system

Abstract

The system according to the embodiment aims to automate the creation of system manuals and keep them up to date. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an extraction unit, and an update unit. The collection unit collects system operation logs or user behavior data. The analysis unit analyzes the data collected by the collection unit. The extraction unit extracts necessary information based on the data analyzed by the analysis unit. The update unit automatically updates the information extracted by the extraction unit to the manual.

Description

[Technical Field]

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

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

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

[0004] In conventional techniques, the work of extracting the necessary information when creating a system manual and keeping it up to date is time-consuming, and there is room for improvement.

[0005] The system according to the embodiment aims to automate the creation of system manuals and keep them up to date. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an extraction unit, and an update unit. The collection unit collects system operation logs or user behavior data. The analysis unit analyzes the data collected by the collection unit. The extraction unit extracts necessary information based on the data analyzed by the analysis unit. The update unit automatically updates the manual with the information extracted by the extraction unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate the creation of the system manual and keep it up to date. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention automates the creation of system manuals, significantly reducing the workload of manual creators. This system automates the task of intentionally extracting necessary parts and automatically keeps the manual up-to-date, ensuring quality. Furthermore, in the future, mouse tracking and on-screen UI integration will eliminate the need for manuals. For example, the system collects system operation logs and user behavior data. It then analyzes the collected data and automatically extracts the necessary parts for the manual. This process involves using generative AI to analyze and extract the data. The extracted information is automatically reflected in the manual, keeping it up-to-date. Furthermore, in the future, mouse tracking technology will be introduced to provide real-time guidance based on user actions, eliminating the need for manuals. This minimizes waste and allows users to focus on more essential tasks. This system significantly reduces the workload of creating manuals and keeps them up-to-date.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, an extraction unit, and an update unit. The collection unit collects system operation logs or user behavior data. The system operation logs include, for example, click history, input history, and error logs. The user behavior data includes, for example, page browsing history, scrolling behavior, and stay time. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm. The extraction unit extracts necessary information based on the data analyzed by the analysis unit. The necessary information includes, for example, user behavior patterns and causes of errors. The update unit automatically reflects the information extracted by the extraction unit in the manual. Methods for automatically reflecting the information include, for example, the frequency of manual updates and the type of information reflected. As a result, the system according to the embodiment significantly reduces the burden of creating manuals and can always keep them up to date.

[0030] The system further includes a guide unit that provides real-time guidance in response to user operations. The guide unit provides real-time guidance in response to user operations. Methods for providing real-time guidance include, for example, pop-up notifications, audio guidance, and tooltips. By providing real-time guidance in response to user operations, the system aims to eliminate the need for manuals.

[0031] The collection unit can analyze the user's past operation history and select the optimal data collection method. For example, the collection unit prioritizes collection of operations that the user has frequently performed in the past. The collection unit can also predict operations that will be performed during a specific time period from the user's past operation history and collect data during that time period. Furthermore, the collection unit can analyze the user's operation history and select the most efficient data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past operation history.

[0032] The collection unit can perform filtering based on the user's current task or area of ​​interest when collecting data. For example, the collection unit collects only data related to the task the user is currently performing. The collection unit can also preferentially collect highly relevant data based on the user's area of ​​interest. Furthermore, the collection unit can filter unnecessary data taking into account the user's current task or area of ​​interest. This makes it possible to collect highly relevant data by filtering data based on the user's current task or area of ​​interest.

[0033] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user is using voice input, the collection unit prioritizes collection of voice data. Also, if the user is using text input, the collection unit can also prioritize collection of text data. Furthermore, if the user is using image input, the collection unit can also prioritize collection of image data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method.

[0034] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also filter highly relevant data based on the user's geographical location information. Furthermore, when the user is moving, the collection unit can also collect optimal data based on the user's current location. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0035] When collecting data, the collection unit can analyze the user's social media activities and collect relevant data. For example, the collection unit collects relevant data based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and prioritize collection of highly relevant data. Furthermore, the collection unit can also collect relevant data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities.

[0036] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can adjust the data collection method based on, for example, feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and select the optimal collection method. Furthermore, the collection unit can also improve the accuracy of data collection by reflecting the user's past feedback. In this way, the optimal collection method can be customized by reflecting the user's past feedback.

