System

Abstract

An object of a system according to an embodiment is to efficiently search for and deepen understanding of contents of an audio book.SOLUTION: A system includes a reception part, a retrieval part, and an answer generation part. The receiving unit receives a voice question from a user. The retrieval part retrieves the contents of the book on the basis of the question received by the question reception part. The answer generation unit generates an answer based on the content retrieved by the retrieval unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to efficiently search for and deepen understanding of the contents of audiobooks.

[0005] The system according to the embodiment aims to efficiently search for and deepen understanding of the contents of audiobooks. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a search unit, and an answer generation unit. The reception unit receives a question by voice from a user. The search unit searches the contents of a book based on the question received by the question reception unit. The answer generation unit generates an answer based on the content searched by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently search and understand the contents of audiobooks. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) An application according to an embodiment of the present invention utilizes AI technology to search the contents of a book and deepen the user's understanding, addressing the problems of missing parts and difficulty understanding technical terms in conventional audiobooks. This application allows the user to simply ask a question by voice, and the AI ​​will accurately and quickly provide the necessary information from the book. This allows the application to help the user gain a deeper understanding of the audiobook and assist in understanding the technical terms and content.

[0029] An application according to an embodiment includes a question receiving unit, a search unit, and an answer generating unit. The question receiving unit receives a question uttered by a user. For example, the user can ask a question such as, "Please tell me the meaning of the technical terminology explained in this chapter." The question receiving unit can also convert the user's voice into text data using speech recognition technology. The search unit searches the contents of a book based on the question received by the question receiving unit. For example, the search unit can search for a relevant part of the book using a keyword search. The search unit can also search for detailed information using a full-text search. The search unit can also search for a specific chapter or section using a metadata search. The answer generating unit generates an answer based on the content searched by the search unit. For example, the answer generating unit generates an appropriate answer using a generation AI (e.g., a text generation AI or a multimodal generation AI). The answer generating unit can also provide a detailed explanation based on the search results. The answer generating unit can also provide additional information related to the user's question. In this way, the application according to an embodiment searches the contents of a book based on the user's voice question and generates an answer, thereby improving the audiobook usage experience. For example, if a user gives an instruction such as "Go to the next chapter," the generative AI will interpret the instruction and play the next chapter. Also, if a user asks a question such as "Tell me all the parts of this book that relate to 'artificial intelligence,'" the generative AI will search for all parts related to that topic and provide them all together.

[0030] The search unit can predict related information based on the user's past search history and present it in advance. For example, the search unit analyzes keywords and phrases that the user has searched for in the past, predicts related information, and presents it in advance. For example, if a user previously searched for "quantum computer," it can automatically display new related information and topics. The search unit can also save the user's search history and refer to it the next time they search. Furthermore, the search unit can learn the user's search patterns and make more accurate predictions. This improves user convenience by predicting related information based on the user's past search history and presenting it in advance.

[0031] The question receiving unit can analyze the tone and speed of the user's voice to understand the intent of the question. For example, the question receiving unit can analyze the tone and speed of the user's voice to develop an algorithm for understanding the intent of the question. For example, if the user is in a hurry, a concise answer can be provided. The question receiving unit can also analyze the voice frequency and volume to estimate the user's emotions. Furthermore, the question receiving unit can analyze the speaking speed and word intervals to estimate the user's intent. In this way, by analyzing the tone and speed of the user's voice and understanding the intent of the question, a more appropriate answer can be provided.

[0032] The search unit can search not only the contents of a book but also related external resources at the same time, providing comprehensive information. For example, in addition to searching the contents of a book, the search unit can simultaneously search related web articles and academic papers, providing comprehensive information. For example, the contents of a book about "quantum computers" can be displayed simultaneously with the latest research papers. The search unit can also refer to a database of external resources to obtain related information. Furthermore, the search unit can search online articles and news sites to provide the latest information. This allows the search unit to simultaneously search related external resources in addition to searching the contents of a book, providing comprehensive information, and deepening the user's understanding.

[0033] The answer generation unit can also provide visual information including images and diagrams when a user asks a question. For example, when a user asks a question, the answer generation unit automatically searches for related images and diagrams to visually aid understanding. For example, it can display a structural diagram of a "quantum computer." The answer generation unit can also provide detailed explanations based on visual information. Furthermore, the answer generation unit can also use videos and animations to aid the user's understanding. In this way, when a user asks a question, visual information including images and diagrams can be provided to aid visual understanding.

[0034] The answer generation unit can provide not only the definition of a technical term but also specific examples and application examples in which the term is used. For example, the answer generation unit provides, in addition to the definition of a technical term, specific examples in which the term is used. For example, the answer generation unit can provide a definition of "quantum computer" along with actual application examples. The answer generation unit can also provide background information and related examples for the technical term. Furthermore, the answer generation unit can deepen the user's understanding by showing application examples of the technical term. In this way, the user's understanding is deepened by providing not only the definition of the technical term but also specific examples and application examples.

[0035] The answer generation unit can explain the historical background and evolution of technical terms, allowing the user to understand the overall picture of the term. For example, the answer generation unit can explain in detail the historical background and evolution of technical terms. For example, the answer generation unit can show the historical background and evolution of "quantum computer." The answer generation unit can also explain the history of the development of technical terms and important events. Furthermore, the answer generation unit can show the evolution of technical terms, allowing the user to understand the overall picture of the term. By explaining the historical background and evolution of technical terms, the answer generation unit can help the user understand the overall picture of the term.

