system

Abstract

The system according to this embodiment aims to provide optimal content tailored to the user's knowledge level and learning goals, thereby enabling efficient knowledge acquisition. [Solution] The system according to the embodiment comprises a summarization unit, a recommendation unit, and a visualization unit. The summarization unit summarizes and extracts various content such as articles, audio, and video. The recommendation unit recommends content based on the content extracted by the summarization unit, according to the user's knowledge level and learning goals. The visualization unit visualizes the user's level of understanding and progress based on the content recommended by the recommendation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully achieved to efficiently extract necessary information from various contents and provide it according to the user's knowledge level and learning goals, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal content according to the user's knowledge level and learning goals and enable the user to efficiently acquire knowledge.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a summarization unit, a recommendation unit, and a visualization unit. The summarization unit summarizes and extracts various content such as articles, audio, and video. The recommendation unit recommends content based on the content extracted by the summarization unit, according to the user's knowledge level and learning goals. The visualization unit visualizes the user's understanding and progress based on the content recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to this embodiment provides optimal content tailored to the user's knowledge level and learning goals, enabling them to acquire knowledge efficiently. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent service according to an embodiment of the present invention is an AI agent service that allows users to quickly access what they "want to know" and "want to learn" in their busy daily lives and acquire knowledge efficiently. The AI ​​agent service summarizes and extracts various content such as articles, audio, and video, and recommends content according to the user's knowledge level and learning goals. Furthermore, the AI ​​agent service visualizes the user's level of understanding and progress, and automatically optimizes the path of information gathering and learning, supporting the user to reach their goals in the shortest possible time. For example, the AI ​​agent service summarizes and extracts various content such as articles, audio, and video. In this process, the AI ​​extracts the important points of each piece of content and summarizes them so that they can be understood in a short time. For example, it can summarize a long article into a few lines or a long video into a few minutes. This allows the user to obtain the necessary information in a short time. Next, the AI ​​agent service recommends the most suitable content according to the user's knowledge level and learning goals. For example, it can recommend content for beginners or content specialized in a particular field. This allows the user to efficiently learn content that suits them. Furthermore, the AI ​​agent service visualizes the user's level of understanding and progress, automatically optimizing information gathering and learning paths. For example, it can display the user's level of understanding and progress using graphs and charts. This allows users to grasp their learning status and clearly identify what they should learn next. This system enables users to acquire knowledge efficiently even in their busy daily lives. For example, business professionals can efficiently acquire knowledge useful for their work, and students and learners can absorb a lot of knowledge in a short time. Researchers and experts can also efficiently conduct research by summarizing broad and advanced information. The AI ​​agent service is designed to solve problems such as information overload, lack of time, and difficulty in visualizing the level of acquisition. For example, regarding the problem that even just searching for information takes time and effort, the AI ​​can summarize and provide information, significantly reducing the time spent on information gathering.Furthermore, addressing the challenge of limited time available for information gathering and learning due to busy schedules, AI can efficiently recommend necessary information and content based on learning progress and knowledge level. Additionally, addressing the difficulty of understanding how well information is being understood and how far learning is progressing, AI can support learning by visualizing progress and comprehension. AI agent services also hold significant importance in the EdTech market. For example, the global EdTech market is projected to exceed 38 trillion yen by 2025, and by providing value such as rapid information acquisition, personalized learning support, and visualization of comprehension and progress, AI agent services are expected to be used by many users. This allows users to acquire knowledge efficiently.

[0029] The AI ​​agent service according to this embodiment comprises a summarization unit, a recommendation unit, and a visualization unit. The summarization unit summarizes and extracts various types of content, such as articles, audio, and video. For example, the summarization unit can condense a long article into a few lines. It can also condense a long video into a few minutes. Furthermore, the summarization unit can summarize audio content so that it can be understood in a short amount of time. For example, the summarization unit can extract the key points of an article and summarize it so that it can be understood in a short amount of time. For example, the summarization unit can extract key scenes from a video and summarize them so that they can be viewed in a short amount of time. For example, the summarization unit can extract key parts of audio content and summarize them so that they can be listened to in a short amount of time. The recommendation unit recommends content based on the content extracted by the summarization unit, according to the user's knowledge level and learning goals. For example, the recommendation unit can recommend content for beginners. It can also recommend content specialized in a particular field. Furthermore, the recommendation unit can recommend the most suitable content according to the user's learning goals. For example, the recommendation unit recommends beginner-friendly content based on the user's knowledge level. The recommendation unit can also recommend content specialized in a specific field. The recommendation unit can also recommend content that is optimal for the user's learning goals. The visualization unit visualizes the user's understanding and progress based on the content recommended by the recommendation unit. The visualization unit can display the user's understanding in graphs or charts. It can also display the user's progress in graphs or charts. Furthermore, the visualization unit can automatically optimize the user's learning path. For example, the visualization unit displays the user's understanding in a graph. The visualization unit can also display the user's progress in a chart. The visualization unit can also automatically optimize the user's learning path. As a result, the AI ​​agent service according to this embodiment allows the user to acquire knowledge efficiently.