[0037] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. Furthermore, the analysis unit can also apply a voice recognition algorithm to voice data. In this way, by applying different analysis algorithms depending on the data category, optimal analysis results can be provided.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can also select the optimal analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0040] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also postpone analysis of older data based on the time when the data was collected. Furthermore, the analysis unit can optimally allocate analysis resources taking into account the time when the data was collected. This enables efficient analysis by determining the analysis priority based on the time when the data was collected.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can optimally adjust the order of analysis taking into account the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0042] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide the user with analysis results that are optimal for the user.

[0043] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between data during extraction. For example, the extraction unit analyzes the interrelationships between data and preferentially extracts highly relevant information. The extraction unit can also filter out unnecessary information by taking into account the interrelationships between data. Furthermore, the extraction unit can improve the accuracy of extraction based on the interrelationships between data. In this way, the accuracy of extraction can be improved by taking into account the interrelationships between data.

[0044] The extraction unit can perform extraction while taking into consideration attribute information of the data submitter. For example, if the data submitter is an expert, the extraction unit can preferentially extract that information. In addition, if the data submitter is a general user, the extraction unit can also filter that information. Furthermore, the extraction unit can improve the accuracy of extraction based on the attribute information of the data submitter. In this way, by taking into consideration the attribute information of the data submitter, highly relevant information can be extracted.

[0045] The extraction unit can weight the extraction based on the frequency of data submission during extraction. For example, the extraction unit prioritizes extraction of data with a high submission frequency. The extraction unit can also postpone data with a low submission frequency. Furthermore, the extraction unit can weight the extraction taking into account the frequency of data submission. Thus, by weighting the extraction based on the frequency of data submission, important information can be preferentially extracted.

[0046] The extraction unit can perform extraction while taking into consideration the geographical distribution of data. For example, if a user is in a specific area, the extraction unit preferentially extracts data related to that area. The extraction unit can also extract highly relevant information based on the geographical distribution of data. Furthermore, the extraction unit can extract optimal information based on the user's current location. This makes it possible to extract highly relevant information by taking into consideration the geographical distribution of data.

[0047] The extraction unit can improve the accuracy of extraction by referring to literature related to the data during extraction. For example, the extraction unit refers to literature related to the data and preferentially extracts highly relevant information. The extraction unit can also filter out unnecessary information based on literature related to the data. Furthermore, the extraction unit can also improve the accuracy of extraction by referring to literature related to the data. In this way, by referring to literature related to the data, the accuracy of extraction can be improved.

[0048] The extraction unit can perform extraction taking into consideration the market value of the data. For example, the extraction unit preferentially extracts data with a high market value. The extraction unit can also postpone data with a low market value. Furthermore, the extraction unit can weight the extraction taking into consideration the market value of the data. This allows important information to be preferentially extracted by taking into consideration the market value of the data.

[0049] The update unit can adjust the level of detail of the update based on the importance of the data during the update. For example, the update unit performs a detailed update on data with high importance. The update unit can also perform a simplified update on data with low importance. Furthermore, the update unit can optimally allocate update resources according to the importance of the data. This allows for efficient updates by adjusting the level of detail of the update based on the importance of the data.

[0050] The update unit can apply different update algorithms depending on the category of data when updating. For example, the update unit applies a natural language processing algorithm to text data. The update unit can also apply an image recognition algorithm to image data. Furthermore, the update unit can also apply a voice recognition algorithm to voice data. In this way, by applying different update algorithms depending on the category of data, optimal update results can be provided.

[0051] During an update, the update unit can improve the accuracy of the update by referring to the user's past update results. The update unit, for example, adjusts the update algorithm based on the user's past update results. The update unit can also analyze the user's past update results to improve the accuracy of the update. Furthermore, the update unit can also select the optimal update method by referring to the user's past update results. In this way, the accuracy of the update can be improved by referring to the user's past update results.

[0052] The update unit can determine the update priority based on the time of data collection during updating. For example, the update unit prioritizes updating the most recent data. The update unit can also postpone updating older data based on the time of data collection. Furthermore, the update unit can optimally allocate update resources taking into account the time of data collection. This allows for efficient updates by determining the update priority based on the time of data collection.