[0036] In addition to assisting in the understanding of technical terms, the answer generation unit can provide related videos and animations to visually aid comprehension. For example, in addition to assisting in the understanding of technical terms, the answer generation unit can provide related videos to visually aid comprehension. For example, the answer generation unit can display a related documentary along with the definition of "quantum computer." The answer generation unit can also use animations to explain technical terms. Furthermore, the answer generation unit can also use infographics to aid in the understanding of technical terms. In this way, in addition to assisting in the understanding of technical terms, the answer generation unit can provide related videos and animations to visually aid comprehension.

[0037] The answer generation unit can provide related quizzes and practice questions to deepen the user's understanding of technical terms and check the user's level of understanding. For example, the answer generation unit can provide related quizzes to deepen the user's understanding of technical terms and check the user's level of understanding. For example, the answer generation unit can display a quiz on the definition of "quantum computer." The answer generation unit can also provide practice questions to check the user's level of understanding. Furthermore, the answer generation unit can provide a time attack quiz to check the user's level of understanding. In this way, the learning effect can be improved by providing related quizzes and practice questions to deepen the user's understanding of technical terms and checking the user's level of understanding.

[0038] The search unit can analyze the user's past listening patterns and present the parts that are most likely to be missed in advance. The search unit, for example, builds a system that analyzes the user's past listening patterns and presents the parts that are most likely to be missed in advance. For example, the search unit identifies parts that the user frequently rewinds and displays them in advance. The search unit can also learn the listening patterns and predict parts that the user is likely to miss. Furthermore, the search unit can highlight parts that are most likely to be missed based on the user's listening patterns. In this way, convenience for the user is improved by analyzing the user's past listening patterns and presenting the parts that are most likely to be missed in advance.

[0039] When searching for a missed portion, the generation AI can automatically generate a summary, allowing the user to grasp the content in a short amount of time. For example, when searching for a missed portion, the generation AI can automatically generate a summary, allowing the user to grasp the content in a short amount of time. For example, the generation AI can concisely summarize the main points of a long chapter. The generation AI can also use a summarization algorithm to extract important points and compress information. Furthermore, the generation AI can provide summaries in response to user questions, allowing the user to grasp the content in a short amount of time. This improves user convenience by allowing the generation AI to automatically generate summaries, allowing the user to grasp the content in a short amount of time.

[0040] The search unit can search for the missed portion and simultaneously present other related chapters and sections, making it easier to understand the overall flow. For example, the search unit can search for the missed portion and simultaneously present other related chapters and sections, making it easier to understand the overall flow. For example, it can display chapters before and after the portion the user missed. The search unit can also automatically select and present highly relevant chapters and sections. Furthermore, the search unit can provide related information based on the user's listening patterns. In this way, in addition to searching for the missed portion, it can simultaneously present other related chapters and sections, making it easier to understand the overall flow.

[0041] When searching for a missed portion, the generation AI can automatically provide relevant visual information to aid visual understanding. For example, when searching for a missed portion, the generation AI can automatically provide relevant visual information to aid visual understanding. For example, it can display charts and illustrations related to the portion the user missed. The generation AI can also provide detailed explanations based on the visual information. Furthermore, the generation AI can also use videos and animations to help the user understand. In this way, the generation AI can automatically provide relevant visual information to aid visual understanding, improving user convenience.

[0042] A voice interface can analyze the tone and speed of a user's voice and generate an optimal response. For example, a voice interface can analyze the tone and speed of a user's voice and build a system that generates an optimal response. For example, if the user is in a hurry, a concise answer can be provided. A voice interface can also use voice recognition technology to convert the user's voice into text data and generate an optimal response. Furthermore, a voice interface can use voice synthesis technology to respond to the user in a natural voice. This improves user convenience by analyzing the tone and speed of a user's voice through the voice interface and generating an optimal response.

[0043] The voice interface can analyze the user's past operation history and make optimal operation suggestions. The voice interface, for example, builds a system that analyzes the user's past operation history and makes optimal operation suggestions. For example, it prioritizes suggestions for functions that the user uses frequently. The voice interface can also save the operation history and use it as a reference the next time the user operates the device. Furthermore, the voice interface can learn the user's operation patterns and make more accurate suggestions. In this way, the user's past operation history can be analyzed through the voice interface and optimal operation suggestions can be made, improving user convenience.

[0044] A voice interface can incorporate gesture recognition to enable a user to operate without using their hands. For example, a voice interface can incorporate gesture recognition to build a system that allows a user to operate without using their hands. For example, a function to turn pages with a specific gesture can be provided. A voice interface can also detect a user's gestures using camera-based recognition technology. A voice interface can also detect a user's gestures using sensor-based recognition technology. This improves user convenience by incorporating gesture recognition in addition to a voice interface to enable a user to operate without using their hands.

[0045] A voice interface can provide a multitasking function that allows a user to operate multiple tasks. For example, a voice interface can be used to build a system that provides a multitasking function that allows a user to operate multiple tasks simultaneously. For example, multiple applications can be operated simultaneously using voice commands. A voice interface can also provide an interface that allows smooth task switching. Furthermore, a voice interface can set task priorities and process important tasks first. This improves user convenience by providing a multitasking function that allows a user to operate multiple tasks simultaneously through a voice interface.

