System

Abstract

An object of a system according to an embodiment is to provide a quick and accurate answer to a question that a user does not know.SOLUTION: A system includes a photographing unit, an answer generation unit, and a question reception unit. The picture-taking unit allows a user to take a picture of a question. The answer generation unit analyzes the photograph of the question taken by the photograph taking unit and generates an answer. The question reception unit receives an additional question from a user.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 for users to quickly and accurately find answers to questions they do not understand.

[0005] The system according to the embodiment aims to provide a quick and accurate answer to a question that the user does not understand. [Means for solving the problem]

[0006] The system according to the embodiment includes a photography unit, an answer generation unit, and a question reception unit. The photography unit allows a user to take a photo of a question. The answer generation unit analyzes the photo of the question taken by the photography unit and generates an answer. The question reception unit receives additional questions from the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide a quick and accurate answer to a question that the user does not understand. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Dekisugi AI system according to an embodiment of the present invention is a system in which a user takes a photo of a problem, a generation AI generates an answer, and accepts follow-up questions. This allows the Dekisugi AI system to quickly and accurately provide an answer and respond to follow-up questions simply by the user taking a photo of the problem.

[0029] The Dekisugi AI system according to the embodiment includes a photography unit, an answer generation unit, and a question reception unit. The photography unit allows a user to take a photo of a question. For example, the photo of the question can be taken using a smartphone camera. The photography unit also has a function of sending the taken photo to the Dekisugi AI. The answer generation unit analyzes the photo of the question taken by the photography unit and generates an answer. For example, the generation AI can analyze the content of the question using image recognition technology and generate an appropriate answer. The generation AI can also generate answers based on a trained database. The question reception unit accepts follow-up questions from the user. For example, the user can ask a question by text input or voice input. The question reception unit sends the question to the generation AI and generates an appropriate answer. In this way, the Dekisugi AI system according to the embodiment allows a user to take a photo of a question, generate an answer, and accept follow-up questions. For example, if a user comes across a problem they don't understand while doing homework or studying for a qualification exam, the Dekisugi AI system can quickly and accurately provide an answer and respond to follow-up questions.

[0030] The photography unit can evaluate the quality of the photograph taken and correct or enhance the image as necessary. For example, when a user takes a photo of a question, the generation AI automatically evaluates the image's resolution and brightness and adjusts the image's sharpness and contrast as necessary. For example, it brightens photos taken in dark environments. The photography unit can also remove noise from images and emphasize text and figures. For example, if handwritten text is unclear, it can emphasize the text to make it easier to read. This improves the accuracy of answer generation by evaluating the quality of the photograph taken and correcting or enhancing it as necessary.

[0031] The answer generation unit can provide related supplemental information based on the photograph. For example, the answer generation unit analyzes a photo of a problem taken by the user, and the generation AI automatically searches for and displays similar questions. For example, it presents past exam questions and practice questions. The answer generation unit can also provide related theory and additional example problems. For example, for a math problem, it can provide related formulas and solution steps. Furthermore, the answer generation unit can provide references and academic papers. For example, for an English grammar question, it can provide grammar rules and example sentences. This provides related supplemental information based on the photograph, deepening the user's understanding.

[0032] The answer generation unit can evaluate the difficulty of the questions and provide answers appropriate to the user's learning level. For example, the answer generation unit analyzes photos of questions taken by the user, and the generation AI automatically evaluates the difficulty of the questions. For example, the difficulty level is scored based on the content and format of the questions. The answer generation unit can also adjust answers based on the user's past performance and current level of understanding. For example, it can provide answers for beginners or answers for advanced learners. Furthermore, the answer generation unit can track the user's learning progress and provide answers appropriate to the user's learning level. For example, it can customize answers based on past learning history. This improves learning effectiveness by evaluating the difficulty of questions and providing answers appropriate to the user's learning level.

[0033] The answer generation unit can provide related video explanations based on the photo of the question. For example, the answer generation unit analyzes photos of questions taken by the user, and the generation AI automatically searches for and displays related video explanations. For example, it links to videos on YouTube or educational platforms. The answer generation unit can also summarize the content of the video explanation and provide it to the user. For example, it can display the important points of the video in text. Furthermore, the answer generation unit can suggest related video explanations based on the user's learning history. For example, it can suggest new videos based on videos watched in the past. This deepens the user's understanding by providing related video explanations based on the photo of the question.

