System

Abstract

An object of a system according to an embodiment is to automatically generate an optimal help page for an individual user based on a question or a browsing history of the user.SOLUTION: A system includes a question analysis part, a browsing history analysis part, and a help page generation part. The question analysis unit analyzes a question of a user. The browsing history analysis unit analyzes a browsing history of the user on the basis of the question of the user analyzed by the question analysis unit. The help page generation unit generates an optimum help page for each user based on the information analyzed by the question analysis unit and the browsing history analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] Conventional technologies have had the problem that help pages contain too much information or are poorly organized, making it difficult for users to find the information they need.

[0005] The system according to the embodiment aims to automatically generate a help page that is optimal for each individual user based on the user's questions and browsing history. [Means for solving the problem]

[0006] The system according to the embodiment includes a question analysis unit, a browsing history analysis unit, and a help page generation unit. The question analysis unit analyzes a user's question. The browsing history analysis unit analyzes the user's browsing history based on the user's question analyzed by the question analysis unit. The help page generation unit generates a help page optimal for each user based on the information analyzed by the question analysis unit and the browsing history analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a help page that is optimal for each individual user based on the user's questions and browsing history. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The automatic help page generation system according to the embodiment of the present invention is a system that automatically generates the most suitable help page for each individual user based on the user's questions and browsing history, thereby enabling the user to quickly and easily find the information they need.

[0029] The automatic help page generation system according to the embodiment includes a question analysis unit, a browsing history analysis unit, and a help page generation unit. The question analysis unit analyzes a user's question. For example, the question analysis unit analyzes a user's question using natural language processing technology and understands its content. The question analysis unit can also convert a voice input into text and analyze it. For example, the question analysis unit can convert voice into text using speech recognition technology and analyze the text. The browsing history analysis unit analyzes the user's browsing history based on the user's question analyzed by the question analysis unit. For example, the browsing history analysis unit analyzes the URLs and browsing times of pages the user has previously viewed to understand the user's interests. The browsing history analysis unit can also suggest related new information and updated information based on the user's browsing history. For example, it can provide new information related to pages the user frequently views. The help page generation unit generates a help page optimized for each individual user based on the information analyzed by the question analysis unit and the browsing history analysis unit. For example, the help page generation unit generates a help page including answers to the user's questions and related FAQs. The help page generation unit can also provide help pages in the optimal display format, taking into account the user's device and browser information. For example, it can adopt a responsive design that is optimal for smartphones. This allows the automatic help page generation system according to the embodiment to generate the optimal help page based on the user's question and browsing history. For example, if a user asks, "What should I do if I forget my password?", GEMINI analyzes the question and generates a help page that provides information on password reset. Furthermore, if the user has frequently viewed pages related to account settings in the past, GEMINI will prioritize displaying help pages related to account settings based on that information.

[0030] The question analysis unit also takes into account the user's past question history, identifying similar question patterns and improving the accuracy of answers. For example, if a user asks, "What should I do if I forget my password?", the question analysis unit will refer to the history of similar questions asked in the past and provide the most appropriate answer. For example, if there have been many questions about password resets in the past, a detailed help page will be generated based on that information. By taking into account the past question history, the question analysis unit improves the accuracy of answers.

[0031] The question analysis unit can infer the user's intent based on the results of question analysis and automatically suggest related additional information and FAQs. For example, if a user asks, "What should I do if I forget my password?", the question analysis unit can infer the intent of the question and automatically suggest additional information and FAQs related to password resets. For example, it can display password reset procedures and FAQs related to security. The question analysis unit can infer the user's intent and automatically suggest related information.

[0032] The question analysis unit compares the analysis results of the question with questions from other users, identifies common problems, and provides common solutions. For example, if a user asks, "What should I do if I forget my password?", the question analysis unit analyzes similar questions from other users and identifies common problems. For example, it provides a common solution for resetting a password. This makes it possible to identify common problems and provide common solutions.

