system

Abstract

The system according to this embodiment aims to enable children to acquire knowledge in the field of social sciences while maintaining their interest. [Solution] The system according to the embodiment comprises a lecture generation unit, an output unit, a reception unit, an answer generation unit, and a provision unit. The lecture generation unit generates audio of lectures on the field of social sciences using AI. The output unit outputs the audio generated by the lecture generation unit through a speaker attached to a stuffed animal. The reception unit receives audio of questions from lecture participants using a microphone attached to the stuffed animal. The answer generation unit generates answers based on the questions received by the reception unit. The provision unit provides the answers generated by the answer generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, in the education of the social science field, there was a problem that it was difficult for children to maintain their interest.

[0005] The system according to the embodiment aims to enable children to acquire knowledge in the social science field while maintaining their interest.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a lecture generation unit, an output unit, a reception unit, an answer generation unit, and a provision unit. The lecture generation unit generates audio of lectures on social sciences using AI. The output unit outputs the audio generated by the lecture generation unit through a speaker attached to a stuffed animal. The reception unit receives audio of questions from lecture participants using a microphone attached to the stuffed animal. The answer generation unit generates answers based on the questions received by the reception unit. The provision unit provides the answers generated by the answer generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows children to acquire knowledge in the field of social sciences while maintaining their interest. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) An educational system according to an embodiment of the present invention is a system that provides educational programs on social science fields such as history, geography, culture, and politics using a stuffed animal that directly or readily incorporates a generative AI. This educational system has the function of generating audio for lectures on social science fields and generating and providing answers to questions from students. For example, the educational system has a lecture generation unit that uses AI to generate audio for lectures on social science fields. This audio is output from a speaker attached to the stuffed animal. Next, a microphone attached to the stuffed animal receives audio questions from students. The reception unit analyzes the question, and the answer generation unit uses the generative AI to generate an answer to the question. The generated answer is provided to the student through the provision unit. For example, if a child asks a question about a historical event, the AI ​​generates an appropriate answer to that question, and the stuffed animal conveys the answer aloud. It is also possible to estimate the student's emotions and generate an answer based on those emotions. Furthermore, it also has the function of analyzing the student's question history and generating an answer based on the analysis results. It can also generate an answer based on the student's current learning status and areas of interest. This system also has the function of determining the student's level of learning and adjusting the difficulty level of the lecture based on that level of learning. For example, a simple explanation can be given to content that students are learning for the first time, and a more detailed explanation can be given to content they already know. This allows children to learn at their own pace and effectively acquire knowledge in the social sciences. In this way, the education system can provide appropriate lectures according to the students' level of learning and effectively acquire knowledge in the social sciences. Note that stuffed animals are robots that imitate humans or animals, but are not limited to such examples.

[0029] The educational system according to this embodiment comprises a lecture generation unit, an output unit, a reception unit, an answer generation unit, and a provision unit. The lecture generation unit generates audio of lectures on social sciences using AI. The lecture generation unit can generate audio of lectures on fields such as history, geography, culture, and politics, for example, using natural language processing technology. The lecture generation unit can also generate audio of lectures using speech synthesis technology. For example, the lecture generation unit inputs the prompt "Generate a lecture on historical events" to an AI model and outputs the generated audio. The output unit outputs the audio generated by the lecture generation unit through a speaker attached to a stuffed animal. The output unit provides the generated lecture audio to students, for example, through the speaker. The output unit also has a function to adjust the tone and speed of the audio. For example, the output unit can adjust the tone and speed of the audio according to the age and comprehension level of the students. The reception unit receives audio of questions from lecture students through a microphone attached to the stuffed animal. The reception unit acquires the students' questions as audio data through the microphone, for example. Furthermore, the reception unit can also convert the student's questions into text data using speech recognition technology. For example, the reception unit analyzes the student's questions using speech recognition technology and saves them as text data. The answer generation unit generates answers based on the questions received by the reception unit. The answer generation unit generates answers to questions using a generation AI. For example, the answer generation unit inputs the prompt "Generate an answer to this question" to the generation AI and outputs the generated answer. The delivery unit provides the answers generated by the answer generation unit. The delivery unit provides the generated answers to the students by voice, for example, through a speaker. The delivery unit also has a function to display the generated answers as text data. For example, the delivery unit displays the generated answers as text data through a display. As a result, the educational system according to this embodiment provides appropriate answers to students' questions and enables them to effectively acquire knowledge in the field of social sciences.