[0030] The summarization unit summarizes and extracts information from various types of content, including articles, audio, and video. For example, it can condense a long article into a few lines. It can also condense a long video into a few minutes. Furthermore, it can summarize audio content so that it can be understood in a short amount of time. Specifically, the summarization unit utilizes natural language processing technology to extract the key points of an article and summarize them for quick understanding. For example, it extracts keywords and important phrases from an article and generates a summary based on them. In the case of videos, the summarization unit uses video analysis technology to extract important scenes and summarize them for quick viewing. For example, it automatically detects scenes that particularly interest viewers or scenes with a large amount of information in a video and combines them to create a shortened version. For audio content, it uses speech recognition technology to convert audio into text and extracts important parts from that text for summarization. For example, it analyzes the audio of a lecture or interview and extracts points that the speaker wants to emphasize or important information so that it can be understood in a short amount of time. By combining these technologies, the summarization unit efficiently summarizes various types of content, enabling users to obtain important information in a short amount of time. Furthermore, the summarization function can customize the style and content of summaries according to the user's preferences and interests. For example, if a user is interested in a particular topic, it will prioritize summarizing information related to that topic. The summarization function can also continuously learn and improve to enhance the accuracy of its summaries. This allows the summarization function to provide the most suitable summary for the user and support efficient information acquisition.

[0031] The recommendation section recommends content based on the content extracted by the summarization section, tailoring it to the user's knowledge level and learning goals. For example, the recommendation section can recommend content suitable for beginners. It can also recommend content specialized in specific fields. Furthermore, it can recommend content that is optimally suited to the user's learning goals. Specifically, the recommendation section analyzes the user's past learning history and interests, and selects the most relevant content based on this analysis. For example, it recommends highly relevant content based on the user's past viewing history of articles and videos. It also suggests what the user should learn next, taking into account their learning progress and understanding. The recommendation section uses machine learning algorithms to analyze user behavior patterns and provide content optimized for each individual user. For example, if a user is interested in a particular field, it recommends the latest research findings and news articles related to that field. If a user wants to acquire a specific skill, it suggests learning materials and training videos related to that skill. Furthermore, the recommendation system collects user feedback and uses it to improve the accuracy of recommendations. For example, users can rate the recommended content, and the system adjusts the next recommendation based on that rating. This allows the recommendation system to provide optimal content tailored to the user's needs and improve learning efficiency.

[0032] The visualization unit visualizes the user's understanding and progress based on the content recommended by the recommendation unit. For example, the visualization unit can display the user's understanding in graphs and charts. It can also display the user's progress in graphs and charts. Furthermore, the visualization unit can automatically optimize the user's learning path. Specifically, the visualization unit collects the user's learning data and visually displays the level of understanding and progress based on it. For example, by recording the content and time the user has studied and displaying it in graphs and charts, it allows users to see at a glance how far they have progressed in their learning. It can also display scores based on the results of quizzes and tests to evaluate the user's understanding. Based on this data, the visualization unit analyzes the user's learning trends and weaknesses and optimizes the learning path. For example, if a user lacks understanding in a particular area, it prioritizes recommending content related to that area. It also supports efficient learning by suggesting what the user should learn next according to their learning progress. Furthermore, the visualization unit can also evaluate relative understanding and progress by comparing the user's learning data with that of other users. This allows users to objectively understand their learning progress and maintain motivation. Through these functions, the visualization unit can effectively support user learning and promote knowledge acquisition.

[0033] The summarization unit can summarize and extract information from various types of content, such as articles, audio, and video. For example, the summarization unit can condense a long article into a few lines. For example, it can condense a long video into a few minutes. For example, it can summarize audio content so that it can be understood in a short amount of time. This allows users to obtain the necessary information quickly by summarizing and extracting information from various types of content. Some or all of the processing described above in the summarization unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the summarization unit can input various types of content, such as articles, audio, and video, into a generative AI, which can then generate a summary.

[0034] The recommendation unit can recommend the most suitable content according to the user's knowledge level and learning goals. For example, the recommendation unit can recommend content for beginners. For example, the recommendation unit can recommend content specialized in a particular field. For example, the recommendation unit can recommend the most suitable content according to the user's learning goals. This allows for efficient learning by recommending the most suitable content according to the user's knowledge level and learning goals. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data on the user's knowledge level and learning goals into a generative AI, which can then recommend the most suitable content.

[0035] The visualization unit can visualize the user's level of understanding and progress, and automatically optimize the information gathering and learning path. For example, the visualization unit can display the user's level of understanding in graphs and charts. For example, the visualization unit can also display the user's progress in graphs and charts. For example, the visualization unit can automatically optimize the user's learning path. This helps users reach their goals in the shortest possible time by visualizing their level of understanding and progress and automatically optimizing the information gathering and learning path. Some or all of the above-described processes in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input data on the user's level of understanding and progress into a generative AI, which can then perform the visualization.

[0036] The visualization unit can display the user's level of understanding and progress in graphs and charts. For example, the visualization unit can display the user's level of understanding in a graph. For example, the visualization unit can also display the user's progress in a chart. For example, the visualization unit can automatically optimize the user's learning path. This makes it easier to grasp the user's learning status by displaying their level of understanding and progress in graphs and charts. Some or all of the above-described processes in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input data on the user's level of understanding and progress into a generative AI, which can then generate graphs and charts.

[0037] The recommendation unit can recommend content suitable for beginners or content specialized in specific fields. For example, the recommendation unit can recommend content suitable for beginners. The recommendation unit can also recommend content specialized in specific fields. For example, the recommendation unit can recommend content that is optimal for the user's learning goals. This allows users to efficiently learn content that suits them by recommending content suitable for beginners or content specialized in specific fields. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data about the user's knowledge level and learning goals into a generative AI, which can then recommend the optimal content.

[0038] The summarization unit can adjust the level of detail in the summary based on the importance of the content. For example, the summarization unit can summarize content containing important information in detail. For example, it can summarize information of low importance concisely. For example, it can also highlight and summarize the most important parts. In this way, by adjusting the level of detail in the summary based on the importance of the content, important information can be summarized in more detail. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input data on the importance of the content into a generative AI, which can then adjust the level of detail in the summary.

