System

Abstract

To provide a system which rapidly specifies causes and countermeasures on the basis of an OS or a vendor name, a failure content, and a log.SOLUTION: A system includes: a reception part, a retrieval part, an analysis part, and a provision part. The reception part receives an input of an OS or a vendor name, a failure content, and a log from a user. The retrieval part retrieves a bug report on internet or public material of a vendor on the basis of the information received by the reception part. The analysis part analyzes the information collected by the retrieval part, and specifies causes and countermeasures. The provision part provides a user with the causes and countermeasures specified by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, the process of identifying the cause and solution based on the OS, vendor name, details of the problem, and logs was time-consuming and laborious.

[0005] The system according to the embodiment aims to quickly identify the cause and a solution based on the OS, vendor name, details of the failure, and logs. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a search unit, an analysis unit, and a provision unit. The reception unit receives input of the OS or vendor name, the fault details, and a log from a user. The search unit searches for bug reports on the Internet or public documents from the vendor based on the information received by the reception unit. The analysis unit analyzes the information collected by the search unit and identifies the cause and a solution. The provision unit provides the user with the cause and solution identified by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can quickly identify the cause and how to deal with a problem based on the OS, vendor name, details of the problem, and logs. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A fault response system according to an embodiment of the present invention is a system that, by specifying an OS and a vendor name and inputting fault details and logs, refers to bug reports on the Internet and documents published by the vendor, and tells the cause and how to deal with the problem. In this fault response system, when a user inputs an OS, a vendor name, fault details, and logs, the AI ​​searches bug reports on the Internet and documents published by the vendor, collects related information, analyzes the cause and how to deal with the problem, and provides it to the user. In addition, this system can be linked to an in-house document management system (for example, an in-house Wiki or Redmine), and it is possible to search in-house cases and document new faults. For example, when a user inputs information such as "operating system A", "vendor B", "application crashes", and "error log: 0x0000001A", the AI ​​searches bug reports on the Internet and documents published by the vendor, and refers to forum posts where similar faults are reported and technical documents published by the vendor. Based on the collected information, the AI ​​analyzes information related to the error log "0x0000001A", identifies that the cause is a memory management problem, and suggests checking the memory and applying a specific patch. In addition, this system is linked to the company's document management system, making it possible to search for internal cases, and for new problems, the AI ​​uses the summary function to newly document them. This enriches the company's knowledge base, allowing for quick response when similar problems occur. This allows the AI ​​to take over the primary investigation by external vendors, improving the efficiency of problem response. For example, by having the AI ​​automatically perform the problem investigation that was previously outsourced to external vendors, time and costs can be reduced. In addition, by enriching the company's knowledge base, the speed of problem response can be improved, and work efficiency can be expected. This allows the problem response system to search for information on the Internet based on the information entered by the user, and provide the cause and solution, improving the efficiency of problem response.

[0029] The fault response system according to the embodiment includes a reception unit, a search unit, an analysis unit, and a provision unit. The reception unit receives input of an OS, a vendor name, fault details, and a log from a user. The information input by the user includes, but is not limited to, an OS or vendor name such as Windows (registered trademark), Linux (registered trademark), or a specific software vendor, fault details such as an error message or a system malfunction, and logs such as a system log, an application log, and an error log. The search unit searches bug reports on the Internet and public documents of vendors based on the information received by the reception unit. The search unit searches bug report sites and forums such as GitHub (registered trademark) Issues, Stack Overflow, Reddit, and Quora, official documents and technical blogs published by vendors, and the like. The analysis unit analyzes the information collected by the search unit and identifies the cause and a countermeasure. The analysis unit identifies the cause based on, for example, the analysis results of error messages and past cases, and proposes a countermeasure such as providing a procedure manual or a script. The provision unit provides the user with the cause and the countermeasure identified by the analysis unit. The providing unit provides information by, for example, email notification, dashboard display, or the like. As a result, the fault response system according to the embodiment can search for information on the Internet based on the user's input information and provide the cause and a solution, thereby making fault response more efficient. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs the cause and solution identified by the analyzing unit and outputs information.

[0030] The reception unit accepts input of the OS, vendor name, fault content, and log from the user. The information input by the user includes, but is not limited to, the OS and vendor name, such as Windows, Linux, and a specific software vendor, fault content such as error messages and system malfunctions, and logs such as system logs, application logs, and error logs. Specifically, the user inputs this information through a dedicated web form or application. The web form is provided with drop-down menus and text boxes, so that the user can easily input the necessary information. For example, the type and version of the OS and the vendor name can be selected from the drop-down menu, and the error message and fault content can be freely written in the text box. In addition, the log file can be easily attached using a file upload function. This allows the reception unit to efficiently collect detailed information from the user. Furthermore, the reception unit automatically formats the input information so that it can be easily analyzed by the subsequent processing department. For example, the reception unit converts the error message and the log content into a specific format and saves it in a database. The reception unit can also perform initial filtering and classification based on the input information and assign it to a specific fault category. This allows the subsequent search unit and analysis unit to efficiently proceed with their work.

[0031] The search unit searches for bug reports on the Internet and published documents from vendors based on the information received by the reception unit. For example, the search unit searches bug reporting sites and forums such as GitHub Issues, Stack Overflow, Reddit, and Quora, as well as official documents and technical blogs published by vendors. Specifically, the search unit automatically searches these sites and forums using the error message and fault content entered by the user as keywords. The search algorithm uses natural language processing technology to preferentially extract highly relevant information. For example, it analyzes a part of the error message to identify threads and documents in which similar problems are reported. The search unit also has a function to automatically classify search results and sort them in order of relevance. This allows users to quickly find the information they need. Furthermore, the search unit stores the search results in a database so that the subsequent analysis unit can easily access them. For example, it stores the URL and summary information of the search results in a database so that the analysis unit can quickly obtain the required information. Furthermore, the search unit collects user feedback and continuously improves the search algorithm in order to improve the accuracy of the search results. This allows the search unit to always provide the latest and most optimal information and improve the efficiency of fault handling.

