system

Abstract

The system according to the embodiment aims to keep students interested in history and culture. [Solution] A system according to an embodiment includes a reception unit, a search unit, a generation unit, and a provision unit. The reception unit receives questions from students. The search unit analyzes the questions received by the reception unit and searches for related information. The generation unit generates answers based on the information searched by the search unit. The provision unit provides the answers generated by the generation unit to students.

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 technology has had the challenge of making it difficult for students to maintain an interest in history and culture.

[0005] The system according to the embodiment aims to keep students interested in history and culture. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a search unit, a generation unit, and a provision unit. The reception unit receives questions from students. The search unit analyzes the questions received by the reception unit and searches for related information. The generation unit generates answers based on the information searched by the search unit. The provision unit provides the answers generated by the generation unit to students. [Effects of the Invention]

[0007] The system according to the embodiment can keep students interested in history and culture. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A virtual history experience system according to an embodiment of the present invention is an interactive virtual history experience system that utilizes AI technology to enable modern students to gain a deeper understanding of history and culture. This system provides a rich, interactive virtual history experience at low cost. Specifically, students first select a historical or cultural theme of interest. Next, an AI generates a virtual history experience based on that theme. This virtual history experience is interactive, with students asking questions and the AI ​​providing appropriate answers. For example, if a student asks, "What was life like in a particular era?", the AI ​​will provide a detailed explanation of life in that era. Furthermore, the AI ​​provides information of an appropriate level of difficulty depending on the student's level of understanding. For example, it provides basic information to beginners and more detailed information to advanced learners. This allows students to learn at their own pace. Furthermore, because this system is provided at low cost, it can be introduced in many schools and educational institutions. This allows students to experience real history and culture, encouraging them to learn with interest. As a result, the virtual history experience system can deepen students' understanding of history and culture by providing appropriate information in response to their questions.

[0029] A virtual history experience system according to an embodiment includes a reception unit, a search unit, a generation unit, and a provision unit. The reception unit receives questions from students. The student's questions include, but are not limited to, questions about life and culture in a particular era. The reception unit may receive questions in text format, for example. The reception unit may also receive questions using voice input. For example, the reception unit may convert the student's voice into text using voice recognition technology and accept the text as a question. The search unit analyzes the question received by the reception unit and searches for related information. The search unit may analyze the question using natural language processing technology, for example. The search unit may also extract important parts of the question using keyword extraction technology and search for related information. For example, the search unit may search for related information from a database to find an answer to the question. The generation unit generates an answer based on the information searched by the search unit. The generation unit may generate an answer using, for example, template-based generation technology. The generation unit may also generate an answer using AI technology. For example, the generation unit may use a generation AI (e.g., LLM) to generate a detailed answer to the question. The providing unit provides the answer generated by the generating unit to the student. The providing unit provides the answer in text format, for example. The providing unit can also provide the answer in audio format. For example, the generated answer is provided to the student as audio using speech synthesis technology. This allows the virtual history experience system according to the embodiment to provide appropriate information in response to the student's question. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the answer using an AI model that receives the answer generated by the generating unit as input and outputs an answer.

[0030] The reception unit can select a history or cultural theme that interests the student. The reception unit, for example, provides an interface for the student to select a theme that interests them. For example, the reception unit can display a list of themes, allowing the student to select one from the list. The reception unit can also suggest themes that interest the student based on the student's past learning history and survey results. For example, the reception unit can suggest appropriate themes based on the themes that the student has studied in the past and the themes of interest that the student answered in the survey. This makes it possible to provide a virtual history experience based on the themes that interest the student. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's learning history data into a generation AI and have the generation AI suggest themes of interest.

[0031] The generation unit can analyze the student's question using AI and search for related information. The generation unit can analyze the student's question using, for example, natural language processing technology. For example, the generation unit can analyze the grammatical structure of the question and extract important keywords. The generation unit can also understand the intent of the question and search for related information using a machine learning algorithm. For example, the generation unit can classify the intent of the question and search for appropriate information based on that. The generation unit can also use deep learning technology to deeply understand the meaning of the question and search for related information. For example, the generation unit can understand the context of the question and search for relevant information based on that. This allows appropriate information for the question to be quickly searched for using AI. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI. For example, the generation unit can input the student's question data into the generation AI and have the generation AI search for related information.

[0032] The generation unit can provide information of appropriate difficulty level according to the student's level of understanding using AI. The generation unit, for example, conducts a test to evaluate the student's level of understanding. For example, the generation unit presents short quizzes or questions to the student and evaluates the student's level of understanding based on the student's answers. The generation unit can also analyze the student's past learning history to evaluate the student's level of understanding. For example, the generation unit evaluates the student's level of understanding based on the content the student has previously studied and test results. Furthermore, the generation unit can use AI technology to evaluate the student's level of understanding in real time and provide information of appropriate difficulty level based on the evaluation. For example, the generation unit can evaluate the accuracy and speed of the student's answers and adjust the difficulty level of the information based on the evaluation. This can improve learning effectiveness by providing information according to the student's level of understanding. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the student's level of understanding data into the generation AI and cause the generation AI to provide information of appropriate difficulty level.

[0033] The providing unit can provide the answer generated by the AI ​​to the student. The providing unit can provide the answer in text format, for example. For example, the providing unit can display the generated answer as text and provide it to the student. The providing unit can also provide the answer in audio format. For example, the providing unit can use speech synthesis technology to provide the generated answer as audio to the student. The providing unit can also provide the answer in an interactive format. For example, the providing unit can provide the generated answer to the student as an interactive chatbot, allowing the student to ask further questions. In this way, by using AI, the generated answer can be quickly provided to the student. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the generated answer data to the generation AI and cause the generation AI to provide the answer.

[0034] The provision unit can provide a cost-effective, rich, interactive virtual history experience. The provision unit, for example, reduces costs by utilizing cloud-based infrastructure. For example, the provision unit optimizes operational costs by dynamically allocating necessary resources using cloud services. The provision unit can also reduce costs by utilizing open-source software. For example, the provision unit reduces development costs by utilizing open-source natural language processing libraries and machine learning frameworks. Furthermore, the provision unit can incorporate high-quality graphics and interactive elements to provide a rich, interactive experience. For example, the provision unit can use 3D modeling technology to realistically recreate historical scenes and allow students to enjoy interacting within them. This allows many students to experience history and culture by providing a low-cost, rich, interactive virtual history experience. Some or all of the above-described processing in the provision unit may be performed using, or without, a generation AI. For example, the provision unit can input cloud service usage data into the generation AI and have the generation AI perform resource optimization.