[0033] The question analysis unit also supports voice input when analyzing questions, and can convert the user's question into text using voice recognition technology for analysis. For example, if a user asks verbally, "What should I do if I forget my password?", GEMINI will convert the question into text using voice recognition technology and analyze it. For example, the voice input can be converted into text and a help page regarding password reset can be generated. This allows the voice input to be converted into text and analyzed.

[0034] When analyzing the browsing history, the browsing history analysis unit takes into account the time spent on pages viewed by the user and can provide information of higher importance with priority. For example, if a user spends a long time on a page related to account settings, the browsing history analysis unit takes that time into account and provides important information related to account settings with priority. For example, detailed instructions for account settings are displayed. This allows the provision of information of higher importance to take into account the time spent on the page.

[0035] The browsing history analysis unit can infer a user's interests and concerns based on their browsing history and automatically suggest related new information and updates. For example, if a user frequently visits pages related to account settings, the browsing history analysis unit can automatically suggest new information and updates related to account settings based on that information. For example, additional information about new account settings functions can be displayed. This allows new information and updates to be suggested based on the user's interests and concerns.

[0036] The browsing history analysis unit can compare the results of the browsing history analysis with the browsing histories of other users to identify common interests and provide related information. For example, if a user frequently views pages related to account settings, the browsing history analysis unit can compare the results with the browsing histories of other users to identify common interests and provide related information. For example, common problems and solutions related to account settings can be provided. This allows common interests and provides related information.

[0037] When analyzing the browsing history, the browsing history analysis unit can summarize the content of pages viewed by the user and provide related information based on the summary information. For example, when a user views a page related to account settings, the browsing history analysis unit summarizes the content of the page and provides related information based on the summary information. For example, the browsing history analysis unit summarizes the account setting procedures and points to note and displays a related help page. This makes it possible to summarize the content of the page and provide related information.

[0038] When generating a help page, the help page generator takes into account the user's past feedback and can optimize the content based on that feedback. For example, if a user provides feedback that "the password reset procedure is difficult to understand," the help page generator will take that feedback into account and optimize the password reset procedure to make it easier to understand. For example, the generator will explain the procedure in detail. This allows the content of the help page to be optimized based on user feedback.

[0039] When generating a help page, the help page generator takes into account the user's device and browser information and provides the page in the optimal display format. For example, if the user is using a smartphone, the help page generator takes into account the device information and provides the help page in the optimal display format for the smartphone. For example, it employs responsive design. This allows the help page to be provided in the optimal display format based on the user's device and browser information.

[0040] When generating a help page, the help page generation unit can refer to the help page browsing history of other users and provide information that solves common problems. For example, if another user feels that the password reset procedure is difficult to understand, the help page generation unit can refer to the browsing history and provide information that solves the common problem. For example, the password reset procedure can be explained in a more understandable way. This makes it possible to refer to the browsing history of other users and provide information that solves common problems.

[0041] When generating a help page, the help page generation unit can automatically generate a multilingual help page based on the user's language setting. For example, if the user has set Japanese as their language setting, the help page generation unit automatically generates a Japanese help page based on that language setting. For example, the password reset procedure can be explained in Japanese. This allows for the automatic generation of a multilingual help page based on the user's language setting.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The automatic help page generation system can also obtain the user's geographic location information and provide information specific to the region. For example, if the user is in a specific region, FAQs and support information related to that region will be displayed preferentially. It can also provide region-specific problems and solutions. For example, it can present solutions to technical problems that only occur in a specific region. This allows the system to provide more appropriate help pages based on the user's geographic location information.

[0044] The help page automatic generation system can also analyze a user's past purchase history and provide help pages related to the purchased products. For example, if a user has purchased a specific product, FAQs and troubleshooting information related to that product can be displayed preferentially. It can also provide information on related accessories and additional features based on the purchase history. This allows the system to provide more relevant help pages based on the user's purchase history.