[0032] The analysis unit analyzes the information collected by the search unit and identifies the cause and the countermeasure. For example, the analysis unit identifies the cause based on the analysis result of the error message and past cases, and proposes a countermeasure such as providing a procedure manual or a script. Specifically, the analysis unit uses natural language processing technology to analyze the collected information and identify the pattern of the error message or the fault content. For example, the analysis unit searches a database for the cause of a specific error message in the past, and infers the cause based on similar cases. In addition, the analysis unit uses a machine learning algorithm to learn past fault data and predict the cause of a new fault. This allows the analysis unit to quickly and accurately identify the cause and propose an appropriate countermeasure. Furthermore, the analysis unit generates specific countermeasure procedures and scripts based on the identified cause. For example, specific procedures such as changing the system settings, reinstalling the software, and executing a specific command are provided to the user. In addition, the analysis unit evaluates the effectiveness of the countermeasure and makes corrections or improvements as necessary. This allows the analysis unit to always provide the optimal countermeasure, and improve the efficiency and accuracy of fault handling.

[0033] The providing unit provides the user with the cause and the countermeasure identified by the analysis unit. The providing unit provides information by, for example, email notification, dashboard display, and the like. Specifically, the providing unit provides information in an appropriate format to convey the cause and the countermeasure received from the analysis unit to the user in an easy-to-understand manner. For example, in the email notification, a report that briefly summarizes the cause and the countermeasure is sent so that the user can respond quickly. In addition, in the dashboard display, the information is provided visually using graphs and charts so that the user can check the information in real time. Furthermore, the providing unit collects user feedback and continuously improves the accuracy and effectiveness of the information provided. For example, the results of the user's implementation of the countermeasure are collected as feedback, and the effectiveness of the countermeasure is evaluated in cooperation with the analysis unit. Furthermore, the providing unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only email notification, but also SMS, push notification, voice call, and the like. As a result, the providing unit can quickly and reliably provide information to the user, and improve the efficiency and accuracy of fault response. Furthermore, the providing unit can also provide information using AI. For example, the causes and countermeasures identified by the analysis unit are input, and an AI model that outputs information is used to provide optimal information to the user. This enables the provision unit to provide more advanced information and support the user in dealing with problems.

[0034] The providing unit can cooperate with an in-house document management system to enable searching in-house cases. The providing unit can cooperate with, for example, an in-house document management system (for example, SharePoint, Confluence, etc.) to enable searching in-house cases. This enriches the in-house knowledge base, and enables a quick response when a similar failure occurs. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can provide information using an AI model that accesses an in-house document management system and searches for related information.

[0035] The providing unit can newly document the new fault using the summarization function. The providing unit can newly document the new fault using the summarization function, for example, by using a summarization algorithm using natural language processing technology. This enriches the in-house knowledge base and enables rapid response to similar faults. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input information on the new fault into the generation AI and document it using the generation AI that generates a summary.

[0036] The search unit can search forum posts on the Internet and technical documents of vendors. The search unit can search forum posts on the Internet, such as Reddit and Quora, and technical documents such as API documents and white papers published by vendors. This allows a wide range of related information to be collected. Some or all of the above-mentioned processing in the search unit can be performed, for example, using AI or can be performed without using AI. For example, the search unit can collect information using an AI model that searches forum posts and technical documents on the Internet.

[0037] The analysis unit can analyze information related to the error log and identify the cause. The analysis unit can analyze information related to the error log and identify the cause, for example, by using a method such as log pattern matching or error message analysis. This allows the cause to be identified quickly. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input information in the error log into AI and perform analysis using an AI model that identifies the cause.

[0038] The analysis unit can propose a countermeasure based on the identified cause. The analysis unit proposes a countermeasure based on the identified cause, for example, by using a method such as providing a procedure manual or a script. This makes it possible to quickly provide an appropriate countermeasure. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the identified cause into AI and provide the countermeasure using an AI model that proposes a countermeasure.

[0039] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit can, for example, analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display OS and vendor names that the user frequently input in the past as candidates. In addition, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user used in the past. Furthermore, the reception unit can predict and suggest the OS and vendor name to be used in a specific time period from the user's past input history. This improves input efficiency. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or can be performed without using AI. For example, the reception unit can input the user's past input history into AI and suggest an input method using an AI model that suggests the optimal input method.

[0040] The reception unit can monitor the current system state of the user in real time when the fault content or log is input, and improve the accuracy of the input content. For example, when the user inputs the fault content, the reception unit monitors the current system state in real time and automatically completes related information. In addition, when the user inputs the log, the accuracy of the log can be confirmed by referring to the current system state. Furthermore, based on the fault content input by the user, the current system state can be analyzed in real time and additional input can be prompted. This improves the accuracy of the input content. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the current system state to AI and have AI execute completion of the input content.

[0041] The reception unit can preferentially acquire highly relevant information based on the geographical location information of the user at the time of input. For example, when the user is in a specific region, the reception unit preferentially acquires information on OS and vendors related to the region. Also, when the user is in a specific country, the reception unit can preferentially acquire information based on the language and regulations of the country. Furthermore, when the user is moving, the optimal information can be acquired in real time based on the current location. This makes it possible to quickly provide appropriate information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and provide information using an AI model that acquires highly relevant information.

[0042] The reception unit can analyze the user's social media activity at the time of input and automatically complement related fault information. The reception unit automatically complements the input contents based on, for example, fault information shared by the user on social media. The reception unit can also complement related fault information by referring to information on technical communities that the user follows on social media. Furthermore, the input contents can also be automatically complemented based on logs and error messages posted by the user on social media. This improves input efficiency. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can provide information using an AI model that inputs the user's social media activity into AI and complements related fault information.

[0043] The search unit can apply an optimal search algorithm by referring to past search history at the time of search. The search unit can apply an optimal search algorithm based on, for example, the OS or vendor name searched for by the user in the past. Also, it is possible to preferentially display highly relevant information from the user's past search history. Furthermore, it is possible to apply an optimal search algorithm based on the fault content or log searched for by the user in the past. This improves the accuracy of the search. A part or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input past search history into AI and perform a search using an AI model that applies an optimal search algorithm.

[0044] The search unit can dynamically adjust the search range according to the input fault content and the level of detail of the log during the search. For example, when the input fault content is detailed, the search unit widens the search range to collect related information. Also, when the input log is detailed, the search unit can narrow the search range to collect specific information. Furthermore, the optimal search range can be dynamically adjusted according to the input fault content and the level of detail of the log. This makes it possible to efficiently collect related information. A part or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the input fault content and the level of detail of the log into AI and perform a search using an AI model that dynamically adjusts the search range.