[0030] The lecture generation unit uses AI to generate audio for lectures in the social sciences. Specifically, it can generate audio for lectures in fields such as history, geography, culture, and politics using natural language processing technology. For example, the lecture generation unit can input a prompt to its AI model such as "Generate a lecture about historical events" and output the generated audio. This prompt can include detailed information such as specific events, eras, and regions. For example, by inputting a prompt such as "Generate a lecture about major events in World War II," the AI ​​will collect relevant information and construct the lecture content. It can also convert the generated text into natural-sounding speech using speech synthesis technology. Speech synthesis technology includes functions to adjust the tone, speed, and accent of the speech, allowing for the generation of optimal speech according to the age and comprehension level of the learners. Furthermore, the lecture generation unit can regularly update the content of the generated lectures to reflect new information and research findings. This ensures that learners are always learning the latest knowledge. The lecture generation unit also supports multiple languages ​​and can generate lectures in different languages. This allows it to accommodate learners who speak different languages ​​and provides a global educational environment.

[0031] The output unit outputs the audio generated by the lecture generation unit through a speaker attached to the stuffed animal. Specifically, it provides the generated lecture audio to the learner through the speaker. The output unit has a function to adjust the tone and speed of the audio, allowing it to be adjusted according to the learner's age and level of understanding. For example, lectures for children can be output in a bright, slow tone, while lectures for adults can be output in a more professional, faster tone. The output unit also has a function to adjust the volume of the audio, allowing it to provide lectures at the optimal volume according to the learner's environment. Furthermore, by coordinating multiple speakers, the output unit can deliver audio uniformly even in large classrooms or halls. This allows learners to clearly hear the lecture content no matter where they are positioned. In addition to audio output, the output unit may also have a display to provide visual information. For example, displaying images or videos related to the lecture content on the display can deepen the learner's understanding. In this way, the output unit can provide both audio and visual information, enhancing the learner's learning effectiveness.

[0032] The reception desk uses microphones attached to stuffed animals to receive audio questions from lecture participants. Specifically, it captures participants' questions as audio data through the microphones. The reception desk can also convert participants' questions into text data using speech recognition technology. For example, if a participant asks, "Please tell me about the background of this event," the reception desk analyzes the audio and saves it as text data. The speech recognition technology includes noise cancellation and audio clearing functions, allowing it to accurately recognize participants' questions. Furthermore, the reception desk can receive questions from multiple participants simultaneously. For example, in large lectures, multiple microphones can be installed and audio data from each microphone processed simultaneously to efficiently receive multiple questions. The reception desk also has a function to classify participants' questions and group related questions. This prevents the same question from being asked multiple times, enabling efficient question answering. In addition, the reception desk can save participants' question history and refer to it later. This allows for easy searching and reuse of past questions and their answers.

[0033] The answer generation unit generates answers based on questions received by the reception unit. Specifically, it uses a generation AI to generate answers to questions. For example, the answer generation unit prompts the generation AI with "Please generate an answer to this question" and outputs the generated answer. The generation AI analyzes the content of the question, collects relevant information, and constructs the answer. For example, in response to the question "Please tell me about the background of this event," the generation AI investigates the historical background and related events and generates a detailed answer. The generation AI can refer to a large number of databases and documents, select the most appropriate information, and generate an answer. Furthermore, the generation AI can adjust the content of the answer according to the learner's level of understanding. For example, it can generate a concise and easy-to-understand answer for beginners and a detailed and specialized answer for experts. In addition, the answer generation unit can provide the generated answer in multiple formats. For example, in addition to an answer in audio format, it can also generate an answer in text format, allowing learners to choose. This enables the answer generation unit to provide quick and appropriate answers to learners' questions, thereby enhancing learning effectiveness.

[0034] The delivery unit provides the answers generated by the generation unit. Specifically, it provides the generated answers to learners audibly through speakers. The delivery unit also has a function to display the generated answers as text data. For example, it can display the generated answers as text data through a display. This allows learners to confirm the answers not only audibly but also visually. Furthermore, the delivery unit has a function to save the generated answers and refer to them later. For example, learners can easily search for and review questions and answers they have received in the past. The delivery unit also has a function to improve the content of the answers based on learner feedback. For example, if learners provide additional questions or comments on an answer, the delivery unit can collect that feedback and update the content of the answer. This allows the delivery unit to always provide the latest and most optimal answers, maximizing the learning effect for learners. Furthermore, the delivery unit can provide answers to multiple learners simultaneously. For example, in a large lecture, it can use multiple displays and speakers to provide answers to all learners simultaneously. This allows the delivery unit to provide answers efficiently and effectively, increasing learner satisfaction.