[0035] The reception unit can analyze the student's past question history and select the optimal reception method. The reception unit, for example, stores the student's past question history in a database and analyzes it. For example, the reception unit prioritizes reception of topics that the student has frequently asked about in the past. The reception unit can also prioritize suggesting question formats (voice, text, etc.) that the student has used in the past. For example, if the student has previously asked a question by voice, the reception unit can suggest that the student ask the question by voice next time. Furthermore, the reception unit can select the optimal reception method for a specific time period based on the student's past question history. For example, if the student has previously asked a question during a specific time period, the reception unit can suggest the optimal reception method for that time period. In this way, by analyzing the past question history, the student can be asked the optimal method. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the student's question history data into the generation AI and have the generation AI select the optimal reception method.

[0036] When receiving questions, the reception unit can filter the questions based on the student's current learning situation and areas of interest. The reception unit, for example, obtains the student's current learning situation from a database and filters the questions based on that information. For example, the reception unit prioritizes receiving questions related to the topic the student is currently studying. The reception unit can also filter relevant questions based on the student's areas of interest. For example, the reception unit prioritizes receiving questions related to topics in which the student has previously shown interest. The reception unit can also filter questions of an appropriate level of difficulty based on the student's learning progress. For example, the reception unit selects questions of an appropriate level of difficulty based on the student's learning progress data. This allows more relevant questions to be received by filtering questions based on the student's learning situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the student's learning situation data into the generation AI and have the generation AI perform question filtering.

[0037] When receiving questions, the reception unit can prioritize relevant questions by taking into account the student's geographical location information. The reception unit, for example, obtains the student's geographical location information from GPS data or an IP address and filters questions based on that information. For example, if the student is in a specific area, the reception unit can prioritize questions about the history and culture of that area. Furthermore, if the student is traveling, the reception unit can prioritize questions related to the student's destinations. For example, the reception unit can prioritize questions about the history and culture of the destinations the student is traveling to. Furthermore, if the student is at school, the reception unit can prioritize questions related to the school curriculum. For example, the reception unit prioritizes questions related to the student's school classes. Thus, by taking the student's geographical location information into account, questions related to the area can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the student's geographical location data into the generation AI and have the generation AI perform question filtering.

[0038] When receiving a question, the reception unit can analyze the student's social media activity and receive relevant questions. The reception unit, for example, retrieves the student's social media activity from a database and analyzes it. For example, the reception unit can prioritize receiving questions related to topics the student has shown interest in on social media. The reception unit can also prioritize receiving questions related to accounts the student follows on social media. For example, the reception unit can prioritize receiving questions related to the content of history and culture accounts the student follows. The reception unit can also prioritize receiving questions related to content the student has shared on social media. For example, the reception unit can prioritize receiving questions related to history and culture content shared by the student. This makes it possible to receive highly relevant questions by analyzing the student's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the student's social media data into the generation AI and have the generation AI filter the questions.

[0039] The search unit can improve search accuracy by taking into account the interrelationships between questions during a search. The search unit, for example, retrieves interrelationships between questions from a database and analyzes them. For example, the search unit analyzes the relevance of questions and prioritizes searching for related information. The search unit can also search for related topics by taking into account the interrelationships between questions. For example, the search unit builds a co-occurrence network of questions and searches for related information based on that. Furthermore, the search unit can search for related literature and materials based on the interrelationships between questions. For example, the search unit calculates a relevance score for questions and searches for related literature and materials based on that score. This makes it possible to search for highly relevant information by taking into account the interrelationships between questions. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit may input question interrelationship data into the generation AI and have the generation AI improve search accuracy.

[0040] The search unit can perform a search while taking into account attribute information of the question submitter. For example, the search unit obtains attribute information of the question submitter from a database and analyzes it. For example, the search unit can search for appropriate information based on the age of the question submitter. The search unit can also search for appropriate information based on the learning level of the question submitter. For example, the search unit can evaluate the learning level of the question submitter and search for appropriate information based on the evaluation. Furthermore, the search unit can also search for appropriate information based on the interests and concerns of the question submitter. For example, the search unit can analyze the interests and concerns of the question submitter and search for appropriate information based on the analysis. In this way, more appropriate information can be searched for by taking into account the attribute information of the question submitter. Some or all of the above-described processing in the search unit can be performed using, or without, a generation AI. For example, the search unit can input attribute data of the question submitter into the generation AI and have the generation AI perform a search.

[0041] The search unit can perform a search taking into account the geographical distribution of questions. For example, the search unit obtains the geographical distribution of questions from a database and analyzes it. For example, the search unit analyzes the geographical distribution of questions and searches for related information. The search unit can also search for information related to a region based on the geographical distribution of questions. For example, the search unit searches for related information based on the number of questions per region. Furthermore, the search unit can search for topics by region taking into account the geographical distribution of questions. For example, the search unit analyzes trends by region and searches for related information based on the trends. In this way, information related to a region can be searched by taking the geographical distribution of questions into account. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input geographical distribution data of questions to the generation AI and have the generation AI perform a search.

[0042] The search unit can improve the accuracy of the search by referring to literature related to the question during the search. The search unit, for example, retrieves literature related to the question from a database and refers to it. For example, the search unit refers to academic papers related to the question to search for appropriate information. The search unit can also search for detailed information by referring to books related to the question. For example, the search unit searches for reliable information based on the contents of books related to the question. Furthermore, the search unit can also search for the latest information by referring to online articles related to the question. For example, the search unit searches for the latest information based on online articles related to the question. This allows the accuracy of the search to be improved by referring to related literature. Some or all of the above-mentioned processing in the search unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the search unit can input literature data related to the question into the generation AI and have the generation AI perform the search.