[0045] During a search, the search unit can include not only related forums and technical documents on the Internet, but also blogs and articles by specific experts as search targets. The search unit can, for example, include posts on technical forums on the Internet as search targets. It can also include technical documents published by vendors as search targets. It can also include blogs and articles written by specific experts as search targets. This allows a wide range of information to be collected. Some or all of the above-mentioned processing in the search unit can be performed, for example, using AI or can be performed without using AI. For example, the search unit can collect information using an AI model that searches forums and technical documents on the Internet, and blogs and articles by experts.

[0046] The search unit can filter search results taking into account information specific to the user's industry during the search. The search unit, for example, reflects technical information specific to the industry to which the user belongs in the search results. The search unit can also filter search results taking into account regulations and standards related to the user's industry. Furthermore, the search results can be filtered taking into account terms and technologies specific to the user's industry. This makes it possible to provide highly relevant information. Some or all of the above-mentioned processing in the search unit may be performed, for example, using AI or may be performed without using AI. For example, the search unit can provide information by inputting information specific to the user's industry into AI and using an AI model that filters search results.

[0047] The analysis unit can optimize the analysis algorithm by referring to past failure data during analysis. The analysis unit can apply an optimal analysis algorithm based on, for example, past failure data. Also, the analysis unit can prioritize analysis of highly relevant information from the past failure data. Furthermore, the analysis algorithm can be dynamically adjusted by referring to the past failure data. This improves the accuracy of the analysis. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input past failure data into AI and perform analysis using an AI model that optimizes the analysis algorithm.

[0048] The analysis unit can dynamically adjust the accuracy of the analysis according to the level of detail of the input error log during analysis. For example, when the input error log is detailed, the analysis unit increases the accuracy of the analysis. Also, when the input error log is brief, the analysis unit can adjust the accuracy of the analysis. Furthermore, the analysis algorithm can be dynamically adjusted according to the level of detail of the input error log. This makes it possible to provide appropriate analysis results. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the level of detail of the input error log to AI and perform analysis using an AI model that dynamically adjusts the accuracy of the analysis.

[0049] During the analysis, the analysis unit can include not only related forums and technical documents on the Internet, but also blogs and articles by specific experts as analysis targets. The analysis unit can, for example, include posts on technical forums on the Internet as analysis targets. It can also include technical documents published by vendors as analysis targets. It can also include blogs and articles written by specific experts as analysis targets. This allows a wide range of information to be analyzed. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can collect information using an AI model that analyzes forums and technical documents on the Internet, and blogs and articles by experts.

[0050] During the analysis, the analysis unit can filter the analysis results taking into account information specific to the user's industry. The analysis unit, for example, reflects technical information specific to the industry to which the user belongs in the analysis results. The analysis results can also be filtered taking into account regulations and standards related to the user's industry. Furthermore, the analysis results can also be filtered taking into account terms and technologies specific to the user's industry. This makes it possible to provide highly relevant information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can provide information by inputting information specific to the user's industry into AI and using an AI model that filters the analysis results.

[0051] The providing unit can select the optimal providing method by referring to the past providing history at the time of providing. The providing unit selects the optimal providing method based on, for example, information received by the user in the past. Also, it is possible to provide highly relevant information preferentially based on the user's past providing history. Furthermore, it is possible to select the optimal providing method based on the format of information received by the user in the past. This improves the accuracy of providing information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information by inputting the past providing history into AI and using an AI model that selects the optimal providing method.

[0052] The providing unit can monitor the current system state of the user in real time at the time of providing, and improve the accuracy of the provided content. For example, when the user receives information, the providing unit monitors the current system state in real time and provides related information. In addition, when the user receives information, the current system state can be referred to to confirm the accuracy of the provided content. Furthermore, when the user receives information, the current system state can be analyzed in real time and additional information can be provided. This improves the accuracy of the provided content. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information by inputting the current system state into AI and using an AI model that improves the accuracy of the provided content.

[0053] The providing unit can provide highly relevant information preferentially based on the geographical location information of the user at the time of providing. For example, when the user is in a specific area, the providing unit can provide information related to the area preferentially. Also, when the user is in a specific country, the providing unit can provide information based on the language and regulations of the country preferentially. Furthermore, when the user is moving, optimal information can be provided in real time based on the current location. This makes it possible to provide appropriate information quickly. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information by inputting the geographical location information of the user into AI and using an AI model that provides highly relevant information.

[0054] The providing unit can analyze the user's social media activity at the time of providing, and automatically complement the related fault information. The providing unit automatically complements the provided content based on, for example, fault information shared by the user on social media. Also, the providing unit can complement the related fault information by referring to information on technical communities that the user follows on social media. Furthermore, the provided content can be automatically complemented based on logs and error messages posted by the user on social media. This improves the accuracy of providing information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs the user's social media activity into AI and complements the related fault information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can automatically suggest related past failure cases based on the contents of the user's input. For example, if the user inputs a specific error message, past cases in which a similar error message occurred are automatically displayed. Also, if the user inputs a specific OS or vendor name, past failure cases related to that OS or vendor can be preferentially displayed. Furthermore, based on the contents of the failure input by the user, related past failure cases can be filtered and the most relevant case can be suggested. This allows the user to quickly refer to past cases and find an appropriate solution.

[0057] The providing unit can suggest related external technical support services based on the contents input by the user. For example, when the user inputs a specific error message, external technical support services corresponding to the error message are automatically displayed. Also, when the user inputs a specific OS or vendor name, external technical support services related to the OS or vendor can be preferentially displayed. Furthermore, based on the contents of the fault input by the user, related external technical support services can be filtered and the most relevant service can be suggested. This allows the user to receive appropriate technical support quickly.

[0058] The providing unit can suggest related training materials and tutorials based on the contents input by the user. For example, when the user inputs a specific error message, the providing unit can automatically display training materials and tutorials corresponding to the error message. Also, when the user inputs a specific OS or vendor name, the providing unit can preferentially display training materials and tutorials related to the OS or vendor. Furthermore, based on the fault contents input by the user, the providing unit can filter related training materials and tutorials and suggest the most relevant materials. This allows the user to quickly refer to appropriate training materials and improve problem-solving skills.