[0035] The answer generation unit can generate answers to questions using a generative AI. For example, the answer generation unit uses the generative AI to generate appropriate answers to the student's questions. For instance, the answer generation unit prompts the generative AI with "Generate an answer to this question" and outputs the generated answer. This allows for the generation of appropriate answers to questions using the generative AI. These models have been trained on large amounts of text data and possess advanced natural language processing capabilities. The generative AI can generate appropriate answers based on the content of the question. For example, the generative AI understands the context of the question and generates an answer based on relevant information. Depending on the content of the question, the generative AI can also generate answers that include detailed explanations and specific examples. This allows the answer generation unit to provide appropriate answers to the student's questions and effectively acquire knowledge in the field of social sciences.

[0036] The answer generation unit can analyze the learner's question history and generate answers based on the analysis results. For example, the answer generation unit uses AI to analyze the learner's question history. For instance, it analyzes the content and frequency of past questions to understand the learner's interests and level of understanding. Based on the analysis results, the answer generation unit generates appropriate answers. For example, it generates answers that include information related to questions the learner has asked in the past. It can also generate detailed answers on topics that learners frequently ask about. Furthermore, based on the learner's question history, the answer generation unit can generate answers in a format that is easy for learners to understand. This allows for the generation of more appropriate answers based on past question history. The question history includes, for example, how past questions are recorded, how long they are stored, and how they are analyzed. This enables the answer generation unit to provide appropriate answers based on the learner's question history, allowing them to effectively acquire knowledge in the social sciences.

[0037] The reception desk can generate responses based on the learner's learning status and areas of interest. For example, the reception desk can use AI to evaluate the learner's learning status. For example, the reception desk can analyze the learner's test results, study time, and progress to understand their learning status. The reception desk can also identify the learner's areas of interest based on survey results and past learning history. For example, the reception desk can analyze topics the learner has shown interest in and their learning history to understand their areas of interest. Based on the learning status and areas of interest, the reception desk generates appropriate responses. For example, the reception desk generates responses in a format that is easy for the learner to understand. The reception desk can also generate responses that include information related to the learner's areas of interest. This allows the reception desk to generate responses that are tailored to the learner's learning status and areas of interest. Learning status includes, for example, test results, study time, and progress. Areas of interest include, for example, survey results, past learning history, and topics the learner has shown interest in. This allows the reception desk to provide appropriate responses based on the learner's learning status and areas of interest, enabling them to effectively acquire knowledge in the social sciences.

[0038] The reception unit is further equipped with a determination unit that assesses the learner's level of understanding, and the lecture generation unit can adjust the difficulty level of the lecture based on the learner's level of understanding. The reception unit, for example, uses AI to determine the learner's level of understanding. For example, the reception unit analyzes the learner's test results, study time, and comprehension evaluation to understand their level of understanding. Based on the determined level of understanding, the lecture generation unit adjusts the difficulty level of the lecture. For example, the lecture generation unit can provide a simple explanation for content that the learner is learning for the first time, and a more detailed explanation for content that the learner already knows. This allows the system to provide lectures of an appropriate difficulty level according to the learner's level of understanding. Level of understanding includes, for example, test results, study time, and comprehension evaluation. Difficulty level of the lecture includes, for example, the depth of content, the frequency of use of technical terms, and the level of detail in the explanation. This allows the reception unit to assess the learner's level of understanding, and the lecture generation unit to adjust the difficulty level of the lecture based on that level of understanding, thereby improving the learner's learning effectiveness.

[0039] The lecture generation unit can analyze a student's past learning history and select appropriate lecture content. For example, the lecture generation unit uses AI to analyze a student's past learning history. For instance, it analyzes the content, study time, and test results a student has previously learned to understand their learning history. Based on the analysis, the lecture generation unit selects appropriate lecture content. For example, it selects new topics related to what the student has previously learned. It can also select lecture content that focuses on areas where the student struggles. Furthermore, it can prioritize incorporating areas of interest into the lecture content. This allows the system to provide optimal lecture content based on past learning history. Past learning history includes, for example, topics studied, study time, and test results. This enables the lecture generation unit to provide appropriate lecture content based on the student's past learning history, allowing them to effectively acquire knowledge in the social sciences.