[0046] The learning support function can analyze a user's learning progress and propose an optimal learning plan. For example, the learning support function can build a system that analyzes a user's learning progress in real time and proposes an optimal learning plan. For example, if a user is lagging behind on a particular topic, a plan to focus on that topic can be proposed. The learning support function can also analyze the level and speed of learning achievement and provide a learning plan that is suitable for the user. Furthermore, the learning support function can propose an optimal learning plan based on the user's learning style. In this way, the learning support function can analyze a user's learning progress and propose an optimal learning plan, thereby improving the user's learning effectiveness.

[0047] The learning support function can analyze a user's past learning history and provide optimal learning resources. The learning support function, for example, analyzes a user's past learning history and builds a system that provides optimal learning resources. For example, it can suggest new resources related to topics the user has previously studied. The learning support function can also save the learning history and refer to it the next time the user studies. Furthermore, the learning support function can learn the user's learning patterns and provide more accurate resources. In this way, the learning support function can analyze a user's past learning history and provide optimal learning resources, thereby improving the user's learning effectiveness.

[0048] The learning support function can provide a social function that allows users to share what they have learned with other users. The learning support function, for example, builds a system that provides a social function that allows users to share what they have learned with other users. For example, it provides a function to share what they have learned on a social networking site. The learning support function can also set the method and scope of sharing. Furthermore, the learning support function can also set privacy settings for sharing. In this way, by providing a social function that allows users to share what they have learned with other users in addition to the learning support function, the learning effect can be improved.

[0049] The learning support function can provide a simulation function that allows users to practice what they have learned. The learning support function, for example, builds a system that provides a simulation function that allows users to practice what they have learned. For example, it provides a function that allows users to try out what they have learned in a virtual environment. The learning support function can also set a simulation scenario so that users can practice it. Furthermore, the learning support function can provide a method for evaluating the simulation and check the user's level of understanding. In this way, the learning effect is improved by providing a simulation function that allows users to practice what they have learned through the learning support function.

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

[0051] The search unit can predict and present related information in advance based on the user's past search history. For example, it can analyze keywords and phrases that the user has previously searched for, and predict and present related information in advance. For example, if a user previously searched for "quantum computer," it can automatically display new related information and topics. The search unit can also save the user's search history and refer to it the next time they search. Furthermore, the search unit can learn the user's search patterns and make more accurate predictions. This improves user convenience by predicting related information based on the user's past search history and presenting it in advance.

[0052] In addition to searching the contents of a book, the search unit can simultaneously search related external resources to provide comprehensive information. For example, in addition to searching the contents of a book, it can simultaneously search related web articles and academic papers to provide comprehensive information. For example, the contents of a book about "quantum computers" can be displayed simultaneously with the latest research papers. The search unit can also refer to a database of external resources to obtain related information. Furthermore, the search unit can search online articles and news sites to provide the latest information. This allows the search unit to simultaneously search related external resources in addition to searching the contents of a book, providing comprehensive information and deepening the user's understanding.

[0053] The answer generation unit can also provide visual information, including images and diagrams, when a user asks a question. For example, when a user asks a question, it can automatically search for related images and diagrams to visually aid understanding. For example, it can display a structural diagram of a "quantum computer." The answer generation unit can also provide detailed explanations based on visual information. Furthermore, the answer generation unit can also use videos and animations to aid the user's understanding. This allows the user to visually aid understanding by providing visual information, including images and diagrams, when asking a question.

[0054] The search unit can analyze the user's past listening patterns and present the parts that are most likely to be missed in advance. For example, a system can be constructed that analyzes the user's past listening patterns and presents the parts that are most likely to be missed in advance. For example, parts that the user frequently rewinds can be identified and displayed in advance. The search unit can also learn the listening patterns and predict parts that the user is likely to miss. Furthermore, the search unit can highlight parts that are most likely to be missed based on the user's listening patterns. In this way, convenience for the user can be improved by analyzing the user's past listening patterns and presenting the parts that are most likely to be missed in advance.

[0055] When searching for a missed portion, the generation AI can automatically generate a summary, allowing the user to grasp the content in a short amount of time. For example, when searching for a missed portion, the generation AI can automatically generate a summary, allowing the user to grasp the content in a short amount of time. For example, the generation AI can concisely summarize the main points of a long chapter. The generation AI can also use a summarization algorithm to extract important points and compress information. Furthermore, the generation AI can provide summaries in response to user questions, allowing the user to grasp the content in a short amount of time. This improves user convenience by allowing the generation AI to automatically generate summaries, allowing the user to grasp the content in a short amount of time.

[0056] The search unit can search for the missed portion and simultaneously present other related chapters and sections, making it easier to understand the overall flow. For example, in addition to searching for the missed portion, other related chapters and sections can be simultaneously presented, making it easier to understand the overall flow. For example, chapters before and after the portion the user missed can be displayed. The search unit can also automatically select and present highly relevant chapters and sections. Furthermore, the search unit can provide related information based on the user's listening patterns. This makes it easier to understand the overall flow by searching for the missed portion and simultaneously presenting other related chapters and sections.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The question receiving unit receives a question from the user through speech. For example, the user can ask, "Please tell me the meaning of the technical terms explained in this chapter." The question receiving unit can also convert the user's speech into text data using speech recognition technology. Step 2: The search unit searches the contents of the book based on the question received by the question receiving unit. For example, the search unit may use a keyword search to find relevant parts of the book. The search unit may also use a full-text search to find detailed information. Furthermore, the search unit may use a metadata search to find a specific chapter or section. Step 3: The answer generation unit generates an answer based on the content searched by the search unit. For example, the answer generation unit generates an appropriate answer using a generation AI (e.g., a text generation AI or a multimodal generation AI). The answer generation unit can also provide a detailed explanation based on the search results. Furthermore, the answer generation unit can also provide additional information related to the user's question.

