System

Abstract

To provide a system that promptly and accurately provide countermeasures for trouble incidents arising in a factory.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit accepts data containing manuals compiled to address trouble events arising in a factory. The analysis unit analyzes the data received by the reception unit. The provision unit provides countermeasures for the trouble events based on the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult to provide rapid and accurate countermeasures to trouble events that occur in factories.

[0005] The system according to the embodiment aims to provide a quick and accurate response to a trouble event that occurs in a factory. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives data that includes a manual for dealing with a trouble event that has occurred in a factory. The analysis unit analyzes the data received by the reception unit. The provision unit provides a countermeasure for dealing with the trouble event based on the result of the analysis by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can quickly and accurately provide countermeasures to trouble events that occur in a factory. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system for responding to trouble events that have occurred in an automobile factory. This system accepts data in which a manual corresponding to a trouble event that has occurred in the factory is compiled. Next, the system analyzes the accepted data and provides information for responding to the trouble event. The provision unit uses a generation AI to learn past trouble cases and identify the optimal countermeasure. The reception unit can also accept manuals related to machine failures and production line stoppages. Furthermore, the system estimates the user's emotions and adjusts the input timing of the trouble event based on the estimated user's emotions. For example, the reception unit that accepts data in which a manual corresponding to a trouble event that has occurred in a factory is configured to accept a manual related to at least one of the trouble events of machine failures and production line stoppages. Next, the analysis unit that analyzes the data accepted by the reception unit learns past trouble cases using a generation AI and identifies the most appropriate countermeasure. Finally, the provision unit that provides countermeasures for the trouble event based on the results of the analysis by the analysis unit estimates the user's emotions and adjusts the input timing of the trouble event based on the estimated user's emotions. This enables the system to respond quickly and appropriately to the trouble event.

[0029] The trouble response system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives data in which a manual corresponding to a trouble event that has occurred in a factory is compiled. For example, the reception unit can receive a manual regarding a machine failure or a production line stop. The reception unit can receive, for example, a manual in a text format or an illustrated format. The analysis unit analyzes the data received by the reception unit. The analysis unit analyzes the data using, for example, a data analysis method or an analysis algorithm. The analysis unit learns past trouble cases using a generation AI and identifies the most appropriate countermeasure. For example, the generation AI learns past trouble cases using a deep learning model or a natural language generation technology. The provision unit provides countermeasures for the trouble event based on the results analyzed by the analysis unit. The provision unit provides countermeasures such as repair procedures and preventive measures. The provision unit learns past trouble cases using a generation AI and identifies the most appropriate countermeasure. For example, the generation AI learns past trouble cases and identifies the optimal countermeasure. This enables the trouble response system according to the embodiment to respond quickly and appropriately to trouble events.

[0030] The reception unit receives data that includes a manual for dealing with a trouble event that occurred in the factory. For example, the reception unit can receive manuals for machine failures and production line stoppages. The reception unit can receive manuals in text format or diagram format, for example. Specifically, the reception unit has an interface for electronically receiving trouble reports and manuals sent from each department in the factory. This includes a function for receiving data through email, a dedicated web portal, or a digital management system in the factory. The reception unit automatically classifies the received data and stores it in an appropriate folder according to the type of trouble and the scope of the impact. For example, a manual for machine failures is stored in a "machine trouble" folder, and a manual for production line stoppages is stored in a "production line trouble" folder. The reception unit also has a conversion function for unifying the format of the received data. For example, it can convert a manual in PDF format into text format, or convert image data into text data using OCR technology. This allows the analysis unit to process the data efficiently. In addition, the reception unit also has a function for monitoring the data reception status in real time and requesting retransmission of data if necessary. This makes it possible to prevent data loss or incomplete data from being received, improving the reliability of the entire system.

[0031] The analysis unit analyzes the data received by the reception unit. The analysis unit analyzes the data using, for example, a data analysis method or an analysis algorithm. The analysis unit uses the generation AI to learn past trouble cases and identify the most appropriate countermeasure. For example, the generation AI learns past trouble cases using a deep learning model or natural language generation technology. Specifically, the analysis unit preprocesses the received data, removes noise, and extracts necessary information. Next, the generation AI refers to a database of past trouble cases and searches for similar trouble events. The deep learning model analyzes data through multiple layers and recognizes patterns to identify the cause and impact of the trouble. The natural language generation technology analyzes the text data and extracts details of the trouble and countermeasures. Furthermore, the generation AI proposes the optimal countermeasure based on the analysis results. For example, for a machine failure, it proposes replacement and repair procedures for specific parts, and for a production line shutdown, it proposes alternative production routes and restart procedures. The analysis unit evaluates these proposals and selects the most effective countermeasure. In addition, the analysis unit compares past trouble cases with current trouble events and analyzes trends and patterns, which helps predict future troubles and develop preventive measures. This enables the analysis unit to respond quickly and accurately to trouble events, improving the factory's operating rate and productivity.