[0045] The automatic help page generation system can also analyze a user's learning history and provide help pages that correspond to their learning progress. For example, if a user is taking a specific online course, it can provide help pages and additional learning resources related to that course. It can also suggest what to learn next or review depending on the user's learning progress. This allows the system to provide more effective help pages based on the user's learning history.

[0046] The automatic help page generation system can also analyze a user's social media activity and provide help pages based on their interests. For example, if a user frequently posts about a particular topic, help pages and FAQs related to that topic will be displayed preferentially. It can also provide related information based on trends and topics on social media. This allows the system to provide more relevant help pages based on the user's social media activity.

[0047] The help page automatic generation system can also analyze the user's device usage and provide device-specific help pages. For example, if a user frequently uses a particular device, it can prioritize the display of FAQs and troubleshooting information related to that device. It can also suggest optimal settings and usage methods based on device usage. This allows the system to provide more appropriate help pages based on the user's device usage.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The question analysis unit analyzes the user's question. For example, the question analysis unit uses natural language processing technology to analyze the question entered by the user and understand its content. The question analysis unit can also convert voice input into text and analyze it. For example, it uses voice recognition technology to convert the voice into text and analyzes the text. Step 2: The browsing history analysis unit analyzes the user's browsing history based on the user's question analyzed by the question analysis unit. For example, the browsing history analysis unit analyzes the URLs of pages the user has previously viewed and the viewing times to understand the user's interests and concerns. The browsing history analysis unit can also suggest related new information or updated information based on the user's browsing history. For example, it can provide new information related to pages the user frequently views. Step 3: The help page generator generates a help page optimized for each individual user based on the information analyzed by the question analyzer and browsing history analyzer. For example, the help page generator generates a help page that includes answers to the user's questions and related FAQs. The help page generator can also provide the help page in the optimal display format, taking into account the user's device and browser information. For example, it can adopt a responsive design that is optimal for smartphones.

[0050] (Example 2) The automatic help page generation system according to the embodiment of the present invention is a system that automatically generates the most suitable help page for each individual user based on the user's questions and browsing history, thereby enabling the user to quickly and easily find the information they need.

[0051] The automatic help page generation system according to the embodiment includes a question analysis unit, a browsing history analysis unit, and a help page generation unit. The question analysis unit analyzes a user's question. For example, the question analysis unit analyzes a user's question using natural language processing technology and understands its content. The question analysis unit can also convert a voice input into text and analyze it. For example, the question analysis unit can convert voice into text using speech recognition technology and analyze the text. The browsing history analysis unit analyzes the user's browsing history based on the user's question analyzed by the question analysis unit. For example, the browsing history analysis unit analyzes the URLs and browsing times of pages the user has previously viewed to understand the user's interests. The browsing history analysis unit can also suggest related new information and updated information based on the user's browsing history. For example, it can provide new information related to pages the user frequently views. The help page generation unit generates a help page optimized for each individual user based on the information analyzed by the question analysis unit and the browsing history analysis unit. For example, the help page generation unit generates a help page including answers to the user's questions and related FAQs. The help page generation unit can also provide help pages in the optimal display format, taking into account the user's device and browser information. For example, it can adopt a responsive design that is optimal for smartphones. This allows the automatic help page generation system according to the embodiment to generate the optimal help page based on the user's question and browsing history. For example, if a user asks, "What should I do if I forget my password?", GEMINI analyzes the question and generates a help page that provides information on password reset. Furthermore, if the user has frequently viewed pages related to account settings in the past, GEMINI will prioritize displaying help pages related to account settings based on that information.

[0052] The question analysis unit also takes into account the user's past question history, identifying similar question patterns and improving the accuracy of answers. For example, if a user asks, "What should I do if I forget my password?", the question analysis unit will refer to the history of similar questions asked in the past and provide the most appropriate answer. For example, if there have been many questions about password resets in the past, a detailed help page will be generated based on that information. By taking into account the past question history, the question analysis unit improves the accuracy of answers.

