system

Abstract

An object of a system according to an embodiment is to provide a person in charge with knowledge about maintenance and management of an RPA.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a specific troubleshooting procedure based on the analysis result obtained by the analysis unit. The provision unit provides the procedure generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was a problem in that knowledge regarding RPA maintenance and management was not provided sufficiently, making it difficult for personnel to respond appropriately.

[0005] The system according to the embodiment aims to provide personnel with knowledge regarding the maintenance and management of RPA. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates specific troubleshooting procedures based on the analysis results obtained by the analysis unit. The provision unit provides the procedures generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide knowledge about the maintenance and management of RPA to those in charge. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses generative AI to provide personnel with RPA maintenance and management knowledge. This system multimodally understands the business operations, characteristics, and error cases of each department and generates specific troubleshooting procedures using natural language processing. For example, the system collects text data, image data, audio data, etc. to understand the business operations, characteristics, and error cases of each department. Next, it analyzes the collected data to understand the business operations, characteristics, and error cases of each department. It then generates specific troubleshooting procedures based on the analysis results. Finally, it provides the generated troubleshooting procedures to personnel so that they can perform RPA maintenance themselves. This enables personnel in each department to perform RPA maintenance themselves, thereby improving maintenance efficiency. For example, the system collects data such as business manuals, error logs, and audio recordings, and uses AI to multimodally understand the business operations, characteristics, and error cases of each department. Next, it analyzes the collected text data, image data, and audio data to understand the business flow and identify the cause of the error. Based on the analysis results, it generates specific troubleshooting procedures in natural language and provides them to personnel. This allows personnel to perform RPA maintenance themselves.

[0029] A maintenance support system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. Examples of the data include, but are not limited to, text data, image data, and audio data. The collection unit collects data such as operation manuals, error logs, and audio recordings. The collection unit can also collect data in real time using a sensor or a camera. For example, the collection unit can collect text data from operation manuals, error log data, and audio recordings. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, data mining or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit analyzes the text data from operation manuals to understand the workflow. The analysis unit can also analyze error log data to identify the cause of an error. The analysis unit can also analyze audio recordings to understand the content and characteristics of the work. The generation unit generates specific troubleshooting procedures based on the analysis results obtained by the analysis unit. The generation can be performed using, for example, a generation AI, but is not limited to, examples. For example, the generation unit generates specific troubleshooting procedures in natural language based on the analysis results. The generation unit can also use a generation AI to generate procedures that specifically indicate how to deal with the cause of an error. The generation unit can also use a generation AI to generate troubleshooting procedures based on a business flow. The provision unit provides the procedures generated by the generation unit. The provision can be performed, for example, by email or dashboard display, but is not limited to these examples. For example, the provision unit can provide a screen that displays the generated procedures so that a person in charge can perform maintenance according to the procedures. The provision unit can also send the generated procedures to a person in charge by email. The provision unit can also display the generated procedures on a dashboard so that a person in charge can check the procedures in real time. This allows the maintenance support system according to the embodiment to efficiently collect, analyze, generate, and provide data.For example, the system collects data such as business manuals, error logs, and voice recordings, and the AI ​​can understand the business content and characteristics of each department, as well as error cases, in a multimodal manner. Next, it analyzes the collected text data, image data, and voice data to understand the business flow and identify the cause of the error. Furthermore, based on the analysis results, it generates specific troubleshooting procedures in natural language and provides them to the person in charge. This allows the person in charge to perform RPA maintenance themselves.

[0030] The collection unit can collect text data, image data, and audio data. The collection unit, for example, collects text data from a business manual. For example, the collection unit collects the text data from the business manual and understands the business flow. The collection unit can also collect error log data. For example, the collection unit collects error log data and identifies the cause of the error. The collection unit can also collect audio recordings. For example, the collection unit collects audio recordings and understands the business content and characteristics. This enables more comprehensive analysis by collecting various data formats. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the text data from the business manual into a generation AI and have the generation AI understand the business flow.

[0031] The analysis unit analyzes the collected text data, image data, and audio data to understand the business content, characteristics, and error cases of each department. The analysis unit, for example, analyzes the text data of a business manual to understand the business flow. For example, the analysis unit analyzes the text data of a business manual to understand the business flow. The analysis unit can also analyze error log data to identify the cause of an error. For example, the analysis unit analyzes error log data to identify the cause of an error. The analysis unit can also analyze audio recordings to understand the business content and characteristics. For example, the analysis unit analyzes audio recordings to understand the business content and characteristics. By understanding the business content, characteristics, and error cases of each department, more appropriate troubleshooting procedures can be generated. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the text data of a business manual into a generation AI and have the generation AI understand the business flow.

[0032] The generation unit can generate specific troubleshooting procedures based on the analysis results. The generation unit, for example, generates specific troubleshooting procedures in natural language based on the analysis results. For example, the generation unit generates specific troubleshooting procedures in natural language based on the analysis results. The generation unit can also use a generation AI to generate procedures that specifically indicate how to deal with the cause of an error. For example, the generation unit uses a generation AI to generate procedures that specifically indicate how to deal with the cause of an error. The generation unit can also use a generation AI to generate troubleshooting procedures based on a business flow. For example, the generation unit uses a generation AI to generate troubleshooting procedures based on a business flow. In this way, troubleshooting can be performed efficiently by generating specific procedures based on the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the analysis results to the generation AI and cause the generation AI to generate specific troubleshooting procedures.

[0033] The providing unit can provide the generated procedure to the person in charge. The providing unit, for example, provides a screen displaying the generated procedure, allowing the person in charge to perform maintenance according to the procedure. For example, the providing unit provides a screen displaying the generated procedure, allowing the person in charge to perform maintenance according to the procedure. The providing unit can also send the generated procedure to the person in charge by email. For example, the providing unit sends the generated procedure to the person in charge by email. The providing unit can also display the generated procedure on a dashboard, allowing the person in charge to check the procedure in real time. For example, the providing unit displays the generated procedure on a dashboard, allowing the person in charge to check the procedure in real time. In this way, by providing the generated procedure to the person in charge, the person in charge can perform maintenance themselves. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit inputs the generated procedure into a generation AI and causes the generation AI to execute a display method for providing the procedure to the person in charge.