[0034] The answer generation unit can evaluate the reliability of answers and provide highly reliable answers preferentially. For example, when the generation AI generates answers from a trained database, the answer generation unit uses an algorithm to evaluate the reliability of the answer. For example, the evaluation is based on the reliability of the information source in the database and the consistency of the answers. The answer generation unit can also evaluate reliability based on the user's past answer history. For example, answers with a high past accuracy rate can be provided preferentially. Furthermore, the answer generation unit can also preferentially use highly reliable information sources. For example, answers can be generated based on academic papers and specialized books. This makes it possible to evaluate the reliability of answers and provide highly reliable answers preferentially, thereby providing highly reliable information to the user.

[0035] The answer generation unit can suggest related learning resources when an answer is provided. For example, the answer generation unit automatically suggests related learning resources when the generation AI provides an answer from a learned database. For example, it displays the chapter or page of a reference book related to the answer. The answer generation unit can also suggest online courses and practice problems. For example, it displays a link to a related online course after the user receives an answer. Furthermore, the answer generation unit can suggest related learning resources based on the user's learning history. For example, it can suggest new resources based on content that has been learned in the past. In this way, the answer generation unit supports the user's learning by suggesting related learning resources when an answer is provided.

[0036] The answer generation unit can refer to the user's past learning history and provide individually customized answers. For example, the generation AI of the answer generation unit refers to the user's past learning history and provides individually customized answers. For example, the answer is adjusted based on the content previously learned and the accuracy rate of answers. The answer generation unit can also track the user's learning progress and provide individually customized answers. For example, it provides answers based on the user's current level of understanding. Furthermore, the answer generation unit can provide answers based on the user's learning style. For example, it provides diagrams for visual learners and audio explanations for auditory learners. In this way, the learning effect is improved by referring to the user's past learning history and providing individually customized answers.

[0037] The answer generation unit can present relevant real-world application examples when providing an answer. For example, the answer generation unit automatically presents relevant real-world application examples when the generation AI provides an answer from a trained database. For example, it displays actual application examples of mathematical formulas. The answer generation unit can also suggest relevant real-world application examples based on the user's learning history. For example, it presents new application examples based on content learned in the past. Furthermore, the answer generation unit can provide use cases in real-world projects or industries. For example, it presents real-world application examples based on the results of scientific experiments. This deepens the user's understanding by presenting relevant real-world application examples when providing an answer.

[0038] The question reception unit can analyze the intent of a user's question and provide the optimal answer. For example, when a user asks a question by text input or voice input, the question reception unit uses natural language processing technology to analyze the intent of the question. For example, in response to a question such as "I don't know how to use this formula," the generation AI can provide a specific example of use. The question reception unit can also analyze the intent of a question based on the user's past question history. For example, if a similar question has been asked in the past, the answer can be used as a reference. Furthermore, the question reception unit can provide the optimal answer based on the user's learning history. For example, it can provide an answer based on the user's current level of understanding. In this way, the intent of the user's question can be analyzed and the optimal answer can be provided, quickly resolving the user's concerns.

[0039] The question receiving unit can provide additional relevant information when providing an answer to a question. For example, when a user inputs a question, the question receiving unit allows the generation AI to automatically provide additional relevant information. For example, similar questions and FAQs can be displayed, allowing the user to access other related information. The question receiving unit can also provide related theories and additional example problems. For example, for a math problem, it can provide related formulas and solution steps. Furthermore, the question receiving unit can provide references and academic papers. For example, for an English grammar question, it can provide grammar rules and example sentences. This provides additional relevant information when providing an answer to a question, thereby deepening the user's understanding.

[0040] The question receiving unit can provide video explanations related to a user's question. For example, when a user inputs a question, the generation AI automatically searches for and displays related video explanations. For example, it links to videos on YouTube or educational platforms. The question receiving unit can also summarize the content of the video explanation and provide it to the user. For example, it can display the important points of the video in text. Furthermore, the question receiving unit can suggest related video explanations based on the user's learning history. For example, it can suggest new videos based on videos watched in the past. This deepens the user's understanding by providing video explanations related to the user's question.

[0041] The question receiving unit can provide an answer format that suits the user's learning style. For example, the question receiving unit uses a generation AI to analyze the user's learning style and select the optimal answer format. For example, it provides text answers to users who prefer text format and video answers to users who prefer visual learning. The question receiving unit can also adjust the answer format based on the user's learning history. For example, it can provide diagrams to visual learners and audio explanations to auditory learners. Furthermore, the question receiving unit can track the user's learning progress and provide an answer format that suits the user's learning style. For example, it can provide an answer format that suits the user's current level of understanding. This improves learning effectiveness by providing an answer format that suits the user's learning style.