[0053] The question analysis unit can infer the user's intent based on the results of question analysis and automatically suggest related additional information and FAQs. For example, if a user asks, "What should I do if I forget my password?", the question analysis unit can infer the intent of the question and automatically suggest additional information and FAQs related to password resets. For example, it can display password reset procedures and FAQs related to security. The question analysis unit can infer the user's intent and automatically suggest related information.

[0054] The question analysis unit uses the emotion estimation function to analyze the emotion contained in the user's question and generate an answer that corresponds to that emotion. For example, if a user asks, "I'm having trouble because I forgot my password," the question analysis unit analyzes the emotion contained in the question and generates an answer that alleviates the user's anxiety. For example, it may provide a reassuring message along with instructions for resetting the password. This allows the system to generate an answer that corresponds to the user's emotion.

[0055] The question analysis unit compares the analysis results of the question with questions from other users, identifies common problems, and provides common solutions. For example, if a user asks, "What should I do if I forget my password?", the question analysis unit analyzes similar questions from other users and identifies common problems. For example, it provides a common solution for resetting a password. This makes it possible to identify common problems and provide common solutions.

[0056] The question analysis unit also supports voice input when analyzing questions, and can convert the user's question into text using voice recognition technology for analysis. For example, if a user asks verbally, "What should I do if I forget my password?", GEMINI will convert the question into text using voice recognition technology and analyze it. For example, the voice input can be converted into text and a help page regarding password reset can be generated. This allows the voice input to be converted into text and analyzed.

[0057] The question analysis unit uses the emotion estimation function to analyze the user's emotions in real time when asking a question and can provide an answer that elicits positive emotions. For example, if a user asks, "I'm having trouble because I forgot my password," the question analysis unit analyzes the user's emotions in real time when asking the question and provides an answer that elicits positive emotions. For example, it may explain the password reset procedure along with a message that gives a sense of security. This allows the system to analyze the user's emotions in real time and provide an answer that elicits positive emotions.

[0058] When analyzing the browsing history, the browsing history analysis unit takes into account the time spent on pages viewed by the user and can provide information of higher importance with priority. For example, if a user spends a long time on a page related to account settings, the browsing history analysis unit takes that time into account and provides important information related to account settings with priority. For example, detailed instructions for account settings are displayed. This allows the provision of information of higher importance to take into account the time spent on the page.

[0059] The browsing history analysis unit can infer a user's interests and concerns based on their browsing history and automatically suggest related new information and updates. For example, if a user frequently visits pages related to account settings, the browsing history analysis unit can automatically suggest new information and updates related to account settings based on that information. For example, additional information about new account settings functions can be displayed. This allows new information and updates to be suggested based on the user's interests and concerns.

[0060] The browsing history analysis unit uses the emotion estimation function to analyze the emotional response of the user to the pages they have viewed, and can provide information that elicits positive emotions. For example, the browsing history analysis unit analyzes the emotional response when the user views a page related to account settings, and provides information that elicits positive emotions. For example, it displays success stories of account settings and user testimonials. This makes it possible to analyze the user's emotional response and provide information that elicits positive emotions.

[0061] The browsing history analysis unit can compare the results of the browsing history analysis with the browsing histories of other users to identify common interests and provide related information. For example, if a user frequently views pages related to account settings, the browsing history analysis unit can compare the results with the browsing histories of other users to identify common interests and provide related information. For example, common problems and solutions related to account settings can be provided. This allows common interests and provides related information.

[0062] When analyzing the browsing history, the browsing history analysis unit can summarize the content of pages viewed by the user and provide related information based on the summary information. For example, when a user views a page related to account settings, the browsing history analysis unit summarizes the content of the page and provides related information based on the summary information. For example, the browsing history analysis unit summarizes the account setting procedures and points to note and displays a related help page. This makes it possible to summarize the content of the page and provide related information.

[0063] The browsing history analysis unit uses the emotion estimation function to analyze the user's emotions in real time when browsing, and can provide information that elicits positive emotions. For example, the browsing history analysis unit analyzes the user's emotions in real time when browsing a page related to account settings, and can provide information that elicits positive emotions. For example, it displays success stories of account settings and user testimonials. This makes it possible to analyze the user's emotions in real time and provide information that elicits positive emotions.