[0059] The search unit can suggest information on related open source projects based on the contents of the user's input. For example, if the user inputs a specific error log, information on open source projects related to the error log can be automatically displayed. In addition, if the user inputs a specific OS or vendor name, information on open source projects related to the OS or vendor can be preferentially displayed. Furthermore, based on the contents of the fault input by the user, information on related open source projects can be filtered and the most relevant project can be suggested. This allows the user to quickly refer to information on open source projects and find resources for solving the problem.

[0060] The analysis unit can suggest related security threat information based on the contents of the user's input. For example, if the user inputs a specific error log, security threat information related to that error log is automatically displayed. Also, if the user inputs a specific OS or vendor name, security threat information related to that OS or vendor can be preferentially displayed. Furthermore, based on the contents of the failure input by the user, related security threat information can be filtered and the most relevant information can be suggested. This allows the user to quickly refer to security threat information and take appropriate measures.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit receives input of the OS, vendor name, fault details, and logs from the user. Information input by the user includes, for example, the OS (such as Windows, Linux, or a specific software vendor) and vendor name, fault details such as error messages and system malfunctions, system logs, application logs, error logs, etc. Step 2: The search unit searches for bug reports on the Internet and documents published by vendors based on the information received by the reception unit. For example, the search unit searches bug reporting sites and forums such as GitHub Issues, Stack Overflow, Reddit, and Quora, as well as official documents and technical blogs published by vendors. Step 3: The analysis unit analyzes the information collected by the search unit and identifies the cause and how to deal with the problem. For example, the analysis unit identifies the cause based on the results of analyzing error messages and past cases, and proposes ways to deal with the problem, such as providing instructions or scripts. Step 4: The providing unit provides the user with the cause and countermeasure identified by the analysis unit. The providing unit provides the information by, for example, email notification, dashboard display, or other methods. Some or all of the processing in the providing unit may be performed using AI, or may be performed without using AI.

[0063] (Example 2) A fault response system according to an embodiment of the present invention is a system that, by specifying an OS and a vendor name and inputting fault details and logs, refers to bug reports on the Internet and documents published by the vendor, and tells the cause and how to deal with the problem. In this fault response system, when a user inputs an OS, a vendor name, fault details, and logs, the AI ​​searches bug reports on the Internet and documents published by the vendor, collects related information, analyzes the cause and how to deal with the problem, and provides it to the user. In addition, this system can be linked to an in-house document management system (for example, an in-house Wiki or Redmine), and it is possible to search in-house cases and document new faults. For example, when a user inputs information such as "operating system A", "vendor B", "application crashes", and "error log: 0x0000001A", the AI ​​searches bug reports on the Internet and documents published by the vendor, and refers to forum posts where similar faults are reported and technical documents published by the vendor. Based on the collected information, the AI ​​analyzes information related to the error log "0x0000001A", identifies that the cause is a memory management problem, and suggests checking the memory and applying a specific patch. In addition, this system is linked to the company's document management system, making it possible to search for internal cases, and for new problems, the AI ​​uses the summary function to newly document them. This enriches the company's knowledge base, allowing for quick response when similar problems occur. This allows the AI ​​to take over the primary investigation by external vendors, improving the efficiency of problem response. For example, by having the AI ​​automatically perform the problem investigation that was previously outsourced to external vendors, time and costs can be reduced. In addition, by enriching the company's knowledge base, the speed of problem response can be improved, and work efficiency can be expected. This allows the problem response system to search for information on the Internet based on the information entered by the user, and provide the cause and solution, improving the efficiency of problem response.

[0064] The failure response system according to the embodiment includes a reception unit, a search unit, an analysis unit, and a provision unit. The reception unit receives input of an OS, a vendor name, a failure content, and a log from a user. The information input by the user includes, for example, an OS or vendor name such as Windows, Linux, or a specific software vendor, a failure content such as an error message or a system malfunction, and logs such as a system log, an application log, and an error log, but is not limited to such examples. The search unit searches bug reports on the Internet and public documents of vendors based on the information received by the reception unit. The search unit searches, for example, bug report sites and forums such as GitHub Issues, Stack Overflow, Reddit, and Quora, official documents and technical blogs published by vendors, and the like. The analysis unit analyzes the information collected by the search unit and identifies the cause and a countermeasure. The analysis unit identifies the cause based on, for example, the analysis result of an error message or past cases, and proposes a countermeasure such as providing a procedure manual or a script. The provision unit provides the user with the cause and the countermeasure identified by the analysis unit. The provision unit provides the information by, for example, a method such as an email notification or a dashboard display. As a result, the fault response system according to the embodiment can search for information on the Internet based on the user's input information and provide the cause and a solution, thereby making fault response more efficient. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that receives the cause and solution identified by the analyzing unit as input and outputs information.

[0065] The reception unit accepts input of the OS, vendor name, fault content, and log from the user. The information input by the user includes, but is not limited to, the OS and vendor name, such as Windows, Linux, and a specific software vendor, fault content such as error messages and system malfunctions, and logs such as system logs, application logs, and error logs. Specifically, the user inputs this information through a dedicated web form or application. The web form is provided with drop-down menus and text boxes, so that the user can easily input the necessary information. For example, the type and version of the OS and the vendor name can be selected from the drop-down menu, and the error message and fault content can be freely written in the text box. In addition, the log file can be easily attached using a file upload function. This allows the reception unit to efficiently collect detailed information from the user. Furthermore, the reception unit automatically formats the input information so that it can be easily analyzed by the subsequent processing department. For example, the reception unit converts the error message and the log content into a specific format and saves it in a database. The reception unit can also perform initial filtering and classification based on the input information and assign it to a specific fault category. This allows the subsequent search unit and analysis unit to efficiently proceed with their work.

[0066] The search unit searches for bug reports on the Internet and published documents from vendors based on the information received by the reception unit. For example, the search unit searches bug reporting sites and forums such as GitHub Issues, Stack Overflow, Reddit, and Quora, as well as official documents and technical blogs published by vendors. Specifically, the search unit automatically searches these sites and forums using the error message and fault content entered by the user as keywords. The search algorithm uses natural language processing technology to preferentially extract highly relevant information. For example, it analyzes a part of the error message to identify threads and documents in which similar problems are reported. The search unit also has a function to automatically classify search results and sort them in order of relevance. This allows users to quickly find the information they need. Furthermore, the search unit stores the search results in a database so that the subsequent analysis unit can easily access them. For example, it stores the URL and summary information of the search results in a database so that the analysis unit can quickly obtain the required information. Furthermore, the search unit collects user feedback and continuously improves the search algorithm in order to improve the accuracy of the search results. This allows the search unit to always provide the latest and most optimal information and improve the efficiency of fault handling.