[0043] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. The generation unit, for example, sets criteria for evaluating the importance of the question. For example, the generation unit evaluates the importance based on the content and impact of the question. The generation unit can also evaluate the importance by taking into account attribute information of the question submitter. For example, the generation unit evaluates the importance based on the age and learning level of the question submitter. The generation unit can also evaluate the importance by taking into account the time and frequency of question submission. For example, the generation unit evaluates the importance based on the date and time of question submission and the frequency of submission. This allows for adjusting the level of detail of the answer depending on the importance of the question, thereby providing a more appropriate answer. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input question importance data into the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0044] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. The generation unit, for example, sets criteria for classifying question categories. For example, the generation unit classifies categories based on the content and theme of the question. The generation unit can also classify categories taking into account attribute information of the question submitter. For example, the generation unit classifies categories based on the age and learning level of the question submitter. The generation unit can also classify categories taking into account the time and frequency of question submission. For example, the generation unit classifies categories based on the date and time of question submission and the frequency of submission. This allows for the application of an optimal generation algorithm depending on the question category, thereby providing a more appropriate answer. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0045] When generating answers, the generation unit can determine the priority of answers based on the time the question was submitted. The generation unit, for example, obtains the time the question was submitted from a database and determines the priority based on that information. For example, the generation unit prioritizes generating answers for recently submitted questions. The generation unit can also prioritize generating answers for questions related to important events. For example, the generation unit prioritizes answers to questions related to a specific event. Furthermore, the generation unit can also prioritize generating answers for questions submitted during a specific time period. For example, the generation unit prioritizes answers to questions submitted during a specific time period. In this way, by determining the priority of answers based on the time the question was submitted, answers can be provided at a more appropriate time. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input question submission time data into the generation AI and have the generation AI determine the priority of answers.

[0046] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. The generation unit, for example, obtains the relevance of questions from a database and adjusts the order of answers based on that information. For example, the generation unit analyzes the relevance of questions and prioritizes related information in the answer. The generation unit can also prioritize related topics in the answer, taking into account the interrelationships between questions. For example, the generation unit builds a co-occurrence network of questions and answers related information based on that network. Furthermore, the generation unit can prioritize related literature and materials in the answer based on the relevance of the questions. For example, the generation unit calculates a relevance score for the question and answers related literature and materials based on that score. This allows for adjusting the order of answers based on the relevance of the questions, thereby providing a more appropriate answer. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input question relevance data into the generation AI and have the generation AI adjust the order of answers.

[0047] When providing an answer, the providing unit can select the optimal delivery method by referring to the student's past learning history. The providing unit, for example, retrieves the student's past learning history from a database and selects the optimal delivery method based on that information. For example, the providing unit prioritizes selecting a delivery method that the student has previously preferred. The providing unit can also analyze the student's past learning history and select the most effective delivery method. For example, the providing unit prioritizes selecting a delivery method that the student has previously shown high learning effectiveness. Furthermore, the providing unit can select a delivery method that suits the student's learning style based on the student's past learning history. For example, if the student has previously preferred visual information, the providing unit provides the answer using a visual method. This allows the answer to be provided in the optimal method by referring to the student's past learning history. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the student's learning history data into the generation AI and have the generation AI select the optimal delivery method.

[0048] When providing an answer, the providing unit can customize the means of providing the answer based on the student's current learning situation. The providing unit, for example, obtains the student's current learning situation from a database and customizes the means of providing the answer based on that information. For example, the providing unit provides the answer in a manner related to the topic the student is currently studying. The providing unit can also provide the answer in an appropriate manner depending on the student's learning progress. For example, the providing unit provides the answer in an appropriate manner based on the student's learning progress data. Furthermore, the providing unit can provide the answer in an optimal manner taking the student's learning situation into consideration. For example, the providing unit analyzes the student's learning situation and provides the answer in an optimal manner based on that analysis. In this way, by customizing the means of providing the answer based on the student's current learning situation, it is possible to provide the answer in a more appropriate manner. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the student's learning situation data into the generation AI and cause the generation AI to customize the means of providing the answer.

[0049] When providing answers, the providing unit can select the optimal providing method by taking into account the student's geographical location information. For example, the providing unit obtains the student's geographical location information from GPS data or an IP address and selects the optimal providing method based on that information. For example, if the student is in a specific area, the providing unit can prioritize providing information related to that area. Also, if the student is traveling, the providing unit can prioritize providing information related to the destination. For example, the providing unit can prioritize providing information related to the history and culture of the destination the student is traveling to. Furthermore, if the student is at school, the providing unit can prioritize providing information related to the school curriculum. For example, the providing unit can prioritize providing information related to the student's school class content. In this way, by taking the student's geographical location information into consideration, it is possible to prioritize providing information related to the region. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the student's geographical location data into the generation AI and cause the generation AI to select the optimal providing method.

[0050] When providing an answer, the providing unit can analyze the student's social media activity and suggest a means of providing the answer. The providing unit, for example, retrieves the student's social media activity from a database and suggests a means of providing the answer based on the retrieved information. For example, the providing unit may prioritize providing information related to topics the student has shown interest in on social media. The providing unit may also prioritize providing information related to accounts the student follows on social media. For example, the providing unit may prioritize providing information related to the content of history and culture accounts the student follows. The providing unit may also prioritize providing information related to content the student has shared on social media. For example, the providing unit may prioritize providing information related to history and culture content shared by the student. This makes it possible to provide highly relevant information by analyzing the student's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the student's social media data into the generation AI and cause the generation AI to suggest a means of providing the answer.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] When accepting a student's question, the reception desk can automatically suggest relevant visual content based on the question. For example, if a student asks about architecture from a particular era, it can display images or 3D models of buildings from that era. Or, if a student asks about a specific historical event, it can provide videos or interactive timelines related to that event. Furthermore, the reception desk can provide data visualizations such as maps and graphs related to the student's question. This makes it easier for students to understand information visually, improving learning outcomes.

[0053] When students are selecting a history or culture topic that interests them, the reception unit can display popular topics selected by other students. For example, the reception unit can display the topics that other students have frequently selected in a ranking format, allowing students to select from among them. The reception unit can also display reviews and ratings for each topic, allowing students to select a topic while taking into account the opinions of other students. Furthermore, the reception unit can suggest recommended learning resources (e.g., books, websites, videos, etc.) related to the topic. This allows students to select a topic that suits them while taking into account the choices and ratings of other students.