[0053] The update unit can adjust the update order based on the relevance of the data when updating. For example, the update unit prioritizes updating highly relevant data. The update unit can also postpone updating less relevant data. Furthermore, the update unit can optimally adjust the update order by taking the relevance of the data into consideration. This allows for efficient updates by adjusting the update order based on the relevance of the data.

[0054] During updating, the update unit can adjust the use of technical terminology in the update according to the user's level of expertise. For example, if the user has technical expertise, the update unit can provide an update result that uses a lot of technical terminology. Also, if the user does not have technical expertise, the update unit can provide an update result in simple language. Furthermore, the update unit can adjust the way the update result is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the update according to the user's level of expertise, it is possible to provide an update result that is optimal for the user.

[0055] When providing guidance, the guide unit can provide the most appropriate guidance by referring to the user's past operation history. The guide unit can provide the most appropriate guidance based on, for example, operations performed by the user in the past. The guide unit can also analyze the user's past operation history and provide the most efficient guidance. Furthermore, the guide unit can select the most appropriate guidance method by referring to the user's past operation history. In this way, the most appropriate guidance can be provided by referring to the user's past operation history.

[0056] When providing a guide, the guide unit can customize the guide content according to the user's current task. For example, the guide unit provides a guide related to the task the user is currently performing. The guide unit can also customize the optimal guide content based on the user's current task. Furthermore, the guide unit can adjust the level of detail of the guide according to the user's current task. This makes it possible to provide the optimal guide by customizing the guide content according to the user's current task.

[0057] The guide unit can improve the guide method by reflecting user feedback when providing guidance. For example, the guide unit adjusts the guide method based on feedback provided by the user. The guide unit can also analyze the user feedback and select the optimal guide method. Furthermore, the guide unit can also improve the accuracy of the guide by reflecting user feedback. In this way, the accuracy of the guide can be improved by reflecting user feedback.

[0058] When providing a guide, the guide unit can provide the most appropriate guide by taking into account the user's geographical location information. For example, if the user is in a specific area, the guide unit can provide a guide related to that area. The guide unit can also provide a highly relevant guide based on the user's geographical location information. Furthermore, the guide unit can also provide the most appropriate guide based on the user's current location. In this way, by taking into account the user's geographical location information, it is possible to provide a highly relevant guide.

[0059] When providing a guide, the guide unit can analyze the user's social media activity and provide a relevant guide. For example, the guide unit can provide a relevant guide based on information shared by the user on social media. The guide unit can also analyze the user's social media activity and provide a highly relevant guide. Furthermore, the guide unit can provide a relevant guide by referring to the activity of the user's friends on social media. In this way, a highly relevant guide can be provided by analyzing the user's social media activity.

[0060] When providing guidance, the guide unit can customize the guidance method by reflecting the user's past feedback. The guide unit adjusts the guidance method based on, for example, feedback provided by the user in the past. The guide unit can also analyze the user's past feedback and select the optimal guidance method. Furthermore, the guide unit can also improve the accuracy of the guidance by reflecting the user's past feedback. In this way, the optimal guidance method can be provided by reflecting the user's past feedback.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The collection unit can analyze the user's past operation history and select the optimal data collection method. For example, it can prioritize collection of operations that the user frequently performed in the past. The collection unit can also predict operations that will be performed during a specific time period from the user's past operation history and collect data during that time period. Furthermore, the collection unit can analyze the user's operation history and select the most efficient data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past operation history.

[0063] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a natural language processing algorithm can be applied to text data. An image recognition algorithm can also be applied to image data. Furthermore, a voice recognition algorithm can be applied to voice data. In this way, by applying different analysis algorithms depending on the data category, optimal analysis results can be provided.

[0064] When collecting data, the collection unit can filter the data based on the user's current task or area of ​​interest. For example, the collection unit collects only data related to the task the user is currently performing. The collection unit can also preferentially collect highly relevant data based on the user's area of ​​interest. Furthermore, the collection unit can filter unnecessary data taking into account the user's current task or area of ​​interest. This makes it possible to collect highly relevant data by filtering data based on the user's current task or area of ​​interest.