[0032] The provision unit provides countermeasures to deal with trouble events based on the results of the analysis by the analysis unit. The provision unit provides countermeasures such as repair procedures and preventive measures. The provision unit uses the generation AI to learn past trouble cases and identify the most appropriate countermeasure. For example, the generation AI learns past trouble cases and identifies the optimal countermeasure. Specifically, the provision unit has an interface for quickly communicating the countermeasures provided by the analysis unit to the factory personnel. This includes a function for sending notifications via email, SMS, a dedicated mobile application, etc. The provision unit provides documents and videos that explain the countermeasures in detail, helping the personnel to respond quickly. For example, it provides videos showing repair procedures and guidelines explaining preventive measures. The provision unit also has a function for monitoring the implementation status of the countermeasures in real time and providing additional instructions and support as necessary. For example, it checks whether the repair has been completed and sends additional instructions if it has not been completed. In addition, the provision unit also has a function for evaluating the effectiveness of the countermeasures and collecting feedback. This enables the provision unit to continuously improve the accuracy and effectiveness of the countermeasures and improve the quality of future countermeasures. This enables the providing department to provide a prompt and appropriate response to trouble events in the factory, thereby improving the factory's operating rate and productivity.

[0033] The providing unit can learn past trouble cases using the generation AI and identify a countermeasure. The providing unit, for example, uses the generation AI to learn past trouble cases and identify the most appropriate countermeasure. The generation AI learns past trouble cases using, for example, a deep learning model or natural language generation technology. For example, the generation AI learns past trouble cases and identifies the optimal countermeasure. In this way, the optimal countermeasure can be provided by learning past trouble cases. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input past trouble cases to the generation AI and cause the generation AI to identify the optimal countermeasure.

[0034] The reception unit can receive a manual regarding at least one of the trouble events of a machine failure or a stop of a production line. The reception unit, for example, receives a manual regarding a machine failure. For example, engine failure or sensor malfunction is included. The reception unit can also receive a manual regarding a stop of a production line. For example, parts shortage or machine trouble is included. Thereby, by receiving a manual regarding a specific trouble event, the accuracy of the response is improved. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input a manual regarding a machine failure to a generation AI and cause the generation AI to execute the reception of the manual.

[0035] The reception unit can analyze the user's past trouble report history and select the optimal reception method when receiving a trouble event. For example, the reception unit analyzes the types of troubles reported by the user in the past and proposes the optimal reception method for similar troubles. The reception unit can also provide a simplified reception method to a user who frequently reports, taking into account the user's past reporting frequency. Furthermore, the reception unit can refer to the user's past trouble resolution time and preferentially receive when a quick response is required. In this way, the optimal reception method can be provided by analyzing the past trouble report history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past trouble report history to the generation AI and cause the generation AI to select the optimal reception method.

[0036] The reception unit can perform filtering based on the user's current work situation and field of interest when receiving a trouble event. For example, the reception unit checks the work content currently being performed by the user and receives only related trouble events. The reception unit can also preferentially receive trouble events with high importance based on the user's field of interest. Furthermore, the reception unit can also temporarily limit the reception of trouble events when the load is high, taking into account the user's workload. In this way, by filtering based on the current work situation and field of interest, it is possible to preferentially receive highly relevant trouble events. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current work situation and field of interest to the generation AI and cause the generation AI to execute filtering.

[0037] When receiving a trouble event, the reception unit can preferentially receive a highly relevant event by taking into account the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially receives a trouble event that occurs in that area. The reception unit can also preferentially receive a nearby trouble event based on the user's location information. Furthermore, when the user is moving, the reception unit can also preferentially receive a trouble event that occurs in the area to which the user is moving. In this way, by taking the geographical location information into account, a highly relevant trouble event can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to perform preferential reception of a highly relevant event.

[0038] The reception unit can analyze the user's social media activity and receive related events when receiving a trouble event. The reception unit, for example, analyzes the user's social media posts and preferentially receives related trouble events. The reception unit can also refer to the posts of the user's followers and friends to receive related trouble events. Furthermore, the reception unit can take into consideration the frequency of the user's social media activities and prioritize reception of trouble events when the user is frequently active. In this way, by analyzing social media activities, related trouble events can be preferentially received. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute reception of related events.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the trouble event during the analysis. For example, the analysis unit performs a detailed analysis on a trouble event with a high level of importance. Furthermore, the analysis unit can perform a simplified analysis on a trouble event with a low level of importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on a trouble event with a medium level of importance. In this way, by adjusting the level of detail of the analysis based on the importance of the trouble event, an appropriate analysis result can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input importance data of the trouble event to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the trouble event during analysis. For example, the analysis unit applies a machine learning algorithm to a machine failure. Also, the analysis unit can apply a statistical analysis algorithm to a production line stop. Furthermore, the analysis unit can select and apply an appropriate analysis algorithm to other trouble events. In this way, by applying an appropriate analysis algorithm depending on the category of the trouble event, the accuracy of the analysis is improved. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the trouble event to the generation AI and cause the generation AI to apply the analysis algorithm.

