System

Abstract

A system according to an embodiment aims to summarize and visually convey educational content.SOLUTION: A system according to an embodiment includes a text analysis unit, a summary generation unit, an avatar generation unit, and a moving image generation unit. The text analysis unit analyzes a text. The summary generation unit generates a summary from the text analyzed by the text analysis unit. The avatar generator may generate an avatar based on the summary generated by the summary generator. The video generation unit generates a video using the avatar generated by the avatar generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately summarize educational content and convey information visually, and there is room for improvement.

[0005] The system according to the embodiment aims to summarize and visually convey educational content. [Means for solving the problem]

[0006] A system according to an embodiment includes a text analysis unit, a summary generation unit, an avatar generation unit, and a video generation unit. The text analysis unit analyzes text. The summary generation unit generates a summary from the text analyzed by the text analysis unit. The avatar generation unit generates an avatar based on the summary generated by the summary generation unit. The video generation unit generates a video using the avatar generated by the avatar generation unit. [Effects of the Invention]

[0007] An embodiment of the system can summarize and visually convey educational content. [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 Edu-generated AI avatar coach according to an embodiment of the present invention is a system that summarizes information from reference books and textbooks and generates videos using virtual avatars based on the summarized content. This allows the Edu-generated AI avatar coach to improve access to and comprehension of educational content, enabling learners to acquire knowledge more effectively.

[0029] An Edu-generating AI avatar coach according to an embodiment includes a text analysis unit, a summary generation unit, an avatar generation unit, and a video generation unit. The text analysis unit analyzes text. For example, the text analysis unit analyzes the content of reference books and documents and extracts important key points. The text analysis unit can also analyze the meaning of text using natural language processing technology. For example, the text analysis unit extracts important events and people from history textbooks and summarizes them concisely. The summary generation unit generates a summary from the text analyzed by the text analysis unit. For example, the summary generation unit generates a summary of the text using a generation AI (e.g., a text generation AI or a multimodal generation AI). The summary generation unit can also generate a summary by extracting important parts of a sentence. For example, the summary generation unit generates a summary by prompting the generation AI, such as "Please summarize the main points of this sentence." The avatar generation unit generates an avatar based on the summary generated by the summary generation unit. For example, the avatar generation unit customizes a virtual avatar and generates a video for the avatar to provide explanatory or educational content. The avatar generation unit can also enable the avatar to speak in real time. For example, the avatar generation unit can change the avatar's appearance, tone of voice, background design, etc. The video generation unit generates a video using the avatar generated by the avatar generation unit. For example, the video generation unit generates a video in which the avatar gives a history lesson and explains important events. The video generation unit can also generate a video in which the avatar combines visual elements to visually convey information. For example, the video generation unit incorporates images and diagrams related to the content the avatar explains into the video. This allows the Edu Generative AI Avatar Coach, according to the embodiment, to function as an educational AI assistant, performing a consistent process from text analysis to video generation. For example, a user can instruct the AI ​​to generate a video on a specific topic. The generation AI analyzes the instruction, collects and summarizes related information, and generates custom content. This allows custom content tailored to individual educational needs to be created.

[0030] The text analysis unit can automatically search for related images and figures when analyzing text and incorporate them into the summary. For example, when the generation AI performs text analysis, the text analysis unit automatically searches for related images and figures and incorporates them into the summary. For example, when summarizing a history textbook, it searches for related historical images and figures and incorporates them into the summary. The text analysis unit also automatically searches for related images and figures when analyzing text and incorporates them into the summary. For example, when summarizing a science textbook, it searches for images and figures of related experiments and incorporates them into the summary. The text analysis unit also automatically searches for related images and figures when the generation AI performs text analysis and incorporates them into the summary. For example, when summarizing a report written by a user, it searches for figures of related data and incorporates them into the summary. In this way, by incorporating related images and figures into the summary, it is possible to generate a summary that is visually easy to understand.

[0031] The text analysis unit can simultaneously analyze multiple reference books and documents and generate a summary by integrating the interrelated information. For example, the generation AI can simultaneously analyze multiple reference books and documents and generate a summary by integrating the interrelated information. For example, the generation AI can analyze a history textbook and related documents and generate an integrated summary. The text analysis unit can also simultaneously analyze multiple reference books and documents and generate a summary by integrating the interrelated information. For example, the generation AI can analyze a science textbook and related research papers and generate an integrated summary. The text analysis unit can also simultaneously analyze multiple reference books and documents and generate a summary by integrating the interrelated information. For example, the generation AI can analyze multiple reports provided by a user and generate an integrated summary. This allows the generation of a summary by integrating multiple reference books and documents.