[0059] (Example 2) An application according to an embodiment of the present invention utilizes AI technology to search the contents of a book and deepen the user's understanding, addressing the problems of missing parts and difficulty understanding technical terms in conventional audiobooks. This application allows the user to simply ask a question by voice, and the AI ​​will accurately and quickly provide the necessary information from the book. This allows the application to help the user gain a deeper understanding of the audiobook and assist in understanding the technical terms and content.

[0060] An application according to an embodiment includes a question receiving unit, a search unit, and an answer generating unit. The question receiving unit receives a question uttered by a user. For example, the user can ask a question such as, "Please tell me the meaning of the technical terminology explained in this chapter." The question receiving unit can also convert the user's voice into text data using speech recognition technology. The search unit searches the contents of a book based on the question received by the question receiving unit. For example, the search unit can search for a relevant part of the book using a keyword search. The search unit can also search for detailed information using a full-text search. The search unit can also search for a specific chapter or section using a metadata search. The answer generating unit generates an answer based on the content searched by the search unit. For example, the answer generating unit generates an appropriate answer using a generation AI (e.g., a text generation AI or a multimodal generation AI). The answer generating unit can also provide a detailed explanation based on the search results. The answer generating unit can also provide additional information related to the user's question. In this way, the application according to an embodiment searches the contents of a book based on the user's voice question and generates an answer, thereby improving the audiobook usage experience. For example, if a user gives an instruction such as "Go to the next chapter," the generative AI will interpret the instruction and play the next chapter. Also, if a user asks a question such as "Tell me all the parts of this book that relate to 'artificial intelligence,'" the generative AI will search for all parts related to that topic and provide them all together.

[0061] The search unit can predict related information based on the user's past search history and present it in advance. For example, the search unit analyzes keywords and phrases that the user has searched for in the past, predicts related information, and presents it in advance. For example, if a user previously searched for "quantum computer," it can automatically display new related information and topics. The search unit can also save the user's search history and refer to it the next time they search. Furthermore, the search unit can learn the user's search patterns and make more accurate predictions. This improves user convenience by predicting related information based on the user's past search history and presenting it in advance.

[0062] The question receiving unit can analyze the tone and speed of the user's voice to understand the intent of the question. For example, the question receiving unit can analyze the tone and speed of the user's voice to develop an algorithm for understanding the intent of the question. For example, if the user is in a hurry, a concise answer can be provided. The question receiving unit can also analyze the voice frequency and volume to estimate the user's emotions. Furthermore, the question receiving unit can analyze the speaking speed and word intervals to estimate the user's intent. In this way, by analyzing the tone and speed of the user's voice and understanding the intent of the question, a more appropriate answer can be provided.

[0063] The answer generation unit can analyze the user's emotions and provide an answer that corresponds to the emotions. For example, the answer generation unit uses an emotion estimation function to analyze the emotions of the user when asking a question in real time and provide an answer that corresponds to the emotions. For example, if the user is confused, the answer generation unit can provide a more detailed explanation. The answer generation unit can also estimate the user's emotions using voice analysis technology and generate an answer that corresponds to the emotions. Furthermore, the answer generation unit can also estimate the user's emotions using facial expression analysis technology and provide an answer that corresponds to the emotions. In this way, by analyzing the user's emotions and providing an answer that corresponds to the emotions, user satisfaction can be improved.

[0064] The search unit can search not only the contents of a book but also related external resources at the same time, providing comprehensive information. For example, in addition to searching the contents of a book, the search unit can simultaneously search related web articles and academic papers, providing comprehensive information. For example, the contents of a book about "quantum computers" can be displayed simultaneously with the latest research papers. The search unit can also refer to a database of external resources to obtain related information. Furthermore, the search unit can search online articles and news sites to provide the latest information. This allows the search unit to simultaneously search related external resources in addition to searching the contents of a book, providing comprehensive information, and deepening the user's understanding.

[0065] The answer generation unit can also provide visual information including images and diagrams when a user asks a question. For example, when a user asks a question, the answer generation unit automatically searches for related images and diagrams to visually aid understanding. For example, it can display a structural diagram of a "quantum computer." The answer generation unit can also provide detailed explanations based on visual information. Furthermore, the answer generation unit can also use videos and animations to aid the user's understanding. In this way, when a user asks a question, visual information including images and diagrams can be provided to aid visual understanding.

[0066] The answer generation unit can use the emotion estimation function to analyze the emotion of the user when asking a question in real time and provide additional information to elicit positive emotions. The answer generation unit, for example, uses the emotion estimation function to analyze the emotion of the user when asking a question in real time and provide additional information to elicit positive emotions. For example, if the user is excited, the answer generation unit can provide additional related, interesting topics. The answer generation unit can also use voice analysis technology to estimate the user's emotion and provide information to elicit positive emotions. Furthermore, the answer generation unit can also use facial expression analysis technology to estimate the user's emotion and provide information to elicit positive emotions. In this way, the emotion estimation function can be used to analyze the emotion of the user when asking a question in real time and provide additional information to elicit positive emotions, thereby improving user satisfaction.