[0039] The summarization unit can apply different summarization algorithms depending on the content category. For example, the summarization unit can apply a news-specific summarization algorithm to news articles. For example, the summarization unit can apply a scholarly paper-specific summarization algorithm to academic papers. For example, the summarization unit can apply an entertainment-specific summarization algorithm to entertainment content. By applying different summarization algorithms depending on the content category, a more appropriate summary can be provided. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input data about the content category into a generative AI, and the generative AI can apply different summarization algorithms.

[0040] The summarization unit can determine the priority of summaries by considering the credibility of the content creators. For example, the summarization unit can prioritize summarizing content from highly credible creators. For example, the summarization unit can also postpone summarizing content from less credible creators. For example, the summarization unit can adjust the level of detail of the summaries based on the credibility of the creators. This allows for the priority provision of highly reliable information by determining the priority of summaries by considering the credibility of the content creators. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input data on the credibility of the content creators into a generative AI, which can then determine the priority of summaries.

[0041] The summarization unit can adjust the order of summaries based on the relevance of the content. For example, the summarization unit can prioritize summarizing content relevant to the user's interests. For example, the summarization unit can postpone summarizing less relevant content. For example, the summarization unit can highlight highly relevant parts in the summary. This allows for the prioritization of information relevant to the user's interests by adjusting the order of summaries based on the relevance of the content. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input data on the relevance of the content into a generative AI, which can then adjust the order of the summaries.

[0042] The recommendation unit can select the most suitable content by referring to the user's learning history. For example, the recommendation unit can recommend relevant content based on what the user has learned in the past. For example, the recommendation unit can suggest what the user should learn next based on their learning history. For example, the recommendation unit can analyze the user's learning history and suggest the most efficient learning route. This allows for efficient learning by selecting the most suitable content by referring to the user's learning history. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data about the user's learning history into a generative AI, which can then select the most suitable content.

[0043] The recommendation unit can customize the content of its recommendations based on the user's current learning status. For example, the recommendation unit can recommend content related to what the user is currently learning. For example, the recommendation unit can also suggest what the user should learn next, depending on their learning progress. For example, the recommendation unit can analyze the user's current learning status and suggest the optimal learning route. This allows for efficient learning by customizing the content of recommendations based on the user's current learning status. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data about the user's current learning status into a generative AI, which can then customize the content of its recommendations.

[0044] The recommendation unit can determine the priority of recommendations by considering the trustworthiness of the content creators. For example, the recommendation unit can prioritize recommending content from trustworthy creators. For example, the recommendation unit can also postpone recommending content from untrustworthy creators. For example, the recommendation unit can adjust the level of detail of recommendations based on the trustworthiness of the creators. This allows for the priority provision of trustworthy information by determining the priority of recommendations by considering the trustworthiness of the content creators. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation unit can input data on the trustworthiness of content creators into a generative AI, and the generative AI can determine the priority of recommendations.

[0045] The recommendation unit can adjust the order of recommendations based on the relevance of the content. For example, the recommendation unit can prioritize recommending content related to the user's interests. For example, the recommendation unit can postpone recommending less relevant content. For example, the recommendation unit can highlight and recommend highly relevant parts. In this way, by adjusting the order of recommendations based on the relevance of the content, information related to the user's interests can be provided preferentially. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data on the relevance of the content into a generative AI, and the generative AI can adjust the order of recommendations.

[0046] The visualization unit can select the optimal display method by referring to the user's learning history. For example, the visualization unit can display relevant information based on what the user has learned in the past. For example, the visualization unit can also display what the user should learn next based on their learning history. For example, the visualization unit can analyze the user's learning history and display the most efficient learning route. This allows for efficient learning by selecting the optimal display method by referring to the user's learning history. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input data about the user's learning history into a generative AI, which can then select the optimal display method.

[0047] The visualization unit can customize the displayed content based on the user's current learning status. For example, the visualization unit can display information related to the content the user is currently learning. For example, the visualization unit can also display the content the user should learn next, according to their learning progress. For example, the visualization unit can analyze the user's current learning status and display the optimal learning route. This allows for efficient learning by customizing the displayed content based on the user's current learning status. Some or all of the above-described processes in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input data about the user's current learning status into a generative AI, which can then customize the displayed content.

[0048] The visualization unit can prioritize displaying highly relevant information by considering the user's geographical location. For example, the visualization unit can prioritize displaying information related to the user's current location. For example, the visualization unit can also display relevant information based on the user's geographical location. For example, the visualization unit can analyze the user's geographical location and display the most relevant information. This allows for the provision of more appropriate information by prioritizing the display of highly relevant information by considering the user's geographical location. Some or all of the above processing in the visualization unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the visualization unit can input data about the user's geographical location into a generating AI, which can then prioritize displaying highly relevant information.

[0049] The visualization unit can analyze a user's social media activity and display relevant information. For example, the visualization unit can prioritize displaying information that the user has shown interest in on social media. For example, the visualization unit can also display relevant information based on the user's social media activity. For example, the visualization unit can analyze a user's social media activity and display the most relevant information. This allows for the provision of more appropriate information by analyzing the user's social media activity and displaying relevant information. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input data about the user's social media activity into a generative AI, and the generative AI can display relevant information.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The summary section can adjust the level of detail based on the importance of the content. For example, content containing important information can be summarized in detail. Conversely, less important information can be summarized concisely. Furthermore, it can highlight the most important parts of the summary. This allows for a more detailed summary of important information by adjusting the level of detail based on the importance of the content.

[0052] The summarization section can apply different summarization algorithms depending on the content category. For example, a news article can be summarized using a summarization algorithm specifically designed for news. Similarly, academic papers can be summarized using a summarization algorithm specifically designed for academic papers. Furthermore, entertainment content can be summarized using an entertainment-specific summarization algorithm. By applying different summarization algorithms according to the content category, a more appropriate summary can be provided.