[0064] When generating a help page, the help page generator takes into account the user's past feedback and can optimize the content based on that feedback. For example, if a user provides feedback that "the password reset procedure is difficult to understand," the help page generator will take that feedback into account and optimize the password reset procedure to make it easier to understand. For example, the generator will explain the procedure in detail. This allows the content of the help page to be optimized based on user feedback.

[0065] When generating a help page, the help page generator takes into account the user's device and browser information and provides the page in the optimal display format. For example, if the user is using a smartphone, the help page generator takes into account the device information and provides the help page in the optimal display format for the smartphone. For example, it employs responsive design. This allows the help page to be provided in the optimal display format based on the user's device and browser information.

[0066] The help page generator uses the emotion estimation function to generate a help page that corresponds to the user's emotions and provides content that elicits positive emotions. For example, if a user feels that the password reset procedure is difficult to understand, the help page generator analyzes that emotion and generates a help page that elicits positive emotions. For example, it explains the procedure along with a message that gives a sense of security. This allows the generator to generate a help page that corresponds to the user's emotions and provides content that elicits positive emotions.

[0067] When generating a help page, the help page generation unit can refer to the help page browsing history of other users and provide information that solves common problems. For example, if another user feels that the password reset procedure is difficult to understand, the help page generation unit can refer to the browsing history and provide information that solves the common problem. For example, the password reset procedure can be explained in a more understandable way. This makes it possible to refer to the browsing history of other users and provide information that solves common problems.

[0068] When generating a help page, the help page generation unit can automatically generate a multilingual help page based on the user's language setting. For example, if the user has set Japanese as their language setting, the help page generation unit automatically generates a Japanese help page based on that language setting. For example, the password reset procedure can be explained in Japanese. This allows for the automatic generation of a multilingual help page based on the user's language setting.

[0069] The help page generator uses the emotion estimation function to generate help pages in real time that correspond to the user's emotions, providing content that elicits positive emotions. For example, if a user feels that the password reset procedure is difficult to understand, the help page generator analyzes that emotion in real time and generates a help page that elicits positive emotions. For example, it explains the procedure along with a message that gives a sense of security. This allows the generator to generate help pages in real time that correspond to the user's emotions and provide content that elicits positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The automatic help page generation system can also obtain the user's geographic location information and provide information specific to the region. For example, if the user is in a specific region, FAQs and support information related to that region will be displayed preferentially. It can also provide region-specific problems and solutions. For example, it can present solutions to technical problems that only occur in a specific region. This allows the system to provide more appropriate help pages based on the user's geographic location information.

[0072] The help page automatic generation system can also analyze a user's past purchase history and provide help pages related to the purchased products. For example, if a user has purchased a specific product, FAQs and troubleshooting information related to that product can be displayed preferentially. It can also provide information on related accessories and additional features based on the purchase history. This allows the system to provide more relevant help pages based on the user's purchase history.

[0073] The automatic help page generation system can also analyze a user's learning history and provide help pages that correspond to their learning progress. For example, if a user is taking a specific online course, it can provide help pages and additional learning resources related to that course. It can also suggest what to learn next or review depending on the user's learning progress. This allows the system to provide more effective help pages based on the user's learning history.

[0074] The automatic help page generation system can also analyze a user's social media activity and provide help pages based on their interests. For example, if a user frequently posts about a particular topic, help pages and FAQs related to that topic will be displayed preferentially. It can also provide related information based on trends and topics on social media. This allows the system to provide more relevant help pages based on the user's social media activity.

[0075] The help page automatic generation system can also analyze the user's device usage and provide device-specific help pages. For example, if a user frequently uses a particular device, it can prioritize the display of FAQs and troubleshooting information related to that device. It can also suggest optimal settings and usage methods based on device usage. This allows the system to provide more appropriate help pages based on the user's device usage.