[0034] The collection unit can analyze each department's past data collection history and select the optimal collection method. The collection unit, for example, analyzes each department's past data collection history and selects the most efficient collection method. For example, the collection unit analyzes each department's past data collection history and selects the most efficient collection method. The collection unit can also customize the collection method based on each department's past data collection history. For example, the collection unit customizes the collection method based on each department's past data collection history. The collection unit can also improve the collection method by referring to each department's past data collection history. For example, the collection unit improves the collection method by referring to each department's past data collection history. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into the generation AI and cause the generation AI to select the optimal collection method.

[0035] When collecting data, the collection unit can filter the data based on the current business situation and areas of interest of each department. For example, the collection unit prioritizes collecting highly relevant data, taking into account the current business situation of each department. For example, the collection unit prioritizes collecting highly relevant data, taking into account the current business situation of each department. The collection unit can also filter the data to be collected based on the areas of interest of each department. For example, the collection unit filters the data to be collected based on the areas of interest of each department. The collection unit can also determine the priority of the data to be collected based on the business situation and areas of interest of each department. For example, the collection unit determines the priority of the data to be collected based on the business situation and areas of interest of each department. In this way, highly relevant data can be collected by filtering the data based on the business situation and areas of interest of each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the business situation and areas of interest of each department to the generation AI and have the generation AI perform data filtering.

[0036] When collecting data, the collection unit can select the optimal collection means according to the input method of each department. The collection unit, for example, selects the optimal collection means according to the input method of each department. For example, the collection unit selects the optimal collection means according to the input method of each department. The collection unit can also prioritize collecting voice data for departments that use voice input. For example, the collection unit can prioritize collecting voice data for departments that use voice input. The collection unit can also prioritize collecting text data for departments that use text input. For example, the collection unit can prioritize collecting text data for departments that use text input. This enables efficient data collection by selecting the optimal collection means according to the input method of each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data of the input method of each department to the generation AI and have the generation AI select the optimal collection means.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of each department. The collection unit, for example, prioritizes collecting highly relevant data based on the geographical location information of each department. For example, the collection unit prioritizes collecting highly relevant data based on the geographical location information of each department. The collection unit can also prioritize collecting data from geographically close departments. For example, the collection unit prioritizes collecting data from geographically close departments. The collection unit can also determine the priority of data to be collected by taking into account the geographical location information. For example, the collection unit determines the priority of data to be collected by taking into account the geographical location information. This allows highly relevant data to be collected efficiently by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0038] The collection unit can analyze the social media activities of each department when collecting data and collect related data. The collection unit, for example, analyzes the social media activities of each department and collects related data. For example, the collection unit analyzes the social media activities of each department and collects related data. The collection unit can also filter the data to be collected based on the content of social media activities. For example, the collection unit filters the data to be collected based on the content of social media activities. The collection unit can also determine the priority of the data to be collected with reference to the social media activities. For example, the collection unit determines the priority of the data to be collected with reference to the social media activities. In this way, related data can be efficiently collected by analyzing the social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on social media activities to a generation AI and cause the generation AI to collect related data.

[0039] When collecting data, the collection unit can customize the collection method by reflecting past feedback from each department. For example, the collection unit customizes the collection method based on past feedback from each department. The collection unit can also improve the collection method by reflecting the feedback content. For example, the collection unit improves the collection method by reflecting the feedback content. The collection unit can also determine the priority of data to be collected by referring to past feedback. For example, the collection unit determines the priority of data to be collected by referring to past feedback. In this way, the collection method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit adjusts the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-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 information on the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. For example, the analysis unit applies an image analysis algorithm to image data. The analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit applies a voice analysis algorithm to voice data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information on the category of data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of each department. The analysis unit, for example, adjusts the analysis algorithm based on past analysis results of each department. For example, the analysis unit adjusts the analysis algorithm based on past analysis results of each department. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to past analysis results. The analysis unit can also analyze past analysis results of each department and improve the analysis method. For example, the analysis unit analyzes past analysis results of each department and improves the analysis method. In this way, the accuracy of the analysis is improved by referring to past analysis results. 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 data of past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the latest data. For example, the analysis unit prioritizes analyzing the latest data. The analysis unit can also postpone analyzing older data. For example, the analysis unit postpones analyzing older data. The analysis unit can also determine the analysis priority based on the time when the data was collected. For example, the analysis unit determines the analysis priority based on the time when the data was collected. In this way, by determining the analysis priority based on the time when the data was collected, the latest data can be analyzed preferentially. 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 information about the time when the data was collected to the generation AI and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. 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 information on the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the expertise level of each department. For example, the analysis unit provides analysis results that use a lot of technical terminology to departments with high expertise. For example, the analysis unit provides analysis results that use a lot of technical terminology to departments with high expertise. The analysis unit can also provide analysis results that are explained in simple language to departments with low expertise. For example, the analysis unit provides analysis results that are explained in simple language to departments with low expertise. The analysis unit can also adjust the use of technical terminology in the analysis according to the expertise level of each department. In this way, by adjusting the use of technical terminology in the analysis according to the expertise level of each department, it is possible to provide analysis results that are easy to understand. 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 information on the expertise level of each department to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0046] The generation unit can adjust the level of detail of the procedure to be generated based on the importance of the analysis result during generation. For example, the generation unit generates detailed procedures for analysis results with high importance. For example, the generation unit generates detailed procedures for analysis results with high importance. The generation unit can also generate simplified procedures for analysis results with low importance. For example, the generation unit generates simplified procedures for analysis results with low importance. The generation unit can also adjust the level of detail of the procedure to be generated according to the importance of the analysis result. For example, the generation unit adjusts the level of detail of the procedure to be generated according to the importance of the analysis result. This enables efficient procedure generation by adjusting the level of detail of the procedure according to the importance of the analysis result. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input information on the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the procedure.

[0047] The generation unit can apply different generation algorithms depending on the category of the analysis result during generation. For example, the generation unit applies a natural language generation algorithm to the analysis result of text data. For example, the generation unit applies a natural language generation algorithm to the analysis result of text data. The generation unit can also apply an image generation algorithm to the analysis result of image data. For example, the generation unit applies an image generation algorithm to the analysis result of image data. The generation unit can also apply a voice generation algorithm to the analysis result of voice data. For example, the generation unit applies a voice generation algorithm to the analysis result of voice data. This improves the accuracy of procedure generation by applying an appropriate generation algorithm depending on the category of the analysis result. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input information on the category of the analysis result to the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0048] During generation, the generation unit can improve the accuracy of generation by referring to past generation results of each department. The generation unit, for example, adjusts the generation algorithm based on past generation results of each department. For example, the generation unit adjusts the generation algorithm based on past generation results of each department. The generation unit can also improve the accuracy of generation by referring to past generation results. For example, the generation unit improves the accuracy of generation by referring to past generation results. The generation unit can also analyze past generation results of each department and improve the generation method. For example, the generation unit analyzes past generation results of each department and improves the generation method. In this way, the accuracy of generation is improved by referring to past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data of past generation results into the generation AI and cause the generation AI to improve the accuracy of generation.