[0053] The recommendation system can select the most suitable content by referring to the user's learning history. For example, it can recommend related content based on what the user has learned in the past. It can also suggest what the user should learn next based on their learning history. Furthermore, it can analyze the user's learning history and suggest the most efficient learning route. In this way, by selecting the most suitable content by referring to the user's learning history, they can learn more efficiently.

[0054] The recommendation system can prioritize recommendations by considering the credibility of the content creators. For example, it can prioritize recommending content from highly reliable creators, while delaying recommendations for content from less reliable creators. Furthermore, it can adjust the level of detail in recommendations based on the creator's credibility. This allows for the priority provision of highly reliable information by prioritizing recommendations based on the credibility of the content creators.

[0055] The visualization unit can prioritize displaying highly relevant information by considering the user's geographical location. For example, it can prioritize displaying information related to the user's current location. It can also display relevant information based on the user's geographical location. Furthermore, it can analyze the user's geographical location and display the most relevant information. By prioritizing the display of highly relevant information while considering the user's geographical location, it can provide more appropriate information.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The summarization section summarizes and extracts information from various types of content, such as articles, audio, and video. For example, it might condense a long article into a few lines, a long video into a few minutes, or summarize audio content so that it can be understood in a short amount of time. The summarization section extracts the key points of an article, the important scenes of a video, and the important parts of audio content, making them easy to understand, view, and listen to in a short amount of time. Step 2: The recommendation section recommends content based on the content extracted by the summarization section, tailored to the user's knowledge level and learning goals. For example, it recommends content for beginners or content specializing in specific fields, providing the optimal content to match the user's learning goals. Step 3: The visualization unit visualizes the user's understanding and progress based on the content recommended by the recommendation unit. For example, it displays the user's understanding and progress in graphs and charts, and automatically optimizes the user's learning path.

[0058] (Example of form 2) The AI ​​agent service according to an embodiment of the present invention is an AI agent service that allows users to quickly access what they "want to know" and "want to learn" in their busy daily lives and acquire knowledge efficiently. The AI ​​agent service summarizes and extracts various content such as articles, audio, and video, and recommends content according to the user's knowledge level and learning goals. Furthermore, the AI ​​agent service visualizes the user's level of understanding and progress, and automatically optimizes the path of information gathering and learning, supporting the user to reach their goals in the shortest possible time. For example, the AI ​​agent service summarizes and extracts various content such as articles, audio, and video. In this process, the AI ​​extracts the important points of each piece of content and summarizes them so that they can be understood in a short time. For example, it can summarize a long article into a few lines or a long video into a few minutes. This allows the user to obtain the necessary information in a short time. Next, the AI ​​agent service recommends the most suitable content according to the user's knowledge level and learning goals. For example, it can recommend content for beginners or content specialized in a particular field. This allows the user to efficiently learn content that suits them. Furthermore, the AI ​​agent service visualizes the user's level of understanding and progress, automatically optimizing information gathering and learning paths. For example, it can display the user's level of understanding and progress using graphs and charts. This allows users to grasp their learning status and clearly identify what they should learn next. This system enables users to acquire knowledge efficiently even in their busy daily lives. For example, business professionals can efficiently acquire knowledge useful for their work, and students and learners can absorb a lot of knowledge in a short time. Researchers and experts can also efficiently conduct research by summarizing broad and advanced information. The AI ​​agent service is designed to solve problems such as information overload, lack of time, and difficulty in visualizing the level of acquisition. For example, regarding the problem that even just searching for information takes time and effort, the AI ​​can summarize and provide information, significantly reducing the time spent on information gathering.Furthermore, addressing the challenge of limited time available for information gathering and learning due to busy schedules, AI can efficiently recommend necessary information and content based on learning progress and knowledge level. Additionally, addressing the difficulty of understanding how well information is being understood and how far learning is progressing, AI can support learning by visualizing progress and comprehension. AI agent services also hold significant importance in the EdTech market. For example, the global EdTech market is projected to exceed 38 trillion yen by 2025, and by providing value such as rapid information acquisition, personalized learning support, and visualization of comprehension and progress, AI agent services are expected to be used by many users. This allows users to acquire knowledge efficiently.

[0059] The AI ​​agent service according to this embodiment comprises a summarization unit, a recommendation unit, and a visualization unit. The summarization unit summarizes and extracts various types of content, such as articles, audio, and video. For example, the summarization unit can condense a long article into a few lines. It can also condense a long video into a few minutes. Furthermore, the summarization unit can summarize audio content so that it can be understood in a short amount of time. For example, the summarization unit can extract the key points of an article and summarize it so that it can be understood in a short amount of time. For example, the summarization unit can extract key scenes from a video and summarize them so that they can be viewed in a short amount of time. For example, the summarization unit can extract key parts of audio content and summarize them so that they can be listened to in a short amount of time. The recommendation unit recommends content based on the content extracted by the summarization unit, according to the user's knowledge level and learning goals. For example, the recommendation unit can recommend content for beginners. It can also recommend content specialized in a particular field. Furthermore, the recommendation unit can recommend the most suitable content according to the user's learning goals. For example, the recommendation unit recommends beginner-friendly content based on the user's knowledge level. The recommendation unit can also recommend content specialized in a specific field. The recommendation unit can also recommend content that is optimal for the user's learning goals. The visualization unit visualizes the user's understanding and progress based on the content recommended by the recommendation unit. The visualization unit can display the user's understanding in graphs or charts. It can also display the user's progress in graphs or charts. Furthermore, the visualization unit can automatically optimize the user's learning path. For example, the visualization unit displays the user's understanding in a graph. The visualization unit can also display the user's progress in a chart. The visualization unit can also automatically optimize the user's learning path. As a result, the AI ​​agent service according to this embodiment allows the user to acquire knowledge efficiently.