[0042] The answer generation unit can analyze data from a problem set provided by the user and classify the difficulty and category of the questions. For example, the answer generation unit analyzes the data from a problem set provided by the user, and the generation AI automatically evaluates the difficulty of the questions. For example, the difficulty is scored based on the content and format of the questions. The answer generation unit can also classify the category of the questions. For example, for a math problem set, it can classify the questions into categories such as algebra, geometry, and calculus. Furthermore, the answer generation unit can adjust the difficulty and category of the questions based on the user's learning history. For example, it adjusts the difficulty of the questions based on past learning history. In this way, by analyzing the data from a problem set provided by the user and classifying the difficulty and category of the questions, learning efficiency is improved.

[0043] The answer generation unit can provide relevant supplementary information when studying a problem set. For example, when a user studies a problem set, the generation AI automatically provides relevant supplementary information. For example, it displays links to explanatory videos and reference materials. The answer generation unit can also provide related theories and additional example problems. For example, for math problems, it can show relevant formulas and solution steps. Furthermore, the answer generation unit can provide references and academic papers. For example, for English grammar questions, it can provide grammar rules and example sentences. This provides relevant supplementary information when studying a problem set, deepening the user's understanding.

[0044] The answer generation unit can analyze data on problem sets provided by the user and suggest other related problem sets or teaching materials. For example, the answer generation unit analyzes data on problem sets provided by the user, and the generation AI automatically suggests other related problem sets or teaching materials. For example, it displays problem sets with the same theme or difficulty level. The answer generation unit can also suggest related teaching materials based on the user's learning history. For example, it can suggest new teaching materials based on content learned in the past. Furthermore, the answer generation unit can track the user's learning progress and suggest other related problem sets or teaching materials. For example, it can suggest teaching materials based on the user's current level of understanding. In this way, the data on problem sets provided by the user can be analyzed and other related problem sets or teaching materials can be suggested, thereby broadening the scope of learning.

[0045] The answer generation unit can track the user's learning progress when studying a problem set and provide feedback according to the progress. For example, when a user studies a problem set, the generation AI automatically tracks the learning progress. For example, it records the percentage of correct answers and the study time. The answer generation unit can also provide feedback according to the user's learning progress. For example, it can provide additional practice problems if progress is lagging behind. Furthermore, the answer generation unit can evaluate progress based on the user's learning history and provide appropriate feedback. For example, it can evaluate progress based on past learning history and indicate areas for improvement. In this way, the efficiency of learning can be improved by tracking the user's learning progress when studying a problem set and providing feedback according to the progress.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The Dekisugi AI system can also be equipped with customization functions according to the user's learning style. For example, it can provide answers that make extensive use of diagrams and videos for visual learners, and audio explanations for auditory learners. It can also provide interactive questions for tactile learners. It can also recommend the optimal learning style based on the user's learning history. This can maximize learning effectiveness by providing customized answers according to the user's learning style.

[0048] The Dekisugi AI system can also track a user's learning progress and provide feedback according to their progress. For example, when a user receives an answer, it can evaluate their progress based on their past learning history and provide appropriate feedback. If progress is slow, it can provide additional practice questions or supplementary materials. If progress is going well, it can provide advice on how to move on to the next step. In this way, by tracking a user's learning progress and providing feedback according to their progress, it is possible to improve learning efficiency.

[0049] The Dekisugi AI system can also provide individually customized study plans based on the user's learning history. For example, it can suggest the optimal study plan for the user based on their past learning history and the accuracy rate of their answers. The study plan can also include daily learning goals and progress management functions. It can also suggest learning materials and resources that suit the user's learning style. This maximizes learning effectiveness by providing an individually customized study plan based on the user's learning history.

[0050] The Dekisugi AI system can also suggest relevant real-world applications based on the user's learning history. For example, it can automatically suggest relevant real-world applications based on what the user has learned. It can display real-world applications of mathematical formulas or real-world applications based on the results of scientific experiments. It can also suggest new applications based on the user's learning history. It can even provide use cases in real-world projects and industries. This helps deepen the user's understanding by connecting what they have learned to the real world.

[0051] The Dekisugi AI system can also analyze the intent of a user's question and provide the most appropriate answer. For example, when a user asks a question by text input or voice input, the generative AI uses natural language processing technology to analyze the intent of the question. Specific use cases and related theories can be provided. The system can also analyze the intent of the question based on the user's past question history and provide the most appropriate answer. Furthermore, it can provide the most appropriate answer based on the user's learning history. This allows the system to analyze the intent of the user's question and provide the most appropriate answer, quickly resolving the user's concerns.