[0049] At the time of generation, the generation unit can determine the priority of the procedures to be generated based on the time when the analysis results were collected. The generation unit, for example, prioritizes generating procedures based on the latest analysis results. For example, the generation unit prioritizes generating procedures based on the latest analysis results. The generation unit can also generate procedures while putting off older analysis results. For example, the generation unit puts off generating procedures while putting off older analysis results. The generation unit can also determine the priority of the procedures to be generated based on the time when the analysis results were collected. For example, the generation unit determines the priority of the procedures to be generated based on the time when the analysis results were collected. In this way, by determining the priority of the procedures based on the time when the analysis results were collected, it is possible to generate procedures based on the latest analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information about the time when the analysis results were collected to the generation AI and have the generation AI determine the priority of the procedures.

[0050] The generation unit can adjust the order of the procedures to be generated based on the relevance of the analysis results during generation. The generation unit, for example, prioritizes generating procedures based on highly relevant analysis results. For example, the generation unit prioritizes generating procedures based on highly relevant analysis results. The generation unit can also postpone generating procedures for analysis results with low relevance. For example, the generation unit postpones generating procedures for analysis results with low relevance. The generation unit can also adjust the order of the procedures to be generated based on the relevance of the analysis results. For example, the generation unit adjusts the order of the procedures to be generated based on the relevance of the analysis results. This enables efficient procedure generation by adjusting the order of the procedures based on the relevance of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information on the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of the procedures.

[0051] During generation, the generation unit can adjust the use of technical terminology in the procedures to be generated according to the expertise level of each department. For example, the generation unit generates procedures that use a lot of technical terminology for departments with high expertise. For example, the generation unit generates procedures that use a lot of technical terminology for departments with high expertise. The generation unit can also generate procedures that are explained in simple language for departments with low expertise. For example, the generation unit generates procedures that are explained in simple language for departments with low expertise. The generation unit can also adjust the use of technical terminology in the procedures to be generated according to the expertise level of each department. In this way, by adjusting the use of technical terminology in the procedures according to the expertise level of each department, it is possible to provide procedures that are easy to understand. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information on the expertise level of each department into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0052] The providing unit can select the optimal display method by referring to the past operation history of each department when providing the data. The providing unit, for example, selects the optimal display method based on the past operation history of each department. For example, the providing unit selects the optimal display method based on the past operation history of each department. The providing unit can also customize the display method by referring to the past operation history. For example, the providing unit customizes the display method by referring to the past operation history. The providing unit can also analyze the operation history of each department and improve the display method. For example, the providing unit analyzes the operation history of each department and improves the display method. In this way, the optimal display method can be selected by referring to the past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data of the past operation history into the generation AI and cause the generation AI to select the optimal display method.

[0053] The providing unit can customize the display content according to the current tasks of each department when providing the information. For example, the providing unit prioritizes displaying highly relevant information taking into account the current tasks of each department. For example, the providing unit prioritizes displaying highly relevant information taking into account the current tasks of each department. The providing unit can also customize the display content based on the current tasks. For example, the providing unit customizes the display content based on the current tasks. The providing unit can also adjust the display content by referring to the task status of each department. For example, the providing unit adjusts the display content by referring to the task status of each department. In this way, highly relevant information can be provided by customizing the display content according to the current tasks. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the task status of each department into the generating AI and cause the generating AI to customize the display content.

[0054] The providing unit can improve the display method by reflecting feedback from each department when providing the data. The providing unit, for example, improves the display method based on feedback from each department. For example, the providing unit improves the display method based on feedback from each department. The providing unit can also customize the display means by reflecting the feedback content. For example, the providing unit customizes the display means by reflecting the feedback content. The providing unit can also adjust the display method by referring to past feedback. For example, the providing unit adjusts the display method by referring to past feedback. In this way, the display method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input feedback data into a generating AI and cause the generating AI to improve the display method.

[0055] The providing unit can select the optimal display method by taking into consideration the device information of each department at the time of providing. The providing unit, for example, selects the optimal display method based on the device information of each department. For example, the providing unit selects the optimal display method based on the device information of each department. The providing unit can also customize the display method according to the type of device. For example, the providing unit customizes the display method according to the type of device. The providing unit can also adjust the display method by referring to the device information. For example, the providing unit adjusts the display method by referring to the device information. In this way, the optimal display method can be selected by taking the device information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input device information data to the generation AI and cause the generation AI to select the optimal display method.

[0056] The providing unit can make the display content multilingual according to the language settings of each department when providing the information. The providing unit, for example, makes the display content multilingual based on the language settings of each department. For example, the providing unit makes the display content multilingual based on the language settings of each department. The providing unit can also automatically translate the display content according to the language settings. For example, the providing unit automatically translates the display content according to the language settings. The providing unit can also adjust the display content by referring to the language settings of each department. For example, the providing unit adjusts the display content by referring to the language settings of each department. This makes it possible to provide information appropriate for each department by making the display content multilingual according to the language settings. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input language setting data to a generation AI and cause the generation AI to perform multilingual support for the display content.

[0057] The providing unit can customize the delivery method by reflecting past feedback from each department when providing the information. The providing unit, for example, customizes the delivery method based on past feedback from each department. For example, the providing unit customizes the delivery method based on past feedback from each department. The providing unit can also improve the delivery means by reflecting the feedback content. For example, the providing unit improves the delivery means by reflecting the feedback content. The providing unit can also adjust the delivery method by referring to past feedback. For example, the providing unit adjusts the delivery method by referring to past feedback. In this way, the delivery method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data of past feedback into the generation AI and cause the generation AI to customize the delivery method.

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

[0059] The collection unit can dynamically adjust the priority of data collection based on the business content of each department. For example, the collection unit prioritizes the collection of data related to a project currently underway by a specific department. The collection unit can also adjust the frequency of data collection taking into account the workload of each department. Furthermore, the collection unit can dynamically change the type of data to be collected in response to changes in the business content of each department. This allows the collection unit to achieve optimal data collection according to the business status of each department.