[0060] The summarization unit summarizes and extracts information from various types of content, including articles, audio, and video. For example, it can condense a long article into a few lines. It can also condense a long video into a few minutes. Furthermore, it can summarize audio content so that it can be understood in a short amount of time. Specifically, the summarization unit utilizes natural language processing technology to extract the key points of an article and summarize them for quick understanding. For example, it extracts keywords and important phrases from an article and generates a summary based on them. In the case of videos, the summarization unit uses video analysis technology to extract important scenes and summarize them for quick viewing. For example, it automatically detects scenes that particularly interest viewers or scenes with a large amount of information in a video and combines them to create a shortened version. For audio content, it uses speech recognition technology to convert audio into text and extracts important parts from that text for summarization. For example, it analyzes the audio of a lecture or interview and extracts points that the speaker wants to emphasize or important information so that it can be understood in a short amount of time. By combining these technologies, the summarization unit efficiently summarizes various types of content, enabling users to obtain important information in a short amount of time. Furthermore, the summarization function can customize the style and content of summaries according to the user's preferences and interests. For example, if a user is interested in a particular topic, it will prioritize summarizing information related to that topic. The summarization function can also continuously learn and improve to enhance the accuracy of its summaries. This allows the summarization function to provide the most suitable summary for the user and support efficient information acquisition.

[0061] The recommendation section recommends content based on the content extracted by the summarization section, tailoring it to the user's knowledge level and learning goals. For example, the recommendation section can recommend content suitable for beginners. It can also recommend content specialized in specific fields. Furthermore, it can recommend content that is optimally suited to the user's learning goals. Specifically, the recommendation section analyzes the user's past learning history and interests, and selects the most relevant content based on this analysis. For example, it recommends highly relevant content based on the user's past viewing history of articles and videos. It also suggests what the user should learn next, taking into account their learning progress and understanding. The recommendation section uses machine learning algorithms to analyze user behavior patterns and provide content optimized for each individual user. For example, if a user is interested in a particular field, it recommends the latest research findings and news articles related to that field. If a user wants to acquire a specific skill, it suggests learning materials and training videos related to that skill. Furthermore, the recommendation system collects user feedback and uses it to improve the accuracy of recommendations. For example, users can rate the recommended content, and the system adjusts the next recommendation based on that rating. This allows the recommendation system to provide optimal content tailored to the user's needs and improve learning efficiency.

[0062] The visualization unit visualizes the user's understanding and progress based on the content recommended by the recommendation unit. For example, the visualization unit can display the user's understanding in graphs and charts. It can also display the user's progress in graphs and charts. Furthermore, the visualization unit can automatically optimize the user's learning path. Specifically, the visualization unit collects the user's learning data and visually displays the level of understanding and progress based on it. For example, by recording the content and time the user has studied and displaying it in graphs and charts, it allows users to see at a glance how far they have progressed in their learning. It can also display scores based on the results of quizzes and tests to evaluate the user's understanding. Based on this data, the visualization unit analyzes the user's learning trends and weaknesses and optimizes the learning path. For example, if a user lacks understanding in a particular area, it prioritizes recommending content related to that area. It also supports efficient learning by suggesting what the user should learn next according to their learning progress. Furthermore, the visualization unit can also evaluate relative understanding and progress by comparing the user's learning data with that of other users. This allows users to objectively understand their learning progress and maintain motivation. Through these functions, the visualization unit can effectively support user learning and promote knowledge acquisition.

[0063] The summarization unit can summarize and extract information from various types of content, such as articles, audio, and video. For example, the summarization unit can condense a long article into a few lines. For example, it can condense a long video into a few minutes. For example, it can summarize audio content so that it can be understood in a short amount of time. This allows users to obtain the necessary information quickly by summarizing and extracting information from various types of content. Some or all of the processing described above in the summarization unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the summarization unit can input various types of content, such as articles, audio, and video, into a generative AI, which can then generate a summary.

[0064] The recommendation unit can recommend the most suitable content according to the user's knowledge level and learning goals. For example, the recommendation unit can recommend content for beginners. For example, the recommendation unit can recommend content specialized in a particular field. For example, the recommendation unit can recommend the most suitable content according to the user's learning goals. This allows for efficient learning by recommending the most suitable content according to the user's knowledge level and learning goals. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data on the user's knowledge level and learning goals into a generative AI, which can then recommend the most suitable content.

[0065] The visualization unit can visualize the user's level of understanding and progress, and automatically optimize the information gathering and learning path. For example, the visualization unit can display the user's level of understanding in graphs and charts. For example, the visualization unit can also display the user's progress in graphs and charts. For example, the visualization unit can automatically optimize the user's learning path. This helps users reach their goals in the shortest possible time by visualizing their level of understanding and progress and automatically optimizing the information gathering and learning path. Some or all of the above-described processes in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input data on the user's level of understanding and progress into a generative AI, which can then perform the visualization.

[0066] The visualization unit can display the user's level of understanding and progress in graphs and charts. For example, the visualization unit can display the user's level of understanding in a graph. For example, the visualization unit can also display the user's progress in a chart. For example, the visualization unit can automatically optimize the user's learning path. This makes it easier to grasp the user's learning status by displaying their level of understanding and progress in graphs and charts. Some or all of the above-described processes in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input data on the user's level of understanding and progress into a generative AI, which can then generate graphs and charts.

