System

Abstract

An object of the system according to the embodiment is to automatically generate a manual based on the specifications and operation method of a new device and to improve the accuracy by reflecting the feedback of the user.SOLUTION: A system includes a generation unit, a collection unit, and an analysis unit. The generation unit analyzes the specification or the operation method of the new device and generates a manual based on the analysis. The collection unit collects feedback from a user. The analysis unit analyzes the information collected by the collection unit to improve the accuracy of the generated AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, creating manuals that correspond to the specifications and operation methods of new equipment takes time and effort, and there is a problem in that it is difficult to make improvements that reflect user feedback.

[0005] The system according to the embodiment aims to automatically generate a manual based on the specifications and operation methods of a new device, and to improve accuracy by reflecting user feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a collection unit, and an analysis unit. The generation unit analyzes the specifications or operation methods of new equipment and generates a manual based on the analysis. The collection unit collects feedback from users. The analysis unit analyzes the data collected by the collection unit to improve the accuracy of the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a manual based on the specifications and operation methods of a new device, and can improve its accuracy by reflecting user feedback. [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 manual generation system according to an embodiment of the present invention analyzes the specifications and operation methods of new devices and generates manuals based on the analysis. This system uses a generation AI to automatically generate manuals covering everything from operation to troubleshooting for various network devices. For example, each time a network device is updated, the generation AI automatically updates the manual. Next, usage data and user feedback from the AI ​​Dynamic Manual are collected to continuously improve the accuracy of the generation AI. This enables customization to meet user needs. For example, the generation AI analyzes the specifications and operation methods of new devices and generates manuals based on the analysis. For example, when a new router is released, a manual is automatically generated that includes instructions for configuring and troubleshooting the router. Next, usage data and user feedback from the AI ​​Dynamic Manual are collected. For example, data is collected on how users use the manual and which parts are difficult to understand. This data is input into the generation AI and used to improve the manual. Furthermore, the accuracy of the generation AI is continuously improved based on the collected data and feedback. For example, improvements are made based on user feedback, such as providing more detailed explanations of specific operation methods. This enables customization to meet user needs. This allows the manual generation system to automatically generate the latest manuals every time the network equipment being sold is updated. Furthermore, by utilizing user feedback, the accuracy of the manuals can be continuously improved, allowing for customization to meet user needs. For example, if the setup method for a new device is complicated, providing a more detailed explanation of that part will make it easier for users to understand.

[0029] A manual generation system according to an embodiment includes a generation unit, a collection unit, and an analysis unit. The generation unit analyzes the specifications or operation methods of a new device and generates a manual based on the specifications or operation methods. For example, the generation unit uses a generation AI to analyze the specifications of the new device and generates a manual based on the results. The generation unit can also analyze the operation methods of the new device and generate a manual based on the results. The generation unit can also analyze troubleshooting methods for the new device and generate a manual based on the results. For example, the generation unit analyzes the configuration method of a new router and generates a configuration manual based on the results. The generation unit can also analyze the operation methods of the new router and generate an operation manual based on the results. The generation unit can also analyze troubleshooting methods for the new router and generate a troubleshooting manual based on the results. The collection unit collects feedback from users. For example, the collection unit collects usage data for the AI ​​Dynamic Manual. The collection unit can also collect user feedback. The collection unit can also collect user usage logs. For example, the collection unit collects information on how users use the manual. The collection unit can also collect which parts of the manual are difficult for users to understand. The collection unit can also collect which parts of the manual are frequently referred to by users. The analysis unit analyzes the data collected by the collection unit and improves the accuracy of the generation AI. For example, the analysis unit analyzes collected usage data and improves the accuracy of the generation AI. The analysis unit can also analyze collected feedback and improve the accuracy of the generation AI. The analysis unit can also analyze collected usage logs and improve the accuracy of the generation AI. For example, the analysis unit can identify parts that users find difficult to understand and provide more detailed explanations of those parts. The analysis unit can also identify parts that users frequently refer to and improve the explanations of those parts. The analysis unit can also update the learning data of the generation AI based on the user's usage logs.As a result, the manual generation system according to the embodiment can automatically generate manuals based on the specifications and operation methods of new equipment, and by collecting and analyzing feedback from users, the accuracy of the generation AI can be continuously improved.

[0030] The generation unit provides the generated manual to the user. For example, the generation unit provides the generated manual online. The generation unit can also provide the generated manual as a printed document. The generation unit can also display the generated manual within an app. For example, the generation unit provides the generated manual on a website. The generation unit can also make the generated manual downloadable in PDF format. The generation unit can also display the generated manual within a mobile app. In this way, by providing the generated manual to the user, the user can use the latest information.

[0031] The analysis unit provides a detailed explanation of a specific operation method based on the feedback. The analysis unit, for example, analyzes feedback from a user and provides a detailed explanation of a specific operation method. The analysis unit can also analyze collected usage data and provide a detailed explanation of a specific operation method. Furthermore, the analysis unit can analyze collected usage logs and provide a detailed explanation of a specific operation method. For example, the analysis unit identifies an operation method that a user finds difficult to understand and provides a detailed explanation of that operation method. The analysis unit can also identify an operation method that a user frequently asks about and provide a detailed explanation of that operation method. Furthermore, the analysis unit can provide a detailed explanation of a specific operation method based on the user's usage log. In this way, providing a detailed explanation of a specific operation method based on feedback deepens the user's understanding.