[0054] When analyzing a student's question, the generator can automatically generate and present to the student additional questions to more accurately understand the intent of the question. For example, if a student asks, "Tell me about life in a particular era," the generator can present an additional question such as, "What specific aspects would you like to know about? (e.g., food, housing, clothing, etc.)" The generator can also generate more detailed questions based on the student's answers to deepen the dialogue. Furthermore, the generator can analyze the student's answers and generate optimal questions to provide related information. This makes it possible to more accurately understand the intent of the student's question and provide appropriate information.

[0055] The generation unit can track the student's learning progress in real time and provide feedback when providing information of appropriate difficulty level according to the student's level of understanding. For example, the generation unit can record the student's correct answer rate for quizzes and questions and evaluate the student's level of understanding. The generation unit can also suggest the next content to study based on the student's learning progress. Furthermore, the generation unit can analyze the student's learning history and suggest reviewing topics that the student found difficult in the past. This allows students to understand their own learning progress and study effectively.

[0056] When providing an AI-generated answer to a student, the provider can suggest additional related information based on the content of the answer. For example, if a student asks about a specific historical event, the provider can provide information about other important events or people related to that event. The provider can also provide references and website links related to the answer, allowing the student to learn more. The provider can also suggest quizzes and activities related to the answer, allowing the student to check what they have learned. This allows the student to further deepen their learning based on the information provided.

[0057] In providing a cost-effective, rich, interactive virtual history experience, the provider can track learning progress and suggest individualized learning plans. For example, the provider can analyze a student's learning history and suggest what content they should study next or what they need to review. The provider can also suggest the optimal learning method based on the student's learning style. For example, a student who prefers visual learning can be suggested a learning plan that includes a lot of visual content. Furthermore, the provider can provide timely feedback based on the student's learning progress. This allows students to learn effectively at their own pace.

[0058] The reception unit can analyze the student's past question history and consider the frequency and patterns of questions when selecting the optimal reception method. For example, if a student frequently asks questions about a particular topic, it will prioritize questions related to that topic. The reception unit can also analyze the patterns of question formats (audio, text, etc.) used by students in the past and suggest the optimal format. Furthermore, the reception unit can analyze the patterns of the time of day and day of the week when students ask questions and suggest the optimal timing to receive them. This makes it possible to receive questions in the optimal way based on the student's question history.

[0059] When accepting questions, the reception unit can monitor the student's learning progress in real time and provide feedback when filtering based on the student's current learning situation and areas of interest. For example, the reception unit can preferentially accept questions related to the student's current learning topic and track their progress. The reception unit can also suggest related questions based on the student's areas of interest. Furthermore, the reception unit can suggest questions of an appropriate level of difficulty based on the student's learning progress. This makes it possible to accept the most appropriate questions based on the student's learning situation and areas of interest.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit accepts questions from students. Student questions may include, but are not limited to, questions about life and culture in a particular era. The reception unit can accept questions in text format or via voice input. In the case of voice input, speech recognition technology is used to convert the student's voice into text and accept the question. Step 2: The search unit analyzes the question received by the reception unit and searches for related information. The search unit analyzes the question using natural language processing technology and keyword extraction technology, and searches for related information from the database. Step 3: The generator generates an answer based on the information retrieved by the searcher. The generator generates the answer using template-based generation technology or AI technology. For example, it uses a generative AI (e.g., LLM) to generate a detailed answer to the question. Step 4: The providing unit provides the answer generated by the generating unit to the student. The providing unit can provide the answer in text format or audio format. In the audio format, the answer generated using speech synthesis technology is provided to the student as audio.

[0062] (Example 2) A virtual history experience system according to an embodiment of the present invention is an interactive virtual history experience system that utilizes AI technology to enable modern students to gain a deeper understanding of history and culture. This system provides a rich, interactive virtual history experience at low cost. Specifically, students first select a historical or cultural theme of interest. Next, an AI generates a virtual history experience based on that theme. This virtual history experience is interactive, with students asking questions and the AI ​​providing appropriate answers. For example, if a student asks, "What was life like in a particular era?", the AI ​​will provide a detailed explanation of life in that era. Furthermore, the AI ​​provides information of an appropriate level of difficulty depending on the student's level of understanding. For example, it provides basic information to beginners and more detailed information to advanced learners. This allows students to learn at their own pace. Furthermore, because this system is provided at low cost, it can be introduced in many schools and educational institutions. This allows students to experience real history and culture, encouraging them to learn with interest. As a result, the virtual history experience system can deepen students' understanding of history and culture by providing appropriate information in response to their questions.

[0063] A virtual history experience system according to an embodiment includes a reception unit, a search unit, a generation unit, and a provision unit. The reception unit receives questions from students. The student's questions include, but are not limited to, questions about life and culture in a particular era. The reception unit may receive questions in text format, for example. The reception unit may also receive questions using voice input. For example, the reception unit may convert the student's voice into text using voice recognition technology and accept the text as a question. The search unit analyzes the question received by the reception unit and searches for related information. The search unit may analyze the question using natural language processing technology, for example. The search unit may also extract important parts of the question using keyword extraction technology and search for related information. For example, the search unit may search for related information from a database to find an answer to the question. The generation unit generates an answer based on the information searched by the search unit. The generation unit may generate an answer using, for example, template-based generation technology. The generation unit may also generate an answer using AI technology. For example, the generation unit may use a generation AI (e.g., LLM) to generate a detailed answer to the question. The providing unit provides the answer generated by the generating unit to the student. The providing unit provides the answer in text format, for example. The providing unit can also provide the answer in audio format. For example, the generated answer is provided to the student as audio using speech synthesis technology. This allows the virtual history experience system according to the embodiment to provide appropriate information in response to the student's question. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the answer using an AI model that receives the answer generated by the generating unit as input and outputs an answer.

[0064] The reception unit can select a history or cultural theme that interests the student. The reception unit, for example, provides an interface for the student to select a theme that interests them. For example, the reception unit can display a list of themes, allowing the student to select one from the list. The reception unit can also suggest themes that interest the student based on the student's past learning history and survey results. For example, the reception unit can suggest appropriate themes based on the themes that the student has studied in the past and the themes of interest that the student answered in the survey. This makes it possible to provide a virtual history experience based on the themes that interest the student. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's learning history data into a generation AI and have the generation AI suggest themes of interest.