[0032] The text analysis unit can simultaneously analyze text in different languages ​​and generate summaries that support multiple languages. For example, the generation AI in the text analysis unit simultaneously analyzes text in different languages ​​and generates summaries that support multiple languages. For example, it analyzes history textbooks in English and Japanese and generates summaries in both languages. The text analysis unit can also simultaneously analyze text in different languages ​​and generate summaries that support multiple languages. For example, it analyzes science textbooks in French and German and generates summaries in both languages. The text analysis unit can also simultaneously analyze text in different languages ​​and generate summaries that support multiple languages. For example, it analyzes literary works in Spanish and Chinese and generates summaries in both languages. This allows the generation AI to analyze text in different languages ​​and generate summaries that support multiple languages.

[0033] The text analysis unit can accept voice input, generate text from the voice, and analyze the text to generate a summary. For example, the text analysis unit uses a generation AI to accept voice input, generate text from the voice, and analyze the text to generate a summary. For example, it analyzes the audio of a lecture to generate a summary. The text analysis unit can also accept voice input, generate text from the voice, and analyze the text to generate a summary. For example, it can analyze the audio of an interview to generate a summary. The text analysis unit can also accept voice input, generate text from the voice, and analyze the text to generate a summary. For example, it can analyze the audio of a meeting to generate a summary. In this way, voice input can be analyzed and a summary can be generated.

[0034] When generating custom content, the summary generation unit can suggest optimal learning content based on the user's learning history and progress. In the summary generation unit, for example, the generation AI takes into account the user's learning history and progress and suggests optimal learning content. For example, it suggests the next topic to learn based on past learning content. The summary generation unit also takes into account the learning history and progress and suggests optimal learning content. For example, it generates content that focuses on areas in which the user is weak. In addition, the summary generation unit takes into account the user's learning history and progress and suggests optimal learning content. For example, it generates custom content that matches the user's learning pace. This makes it possible to suggest optimal learning content based on the user's learning history and progress.

[0035] When generating custom content, the summary generation unit can automatically add related topics based on the user's interests and concerns. In the summary generation unit, for example, the generation AI automatically adds related topics based on the user's interests and concerns. For example, if the user is interested in history, related historical events are added. The summary generation unit also automatically adds related topics based on the user's interests and concerns. For example, if the user is interested in science, related scientific topics are added. The summary generation unit also automatically adds related topics based on the user's interests and concerns. For example, if the user is interested in literature, related literary works are added. In this way, related topics can be added based on the user's interests and concerns.

[0036] When generating custom content, the summary generation unit can generate content that corresponds to different learning styles. In the summary generation unit, for example, the generation AI generates content that corresponds to different learning styles. For example, it generates content that makes extensive use of diagrams and graphs for visual learners. In addition, the summary generation unit generates content that corresponds to different learning styles. For example, it generates content that includes audio commentary for auditory learners. In addition, the summary generation unit generates content that corresponds to different learning styles. For example, it generates content that includes interactive elements for tactile learners. In this way, custom content that corresponds to different learning styles can be generated.

[0037] When generating custom content, the summary generation unit can adjust the length of the content so that the user can complete the study within the time specified by the user. The summary generation unit, for example, adjusts the length of the content so that the generation AI can complete the study within the time specified by the user. For example, it summarizes the content so that it can be studied in 30 minutes or less. The summary generation unit also adjusts the length of the content so that the study can be completed within the specified time. For example, it summarizes the content so that it can be studied in 1 hour or less. The summary generation unit also adjusts the length of the content so that the generation AI can complete the study within the time specified by the user. For example, it summarizes the content so that it can be studied in 15 minutes or less. In this way, the length of the content can be adjusted so that the user can complete the study within the time specified by the user.

[0038] The avatar generation unit provides customization options according to the user's preferences, and can adjust the avatar's appearance and voice. In the avatar generation unit, for example, the generation AI provides customization options according to the user's preferences and adjusts the avatar's appearance and voice. For example, the hairstyle and clothing selected by the user are reflected in the avatar. The avatar generation unit also provides customization options according to the user's preferences and adjusts the avatar's appearance and voice. For example, the voice tone and accent selected by the user are reflected in the avatar. In addition, the avatar generation unit provides customization options according to the user's preferences and adjusts the avatar's appearance and voice. For example, the avatar's behavior is adjusted according to the background and scenario selected by the user. This allows the avatar generation unit to provide customization options according to the user's preferences and adjust the avatar's appearance and voice.