[0032] When analyzing the specifications and operation methods of a new device, the generation unit improves the accuracy of the analysis by referring to data on similar devices from the past. For example, the generation unit analyzes the specifications of the new device by referring to data on devices from the same manufacturer that were released in the past. The generation unit can also analyze operation methods by referring to data on other devices with similar functions. Furthermore, the generation unit can analyze troubleshooting methods for the new device by referring to past troubleshooting data. For example, the generation unit analyzes the specifications of a new router by referring to data on routers from the same manufacturer that were released in the past. The generation unit can also analyze operation methods for the new router by referring to data on routers from other manufacturers that have similar functions. Furthermore, the generation unit can analyze troubleshooting methods for the new router by referring to past troubleshooting data. In this way, the analysis accuracy is improved by referring to data on similar devices from the past.

[0033] The generation unit customizes the generated manual according to the user's skill level. For example, the generation unit generates a manual for beginners that mainly explains basic operation methods. The generation unit can also generate a manual for intermediate users that includes advanced operation methods and troubleshooting. The generation unit can also generate a manual for advanced users that includes detailed technical information and advanced setting methods. For example, the generation unit generates a manual for beginners that mainly explains basic operation methods. The generation unit can also generate a manual for intermediate users that includes advanced operation methods and troubleshooting. The generation unit can also generate a manual for advanced users that includes detailed technical information and advanced setting methods. This makes it possible to provide a more appropriate manual by customizing according to the user's skill level.

[0034] The generation unit adds specific examples according to the user's usage environment to the generated manual. The generation unit generates a manual including specific examples tailored to, for example, a home usage environment. The generation unit can also generate a manual including specific examples tailored to a business usage environment. The generation unit can also generate a manual including specific examples tailored to a usage environment for educational institutions. For example, the generation unit generates a manual including specific examples tailored to a home usage environment. The generation unit can also generate a manual including specific examples tailored to a business usage environment. The generation unit can also generate a manual including specific examples tailored to a usage environment for educational institutions. In this way, by adding specific examples tailored to the user's usage environment, the practicality of the manual is improved.

[0035] When analyzing the specifications and operation methods of a new device, the generation unit improves the accuracy of the analysis by referring to data on similar devices from other manufacturers. For example, the generation unit analyzes the specifications of the new device by referring to data on devices with similar functions from other manufacturers. The generation unit can also analyze troubleshooting methods for the new device by referring to troubleshooting data from other manufacturers. Furthermore, the generation unit can analyze operating methods for the new device by referring to operating manuals from other manufacturers. For example, the generation unit analyzes the specifications of a new router by referring to data on routers with similar functions from other manufacturers. The generation unit can also analyze troubleshooting methods for the new router by referring to troubleshooting data from other manufacturers. Furthermore, the generation unit can analyze operating methods for the new router by referring to operating manuals from other manufacturers. In this way, by referring to data on similar devices from other manufacturers, the accuracy of the analysis is improved.

[0036] The generation unit provides multilingual support for the generated manual in accordance with the user's language settings. The generation unit, for example, automatically translates the manual based on the language settings of the user's device. The generation unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the generation unit can provide the manual in that language. For example, the generation unit automatically translates the manual based on the language settings of the user's device. The generation unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the generation unit can provide the manual in that language. This allows for multilingual support in accordance with the user's language settings, making it possible to accommodate a larger number of users.

[0037] The generation unit continuously updates the content of the generated manual by reflecting user feedback. For example, the generation unit provides detailed explanations of specific operation methods based on user feedback. The generation unit can also improve the layout of the manual based on user feedback. The generation unit can also add new troubleshooting methods based on user feedback. For example, the generation unit provides detailed explanations of specific operation methods based on user feedback. The generation unit can also improve the layout of the manual based on user feedback. The generation unit can also add new troubleshooting methods based on user feedback. In this way, by continuously updating the content by reflecting user feedback, the accuracy of the manual is improved.

[0038] When collecting feedback, the collection unit improves the accuracy of the collection by referring to the user's past usage history. For example, the collection unit presents related questions based on feedback provided by the user in the past. The collection unit can also preferentially collect feedback on specific functions from the user's past usage history. Furthermore, the collection unit can analyze the user's past usage history and suggest an optimal feedback collection method. For example, the collection unit presents related questions based on feedback provided by the user in the past. The collection unit can also preferentially collect feedback on specific functions from the user's past usage history. Furthermore, the collection unit can analyze the user's past usage history and suggest an optimal feedback collection method. In this way, by referring to the user's past usage history, the accuracy of feedback collection is improved.

[0039] The collection unit selects an optimal collection means based on the user's device information when collecting feedback. For example, if the user is using a smartphone, the collection unit provides a mobile-friendly feedback form. Furthermore, if the user is using a tablet, the collection unit can provide a feedback form optimized for a large screen. Furthermore, if the user is using a desktop, the collection unit can provide a detailed feedback form. For example, if the user is using a smartphone, the collection unit provides a mobile-friendly feedback form. Furthermore, if the user is using a tablet, the collection unit can provide a feedback form optimized for a large screen. Furthermore, if the user is using a desktop, the collection unit can provide a detailed feedback form. In this way, the optimal feedback collection means can be provided by taking the user's device information into consideration.