[0040] The lecture generation unit can apply different lecture styles depending on the age and comprehension level of the students. For example, the lecture generation unit uses AI to evaluate the age and comprehension level of the students. For instance, the lecture generation unit analyzes the students' age group, comprehension test results, and past learning history to determine their age and comprehension level. Based on the evaluation results, the lecture generation unit applies an appropriate lecture style. For example, for children, the lecture generation unit applies a lecture style that uses simple language and many illustrations. For adults, the lecture generation unit can also apply a lecture style that includes detailed explanations with specialized terminology. Furthermore, for seniors, the lecture generation unit can apply a lecture style that proceeds at a slow pace and makes frequent use of repetition. This allows the lecture generation unit to provide an appropriate lecture style according to the age and comprehension level of the students. Age and comprehension level include, for example, age group, comprehension test results, and past learning history. As a result, the lecture generation unit can provide an appropriate lecture style based on the age and comprehension level of the students, enabling them to effectively acquire knowledge in the field of social sciences.

[0041] The lecture generation system can incorporate relevant case studies based on the geographical background of the students. For example, the lecture generation system can use AI to evaluate the geographical background of the students. For instance, the lecture generation system can analyze the culture, history, and economic conditions of the region where the students live to understand their geographical background. Based on the evaluation results, the lecture generation system incorporates appropriate case studies into the lectures. For example, the lecture generation system can incorporate historical events from the region where the students live into the lectures. The lecture generation system can also incorporate case studies related to the culture and traditions of the students' country into the lectures. Furthermore, the lecture generation system can introduce political events that occurred in the students' region as part of the lectures. This allows the system to provide appropriate case studies tailored to the geographical background of the students. Geographical background includes, for example, the culture, history, and economic conditions of the region. As a result, the lecture generation system can incorporate appropriate case studies into the lectures based on the geographical background of the students, enabling them to effectively acquire knowledge in the field of social science.

[0042] The lecture generation unit can adjust the order of lectures based on the students' interests. For example, the lecture generation unit uses AI to evaluate students' interests. For example, the lecture generation unit analyzes survey results and past learning history to understand students' interests. Based on the evaluation results, the lecture generation unit adjusts the order of lectures appropriately. For example, the lecture generation unit will address topics that students are interested in first. The lecture generation unit can also construct the order of lectures to focus on areas of high interest to students. Furthermore, the lecture generation unit can postpone topics that students are less interested in and start lectures with content that will pique their interest. This allows the lecture generation unit to provide an appropriate order of lectures that matches the students' interests. Interests include, for example, survey results, past learning history, and topics that students have shown interest in. As a result, the lecture generation unit can provide an appropriate order of lectures based on students' interests, enabling them to effectively acquire knowledge in the field of social sciences.

[0043] The output unit can adjust the frequency of the audio according to the learner's auditory characteristics. For example, the output unit uses AI to evaluate the learner's auditory characteristics. For example, the output unit analyzes the learner's hearing test results and the frequency characteristics of the audio to understand their auditory characteristics. Based on the evaluation results, the output unit adjusts the frequency of the audio. For example, the output unit outputs audio with suppressed high frequencies for elderly people. The output unit can also output clear, high-frequency audio for children. Furthermore, the output unit can output audio that emphasizes specific frequency bands for learners with hearing impairments. This allows the output unit to provide appropriate audio according to the learner's auditory characteristics. Auditory characteristics include, for example, hearing test results, audio frequency characteristics, and volume preferences. As a result, the output unit can provide appropriate audio based on the learner's auditory characteristics, enabling them to effectively acquire knowledge in the social sciences.

[0044] The output unit can detect the learner's ambient sounds and automatically adjust the audio volume. For example, the output unit uses AI to evaluate the learner's ambient sounds. For instance, it analyzes the volume of surrounding sounds and background noise to understand the ambient sounds. Based on the evaluation results, the output unit adjusts the audio volume. For example, if the learner is in a noisy environment, the output unit increases the volume. Conversely, if the learner is in a quiet environment, the output unit can decrease the volume. Furthermore, if the learner is using headphones, the output unit can automatically adjust the volume to an appropriate level. This allows the system to provide an appropriate audio volume based on the learner's ambient sounds. Ambient sounds include, for example, background noise, ambient volume, and the detection of specific sounds. This enables the output unit to provide an appropriate audio volume based on the learner's ambient sounds, allowing them to effectively acquire knowledge in the social sciences.