[0039] The avatar generation unit can receive user feedback in real time and adjust the avatar's movements and speech. In the avatar generation unit, for example, the generation AI receives user feedback in real time and adjusts the avatar's movements and speech. For example, if the user instructs, "Speak more slowly," the avatar adjusts the speed at which it speaks. The avatar generation unit also receives user feedback in real time and adjusts the avatar's movements and speech. For example, if the user instructs, "Explain this part in more detail," the avatar adds a detailed explanation. The avatar generation unit also receives user feedback in real time and adjusts the avatar's movements and speech. For example, if the user instructs, "Change the background," the avatar's background is changed. This allows the avatar to receive user feedback in real time and adjust the avatar's movements and speech.

[0040] The avatar generation unit generates avatars that correspond to different cultures and regions, making it possible to accommodate global users. For example, the generation AI of the avatar generation unit generates avatars that correspond to different cultures and regions, making it possible to accommodate global users. For example, the avatar's clothing and language are customized to suit the region. The avatar generation unit also generates avatars that correspond to different cultures and regions, making it possible to accommodate global users. For example, the avatar's gestures and expressions are adjusted to suit the culture. The avatar generation unit also generates avatars that correspond to different cultures and regions, making it possible to accommodate global users. For example, the avatar's background and scenario are changed to suit the characteristics of the region. This makes it possible to generate avatars that correspond to different cultures and regions, making it possible to accommodate global users.

[0041] The avatar generation unit can adjust the avatar's actions and speech according to the background and scenario selected by the user. For example, the generation AI of the avatar generation unit adjusts the avatar's actions and speech according to the background and scenario selected by the user. For example, the content of what the avatar says is adjusted to match the historical background selected by the user. The avatar generation unit also adjusts the avatar's actions and speech according to the background and scenario selected by the user. For example, the avatar explains an experiment according to a scientific scenario selected by the user. The avatar generation unit also adjusts the avatar's actions and speech according to the background and scenario selected by the generation AI. For example, the avatar tells a story according to a literary background selected by the user. This allows the avatar's actions and speech to be adjusted according to the background and scenario selected by the user.

[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 Edu-generated AI avatar coach can also be customized to fit a user's learning style. For example, for visual learners, infographics and animations related to the content the avatar is explaining can be added. For auditory learners, background music and sound effects can be incorporated in addition to the avatar's audio commentary. Furthermore, interactive quizzes and simulations can be provided for tactile learners. This allows for the optimal learning experience to be tailored to each user's learning style.

[0044] The Edu-generated AI avatar coach can also suggest optimal learning content based on the user's learning history and progress. For example, it can suggest the next topic to study based on past learning content. It can also generate content that focuses on areas in which the user is weak. It can also generate custom content that matches the user's learning pace. This allows it to suggest optimal learning content based on the user's learning history and progress.

[0045] The Edu-generated AI avatar coach can also automatically add related topics based on the user's interests. For example, if a user is interested in history, related historical events can be added. If a user is interested in science, related scientific topics can be added. Furthermore, if a user is interested in literature, related literary works can be added. This allows related topics to be added based on the user's interests.

[0046] The Edu-generated AI avatar coach can also adjust the length of the content so that the user can complete the learning within the time specified by the user. For example, it can summarize the content so that it can be learned in 30 minutes or less, or it can summarize the content so that it can be learned in 1 hour or less, or it can summarize the content so that it can be learned in 15 minutes or less. This allows the length of the content to be adjusted so that the user can complete the learning within the time specified by the user.

[0047] The Edu-generated AI avatar coach can also receive user feedback in real time and adjust the avatar's behavior and speech accordingly. For example, if the user instructs the avatar to "speak more slowly," the avatar can adjust its speaking speed. If the user instructs the avatar to "explain this part in more detail," the avatar can add a detailed explanation. Furthermore, if the user instructs the avatar to "change the background," the avatar's background can be changed. This allows the avatar to receive user feedback in real time and adjust the avatar's behavior and speech accordingly.