[0067] The analysis unit analyzes the information collected by the search unit and identifies the cause and the countermeasure. For example, the analysis unit identifies the cause based on the analysis result of the error message and past cases, and proposes a countermeasure such as providing a procedure manual or a script. Specifically, the analysis unit uses natural language processing technology to analyze the collected information and identify the pattern of the error message or the fault content. For example, the analysis unit searches a database for the cause of a specific error message in the past, and infers the cause based on similar cases. In addition, the analysis unit uses a machine learning algorithm to learn past fault data and predict the cause of a new fault. This allows the analysis unit to quickly and accurately identify the cause and propose an appropriate countermeasure. Furthermore, the analysis unit generates specific countermeasure procedures and scripts based on the identified cause. For example, specific procedures such as changing the system settings, reinstalling the software, and executing a specific command are provided to the user. In addition, the analysis unit evaluates the effectiveness of the countermeasure and makes corrections or improvements as necessary. This allows the analysis unit to always provide the optimal countermeasure, and improve the efficiency and accuracy of fault handling.

[0068] The providing unit provides the user with the cause and the countermeasure identified by the analysis unit. The providing unit provides information by, for example, email notification, dashboard display, and the like. Specifically, the providing unit provides information in an appropriate format to convey the cause and the countermeasure received from the analysis unit to the user in an easy-to-understand manner. For example, in the email notification, a report that briefly summarizes the cause and the countermeasure is sent so that the user can respond quickly. In addition, in the dashboard display, the information is provided visually using graphs and charts so that the user can check the information in real time. Furthermore, the providing unit collects user feedback and continuously improves the accuracy and effectiveness of the information provided. For example, the results of the user's implementation of the countermeasure are collected as feedback, and the effectiveness of the countermeasure is evaluated in cooperation with the analysis unit. Furthermore, the providing unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only email notification, but also SMS, push notification, voice call, and the like. As a result, the providing unit can quickly and reliably provide information to the user, and improve the efficiency and accuracy of fault response. Furthermore, the providing unit can also provide information using AI. For example, the causes and countermeasures identified by the analysis unit are input, and an AI model that outputs information is used to provide optimal information to the user. This enables the provision unit to provide more advanced information and support the user in dealing with problems.

[0069] The providing unit can cooperate with an in-house document management system to enable searching in-house cases. The providing unit can cooperate with, for example, an in-house document management system (for example, SharePoint, Confluence, etc.) to enable searching in-house cases. This enriches the in-house knowledge base, and enables a quick response when a similar failure occurs. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can provide information using an AI model that accesses an in-house document management system and searches for related information.

[0070] The providing unit can newly document the new fault using the summarization function. The providing unit can newly document the new fault using the summarization function, for example, by using a summarization algorithm using natural language processing technology. This enriches the in-house knowledge base and enables rapid response to similar faults. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input information on the new fault into the generation AI and document it using the generation AI that generates a summary.

[0071] The search unit can search forum posts on the Internet and technical documents of vendors. The search unit can search forum posts on the Internet, such as Reddit and Quora, and technical documents such as API documents and white papers published by vendors. This allows a wide range of related information to be collected. Some or all of the above-mentioned processing in the search unit can be performed, for example, using AI or can be performed without using AI. For example, the search unit can collect information using an AI model that searches forum posts and technical documents on the Internet.

[0072] The analysis unit can analyze information related to the error log and identify the cause. The analysis unit can analyze information related to the error log and identify the cause, for example, by using a method such as log pattern matching or error message analysis. This allows the cause to be identified quickly. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input information in the error log into AI and perform analysis using an AI model that identifies the cause.

[0073] The analysis unit can propose a countermeasure based on the identified cause. The analysis unit proposes a countermeasure based on the identified cause, for example, by using a method such as providing a procedure manual or a script. This makes it possible to quickly provide an appropriate countermeasure. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the identified cause into AI and provide the countermeasure using an AI model that proposes a countermeasure.

[0074] The reception unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user's emotion. The reception unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis, and adjust the display method of the input interface based on the estimated user's emotion. For example, when the user is stressed, a simple interface is provided to minimize the input procedure. Also, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, when the user is in a hurry, voice input can be prioritized to quickly input the OS, vendor name, fault details, and log. This reduces the user's stress and improves input efficiency. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit can, for example, analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display OS and vendor names that the user frequently input in the past as candidates. In addition, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user used in the past. Furthermore, the reception unit can predict and suggest the OS and vendor name to be used in a specific time period from the user's past input history. This improves input efficiency. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or can be performed without using AI. For example, the reception unit can input the user's past input history into AI and suggest an input method using an AI model that suggests the optimal input method.

[0076] The reception unit can monitor the current system state of the user in real time when the fault content or log is input, and improve the accuracy of the input content. For example, when the user inputs the fault content, the reception unit monitors the current system state in real time and automatically completes related information. In addition, when the user inputs the log, the accuracy of the log can be confirmed by referring to the current system state. Furthermore, based on the fault content input by the user, the current system state can be analyzed in real time and additional input can be prompted. This improves the accuracy of the input content. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the current system state to AI and have AI execute completion of the input content.

[0077] The reception unit can estimate the user's emotion and determine the priority of the input contents based on the estimated user's emotion. The reception unit can estimate the user's emotion using, for example, a method such as facial expression recognition or voice analysis, and determine the priority of the input contents based on the estimated user's emotion. For example, when the user is nervous, important input items are preferentially displayed to quickly complete the input. In addition, when the user is relaxed, detailed input items can be displayed and a customizable input method can be provided. Furthermore, when the user is in a hurry, the most important input items can be preferentially displayed to quickly complete the input. This allows important information to be quickly input. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0078] The reception unit can preferentially acquire highly relevant information based on the geographical location information of the user at the time of input. For example, when the user is in a specific region, the reception unit preferentially acquires information on OS and vendors related to the region. Also, when the user is in a specific country, the reception unit can preferentially acquire information based on the language and regulations of the country. Furthermore, when the user is moving, the optimal information can be acquired in real time based on the current location. This makes it possible to quickly provide appropriate information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and provide information using an AI model that acquires highly relevant information.