[0065] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between data during extraction. For example, the extraction unit analyzes the interrelationships between data and preferentially extracts highly relevant information. The extraction unit can also filter out unnecessary information by taking into account the interrelationships between data. Furthermore, the extraction unit can improve the accuracy of extraction based on the interrelationships between data. In this way, the accuracy of extraction can be improved by taking into account the interrelationships between data.

[0066] When providing guidance, the guide unit can provide the most appropriate guidance by referring to the user's past operation history. For example, the guide unit can provide the most appropriate guidance based on the operations performed by the user in the past. The guide unit can also analyze the user's past operation history and provide the most efficient guidance. Furthermore, the guide unit can select the most appropriate guidance method by referring to the user's past operation history. In this way, the guide unit can provide the most appropriate guidance by referring to the user's past operation history.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects system operation logs or user behavior data. The system operation logs include, for example, click history, input history, and error logs, and the user behavior data includes, for example, page browsing history, scrolling behavior, and stay time. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms. Step 3: The extraction unit extracts necessary information based on the data analyzed by the analysis unit. The necessary information includes, for example, the user's behavioral patterns and the cause of the error. Step 4: The update unit automatically updates the manual with the information extracted by the extraction unit. The method of automatically updating the manual may include, for example, the frequency of updating the manual and the type of information to be updated.

[0069] (Example 2) A system according to an embodiment of the present invention automates the creation of system manuals, significantly reducing the workload of manual creators. This system automates the task of intentionally extracting necessary parts and automatically keeps the manual up-to-date, ensuring quality. Furthermore, in the future, mouse tracking and on-screen UI integration will eliminate the need for manuals. For example, the system collects system operation logs and user behavior data. It then analyzes the collected data and automatically extracts the necessary parts for the manual. This process involves using generative AI to analyze and extract the data. The extracted information is automatically reflected in the manual, keeping it up-to-date. Furthermore, in the future, mouse tracking technology will be introduced to provide real-time guidance based on user actions, eliminating the need for manuals. This minimizes waste and allows users to focus on more essential tasks. This system significantly reduces the workload of creating manuals and keeps them up-to-date.

[0070] The system according to the embodiment includes a collection unit, an analysis unit, an extraction unit, and an update unit. The collection unit collects system operation logs or user behavior data. The system operation logs include, for example, click history, input history, and error logs. The user behavior data includes, for example, page browsing history, scrolling behavior, and stay time. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm. The extraction unit extracts necessary information based on the data analyzed by the analysis unit. The necessary information includes, for example, user behavior patterns and causes of errors. The update unit automatically reflects the information extracted by the extraction unit in the manual. Methods for automatically reflecting the information include, for example, the frequency of manual updates and the type of information reflected. As a result, the system according to the embodiment significantly reduces the burden of creating manuals and can always keep them up to date.

[0071] The system further includes a guide unit that provides real-time guidance in response to user operations. The guide unit provides real-time guidance in response to user operations. Methods for providing real-time guidance include, for example, pop-up notifications, audio guidance, and tooltips. By providing real-time guidance in response to user operations, the system aims to eliminate the need for manuals.

[0072] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. The collection unit can also increase the frequency of data collection to collect more detailed data if the user is relaxed. Furthermore, if the user is concentrating, the collection unit can adjust the timing of data collection so as not to interfere with the user's work. In this way, adjusting the timing of data collection according to the user's emotions reduces the user's burden. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The collection unit can analyze the user's past operation history and select the optimal data collection method. For example, the collection unit prioritizes collection of operations that the user has frequently performed in the past. The collection unit can also predict operations that will be performed during a specific time period from the user's past operation history and collect data during that time period. Furthermore, the collection unit can analyze the user's operation history and select the most efficient data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past operation history.

[0074] The collection unit can perform filtering based on the user's current task or area of ​​interest when collecting data. For example, the collection unit collects only data related to the task the user is currently performing. The collection unit can also preferentially collect highly relevant data based on the user's area of ​​interest. Furthermore, the collection unit can filter unnecessary data taking into account the user's current task or area of ​​interest. This makes it possible to collect highly relevant data by filtering data based on the user's current task or area of ​​interest.