[0048] The Edu Generative AI Avatar Coach can also generate avatars that are compatible with different cultures and regions, making it suitable for global users. For example, the avatar's clothing and language can be customized to suit the region. The avatar's gestures and expressions can also be adjusted to suit the culture. Furthermore, the avatar's background and scenario can be changed to suit the characteristics of the region. This allows the generation of avatars that are compatible with different cultures and regions, making it suitable for global users.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The text analysis unit analyzes the text. For example, the text analysis unit analyzes the content of reference books and documents and extracts important key points. The text analysis unit can also analyze the meaning of the text using natural language processing techniques. For example, the text analysis unit extracts important events and people from history textbooks and summarizes them concisely. Step 2: The summary generation unit generates a summary from the text analyzed by the text analysis unit. For example, the summary generation unit generates a summary of the text using a generation AI (e.g., a text generation AI or a multimodal generation AI). The summary generation unit can also generate a summary by extracting important parts of a sentence. For example, the summary generation unit generates a summary by prompting the generation AI, such as "Please summarize the main points of this sentence." Step 3: The avatar generator generates an avatar based on the summary generated by the summary generator. For example, the avatar generator customizes the virtual avatar and generates a video for the avatar to provide explanatory or educational content. The avatar generator can also enable the avatar to speak in real time. For example, the avatar generator can change the avatar's appearance, tone of voice, background design, etc. Step 4: The video generation unit generates a video using the avatar generated by the avatar generation unit. For example, the video generation unit generates a video in which the avatar gives a history lesson and explains important events. The video generation unit can also generate a video in which the avatar combines visual elements to visually convey information. For example, the video generation unit incorporates images and diagrams related to the content explained by the avatar into the video.

[0051] (Example 2) The Edu-generated AI avatar coach according to an embodiment of the present invention is a system that summarizes information from reference books and textbooks and generates videos using virtual avatars based on the summarized content. This allows the Edu-generated AI avatar coach to improve access to and comprehension of educational content, enabling learners to acquire knowledge more effectively.

[0052] An Edu-generating AI avatar coach according to an embodiment includes a text analysis unit, a summary generation unit, an avatar generation unit, and a video generation unit. The text analysis unit analyzes text. For example, the text analysis unit analyzes the content of reference books and documents and extracts important key points. The text analysis unit can also analyze the meaning of text using natural language processing technology. For example, the text analysis unit extracts important events and people from history textbooks and summarizes them concisely. The summary generation unit generates a summary from the text analyzed by the text analysis unit. For example, the summary generation unit generates a summary of the text using a generation AI (e.g., a text generation AI or a multimodal generation AI). The summary generation unit can also generate a summary by extracting important parts of a sentence. For example, the summary generation unit generates a summary by prompting the generation AI, such as "Please summarize the main points of this sentence." The avatar generation unit generates an avatar based on the summary generated by the summary generation unit. For example, the avatar generation unit customizes a virtual avatar and generates a video for the avatar to provide explanatory or educational content. The avatar generation unit can also enable the avatar to speak in real time. For example, the avatar generation unit can change the avatar's appearance, tone of voice, background design, etc. The video generation unit generates a video using the avatar generated by the avatar generation unit. For example, the video generation unit generates a video in which the avatar gives a history lesson and explains important events. The video generation unit can also generate a video in which the avatar combines visual elements to visually convey information. For example, the video generation unit incorporates images and diagrams related to the content the avatar explains into the video. This allows the Edu Generative AI Avatar Coach, according to the embodiment, to function as an educational AI assistant, performing a consistent process from text analysis to video generation. For example, a user can instruct the AI ​​to generate a video on a specific topic. The generation AI analyzes the instruction, collects and summarizes related information, and generates custom content. This allows custom content tailored to individual educational needs to be created.

[0053] The text analysis unit uses the emotion estimation function when analyzing text to extract emotional nuances from the text and reflect them in the summary. For example, when the generation AI performs text analysis, the text analysis unit uses the emotion estimation function to extract emotional nuances from the text. For example, in a history textbook, the text analysis unit extracts emotional reactions to a specific event and reflects those emotions in the summary. The text analysis unit also uses the emotion estimation function to extract emotional nuances from the text and reflect them in the summary. For example, when summarizing a literary work, a summary that reflects the emotions of the characters is generated. The text analysis unit also uses the emotion estimation function when the generation AI performs text analysis to extract emotional nuances from the text and reflect them in the summary. For example, the text analysis unit extracts emotional elements from an essay written by a user and generates a summary that reflects those emotions. This makes it possible to generate a summary that reflects the emotional nuances in the text.