[0060] The analysis department monitors the business performance of each department in real time and can issue alerts if it detects an abnormality. For example, the analysis department detects unusual patterns in business flows and notifies the person in charge. The analysis department can also identify abnormal performance by comparing it with past data and discover problems early. Furthermore, the analysis department can propose specific countermeasures based on the results of anomaly detection. This allows the analysis department to improve the stability of business operations.

[0061] The provision department can adjust the timing of providing procedures based on the work schedule of each department. For example, the provision department refrains from providing procedures during busy hours and provides procedures during quieter hours. The provision department can also adjust the frequency of providing procedures, taking into account the work schedule of each department. Furthermore, the provision department can provide procedures immediately after a specific task is completed, thereby achieving timely support. This allows the provision department to provide optimal procedures according to the work schedule of each department.

[0062] The analysis department can evaluate the operational efficiency of each department and propose improvements. For example, the analysis department can identify unnecessary steps in a business flow and make proposals for improving efficiency. The analysis department can also compare the operational performance of each department and share best practices. Furthermore, the analysis department can provide specific action plans for improving operational efficiency. This allows the analysis department to improve the operational efficiency of each department.

[0063] The provisioning department can customize the method of providing procedures based on the business operations of each department. For example, the provisioning department can provide detailed technical procedures to departments that perform technical work. It can also provide concise procedures that focus on the main points to departments that perform administrative work. Furthermore, the provisioning department can change the format in which procedures are provided depending on the business operations of each department. This allows the provisioning department to provide procedures that are optimal for the business operations of each department.

[0064] The analysis department can customize the format in which the analysis results are provided based on the business operations of each department. For example, it can provide detailed technical reports to departments that perform technical work. It can also provide concise reports that focus on the main points to departments that perform administrative work. Furthermore, the analysis department can change the format in which the analysis results are provided based on the business operations of each department. This allows the analysis department to provide optimal analysis results that are suited to the business operations of each department.

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

[0066] Step 1: The collection unit collects data. This data includes text data, image data, and audio data. For example, data such as business manuals, error logs, and audio recordings may be collected. The collection unit can also collect data in real time using sensors and cameras. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using data mining and machine learning algorithms. For example, the text data of a business manual can be analyzed to understand the business flow. Error log data can also be analyzed to identify the cause of an error. Furthermore, audio recordings can be analyzed to understand the content and characteristics of business operations. Step 3: The generation unit generates specific troubleshooting procedures based on the analysis results obtained by the analysis unit. This is done using a generation AI. For example, specific troubleshooting procedures can be generated in natural language based on the analysis results. It can also generate procedures that specifically show how to deal with the cause of an error. It can also generate troubleshooting procedures based on business processes. Step 4: The provision unit provides the procedures generated by the generation unit. Provision is performed by methods such as email or dashboard display. For example, a screen displaying the generated procedures is provided so that the person in charge can perform maintenance according to the procedures. The generated procedures can also be sent to the person in charge by email. Furthermore, the generated procedures can also be displayed on a dashboard so that the person in charge can check the procedures in real time.

[0067] (Example 2) A system according to an embodiment of the present invention uses generative AI to provide personnel with RPA maintenance and management knowledge. This system multimodally understands the business operations, characteristics, and error cases of each department and generates specific troubleshooting procedures using natural language processing. For example, the system collects text data, image data, audio data, etc. to understand the business operations, characteristics, and error cases of each department. Next, it analyzes the collected data to understand the business operations, characteristics, and error cases of each department. It then generates specific troubleshooting procedures based on the analysis results. Finally, it provides the generated troubleshooting procedures to personnel so that they can perform RPA maintenance themselves. This enables personnel in each department to perform RPA maintenance themselves, thereby improving maintenance efficiency. For example, the system collects data such as business manuals, error logs, and audio recordings, and uses AI to multimodally understand the business operations, characteristics, and error cases of each department. Next, it analyzes the collected text data, image data, and audio data to understand the business flow and identify the cause of the error. Based on the analysis results, it generates specific troubleshooting procedures in natural language and provides them to personnel. This allows personnel to perform RPA maintenance themselves.

[0068] A maintenance support system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. Examples of the data include, but are not limited to, text data, image data, and audio data. The collection unit collects data such as operation manuals, error logs, and audio recordings. The collection unit can also collect data in real time using a sensor or a camera. For example, the collection unit can collect text data from operation manuals, error log data, and audio recordings. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, data mining or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit analyzes the text data from operation manuals to understand the workflow. The analysis unit can also analyze error log data to identify the cause of an error. The analysis unit can also analyze audio recordings to understand the content and characteristics of the work. The generation unit generates specific troubleshooting procedures based on the analysis results obtained by the analysis unit. The generation can be performed using, for example, a generation AI, but is not limited to, examples. For example, the generation unit generates specific troubleshooting procedures in natural language based on the analysis results. The generation unit can also use a generation AI to generate procedures that specifically indicate how to deal with the cause of an error. The generation unit can also use a generation AI to generate troubleshooting procedures based on a business flow. The provision unit provides the procedures generated by the generation unit. The provision can be performed, for example, by email or dashboard display, but is not limited to these examples. For example, the provision unit can provide a screen that displays the generated procedures so that a person in charge can perform maintenance according to the procedures. The provision unit can also send the generated procedures to a person in charge by email. The provision unit can also display the generated procedures on a dashboard so that a person in charge can check the procedures in real time. This allows the maintenance support system according to the embodiment to efficiently collect, analyze, generate, and provide data.For example, the system collects data such as business manuals, error logs, and voice recordings, and the AI ​​can understand the business content and characteristics of each department, as well as error cases, in a multimodal manner. Next, it analyzes the collected text data, image data, and voice data to understand the business flow and identify the cause of the error. Furthermore, based on the analysis results, it generates specific troubleshooting procedures in natural language and provides them to the person in charge. This allows the person in charge to perform RPA maintenance themselves.

[0069] The collection unit can collect text data, image data, and audio data. The collection unit, for example, collects text data from a business manual. For example, the collection unit collects the text data from the business manual and understands the business flow. The collection unit can also collect error log data. For example, the collection unit collects error log data and identifies the cause of the error. The collection unit can also collect audio recordings. For example, the collection unit collects audio recordings and understands the business content and characteristics. This enables more comprehensive analysis by collecting various data formats. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the text data from the business manual into a generation AI and have the generation AI understand the business flow.