[0040] When collecting feedback, the collection unit prioritizes collecting highly relevant feedback 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 feedback related to the area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting feedback related to the travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting feedback related to home use. For example, when the user is in a specific area, the collection unit prioritizes collecting feedback related to the area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting feedback related to the travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting feedback related to home use. In this way, highly relevant feedback can be collected preferentially by taking into account the user's geographical location information.

[0041] When collecting feedback, the collection unit analyzes the user's social media activities and collects related feedback. For example, the collection unit collects feedback regarding issues mentioned by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect related feedback. Furthermore, the collection unit can also collect related feedback by referring to the activities of the user's friends on social media. For example, the collection unit collects feedback regarding issues mentioned by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect related feedback. Furthermore, the collection unit can also collect related feedback by referring to the activities of the user's friends on social media. In this way, it is possible to collect related feedback by analyzing the user's social media activities.

[0042] When analyzing collected data, the analysis unit refers to past feedback data to improve the accuracy of the analysis. The analysis unit, for example, analyzes current data based on past feedback data. The analysis unit can also extract specific patterns from the past feedback data and use them in the analysis. Furthermore, the analysis unit can refer to past feedback data to improve the accuracy of the analysis results. For example, the analysis unit analyzes current data based on past feedback data. The analysis unit can also extract specific patterns from the past feedback data and use them in the analysis. Furthermore, the analysis unit can refer to past feedback data to improve the accuracy of the analysis results. In this way, by referring to past feedback data, the analysis accuracy is improved.

[0043] The analysis unit improves the algorithm of the generation AI based on the analysis results. For example, the analysis unit adjusts the algorithm of the generation AI based on the analysis results, thereby improving accuracy. The analysis unit can also update the learning data of the generation AI based on data obtained from the analysis results. Furthermore, the analysis unit can optimize the parameters of the generation AI based on the analysis results. For example, the analysis unit adjusts the algorithm of the generation AI based on the analysis results, thereby improving accuracy. The analysis unit can also update the learning data of the generation AI based on data obtained from the analysis results. Furthermore, the analysis unit can optimize the parameters of the generation AI based on the analysis results. In this way, by optimizing the algorithm of the generation AI based on the analysis results, accuracy is improved.

[0044] The analysis unit provides a detailed explanation of a specific operation method based on the analysis result. For example, the analysis unit identifies an operation method that is difficult for the user to understand from the analysis result and provides a detailed explanation of the operation method. The analysis unit can also add supplemental information about the specific operation method based on the analysis result. Furthermore, the analysis unit can identify an operation method that is frequently asked about by the analysis result and provide a detailed explanation of the operation method. For example, the analysis unit identifies an operation method that is difficult for the user to understand from the analysis result and provides a detailed explanation of the operation method. The analysis unit can also add supplemental information about the specific operation method based on the analysis result. Furthermore, the analysis unit can identify an operation method that is frequently asked about by the analysis result and provide a detailed explanation of the operation method. In this way, by providing a detailed explanation of the specific operation method based on the analysis result, the user's understanding is deepened.

[0045] When analyzing collected data, the analysis unit integrates information from other data sources to improve the accuracy of the analysis. For example, the analysis unit integrates information from other data sources to improve the accuracy of the analysis. The analysis unit can also refer to information from other data sources to improve the accuracy of the analysis results. Furthermore, the analysis unit can complement the analysis results based on information from other data sources. For example, the analysis unit integrates information from other data sources to improve the accuracy of the analysis. The analysis unit can also refer to information from other data sources to improve the accuracy of the analysis results. Furthermore, the analysis unit can complement the analysis results based on information from other data sources. In this way, the analysis accuracy is improved by integrating information from other data sources.

[0046] The analysis unit updates the learning data of the generative AI based on the analysis results. The analysis unit, for example, adds and updates the learning data of the generative AI based on the analysis results. The analysis unit can also optimize the learning data of the generative AI based on new knowledge obtained from the analysis results. The analysis unit can also expand the learning dataset of the generative AI based on the analysis results. For example, the analysis unit adds and updates the learning data of the generative AI based on the analysis results. The analysis unit can also optimize the learning data of the generative AI based on new knowledge obtained from the analysis results. The analysis unit can also expand the learning dataset of the generative AI based on the analysis results. In this way, by updating the learning data of the generative AI based on the analysis results, the accuracy of the generative AI is improved.

[0047] The analysis unit performs customization according to the user's needs based on the analysis results. The analysis unit, for example, provides a manual customized according to the user's needs based on the analysis results. The analysis unit can also generate a manual that emphasizes specific functions or operation methods based on the user's needs obtained from the analysis results. The analysis unit can also perform customization according to the user's usage environment based on the analysis results. For example, the analysis unit provides a manual customized according to the user's needs based on the analysis results. The analysis unit can also generate a manual that emphasizes specific functions or operation methods based on the user's needs obtained from the analysis results. The analysis unit can also perform customization according to the user's usage environment based on the analysis results. In this way, a more appropriate manual can be provided by customizing according to the user's needs based on the analysis results.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The generator can also customize the generated manual by reflecting past user feedback. For example, if there has been a lot of feedback in the past about a specific operation method, that part can be explained in more detail. Specific troubleshooting methods can also be added based on past feedback. Furthermore, the layout and structure of the manual can be improved based on past feedback. This makes it possible to provide a more user-friendly manual by reflecting past user feedback.