[0079] The reception unit can analyze the user's social media activity at the time of input and automatically complement related fault information. The reception unit automatically complements the input contents based on, for example, fault information shared by the user on social media. The reception unit can also complement related fault information by referring to information on technical communities that the user follows on social media. Furthermore, the input contents can also be automatically complemented based on logs and error messages posted by the user on social media. This improves input efficiency. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can provide information using an AI model that inputs the user's social media activity into AI and complements related fault information.

[0080] The search unit can estimate the user's emotion and adjust the display method of the search results based on the estimated user's emotion. The search unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis, and adjust the display method of the search results based on the estimated user's emotion. For example, when the user is nervous, a simple search result with high visibility is displayed. When the user is relaxed, a search result including detailed information can be displayed. Furthermore, when the user is in a hurry, a search result that focuses on the main points can be displayed. This reduces the user's stress and improves search efficiency. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0081] The search unit can apply an optimal search algorithm by referring to past search history at the time of search. The search unit can apply an optimal search algorithm based on, for example, the OS or vendor name searched for by the user in the past. Also, it is possible to preferentially display highly relevant information from the user's past search history. Furthermore, it is possible to apply an optimal search algorithm based on the fault content or log searched for by the user in the past. This improves the accuracy of the search. A part or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input past search history into AI and perform a search using an AI model that applies an optimal search algorithm.

[0082] The search unit can dynamically adjust the search range according to the input fault content and the level of detail of the log during the search. For example, when the input fault content is detailed, the search unit widens the search range to collect related information. Also, when the input log is detailed, the search unit can narrow the search range to collect specific information. Furthermore, the optimal search range can be dynamically adjusted according to the input fault content and the level of detail of the log. This makes it possible to efficiently collect related information. A part or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the input fault content and the level of detail of the log into AI and perform a search using an AI model that dynamically adjusts the search range.

[0083] The search unit can estimate the user's emotion and determine the priority of the search results based on the estimated user's emotion. The search unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis, and determine the priority of the search results based on the estimated user's emotion. For example, when the user is nervous, important information is preferentially displayed. Also, when the user is relaxed, detailed information can be preferentially displayed. Furthermore, when the user is in a hurry, information with the main points can be preferentially displayed. This makes it possible to provide important information quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0084] During a search, the search unit can include not only related forums and technical documents on the Internet, but also blogs and articles by specific experts as search targets. The search unit can, for example, include posts on technical forums on the Internet as search targets. It can also include technical documents published by vendors as search targets. It can also include blogs and articles written by specific experts as search targets. This allows a wide range of information to be collected. Some or all of the above-mentioned processing in the search unit can be performed, for example, using AI or can be performed without using AI. For example, the search unit can collect information using an AI model that searches forums and technical documents on the Internet, and blogs and articles by experts.

[0085] The search unit can filter search results taking into account information specific to the user's industry during the search. The search unit, for example, reflects technical information specific to the industry to which the user belongs in the search results. The search unit can also filter search results taking into account regulations and standards related to the user's industry. Furthermore, the search results can be filtered taking into account terms and technologies specific to the user's industry. This makes it possible to provide highly relevant information. Some or all of the above-mentioned processing in the search unit may be performed, for example, using AI or may be performed without using AI. For example, the search unit can provide information by inputting information specific to the user's industry into AI and using an AI model that filters search results.

[0086] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis, and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible analysis result is displayed. When the user is relaxed, an analysis result including detailed information can also be displayed. Furthermore, when the user is in a hurry, an analysis result with the main points can also be displayed. This reduces the user's stress and improves understanding of the analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0087] The analysis unit can optimize the analysis algorithm by referring to past failure data during analysis. The analysis unit can apply an optimal analysis algorithm based on, for example, past failure data. Also, the analysis unit can prioritize analysis of highly relevant information from the past failure data. Furthermore, the analysis algorithm can be dynamically adjusted by referring to the past failure data. This improves the accuracy of the analysis. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input past failure data into AI and perform analysis using an AI model that optimizes the analysis algorithm.

[0088] The analysis unit can dynamically adjust the accuracy of the analysis according to the level of detail of the input error log during analysis. For example, when the input error log is detailed, the analysis unit increases the accuracy of the analysis. Also, when the input error log is brief, the analysis unit can adjust the accuracy of the analysis. Furthermore, the analysis algorithm can be dynamically adjusted according to the level of detail of the input error log. This makes it possible to provide appropriate analysis results. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the level of detail of the input error log to AI and perform analysis using an AI model that dynamically adjusts the accuracy of the analysis.

[0089] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. The analysis unit can estimate the user's emotion using, for example, a method such as facial expression recognition or voice analysis, and determine the priority of the analysis results based on the estimated user's emotion. For example, when the user is nervous, important information can be displayed preferentially. In addition, when the user is relaxed, detailed information can be displayed preferentially. Furthermore, when the user is in a hurry, information that covers the main points can be displayed preferentially. This makes it possible to provide important information quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0090] During the analysis, the analysis unit can include not only related forums and technical documents on the Internet, but also blogs and articles by specific experts as analysis targets. The analysis unit can, for example, include posts on technical forums on the Internet as analysis targets. It can also include technical documents published by vendors as analysis targets. It can also include blogs and articles written by specific experts as analysis targets. This allows a wide range of information to be analyzed. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can collect information using an AI model that analyzes forums and technical documents on the Internet, and blogs and articles by experts.

[0091] During the analysis, the analysis unit can filter the analysis results taking into account information specific to the user's industry. The analysis unit, for example, reflects technical information specific to the industry to which the user belongs in the analysis results. The analysis results can also be filtered taking into account regulations and standards related to the user's industry. Furthermore, the analysis results can also be filtered taking into account terms and technologies specific to the user's industry. This makes it possible to provide highly relevant information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can provide information by inputting information specific to the user's industry into AI and using an AI model that filters the analysis results.

[0092] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis, and adjust the display method of the information to be provided based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible display method is provided. Also, when the user is relaxed, a display method including detailed information can be provided. Furthermore, when the user is in a hurry, a display method that focuses on the main points can be provided. This reduces the user's stress and improves the understanding of the information. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0093] The providing unit can select the optimal providing method by referring to the past providing history at the time of providing. The providing unit selects the optimal providing method based on, for example, information received by the user in the past. Also, it is possible to provide highly relevant information preferentially based on the user's past providing history. Furthermore, it is possible to select the optimal providing method based on the format of information received by the user in the past. This improves the accuracy of providing information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information by inputting the past providing history into AI and using an AI model that selects the optimal providing method.