[0075] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user is using voice input, the collection unit prioritizes collection of voice data. Also, if the user is using text input, the collection unit can also prioritize collection of text data. Furthermore, if the user is using image input, the collection unit can also prioritize collection of image data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method.

[0076] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting data of high importance. The collection unit can also prioritize collecting detailed data when the user is relaxed. Furthermore, when the user is concentrating, the collection unit can also prioritize collecting data related to work. In this way, by determining the priority of data to be collected according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0077] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also filter highly relevant data based on the user's geographical location information. Furthermore, when the user is moving, the collection unit can also collect optimal data based on the user's current location. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0078] When collecting data, the collection unit can analyze the user's social media activities and collect relevant data. For example, the collection unit collects relevant data based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and prioritize collection of highly relevant data. Furthermore, the collection unit can also collect relevant data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities.

[0079] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can adjust the data collection method based on, for example, feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and select the optimal collection method. Furthermore, the collection unit can also improve the accuracy of data collection by reflecting the user's past feedback. In this way, the optimal collection method can be customized by reflecting the user's past feedback.

[0080] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows the analysis presentation method to be adjusted according to the user's emotions, thereby providing optimal analysis results for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. Furthermore, the analysis unit can also apply a voice recognition algorithm to voice data. In this way, by applying different analysis algorithms depending on the data category, optimal analysis results can be provided.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can also select the optimal analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows the analysis result to be optimally tailored to the user by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also postpone analysis of older data based on the time when the data was collected. Furthermore, the analysis unit can optimally allocate analysis resources taking into account the time when the data was collected. This enables efficient analysis by determining the analysis priority based on the time when the data was collected.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can optimally adjust the order of analysis taking into account the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide the user with analysis results that are optimal for the user.

[0088] The extraction unit can estimate the user's emotions and determine the priority of information to be extracted based on the estimated user emotions. For example, when the user is feeling stressed, the extraction unit can prioritize extracting information of high importance. Furthermore, when the user is relaxed, the extraction unit can also prioritize extracting detailed information. Furthermore, when the user is concentrating, the extraction unit can prioritize extracting information related to work. In this way, by determining the priority of information to be extracted according to the user's emotions, important information can be preferentially extracted. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between data during extraction. For example, the extraction unit analyzes the interrelationships between data and preferentially extracts highly relevant information. The extraction unit can also filter out unnecessary information by taking into account the interrelationships between data. Furthermore, the extraction unit can improve the accuracy of extraction based on the interrelationships between data. In this way, the accuracy of extraction can be improved by taking into account the interrelationships between data.

[0090] The extraction unit can perform extraction while taking into consideration attribute information of the data submitter. For example, if the data submitter is an expert, the extraction unit can preferentially extract that information. In addition, if the data submitter is a general user, the extraction unit can also filter that information. Furthermore, the extraction unit can improve the accuracy of extraction based on the attribute information of the data submitter. In this way, by taking into consideration the attribute information of the data submitter, highly relevant information can be extracted.

[0091] The extraction unit can weight the extraction based on the frequency of data submission during extraction. For example, the extraction unit prioritizes extraction of data with a high submission frequency. The extraction unit can also postpone data with a low submission frequency. Furthermore, the extraction unit can weight the extraction taking into account the frequency of data submission. Thus, by weighting the extraction based on the frequency of data submission, important information can be preferentially extracted.

[0092] The extraction unit can estimate the user's emotions and adjust the display method of the extracted information based on the estimated user emotions. For example, if the user is nervous, the extraction unit can provide a simple, highly visible display method. If the user is relaxed, the extraction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the extraction unit can also provide a display method that focuses on the main points. This allows the optimal information to be provided to the user by adjusting the information display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The extraction unit can perform extraction while taking into consideration the geographical distribution of data. For example, if a user is in a specific area, the extraction unit preferentially extracts data related to that area. The extraction unit can also extract highly relevant information based on the geographical distribution of data. Furthermore, the extraction unit can extract optimal information based on the user's current location. This makes it possible to extract highly relevant information by taking into consideration the geographical distribution of data.