[0054] The text analysis unit can automatically search for related images and figures when analyzing text and incorporate them into the summary. For example, when the generation AI performs text analysis, the text analysis unit automatically searches for related images and figures and incorporates them into the summary. For example, when summarizing a history textbook, it searches for related historical images and figures and incorporates them into the summary. The text analysis unit also automatically searches for related images and figures when analyzing text and incorporates them into the summary. For example, when summarizing a science textbook, it searches for images and figures of related experiments and incorporates them into the summary. The text analysis unit also automatically searches for related images and figures when the generation AI performs text analysis and incorporates them into the summary. For example, when summarizing a report written by a user, it searches for figures of related data and incorporates them into the summary. In this way, by incorporating related images and figures into the summary, it is possible to generate a summary that is visually easy to understand.

[0055] The text analysis unit can simultaneously analyze multiple reference books and documents and generate a summary by integrating the interrelated information. For example, the generation AI can simultaneously analyze multiple reference books and documents and generate a summary by integrating the interrelated information. For example, the generation AI can analyze a history textbook and related documents and generate an integrated summary. The text analysis unit can also simultaneously analyze multiple reference books and documents and generate a summary by integrating the interrelated information. For example, the generation AI can analyze a science textbook and related research papers and generate an integrated summary. The text analysis unit can also simultaneously analyze multiple reference books and documents and generate a summary by integrating the interrelated information. For example, the generation AI can analyze multiple reports provided by a user and generate an integrated summary. This allows the generation of a summary by integrating multiple reference books and documents.

[0056] The text analysis unit can simultaneously analyze text in different languages ​​and generate summaries that support multiple languages. For example, the generation AI in the text analysis unit simultaneously analyzes text in different languages ​​and generates summaries that support multiple languages. For example, it analyzes history textbooks in English and Japanese and generates summaries in both languages. The text analysis unit can also simultaneously analyze text in different languages ​​and generate summaries that support multiple languages. For example, it analyzes science textbooks in French and German and generates summaries in both languages. The text analysis unit can also simultaneously analyze text in different languages ​​and generate summaries that support multiple languages. For example, it analyzes literary works in Spanish and Chinese and generates summaries in both languages. This allows the generation AI to analyze text in different languages ​​and generate summaries that support multiple languages.

[0057] The text analysis unit can accept voice input, generate text from the voice, and analyze the text to generate a summary. For example, the text analysis unit uses a generation AI to accept voice input, generate text from the voice, and analyze the text to generate a summary. For example, it analyzes the audio of a lecture to generate a summary. The text analysis unit can also accept voice input, generate text from the voice, and analyze the text to generate a summary. For example, it can analyze the audio of an interview to generate a summary. The text analysis unit can also accept voice input, generate text from the voice, and analyze the text to generate a summary. For example, it can analyze the audio of a meeting to generate a summary. In this way, voice input can be analyzed and a summary can be generated.

[0058] The text analysis unit can use the emotion estimation function to analyze the emotion of text entered by the user and generate a summary that elicits positive emotions. For example, the generation AI in the text analysis unit uses the emotion estimation function to analyze the emotion of text entered by the user and generate a summary that elicits positive emotions. For example, it analyzes the emotion of an essay written by the user and generates a positive summary. The text analysis unit also uses the emotion estimation function to analyze the emotion of text entered by the user and generate a summary that elicits positive emotions. For example, it analyzes the emotion of a report written by the user and generates a positive summary. The text analysis unit also uses the emotion estimation function to analyze the emotion of text entered by the user and generate a summary that elicits positive emotions. For example, it analyzes the emotion of a diary written by the user and generates a positive summary. In this way, it is possible to analyze the user's emotions and generate a summary that elicits positive emotions.

[0059] When generating custom content, the summary generation unit can use the emotion estimation function to generate content that corresponds to the user's emotional state. For example, the generation AI in the summary generation unit uses the emotion estimation function to generate content that corresponds to the user's emotional state. For example, if the user is feeling stressed, the summary generation unit generates content that helps the user relax. The summary generation unit also uses the emotion estimation function to generate content that corresponds to the user's emotional state. For example, if the user is excited, the summary generation unit generates content that helps the user concentrate. The summary generation unit also uses the emotion estimation function to generate content that corresponds to the user's emotional state. For example, if the user is sad, the summary generation unit generates content that brightens the user's mood. This makes it possible to generate custom content that corresponds to the user's emotional state.

[0060] When generating custom content, the summary generation unit can suggest optimal learning content based on the user's learning history and progress. In the summary generation unit, for example, the generation AI takes into account the user's learning history and progress and suggests optimal learning content. For example, it suggests the next topic to learn based on past learning content. The summary generation unit also takes into account the learning history and progress and suggests optimal learning content. For example, it generates content that focuses on areas in which the user is weak. In addition, the summary generation unit takes into account the user's learning history and progress and suggests optimal learning content. For example, it generates custom content that matches the user's learning pace. This makes it possible to suggest optimal learning content based on the user's learning history and progress.