[0041] During the analysis, the analysis unit can determine the priority of the analysis based on the occurrence time of the trouble event. For example, the analysis unit gives priority to analyzing a trouble event that occurred recently. The analysis unit can also give priority to analyzing a trouble event that occurred frequently in the past. Furthermore, the analysis unit can also give priority to analyzing a trouble event that occurs in a specific time period. This allows for a quick response by determining the priority of the analysis based on the occurrence time of the trouble event. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the occurrence time data of the trouble event to the generation AI and cause the generation AI to determine the priority of the analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of trouble events during analysis. For example, the analysis unit prioritizes analysis of highly related trouble events. Also, the analysis unit can postpone analysis of less related trouble events. Furthermore, when multiple trouble events are related, the analysis unit can analyze them in order of relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of trouble events. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input relevance data of trouble events to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] The providing unit can adjust the level of detail of the countermeasure to be provided based on the importance of the trouble event at the time of providing. For example, the providing unit provides a detailed countermeasure for a trouble event of high importance. Also, the providing unit can provide a simplified countermeasure for a trouble event of low importance. Furthermore, the providing unit can also provide a countermeasure of moderate detail for a trouble event of medium importance. As a result, an appropriate countermeasure can be provided by adjusting the level of detail of the countermeasure based on the importance of the trouble event. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the importance data of the trouble event to the generating AI and cause the generating AI to adjust the level of detail of the countermeasure.

[0044] The providing unit can apply different countermeasures depending on the category of the trouble event at the time of providing. For example, the providing unit provides a machine repair procedure for a machine failure. In addition, the providing unit can provide a production restart procedure for a production line stop. Furthermore, the providing unit can select and provide an appropriate countermeasure for other trouble events. This improves the accuracy of the response by applying an appropriate countermeasure depending on the category of the trouble event. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the category data of the trouble event to the generating AI and cause the generating AI to apply the countermeasure.

[0045] The providing unit can determine the priority of countermeasures based on the occurrence time of the trouble event at the time of providing. For example, the providing unit can provide preferentially countermeasures for a trouble event that has recently occurred. The providing unit can also provide preferentially countermeasures for a trouble event that has frequently occurred in the past. Furthermore, the providing unit can also provide preferentially countermeasures for a trouble event that occurs in a specific time period. This enables a quick response by determining the priority of countermeasures based on the occurrence time of the trouble event. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the occurrence time data of the trouble event to the generating AI and cause the generating AI to execute the determination of the priority of the countermeasures.

[0046] The providing unit can adjust the order of countermeasures based on the relevance of trouble events at the time of providing. For example, the providing unit provides countermeasures for highly related trouble events with priority. Also, the providing unit can postpone countermeasures for less related trouble events. Furthermore, when multiple trouble events are related, the providing unit can provide countermeasures in order of relevance. This allows for efficient response by adjusting the order of countermeasures based on the relevance of trouble events. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input relevance data of trouble events to the generating AI and cause the generating AI to adjust the order of countermeasures.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reception unit can analyze the user's past trouble report history and select the optimal reception method. For example, the reception unit can analyze the types of troubles that the user has reported in the past and propose the optimal reception method for similar troubles. The reception unit can also provide a simplified reception method to a user who frequently reports, taking into account the user's past reporting frequency. Furthermore, the reception unit can refer to the user's past trouble resolution time and preferentially accept when a quick response is required. In this way, the optimal reception method can be provided by analyzing the past trouble report history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past trouble report history to the generation AI and cause the generation AI to select the optimal reception method.

[0049] The analysis unit can determine the priority of the analysis based on the frequency of occurrence of the trouble event. For example, the analysis unit gives priority to the analysis of the trouble event that occurs frequently. The analysis unit can also postpone the analysis of the trouble event that occurs infrequently. Furthermore, the analysis unit can give priority to the analysis of the trouble events that occur in a specific period during the period. This enables efficient analysis by determining the priority of the analysis based on the frequency of occurrence of the trouble event. For example, a part or all of the above-mentioned processing in the analysis unit may be performed using AI or may be performed without using AI. For example, the analysis unit can input the occurrence frequency data of the trouble event to the generation AI and cause the generation AI to determine the priority of the analysis.

[0050] The analysis unit can determine the priority of the analysis based on the location where the trouble event occurs. For example, the analysis is prioritized for a trouble event that frequently occurs in a specific area. The analysis unit can also determine the priority of the analysis for a trouble event whose occurrence location is limited based on the location. Furthermore, the analysis unit can also determine the priority of the analysis for a trouble event that occurs in multiple locations based on the importance of the occurrence location. This enables efficient analysis by determining the priority of the analysis based on the occurrence location of the trouble event. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the occurrence location data of the trouble event to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0051] The reception unit can perform filtering based on the user's current work status and field of interest. For example, the reception unit checks the work content currently being performed by the user and receives only related trouble events. The reception unit can also preferentially receive trouble events with high importance based on the user's field of interest. Furthermore, the reception unit can take into account the user's workload and temporarily limit the reception of trouble events when the load is high. In this way, by filtering based on the current work status and field of interest, it is possible to preferentially receive highly relevant trouble events. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current work status and field of interest to the generation AI and cause the generation AI to execute filtering.

[0052] The providing unit can determine the priority of countermeasures based on the occurrence time of the trouble event. For example, the providing unit can provide preferentially countermeasures for a trouble event that has recently occurred. The providing unit can also provide preferentially countermeasures for a trouble event that has frequently occurred in the past. Furthermore, the providing unit can also provide preferentially countermeasures for a trouble event that occurs in a specific time period. This enables a quick response by determining the priority of countermeasures based on the occurrence time of the trouble event. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input data on the occurrence time of the trouble event to the generating AI and cause the generating AI to determine the priority of the countermeasures.