[0067] The recommendation unit can recommend content suitable for beginners or content specialized in specific fields. For example, the recommendation unit can recommend content suitable for beginners. The recommendation unit can also recommend content specialized in specific fields. For example, the recommendation unit can recommend content that is optimal for the user's learning goals. This allows users to efficiently learn content that suits them by recommending content suitable for beginners or content specialized in specific fields. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data about the user's knowledge level and learning goals into a generative AI, which can then recommend the optimal content.

[0068] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is stressed, the summarization unit can provide a simple, to-the-point summary. If the user is relaxed, the summarization unit can also provide a summary that includes detailed information. If the user is in a hurry, the summarization unit can adjust the length of the summary to allow for quick comprehension. This allows for the provision of a more appropriate summary by adjusting the way the summary is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using a generative AI, or not. For example, the summarization unit can input user emotion data into a generative AI, which can then adjust the way the summary is presented.

[0069] The summarization unit can adjust the level of detail in the summary based on the importance of the content. For example, the summarization unit can summarize content containing important information in detail. For example, it can summarize information of low importance concisely. For example, it can also highlight and summarize the most important parts. In this way, by adjusting the level of detail in the summary based on the importance of the content, important information can be summarized in more detail. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input data on the importance of the content into a generative AI, which can then adjust the level of detail in the summary.

[0070] The summarization unit can apply different summarization algorithms depending on the content category. For example, the summarization unit can apply a news-specific summarization algorithm to news articles. For example, the summarization unit can apply a scholarly paper-specific summarization algorithm to academic papers. For example, the summarization unit can apply an entertainment-specific summarization algorithm to entertainment content. By applying different summarization algorithms depending on the content category, a more appropriate summary can be provided. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input data about the content category into a generative AI, and the generative AI can apply different summarization algorithms.

[0071] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is stressed, the summarization unit can provide a short, concise summary. For example, if the user is relaxed, the summarization unit can provide a longer summary containing more detailed information. For example, if the user is in a hurry, the summarization unit can adjust the length of the summary to allow for quick comprehension. This allows for the provision of more appropriate summaries by adjusting the length of the summary according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using a generative AI, or not. For example, the summarization unit can input user emotion data into a generative AI, which can then adjust the length of the summary.

[0072] The summarization unit can determine the priority of summaries by considering the credibility of the content creators. For example, the summarization unit can prioritize summarizing content from highly credible creators. For example, the summarization unit can also postpone summarizing content from less credible creators. For example, the summarization unit can adjust the level of detail of the summaries based on the credibility of the creators. This allows for the priority provision of highly reliable information by determining the priority of summaries by considering the credibility of the content creators. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input data on the credibility of the content creators into a generative AI, which can then determine the priority of summaries.

[0073] The summarization unit can adjust the order of summaries based on the relevance of the content. For example, the summarization unit can prioritize summarizing content relevant to the user's interests. For example, the summarization unit can postpone summarizing less relevant content. For example, the summarization unit can highlight highly relevant parts in the summary. This allows for the prioritization of information relevant to the user's interests by adjusting the order of summaries based on the relevance of the content. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input data on the relevance of the content into a generative AI, which can then adjust the order of the summaries.

[0074] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated emotions. For example, if the user is stressed, the recommendation unit can provide simple, to-the-point recommendations. If the user is relaxed, the recommendation unit can provide recommendations that include detailed information. If the user is in a hurry, the recommendation unit can adjust the length of the recommendations so that they can be understood quickly. This allows for the provision of more appropriate recommendations by adjusting the way recommendations are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or not. For example, the recommendation unit can input user emotion data into a generative AI, which can then adjust the way recommendations are presented.

[0075] The recommendation unit can select the most suitable content by referring to the user's learning history. For example, the recommendation unit can recommend relevant content based on what the user has learned in the past. For example, the recommendation unit can suggest what the user should learn next based on their learning history. For example, the recommendation unit can analyze the user's learning history and suggest the most efficient learning route. This allows for efficient learning by selecting the most suitable content by referring to the user's learning history. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data about the user's learning history into a generative AI, which can then select the most suitable content.

[0076] The recommendation unit can customize the content of its recommendations based on the user's current learning status. For example, the recommendation unit can recommend content related to what the user is currently learning. For example, the recommendation unit can also suggest what the user should learn next, depending on their learning progress. For example, the recommendation unit can analyze the user's current learning status and suggest the optimal learning route. This allows for efficient learning by customizing the content of recommendations based on the user's current learning status. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data about the user's current learning status into a generative AI, which can then customize the content of its recommendations.

[0077] The recommendation unit can estimate the user's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the user is stressed, the recommendation unit can provide short, concise recommendations. For example, if the user is relaxed, the recommendation unit can provide longer recommendations that include more detailed information. For example, if the user is in a hurry, the recommendation unit can adjust the length of recommendations to be easily understood in a short time. This allows for more appropriate recommendations to be provided by adjusting the length of recommendations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or not. For example, the recommendation unit can input user emotion data into a generative AI, which can then adjust the length of the recommendations.

[0078] The recommendation unit can determine the priority of recommendations by considering the trustworthiness of the content creators. For example, the recommendation unit can prioritize recommending content from trustworthy creators. For example, the recommendation unit can also postpone recommending content from untrustworthy creators. For example, the recommendation unit can adjust the level of detail of recommendations based on the trustworthiness of the creators. This allows for the priority provision of trustworthy information by determining the priority of recommendations by considering the trustworthiness of the content creators. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation unit can input data on the trustworthiness of content creators into a generative AI, and the generative AI can determine the priority of recommendations.