[0094] The extraction unit can improve the accuracy of extraction by referring to literature related to the data during extraction. For example, the extraction unit refers to literature related to the data and preferentially extracts highly relevant information. The extraction unit can also filter out unnecessary information based on literature related to the data. Furthermore, the extraction unit can also improve the accuracy of extraction by referring to literature related to the data. In this way, by referring to literature related to the data, the accuracy of extraction can be improved.

[0095] The extraction unit can perform extraction taking into consideration the market value of the data. For example, the extraction unit preferentially extracts data with a high market value. The extraction unit can also postpone data with a low market value. Furthermore, the extraction unit can weight the extraction taking into consideration the market value of the data. This allows important information to be preferentially extracted by taking into consideration the market value of the data.

[0096] The update unit can estimate the user's emotions and adjust the timing of updates based on the estimated user emotions. For example, when the user is feeling stressed, the update unit reduces the frequency of updates to reduce the burden on the user. Furthermore, when the user is relaxed, the update unit can increase the frequency of updates to provide more detailed information. Furthermore, when the user is concentrating, the update unit can adjust the timing of updates to avoid interfering with the user's work. In this way, the burden on the user is reduced by adjusting the timing of updates according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The update unit can adjust the level of detail of the update based on the importance of the data during the update. For example, the update unit performs a detailed update on data with high importance. The update unit can also perform a simplified update on data with low importance. Furthermore, the update unit can optimally allocate update resources according to the importance of the data. This allows for efficient updates by adjusting the level of detail of the update based on the importance of the data.

[0098] The update unit can apply different update algorithms depending on the category of data when updating. For example, the update unit applies a natural language processing algorithm to text data. The update unit can also apply an image recognition algorithm to image data. Furthermore, the update unit can also apply a voice recognition algorithm to voice data. In this way, by applying different update algorithms depending on the category of data, optimal update results can be provided.

[0099] During an update, the update unit can improve the accuracy of the update by referring to the user's past update results. The update unit, for example, adjusts the update algorithm based on the user's past update results. The update unit can also analyze the user's past update results to improve the accuracy of the update. Furthermore, the update unit can also select the optimal update method by referring to the user's past update results. In this way, the accuracy of the update can be improved by referring to the user's past update results.

[0100] The update unit can estimate the user's emotions and determine the update priority based on the estimated user's emotions. For example, when the user is feeling stressed, the update unit prioritizes updating data with high importance. Furthermore, when the user is relaxed, the update unit can also prioritize updating detailed data. Furthermore, when the user is concentrating, the update unit can also prioritize updating data related to work. In this way, by determining the update priority according to the user's emotions, important data can be updated with priority. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0101] The update unit can determine the update priority based on the time of data collection during updating. For example, the update unit prioritizes updating the most recent data. The update unit can also postpone updating older data based on the time of data collection. Furthermore, the update unit can optimally allocate update resources taking into account the time of data collection. This allows for efficient updates by determining the update priority based on the time of data collection.

[0102] The update unit can adjust the update order based on the relevance of the data when updating. For example, the update unit prioritizes updating highly relevant data. The update unit can also postpone updating less relevant data. Furthermore, the update unit can optimally adjust the update order by taking the relevance of the data into consideration. This allows for efficient updates by adjusting the update order based on the relevance of the data.

[0103] During updating, the update unit can adjust the use of technical terminology in the update according to the user's level of expertise. For example, if the user has technical expertise, the update unit can provide an update result that uses a lot of technical terminology. Also, if the user does not have technical expertise, the update unit can provide an update result in simple language. Furthermore, the update unit can adjust the way the update result is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the update according to the user's level of expertise, it is possible to provide an update result that is optimal for the user.

[0104] The guide unit can estimate the user's emotions and adjust the guide display method based on the estimated user emotions. For example, when the user is nervous, the guide unit provides a simple, highly visible display method. When the user is relaxed, the guide unit can also provide a display method including detailed information. When the user is in a hurry, the guide unit can also provide a display method that focuses on the main points. This allows the guide display method to be adjusted according to the user's emotions, thereby providing the optimal guide for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] When providing guidance, the guide unit can provide the most appropriate guidance by referring to the user's past operation history. The guide unit can provide the most appropriate guidance based on, for example, operations performed by the user in the past. The guide unit can also analyze the user's past operation history and provide the most efficient guidance. Furthermore, the guide unit can select the most appropriate guidance method by referring to the user's past operation history. In this way, the most appropriate guidance can be provided by referring to the user's past operation history.