[0053] The analysis unit can apply different analysis algorithms depending on the category of the trouble event. For example, a machine learning algorithm can be applied to a machine failure. The analysis unit can also apply a statistical analysis algorithm to a production line stop. Furthermore, the analysis unit can select and apply an appropriate analysis algorithm to other trouble events. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of the trouble event. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the trouble event to the generation AI and cause the generation AI to apply the analysis algorithm.

[0054] The process flow of the first embodiment will be briefly described below.

[0055] Step 1: The reception department accepts data that contains manuals for dealing with troubles that occur in the factory. For example, manuals for machine breakdowns and production line stoppages can be accepted in text or diagram format. Step 2: The analysis unit analyzes the data received by the reception unit. The analysis unit analyzes the data using data analysis methods and analysis algorithms, and uses a generation AI to learn from past trouble cases and identify the most appropriate way to deal with the problem. For example, a deep learning model or natural language generation technology is used. Step 3: The provision unit provides countermeasures to deal with trouble events based on the results of the analysis by the analysis unit. The provision unit provides countermeasures such as repair procedures and preventive measures, and uses a generation AI to learn from past trouble cases and identify the most appropriate countermeasure.

[0056] (Example 2) The system according to the embodiment of the present invention is a system for responding to trouble events that have occurred in an automobile factory. This system accepts data in which a manual corresponding to a trouble event that has occurred in the factory is compiled. Next, the system analyzes the accepted data and provides information for responding to the trouble event. The provision unit uses a generation AI to learn past trouble cases and identify the optimal countermeasure. The reception unit can also accept manuals related to machine failures and production line stoppages. Furthermore, the system estimates the user's emotions and adjusts the input timing of the trouble event based on the estimated user's emotions. For example, the reception unit that accepts data in which a manual corresponding to a trouble event that has occurred in a factory is configured to accept a manual related to at least one of the trouble events of machine failures and production line stoppages. Next, the analysis unit that analyzes the data accepted by the reception unit learns past trouble cases using a generation AI and identifies the most appropriate countermeasure. Finally, the provision unit that provides countermeasures for the trouble event based on the results of the analysis by the analysis unit estimates the user's emotions and adjusts the input timing of the trouble event based on the estimated user's emotions. This enables the system to respond quickly and appropriately to the trouble event.

[0057] The trouble response system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives data in which a manual corresponding to a trouble event that has occurred in a factory is compiled. For example, the reception unit can receive a manual regarding a machine failure or a production line stop. The reception unit can receive, for example, a manual in a text format or an illustrated format. The analysis unit analyzes the data received by the reception unit. The analysis unit analyzes the data using, for example, a data analysis method or an analysis algorithm. The analysis unit learns past trouble cases using a generation AI and identifies the most appropriate countermeasure. For example, the generation AI learns past trouble cases using a deep learning model or a natural language generation technology. The provision unit provides countermeasures for the trouble event based on the results analyzed by the analysis unit. The provision unit provides countermeasures such as repair procedures and preventive measures. The provision unit learns past trouble cases using a generation AI and identifies the most appropriate countermeasure. For example, the generation AI learns past trouble cases and identifies the optimal countermeasure. This enables the trouble response system according to the embodiment to respond quickly and appropriately to trouble events.

[0058] The reception unit receives data that includes a manual for dealing with a trouble event that occurred in the factory. For example, the reception unit can receive manuals for machine failures and production line stoppages. The reception unit can receive manuals in text format or diagram format, for example. Specifically, the reception unit has an interface for electronically receiving trouble reports and manuals sent from each department in the factory. This includes a function for receiving data through email, a dedicated web portal, or a digital management system in the factory. The reception unit automatically classifies the received data and stores it in an appropriate folder according to the type of trouble and the scope of the impact. For example, a manual for machine failures is stored in a "machine trouble" folder, and a manual for production line stoppages is stored in a "production line trouble" folder. The reception unit also has a conversion function for unifying the format of the received data. For example, it can convert a manual in PDF format into text format, or convert image data into text data using OCR technology. This allows the analysis unit to process the data efficiently. In addition, the reception unit also has a function for monitoring the data reception status in real time and requesting retransmission of data if necessary. This makes it possible to prevent data loss or incomplete data from being received, improving the reliability of the entire system.

[0059] The analysis unit analyzes the data received by the reception unit. The analysis unit analyzes the data using, for example, a data analysis method or an analysis algorithm. The analysis unit uses the generation AI to learn past trouble cases and identify the most appropriate countermeasure. For example, the generation AI learns past trouble cases using a deep learning model or natural language generation technology. Specifically, the analysis unit preprocesses the received data, removes noise, and extracts necessary information. Next, the generation AI refers to a database of past trouble cases and searches for similar trouble events. The deep learning model analyzes data through multiple layers and recognizes patterns to identify the cause and impact of the trouble. The natural language generation technology analyzes the text data and extracts details of the trouble and countermeasures. Furthermore, the generation AI proposes the optimal countermeasure based on the analysis results. For example, for a machine failure, it proposes replacement and repair procedures for specific parts, and for a production line shutdown, it proposes alternative production routes and restart procedures. The analysis unit evaluates these proposals and selects the most effective countermeasure. In addition, the analysis unit compares past trouble cases with current trouble events and analyzes trends and patterns, which helps predict future troubles and develop preventive measures. This enables the analysis unit to respond quickly and accurately to trouble events, improving the factory's operating rate and productivity.