[0067] The answer generation unit can provide not only the definition of a technical term but also specific examples and application examples in which the term is used. For example, the answer generation unit provides, in addition to the definition of a technical term, specific examples in which the term is used. For example, the answer generation unit can provide a definition of "quantum computer" along with actual application examples. The answer generation unit can also provide background information and related examples for the technical term. Furthermore, the answer generation unit can deepen the user's understanding by showing application examples of the technical term. In this way, the user's understanding is deepened by providing not only the definition of the technical term but also specific examples and application examples.

[0068] The answer generation unit can explain the historical background and evolution of technical terms, allowing the user to understand the overall picture of the term. For example, the answer generation unit can explain in detail the historical background and evolution of technical terms. For example, the answer generation unit can show the historical background and evolution of "quantum computer." The answer generation unit can also explain the history of the development of technical terms and important events. Furthermore, the answer generation unit can show the evolution of technical terms, allowing the user to understand the overall picture of the term. By explaining the historical background and evolution of technical terms, the answer generation unit can help the user understand the overall picture of the term.

[0069] The emotion estimation function can analyze the emotions a user feels when trying to understand technical terms and provide additional explanations if the user finds it difficult to understand. For example, the emotion estimation function can analyze the emotions a user feels when trying to understand technical terms in real time and provide additional explanations if the user finds it difficult to understand. For example, if the user is confused, a more detailed explanation can be provided. The emotion estimation function can also estimate the user's emotions using voice analysis technology and provide additional explanations if the user finds it difficult to understand. Furthermore, the emotion estimation function can estimate the user's emotions using facial expression analysis technology and provide additional explanations if the user finds it difficult to understand. In this way, the emotion estimation function can analyze the emotions a user feels when trying to understand technical terms and provide additional explanations if the user finds it difficult to understand, thereby deepening the user's understanding.

[0070] In addition to assisting in the understanding of technical terms, the answer generation unit can provide related videos and animations to visually aid comprehension. For example, in addition to assisting in the understanding of technical terms, the answer generation unit can provide related videos to visually aid comprehension. For example, the answer generation unit can display a related documentary along with the definition of "quantum computer." The answer generation unit can also use animations to explain technical terms. Furthermore, the answer generation unit can also use infographics to aid in the understanding of technical terms. In this way, in addition to assisting in the understanding of technical terms, the answer generation unit can provide related videos and animations to visually aid comprehension.

[0071] The answer generation unit can provide related quizzes and practice questions to deepen the user's understanding of technical terms and check the user's level of understanding. For example, the answer generation unit can provide related quizzes to deepen the user's understanding of technical terms and check the user's level of understanding. For example, the answer generation unit can display a quiz on the definition of "quantum computer." The answer generation unit can also provide practice questions to check the user's level of understanding. Furthermore, the answer generation unit can provide a time attack quiz to check the user's level of understanding. In this way, the learning effect can be improved by providing related quizzes and practice questions to deepen the user's understanding of technical terms and checking the user's level of understanding.

[0072] The emotion estimation function can analyze the emotions a user feels when understanding technical terms in real time and provide additional information to elicit positive emotions. For example, the emotion estimation function can analyze the emotions a user feels when understanding technical terms in real time and provide additional information to elicit positive emotions. For example, if the user is confused, it can provide success stories. The emotion estimation function can also estimate the user's emotions using voice analysis technology and provide information to elicit positive emotions. Furthermore, the emotion estimation function can also estimate the user's emotions using facial expression analysis technology and provide information to elicit positive emotions. In this way, the emotion estimation function can analyze the emotions a user feels when understanding technical terms in real time and provide additional information to elicit positive emotions, thereby improving learning effectiveness.

[0073] The search unit can analyze the user's past listening patterns and present the parts that are most likely to be missed in advance. The search unit, for example, builds a system that analyzes the user's past listening patterns and presents the parts that are most likely to be missed in advance. For example, the search unit identifies parts that the user frequently rewinds and displays them in advance. The search unit can also learn the listening patterns and predict parts that the user is likely to miss. Furthermore, the search unit can highlight parts that are most likely to be missed based on the user's listening patterns. In this way, convenience for the user is improved by analyzing the user's past listening patterns and presenting the parts that are most likely to be missed in advance.

[0074] When searching for a missed portion, the generation AI can automatically generate a summary, allowing the user to grasp the content in a short amount of time. For example, when searching for a missed portion, the generation AI can automatically generate a summary, allowing the user to grasp the content in a short amount of time. For example, the generation AI can concisely summarize the main points of a long chapter. The generation AI can also use a summarization algorithm to extract important points and compress information. Furthermore, the generation AI can provide summaries in response to user questions, allowing the user to grasp the content in a short amount of time. This improves user convenience by allowing the generation AI to automatically generate summaries, allowing the user to grasp the content in a short amount of time.