[0079] The recommendation unit can adjust the order of recommendations based on the relevance of the content. For example, the recommendation unit can prioritize recommending content related to the user's interests. For example, the recommendation unit can postpone recommending less relevant content. For example, the recommendation unit can highlight and recommend highly relevant parts. In this way, by adjusting the order of recommendations based on the relevance of the content, information related to the user's interests can be provided preferentially. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data on the relevance of the content into a generative AI, and the generative AI can adjust the order of recommendations.

[0080] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user emotions. For example, if the user is stressed, the visualization unit can provide a simple and highly visible display method. For example, if the user is relaxed, the visualization unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the visualization unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the visualization according to the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the visualization unit may be performed using a generative AI, for example, or without a generative AI. For example, the visualization unit can input user emotion data into a generative AI, and the generative AI can adjust the display method of the visualization.

[0081] The visualization unit can select the optimal display method by referring to the user's learning history. For example, the visualization unit can display relevant information based on what the user has learned in the past. For example, the visualization unit can also display what the user should learn next based on their learning history. For example, the visualization unit can analyze the user's learning history and display the most efficient learning route. This allows for efficient learning by selecting the optimal display method by referring to the user's learning history. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input data about the user's learning history into a generative AI, which can then select the optimal display method.

[0082] The visualization unit can customize the displayed content based on the user's current learning status. For example, the visualization unit can display information related to the content the user is currently learning. For example, the visualization unit can also display the content the user should learn next, according to their learning progress. For example, the visualization unit can analyze the user's current learning status and display the optimal learning route. This allows for efficient learning by customizing the displayed content based on the user's current learning status. Some or all of the above-described processes in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input data about the user's current learning status into a generative AI, which can then customize the displayed content.

[0083] The visualization unit can estimate the user's emotions and determine the visualization priority based on the estimated emotions. For example, if the user is stressed, the visualization unit can prioritize displaying important information. For example, if the user is relaxed, the visualization unit can prioritize displaying detailed information. For example, if the user is in a hurry, the visualization unit can prioritize displaying concise information. This allows for the provision of more appropriate information by determining the visualization priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the visualization unit may be performed using a generative AI, or not. For example, the visualization unit can input user emotion data into a generative AI, which can then determine the visualization priority.

[0084] The visualization unit can prioritize displaying highly relevant information by considering the user's geographical location. For example, the visualization unit can prioritize displaying information related to the user's current location. For example, the visualization unit can also display relevant information based on the user's geographical location. For example, the visualization unit can analyze the user's geographical location and display the most relevant information. This allows for the provision of more appropriate information by prioritizing the display of highly relevant information by considering the user's geographical location. Some or all of the above processing in the visualization unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the visualization unit can input data about the user's geographical location into a generating AI, which can then prioritize displaying highly relevant information.

[0085] The visualization unit can analyze a user's social media activity and display relevant information. For example, the visualization unit can prioritize displaying information that the user has shown interest in on social media. For example, the visualization unit can also display relevant information based on the user's social media activity. For example, the visualization unit can analyze a user's social media activity and display the most relevant information. This allows for the provision of more appropriate information by analyzing the user's social media activity and displaying relevant information. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input data about the user's social media activity into a generative AI, and the generative AI can display relevant information.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The summarization function can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, if the user is stressed, it can provide a simple, to-the-point summary. If the user is relaxed, it can provide a summary with more detailed information. Furthermore, if the user is in a hurry, the length of the summary can be adjusted to allow for quick comprehension. By adjusting the way the summary is presented according to the user's emotions, a more appropriate summary can be provided.

[0088] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if a user is stressed, it can provide simple, to-the-point recommendations. If the user is relaxed, it can provide recommendations that include more detailed information. Furthermore, if the user is in a hurry, the length of the recommendations can be adjusted to allow for quick understanding. By adjusting the way recommendations are presented according to the user's emotions, the system can provide more appropriate recommendations.

[0089] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of the visualization according to the user's emotions, a more appropriate display can be provided.

[0090] The recommendation system can estimate the user's emotions and adjust the length of recommendations based on that estimation. For example, if a user is stressed, it can provide short, concise recommendations. If a user is relaxed, it can provide longer recommendations with more detailed information. Furthermore, if a user is in a hurry, the recommendation length can be adjusted to allow for quick comprehension. By adjusting the recommendation length according to the user's emotions, the system can provide more appropriate recommendations.

[0091] The visualization unit can estimate the user's emotions and determine the priority of visualizations based on those emotions. For example, if the user is stressed, important information can be displayed preferentially. If the user is relaxed, detailed information can be displayed preferentially. Furthermore, if the user is in a hurry, concise information can be displayed preferentially. In this way, by determining the priority of visualizations according to the user's emotions, more appropriate information can be provided.

[0092] The summary section can adjust the level of detail based on the importance of the content. For example, content containing important information can be summarized in detail. Conversely, less important information can be summarized concisely. Furthermore, it can highlight the most important parts of the summary. This allows for a more detailed summary of important information by adjusting the level of detail based on the importance of the content.

[0093] The summarization section can apply different summarization algorithms depending on the content category. For example, a news article can be summarized using a summarization algorithm specifically designed for news. Similarly, academic papers can be summarized using a summarization algorithm specifically designed for academic papers. Furthermore, entertainment content can be summarized using an entertainment-specific summarization algorithm. By applying different summarization algorithms according to the content category, a more appropriate summary can be provided.

[0094] The recommendation system can select the most suitable content by referring to the user's learning history. For example, it can recommend related content based on what the user has learned in the past. It can also suggest what the user should learn next based on their learning history. Furthermore, it can analyze the user's learning history and suggest the most efficient learning route. In this way, by selecting the most suitable content by referring to the user's learning history, they can learn more efficiently.