[0050] The generator can also emphasize important information in the generated manual based on the frequency of use by the user. For example, it can highlight the operation methods that the user frequently refers to. It can also prominently display troubleshooting methods that the user frequently uses. It can also identify parts where the user spends a lot of time and explain those parts in more detail. Thus, by emphasizing important information based on the frequency of use by the user, the practicality of the manual is improved.

[0051] The generation unit can also provide the generated manual with an optimal display format according to the characteristics of the user's device. For example, a mobile-friendly display format can be provided for a user using a smartphone. A display format optimized for a large screen can be provided for a user using a tablet. Furthermore, a display format including detailed information can be provided for a user using a desktop. This improves the usability of the manual by providing an optimal display format according to the characteristics of the user's device.

[0052] The generator can also customize the generated manual according to the user's learning style. For example, a manual containing many illustrations and videos can be provided to a visual learner. A manual including audio guides can also be provided to an auditory learner. Furthermore, a step-by-step practical guide can be provided to a practical learner. In this way, the effectiveness of the manual can be improved by customizing it according to the user's learning style.

[0053] The generator can also customize the generated manual to reflect the user's cultural background. For example, the generator can provide a manual that reflects common practices and terminology in a particular culture. The generator can also provide a manual that includes culturally appropriate examples and scenarios. Furthermore, the generator can use a presentation method that takes cultural sensitivity into account. This customization to reflect the user's cultural background can facilitate understanding of the manual.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The generator analyzes the specifications and operation methods of the new device and generates a manual based on them. The generator uses generative AI to analyze the specifications, operation methods, and troubleshooting methods of the new device and generates a manual based on the results. For example, it analyzes how to set up, operate, and troubleshoot a new router and generates the respective manuals. Step 2: The collection unit collects user feedback. The collection unit collects usage data and user usage logs for the AI ​​Dynamic Manual to gather information on how users use the manual, which parts are difficult to understand, and which parts they frequently refer to. Step 3: The analysis unit analyzes the data collected by the collection unit to improve the accuracy of the generation AI. The analysis unit analyzes the collected usage data, feedback, and usage logs to identify parts that users find difficult to understand or frequently refer to, and provides more detailed explanations or improves those parts. The analysis unit also updates the learning data of the generation AI based on the user's usage logs.

[0056] (Example 2) A manual generation system according to an embodiment of the present invention analyzes the specifications and operation methods of new devices and generates manuals based on the analysis. This system uses a generation AI to automatically generate manuals covering everything from operation to troubleshooting for various network devices. For example, each time a network device is updated, the generation AI automatically updates the manual. Next, usage data and user feedback from the AI ​​Dynamic Manual are collected to continuously improve the accuracy of the generation AI. This enables customization to meet user needs. For example, the generation AI analyzes the specifications and operation methods of new devices and generates manuals based on the analysis. For example, when a new router is released, a manual is automatically generated that includes instructions for configuring and troubleshooting the router. Next, usage data and user feedback from the AI ​​Dynamic Manual are collected. For example, data is collected on how users use the manual and which parts are difficult to understand. This data is input into the generation AI and used to improve the manual. Furthermore, the accuracy of the generation AI is continuously improved based on the collected data and feedback. For example, improvements are made based on user feedback, such as providing more detailed explanations of specific operation methods. This enables customization to meet user needs. This allows the manual generation system to automatically generate the latest manuals every time the network equipment being sold is updated. Furthermore, by utilizing user feedback, the accuracy of the manuals can be continuously improved, allowing for customization to meet user needs. For example, if the setup method for a new device is complicated, providing a more detailed explanation of that part will make it easier for users to understand.

[0057] A manual generation system according to an embodiment includes a generation unit, a collection unit, and an analysis unit. The generation unit analyzes the specifications or operation methods of a new device and generates a manual based on the specifications or operation methods. For example, the generation unit uses a generation AI to analyze the specifications of the new device and generates a manual based on the results. The generation unit can also analyze the operation methods of the new device and generate a manual based on the results. The generation unit can also analyze troubleshooting methods for the new device and generate a manual based on the results. For example, the generation unit analyzes the configuration method of a new router and generates a configuration manual based on the results. The generation unit can also analyze the operation methods of the new router and generate an operation manual based on the results. The generation unit can also analyze troubleshooting methods for the new router and generate a troubleshooting manual based on the results. The collection unit collects feedback from users. For example, the collection unit collects usage data for the AI ​​Dynamic Manual. The collection unit can also collect user feedback. The collection unit can also collect user usage logs. For example, the collection unit collects information on how users use the manual. The collection unit can also collect which parts of the manual are difficult for users to understand. The collection unit can also collect which parts of the manual are frequently referred to by users. The analysis unit analyzes the data collected by the collection unit and improves the accuracy of the generation AI. For example, the analysis unit analyzes collected usage data and improves the accuracy of the generation AI. The analysis unit can also analyze collected feedback and improve the accuracy of the generation AI. The analysis unit can also analyze collected usage logs and improve the accuracy of the generation AI. For example, the analysis unit can identify parts that users find difficult to understand and provide more detailed explanations of those parts. The analysis unit can also identify parts that users frequently refer to and improve the explanations of those parts. The analysis unit can also update the learning data of the generation AI based on the user's usage logs.As a result, the manual generation system according to the embodiment can automatically generate manuals based on the specifications and operation methods of new equipment, and by collecting and analyzing feedback from users, the accuracy of the generation AI can be continuously improved.