[0061] When generating custom content, the summary generation unit can automatically add related topics based on the user's interests and concerns. In the summary generation unit, for example, the generation AI automatically adds related topics based on the user's interests and concerns. For example, if the user is interested in history, related historical events are added. The summary generation unit also automatically adds related topics based on the user's interests and concerns. For example, if the user is interested in science, related scientific topics are added. The summary generation unit also automatically adds related topics based on the user's interests and concerns. For example, if the user is interested in literature, related literary works are added. In this way, related topics can be added based on the user's interests and concerns.

[0062] When generating custom content, the summary generation unit can generate content that corresponds to different learning styles. In the summary generation unit, for example, the generation AI generates content that corresponds to different learning styles. For example, it generates content that makes extensive use of diagrams and graphs for visual learners. In addition, the summary generation unit generates content that corresponds to different learning styles. For example, it generates content that includes audio commentary for auditory learners. In addition, the summary generation unit generates content that corresponds to different learning styles. For example, it generates content that includes interactive elements for tactile learners. In this way, custom content that corresponds to different learning styles can be generated.

[0063] When generating custom content, the summary generation unit can adjust the length of the content so that the user can complete the study within the time specified by the user. The summary generation unit, for example, adjusts the length of the content so that the generation AI can complete the study within the time specified by the user. For example, it summarizes the content so that it can be studied in 30 minutes or less. The summary generation unit also adjusts the length of the content so that the study can be completed within the specified time. For example, it summarizes the content so that it can be studied in 1 hour or less. The summary generation unit also adjusts the length of the content so that the generation AI can complete the study within the time specified by the user. For example, it summarizes the content so that it can be studied in 15 minutes or less. In this way, the length of the content can be adjusted so that the user can complete the study within the time specified by the user.

[0064] When generating custom content, the summary generation unit can use the emotion estimation function to suggest a learning pace that corresponds to the user's emotions. In the summary generation unit, for example, the generation AI uses the emotion estimation function to suggest a learning pace that corresponds to the user's emotions. For example, if the user is feeling stressed, the summary generation unit suggests progressing through the learning at a slower pace. In addition, the summary generation unit uses the emotion estimation function to suggest a learning pace that corresponds to the user's emotions. For example, if the user is excited, the summary generation unit suggests progressing through the learning at a shorter pace to improve concentration. In addition, the summary generation unit uses the emotion estimation function to suggest a learning pace that corresponds to the user's emotions. For example, if the user is tired, the summary generation unit suggests progressing through the learning with breaks. In this way, a learning pace that corresponds to the user's emotions can be suggested.

[0065] The avatar generation unit can use the emotion estimation function to reflect facial expressions and gestures corresponding to the user's emotions on the avatar. In the avatar generation unit, for example, the generation AI uses the emotion estimation function to reflect facial expressions and gestures corresponding to the user's emotions on the avatar. For example, if the user is happy, the avatar speaks with a smile. The avatar generation unit also uses the emotion estimation function to reflect facial expressions and gestures corresponding to the user's emotions on the avatar. For example, if the user is surprised, the avatar has a surprised expression. The avatar generation unit also uses the emotion estimation function to reflect facial expressions and gestures corresponding to the user's emotions on the avatar. For example, if the user is sad, the avatar has a sad expression. In this way, facial expressions and gestures corresponding to the user's emotions can be reflected on the avatar.

[0066] The avatar generation unit provides customization options according to the user's preferences, and can adjust the avatar's appearance and voice. In the avatar generation unit, for example, the generation AI provides customization options according to the user's preferences and adjusts the avatar's appearance and voice. For example, the hairstyle and clothing selected by the user are reflected in the avatar. The avatar generation unit also provides customization options according to the user's preferences and adjusts the avatar's appearance and voice. For example, the voice tone and accent selected by the user are reflected in the avatar. In addition, the avatar generation unit provides customization options according to the user's preferences and adjusts the avatar's appearance and voice. For example, the avatar's behavior is adjusted according to the background and scenario selected by the user. This allows the avatar generation unit to provide customization options according to the user's preferences and adjust the avatar's appearance and voice.

