The data collection unit can also change the type of data to be collected according to the frequency of equipment use. This allows for efficient data collection by adjusting the frequency of data collection based on the frequency of equipment use. 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 equipment usage frequency data into a generating AI and have the generating AI adjust the frequency of data collection.

[0101] The analysis unit can determine the priority of analysis based on the cause of the anomaly. For example, the analysis unit will prioritize the analysis of anomalies with clear causes. For example, the analysis unit may postpone the analysis of anomalies with unknown causes. The analysis unit may also adjust the level of detail of the analysis according to the cause of the anomaly. This enables efficient anomaly analysis by determining the priority of analysis based on the cause of the anomaly. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input anomaly cause data into a generating AI and have the generating AI determine the priority of analysis.

[0102] The suggestion unit can estimate the user's emotions and adjust the level of detail of its suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the level of detail of suggestions according to the user's emotions, appropriate suggestions can be made. 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 AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail of its suggestions.

[0103] The quality control department can estimate the user's emotions and determine quality control priorities based on those estimated emotions. For example, if the user is stressed, the quality control department can prioritize critical quality control tasks. If the user is relaxed, the quality control department can also prioritize overall quality control tasks. If the user is in a hurry, the quality control department can also prioritize quality control tasks that show signs of anomalies. This allows for prioritizing critical quality control tasks according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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 quality control department may be performed using AI or not. For example, the quality control department can input user emotion data into a generative AI and have the generative AI determine quality control priorities.

[0104] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a highly visible display is possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a 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 AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0105] The data collection unit can estimate the user's emotions and determine the priority of sensor data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important sensor data. For example, if the user is relaxed, the data collection unit can also perform overall data collection. For example, if the user is in a hurry, the data collection unit can also prioritize collecting data that shows signs of anomalies. This allows for the priority collection of important data by determining the priority of sensor 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 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 emotion estimation.

[0106] The suggestion unit can estimate the user's emotions and adjust the preventive maintenance suggestion method based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can provide quick suggestions to address anomalies early. If the user is relaxed, the suggestion unit can also provide detailed suggestions to identify the cause of anomalies. If the user is in a hurry, the suggestion unit can prioritize suggesting only important anomalies. This allows for quick and detailed suggestions by adjusting the preventive maintenance suggestion method 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the preventive maintenance suggestion method.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data acquisition unit collects sensor data and product data. For example, it collects temperature data using a temperature sensor, vibration data using a vibration sensor, and pressure data using a pressure sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts anomalies. For example, it analyzes the collected temperature data to detect abnormal temperature changes, analyzes the vibration data to detect abnormal vibration patterns, and analyzes the pressure data to detect abnormal pressure changes. Step 3: The proposal unit suggests the optimal timing for preventive maintenance based on the anomalies predicted by the analysis unit. For example, if an anomaly is predicted, it suggests the optimal time to replace machine parts, perform machine maintenance, or inspect the machine. Step 4: The Quality Control Department proposes an optimal quality control schedule based on the preventive maintenance schedule proposed by the Proposal Department. For example, they propose an optimal quality control schedule to improve product quality, a schedule to identify factors that affect quality in the product manufacturing process and perform quality control at the optimal time, and a schedule to optimize the frequency of product quality inspections.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and quality control unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data such as temperature, vibration, and pressure using the sensors of the smart device 14. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to predict anomalies. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and proposes the optimal timing for preventive maintenance based on the predicted anomalies. The quality control unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and proposes an optimal quality control schedule based on the preventive maintenance schedule. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and quality control unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data such as temperature, vibration, and pressure using the sensors of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to predict anomalies. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and proposes the optimal timing for preventive maintenance based on the predicted anomalies. The quality control unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and proposes an optimal quality control schedule based on the preventive maintenance schedule. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and quality control unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data such as temperature, vibration, and pressure using the sensors of the headset terminal 314. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to predict anomalies. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes the optimal timing for preventive maintenance based on the predicted anomalies. The quality control unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes an optimal quality control schedule based on the preventive maintenance schedule. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and quality control unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data such as temperature, vibration, and pressure using the sensors of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to predict anomalies. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes the optimal timing for preventive maintenance based on the predicted anomalies. The quality control unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes an optimal quality control schedule based on the preventive maintenance schedule. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects sensor data and product data, An analysis unit analyzes the data collected by the aforementioned collection unit and predicts anomalies, A proposal unit that proposes the optimal timing for preventive maintenance based on the anomalies predicted by the analysis unit, The system comprises a quality control unit that proposes an optimal quality control schedule based on the preventive maintenance schedule proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects various sensor data such as temperature, vibration, and pressure. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to detect signs of anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, When an anomaly is predicted, we propose the optimal timing for preventive maintenance. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Quality Control Department We propose an optimal schedule for quality control. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of sensor data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past anomaly data to select the optimal sensor placement. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting sensor data, filtering is performed based on the operating status of the equipment. 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 determines the priority of sensor data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting sensor data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of the equipment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting sensor data, analyze the equipment's maintenance history and collect relevant data. 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 anomaly analysis method based on the estimated user 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 severity of the anomalies. 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 category of the anomaly. 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 how the analysis results are displayed based on the estimated 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 anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the correlation of the anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, The system estimates user sentiment and adjusts the preventive maintenance recommendations based on the estimated user sentiment. 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 severity of the anomaly. 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 anomaly category. 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 determines the priority of suggestions based on the estimated user 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 anomaly occurred. 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 anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Quality Control Department We estimate user sentiment and adjust quality control suggestions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Quality Control Department When proposing quality control solutions, adjust the level of detail in the proposal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Quality Control Department When proposing quality control solutions, different proposal algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Quality Control Department Estimate user sentiment and determine quality control priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Quality Control Department When proposing quality control measures, prioritize the proposals based on the product's manufacturing date. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Quality Control Department When proposing quality control solutions, adjust the order of suggestions based on their relevance to the product. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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 sensor data and product data, An analysis unit analyzes the data collected by the aforementioned collection unit and predicts anomalies, A proposal unit that proposes the optimal timing for preventive maintenance based on the anomalies predicted by the analysis unit, The system comprises a quality control unit that proposes an optimal quality control schedule based on the preventive maintenance schedule proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects various sensor data such as temperature, vibration, and pressure. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to detect signs of anomalies. The system according to feature 1.

4. The aforementioned proposal section is, When an anomaly is predicted, we propose the optimal timing for preventive maintenance. The system according to feature 1.

5. The aforementioned Quality Control Department We propose an optimal schedule for quality control. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of sensor data collection based on the estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze past anomaly data to select the optimal sensor placement. The system according to feature 1.

8. The aforementioned collection unit is When collecting sensor data, filtering is performed based on the operating status of the equipment. The system according to feature 1.

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

10. The aforementioned collection unit is When collecting sensor data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of the equipment. The system according to feature 1.

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