[0075] The emotion estimation function can analyze the emotion of the user when searching for the missed portion and provide additional information to reduce stress. The emotion estimation function, for example, analyzes the emotion of the user when searching for the missed portion in real time and provides additional information to reduce stress. For example, if the user is feeling anxious, it can provide information to help the user relax. The emotion estimation function can also estimate the user's emotion using voice analysis technology and provide information to reduce stress. Furthermore, the emotion estimation function can also estimate the user's emotion using facial expression analysis technology and provide information to reduce stress. In this way, the emotion estimation function can be used to analyze the emotion of the user when searching for the missed portion and provide additional information to reduce stress, thereby improving user convenience.

[0076] The search unit can search for the missed portion and simultaneously present other related chapters and sections, making it easier to understand the overall flow. For example, the search unit can search for the missed portion and simultaneously present other related chapters and sections, making it easier to understand the overall flow. For example, it can display chapters before and after the portion the user missed. The search unit can also automatically select and present highly relevant chapters and sections. Furthermore, the search unit can provide related information based on the user's listening patterns. In this way, in addition to searching for the missed portion, it can simultaneously present other related chapters and sections, making it easier to understand the overall flow.

[0077] When searching for a missed portion, the generation AI can automatically provide relevant visual information to aid visual understanding. For example, when searching for a missed portion, the generation AI can automatically provide relevant visual information to aid visual understanding. For example, it can display charts and illustrations related to the portion the user missed. The generation AI can also provide detailed explanations based on the visual information. Furthermore, the generation AI can also use videos and animations to help the user understand. In this way, the generation AI can automatically provide relevant visual information to aid visual understanding, improving user convenience.

[0078] The emotion estimation function can analyze the user's emotions in real time when searching for the missed part and provide additional information to elicit positive emotions. The emotion estimation function can, for example, analyze the user's emotions in real time when searching for the missed part and provide additional information to elicit positive emotions. For example, if the user is excited, the emotion estimation function can provide additional related, interesting topics. The emotion estimation function can also estimate the user's emotions using voice analysis technology and provide information to elicit positive emotions. The emotion estimation function can also estimate the user's emotions using facial expression analysis technology and provide information to elicit positive emotions. In this way, the emotion estimation function can analyze the user's emotions in real time when searching for the missed part and provide additional information to elicit positive emotions, thereby improving user convenience.

[0079] A voice interface can analyze the tone and speed of a user's voice and generate an optimal response. For example, a voice interface can analyze the tone and speed of a user's voice and build a system that generates an optimal response. For example, if the user is in a hurry, a concise answer can be provided. A voice interface can also use voice recognition technology to convert the user's voice into text data and generate an optimal response. Furthermore, a voice interface can use voice synthesis technology to respond to the user in a natural voice. This improves user convenience by analyzing the tone and speed of a user's voice through the voice interface and generating an optimal response.

[0080] The voice interface can analyze the user's past operation history and make optimal operation suggestions. The voice interface, for example, builds a system that analyzes the user's past operation history and makes optimal operation suggestions. For example, it prioritizes suggestions for functions that the user uses frequently. The voice interface can also save the operation history and use it as a reference the next time the user operates the device. Furthermore, the voice interface can learn the user's operation patterns and make more accurate suggestions. In this way, the user's past operation history can be analyzed through the voice interface and optimal operation suggestions can be made, improving user convenience.

[0081] The emotion estimation function can analyze the emotions of a user when operating a voice interface and provide additional information to elicit positive emotions. For example, the emotion estimation function can analyze the emotions of a user when operating a voice interface in real time and provide additional information to elicit positive emotions. For example, if the user is excited, the emotion estimation function can provide additional related, interesting topics. The emotion estimation function can also estimate the user's emotions using voice analysis technology and provide information to elicit positive emotions. Furthermore, the emotion estimation function can also estimate the user's emotions using facial expression analysis technology and provide information to elicit positive emotions. In this way, the emotion estimation function can analyze the emotions of a user when operating a voice interface and provide additional information to elicit positive emotions, thereby improving user convenience.

[0082] A voice interface can incorporate gesture recognition to enable a user to operate without using their hands. For example, a voice interface can incorporate gesture recognition to build a system that allows a user to operate without using their hands. For example, a function to turn pages with a specific gesture can be provided. A voice interface can also detect a user's gestures using camera-based recognition technology. A voice interface can also detect a user's gestures using sensor-based recognition technology. This improves user convenience by incorporating gesture recognition in addition to a voice interface to enable a user to operate without using their hands.

[0083] A voice interface can provide a multitasking function that allows a user to operate multiple tasks. For example, a voice interface can be used to build a system that provides a multitasking function that allows a user to operate multiple tasks simultaneously. For example, multiple applications can be operated simultaneously using voice commands. A voice interface can also provide an interface that allows smooth task switching. Furthermore, a voice interface can set task priorities and process important tasks first. This improves user convenience by providing a multitasking function that allows a user to operate multiple tasks simultaneously through a voice interface.

[0084] The emotion estimation function can analyze the emotions of a user when operating a voice interface in real time and provide additional information to elicit positive emotions. The emotion estimation function can, for example, analyze the emotions of a user when operating a voice interface in real time and provide additional information to elicit positive emotions. For example, if the user is excited, the emotion estimation function can provide additional related, interesting topics. The emotion estimation function can also estimate the user's emotions using voice analysis technology and provide information to elicit positive emotions. Furthermore, the emotion estimation function can also estimate the user's emotions using facial expression analysis technology and provide information to elicit positive emotions. In this way, the emotion estimation function can analyze the emotions of a user when operating a voice interface in real time and provide additional information to elicit positive emotions, thereby improving user convenience.