[0070] The analysis unit analyzes the collected text data, image data, and audio data to understand the business content, characteristics, and error cases of each department. The analysis unit, for example, analyzes the text data of a business manual to understand the business flow. For example, the analysis unit analyzes the text data of a business manual to understand the business flow. The analysis unit can also analyze error log data to identify the cause of an error. For example, the analysis unit analyzes error log data to identify the cause of an error. The analysis unit can also analyze audio recordings to understand the business content and characteristics. For example, the analysis unit analyzes audio recordings to understand the business content and characteristics. By understanding the business content, characteristics, and error cases of each department, more appropriate troubleshooting procedures can be generated. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the text data of a business manual into a generation AI and have the generation AI understand the business flow.

[0071] The generation unit can generate specific troubleshooting procedures based on the analysis results. The generation unit, for example, generates specific troubleshooting procedures in natural language based on the analysis results. For example, the generation unit generates specific troubleshooting procedures in natural language based on the analysis results. The generation unit can also use a generation AI to generate procedures that specifically indicate how to deal with the cause of an error. For example, the generation unit uses a generation AI to generate procedures that specifically indicate how to deal with the cause of an error. The generation unit can also use a generation AI to generate troubleshooting procedures based on a business flow. For example, the generation unit uses a generation AI to generate troubleshooting procedures based on a business flow. In this way, troubleshooting can be performed efficiently by generating specific procedures based on the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the analysis results to the generation AI and cause the generation AI to generate specific troubleshooting procedures.

[0072] The providing unit can provide the generated procedure to the person in charge. The providing unit, for example, provides a screen displaying the generated procedure, allowing the person in charge to perform maintenance according to the procedure. For example, the providing unit provides a screen displaying the generated procedure, allowing the person in charge to perform maintenance according to the procedure. The providing unit can also send the generated procedure to the person in charge by email. For example, the providing unit sends the generated procedure to the person in charge by email. The providing unit can also display the generated procedure on a dashboard, allowing the person in charge to check the procedure in real time. For example, the providing unit displays the generated procedure on a dashboard, allowing the person in charge to check the procedure in real time. In this way, by providing the generated procedure to the person in charge, the person in charge can perform maintenance themselves. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit inputs the generated procedure into a generation AI and causes the generation AI to execute a display method for providing the procedure to the person in charge.

[0073] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. The collection unit can also increase the frequency of data collection to collect more detailed data when the user is relaxed. For example, when the user is relaxed, the collection unit increases the frequency of data collection to collect more detailed data. The collection unit can also adjust the timing of data collection to quickly collect necessary data when the user is in a hurry. For example, when the user is in a hurry, the collection unit adjusts the timing of data collection to quickly collect necessary data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to adjust the timing of data collection.

[0074] The collection unit can analyze each department's past data collection history and select the optimal collection method. The collection unit, for example, analyzes each department's past data collection history and selects the most efficient collection method. For example, the collection unit analyzes each department's past data collection history and selects the most efficient collection method. The collection unit can also customize the collection method based on each department's past data collection history. For example, the collection unit customizes the collection method based on each department's past data collection history. The collection unit can also improve the collection method by referring to each department's past data collection history. For example, the collection unit improves the collection method by referring to each department's past data collection history. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into the generation AI and cause the generation AI to select the optimal collection method.

[0075] When collecting data, the collection unit can filter the data based on the current business situation and areas of interest of each department. For example, the collection unit prioritizes collecting highly relevant data, taking into account the current business situation of each department. For example, the collection unit prioritizes collecting highly relevant data, taking into account the current business situation of each department. The collection unit can also filter the data to be collected based on the areas of interest of each department. For example, the collection unit filters the data to be collected based on the areas of interest of each department. The collection unit can also determine the priority of the data to be collected based on the business situation and areas of interest of each department. For example, the collection unit determines the priority of the data to be collected based on the business situation and areas of interest of each department. In this way, highly relevant data can be collected by filtering the data based on the business situation and areas of interest of each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the business situation and areas of interest of each department to the generation AI and have the generation AI perform data filtering.

[0076] When collecting data, the collection unit can select the optimal collection means according to the input method of each department. The collection unit, for example, selects the optimal collection means according to the input method of each department. For example, the collection unit selects the optimal collection means according to the input method of each department. The collection unit can also prioritize collecting voice data for departments that use voice input. For example, the collection unit can prioritize collecting voice data for departments that use voice input. The collection unit can also prioritize collecting text data for departments that use text input. For example, the collection unit can prioritize collecting text data for departments that use text input. This enables efficient data collection by selecting the optimal collection means according to the input method of each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data of the input method of each department to the generation AI and have the generation AI select the optimal collection means.

[0077] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit postpones the collection of less important data. For example, if the user is feeling stressed, the collection unit postpones the collection of less important data. The collection unit can also prioritize the collection of detailed data if the user is relaxed. For example, if the user is relaxed, the collection unit prioritizes the collection of detailed data. The collection unit can also prioritize the collection of more important data if the user is in a hurry. For example, if the user is in a hurry, the collection unit prioritizes the collection of more important data. In this way, by determining the priority of data according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI determine the priority of the data.

[0078] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of each department. The collection unit, for example, prioritizes collecting highly relevant data based on the geographical location information of each department. For example, the collection unit prioritizes collecting highly relevant data based on the geographical location information of each department. The collection unit can also prioritize collecting data from geographically close departments. For example, the collection unit prioritizes collecting data from geographically close departments. The collection unit can also determine the priority of data to be collected by taking into account the geographical location information. For example, the collection unit determines the priority of data to be collected by taking into account the geographical location information. This allows highly relevant data to be collected efficiently by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0079] The collection unit can analyze the social media activities of each department when collecting data and collect related data. The collection unit, for example, analyzes the social media activities of each department and collects related data. For example, the collection unit analyzes the social media activities of each department and collects related data. The collection unit can also filter the data to be collected based on the content of social media activities. For example, the collection unit filters the data to be collected based on the content of social media activities. The collection unit can also determine the priority of the data to be collected with reference to the social media activities. For example, the collection unit determines the priority of the data to be collected with reference to the social media activities. In this way, related data can be efficiently collected by analyzing the social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on social media activities to a generation AI and cause the generation AI to collect related data.