[0060] The provision unit provides countermeasures to deal with trouble events based on the results of the analysis by the analysis unit. The provision unit provides countermeasures such as repair procedures and preventive measures. The provision unit uses the generation AI to learn past trouble cases and identify the most appropriate countermeasure. For example, the generation AI learns past trouble cases and identifies the optimal countermeasure. Specifically, the provision unit has an interface for quickly communicating the countermeasures provided by the analysis unit to the factory personnel. This includes a function for sending notifications via email, SMS, a dedicated mobile application, etc. The provision unit provides documents and videos that explain the countermeasures in detail, helping the personnel to respond quickly. For example, it provides videos showing repair procedures and guidelines explaining preventive measures. The provision unit also has a function for monitoring the implementation status of the countermeasures in real time and providing additional instructions and support as necessary. For example, it checks whether the repair has been completed and sends additional instructions if it has not been completed. In addition, the provision unit also has a function for evaluating the effectiveness of the countermeasures and collecting feedback. This enables the provision unit to continuously improve the accuracy and effectiveness of the countermeasures and improve the quality of future countermeasures. This enables the providing department to provide a prompt and appropriate response to trouble events in the factory, thereby improving the factory's operating rate and productivity.

[0061] The providing unit can learn past trouble cases using the generation AI and identify a countermeasure. The providing unit, for example, uses the generation AI to learn past trouble cases and identify the most appropriate countermeasure. The generation AI learns past trouble cases using, for example, a deep learning model or natural language generation technology. For example, the generation AI learns past trouble cases and identifies the optimal countermeasure. In this way, the optimal countermeasure can be provided by learning past trouble cases. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input past trouble cases to the generation AI and cause the generation AI to identify the optimal countermeasure.

[0062] The reception unit can receive a manual regarding at least one of the trouble events of a machine failure or a stop of a production line. The reception unit, for example, receives a manual regarding a machine failure. For example, engine failure or sensor malfunction is included. The reception unit can also receive a manual regarding a stop of a production line. For example, parts shortage or machine trouble is included. Thereby, by receiving a manual regarding a specific trouble event, the accuracy of the response is improved. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input a manual regarding a machine failure to a generation AI and cause the generation AI to execute the reception of the manual.

[0063] The reception unit can estimate the user's emotion and adjust the input timing of the trouble event based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit delays the input timing and waits until the user calms down. Also, when the user is relaxed, the reception unit can immediately receive the input. Furthermore, when the user is in a hurry, the reception unit can also advance the input timing to quickly respond. This allows the input timing to be adjusted according to the user's emotion, thereby reducing the burden on the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0064] The reception unit can analyze the user's past trouble report history and select the optimal reception method when receiving a trouble event. For example, the reception unit analyzes the types of troubles reported by the user in the past and proposes the optimal reception method for similar troubles. The reception unit can also provide a simplified reception method to a user who frequently reports, taking into account the user's past reporting frequency. Furthermore, the reception unit can refer to the user's past trouble resolution time and preferentially receive when a quick response is required. In this way, the optimal reception method can be provided by analyzing the past trouble report history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past trouble report history to the generation AI and cause the generation AI to select the optimal reception method.

[0065] The reception unit can perform filtering based on the user's current work situation and field of interest when receiving a trouble event. For example, the reception unit checks the work content currently being performed by the user and receives only related trouble events. The reception unit can also preferentially receive trouble events with high importance based on the user's field of interest. Furthermore, the reception unit can also temporarily limit the reception of trouble events when the load is high, taking into account the user's workload. In this way, by filtering based on the current work situation and field of interest, it is possible to preferentially receive highly relevant trouble events. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current work situation and field of interest to the generation AI and cause the generation AI to execute filtering.

[0066] The reception unit can estimate the user's emotion and determine the priority of the trouble events to be received based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit preferentially receives trouble events with a high level of importance. Also, when the user is relaxed, the reception unit can receive trouble events with normal priority. Furthermore, when the user is in a hurry, the reception unit can also preferentially receive trouble events that require a quick response. In this way, by determining the priority based on the user's emotion, important trouble events can be preferentially received. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0067] When receiving a trouble event, the reception unit can preferentially receive a highly relevant event by taking into account the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially receives a trouble event that occurs in that area. The reception unit can also preferentially receive a nearby trouble event based on the user's location information. Furthermore, when the user is moving, the reception unit can also preferentially receive a trouble event that occurs in the area to which the user is moving. In this way, by taking the geographical location information into account, a highly relevant trouble event can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to perform preferential reception of a highly relevant event.

[0068] The reception unit can analyze the user's social media activity and receive related events when receiving a trouble event. The reception unit, for example, analyzes the user's social media posts and preferentially receives related trouble events. The reception unit can also refer to the posts of the user's followers and friends to receive related trouble events. Furthermore, the reception unit can take into consideration the frequency of the user's social media activities and prioritize reception of trouble events when the user is frequently active. In this way, by analyzing social media activities, related trouble events can be preferentially received. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute reception of related events.