[0106] When providing a guide, the guide unit can customize the guide content according to the user's current task. For example, the guide unit provides a guide related to the task the user is currently performing. The guide unit can also customize the optimal guide content based on the user's current task. Furthermore, the guide unit can adjust the level of detail of the guide according to the user's current task. This makes it possible to provide the optimal guide by customizing the guide content according to the user's current task.

[0107] The guide unit can improve the guide method by reflecting user feedback when providing guidance. For example, the guide unit adjusts the guide method based on feedback provided by the user. The guide unit can also analyze the user feedback and select the optimal guide method. Furthermore, the guide unit can also improve the accuracy of the guide by reflecting user feedback. In this way, the accuracy of the guide can be improved by reflecting user feedback.

[0108] The guide unit can estimate the user's emotions and determine the priority of guides based on the estimated user emotions. For example, when the user is feeling stressed, the guide unit can prioritize providing highly important guides. Furthermore, when the user is relaxed, the guide unit can also prioritize providing detailed guides. Furthermore, when the user is concentrating, the guide unit can prioritize providing task-related guides. In this way, by determining the priority of guides according to the user's emotions, important guides can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] When providing a guide, the guide unit can provide the most appropriate guide by taking into account the user's geographical location information. For example, if the user is in a specific area, the guide unit can provide a guide related to that area. The guide unit can also provide a highly relevant guide based on the user's geographical location information. Furthermore, the guide unit can also provide the most appropriate guide based on the user's current location. In this way, by taking into account the user's geographical location information, it is possible to provide a highly relevant guide.

[0110] When providing a guide, the guide unit can analyze the user's social media activity and provide a relevant guide. For example, the guide unit can provide a relevant guide based on information shared by the user on social media. The guide unit can also analyze the user's social media activity and provide a highly relevant guide. Furthermore, the guide unit can provide a relevant guide by referring to the activity of the user's friends on social media. In this way, a highly relevant guide can be provided by analyzing the user's social media activity.

[0111] When providing guidance, the guide unit can customize the guidance method by reflecting the user's past feedback. The guide unit adjusts the guidance method based on, for example, feedback provided by the user in the past. The guide unit can also analyze the user's past feedback and select the optimal guidance method. Furthermore, the guide unit can also improve the accuracy of the guidance by reflecting the user's past feedback. In this way, the optimal guidance method can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, extraction unit, update unit, and guide unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects system operation logs and user behavior data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts necessary information based on the analyzed data. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically updates the extracted information in the manual. The guide unit is realized, for example, by the control unit 46A of the smart device 14 and provides real-time guidance in response to user operations. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, extraction unit, update unit, and guide unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects system operation logs and user behavior data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The extraction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, extracts necessary information based on the analyzed data. The update unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically updates the extracted information in the manual. The guide unit, realized, for example, by the control unit 46A of the smart glasses 214, provides real-time guidance in response to user operations. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, extraction unit, update unit, and guide unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects system operation logs and user behavior data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts necessary information based on the analyzed data. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically updates the extracted information in the manual. The guide unit is realized, for example, by the control unit 46A of the headset terminal 314 and provides real-time guidance in response to user operations. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, extraction unit, update unit, and guide unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects system operation logs and user behavior data using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts necessary information based on the analyzed data. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically updates the extracted information in the manual. The guide unit is realized, for example, by the control unit 46A of the robot 414 and provides real-time guidance in response to user operations.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize analysis of important data. Also, if the user is relaxed, it can prioritize analysis of detailed data. Furthermore, if the user is concentrating, it can prioritize analysis of work-related data. In this way, by determining the analysis priority according to the user's emotions, it is possible to prioritize analysis of important data.

[0114] The collection unit can analyze the user's past operation history and select the optimal data collection method. For example, it can prioritize collection of operations that the user frequently performed in the past. The collection unit can also predict operations that will be performed during a specific time period from the user's past operation history and collect data during that time period. Furthermore, the collection unit can analyze the user's operation history and select the most efficient data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past operation history.