[0052] The Dekisugi AI system can also provide relevant video explanations in response to user questions. For example, when a user enters a question, the generation AI automatically searches for and displays relevant video explanations. It can link to videos on YouTube or educational platforms. It can also summarize the content of the video explanations and provide it to the user. It can also suggest related video explanations based on the user's learning history. This allows the user to deepen their understanding by providing relevant video explanations in response to their question.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The user takes a photo of the problem using the photography unit. For example, the user can take the photo using a smartphone camera. The photography unit also has the function of sending the taken photo to Dekisugi AI. Step 2: The answer generation unit analyzes the photo of the problem taken by the photography unit and generates an answer. For example, the generation AI can analyze the content of the problem using image recognition technology and generate an appropriate answer. The generation AI can also generate answers based on a trained database. Step 3: The question accepting unit accepts additional questions from the user. For example, the user can ask questions by text input or voice input. The question accepting unit sends the questions to the generation AI, which generates appropriate answers.

[0055] (Example 2) The Dekisugi AI system according to an embodiment of the present invention is a system in which a user takes a photo of a problem, a generation AI generates an answer, and accepts follow-up questions. This allows the Dekisugi AI system to quickly and accurately provide an answer and respond to follow-up questions simply by the user taking a photo of the problem.

[0056] The Dekisugi AI system according to the embodiment includes a photography unit, an answer generation unit, and a question reception unit. The photography unit allows a user to take a photo of a question. For example, the photo of the question can be taken using a smartphone camera. The photography unit also has a function of sending the taken photo to the Dekisugi AI. The answer generation unit analyzes the photo of the question taken by the photography unit and generates an answer. For example, the generation AI can analyze the content of the question using image recognition technology and generate an appropriate answer. The generation AI can also generate answers based on a trained database. The question reception unit accepts follow-up questions from the user. For example, the user can ask a question by text input or voice input. The question reception unit sends the question to the generation AI and generates an appropriate answer. In this way, the Dekisugi AI system according to the embodiment allows a user to take a photo of a question, generate an answer, and accept follow-up questions. For example, if a user comes across a problem they don't understand while doing homework or studying for a qualification exam, the Dekisugi AI system can quickly and accurately provide an answer and respond to follow-up questions.

[0057] The photography unit can evaluate the quality of the photograph taken and correct or enhance the image as necessary. For example, when a user takes a photo of a question, the generation AI automatically evaluates the image's resolution and brightness and adjusts the image's sharpness and contrast as necessary. For example, it brightens photos taken in dark environments. The photography unit can also remove noise from images and emphasize text and figures. For example, if handwritten text is unclear, it can emphasize the text to make it easier to read. This improves the accuracy of answer generation by evaluating the quality of the photograph taken and correcting or enhancing it as necessary.

[0058] The answer generation unit can provide related supplemental information based on the photograph. For example, the answer generation unit analyzes a photo of a problem taken by the user, and the generation AI automatically searches for and displays similar questions. For example, it presents past exam questions and practice questions. The answer generation unit can also provide related theory and additional example problems. For example, for a math problem, it can provide related formulas and solution steps. Furthermore, the answer generation unit can provide references and academic papers. For example, for an English grammar question, it can provide grammar rules and example sentences. This provides related supplemental information based on the photograph, deepening the user's understanding.

[0059] The answer generation unit can estimate the user's emotions and provide advice to reduce stress. For example, when the user takes a photo of the problem, the generation AI analyzes their facial expressions and voice tone to estimate their stress level. For example, if the user is nervous, the generation AI can display advice to relax. The answer generation unit can also analyze the user's emotions when solving the problem and provide positive feedback. For example, it can display messages such as "Good job!" or "Keep it up!". The answer generation unit can also provide relaxation techniques and positive feedback. For example, it can show how to take deep breaths or stretch. This allows the user's emotions to be reflected in the evaluation, enabling a more comprehensive evaluation.

[0060] The answer generation unit can evaluate the difficulty of the questions and provide answers appropriate to the user's learning level. For example, the answer generation unit analyzes photos of questions taken by the user, and the generation AI automatically evaluates the difficulty of the questions. For example, the difficulty level is scored based on the content and format of the questions. The answer generation unit can also adjust answers based on the user's past performance and current level of understanding. For example, it can provide answers for beginners or answers for advanced learners. Furthermore, the answer generation unit can track the user's learning progress and provide answers appropriate to the user's learning level. For example, it can customize answers based on past learning history. This improves learning effectiveness by evaluating the difficulty of questions and providing answers appropriate to the user's learning level.