[0065] The generation unit can analyze the student's question using AI and search for related information. The generation unit can analyze the student's question using, for example, natural language processing technology. For example, the generation unit can analyze the grammatical structure of the question and extract important keywords. The generation unit can also understand the intent of the question and search for related information using a machine learning algorithm. For example, the generation unit can classify the intent of the question and search for appropriate information based on that. The generation unit can also use deep learning technology to deeply understand the meaning of the question and search for related information. For example, the generation unit can understand the context of the question and search for relevant information based on that. This allows appropriate information for the question to be quickly searched for using AI. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI. For example, the generation unit can input the student's question data into the generation AI and have the generation AI search for related information.

[0066] The generation unit can provide information of appropriate difficulty level according to the student's level of understanding using AI. The generation unit, for example, conducts a test to evaluate the student's level of understanding. For example, the generation unit presents short quizzes or questions to the student and evaluates the student's level of understanding based on the student's answers. The generation unit can also analyze the student's past learning history to evaluate the student's level of understanding. For example, the generation unit evaluates the student's level of understanding based on the content the student has previously studied and test results. Furthermore, the generation unit can use AI technology to evaluate the student's level of understanding in real time and provide information of appropriate difficulty level based on the evaluation. For example, the generation unit can evaluate the accuracy and speed of the student's answers and adjust the difficulty level of the information based on the evaluation. This can improve learning effectiveness by providing information according to the student's level of understanding. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the student's level of understanding data into the generation AI and cause the generation AI to provide information of appropriate difficulty level.

[0067] The providing unit can provide the answer generated by the AI ​​to the student. The providing unit can provide the answer in text format, for example. For example, the providing unit can display the generated answer as text and provide it to the student. The providing unit can also provide the answer in audio format. For example, the providing unit can use speech synthesis technology to provide the generated answer as audio to the student. The providing unit can also provide the answer in an interactive format. For example, the providing unit can provide the generated answer to the student as an interactive chatbot, allowing the student to ask further questions. In this way, by using AI, the generated answer can be quickly provided to the student. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the generated answer data to the generation AI and cause the generation AI to provide the answer.

[0068] The provision unit can provide a cost-effective, rich, interactive virtual history experience. The provision unit, for example, reduces costs by utilizing cloud-based infrastructure. For example, the provision unit optimizes operational costs by dynamically allocating necessary resources using cloud services. The provision unit can also reduce costs by utilizing open-source software. For example, the provision unit reduces development costs by utilizing open-source natural language processing libraries and machine learning frameworks. Furthermore, the provision unit can incorporate high-quality graphics and interactive elements to provide a rich, interactive experience. For example, the provision unit can use 3D modeling technology to realistically recreate historical scenes and allow students to enjoy interacting within them. This allows many students to experience history and culture by providing a low-cost, rich, interactive virtual history experience. Some or all of the above-described processing in the provision unit may be performed using, or without, a generation AI. For example, the provision unit can input cloud service usage data into the generation AI and have the generation AI perform resource optimization.

[0069] The reception unit can estimate the student's emotions and adjust the timing of accepting questions based on the estimated student's emotions. For example, the reception unit captures the student's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions and adjusts the timing of accepting questions. The reception unit can also record the student's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of accepting questions. Furthermore, the reception unit can collect the student's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on fluctuations in heart rate and adjusts the timing of accepting questions. This allows the timing of accepting questions to be adjusted according to the student's emotions, thereby allowing questions to be accepted at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input student emotion data into the generation AI and cause the generation AI to adjust the timing of receiving questions.

[0070] The reception unit can analyze the student's past question history and select the optimal reception method. The reception unit, for example, stores the student's past question history in a database and analyzes it. For example, the reception unit prioritizes reception of topics that the student has frequently asked about in the past. The reception unit can also prioritize suggesting question formats (voice, text, etc.) that the student has used in the past. For example, if the student has previously asked a question by voice, the reception unit can suggest that the student ask the question by voice next time. Furthermore, the reception unit can select the optimal reception method for a specific time period based on the student's past question history. For example, if the student has previously asked a question during a specific time period, the reception unit can suggest the optimal reception method for that time period. In this way, by analyzing the past question history, the student can be asked the optimal method. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the student's question history data into the generation AI and have the generation AI select the optimal reception method.

[0071] When receiving questions, the reception unit can filter the questions based on the student's current learning situation and areas of interest. The reception unit, for example, obtains the student's current learning situation from a database and filters the questions based on that information. For example, the reception unit prioritizes receiving questions related to the topic the student is currently studying. The reception unit can also filter relevant questions based on the student's areas of interest. For example, the reception unit prioritizes receiving questions related to topics in which the student has previously shown interest. The reception unit can also filter questions of an appropriate level of difficulty based on the student's learning progress. For example, the reception unit selects questions of an appropriate level of difficulty based on the student's learning progress data. This allows more relevant questions to be received by filtering questions based on the student's learning situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the student's learning situation data into the generation AI and have the generation AI perform question filtering.

[0072] The reception unit can estimate the student's emotions and prioritize questions based on the estimated student's emotions. For example, the reception unit captures the student's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions and prioritizes questions. The reception unit can also record the student's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and prioritizes questions. The reception unit can also collect the student's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations and prioritizes questions. This allows the system to prioritize questions based on the student's emotions, thereby allowing more appropriate questions to be accepted preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may input student emotion data into the generation AI and have the generation AI determine the priority of questions.

[0073] When receiving questions, the reception unit can prioritize relevant questions by taking into account the student's geographical location information. The reception unit, for example, obtains the student's geographical location information from GPS data or an IP address and filters questions based on that information. For example, if the student is in a specific area, the reception unit can prioritize questions about the history and culture of that area. Furthermore, if the student is traveling, the reception unit can prioritize questions related to the student's destinations. For example, the reception unit can prioritize questions about the history and culture of the destinations the student is traveling to. Furthermore, if the student is at school, the reception unit can prioritize questions related to the school curriculum. For example, the reception unit prioritizes questions related to the student's school classes. Thus, by taking the student's geographical location information into account, questions related to the area can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the student's geographical location data into the generation AI and have the generation AI perform question filtering.

[0074] When receiving a question, the reception unit can analyze the student's social media activity and receive relevant questions. The reception unit, for example, retrieves the student's social media activity from a database and analyzes it. For example, the reception unit can prioritize receiving questions related to topics the student has shown interest in on social media. The reception unit can also prioritize receiving questions related to accounts the student follows on social media. For example, the reception unit can prioritize receiving questions related to the content of history and culture accounts the student follows. The reception unit can also prioritize receiving questions related to content the student has shared on social media. For example, the reception unit can prioritize receiving questions related to history and culture content shared by the student. This makes it possible to receive highly relevant questions by analyzing the student's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the student's social media data into the generation AI and have the generation AI filter the questions.