[0069] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit provides a simple and highly visible analysis result. In addition, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can also provide a concise analysis result with the main points. In this way, by adjusting the expression method of the analysis based on the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the analysis.

[0070] The analysis unit can adjust the level of detail of the analysis based on the importance of the trouble event during the analysis. For example, the analysis unit performs a detailed analysis on a trouble event with a high level of importance. Furthermore, the analysis unit can perform a simplified analysis on a trouble event with a low level of importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on a trouble event with a medium level of importance. In this way, by adjusting the level of detail of the analysis based on the importance of the trouble event, an appropriate analysis result can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input importance data of the trouble event to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0071] The analysis unit can apply different analysis algorithms depending on the category of the trouble event during analysis. For example, the analysis unit applies a machine learning algorithm to a machine failure. Also, the analysis unit can apply a statistical analysis algorithm to a production line stop. Furthermore, the analysis unit can select and apply an appropriate analysis algorithm to other trouble events. In this way, by applying an appropriate analysis algorithm depending on the category of the trouble event, the accuracy of the analysis is improved. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the trouble event to the generation AI and cause the generation AI to apply the analysis algorithm.

[0072] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit provides a short and to the point analysis result. In addition, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can provide a concise analysis result so that the user can understand it quickly. In this way, by adjusting the length of the analysis based on the user's emotion, an analysis result of an appropriate length for the user can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the analysis.

[0073] During the analysis, the analysis unit can determine the priority of the analysis based on the occurrence time of the trouble event. For example, the analysis unit gives priority to analyzing a trouble event that occurred recently. The analysis unit can also give priority to analyzing a trouble event that occurred frequently in the past. Furthermore, the analysis unit can also give priority to analyzing a trouble event that occurs in a specific time period. This allows for a quick response by determining the priority of the analysis based on the occurrence time of the trouble event. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the occurrence time data of the trouble event to the generation AI and cause the generation AI to determine the priority of the analysis.

[0074] The analysis unit can adjust the order of analysis based on the relevance of trouble events during analysis. For example, the analysis unit prioritizes analysis of highly related trouble events. Also, the analysis unit can postpone analysis of less related trouble events. Furthermore, when multiple trouble events are related, the analysis unit can analyze them in order of relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of trouble events. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input relevance data of trouble events to the generation AI and cause the generation AI to adjust the order of analysis.

[0075] The providing unit can estimate the user's emotion and adjust the expression method of the countermeasure to be provided based on the estimated user's emotion. For example, when the user is stressed, the providing unit provides a simple and highly visible countermeasure. In addition, when the user is relaxed, the providing unit can provide a detailed countermeasure. Furthermore, when the user is in a hurry, the providing unit can also provide a concise countermeasure that covers the main points. In this way, by adjusting the expression method of the countermeasure based on the user's emotion, it is possible to provide a countermeasure that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the countermeasure.

[0076] The providing unit can adjust the level of detail of the countermeasure to be provided based on the importance of the trouble event at the time of providing. For example, the providing unit provides a detailed countermeasure for a trouble event of high importance. Also, the providing unit can provide a simplified countermeasure for a trouble event of low importance. Furthermore, the providing unit can also provide a countermeasure of moderate detail for a trouble event of medium importance. As a result, an appropriate countermeasure can be provided by adjusting the level of detail of the countermeasure based on the importance of the trouble event. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the importance data of the trouble event to the generating AI and cause the generating AI to adjust the level of detail of the countermeasure.

[0077] The providing unit can apply different countermeasures depending on the category of the trouble event at the time of providing. For example, the providing unit provides a machine repair procedure for a machine failure. In addition, the providing unit can provide a production restart procedure for a production line stop. Furthermore, the providing unit can select and provide an appropriate countermeasure for other trouble events. This improves the accuracy of the response by applying an appropriate countermeasure depending on the category of the trouble event. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the category data of the trouble event to the generating AI and cause the generating AI to apply the countermeasure.

[0078] The providing unit can estimate the user's emotion and determine the priority of countermeasures to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can provide countermeasures with a high priority. Also, when the user is relaxed, the providing unit can provide countermeasures with normal priority. Furthermore, when the user is in a hurry, the providing unit can also provide countermeasures that require a quick response with priority. In this way, by determining the priority of countermeasures based on the user's emotion, important countermeasures can be provided with priority. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of countermeasures.

[0079] The providing unit can determine the priority of countermeasures based on the occurrence time of the trouble event at the time of providing. For example, the providing unit can provide preferentially countermeasures for a trouble event that has recently occurred. The providing unit can also provide preferentially countermeasures for a trouble event that has frequently occurred in the past. Furthermore, the providing unit can also provide preferentially countermeasures for a trouble event that occurs in a specific time period. This enables a quick response by determining the priority of countermeasures based on the occurrence time of the trouble event. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the occurrence time data of the trouble event to the generating AI and cause the generating AI to execute the determination of the priority of the countermeasures.