[0094] The providing unit can monitor the current system state of the user in real time at the time of providing, and improve the accuracy of the provided content. For example, when the user receives information, the providing unit monitors the current system state in real time and provides related information. In addition, when the user receives information, the current system state can be referred to to confirm the accuracy of the provided content. Furthermore, when the user receives information, the current system state can be analyzed in real time and additional information can be provided. This improves the accuracy of the provided content. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information by inputting the current system state into AI and using an AI model that improves the accuracy of the provided content.

[0095] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion using, for example, a method such as facial expression recognition or voice analysis, and determine the priority of information to be provided based on the estimated user's emotion. For example, when the user is nervous, important information can be provided preferentially. In addition, when the user is relaxed, detailed information can be provided preferentially. Furthermore, when the user is in a hurry, information that covers the main points can be provided preferentially. This makes it possible to provide important information quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0096] The providing unit can provide highly relevant information preferentially based on the geographical location information of the user at the time of providing. For example, when the user is in a specific area, the providing unit can provide information related to the area preferentially. Also, when the user is in a specific country, the providing unit can provide information based on the language and regulations of the country preferentially. Furthermore, when the user is moving, optimal information can be provided in real time based on the current location. This makes it possible to provide appropriate information quickly. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information by inputting the geographical location information of the user into AI and using an AI model that provides highly relevant information.

[0097] The providing unit can analyze the user's social media activity at the time of providing, and automatically complement the related fault information. The providing unit automatically complements the provided content based on, for example, fault information shared by the user on social media. Also, the providing unit can complement the related fault information by referring to information on technical communities that the user follows on social media. Furthermore, the provided content can be automatically complemented based on logs and error messages posted by the user on social media. This improves the accuracy of providing information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs the user's social media activity into AI and complements the related fault information.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The reception unit can automatically suggest related past failure cases based on the contents of the user's input. For example, if the user inputs a specific error message, past cases in which a similar error message occurred are automatically displayed. Also, if the user inputs a specific OS or vendor name, past failure cases related to that OS or vendor can be preferentially displayed. Furthermore, based on the contents of the failure input by the user, related past failure cases can be filtered and the most relevant case can be suggested. This allows the user to quickly refer to past cases and find an appropriate solution.

[0100] The providing unit can suggest related external technical support services based on the contents input by the user. For example, when the user inputs a specific error message, external technical support services corresponding to the error message are automatically displayed. Also, when the user inputs a specific OS or vendor name, external technical support services related to the OS or vendor can be preferentially displayed. Furthermore, based on the contents of the fault input by the user, related external technical support services can be filtered and the most relevant service can be suggested. This allows the user to receive appropriate technical support quickly.

[0101] The providing unit can suggest related training materials and tutorials based on the contents input by the user. For example, when the user inputs a specific error message, the providing unit can automatically display training materials and tutorials corresponding to the error message. Also, when the user inputs a specific OS or vendor name, the providing unit can preferentially display training materials and tutorials related to the OS or vendor. Furthermore, based on the fault contents input by the user, the providing unit can filter related training materials and tutorials and suggest the most relevant materials. This allows the user to quickly refer to appropriate training materials and improve problem-solving skills.

[0102] The search unit can suggest information on related open source projects based on the contents of the user's input. For example, if the user inputs a specific error log, information on open source projects related to the error log can be automatically displayed. In addition, if the user inputs a specific OS or vendor name, information on open source projects related to the OS or vendor can be preferentially displayed. Furthermore, based on the contents of the fault input by the user, information on related open source projects can be filtered and the most relevant project can be suggested. This allows the user to quickly refer to information on open source projects and find resources for solving the problem.

[0103] The analysis unit can suggest related security threat information based on the contents of the user's input. For example, if the user inputs a specific error log, security threat information related to that error log is automatically displayed. Also, if the user inputs a specific OS or vendor name, security threat information related to that OS or vendor can be preferentially displayed. Furthermore, based on the contents of the failure input by the user, related security threat information can be filtered and the most relevant information can be suggested. This allows the user to quickly refer to security threat information and take appropriate measures.

[0104] The reception unit can estimate the user's emotions and adjust the confirmation message for the input contents based on the estimated user's emotions. For example, if the user is stressed, the confirmation message can be made brief, allowing the user to quickly confirm the input contents. Also, if the user is relaxed, a detailed confirmation message can be displayed, allowing the user to carefully confirm the input contents. Furthermore, if the user is in a hurry, important confirmation items can be displayed with priority, allowing the user to quickly complete the confirmation. This reduces the user's stress and makes the confirmation of the input contents more efficient.

[0105] The search unit can estimate the user's emotions and adjust the filtering method of search results based on the estimated user's emotions. For example, if the user is nervous, important information can be displayed preferentially, allowing the user to quickly find the information he or she needs. If the user is relaxed, search results including detailed information can be displayed, allowing the user to carefully check the information. If the user is in a hurry, information that focuses on the main points can be displayed preferentially, allowing the user to quickly find the information he or she needs. This can reduce the user's stress and improve search efficiency.

[0106] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis results based on the estimated user's emotions. For example, if the user is nervous, simple and highly visible analysis results can be displayed to enable quick understanding. If the user is relaxed, analysis results including detailed information can be displayed to allow the user to carefully review the information. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be displayed to provide the necessary information quickly. This can reduce the user's stress and improve understanding of the analysis results.

[0107] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the information can be provided in a simple, highly visible format to enable the user to quickly understand it. If the user is relaxed, the information can be provided in a format including detailed information to enable the user to carefully review the information. Furthermore, if the user is in a hurry, the information can be provided in a format that focuses on the main points to quickly provide the user with the necessary information. This can reduce the user's stress and improve their understanding of the information.