[0075] The search unit can estimate the student's emotions and adjust search criteria based on the estimated student's emotions. For example, the search unit captures the student's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the search unit calculates an emotion score based on changes in facial expressions and adjusts the search criteria. The search unit can also record the student's voice and estimate the emotion using voice analysis technology. For example, the search unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the search criteria. Furthermore, the search unit can collect the student's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the search unit calculates an emotion score based on heart rate fluctuations and adjusts the search criteria. This allows the search criteria to be adjusted according to the student's emotions, thereby retrieving more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the search unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the search unit may input student emotion data into the generation AI and have the generation AI adjust the search criteria.

[0076] The search unit can improve search accuracy by taking into account the interrelationships between questions during a search. The search unit, for example, retrieves interrelationships between questions from a database and analyzes them. For example, the search unit analyzes the relevance of questions and prioritizes searching for related information. The search unit can also search for related topics by taking into account the interrelationships between questions. For example, the search unit builds a co-occurrence network of questions and searches for related information based on that. Furthermore, the search unit can search for related literature and materials based on the interrelationships between questions. For example, the search unit calculates a relevance score for questions and searches for related literature and materials based on that score. This makes it possible to search for highly relevant information by taking into account the interrelationships between questions. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit may input question interrelationship data into the generation AI and have the generation AI improve search accuracy.

[0077] The search unit can perform a search while taking into account attribute information of the question submitter. For example, the search unit obtains attribute information of the question submitter from a database and analyzes it. For example, the search unit can search for appropriate information based on the age of the question submitter. The search unit can also search for appropriate information based on the learning level of the question submitter. For example, the search unit can evaluate the learning level of the question submitter and search for appropriate information based on the evaluation. Furthermore, the search unit can also search for appropriate information based on the interests and concerns of the question submitter. For example, the search unit can analyze the interests and concerns of the question submitter and search for appropriate information based on the analysis. In this way, more appropriate information can be searched for by taking into account the attribute information of the question submitter. Some or all of the above-described processing in the search unit can be performed using, or without, a generation AI. For example, the search unit can input attribute data of the question submitter into the generation AI and have the generation AI perform a search.

[0078] The search unit can estimate the student's emotions and adjust the display order of search results based on the estimated student's emotions. For example, the search unit captures the student's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. For example, the search unit calculates an emotion score based on changes in facial expressions and adjusts the display order of search results. The search unit can also record the student's voice and estimate the emotion using voice analysis technology. For example, the search unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display order of search results. Furthermore, the search unit can collect the student's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the search unit calculates an emotion score based on heart rate fluctuations and adjusts the display order of search results. This allows the display order of search results to be adjusted according to the student's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit may be performed using, for example, a generation AI. For example, the search unit may input student emotion data into the generation AI and have the generation AI adjust the display order of search results.

[0079] The search unit can perform a search taking into account the geographical distribution of questions. For example, the search unit obtains the geographical distribution of questions from a database and analyzes it. For example, the search unit analyzes the geographical distribution of questions and searches for related information. The search unit can also search for information related to a region based on the geographical distribution of questions. For example, the search unit searches for related information based on the number of questions per region. Furthermore, the search unit can search for topics by region taking into account the geographical distribution of questions. For example, the search unit analyzes trends by region and searches for related information based on the trends. In this way, information related to a region can be searched by taking the geographical distribution of questions into account. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input geographical distribution data of questions to the generation AI and have the generation AI perform a search.

[0080] The search unit can improve the accuracy of the search by referring to literature related to the question during the search. The search unit, for example, retrieves literature related to the question from a database and refers to it. For example, the search unit refers to academic papers related to the question to search for appropriate information. The search unit can also search for detailed information by referring to books related to the question. For example, the search unit searches for reliable information based on the contents of books related to the question. Furthermore, the search unit can also search for the latest information by referring to online articles related to the question. For example, the search unit searches for the latest information based on online articles related to the question. This allows the accuracy of the search to be improved by referring to related literature. Some or all of the above-mentioned processing in the search unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the search unit can input literature data related to the question into the generation AI and have the generation AI perform the search.

[0081] The generation unit can estimate the student's emotions and adjust the way the answer is presented based on the estimated student's emotions. For example, the generation unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression and adjusts the way the answer is presented. The generation unit can also record the student's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the way the answer is presented. Furthermore, the generation unit can collect the student's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations and adjusts the way the answer is presented. This allows the way the answer is presented to be adjusted according to the student's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may input the student's emotional data into the generation AI and have the generation AI adjust the way the answer is expressed.

[0082] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. The generation unit, for example, sets criteria for evaluating the importance of the question. For example, the generation unit evaluates the importance based on the content and impact of the question. The generation unit can also evaluate the importance by taking into account attribute information of the question submitter. For example, the generation unit evaluates the importance based on the age and learning level of the question submitter. The generation unit can also evaluate the importance by taking into account the time and frequency of question submission. For example, the generation unit evaluates the importance based on the date and time of question submission and the frequency of submission. This allows for adjusting the level of detail of the answer depending on the importance of the question, thereby providing a more appropriate answer. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input question importance data into the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0083] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. The generation unit, for example, sets criteria for classifying question categories. For example, the generation unit classifies categories based on the content and theme of the question. The generation unit can also classify categories taking into account attribute information of the question submitter. For example, the generation unit classifies categories based on the age and learning level of the question submitter. The generation unit can also classify categories taking into account the time and frequency of question submission. For example, the generation unit classifies categories based on the date and time of question submission and the frequency of submission. This allows for the application of an optimal generation algorithm depending on the question category, thereby providing a more appropriate answer. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0084] The generation unit can estimate the student's emotion and adjust the length of the answer based on the estimated emotion. For example, the generation unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression and adjusts the length of the answer. The generation unit can also record the student's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the answer. Furthermore, the generation unit can collect the student's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations and adjusts the length of the answer. This allows the student to provide a more appropriate answer by adjusting the length of the answer according to the student's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may input student emotion data into the generation AI and have the generation AI adjust the length of the answer.