[0061] The answer generation unit can provide related video explanations based on the photo of the question. For example, the answer generation unit analyzes photos of questions taken by the user, and the generation AI automatically searches for and displays related video explanations. For example, it links to videos on YouTube or educational platforms. The answer generation unit can also summarize the content of the video explanation and provide it to the user. For example, it can display the important points of the video in text. Furthermore, the answer generation unit can suggest related video explanations based on the user's learning history. For example, it can suggest new videos based on videos watched in the past. This deepens the user's understanding by providing related video explanations based on the photo of the question.

[0062] The answer generation unit can estimate the user's emotions and provide an interface for eliciting positive emotions. For example, when the user takes a photo of a problem, the generation AI analyzes the user's facial expressions and vocal tone to provide an interface for eliciting positive emotions. For example, it plays an encouraging message or positive music. The answer generation unit can also analyze the user's emotions and provide positive feedback. For example, it can display messages such as "Good job!" or "Keep it up!". Furthermore, the answer generation unit can track the user's emotions and provide an interface for improving their motivation to learn. For example, it can set goals and manage progress to increase motivation. In this way, the user's motivation to learn is improved by estimating their emotions and providing an interface for eliciting positive emotions.

[0063] The answer generation unit can evaluate the reliability of answers and provide highly reliable answers preferentially. For example, when the generation AI generates answers from a trained database, the answer generation unit uses an algorithm to evaluate the reliability of the answer. For example, the evaluation is based on the reliability of the information source in the database and the consistency of the answers. The answer generation unit can also evaluate reliability based on the user's past answer history. For example, answers with a high past accuracy rate can be provided preferentially. Furthermore, the answer generation unit can also preferentially use highly reliable information sources. For example, answers can be generated based on academic papers and specialized books. This makes it possible to evaluate the reliability of answers and provide highly reliable answers preferentially, thereby providing highly reliable information to the user.

[0064] The answer generation unit can suggest related learning resources when an answer is provided. For example, the answer generation unit automatically suggests related learning resources when the generation AI provides an answer from a learned database. For example, it displays the chapter or page of a reference book related to the answer. The answer generation unit can also suggest online courses and practice problems. For example, it displays a link to a related online course after the user receives an answer. Furthermore, the answer generation unit can suggest related learning resources based on the user's learning history. For example, it can suggest new resources based on content that has been learned in the past. In this way, the answer generation unit supports the user's learning by suggesting related learning resources when an answer is provided.

[0065] The answer generation unit can estimate the user's emotions and provide additional explanations to improve comprehension. For example, when the user receives an answer, the generation AI analyzes the user's facial expressions and tone of voice and provides additional explanations to improve comprehension. For example, if the user is confused, a detailed explanation is displayed. The answer generation unit can also analyze the user's emotions and provide visual aids to improve comprehension. For example, explanations are provided using illustrations or videos. Furthermore, the answer generation unit can provide additional explanations based on the user's learning history. For example, a more detailed explanation is provided for content that was difficult to understand in the past. This improves learning effectiveness by estimating the user's emotions and providing additional explanations to improve comprehension.

[0066] The answer generation unit can refer to the user's past learning history and provide individually customized answers. For example, the generation AI of the answer generation unit refers to the user's past learning history and provides individually customized answers. For example, the answer is adjusted based on the content previously learned and the accuracy rate of answers. The answer generation unit can also track the user's learning progress and provide individually customized answers. For example, it provides answers based on the user's current level of understanding. Furthermore, the answer generation unit can provide answers based on the user's learning style. For example, it provides diagrams for visual learners and audio explanations for auditory learners. In this way, the learning effect is improved by referring to the user's past learning history and providing individually customized answers.

[0067] The answer generation unit can present relevant real-world application examples when providing an answer. For example, the answer generation unit automatically presents relevant real-world application examples when the generation AI provides an answer from a trained database. For example, it displays actual application examples of mathematical formulas. The answer generation unit can also suggest relevant real-world application examples based on the user's learning history. For example, it presents new application examples based on content learned in the past. Furthermore, the answer generation unit can provide use cases in real-world projects or industries. For example, it presents real-world application examples based on the results of scientific experiments. This deepens the user's understanding by presenting relevant real-world application examples when providing an answer.