[0115] The guide unit can estimate the user's emotions and adjust the display method of the guide based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the guide according to the user's emotions, it is possible to provide the optimal guide for the user.

[0116] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a natural language processing algorithm can be applied to text data. An image recognition algorithm can also be applied to image data. Furthermore, a voice recognition algorithm can be applied to voice data. In this way, by applying different analysis algorithms depending on the data category, optimal analysis results can be provided.

[0117] When collecting data, the collection unit can filter the data based on the user's current task or area of ​​interest. For example, the collection unit collects only data related to the task the user is currently performing. The collection unit can also preferentially collect highly relevant data based on the user's area of ​​interest. Furthermore, the collection unit can filter unnecessary data taking into account the user's current task or area of ​​interest. This makes it possible to collect highly relevant data by filtering data based on the user's current task or area of ​​interest.

[0118] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can also provide detailed analysis results. Furthermore, if the user is in a hurry, it can also provide analysis results that focus on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide the most suitable analysis results for the user.

[0119] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between data during extraction. For example, the extraction unit analyzes the interrelationships between data and preferentially extracts highly relevant information. The extraction unit can also filter out unnecessary information by taking into account the interrelationships between data. Furthermore, the extraction unit can improve the accuracy of extraction based on the interrelationships between data. In this way, the accuracy of extraction can be improved by taking into account the interrelationships between data.

[0120] The update unit can estimate the user's emotions and adjust the timing of updates based on the estimated user emotions. For example, if the user is feeling stressed, the update frequency can be reduced to reduce the burden on the user. Also, if the user is relaxed, the update frequency can be increased to provide more detailed information. Furthermore, if the user is concentrating, the update timing can be adjusted so as not to interfere with the user's work. In this way, the burden on the user can be reduced by adjusting the update timing according to the user's emotions.

[0121] When providing guidance, the guide unit can provide the most appropriate guidance by referring to the user's past operation history. For example, the guide unit can provide the most appropriate guidance based on the operations performed by the user in the past. The guide unit can also analyze the user's past operation history and provide the most efficient guidance. Furthermore, the guide unit can select the most appropriate guidance method by referring to the user's past operation history. In this way, the guide unit can provide the most appropriate guidance by referring to the user's past operation history.

[0122] The extraction unit can estimate the user's emotions and determine the priority of information to be extracted based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize extraction of information of high importance. Also, if the user is relaxed, it can prioritize extraction of detailed information. Furthermore, if the user is concentrating, it can prioritize extraction of information related to work. In this way, by determining the priority of information to be extracted according to the user's emotions, it is possible to preferentially extract important information.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The collection unit collects system operation logs or user behavior data. The system operation logs include, for example, click history, input history, and error logs, and the user behavior data includes, for example, page browsing history, scrolling behavior, and stay time. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms. Step 3: The extraction unit extracts necessary information based on the data analyzed by the analysis unit. The necessary information includes, for example, the user's behavioral patterns and the cause of the error. Step 4: The update unit automatically updates the manual with the information extracted by the extraction unit. The method of automatically updating the manual may include, for example, the frequency of updating the manual and the type of information to be updated.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] 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.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] 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.

[0153] 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.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] 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.

[0165] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0168] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0169] 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.

[0170] 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.

[0171] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0172] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0173] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0175] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0180] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0181] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0182] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0185] 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.

[0186] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0187] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0188] 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.

[0189] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0190] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0191] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0193] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0194] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0195] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0196] [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects system operation logs or user behavior data; an analysis unit that analyzes the data collected by the collection unit; an extraction unit that extracts necessary information based on the data analyzed by the analysis unit; an update unit that automatically reflects the information extracted by the extraction unit in the manual. A system characterized by:

2. A guide unit is provided that provides real-time guidance in response to user operations.

2. The system of claim 1.

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

4. The collecting unit Analyze users' past operation history and select the optimal data collection method 2. The system of claim 1.

5. The collecting unit Filtering data collection based on the user's current task or area of ​​interest 2. The system of claim 1.

6. The collecting unit When collecting data, select the optimal collection method depending on the user's input method 2. The system of claim 1.

7. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit When collecting data, consider the user's geographic location to prioritize collecting the most relevant data.

2. The system of claim 1.

9. The collecting unit When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1.

Images