[0045] The output unit can select the optimal audio format considering the learner's device information. For example, the output unit uses AI to evaluate the learner's device information. For instance, it analyzes the type and performance of the device the learner uses to understand the device information. Based on the evaluation results, the output unit selects the optimal audio format. For example, it selects a compressed audio format for smartphones. It can also select an uncompressed audio format for devices requiring high sound quality. Furthermore, it can select a low-bitrate audio format for environments with unstable internet connections. This allows the output unit to provide an appropriate audio format according to the learner's device information. Device information includes, for example, the type of device used, its performance, and supported formats. This enables the output unit to provide an appropriate audio format based on the learner's device information, allowing them to effectively acquire knowledge in the social sciences.

[0046] The output unit can provide multilingual audio based on the learner's language settings. For example, the output unit uses AI to evaluate the learner's language settings. For instance, it analyzes the learner's device language settings and the languages ​​they use to understand their language preferences. Based on the evaluation, the output unit provides multilingual audio. For example, it automatically selects audio based on the learner's device language settings. The output unit can also provide a language switching function if the learner uses multiple languages. Furthermore, if the learner selects a specific language, the output unit can provide audio in that language. This allows the output unit to provide appropriate audio according to the learner's language settings. Language settings include, for example, the languages ​​used, language priority, and multilingual support methods. This enables the output unit to provide appropriate audio based on the learner's language settings, allowing them to effectively acquire knowledge in the social sciences.

[0047] The reception desk can select an appropriate question handling method by referring to the participant's past question history. For example, the reception desk may use AI to evaluate the participant's past question history. For example, the reception desk may analyze the content and frequency of past questions to understand the question history. Based on the evaluation results, the reception desk selects the optimal question handling method. For example, the reception desk may prioritize providing question formats that the participant has used in the past. The reception desk may also suggest specific question formats based on the participant's past question history. Furthermore, the reception desk may automatically select question formats that the participant frequently uses. This allows the reception desk to provide the optimal question handling method based on the past question history. The question history includes, for example, how past questions are recorded, how long they are kept, and how they are analyzed. This allows the reception desk to provide an appropriate question handling method based on the participant's past question history, enabling them to effectively acquire knowledge in the field of social sciences.

[0048] The reception desk can analyze the learner's voice characteristics and perform noise cancellation. For example, the reception desk uses AI to evaluate the learner's voice characteristics. For instance, it analyzes the learner's voice frequency characteristics, volume, and noise level to understand their voice characteristics. Based on the evaluation results, the reception desk performs noise cancellation. For example, it analyzes the learner's voice characteristics and removes background noise. The reception desk can also acquire clear audio based on the learner's voice characteristics. Furthermore, the reception desk can enhance noise cancellation if the learner is in a noisy environment. This allows for the provision of appropriate noise cancellation tailored to the learner's voice characteristics. Voice characteristics include, for example, the frequency characteristics, volume, and noise level of the voice. This enables the reception desk to provide appropriate noise cancellation based on the learner's voice characteristics, allowing them to effectively acquire knowledge in the social sciences.

[0049] The reception desk can provide the optimal question submission method considering the participant's device information. For example, the reception desk can use AI to evaluate the participant's device information. For example, the reception desk can analyze the type and performance of the device the participant is using to understand the device information. Based on the evaluation results, the reception desk provides the optimal question submission method. For example, the reception desk can provide an interface for smartphones that allows for easy question input via touch operation. The reception desk can also provide a question submission method that prioritizes keyboard input for PCs. Furthermore, the reception desk can provide a question submission method that prioritizes voice input for smartwatches. This allows the reception desk to provide an appropriate question submission method according to the participant's device information. Device information includes, for example, the type of device used, the device's performance, and the supported formats. This allows the reception desk to provide an appropriate question submission method based on the participant's device information, enabling them to effectively acquire knowledge in the field of social sciences.

[0050] The reception desk can handle questions in multiple languages ​​based on the learner's language settings. For example, the reception desk can use AI to evaluate the learner's language settings. For instance, the reception desk can analyze the language settings of the learner's device and the languages ​​they use to understand their language preferences. Based on the evaluation results, the reception desk can handle questions in multiple languages. For example, the reception desk can automatically set the language for question acceptance based on the language settings of the learner's device. The reception desk can also provide a language switching function if the learner uses multiple languages. Furthermore, if the learner selects a specific language, the reception desk can accept questions in that language. This allows the reception desk to provide appropriate question acceptance based on the learner's language settings. Language settings include, for example, the languages ​​used, language priority, and multilingual support methods. This enables the reception desk to provide appropriate question acceptance based on the learner's language settings, allowing them to effectively acquire knowledge in the social sciences.