[0080] The providing unit can adjust the order of countermeasures based on the relevance of trouble events at the time of providing. For example, the providing unit provides countermeasures for highly related trouble events with priority. Also, the providing unit can postpone countermeasures for less related trouble events. Furthermore, when multiple trouble events are related, the providing unit can provide countermeasures in order of relevance. This allows for efficient response by adjusting the order of countermeasures based on the relevance of trouble events. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input relevance data of trouble events to the generating AI and cause the generating AI to adjust the order of countermeasures.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The reception unit can analyze the user's past trouble report history and select the optimal reception method. For example, the reception unit can analyze the types of troubles that the user has reported in the past and propose the optimal reception method for similar troubles. The reception unit can also provide a simplified reception method to a user who frequently reports, taking into account the user's past reporting frequency. Furthermore, the reception unit can refer to the user's past trouble resolution time and preferentially accept when a quick response is required. In this way, the optimal reception method can be provided by analyzing the past trouble report history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past trouble report history to the generation AI and cause the generation AI to select the optimal reception method.

[0083] The analysis unit can determine the priority of the analysis based on the frequency of occurrence of the trouble event. For example, the analysis unit gives priority to the analysis of the trouble event that occurs frequently. The analysis unit can also postpone the analysis of the trouble event that occurs infrequently. Furthermore, the analysis unit can give priority to the analysis of the trouble events that occur in a specific period during the period. This enables efficient analysis by determining the priority of the analysis based on the frequency of occurrence of the trouble event. For example, a part or all of the above-mentioned processing in the analysis unit may be performed using AI or may be performed without using AI. For example, the analysis unit can input the occurrence frequency data of the trouble event to the generation AI and cause the generation AI to determine the priority of the analysis.

[0084] The providing unit can estimate the user's emotion and adjust the expression method of the countermeasure to be provided based on the estimated user's emotion. For example, when the user is stressed, a simple and highly visible countermeasure is provided. In addition, when the user is relaxed, the providing unit can provide a detailed countermeasure. Furthermore, when the user is in a hurry, the providing unit can also provide a concise countermeasure that covers the main points. In this way, by adjusting the expression method of the countermeasure based on the user's emotion, it is possible to provide a countermeasure that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the countermeasure.

[0085] The analysis unit can determine the priority of the analysis based on the location where the trouble event occurs. For example, the analysis is prioritized for a trouble event that frequently occurs in a specific area. The analysis unit can also determine the priority of the analysis for a trouble event whose occurrence location is limited based on the location. Furthermore, the analysis unit can also determine the priority of the analysis for a trouble event that occurs in multiple locations based on the importance of the occurrence location. This enables efficient analysis by determining the priority of the analysis based on the occurrence location of the trouble event. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the occurrence location data of the trouble event to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0086] The providing unit can estimate the user's emotion and determine the priority of countermeasures to be provided based on the estimated user's emotion. For example, when the user is stressed, countermeasures with a high importance are provided preferentially. In addition, when the user is relaxed, the providing unit can provide countermeasures with normal priority. Furthermore, when the user is in a hurry, the providing unit can also provide countermeasures that require a quick response preferentially. In this way, by determining the priority of countermeasures based on the user's emotion, important countermeasures can be provided preferentially. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of countermeasures.

[0087] The reception unit can perform filtering based on the user's current work status and field of interest. For example, the reception unit checks the work content currently being performed by the user and receives only related trouble events. The reception unit can also preferentially receive trouble events with high importance based on the user's field of interest. Furthermore, the reception unit can take into account the user's workload and temporarily limit the reception of trouble events when the load is high. In this way, by filtering based on the current work status and field of interest, it is possible to preferentially receive highly relevant trouble events. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current work status and field of interest to the generation AI and cause the generation AI to execute filtering.

[0088] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is stressed, a simple and highly visible analysis result is provided. In addition, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can also provide a concise analysis result with the main points. In this way, by adjusting the expression method of the analysis based on the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the analysis.

[0089] The providing unit can determine the priority of countermeasures based on the occurrence time of the trouble event. For example, the providing unit can provide preferentially countermeasures for a trouble event that has recently occurred. The providing unit can also provide preferentially countermeasures for a trouble event that has frequently occurred in the past. Furthermore, the providing unit can also provide preferentially countermeasures for a trouble event that occurs in a specific time period. This enables a quick response by determining the priority of countermeasures based on the occurrence time of the trouble event. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input data on the occurrence time of the trouble event to the generating AI and cause the generating AI to determine the priority of the countermeasures.

[0090] The reception unit can estimate the user's emotion and determine the priority of the trouble events to be received based on the estimated user's emotion. For example, when the user is stressed, the reception unit receives the trouble events with high importance with priority. Also, when the user is relaxed, the reception unit can receive the trouble events with normal priority. Furthermore, when the user is in a hurry, the reception unit can also receive the trouble events that require a quick response with priority. In this way, by determining the priority based on the user's emotion, it is possible to receive the important trouble events with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0091] The analysis unit can apply different analysis algorithms depending on the category of the trouble event. For example, a machine learning algorithm can be applied to a machine failure. The analysis unit can also apply a statistical analysis algorithm to a production line stop. Furthermore, the analysis unit can select and apply an appropriate analysis algorithm to other trouble events. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of the trouble event. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the trouble event to the generation AI and cause the generation AI to apply the analysis algorithm.