[0058] The generation unit provides the generated manual to the user. For example, the generation unit provides the generated manual online. The generation unit can also provide the generated manual as a printed document. The generation unit can also display the generated manual within an app. For example, the generation unit provides the generated manual on a website. The generation unit can also make the generated manual downloadable in PDF format. The generation unit can also display the generated manual within a mobile app. In this way, by providing the generated manual to the user, the user can use the latest information.

[0059] The analysis unit provides a detailed explanation of a specific operation method based on the feedback. The analysis unit, for example, analyzes feedback from a user and provides a detailed explanation of a specific operation method. The analysis unit can also analyze collected usage data and provide a detailed explanation of a specific operation method. Furthermore, the analysis unit can analyze collected usage logs and provide a detailed explanation of a specific operation method. For example, the analysis unit identifies an operation method that a user finds difficult to understand and provides a detailed explanation of that operation method. The analysis unit can also identify an operation method that a user frequently asks about and provide a detailed explanation of that operation method. Furthermore, the analysis unit can provide a detailed explanation of a specific operation method based on the user's usage log. In this way, providing a detailed explanation of a specific operation method based on feedback deepens the user's understanding.

[0060] The generation unit estimates the user's emotions and adjusts the way the manual is presented based on the estimated user emotions. For example, the generation unit estimates the user's emotions and adjusts the way the manual is presented based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit generates a manual using concise and intuitive expressions. Furthermore, if the user is relaxed, the generation unit can generate a manual that includes detailed explanations and supplementary information. Furthermore, if the user is in a hurry, the generation unit can generate a short manual that focuses on the main points. For example, the generation unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the manual to be presented in an expression style that corresponds to the user's emotions, thereby facilitating the user's understanding. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0061] When analyzing the specifications and operation methods of a new device, the generation unit improves the accuracy of the analysis by referring to data on similar devices from the past. For example, the generation unit analyzes the specifications of the new device by referring to data on devices from the same manufacturer that were released in the past. The generation unit can also analyze operation methods by referring to data on other devices with similar functions. Furthermore, the generation unit can analyze troubleshooting methods for the new device by referring to past troubleshooting data. For example, the generation unit analyzes the specifications of a new router by referring to data on routers from the same manufacturer that were released in the past. The generation unit can also analyze operation methods for the new router by referring to data on routers from other manufacturers that have similar functions. Furthermore, the generation unit can analyze troubleshooting methods for the new router by referring to past troubleshooting data. In this way, the analysis accuracy is improved by referring to data on similar devices from the past.

[0062] The generation unit customizes the generated manual according to the user's skill level. For example, the generation unit generates a manual for beginners that mainly explains basic operation methods. The generation unit can also generate a manual for intermediate users that includes advanced operation methods and troubleshooting. The generation unit can also generate a manual for advanced users that includes detailed technical information and advanced setting methods. For example, the generation unit generates a manual for beginners that mainly explains basic operation methods. The generation unit can also generate a manual for intermediate users that includes advanced operation methods and troubleshooting. The generation unit can also generate a manual for advanced users that includes detailed technical information and advanced setting methods. This makes it possible to provide a more appropriate manual by customizing according to the user's skill level.

[0063] The generation unit adds specific examples according to the user's usage environment to the generated manual. The generation unit generates a manual including specific examples tailored to, for example, a home usage environment. The generation unit can also generate a manual including specific examples tailored to a business usage environment. The generation unit can also generate a manual including specific examples tailored to a usage environment for educational institutions. For example, the generation unit generates a manual including specific examples tailored to a home usage environment. The generation unit can also generate a manual including specific examples tailored to a business usage environment. The generation unit can also generate a manual including specific examples tailored to a usage environment for educational institutions. In this way, by adding specific examples tailored to the user's usage environment, the practicality of the manual is improved.

[0064] The generation unit estimates the user's emotions and adjusts the length of the manual based on the estimated user emotions. For example, the generation unit estimates the user's emotions and adjusts the length of the manual based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit generates a short, concise manual that covers the main points. Furthermore, if the user is relaxed, the generation unit can generate a longer manual with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate a concise, quickly understandable manual. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This promotes user understanding by providing the length of the manual according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0065] When analyzing the specifications and operation methods of a new device, the generation unit improves the accuracy of the analysis by referring to data on similar devices from other manufacturers. For example, the generation unit analyzes the specifications of the new device by referring to data on devices with similar functions from other manufacturers. The generation unit can also analyze troubleshooting methods for the new device by referring to troubleshooting data from other manufacturers. Furthermore, the generation unit can analyze operating methods for the new device by referring to operating manuals from other manufacturers. For example, the generation unit analyzes the specifications of a new router by referring to data on routers with similar functions from other manufacturers. The generation unit can also analyze troubleshooting methods for the new router by referring to troubleshooting data from other manufacturers. Furthermore, the generation unit can analyze operating methods for the new router by referring to operating manuals from other manufacturers. In this way, by referring to data on similar devices from other manufacturers, the accuracy of the analysis is improved.