[0085] When generating answers, the generation unit can determine the priority of answers based on the time the question was submitted. The generation unit, for example, obtains the time the question was submitted from a database and determines the priority based on that information. For example, the generation unit prioritizes generating answers for recently submitted questions. The generation unit can also prioritize generating answers for questions related to important events. For example, the generation unit prioritizes answers to questions related to a specific event. Furthermore, the generation unit can also prioritize generating answers for questions submitted during a specific time period. For example, the generation unit prioritizes answers to questions submitted during a specific time period. In this way, by determining the priority of answers based on the time the question was submitted, answers can be provided at a more appropriate time. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input question submission time data into the generation AI and have the generation AI determine the priority of answers.

[0086] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. The generation unit, for example, obtains the relevance of questions from a database and adjusts the order of answers based on that information. For example, the generation unit analyzes the relevance of questions and prioritizes related information in the answer. The generation unit can also prioritize related topics in the answer, taking into account the interrelationships between questions. For example, the generation unit builds a co-occurrence network of questions and answers related information based on that network. Furthermore, the generation unit can prioritize related literature and materials in the answer based on the relevance of the questions. For example, the generation unit calculates a relevance score for the question and answers related literature and materials based on that score. This allows for adjusting the order of answers based on the relevance of the questions, thereby providing a more appropriate answer. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input question relevance data into the generation AI and have the generation AI adjust the order of answers.

[0087] The providing unit can estimate the student's emotion and adjust the answer providing method based on the estimated student's emotion. For example, the providing unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression and adjusts the answer providing method. The providing unit can also record the student's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the answer providing method. Furthermore, the providing unit can collect the student's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations and adjusts the answer providing method. This allows the answer to be provided in a more appropriate manner by adjusting the answer providing method according to the student's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit may input student emotion data into the generation AI and have the generation AI adjust the method of providing answers.

[0088] When providing an answer, the providing unit can select the optimal delivery method by referring to the student's past learning history. The providing unit, for example, retrieves the student's past learning history from a database and selects the optimal delivery method based on that information. For example, the providing unit prioritizes selecting a delivery method that the student has previously preferred. The providing unit can also analyze the student's past learning history and select the most effective delivery method. For example, the providing unit prioritizes selecting a delivery method that the student has previously shown high learning effectiveness. Furthermore, the providing unit can select a delivery method that suits the student's learning style based on the student's past learning history. For example, if the student has previously preferred visual information, the providing unit provides the answer using a visual method. This allows the answer to be provided in the optimal method by referring to the student's past learning history. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the student's learning history data into the generation AI and have the generation AI select the optimal delivery method.

[0089] When providing an answer, the providing unit can customize the means of providing the answer based on the student's current learning situation. The providing unit, for example, obtains the student's current learning situation from a database and customizes the means of providing the answer based on that information. For example, the providing unit provides the answer in a manner related to the topic the student is currently studying. The providing unit can also provide the answer in an appropriate manner depending on the student's learning progress. For example, the providing unit provides the answer in an appropriate manner based on the student's learning progress data. Furthermore, the providing unit can provide the answer in an optimal manner taking the student's learning situation into consideration. For example, the providing unit analyzes the student's learning situation and provides the answer in an optimal manner based on that analysis. In this way, by customizing the means of providing the answer based on the student's current learning situation, it is possible to provide the answer in a more appropriate manner. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the student's learning situation data into the generation AI and cause the generation AI to customize the means of providing the answer.

[0090] The providing unit can estimate the student's emotions and determine the order in which the answers will be provided based on the estimated student's emotions. For example, the providing unit captures the student's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expressions and determines the order in which the answers will be provided. The providing unit can also record the student's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the order in which the answers will be provided. Furthermore, the providing unit can collect the student's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations and determines the order in which the answers will be provided. This allows the answers to be provided in a more appropriate order by determining the order in which the answers will be provided based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit may input student emotion data into the generation AI and have the generation AI determine the order in which answers are provided.

[0091] When providing answers, the providing unit can select the optimal providing method by taking into account the student's geographical location information. For example, the providing unit obtains the student's geographical location information from GPS data or an IP address and selects the optimal providing method based on that information. For example, if the student is in a specific area, the providing unit can prioritize providing information related to that area. Also, if the student is traveling, the providing unit can prioritize providing information related to the destination. For example, the providing unit can prioritize providing information related to the history and culture of the destination the student is traveling to. Furthermore, if the student is at school, the providing unit can prioritize providing information related to the school curriculum. For example, the providing unit can prioritize providing information related to the student's school class content. In this way, by taking the student's geographical location information into consideration, it is possible to prioritize providing information related to the region. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the student's geographical location data into the generation AI and cause the generation AI to select the optimal providing method.

[0092] When providing an answer, the providing unit can analyze the student's social media activity and suggest a means of providing the answer. The providing unit, for example, retrieves the student's social media activity from a database and suggests a means of providing the answer based on the retrieved information. For example, the providing unit may prioritize providing information related to topics the student has shown interest in on social media. The providing unit may also prioritize providing information related to accounts the student follows on social media. For example, the providing unit may prioritize providing information related to the content of history and culture accounts the student follows. The providing unit may also prioritize providing information related to content the student has shared on social media. For example, the providing unit may prioritize providing information related to history and culture content shared by the student. This makes it possible to provide highly relevant information by analyzing the student's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the student's social media data into the generation AI and cause the generation AI to suggest a means of providing the answer. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, search unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives questions from students using a touch panel 38A or a microphone 38B of the smart device 14. The search unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions and searches for related information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers based on the searched information. The provision unit provides answers to students using a display 40A or a speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, search unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a question from a student using the microphone 238 of the smart glasses 214. The search unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the question and searches for related information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer based on the searched information. The provision unit provides the answer to the student using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, search unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a question from a student using the microphone 238 of the headset type terminal 314. The search unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the question and searches for related information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer based on the searched information. The provision unit provides the answer to the student using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, search unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives questions from students using the microphone 238 of the robot 414. The search unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions and searches for related information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers based on the searched information. The provision unit provides answers to students using the speaker 240 of the robot 414.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] When accepting a student's question, the reception desk can automatically suggest relevant visual content based on the question. For example, if a student asks about architecture from a particular era, it can display images or 3D models of buildings from that era. Or, if a student asks about a specific historical event, it can provide videos or interactive timelines related to that event. Furthermore, the reception desk can provide data visualizations such as maps and graphs related to the student's question. This makes it easier for students to understand information visually, improving learning outcomes.