[0067] The avatar generation unit can receive user feedback in real time and adjust the avatar's movements and speech. In the avatar generation unit, for example, the generation AI receives user feedback in real time and adjusts the avatar's movements and speech. For example, if the user instructs, "Speak more slowly," the avatar adjusts the speed at which it speaks. The avatar generation unit also receives user feedback in real time and adjusts the avatar's movements and speech. For example, if the user instructs, "Explain this part in more detail," the avatar adds a detailed explanation. The avatar generation unit also receives user feedback in real time and adjusts the avatar's movements and speech. For example, if the user instructs, "Change the background," the avatar's background is changed. This allows the avatar to receive user feedback in real time and adjust the avatar's movements and speech.

[0068] The avatar generation unit generates avatars that correspond to different cultures and regions, making it possible to accommodate global users. For example, the generation AI of the avatar generation unit generates avatars that correspond to different cultures and regions, making it possible to accommodate global users. For example, the avatar's clothing and language are customized to suit the region. The avatar generation unit also generates avatars that correspond to different cultures and regions, making it possible to accommodate global users. For example, the avatar's gestures and expressions are adjusted to suit the culture. The avatar generation unit also generates avatars that correspond to different cultures and regions, making it possible to accommodate global users. For example, the avatar's background and scenario are changed to suit the characteristics of the region. This makes it possible to generate avatars that correspond to different cultures and regions, making it possible to accommodate global users.

[0069] The avatar generation unit can adjust the avatar's actions and speech according to the background and scenario selected by the user. For example, the generation AI of the avatar generation unit adjusts the avatar's actions and speech according to the background and scenario selected by the user. For example, the content of what the avatar says is adjusted to match the historical background selected by the user. The avatar generation unit also adjusts the avatar's actions and speech according to the background and scenario selected by the user. For example, the avatar explains an experiment according to a scientific scenario selected by the user. The avatar generation unit also adjusts the avatar's actions and speech according to the background and scenario selected by the generation AI. For example, the avatar tells a story according to a literary background selected by the user. This allows the avatar's actions and speech to be adjusted according to the background and scenario selected by the user.

[0070] The avatar generation unit can use the emotion estimation function to adjust the tone and speaking style of the avatar according to the user's emotions. In the avatar generation unit, for example, the generation AI uses the emotion estimation function to adjust the tone and speaking style of the avatar according to the user's emotions. For example, if the user is depressed, the avatar speaks in a gentle tone. In addition, the avatar generation unit uses the emotion estimation function to adjust the tone and speaking style of the avatar according to the user's emotions. For example, if the user is excited, the avatar speaks in an energetic tone. In addition, the avatar generation unit uses the emotion estimation function to adjust the tone and speaking style of the avatar according to the user's emotions. For example, if the user is tired, the avatar speaks in a calm tone. In this way, the avatar's tone and speaking style can be adjusted according to the user's emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The Edu-generated AI avatar coach can also be customized to fit a user's learning style. For example, for visual learners, infographics and animations related to the content the avatar is explaining can be added. For auditory learners, background music and sound effects can be incorporated in addition to the avatar's audio commentary. Furthermore, interactive quizzes and simulations can be provided for tactile learners. This allows for the optimal learning experience to be tailored to each user's learning style.

[0073] The Edu-generated AI avatar coach can also adjust learning content based on the user's emotional state. For example, if a user is stressed, it can provide relaxing content and a learning pace. If a user is excited, it can provide short learning content to improve focus. If a user is sad, it can provide positive content to brighten the mood. This allows for an optimal learning experience tailored to the user's emotional state.

[0074] The Edu-generated AI avatar coach can also suggest optimal learning content based on the user's learning history and progress. For example, it can suggest the next topic to study based on past learning content. It can also generate content that focuses on areas in which the user is weak. It can also generate custom content that matches the user's learning pace. This allows it to suggest optimal learning content based on the user's learning history and progress.

[0075] The Edu-generated AI avatar coach can also automatically add related topics based on the user's interests. For example, if a user is interested in history, related historical events can be added. If a user is interested in science, related scientific topics can be added. Furthermore, if a user is interested in literature, related literary works can be added. This allows related topics to be added based on the user's interests.

[0076] The Edu-generated AI avatar coach can also adjust the length of the content so that the user can complete the learning within the time specified by the user. For example, it can summarize the content so that it can be learned in 30 minutes or less, or it can summarize the content so that it can be learned in 1 hour or less, or it can summarize the content so that it can be learned in 15 minutes or less. This allows the length of the content to be adjusted so that the user can complete the learning within the time specified by the user.