[0108] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, when the user is nervous, important information can be provided quickly, allowing the user to respond quickly. When the user is relaxed, detailed information can be provided at an appropriate time, allowing the user to carefully check the information. Furthermore, when the user is in a hurry, the most important information can be provided preferentially, allowing the user to respond quickly. This can reduce the user's stress and make the provision of information more efficient.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The reception unit receives input of the OS, vendor name, fault details, and logs from the user. Information input by the user includes, for example, the OS (such as Windows, Linux, or a specific software vendor) and vendor name, fault details such as error messages and system malfunctions, system logs, application logs, error logs, etc. Step 2: The search unit searches for bug reports on the Internet and documents published by vendors based on the information received by the reception unit. For example, the search unit searches bug reporting sites and forums such as GitHub Issues, Stack Overflow, Reddit, and Quora, as well as official documents and technical blogs published by vendors. Step 3: The analysis unit analyzes the information collected by the search unit and identifies the cause and how to deal with the problem. For example, the analysis unit identifies the cause based on the results of analyzing error messages and past cases, and proposes ways to deal with the problem, such as providing instructions or scripts. Step 4: The providing unit provides the user with the cause and countermeasure identified by the analysis unit. The providing unit provides the information by, for example, email notification, dashboard display, or other methods. Some or all of the processing in the providing unit may be performed using AI, or may be performed without using AI.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned reception unit, search unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and receives input of the OS, vendor name, fault content, and log from the user. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and searches for bug reports on the Internet and public documents of the vendor. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to identify the cause and the countermeasure. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the analysis result to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned reception unit, search unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and receives input of the OS, vendor name, fault content, and log from the user. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and searches for bug reports on the Internet and public documents of the vendor. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and identifies the cause and the countermeasure. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the analysis result to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] 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.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] 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.

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

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

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned reception unit, search unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and receives input of the OS, vendor name, fault content, and log from the user. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and searches for bug reports on the Internet and public documents of the vendor. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to identify the cause and the countermeasure. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the analysis result to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] 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.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] 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.

[0156] 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.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] Each of the multiple elements including the above-mentioned reception unit, search unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and receives input of the OS, vendor name, fault content, and log from the user. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and searches for bug reports on the Internet and public documents of the vendor. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to identify the cause and the countermeasure. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the analysis result to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) a reception unit that receives input of an OS or vendor name, a fault description, and a log from a user; a search unit that searches for bug reports on the Internet or public documents of vendors based on the information received by the reception unit; an analysis unit that analyzes the information collected by the search unit and identifies a cause and a countermeasure; A providing unit that provides a user with the cause and a solution identified by the analysis unit. A system characterized by: (Appendix 2) The providing unit is Link with the company's document management system to enable searching within the company 2. The system of claim 1, (Appendix 3) The providing unit is Use the Summary feature to document new defects 2. The system of claim 1, (Appendix 4) The search unit is Searching forum posts or vendor technical documentation on the Internet 2. The system of claim 1, (Appendix 5) The analysis unit is Analyze the information related to the error log and identify the cause 2. The system of claim 1, (Appendix 6) The analysis unit is Suggest solutions based on identified causes 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and adjust a display method of an input interface based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past input history and suggest the optimal input method 2. The system of claim 1, (Appendix 9) The reception unit is When entering information about a problem or a log, the system monitors the user's current system status in real time to improve the accuracy of the information entered. 2. The system of claim 1, (Appendix 10) The reception unit is Infer user emotions and prioritize input content based on the inferred user emotions 2. The system of claim 1, (Appendix 11) The reception unit is As you type, it prioritizes relevant information based on your geographic location 2. The system of claim 1, (Appendix 12) The reception unit is Analyzes your social media activity and automatically completes relevant disability information as you type 2. The system of claim 1, (Appendix 13) The search unit is Infer user sentiment and adjust how search results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The search unit is When searching, it refers to your past search history and applies the most appropriate search algorithm 2. The system of claim 1, (Appendix 15) The search unit is During a search, the search scope is dynamically adjusted according to the input error content and log detail level. 2. The system of claim 1, (Appendix 16) The search unit is Infer user sentiment and prioritize search results based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The search unit is When searching, include relevant forums and technical documents on the Internet, as well as blogs and articles by specific experts 2. The system of claim 1, (Appendix 18) The search unit is When searching, filter search results by taking into account the user's industry-specific information 2. The system of claim 1, (Appendix 19) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The analysis unit is During analysis, past failure data is referenced to optimize the analysis algorithm 2. The system of claim 1, (Appendix 21) The analysis unit is During analysis, the accuracy of analysis is dynamically adjusted according to the level of detail of the input error log. 2. The system of claim 1, (Appendix 22) The analysis unit is Infer user emotions and prioritize analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The analysis unit is The analysis will include relevant forums and technical documents on the Internet, as well as blogs and articles by specific experts. 2. The system of claim 1, (Appendix 24) The analysis unit is During analysis, filter the analysis results by taking into account the user's industry-specific information 2. The system of claim 1, (Appendix 25) The providing unit is Estimate the user's emotions and adjust the way information is displayed based on the estimated user's emotions 2. The system of claim 1, (Appendix 26) The providing unit is When providing information, the optimal method of providing the information is selected by referring to past provision history. 2. The system of claim 1, (Appendix 27) The providing unit is When providing information, the current system status of the user is monitored in real time to improve the accuracy of the information provided. 2. The system of claim 1, (Appendix 28) The providing unit is Estimate the user's emotions and prioritize the information to be provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 29) The providing unit is When provided, the most relevant information is prioritized based on the user's geographic location 2. The system of claim 1, (Appendix 30) The providing unit is When provided, the system will analyze users' social media activity and automatically complete relevant outage information. 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of an OS or vendor name, a fault description, and a log from a user; a search unit that searches for bug reports on the Internet or public documents of vendors based on the information received by the reception unit; an analysis unit that analyzes the information collected by the search unit and identifies a cause and a countermeasure; A providing unit that provides a user with the cause and a solution identified by the analysis unit. A system characterized by:

2. The providing unit is Link with the company's document management system to enable searching within the company 2. The system of claim 1 .

3. The providing unit is Use the Summary feature to document new defects 2. The system of claim 1 .

4. The search unit is Searching forum posts or vendor technical documentation on the Internet 2. The system of claim 1 .

5. The analysis unit is Analyze the information related to the error log and identify the cause 2. The system of claim 1 .

6. The analysis unit is Suggest solutions based on identified causes 2. The system of claim 1 .

7. The reception unit is To estimate a user's emotion and adjust a display method of an input interface based on the estimated user's emotion.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past input history and suggest the optimal input method 2. The system of claim 1 .

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