[0080] When collecting data, the collection unit can customize the collection method by reflecting past feedback from each department. For example, the collection unit customizes the collection method based on past feedback from each department. The collection unit can also improve the collection method by reflecting the feedback content. For example, the collection unit improves the collection method by reflecting the feedback content. The collection unit can also determine the priority of data to be collected by referring to past feedback. For example, the collection unit determines the priority of data to be collected by referring to past feedback. In this way, the collection method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0081] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, when the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, when the user is nervous, the analysis unit provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, when the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a summary analysis result when the user is in a hurry. For example, when the user is in a hurry, the analysis unit provides a summary analysis result. This allows the analysis result to be easily understood by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described 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 emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit adjusts the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-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 information on the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. For example, the analysis unit applies an image analysis algorithm to image data. The analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit applies a voice analysis algorithm to voice data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information on the category of data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of each department. The analysis unit, for example, adjusts the analysis algorithm based on past analysis results of each department. For example, the analysis unit adjusts the analysis algorithm based on past analysis results of each department. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to past analysis results. The analysis unit can also analyze past analysis results of each department and improve the analysis method. For example, the analysis unit analyzes past analysis results of each department and improves the analysis method. In this way, the accuracy of the analysis is improved by referring to past analysis results. 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 data of past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the analysis.

[0086] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the latest data. For example, the analysis unit prioritizes analyzing the latest data. The analysis unit can also postpone analyzing older data. For example, the analysis unit postpones analyzing older data. The analysis unit can also determine the analysis priority based on the time when the data was collected. For example, the analysis unit determines the analysis priority based on the time when the data was collected. In this way, by determining the analysis priority based on the time when the data was collected, the latest data can be analyzed preferentially. 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 information about the time when the data was collected to the generation AI and have the generation AI determine the analysis priority.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. 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 information on the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0088] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the expertise level of each department. For example, the analysis unit provides analysis results that use a lot of technical terminology to departments with high expertise. For example, the analysis unit provides analysis results that use a lot of technical terminology to departments with high expertise. The analysis unit can also provide analysis results that are explained in simple language to departments with low expertise. For example, the analysis unit provides analysis results that are explained in simple language to departments with low expertise. The analysis unit can also adjust the use of technical terminology in the analysis according to the expertise level of each department. In this way, by adjusting the use of technical terminology in the analysis according to the expertise level of each department, it is possible to provide analysis results that are easy to understand. 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 information on the expertise level of each department to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0089] The generation unit can estimate the user's emotions and adjust the expression method of the generated instructions based on the estimated user's emotions. For example, if the user is nervous, the generation unit generates simple, highly visible instructions. For example, if the user is nervous, the generation unit generates simple, highly visible instructions. The generation unit can also generate detailed instructions if the user is relaxed. For example, if the user is relaxed, the generation unit generates detailed instructions. The generation unit can also generate instructions that focus on the main points if the user is in a hurry. For example, if the user is in a hurry, the generation unit generates instructions that focus on the main points. In this way, by adjusting the expression method of the instructions according to the user's emotions, it is possible to provide instructions that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the procedure is expressed.

[0090] The generation unit can adjust the level of detail of the procedure to be generated based on the importance of the analysis result during generation. For example, the generation unit generates detailed procedures for analysis results with high importance. For example, the generation unit generates detailed procedures for analysis results with high importance. The generation unit can also generate simplified procedures for analysis results with low importance. For example, the generation unit generates simplified procedures for analysis results with low importance. The generation unit can also adjust the level of detail of the procedure to be generated according to the importance of the analysis result. For example, the generation unit adjusts the level of detail of the procedure to be generated according to the importance of the analysis result. This enables efficient procedure generation by adjusting the level of detail of the procedure according to the importance of the analysis result. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input information on the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the procedure.

[0091] The generation unit can apply different generation algorithms depending on the category of the analysis result during generation. For example, the generation unit applies a natural language generation algorithm to the analysis result of text data. For example, the generation unit applies a natural language generation algorithm to the analysis result of text data. The generation unit can also apply an image generation algorithm to the analysis result of image data. For example, the generation unit applies an image generation algorithm to the analysis result of image data. The generation unit can also apply a voice generation algorithm to the analysis result of voice data. For example, the generation unit applies a voice generation algorithm to the analysis result of voice data. This improves the accuracy of procedure generation by applying an appropriate generation algorithm depending on the category of the analysis result. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input information on the category of the analysis result to the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0092] During generation, the generation unit can improve the accuracy of generation by referring to past generation results of each department. The generation unit, for example, adjusts the generation algorithm based on past generation results of each department. For example, the generation unit adjusts the generation algorithm based on past generation results of each department. The generation unit can also improve the accuracy of generation by referring to past generation results. For example, the generation unit improves the accuracy of generation by referring to past generation results. The generation unit can also analyze past generation results of each department and improve the generation method. For example, the generation unit analyzes past generation results of each department and improves the generation method. In this way, the accuracy of generation is improved by referring to past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data of past generation results into the generation AI and cause the generation AI to improve the accuracy of generation.

[0093] The generation unit can estimate the user's emotions and adjust the length of the instructions to be generated based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit generates short, concise instructions. For example, if the user is in a hurry, the generation unit generates short, concise instructions. The generation unit can also generate detailed instructions if the user is relaxed. For example, if the user is relaxed, the generation unit generates detailed instructions. The generation unit can also generate visually stimulating instructions if the user is excited. For example, if the user is excited, the generation unit generates visually stimulating instructions. This allows the user to be provided with instructions of an appropriate length by adjusting the length of the instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the procedure.

[0094] At the time of generation, the generation unit can determine the priority of the procedures to be generated based on the time when the analysis results were collected. The generation unit, for example, prioritizes generating procedures based on the latest analysis results. For example, the generation unit prioritizes generating procedures based on the latest analysis results. The generation unit can also generate procedures while putting off older analysis results. For example, the generation unit puts off generating procedures while putting off older analysis results. The generation unit can also determine the priority of the procedures to be generated based on the time when the analysis results were collected. For example, the generation unit determines the priority of the procedures to be generated based on the time when the analysis results were collected. In this way, by determining the priority of the procedures based on the time when the analysis results were collected, it is possible to generate procedures based on the latest analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information about the time when the analysis results were collected to the generation AI and have the generation AI determine the priority of the procedures.

[0095] The generation unit can adjust the order of the procedures to be generated based on the relevance of the analysis results during generation. The generation unit, for example, prioritizes generating procedures based on highly relevant analysis results. For example, the generation unit prioritizes generating procedures based on highly relevant analysis results. The generation unit can also postpone generating procedures for analysis results with low relevance. For example, the generation unit postpones generating procedures for analysis results with low relevance. The generation unit can also adjust the order of the procedures to be generated based on the relevance of the analysis results. For example, the generation unit adjusts the order of the procedures to be generated based on the relevance of the analysis results. This enables efficient procedure generation by adjusting the order of the procedures based on the relevance of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information on the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of the procedures.