[0077] The Edu-generated AI avatar coach can also use its emotion estimation function to suggest a learning pace that matches the user's emotions. For example, if the user is feeling stressed, it can suggest studying at a slower pace. If the user is excited, it can suggest studying for a shorter period of time to improve concentration. Furthermore, if the user is tired, it can suggest taking breaks while studying. This makes it possible to suggest a learning pace that matches the user's emotions.

[0078] The Edu-generated AI Avatar Coach also uses an emotion estimation function to reflect facial expressions and gestures that correspond to the user's emotions on the avatar. For example, if the user is happy, the avatar can speak with a smile. If the user is surprised, the avatar can have a surprised expression. If the user is sad, the avatar can have a sad expression. This allows the avatar to reflect facial expressions and gestures that correspond to the user's emotions.

[0079] The Edu-generated AI avatar coach can also receive user feedback in real time and adjust the avatar's behavior and speech accordingly. For example, if the user instructs the avatar to "speak more slowly," the avatar can adjust its speaking speed. If the user instructs the avatar to "explain this part in more detail," the avatar can add a detailed explanation. Furthermore, if the user instructs the avatar to "change the background," the avatar's background can be changed. This allows the avatar to receive user feedback in real time and adjust the avatar's behavior and speech accordingly.

[0080] The Edu Generative AI Avatar Coach can also generate avatars that are compatible with different cultures and regions, making it suitable for global users. For example, the avatar's clothing and language can be customized to suit the region. The avatar's gestures and expressions can also be adjusted to suit the culture. Furthermore, the avatar's background and scenario can be changed to suit the characteristics of the region. This allows the generation of avatars that are compatible with different cultures and regions, making it suitable for global users.

[0081] The Edu-generated AI Avatar Coach can further use its emotion estimation function to adjust the avatar's tone and speaking style according to the user's emotions. For example, if the user is depressed, the avatar can speak in a gentle tone. If the user is excited, the avatar can speak in an energetic tone. If the user is tired, the avatar can speak in a calm tone. This allows the avatar's tone and speaking style to be adjusted according to the user's emotions.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The text analysis unit analyzes the text. For example, the text analysis unit analyzes the content of reference books and documents and extracts important key points. The text analysis unit can also analyze the meaning of the text using natural language processing techniques. For example, the text analysis unit extracts important events and people from history textbooks and summarizes them concisely. Step 2: The summary generation unit generates a summary from the text analyzed by the text analysis unit. For example, the summary generation unit generates a summary of the text using a generation AI (e.g., a text generation AI or a multimodal generation AI). The summary generation unit can also generate a summary by extracting important parts of a sentence. For example, the summary generation unit generates a summary by prompting the generation AI, such as "Please summarize the main points of this sentence." Step 3: The avatar generator generates an avatar based on the summary generated by the summary generator. For example, the avatar generator customizes the virtual avatar and generates a video for the avatar to provide explanatory or educational content. The avatar generator can also enable the avatar to speak in real time. For example, the avatar generator can change the avatar's appearance, tone of voice, background design, etc. Step 4: The video generation unit generates a video using the avatar generated by the avatar generation unit. For example, the video generation unit generates a video in which the avatar gives a history lesson and explains important events. The video generation unit can also generate a video in which the avatar combines visual elements to visually convey information. For example, the video generation unit incorporates images and diagrams related to the content explained by the avatar into the video.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 text analysis unit that analyzes text; a summary generation unit that generates a summary from the text analyzed by the text analysis unit; an avatar generation unit that generates an avatar based on the summary generated by the summary generation unit; a video generation unit that generates a video using the avatar generated by the avatar generation unit. A system characterized by:

2. The text analysis unit Automatically search for relevant images and charts during text analysis and incorporate them into the summary 2. The system of claim 1.

3. The text analysis unit Analyze texts in different languages ​​simultaneously and generate multilingual summaries 2. The system of claim 1.

4. The summary generation unit When generating custom content, generate content according to the user's emotional state 2. The system of claim 1.

5. The avatar generation unit The avatar is made to reflect facial expressions and gestures according to the user's emotions.

2. The system of claim 1.

6. The text analysis unit Extract emotional nuances from the text during text analysis and reflect them in the summary.

2. The system of claim 1.

7. The text analysis unit Analyzes the sentiment of user-entered text and generates summaries that evoke positive sentiment.

2. The system of claim 1.

8. The avatar generation unit Adjusting the avatar's tone and speaking style according to the user's emotions 2. The system of claim 1.

Images