[0066] The generation unit provides multilingual support for the generated manual in accordance with the user's language settings. The generation unit, for example, automatically translates the manual based on the language settings of the user's device. The generation unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the generation unit can provide the manual in that language. For example, the generation unit automatically translates the manual based on the language settings of the user's device. The generation unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the generation unit can provide the manual in that language. This allows for multilingual support in accordance with the user's language settings, making it possible to accommodate a larger number of users.

[0067] The generation unit continuously updates the content of the generated manual by reflecting user feedback. For example, the generation unit provides detailed explanations of specific operation methods based on user feedback. The generation unit can also improve the layout of the manual based on user feedback. The generation unit can also add new troubleshooting methods based on user feedback. For example, the generation unit provides detailed explanations of specific operation methods based on user feedback. The generation unit can also improve the layout of the manual based on user feedback. The generation unit can also add new troubleshooting methods based on user feedback. In this way, by continuously updating the content by reflecting user feedback, the accuracy of the manual is improved.

[0068] The collection unit estimates the user's emotions and adjusts the feedback collection method based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the feedback collection method based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects feedback in the form of a simple questionnaire. Furthermore, if the user is relaxed, the collection unit can also provide a detailed feedback form. Furthermore, if the user is in a hurry, the collection unit can collect feedback through voice input. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Alternatively, the collection unit can record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. This allows for more appropriate feedback collection by providing a feedback collection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0069] When collecting feedback, the collection unit improves the accuracy of the collection by referring to the user's past usage history. For example, the collection unit presents related questions based on feedback provided by the user in the past. The collection unit can also preferentially collect feedback on specific functions from the user's past usage history. Furthermore, the collection unit can analyze the user's past usage history and suggest an optimal feedback collection method. For example, the collection unit presents related questions based on feedback provided by the user in the past. The collection unit can also preferentially collect feedback on specific functions from the user's past usage history. Furthermore, the collection unit can analyze the user's past usage history and suggest an optimal feedback collection method. In this way, by referring to the user's past usage history, the accuracy of feedback collection is improved.

[0070] The collection unit selects an optimal collection means based on the user's device information when collecting feedback. For example, if the user is using a smartphone, the collection unit provides a mobile-friendly feedback form. Furthermore, if the user is using a tablet, the collection unit can provide a feedback form optimized for a large screen. Furthermore, if the user is using a desktop, the collection unit can provide a detailed feedback form. For example, if the user is using a smartphone, the collection unit provides a mobile-friendly feedback form. Furthermore, if the user is using a tablet, the collection unit can provide a feedback form optimized for a large screen. Furthermore, if the user is using a desktop, the collection unit can provide a detailed feedback form. In this way, the optimal feedback collection means can be provided by taking the user's device information into consideration.

[0071] The collection unit estimates the user's emotions and determines the priority of the feedback to be collected based on the estimated user emotions. For example, the collection unit estimates the user's emotions and determines the priority of the feedback to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting important feedback. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed feedback. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting concise feedback. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. In this way, by prioritizing the feedback according to the user's emotions, important feedback can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0072] When collecting feedback, the collection unit prioritizes collecting highly relevant feedback 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 feedback related to the area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting feedback related to the travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting feedback related to home use. For example, when the user is in a specific area, the collection unit prioritizes collecting feedback related to the area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting feedback related to the travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting feedback related to home use. In this way, highly relevant feedback can be collected preferentially by taking into account the user's geographical location information.

[0073] When collecting feedback, the collection unit analyzes the user's social media activities and collects related feedback. For example, the collection unit collects feedback regarding issues mentioned by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect related feedback. Furthermore, the collection unit can also collect related feedback by referring to the activities of the user's friends on social media. For example, the collection unit collects feedback regarding issues mentioned by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect related feedback. Furthermore, the collection unit can also collect related feedback by referring to the activities of the user's friends on social media. In this way, it is possible to collect related feedback by analyzing the user's social media activities.

[0074] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a concise and highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This facilitates understanding of the analysis results by providing a display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0075] When analyzing collected data, the analysis unit refers to past feedback data to improve the accuracy of the analysis. The analysis unit, for example, analyzes current data based on past feedback data. The analysis unit can also extract specific patterns from the past feedback data and use them in the analysis. Furthermore, the analysis unit can refer to past feedback data to improve the accuracy of the analysis results. For example, the analysis unit analyzes current data based on past feedback data. The analysis unit can also extract specific patterns from the past feedback data and use them in the analysis. Furthermore, the analysis unit can refer to past feedback data to improve the accuracy of the analysis results. In this way, by referring to past feedback data, the analysis accuracy is improved.