[0096] During generation, the generation unit can adjust the use of technical terminology in the procedures to be generated according to the expertise level of each department. For example, the generation unit generates procedures that use a lot of technical terminology for departments with high expertise. For example, the generation unit generates procedures that use a lot of technical terminology for departments with high expertise. The generation unit can also generate procedures that are explained in simple language for departments with low expertise. For example, the generation unit generates procedures that are explained in simple language for departments with low expertise. The generation unit can also adjust the use of technical terminology in the procedures to be generated according to the expertise level of each department. In this way, by adjusting the use of technical terminology in the procedures according to the expertise level of each department, it is possible to provide procedures that are easy to understand. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information on the expertise level of each department into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0097] The providing unit can estimate the user's emotions and adjust the display method of the provided procedures based on the estimated user's emotions. For example, when the user is nervous, the providing unit provides a simple, highly visible display method. For example, when the user is nervous, the providing unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. For example, when the user is relaxed, the providing unit provides a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that emphasizes the main points. For example, when the user is in a hurry, the providing unit provides a display method that emphasizes the main points. This allows the display method to be adjusted according to the user's emotions, thereby enabling a highly visible display for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or without AI. For example, the providing unit can input the user's emotion data into the generating AI and cause the generating AI to adjust the display method.

[0098] The providing unit can select the optimal display method by referring to the past operation history of each department when providing the data. The providing unit, for example, selects the optimal display method based on the past operation history of each department. For example, the providing unit selects the optimal display method based on the past operation history of each department. The providing unit can also customize the display method by referring to the past operation history. For example, the providing unit customizes the display method by referring to the past operation history. The providing unit can also analyze the operation history of each department and improve the display method. For example, the providing unit analyzes the operation history of each department and improves the display method. In this way, the optimal display method can be selected by referring to the past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data of the past operation history into the generation AI and cause the generation AI to select the optimal display method.

[0099] The providing unit can customize the display content according to the current tasks of each department when providing the information. For example, the providing unit prioritizes displaying highly relevant information taking into account the current tasks of each department. For example, the providing unit prioritizes displaying highly relevant information taking into account the current tasks of each department. The providing unit can also customize the display content based on the current tasks. For example, the providing unit customizes the display content based on the current tasks. The providing unit can also adjust the display content by referring to the task status of each department. For example, the providing unit adjusts the display content by referring to the task status of each department. In this way, highly relevant information can be provided by customizing the display content according to the current tasks. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the task status of each department into the generating AI and cause the generating AI to customize the display content.

[0100] The providing unit can improve the display method by reflecting feedback from each department when providing the data. The providing unit, for example, improves the display method based on feedback from each department. For example, the providing unit improves the display method based on feedback from each department. The providing unit can also customize the display means by reflecting the feedback content. For example, the providing unit customizes the display means by reflecting the feedback content. The providing unit can also adjust the display method by referring to past feedback. For example, the providing unit adjusts the display method by referring to past feedback. In this way, the display method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input feedback data into a generating AI and cause the generating AI to improve the display method.

[0101] The providing unit can estimate the user's emotions and adjust the operation procedures to be provided based on the estimated user's emotions. For example, when the user is nervous, the providing unit provides simple and intuitive operation procedures. For example, when the user is nervous, the providing unit provides simple and intuitive operation procedures. The providing unit can also provide detailed operation procedures when the user is relaxed. For example, when the user is relaxed, the providing unit provides detailed operation procedures. The providing unit can also provide operation procedures that emphasize the main points when the user is in a hurry. For example, when the user is in a hurry, the providing unit provides operation procedures that emphasize the main points. This allows the operation procedures to be adjusted according to the user's emotions, enabling intuitive operation for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-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 emotional data into the generating AI and cause the generating AI to adjust the operating procedure.

[0102] The providing unit can select the optimal display method by taking into consideration the device information of each department at the time of providing. The providing unit, for example, selects the optimal display method based on the device information of each department. For example, the providing unit selects the optimal display method based on the device information of each department. The providing unit can also customize the display method according to the type of device. For example, the providing unit customizes the display method according to the type of device. The providing unit can also adjust the display method by referring to the device information. For example, the providing unit adjusts the display method by referring to the device information. In this way, the optimal display method can be selected by taking the device information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input device information data to the generation AI and cause the generation AI to select the optimal display method.

[0103] The providing unit can make the display content multilingual according to the language settings of each department when providing the information. The providing unit, for example, makes the display content multilingual based on the language settings of each department. For example, the providing unit makes the display content multilingual based on the language settings of each department. The providing unit can also automatically translate the display content according to the language settings. For example, the providing unit automatically translates the display content according to the language settings. The providing unit can also adjust the display content by referring to the language settings of each department. For example, the providing unit adjusts the display content by referring to the language settings of each department. This makes it possible to provide information appropriate for each department by making the display content multilingual according to the language settings. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input language setting data to a generation AI and cause the generation AI to perform multilingual support for the display content.

[0104] The providing unit can customize the delivery method by reflecting past feedback from each department when providing the information. The providing unit, for example, customizes the delivery method based on past feedback from each department. For example, the providing unit customizes the delivery method based on past feedback from each department. The providing unit can also improve the delivery means by reflecting the feedback content. For example, the providing unit improves the delivery means by reflecting the feedback content. The providing unit can also adjust the delivery method by referring to past feedback. For example, the providing unit adjusts the delivery method by referring to past feedback. In this way, the delivery method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data of past feedback into the generation AI and cause the generation AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect text data, image data, and audio data using the camera 42 and microphone 38B of the smart device 14. The collection unit can also collect business manuals and error logs via the communication I / F 26 of the data processing device 12. For example, the analysis unit analyzes data collected by the specific processing unit 290 of the data processing device 12, understands the business flow, and identifies the cause of the error. For example, the generation unit generates specific troubleshooting procedures based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the procedures generated by the control unit 46A of the smart device 14 to a person in charge. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect text data, image data, and audio data using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also collect business manuals and error logs via the communication I / F 26 of the data processing device 12. For example, the analysis unit analyzes data collected by the specific processing unit 290 of the data processing device 12, understands the business flow, and identifies the cause of the error. For example, the generation unit generates specific troubleshooting procedures based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the procedures generated by the control unit 46A of the smart glasses 214 to a person in charge. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect text data, image data, and audio data using the camera 42 and microphone 238 of the headset-type terminal 314. The collection unit can also collect business manuals and error logs via the communication I / F 26 of the data processing device 12. For example, the analysis unit analyzes data collected by the specific processing unit 290 of the data processing device 12, understands the business flow, and identifies the cause of the error. For example, the generation unit generates specific troubleshooting procedures based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the procedures generated by the control unit 46A of the headset-type terminal 314 to a person in charge. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect text data, image data, and audio data using the camera 42 and microphone 238 of the robot 414. The collection unit can also collect business manuals and error logs via the communication I / F 26 of the data processing device 12. For example, the analysis unit analyzes data collected by the specific processing unit 290 of the data processing device 12, understands the business flow, and identifies the cause of the error. For example, the generation unit generates specific troubleshooting procedures based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the procedures generated by the control unit 46A of the robot 414 to a person in charge.