[0092] The process flow of the second embodiment will be briefly described below.

[0093] Step 1: The reception department accepts data that contains manuals for dealing with troubles that occur in the factory. For example, manuals for machine breakdowns and production line stoppages can be accepted in text or diagram format. Step 2: The analysis unit analyzes the data received by the reception unit. The analysis unit analyzes the data using data analysis methods and analysis algorithms, and uses a generation AI to learn from past trouble cases and identify the most appropriate way to deal with the problem. For example, a deep learning model or natural language generation technology is used. Step 3: The provision unit provides countermeasures to deal with trouble events based on the results of the analysis by the analysis unit. The provision unit provides countermeasures such as repair procedures and preventive measures, and uses a generation AI to learn from past trouble cases and identify the most appropriate countermeasure.

[0094] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0096] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0097] For example, the reception unit can receive manual data related to a trouble event via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and learns past trouble cases using a generation AI to identify the optimal countermeasure. For example, the provision unit is realized by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides information corresponding to the trouble event based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0105] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0108] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] For example, the reception unit can receive manual data related to a trouble event via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and learns past trouble cases using a generation AI and identifies an optimal countermeasure. For example, the provision unit is realized by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides information corresponding to the trouble event based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] For example, the reception unit can receive manual data related to a trouble event via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and learns past trouble cases using a generation AI and identifies the optimal countermeasure. For example, the provision unit is realized by the speaker 240 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and provides information corresponding to the trouble event based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] For example, the reception unit can receive manual data related to a trouble event via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and learns past trouble cases using a generation AI and identifies the optimal countermeasure. For example, the provision unit is realized by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides information corresponding to the trouble event based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0148] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0152] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] In addition, 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 upon request from the data processing device 12.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0159] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0163] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0165] (Appendix 1) A reception section that receives data that includes a manual for dealing with troubles that occur in the factory; an analysis unit that analyzes the data accepted by the acceptance unit; A provision unit that provides a countermeasure against the trouble event based on the result of the analysis by the analysis unit. A system characterized by: (Appendix 2) The providing unit is Generative AI learns from past trouble cases and identifies countermeasures 2. The system of claim 1, (Appendix 3) The reception unit is Accept manuals regarding at least one of the following trouble events: machine failure or production line stoppage 2. The system of claim 1, (Appendix 4) The reception unit is Estimate the user's emotions and adjust the input timing of trouble events based on the estimated user emotions 2. The system of claim 1, (Appendix 5) The reception unit is When receiving a trouble event, analyze the user's past trouble report history and select the reception method. 2. The system of claim 1, (Appendix 6) The reception unit is When a trouble incident is received, it is filtered based on the user's current work situation and areas of interest. 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and determine the priority of trouble incidents to be accepted based on the estimated user's emotions 2. The system of claim 1, (Appendix 8) The reception unit is When accepting trouble cases, the system takes into account the user's geographic location information and prioritizes cases that are highly relevant. 2. The system of claim 1, (Appendix 9) The reception unit is When a trouble incident is received, the user's social media activity is analyzed and related incidents are received. 2. The system of claim 1, (Appendix 10) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 11) The analysis unit is During analysis, adjust the level of detail based on the importance of the problem 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of the trouble event. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, prioritize analysis based on when the problem occurred. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the order of analysis based on the relevance of trouble events 2. The system of claim 1, (Appendix 16) The providing unit is Inferring a user's emotions and adjusting the way in which countermeasures are presented based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The providing unit is When providing solutions, adjust the level of detail of the solutions provided based on the severity of the problem. 2. The system of claim 1, (Appendix 18) The providing unit is When provided, different measures will be applied depending on the category of the trouble event. 2. The system of claim 1, (Appendix 19) The providing unit is Inferring user emotions and prioritizing measures to be provided based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The providing unit is At the time of provision, the priority of countermeasures will be determined based on the timing of the trouble occurrence. 2. The system of claim 1, (Appendix 21) The providing unit is When provided, adjust the order of actions based on the relevance of the problem event 2. The system of claim 1, [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section that receives data that includes a manual for dealing with troubles that occur in the factory; an analysis unit that analyzes the data accepted by the acceptance unit; A provision unit that provides a countermeasure against the trouble event based on the result of the analysis by the analysis unit. A system characterized by:

2. The providing unit is Generative AI learns from past trouble cases and identifies countermeasures 2. The system of claim 1 .

3. The reception unit is Accept manuals regarding at least one of the following trouble events: machine failure or production line stoppage 2. The system of claim 1 .

4. The reception unit is Estimate the user's emotions and adjust the input timing of trouble events based on the estimated user emotions 2. The system of claim 1 .

5. The reception unit is When receiving a trouble event, analyze the user's past trouble report history and select the reception method.

2. The system of claim 1 .

6. The reception unit is When a trouble incident is received, it is filtered based on the user's current work situation and areas of interest.

2. The system of claim 1 .

7. The reception unit is Estimate the user's emotions and determine the priority of trouble incidents to be accepted based on the estimated user's emotions 2. The system of claim 1 .

8. The reception unit is When accepting trouble cases, the system takes into account the user's geographic location information and prioritizes cases that are highly relevant.

2. The system of claim 1 .

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