[0076] The analysis unit improves the algorithm of the generation AI based on the analysis results. For example, the analysis unit adjusts the algorithm of the generation AI based on the analysis results, thereby improving accuracy. The analysis unit can also update the learning data of the generation AI based on data obtained from the analysis results. Furthermore, the analysis unit can optimize the parameters of the generation AI based on the analysis results. For example, the analysis unit adjusts the algorithm of the generation AI based on the analysis results, thereby improving accuracy. The analysis unit can also update the learning data of the generation AI based on data obtained from the analysis results. Furthermore, the analysis unit can optimize the parameters of the generation AI based on the analysis results. In this way, by optimizing the algorithm of the generation AI based on the analysis results, accuracy is improved.

[0077] The analysis unit provides a detailed explanation of a specific operation method based on the analysis result. For example, the analysis unit identifies an operation method that is difficult for the user to understand from the analysis result and provides a detailed explanation of the operation method. The analysis unit can also add supplemental information about the specific operation method based on the analysis result. Furthermore, the analysis unit can identify an operation method that is frequently asked about by the analysis result and provide a detailed explanation of the operation method. For example, the analysis unit identifies an operation method that is difficult for the user to understand from the analysis result and provides a detailed explanation of the operation method. The analysis unit can also add supplemental information about the specific operation method based on the analysis result. Furthermore, the analysis unit can identify an operation method that is frequently asked about by the analysis result and provide a detailed explanation of the operation method. In this way, by providing a detailed explanation of the specific operation method based on the analysis result, the user's understanding is deepened.

[0078] The analysis unit estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit prioritizes displaying important analysis results. The analysis unit can also prioritize displaying detailed analysis results when the user is relaxed. Furthermore, the analysis unit can prioritize displaying concise analysis results when the user is in a hurry. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the system to prioritize analysis results according to the user's emotions, thereby providing important information preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] When analyzing collected data, the analysis unit integrates information from other data sources to improve the accuracy of the analysis. For example, the analysis unit integrates information from other data sources to improve the accuracy of the analysis. The analysis unit can also refer to information from other data sources to improve the accuracy of the analysis results. Furthermore, the analysis unit can complement the analysis results based on information from other data sources. For example, the analysis unit integrates information from other data sources to improve the accuracy of the analysis. The analysis unit can also refer to information from other data sources to improve the accuracy of the analysis results. Furthermore, the analysis unit can complement the analysis results based on information from other data sources. In this way, the analysis accuracy is improved by integrating information from other data sources.

[0080] The analysis unit updates the learning data of the generative AI based on the analysis results. The analysis unit, for example, adds and updates the learning data of the generative AI based on the analysis results. The analysis unit can also optimize the learning data of the generative AI based on new knowledge obtained from the analysis results. The analysis unit can also expand the learning dataset of the generative AI based on the analysis results. For example, the analysis unit adds and updates the learning data of the generative AI based on the analysis results. The analysis unit can also optimize the learning data of the generative AI based on new knowledge obtained from the analysis results. The analysis unit can also expand the learning dataset of the generative AI based on the analysis results. In this way, by updating the learning data of the generative AI based on the analysis results, the accuracy of the generative AI is improved.

[0081] The analysis unit performs customization according to the user's needs based on the analysis results. The analysis unit, for example, provides a manual customized according to the user's needs based on the analysis results. The analysis unit can also generate a manual that emphasizes specific functions or operation methods based on the user's needs obtained from the analysis results. The analysis unit can also perform customization according to the user's usage environment based on the analysis results. For example, the analysis unit provides a manual customized according to the user's needs based on the analysis results. The analysis unit can also generate a manual that emphasizes specific functions or operation methods based on the user's needs obtained from the analysis results. The analysis unit can also perform customization according to the user's usage environment based on the analysis results. In this way, a more appropriate manual can be provided by customizing according to the user's needs based on the analysis results. === Hard Collateral 1-1 === For example, each of the multiple elements including the generation unit, collection unit, and analysis unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the specifications and operation methods of new equipment and generates a manual based on the analysis. The collection unit is realized by the control unit 46A of the smart device 14, and collects feedback from users. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to improve the accuracy of the generation AI. === Hard Collateral 1-2 === For example, each of the multiple elements including the generation unit, collection unit, and analysis unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the specifications and operation methods of new devices and generates manuals based on the analysis. The collection unit is realized by the control unit 46A of the smart glasses 214, and collects feedback from users. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to improve the accuracy of the generation AI. === Hard Collateral 1-3 === For example, each of the multiple elements including the generation unit, collection unit, and analysis unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the specifications and operation methods of new equipment, and generates a manual based on the analysis. The collection unit is realized by the control unit 46A of the headset type terminal 314, and collects feedback from users. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and improves the accuracy of the generation AI. === Hard Collateral 1-4 === For example, each of the multiple elements including the generation unit, collection unit, and analysis unit is realized by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the specifications and operation methods of new equipment and generates a manual based on the analysis. The collection unit is realized by the control unit 46A of the robot 414, and collects feedback from users. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to improve the accuracy of the generation AI.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The generation unit can also estimate the user's emotions and adjust the format of the manual based on the estimated emotions. For example, if the user is feeling stressed, a visually simple and intuitive format can be provided. If the user is relaxed, a format with many detailed explanations and illustrations can be provided. Furthermore, if the user is in a hurry, a format that summarizes the main points concisely can be provided. In this way, providing the manual in a format that corresponds to the user's emotions promotes the user's understanding.