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

[0106] The collection unit can dynamically adjust the priority of data collection based on the business content of each department. For example, the collection unit prioritizes the collection of data related to a project currently underway by a specific department. The collection unit can also adjust the frequency of data collection taking into account the workload of each department. Furthermore, the collection unit can dynamically change the type of data to be collected in response to changes in the business content of each department. This allows the collection unit to achieve optimal data collection according to the business status of each department.

[0107] The analysis department monitors the business performance of each department in real time and can issue alerts if it detects an abnormality. For example, the analysis department detects unusual patterns in business flows and notifies the person in charge. The analysis department can also identify abnormal performance by comparing it with past data and discover problems early. Furthermore, the analysis department can propose specific countermeasures based on the results of anomaly detection. This allows the analysis department to improve the stability of business operations.

[0108] The generation unit can estimate the user's emotions and adjust the difficulty of the generated instructions based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can generate simple and intuitive instructions. If the user is relaxed, the generation unit can also generate detailed and complex instructions. Furthermore, if the user is in a hurry, the generation unit can generate short instructions that focus on the main points. This allows the generation unit to provide optimal instructions according to the user's emotions.

[0109] The provision department can adjust the timing of providing procedures based on the work schedule of each department. For example, the provision department refrains from providing procedures during busy hours and provides procedures during quieter hours. The provision department can also adjust the frequency of providing procedures, taking into account the work schedule of each department. Furthermore, the provision department can provide procedures immediately after a specific task is completed, thereby achieving timely support. This allows the provision department to provide optimal procedures according to the work schedule of each department.

[0110] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the types of data to be collected to reduce the burden on the user. Also, if the user is relaxed, detailed data can be collected. Furthermore, if the user is in a hurry, only important data can be collected preferentially. This allows the collection unit to achieve optimal data collection according to the user's emotions.

[0111] The analysis department can evaluate the operational efficiency of each department and propose improvements. For example, the analysis department can identify unnecessary steps in a business flow and make proposals for improving efficiency. The analysis department can also compare the operational performance of each department and share best practices. Furthermore, the analysis department can provide specific action plans for improving operational efficiency. This allows the analysis department to improve the operational efficiency of each department.

[0112] The generation unit can estimate the user's emotions and adjust the visual representation of the generated procedure based on the estimated user's emotions. For example, if the user is nervous, the generation unit can use simple, highly visible graphics. If the user is relaxed, the generation unit can generate procedures that include detailed illustrations. Furthermore, if the user is in a hurry, the generation unit can generate visual procedures that emphasize key points. In this way, the generation unit can provide an optimal visual representation according to the user's emotions.

[0113] The provisioning department can customize the method of providing procedures based on the business operations of each department. For example, the provisioning department can provide detailed technical procedures to departments that perform technical work. It can also provide concise procedures that focus on the main points to departments that perform administrative work. Furthermore, the provisioning department can change the format in which procedures are provided depending on the business operations of each department. This allows the provisioning department to provide procedures that are optimal for the business operations of each department.

[0114] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can use a non-invasive data collection method to reduce the user's burden. Alternatively, if the user is relaxed, the collection unit can use an interactive data collection method to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can use a simple data collection method to quickly collect data. This allows the collection unit to realize the optimal data collection method according to the user's emotions.

[0115] The analysis department can customize the format in which the analysis results are provided based on the business operations of each department. For example, it can provide detailed technical reports to departments that perform technical work. It can also provide concise reports that focus on the main points to departments that perform administrative work. Furthermore, the analysis department can change the format in which the analysis results are provided based on the business operations of each department. This allows the analysis department to provide optimal analysis results that are suited to the business operations of each department.

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

[0117] Step 1: The collection unit collects data. This data includes text data, image data, and audio data. For example, data such as business manuals, error logs, and audio recordings may be collected. The collection unit can also collect data in real time using sensors and cameras. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using data mining and machine learning algorithms. For example, the text data of a business manual can be analyzed to understand the business flow. Error log data can also be analyzed to identify the cause of an error. Furthermore, audio recordings can be analyzed to understand the content and characteristics of business operations. Step 3: The generation unit generates specific troubleshooting procedures based on the analysis results obtained by the analysis unit. This is done using a generation AI. For example, specific troubleshooting procedures can be generated in natural language based on the analysis results. It can also generate procedures that specifically show how to deal with the cause of an error. It can also generate troubleshooting procedures based on business processes. Step 4: The provision unit provides the procedures generated by the generation unit. Provision is performed by methods such as email or dashboard display. For example, a screen displaying the generated procedures is provided so that the person in charge can perform maintenance according to the procedures. The generated procedures can also be sent to the person in charge by email. Furthermore, the generated procedures can also be displayed on a dashboard so that the person in charge can check the procedures in real time.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates a specific troubleshooting procedure based on the analysis result obtained by the analysis unit; a providing unit that provides the procedure generated by the generating unit. A system characterized by:

2. The collecting unit Collect text data, image data, and audio data 2. The system of claim 1.

3. The analysis unit Analyze collected text data, image data, and audio data to understand the business operations, characteristics, and error cases of each department.

2. The system of claim 1.

4. The generation unit Generate specific troubleshooting steps based on the analysis results 2. The system of claim 1.

5. The providing unit Provide generated instructions to the person in charge 2. The system of claim 1.

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze each department's past data collection history and select the optimal collection method 2. The system of claim 1.

8. The collecting unit When collecting data, filter it based on each department's current business situation and areas of interest.

2. The system of claim 1.

Images