[0095] The recommendation system can prioritize recommendations by considering the credibility of the content creators. For example, it can prioritize recommending content from highly reliable creators, while delaying recommendations for content from less reliable creators. Furthermore, it can adjust the level of detail in recommendations based on the creator's credibility. This allows for the priority provision of highly reliable information by prioritizing recommendations based on the credibility of the content creators.

[0096] The visualization unit can prioritize displaying highly relevant information by considering the user's geographical location. For example, it can prioritize displaying information related to the user's current location. It can also display relevant information based on the user's geographical location. Furthermore, it can analyze the user's geographical location and display the most relevant information. By prioritizing the display of highly relevant information while considering the user's geographical location, it can provide more appropriate information.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The summarization section summarizes and extracts information from various types of content, such as articles, audio, and video. For example, it might condense a long article into a few lines, a long video into a few minutes, or summarize audio content so that it can be understood in a short amount of time. The summarization section extracts the key points of an article, the important scenes of a video, and the important parts of audio content, making them easy to understand, view, and listen to in a short amount of time. Step 2: The recommendation section recommends content based on the content extracted by the summarization section, tailored to the user's knowledge level and learning goals. For example, it recommends content for beginners or content specializing in specific fields, providing the optimal content to match the user's learning goals. Step 3: The visualization unit visualizes the user's understanding and progress based on the content recommended by the recommendation unit. For example, it displays the user's understanding and progress in graphs and charts, and automatically optimizes the user's learning path.

[0099] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0102] Each of the multiple elements described above, including the summarization unit, recommendation unit, and visualization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the summarization unit is implemented by the control unit 46A of the smart device 14 and summarizes and extracts various content such as articles, audio, and video. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends content according to the user's knowledge level and learning goals. The visualization unit is implemented by the control unit 46A of the smart device 14 and visualizes the user's understanding and progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the summarization unit, recommendation unit, and visualization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the summarization unit is implemented by the control unit 46A of the smart glasses 214 and summarizes and extracts various content such as articles, audio, and video. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends content according to the user's knowledge level and learning goals. The visualization unit is implemented by the control unit 46A of the smart glasses 214 and visualizes the user's understanding and progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the summarization unit, recommendation unit, and visualization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the summarization unit is implemented by the control unit 46A of the headset terminal 314 and summarizes and extracts various content such as articles, audio, and video. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends content according to the user's knowledge level and learning goals. The visualization unit is implemented by the control unit 46A of the headset terminal 314 and visualizes the user's understanding and progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the summarization unit, recommendation unit, and visualization unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the summarization unit is implemented by the control unit 46A of the robot 414 and summarizes and extracts various content such as articles, audio, and video. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends content according to the user's knowledge level and learning goals. The visualization unit is implemented by the control unit 46A of the robot 414 and visualizes the user's understanding and progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A summarization unit that summarizes and extracts diverse content such as articles, audio, and video, Based on the content extracted by the summarization unit, a recommendation unit recommends content according to the user's knowledge level and learning goals, The system includes a visualization unit that visualizes the user's level of understanding and progress based on the content recommended by the recommendation unit. A system characterized by the following features. (Note 2) The summary section above is, Summarize and extract information from diverse content such as articles, audio, and video. The system described in Appendix 1, characterized by the features described herein. (Note 3) The recommendation unit is, Recommends the most suitable content based on the user's knowledge level and learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned visualization unit, Visualize user understanding and progress, and automatically optimize information gathering and learning paths. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned visualization unit, Display user understanding and progress using graphs and charts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The recommendation unit is, Recommends content for beginners or content specializing in specific fields. The system described in Appendix 1, characterized by the features described herein. (Note 7) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The summary section above is, Adjust the level of detail in the summary based on the importance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 9) The summary section above is, Apply different summarization algorithms depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 10) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The summary section above is, Prioritize summaries by considering the credibility of the content creators. The system described in Appendix 1, characterized by the features described herein. (Note 12) The summary section above is, Adjust the order of summaries based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 13) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The recommendation unit is, Select the most suitable content by referring to the user's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The recommendation unit is, Customize recommendations based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 16) The recommendation unit is, It estimates the user's emotions and adjusts the length of recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The recommendation unit is, Prioritizing recommendations takes into account the credibility of the content creators. The system described in Appendix 1, characterized by the features described herein. (Note 18) The recommendation unit is, Adjust the order of recommendations based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display method of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned visualization unit, The optimal display method is selected by referring to the user's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visualization unit, Customize the displayed content based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visualization unit, It estimates the user's emotions and determines the visualization priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned visualization unit, The system prioritizes displaying highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned visualization unit, Analyzes users' social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A summarization unit that summarizes and extracts diverse content such as articles, audio, and video, Based on the content extracted by the summarization unit, a recommendation unit recommends content according to the user's knowledge level and learning goals, The system includes a visualization unit that visualizes the user's level of understanding and progress based on the content recommended by the recommendation unit. A system characterized by the following features.

2. The summary section above is, Summarize and extract information from diverse content such as articles, audio, and video. The system according to feature 1.

3. The recommendation unit is, Recommends the most suitable content based on the user's knowledge level and learning goals. The system according to feature 1.

4. The aforementioned visualization unit, Visualize user understanding and progress, and automatically optimize information gathering and learning paths. The system according to feature 1.

5. The aforementioned visualization unit, Display user understanding and progress using graphs and charts. The system according to feature 1.

6. The recommendation unit is, Recommends content for beginners or content specializing in specific fields. The system according to feature 1.

7. The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system according to feature 1.

8. The summary section above is, Adjust the level of detail in the summary based on the importance of the content. The system according to feature 1.

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