[0076] The automatic help page generation system can also estimate the user's emotions and provide a help page that helps the user relax based on the estimated emotions. For example, if the user is feeling stressed, the system can generate a help page that uses colors and designs that have a relaxing effect. It can also provide music and messages that have a relaxing effect. In this way, it is possible to provide a help page that helps the user relax according to their emotions.

[0077] The automatic help page generation system can also estimate the user's emotions and provide a help page that will motivate the user based on the estimated emotions. For example, if the user is feeling unmotivated, the system can generate a help page that includes encouraging messages and success stories. It can also provide hints and advice to increase motivation. This makes it possible to provide a help page that will motivate the user based on their emotions.

[0078] The automatic help page generation system can also estimate the user's emotions and provide a help page that puts the user at ease based on the estimated emotions. For example, if the user is feeling anxious, the system generates a help page that includes a reassuring message and support information. It can also provide specific procedures and solutions that put the user at ease. This makes it possible to provide a reassuring help page according to the user's emotions.

[0079] The automatic help page generation system can also estimate the user's emotions and provide a help page that the user can enjoy based on the estimated emotions. For example, if the user is bored, the system can generate a help page that includes a fun design and interactive elements. It can also provide a help page that incorporates game elements and quizzes. This makes it possible to provide an enjoyable help page according to the user's emotions.

[0080] The automatic help page generation system can also estimate a user's emotions and provide a help page that helps the user gain confidence based on the estimated emotions. For example, if a user has lost confidence, the system can generate a help page that includes success stories and positive messages to help the user regain their confidence. It can also provide specific steps and advice to help the user gain confidence. In this way, it can provide a help page that helps the user gain confidence according to the user's emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The question analysis unit analyzes the user's question. For example, the question analysis unit uses natural language processing technology to analyze the question entered by the user and understand its content. The question analysis unit can also convert voice input into text and analyze it. For example, it uses voice recognition technology to convert the voice into text and analyzes the text. Step 2: The browsing history analysis unit analyzes the user's browsing history based on the user's question analyzed by the question analysis unit. For example, the browsing history analysis unit analyzes the URLs of pages the user has previously viewed and the viewing times to understand the user's interests and concerns. The browsing history analysis unit can also suggest related new information or updated information based on the user's browsing history. For example, it can provide new information related to pages the user frequently views. Step 3: The help page generator generates a help page optimized for each individual user based on the information analyzed by the question analyzer and browsing history analyzer. For example, the help page generator generates a help page that includes answers to the user's questions and related FAQs. The help page generator can also provide the help page in the optimal display format, taking into account the user's device and browser information. For example, it can adopt a responsive design that is optimal for smartphones.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 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 question analysis unit that analyzes a user's question; a browsing history analysis unit that analyzes the browsing history of the user based on the question of the user analyzed by the question analysis unit; a help page generation unit that generates an optimal help page for each user based on the information analyzed by the question analysis unit and the browsing history analysis unit. A system characterized by:

2. The question analysis unit Taking into account the user's past question history, similar question patterns are identified to improve the accuracy of answers.

2. The system of claim 1.

3. The browsing history analysis unit When analyzing the browsing history, the time spent on pages viewed by the user is taken into consideration, and information of high importance is provided preferentially.

2. The system of claim 1.

4. The help page generation unit Taking the user's past feedback into account when generating the help page and optimizing the content based on the feedback 2. The system of claim 1.

5. The question analysis unit Analyzing the emotion contained in the user's question and generating an answer according to the emotion 2. The system of claim 1.

6. The browsing history analysis unit Analyzing the emotional response of the user to the page viewed by the user and providing the information that elicits positive emotions 2. The system of claim 1.

7. The help page generation unit The help page is generated in accordance with the user's emotions, and content that elicits positive emotions is provided.

2. The system of claim 1.

8. The help page generation unit The help page is generated in real time according to the user's emotions, and content that elicits positive emotions is provided.

2. The system of claim 1.

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