[0051] The answer generation unit can generate optimal answers by analyzing the learner's past question history. For example, the answer generation unit uses AI to evaluate the learner's past question history. For instance, it analyzes the content and frequency of past questions to understand the question history. Based on the evaluation results, the answer generation unit generates the optimal answer. For example, it generates relevant answers based on the learner's past questions. The answer generation unit can also generate detailed answers from the learner's past question history. Furthermore, the answer generation unit can prioritize generating answers on topics that learners frequently ask about. This allows the system to provide optimal answers based on past question history. Question history includes, for example, how past questions are recorded, how long they are stored, and how they are analyzed. This enables the answer generation unit to provide appropriate answers based on the learner's past question history, allowing them to effectively acquire knowledge in the social sciences.

[0052] The answer generation unit can apply different answer styles depending on the learner's age and level of understanding. For example, the answer generation unit uses AI to evaluate the learner's age and level of understanding. For instance, it analyzes the learner's age group, comprehension test results, and past learning history to determine their age and level of understanding. Based on the evaluation results, the answer generation unit applies an appropriate answer style. For example, for children, the answer generation unit generates answers that use simple language and illustrations. For adults, the answer generation unit can also generate detailed answers that include specialized terminology. Furthermore, for seniors, the answer generation unit can generate answers that proceed at a slow pace and make frequent use of repetition. This allows the system to provide appropriate answers tailored to the learner's age and level of understanding. Age and level of understanding include, for example, age group, comprehension test results, and past learning history. This enables the answer generation unit to provide appropriate answers based on the learner's age and level of understanding, allowing them to effectively acquire knowledge in the social sciences.

[0053] The answer generation unit can incorporate relevant examples considering the learner's geographical background. For example, the answer generation unit uses AI to evaluate the learner's geographical background. For instance, the answer generation unit analyzes the culture, history, and economic conditions of the area where the learner lives to understand their geographical background. Based on the evaluation results, the answer generation unit incorporates appropriate examples into the answers. For example, the answer generation unit can incorporate historical events from the area where the learner lives into the answers. The answer generation unit can also incorporate examples related to the culture and traditions of the learner's country into the answers. Furthermore, the answer generation unit can introduce political events that occurred in the learner's region as part of the answers. This allows the answer generation unit to provide appropriate examples tailored to the learner's geographical background. Geographical background includes, for example, the local culture, history, and economic conditions. As a result, the answer generation unit can incorporate appropriate examples into the answers based on the learner's geographical background, enabling them to effectively acquire knowledge in the field of social science.

[0054] The answer generation unit can adjust the order of answers based on the learner's interests. For example, the answer generation unit uses AI to evaluate the learner's interests. For example, the answer generation unit analyzes survey results and past learning history to understand the learner's interests. Based on the evaluation results, the answer generation unit adjusts the order of answers appropriately. For example, the answer generation unit will address topics that the learner is interested in first. The answer generation unit can also construct the order of answers focusing on areas of high interest to the learner. Furthermore, the answer generation unit can postpone topics of little interest to the learner and start with content that is likely to interest them. This allows the answer generation unit to provide an appropriate order of answers that matches the learner's interests. Interests include, for example, survey results, past learning history, and topics that the learner has shown interest in. As a result, the answer generation unit can provide an appropriate order of answers based on the learner's interests, enabling them to effectively acquire knowledge in the field of social sciences.

[0055] The delivery department can select the optimal delivery method by referring to the learner's past learning history. For example, the delivery department can use AI to evaluate the learner's past learning history. For example, the delivery department can analyze past learning content, study time, and test results to understand the learning history. Based on the evaluation results, the delivery department selects the optimal delivery method. For example, the delivery department can prioritize delivery methods that the learner has used in the past. The delivery department can also suggest specific delivery methods based on the learner's past learning history. Furthermore, the delivery department can automatically select delivery methods that the learner frequently uses. This allows the delivery department to provide the optimal delivery method based on past learning history. Learning history includes, for example, topics studied, study time, and test results. This allows the delivery department to provide appropriate delivery methods based on the learner's past learning history, enabling them to effectively acquire knowledge in the social sciences.