[0095] When students are selecting a history or culture topic that interests them, the reception unit can display popular topics selected by other students. For example, the reception unit can display the topics that other students have frequently selected in a ranking format, allowing students to select from among them. The reception unit can also display reviews and ratings for each topic, allowing students to select a topic while taking into account the opinions of other students. Furthermore, the reception unit can suggest recommended learning resources (e.g., books, websites, videos, etc.) related to the topic. This allows students to select a topic that suits them while taking into account the choices and ratings of other students.

[0096] When analyzing a student's question, the generator can automatically generate and present to the student additional questions to more accurately understand the intent of the question. For example, if a student asks, "Tell me about life in a particular era," the generator can present an additional question such as, "What specific aspects would you like to know about? (e.g., food, housing, clothing, etc.)" The generator can also generate more detailed questions based on the student's answers to deepen the dialogue. Furthermore, the generator can analyze the student's answers and generate optimal questions to provide related information. This makes it possible to more accurately understand the intent of the student's question and provide appropriate information.

[0097] The generation unit can track the student's learning progress in real time and provide feedback when providing information of appropriate difficulty level according to the student's level of understanding. For example, the generation unit can record the student's correct answer rate for quizzes and questions and evaluate the student's level of understanding. The generation unit can also suggest the next content to study based on the student's learning progress. Furthermore, the generation unit can analyze the student's learning history and suggest reviewing topics that the student found difficult in the past. This allows students to understand their own learning progress and study effectively.

[0098] When providing an AI-generated answer to a student, the provider can suggest additional related information based on the content of the answer. For example, if a student asks about a specific historical event, the provider can provide information about other important events or people related to that event. The provider can also provide references and website links related to the answer, allowing the student to learn more. The provider can also suggest quizzes and activities related to the answer, allowing the student to check what they have learned. This allows the student to further deepen their learning based on the information provided.

[0099] In providing a cost-effective, rich, interactive virtual history experience, the provider can track learning progress and suggest individualized learning plans. For example, the provider can analyze a student's learning history and suggest what content they should study next or what they need to review. The provider can also suggest the optimal learning method based on the student's learning style. For example, a student who prefers visual learning can be suggested a learning plan that includes a lot of visual content. Furthermore, the provider can provide timely feedback based on the student's learning progress. This allows students to learn effectively at their own pace.

[0100] The reception unit can estimate the student's emotions and customize the way questions are accepted based on the estimated student's emotions. For example, if a student is nervous, the reception unit can accept questions in a gentle tone that will help them relax. On the other hand, if a student is excited, the reception unit can take advantage of that excitement and actively accept questions. Furthermore, the reception unit can change the format of questions depending on the student's emotions. For example, if a student is tired, it can present simple multiple-choice questions. This makes it possible to accept questions in the optimal way according to the student's emotions.

[0101] The reception unit can analyze the student's past question history and consider the frequency and patterns of questions when selecting the optimal reception method. For example, if a student frequently asks questions about a particular topic, it will prioritize questions related to that topic. The reception unit can also analyze the patterns of question formats (audio, text, etc.) used by students in the past and suggest the optimal format. Furthermore, the reception unit can analyze the patterns of the time of day and day of the week when students ask questions and suggest the optimal timing to receive them. This makes it possible to receive questions in the optimal way based on the student's question history.

[0102] When accepting questions, the reception unit can monitor the student's learning progress in real time and provide feedback when filtering based on the student's current learning situation and areas of interest. For example, the reception unit can preferentially accept questions related to the student's current learning topic and track their progress. The reception unit can also suggest related questions based on the student's areas of interest. Furthermore, the reception unit can suggest questions of an appropriate level of difficulty based on the student's learning progress. This makes it possible to accept the most appropriate questions based on the student's learning situation and areas of interest.

[0103] The reception unit can estimate a student's emotions and provide appropriate feedback when prioritizing questions to accept based on the estimated student's emotions by monitoring changes in emotions in real time. For example, if a student is excited, the reception unit can take advantage of that excitement and actively accept questions. If a student is depressed, the reception unit can accept questions in a gentle tone and provide encouraging messages. Furthermore, the reception unit can change the format of questions depending on the student's emotions. For example, if a student is tired, the reception unit can present simple multiple-choice questions. This allows questions to be accepted in the optimal manner according to the student's emotions.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit accepts questions from students. Student questions may include, but are not limited to, questions about life and culture in a particular era. The reception unit can accept questions in text format or via voice input. In the case of voice input, speech recognition technology is used to convert the student's voice into text and accept the question. Step 2: The search unit analyzes the question received by the reception unit and searches for related information. The search unit analyzes the question using natural language processing technology and keyword extraction technology, and searches for related information from the database. Step 3: The generator generates an answer based on the information retrieved by the searcher. The generator generates the answer using template-based generation technology or AI technology. For example, it uses a generative AI (e.g., LLM) to generate a detailed answer to the question. Step 4: The providing unit provides the answer generated by the generating unit to the student. The providing unit can provide the answer in text format or audio format. In the audio format, the answer generated using speech synthesis technology is provided to the student as audio.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0134] 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.

[0135] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk to answer questions from students, a search unit that analyzes the question received by the reception unit and searches for related information; a generation unit that generates an answer based on the information searched by the search unit; a providing unit that provides the answer generated by the generating unit to the student; Equipped with A system characterized by:

2. The reception unit Choose historical and cultural topics that interest students 2. The system of claim 1.

3. The generation unit AI analyzes students' questions and searches for relevant information 2. The system of claim 1.

4. The generation unit AI provides appropriate level of difficulty information according to the student's level of understanding 2. The system of claim 1.

5. The providing unit Providing students with AI-generated answers 2. The system of claim 1.

6. The providing unit Delivering cost-effective, rich, interactive virtual history experiences 2. The system of claim 1.

7. The reception unit Estimate the student's emotions and adjust the timing of accepting questions based on the estimated student emotions 2. The system of claim 1.

8. The reception unit Analyze students' past question history and select the most appropriate method of acceptance 2. The system of claim 1.

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