[0085] The learning support function can analyze a user's learning progress and propose an optimal learning plan. For example, the learning support function can build a system that analyzes a user's learning progress in real time and proposes an optimal learning plan. For example, if a user is lagging behind on a particular topic, a plan to focus on that topic can be proposed. The learning support function can also analyze the level and speed of learning achievement and provide a learning plan that is suitable for the user. Furthermore, the learning support function can propose an optimal learning plan based on the user's learning style. In this way, the learning support function can analyze a user's learning progress and propose an optimal learning plan, thereby improving the user's learning effectiveness.

[0086] The learning support function can analyze a user's past learning history and provide optimal learning resources. The learning support function, for example, analyzes a user's past learning history and builds a system that provides optimal learning resources. For example, it can suggest new resources related to topics the user has previously studied. The learning support function can also save the learning history and refer to it the next time the user studies. Furthermore, the learning support function can learn the user's learning patterns and provide more accurate resources. In this way, the learning support function can analyze a user's past learning history and provide optimal learning resources, thereby improving the user's learning effectiveness.

[0087] The emotion estimation function can analyze the emotions of a user when using a learning assistance function and provide additional information to elicit positive emotions. For example, the emotion estimation function can analyze the emotions of a user when using a learning assistance function in real time and provide additional information to elicit positive emotions. For example, if the user is excited, the emotion estimation function can provide additional related, interesting topics. The emotion estimation function can also estimate the user's emotions using voice analysis technology and provide information to elicit positive emotions. Furthermore, the emotion estimation function can estimate the user's emotions using facial expression analysis technology and provide information to elicit positive emotions. In this way, the emotion estimation function can analyze the emotions of a user when using a learning assistance function and provide additional information to elicit positive emotions, thereby improving the user's learning effectiveness.

[0088] The learning support function can provide a social function that allows users to share what they have learned with other users. The learning support function, for example, builds a system that provides a social function that allows users to share what they have learned with other users. For example, it provides a function to share what they have learned on a social networking site. The learning support function can also set the method and scope of sharing. Furthermore, the learning support function can also set privacy settings for sharing. In this way, by providing a social function that allows users to share what they have learned with other users in addition to the learning support function, the learning effect can be improved.

[0089] The learning support function can provide a simulation function that allows users to practice what they have learned. The learning support function, for example, builds a system that provides a simulation function that allows users to practice what they have learned. For example, it provides a function that allows users to try out what they have learned in a virtual environment. The learning support function can also set a simulation scenario so that users can practice it. Furthermore, the learning support function can provide a method for evaluating the simulation and check the user's level of understanding. In this way, the learning effect is improved by providing a simulation function that allows users to practice what they have learned through the learning support function.

[0090] The emotion estimation function can analyze the emotions of a user when using a learning assistance function in real time and provide additional information to elicit positive emotions. For example, the emotion estimation function can analyze the emotions of a user when using a learning assistance function in real time and provide additional information to elicit positive emotions. For example, if the user is excited, the emotion estimation function can provide additional related and interesting topics. The emotion estimation function can also estimate the user's emotions using voice analysis technology and provide information to elicit positive emotions. Furthermore, the emotion estimation function can also estimate the user's emotions using facial expression analysis technology and provide information to elicit positive emotions. In this way, the emotion estimation function can analyze the emotions of a user when using a learning assistance function in real time and provide additional information to elicit positive emotions, thereby improving learning effectiveness.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The question receiving unit can analyze the tone and speed of the user's voice to understand the intent of the question. For example, if the user is in a hurry, a concise answer can be provided. The question receiving unit can also analyze the voice frequency and volume to estimate the user's emotions. Furthermore, the question receiving unit can analyze the speaking speed and word intervals to estimate the user's intent. In this way, by analyzing the tone and speed of the user's voice and understanding the intent of the question, a more appropriate answer can be provided.

[0093] The search unit can predict and present related information in advance based on the user's past search history. For example, it can analyze keywords and phrases that the user has previously searched for, and predict and present related information in advance. For example, if a user previously searched for "quantum computer," it can automatically display new related information and topics. The search unit can also save the user's search history and refer to it the next time they search. Furthermore, the search unit can learn the user's search patterns and make more accurate predictions. This improves user convenience by predicting related information based on the user's past search history and presenting it in advance.

[0094] The answer generation unit can analyze the user's emotions and provide an answer that corresponds to the emotions. For example, the emotion estimation function can be used to analyze the emotions of the user when asking a question in real time and provide an answer that corresponds to the emotions. For example, if the user is confused, a more detailed explanation can be provided. The answer generation unit can also estimate the user's emotions using voice analysis technology and generate an answer that corresponds to the emotions. Furthermore, the answer generation unit can also estimate the user's emotions using facial expression analysis technology and provide an answer that corresponds to the emotions. In this way, by analyzing the user's emotions and providing an answer that corresponds to the emotions, user satisfaction can be improved.

[0095] In addition to searching the contents of a book, the search unit can simultaneously search related external resources to provide comprehensive information. For example, in addition to searching the contents of a book, it can simultaneously search related web articles and academic papers to provide comprehensive information. For example, the contents of a book about "quantum computers" can be displayed simultaneously with the latest research papers. The search unit can also refer to a database of external resources to obtain related information. Furthermore, the search unit can search online articles and news sites to provide the latest information. This allows the search unit to simultaneously search related external resources in addition to searching the contents of a book, providing comprehensive information and deepening the user's understanding.