[0068] The answer generation unit can estimate the user's emotions and provide feedback to elicit positive emotions. For example, when the user receives an answer, the generation AI analyzes the user's facial expressions and voice tone and provides feedback to elicit positive emotions. For example, messages such as "Good job!" or "Keep it up!" are displayed. The answer generation unit can also analyze the user's emotions and provide positive feedback. For example, an encouraging message is displayed if the user is confused. Furthermore, the answer generation unit can track the user's emotions and provide feedback to improve their motivation to learn. For example, goal setting and progress management are performed to increase motivation. In this way, the user's motivation to learn is improved by estimating the user's emotions and providing feedback to elicit positive emotions.

[0069] The question reception unit can analyze the intent of a user's question and provide the optimal answer. For example, when a user asks a question by text input or voice input, the question reception unit uses natural language processing technology to analyze the intent of the question. For example, in response to a question such as "I don't know how to use this formula," the generation AI can provide a specific example of use. The question reception unit can also analyze the intent of a question based on the user's past question history. For example, if a similar question has been asked in the past, the answer can be used as a reference. Furthermore, the question reception unit can provide the optimal answer based on the user's learning history. For example, it can provide an answer based on the user's current level of understanding. In this way, the intent of the user's question can be analyzed and the optimal answer can be provided, quickly resolving the user's concerns.

[0070] The question receiving unit can provide additional relevant information when providing an answer to a question. For example, when a user inputs a question, the question receiving unit allows the generation AI to automatically provide additional relevant information. For example, similar questions and FAQs can be displayed, allowing the user to access other related information. The question receiving unit can also provide related theories and additional example problems. For example, for a math problem, it can provide related formulas and solution steps. Furthermore, the question receiving unit can provide references and academic papers. For example, for an English grammar question, it can provide grammar rules and example sentences. This provides additional relevant information when providing an answer to a question, thereby deepening the user's understanding.

[0071] The question receiving unit can estimate the user's emotions and provide advice to reduce stress. For example, when a user inputs a question, the question receiving unit uses a generation AI to analyze facial expressions and voice tone to estimate the stress level. For example, if the user is nervous, the question receiving unit can display advice to relax. The question receiving unit can also analyze the emotions the user feels when asking a question and provide positive feedback. For example, it can display messages such as "Good job!" or "Keep it up!" The question receiving unit can also provide relaxation techniques and positive feedback. For example, it can show how to take deep breaths or stretch. This improves the user's learning experience by estimating the user's emotions and providing advice to reduce stress.

[0072] The question receiving unit can provide video explanations related to a user's question. For example, when a user inputs a question, the generation AI automatically searches for and displays related video explanations. For example, it links to videos on YouTube or educational platforms. The question receiving unit can also summarize the content of the video explanation and provide it to the user. For example, it can display the important points of the video in text. Furthermore, the question receiving unit can suggest related video explanations based on the user's learning history. For example, it can suggest new videos based on videos watched in the past. This deepens the user's understanding by providing video explanations related to the user's question.

[0073] The question receiving unit can provide an answer format that suits the user's learning style. For example, the question receiving unit uses a generation AI to analyze the user's learning style and select the optimal answer format. For example, it provides text answers to users who prefer text format and video answers to users who prefer visual learning. The question receiving unit can also adjust the answer format based on the user's learning history. For example, it can provide diagrams to visual learners and audio explanations to auditory learners. Furthermore, the question receiving unit can track the user's learning progress and provide an answer format that suits the user's learning style. For example, it can provide an answer format that suits the user's current level of understanding. This improves learning effectiveness by providing an answer format that suits the user's learning style.

[0074] The question receiving unit can estimate the user's emotions and provide an interface for eliciting positive emotions. For example, when a user inputs a question, the question receiving unit uses a generation AI to analyze facial expressions and voice tones to provide an interface for eliciting positive emotions. For example, it can play an encouraging message or positive music. The question receiving unit can also analyze the user's emotions and provide positive feedback. For example, it can display messages such as "Good job!" or "Keep it up!". Furthermore, the question receiving unit can track the user's emotions and provide an interface for improving their motivation to learn. For example, it can set goals and manage progress to increase motivation. In this way, the user's emotions can be estimated and an interface for eliciting positive emotions can be provided, thereby improving their motivation to learn.

[0075] The answer generation unit can analyze data from a problem set provided by the user and classify the difficulty and category of the questions. For example, the answer generation unit analyzes the data from a problem set provided by the user, and the generation AI automatically evaluates the difficulty of the questions. For example, the difficulty is scored based on the content and format of the questions. The answer generation unit can also classify the category of the questions. For example, for a math problem set, it can classify the questions into categories such as algebra, geometry, and calculus. Furthermore, the answer generation unit can adjust the difficulty and category of the questions based on the user's learning history. For example, it adjusts the difficulty of the questions based on past learning history. In this way, by analyzing the data from a problem set provided by the user and classifying the difficulty and category of the questions, learning efficiency is improved.