[0056] The service provider can select the optimal delivery format considering the learner's device information. For example, the service provider can use AI to evaluate the learner's device information. For example, the service provider can analyze the type and performance of the device used by the learner to understand the device information. Based on the evaluation results, the service provider selects the optimal delivery format. For example, the service provider can select a compressed delivery format for smartphones. The service provider can also select an uncompressed delivery format for devices that require high sound quality. Furthermore, the service provider can select a low-bitrate delivery format for environments with unstable internet connections. This allows the service provider to provide an appropriate delivery format according to the learner's device information. Device information includes, for example, the type of device used, the device's performance, and the supported formats. This allows the service provider to provide an appropriate delivery format based on the learner's device information, enabling them to effectively acquire knowledge in the field of social sciences.

[0057] The service provider can provide multilingual answers based on the learner's language settings. For example, the service provider can use AI to evaluate the learner's language settings. For instance, the service provider can analyze the language settings of the learner's device and the languages ​​they use to understand their language preferences. Based on the evaluation results, the service provider can provide multilingual answers. For example, the service provider can automatically set the language of the answer based on the language settings of the learner's device. The service provider can also provide a language switching function if the learner uses multiple languages. Furthermore, the service provider can provide answers in a specific language if the learner selects that language. This allows the service provider to provide appropriate answers according to the learner's language settings. Language settings include, for example, the languages ​​used, language priority, and multilingual support methods. This enables the service provider to provide appropriate answers based on the learner's language settings and effectively acquire knowledge in the social sciences.

[0058] The system can detect the learner's ambient sounds and automatically adjust the audio volume. For example, the system uses AI to evaluate the learner's ambient sounds. For instance, it analyzes the volume of surrounding sounds and background noise to understand the ambient noise. Based on the evaluation results, the system adjusts the audio volume. For example, if the learner is in a noisy environment, the system increases the volume. Conversely, if the learner is in a quiet environment, the system can decrease the volume. Furthermore, if the learner is using headphones, the system can automatically adjust the volume to an appropriate level. This allows the system to provide an appropriate audio volume based on the learner's ambient sounds. Ambient sounds include, for example, background noise, ambient volume, and the detection of specific sounds. This enables the system to provide an appropriate audio volume based on the learner's ambient sounds, allowing them to effectively acquire knowledge in the social sciences.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The educational system can provide customized learning plans based on the learner's learning style. For example, visual learners can be provided with lecture materials that make extensive use of diagrams and graphs. Auditory learners can be provided with lectures that incorporate audio and music. Furthermore, experiential learners can learn through actual experiences and simulations. This allows for the provision of an optimal learning plan tailored to the learner's learning style, enabling them to effectively acquire knowledge in the social sciences.

[0061] The educational system can analyze students' learning history and provide feedback based on their learning progress. For example, if a student is struggling with a particular topic, it can provide additional materials or practice exercises on that topic. Conversely, if a student understands a particular topic, it can provide advanced content related to that topic. Furthermore, it can evaluate learning progress and provide appropriate feedback based on the student's learning history. This allows for effective feedback based on the student's learning history, enabling them to effectively acquire knowledge in the social sciences.

[0062] Educational systems can assess learners' learning environments and provide the optimal learning environment. For example, if a learner is in a noisy environment, noise cancellation can be provided. If a learner is in a quiet environment, music or audio can be provided to enhance concentration. Furthermore, if a learner is studying on the go, learning content optimized for mobile devices can be provided. This allows for the provision of an optimal learning environment tailored to each learner's circumstances, enabling them to effectively acquire knowledge in the social sciences.

[0063] The educational system can analyze students' learning history and provide customized learning plans based on their progress. For example, if a student is struggling with a particular topic, it can provide additional materials and practice exercises on that topic. Conversely, if a student understands a particular topic, it can provide advanced content related to that topic. Furthermore, it can evaluate learning progress based on the student's learning history and provide appropriate learning plans. This allows for the provision of effective learning plans based on the student's learning history, enabling them to effectively acquire knowledge in the social sciences.