[0096] The answer generation unit can also provide visual information, including images and diagrams, when a user asks a question. For example, when a user asks a question, it can automatically search for related images and diagrams to visually aid understanding. For example, it can display a structural diagram of a "quantum computer." The answer generation unit can also provide detailed explanations based on visual information. Furthermore, the answer generation unit can also use videos and animations to aid the user's understanding. This allows the user to visually aid understanding by providing visual information, including images and diagrams, when asking a question.

[0097] The answer generation unit can use the emotion estimation function to analyze the emotion of the user when asking a question in real time and provide additional information to elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotion of the user when asking a question in real time and provide additional information to elicit positive emotions. For example, if the user is excited, additional related and interesting topics can be provided. The answer generation unit can also use voice analysis technology to estimate the user's emotion and provide information to elicit positive emotions. Furthermore, the answer generation unit can use facial expression analysis technology to estimate the user's emotion and provide information to elicit positive emotions. In this way, the emotion estimation function can be used to analyze the emotion of the user when asking a question in real time and provide additional information to elicit positive emotions, thereby improving user satisfaction.

[0098] The search unit can analyze the user's past listening patterns and present the parts that are most likely to be missed in advance. For example, a system can be constructed that analyzes the user's past listening patterns and presents the parts that are most likely to be missed in advance. For example, parts that the user frequently rewinds can be identified and displayed in advance. The search unit can also learn the listening patterns and predict parts that the user is likely to miss. Furthermore, the search unit can highlight parts that are most likely to be missed based on the user's listening patterns. In this way, convenience for the user can be improved by analyzing the user's past listening patterns and presenting the parts that are most likely to be missed in advance.

[0099] When searching for a missed portion, the generation AI can automatically generate a summary, allowing the user to grasp the content in a short amount of time. For example, when searching for a missed portion, the generation AI can automatically generate a summary, allowing the user to grasp the content in a short amount of time. For example, the generation AI can concisely summarize the main points of a long chapter. The generation AI can also use a summarization algorithm to extract important points and compress information. Furthermore, the generation AI can provide summaries in response to user questions, allowing the user to grasp the content in a short amount of time. This improves user convenience by allowing the generation AI to automatically generate summaries, allowing the user to grasp the content in a short amount of time.

[0100] The emotion estimation function can analyze the emotion of a user when searching for a missed portion and provide additional information to reduce stress. For example, the emotion of a user when searching for a missed portion can be analyzed in real time and additional information to reduce stress can be provided. For example, if the user is feeling anxious, information to help the user relax can be provided. The emotion estimation function can also estimate the user's emotion using voice analysis technology and provide information to reduce stress. Furthermore, the emotion estimation function can estimate the user's emotion using facial expression analysis technology and provide information to reduce stress. In this way, the emotion estimation function can analyze the emotion of a user when searching for a missed portion and provide additional information to reduce stress, thereby improving user convenience.

[0101] The search unit can search for the missed portion and simultaneously present other related chapters and sections, making it easier to understand the overall flow. For example, in addition to searching for the missed portion, other related chapters and sections can be simultaneously presented, making it easier to understand the overall flow. For example, chapters before and after the portion the user missed can be displayed. The search unit can also automatically select and present highly relevant chapters and sections. Furthermore, the search unit can provide related information based on the user's listening patterns. This makes it easier to understand the overall flow by searching for the missed portion and simultaneously presenting other related chapters and sections.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The question receiving unit receives a question from the user through speech. For example, the user can ask, "Please tell me the meaning of the technical terms explained in this chapter." The question receiving unit can also convert the user's speech into text data using speech recognition technology. Step 2: The search unit searches the contents of the book based on the question received by the question receiving unit. For example, the search unit may use a keyword search to find relevant parts of the book. The search unit may also use a full-text search to find detailed information. Furthermore, the search unit may use a metadata search to find a specific chapter or section. Step 3: The answer generation unit generates an answer based on the content searched by the search unit. For example, the answer generation unit generates an appropriate answer using a generation AI (e.g., a text generation AI or a multimodal generation AI). The answer generation unit can also provide a detailed explanation based on the search results. Furthermore, the answer generation unit can also provide additional information related to the user's question.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. a reception unit that receives questions by voice from a user; a search unit that searches the contents of a book based on the question received by the question receiving unit; an answer generation unit that generates an answer based on the content searched by the search unit; A system characterized by:

2. The search section is Predicts relevant information based on the user's past search history and presents it in advance 2. The system of claim 1.

3. The search section is In addition to searching the contents of the book, it also searches related external resources to provide comprehensive information 2. The system of claim 1.

4. The search section is Analyzes the user's past listening patterns and presents the parts they are most likely to miss in advance 2. The system of claim 1.

5. The voice interface is Analyzes the tone and speed of the user's voice to generate the optimal response 2. The system of claim 1.

6. The emotion estimation function Analyze users' emotions when using learning support functions and provide additional information to elicit positive emotions 2. The system of claim 1.

7. The answer generation unit: Analyze user emotions and provide answers that correspond to those emotions 2. The system of claim 1.

8. The emotion estimation function Analyze users' emotions when searching for missed parts and provide additional information to reduce stress 2. The system of claim 1.

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