[0076] The answer generation unit can provide relevant supplementary information when studying a problem set. For example, when a user studies a problem set, the generation AI automatically provides relevant supplementary information. For example, it displays links to explanatory videos and reference materials. The answer generation unit can also provide related theories and additional example problems. For example, for math problems, it can show relevant formulas and solution steps. Furthermore, the answer generation unit can provide references and academic papers. For example, for English grammar questions, it can provide grammar rules and example sentences. This provides relevant supplementary information when studying a problem set, deepening the user's understanding.

[0077] The answer generation unit can estimate the user's emotions and provide advice to improve learning efficiency. For example, when a user is studying a problem set, the generation AI analyzes their facial expressions and voice tone to provide advice to improve learning efficiency. For example, it may recommend taking a break if the user is tired. The answer generation unit can also analyze the user's emotions and provide positive feedback. For example, it may display messages such as "Good job!" or "Keep it up!". Furthermore, the answer generation unit can provide relaxation techniques and positive feedback. For example, it may show how to take deep breaths or stretch. This improves learning effectiveness by estimating the user's emotions and providing advice to improve learning efficiency.

[0078] The answer generation unit can analyze data on problem sets provided by the user and suggest other related problem sets or teaching materials. For example, the answer generation unit analyzes data on problem sets provided by the user, and the generation AI automatically suggests other related problem sets or teaching materials. For example, it displays problem sets with the same theme or difficulty level. The answer generation unit can also suggest related teaching materials based on the user's learning history. For example, it can suggest new teaching materials based on content learned in the past. Furthermore, the answer generation unit can track the user's learning progress and suggest other related problem sets or teaching materials. For example, it can suggest teaching materials based on the user's current level of understanding. In this way, the data on problem sets provided by the user can be analyzed and other related problem sets or teaching materials can be suggested, thereby broadening the scope of learning.

[0079] The answer generation unit can track the user's learning progress when studying a problem set and provide feedback according to the progress. For example, when a user studies a problem set, the generation AI automatically tracks the learning progress. For example, it records the percentage of correct answers and the study time. The answer generation unit can also provide feedback according to the user's learning progress. For example, it can provide additional practice problems if progress is lagging behind. Furthermore, the answer generation unit can evaluate progress based on the user's learning history and provide appropriate feedback. For example, it can evaluate progress based on past learning history and indicate areas for improvement. In this way, the efficiency of learning can be improved by tracking the user's learning progress when studying a problem set and providing feedback according to the progress.

[0080] The answer generation unit can estimate the user's emotions and provide an interface for eliciting positive emotions. For example, when a user is studying a problem set, the generation AI analyzes their facial expressions and vocal tone to provide an interface for eliciting positive emotions. For example, it plays encouraging messages or positive music. The answer generation unit can also analyze the user's emotions and provide positive feedback. For example, it displays messages such as "Good job!" or "Keep it up!". Furthermore, the answer generation unit can track the user's emotions and provide an interface for improving their motivation to learn. For example, it can set goals and manage progress to increase motivation. In this way, the user's motivation to learn is improved by estimating their emotions and providing an interface for eliciting positive emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The Dekisugi AI system can also be equipped with customization functions according to the user's learning style. For example, it can provide answers that make extensive use of diagrams and videos for visual learners, and audio explanations for auditory learners. It can also provide interactive questions for tactile learners. It can also recommend the optimal learning style based on the user's learning history. This can maximize learning effectiveness by providing customized answers according to the user's learning style.

[0083] The Dekisugi AI system can also track a user's learning progress and provide feedback according to their progress. For example, when a user receives an answer, it can evaluate their progress based on their past learning history and provide appropriate feedback. If progress is slow, it can provide additional practice questions or supplementary materials. If progress is going well, it can provide advice on how to move on to the next step. In this way, by tracking a user's learning progress and providing feedback according to their progress, it is possible to improve learning efficiency.

[0084] The Dekisugi AI system can also provide individually customized study plans based on the user's learning history. For example, it can suggest the optimal study plan for the user based on their past learning history and the accuracy rate of their answers. The study plan can also include daily learning goals and progress management functions. It can also suggest learning materials and resources that suit the user's learning style. This maximizes learning effectiveness by providing an individually customized study plan based on the user's learning history.