[0084] The generator can also customize the generated manual by reflecting past user feedback. For example, if there has been a lot of feedback in the past about a specific operation method, that part can be explained in more detail. Specific troubleshooting methods can also be added based on past feedback. Furthermore, the layout and structure of the manual can be improved based on past feedback. This makes it possible to provide a more user-friendly manual by reflecting past user feedback.

[0085] The analysis unit can also estimate the user's emotions and adjust the method of providing feedback on the analysis results based on the estimated emotions. For example, if the user is feeling stressed, it can provide concise, to-the-point feedback. If the user is relaxed, it can provide feedback including detailed analysis results. Furthermore, if the user is in a hurry, it can provide feedback in a format that can be quickly understood. In this way, providing a feedback method that corresponds to the user's emotions promotes understanding of the analysis results.

[0086] The generator can also emphasize important information in the generated manual based on the frequency of use by the user. For example, it can highlight the operation methods that the user frequently refers to. It can also prominently display troubleshooting methods that the user frequently uses. It can also identify parts where the user spends a lot of time and explain those parts in more detail. Thus, by emphasizing important information based on the frequency of use by the user, the practicality of the manual is improved.

[0087] The collection unit can also estimate the user's emotions and adjust the timing of feedback collection based on the estimated emotions. For example, if the user is feeling stressed, the collection of feedback can be postponed. Also, if the user is relaxed, detailed feedback can be requested. Furthermore, if the user is in a hurry, brief feedback can be requested. In this way, by providing the feedback collection timing according to the user's emotions, more appropriate feedback can be collected.

[0088] The generation unit can also provide the generated manual with an optimal display format according to the characteristics of the user's device. For example, a mobile-friendly display format can be provided for a user using a smartphone. A display format optimized for a large screen can be provided for a user using a tablet. Furthermore, a display format including detailed information can be provided for a user using a desktop. This improves the usability of the manual by providing an optimal display format according to the characteristics of the user's device.

[0089] The analysis unit can also estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, a concise and visually easy-to-understand notification can be provided. Alternatively, if the user is relaxed, a notification including detailed analysis results can be provided. Furthermore, if the user is in a hurry, a quick notification that covers the main points can be provided. In this way, providing a notification method that corresponds to the user's emotions promotes understanding of the analysis results.

[0090] The generator can also customize the generated manual according to the user's learning style. For example, a manual containing many illustrations and videos can be provided to a visual learner. A manual including audio guides can also be provided to an auditory learner. Furthermore, a step-by-step practical guide can be provided to a practical learner. In this way, the effectiveness of the manual can be improved by customizing it according to the user's learning style.

[0091] The collection unit can also estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is feeling stressed, feedback mainly consisting of simple questions can be collected. On the other hand, if the user is relaxed, feedback including detailed questions can be collected. Furthermore, if the user is in a hurry, feedback can be collected in a format that allows for quick responses. This makes it possible to collect more appropriate feedback by providing feedback content that corresponds to the user's emotions.

[0092] The generator can also customize the generated manual to reflect the user's cultural background. For example, the generator can provide a manual that reflects common practices and terminology in a particular culture. The generator can also provide a manual that includes culturally appropriate examples and scenarios. Furthermore, the generator can use a presentation method that takes cultural sensitivity into account. This customization to reflect the user's cultural background can facilitate understanding of the manual.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The generator analyzes the specifications and operation methods of the new device and generates a manual based on them. The generator uses generative AI to analyze the specifications, operation methods, and troubleshooting methods of the new device and generates a manual based on the results. For example, it analyzes how to set up, operate, and troubleshoot a new router and generates the respective manuals. Step 2: The collection unit collects user feedback. The collection unit collects usage data and user usage logs for the AI ​​Dynamic Manual to gather information on how users use the manual, which parts are difficult to understand, and which parts they frequently refer to. Step 3: The analysis unit analyzes the data collected by the collection unit to improve the accuracy of the generation AI. The analysis unit analyzes the collected usage data, feedback, and usage logs to identify parts that users find difficult to understand or frequently refer to, and provides more detailed explanations or improves those parts. The analysis unit also updates the learning data of the generation AI based on the user's usage logs.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0118] 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.

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

[0120] 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).

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

[0122] 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.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0125] 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.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0134] 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.

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

[0136] 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).

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

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] [Explanation of symbols]

[0167] 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 system comprising: a generation unit that analyzes the specifications or operating methods of new equipment and generates a manual based on the analysis; a collection unit that collects feedback from users; and an analysis unit that analyzes the data collected by the collection unit and improves the accuracy of the generation AI.

2. The generation unit Providing the generated manual to the user 2. The system of claim 1.

3. The analysis unit Expanding instructions for specific tasks based on feedback 2. The system of claim 1.

4. The system according to claim 1 , wherein the generation unit estimates a user's emotion and adjusts the expression method of the manual based on the estimated user's emotion.

5. The generation unit When analyzing the specifications and operation methods of new equipment, improve the accuracy of the analysis by referring to data from similar equipment in the past.

2. The system of claim 1.

6. The generation unit Customize the generated manual according to the user's skill level 2. The system of claim 1.

7. The generation unit Add specific examples to the generated manual that correspond to the user's environment 2. The system of claim 1.

8. The system according to claim 1 , wherein the generation unit estimates a user's emotion and adjusts the length of the manual based on the estimated user's emotion.

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