[0085] The Dekisugi AI system can also estimate the user's emotions and provide advice to improve learning efficiency. For example, when a user solves a problem, the generation AI analyzes their facial expressions and voice tone to provide advice to improve learning efficiency. If the user is tired, it will recommend taking a break. If the user is concentrating, it will advise them to continue studying. It can also provide relaxation techniques and positive feedback. This makes it possible to improve learning effectiveness by estimating the user's emotions and providing advice to improve learning efficiency.

[0086] The Dekisugi AI system can further estimate the user's emotions and provide an interface to elicit positive emotions. For example, when a user solves a problem, the generative AI analyzes their facial expressions and vocal tone to provide an interface to elicit positive emotions. It can play encouraging messages or positive music. It can also analyze the user's emotions and provide positive feedback. It can also track the user's emotions and provide an interface to improve their motivation to learn. This can improve their motivation to learn by providing an interface to estimate the user's emotions and elicit positive emotions.

[0087] The Dekisugi AI system can also suggest relevant real-world applications based on the user's learning history. For example, it can automatically suggest relevant real-world applications based on what the user has learned. It can display real-world applications of mathematical formulas or real-world applications based on the results of scientific experiments. It can also suggest new applications based on the user's learning history. It can even provide use cases in real-world projects and industries. This helps deepen the user's understanding by connecting what they have learned to the real world.

[0088] The Dekisugi AI system can also estimate the user's emotions and provide additional explanations to improve comprehension. For example, when the user receives an answer, the generation AI analyzes facial expressions and tone of voice to provide additional explanations to improve comprehension. If the user is confused, a detailed explanation is displayed. The system can also analyze the user's emotions and provide visual aids to improve comprehension. Furthermore, it can provide additional explanations based on the user's learning history. This can improve learning effectiveness by estimating the user's emotions and providing additional explanations to improve comprehension.

[0089] The Dekisugi AI system can also analyze the intent of a user's question and provide the most appropriate answer. For example, when a user asks a question by text input or voice input, the generative AI uses natural language processing technology to analyze the intent of the question. Specific use cases and related theories can be provided. The system can also analyze the intent of the question based on the user's past question history and provide the most appropriate answer. Furthermore, it can provide the most appropriate answer based on the user's learning history. This allows the system to analyze the intent of the user's question and provide the most appropriate answer, quickly resolving the user's concerns.

[0090] The Dekisugi AI system can further estimate the user's emotions and provide advice to reduce stress. For example, when a user inputs a question, the generative AI analyzes their facial expressions and voice tone to estimate their stress level. If they are tense, it displays advice to relax. It can also analyze the user's emotions and provide positive feedback. It can also provide relaxation techniques and positive feedback. This improves the user's learning experience by estimating their emotions and providing advice to reduce stress.

[0091] The Dekisugi AI system can also provide relevant video explanations in response to user questions. For example, when a user enters a question, the generation AI automatically searches for and displays relevant video explanations. It can link to videos on YouTube or educational platforms. It can also summarize the content of the video explanations and provide it to the user. It can also suggest related video explanations based on the user's learning history. This allows the user to deepen their understanding by providing relevant video explanations in response to their question.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The user takes a photo of the problem using the photography unit. For example, the user can take the photo using a smartphone camera. The photography unit also has the function of sending the taken photo to Dekisugi AI. Step 2: The answer generation unit analyzes the photo of the problem taken by the photography unit and generates an answer. For example, the generation AI can analyze the content of the problem using image recognition technology and generate an appropriate answer. The generation AI can also generate answers based on a trained database. Step 3: The question accepting unit accepts additional questions from the user. For example, the user can ask questions by text input or voice input. The question accepting unit sends the questions to the generation AI, which generates appropriate answers.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 photography unit where the user takes a photo of the problem; an answer generation unit that analyzes the photograph of the question taken by the photography unit and generates an answer; a question receiving unit that receives additional questions from the user A system characterized by:

2. The photography unit is Evaluate the quality of the photographs taken and correct or enhance the images as needed 2. The system of claim 1.

3. The answer generation unit Evaluating the difficulty of the question and providing the answer according to the learning level of the user 2. The system of claim 1.

4. The answer generation unit Evaluate the reliability of the answers and provide the answers with higher reliability preferentially.

2. The system of claim 1.

5. The question receiving unit Analyzing the intent of the user's question and providing the most appropriate answer 2. The system of claim 1.

6. The answer generation unit Estimating the user's emotions and providing advice to reduce stress 2. The system of claim 1.

7. The answer generation unit Inferring the user's feelings and providing additional commentary to improve comprehension 2. The system of claim 1.

8. The question receiving unit To estimate the user's emotions and provide an interface for eliciting positive emotions.

2. The system of claim 1.

Images