[0064] The educational system can analyze students' learning history and provide customized tests based on their learning progress. For example, if a student is struggling with a particular topic, a test on that topic can be provided. Conversely, if a student understands a particular topic, an advanced test related to that topic can be provided. Furthermore, the system can assess learning progress based on the student's learning history and provide appropriate tests. This allows for effective testing based on the student's learning history, enabling them to effectively acquire knowledge in the social sciences.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The lecture generation unit generates audio for lectures in the social sciences using AI. For example, it can use natural language processing technology to generate audio for lectures in fields such as history, geography, culture, and politics. It can also use speech synthesis technology to generate lecture audio. Specifically, the prompt "Generate a lecture about historical events" is input to the AI ​​model, and the generated audio is output. Step 2: The output unit outputs the audio generated by the lecture generation unit through a speaker attached to the stuffed animal. For example, the generated lecture audio is provided to the student through the speaker. It also has a function to adjust the tone and speed of the audio, allowing it to be adjusted according to the student's age and level of understanding. Step 3: The reception desk receives audio questions from lecture participants via a microphone attached to a stuffed animal. For example, the microphone captures the participant's question as audio data. Alternatively, speech recognition technology can be used to convert the participant's question into text data. Specifically, the participant's question is analyzed using speech recognition technology and saved as text data. Step 4: The answer generation unit generates answers based on the questions received by the reception unit. It uses a generation AI to generate answers to questions. For example, it prompts the generation AI with "Please generate an answer to this question" and outputs the generated answer. Step 5: The providing unit provides the answers generated by the generating unit. For example, it provides the generated answers to the learners via audio through a speaker. It also has a function to display the generated answers as text data, and displays the generated answers as text data through a display.

[0067] (Example of form 2) An educational system according to an embodiment of the present invention is a system that provides educational programs on social science fields such as history, geography, culture, and politics using a stuffed animal that directly or readily incorporates a generative AI. This educational system has the function of generating audio for lectures on social science fields and generating and providing answers to questions from students. For example, the educational system has a lecture generation unit that uses AI to generate audio for lectures on social science fields. This audio is output from a speaker attached to the stuffed animal. Next, a microphone attached to the stuffed animal receives audio questions from students. The reception unit analyzes the question, and the answer generation unit uses the generative AI to generate an answer to the question. The generated answer is provided to the student through the provision unit. For example, if a child asks a question about a historical event, the AI ​​generates an appropriate answer to that question, and the stuffed animal conveys the answer aloud. It is also possible to estimate the student's emotions and generate an answer based on those emotions. Furthermore, it also has the function of analyzing the student's question history and generating an answer based on the analysis results. It can also generate an answer based on the student's current learning status and areas of interest. This system also has the function of determining the student's level of learning and adjusting the difficulty level of the lecture based on that level of learning. For example, a simple explanation can be given to content that students are learning for the first time, and a more detailed explanation can be given to content they already know. This allows children to learn at their own pace and effectively acquire knowledge in the social sciences. In this way, the education system can provide appropriate lectures according to the students' level of learning and effectively acquire knowledge in the social sciences. Note that stuffed animals are robots that imitate humans or animals, but are not limited to such examples.

[0068] The educational system according to this embodiment comprises a lecture generation unit, an output unit, a reception unit, an answer generation unit, and a provision unit. The lecture generation unit generates audio of lectures on social sciences using AI. The lecture generation unit can generate audio of lectures on fields such as history, geography, culture, and politics, for example, using natural language processing technology. The lecture generation unit can also generate audio of lectures using speech synthesis technology. For example, the lecture generation unit inputs the prompt "Generate a lecture on historical events" to an AI model and outputs the generated audio. The output unit outputs the audio generated by the lecture generation unit through a speaker attached to a stuffed animal. The output unit provides the generated lecture audio to students, for example, through the speaker. The output unit also has a function to adjust the tone and speed of the audio. For example, the output unit can adjust the tone and speed of the audio according to the age and comprehension level of the students. The reception unit receives audio of questions from lecture students through a microphone attached to the stuffed animal. The reception unit acquires the students' questions as audio data through the microphone, for example. Furthermore, the reception unit can also convert the student's questions into text data using speech recognition technology. For example, the reception unit analyzes the student's questions using speech recognition technology and saves them as text data. The answer generation unit generates answers based on the questions received by the reception unit. The answer generation unit generates answers to questions using a generation AI. For example, the answer generation unit inputs the prompt "Generate an answer to this question" to the generation AI and outputs the generated answer. The delivery unit provides the answers generated by the generation unit. The delivery unit provides the generated answers to the students by voice, for example, through a speaker. The delivery unit also has a function to display the generated answers as text data. For example, the delivery unit displays the generated answers as text data through a display. As a result, the educational system according to this embodiment provides appropriate answers to students' questions and enables them to effectively acquire knowledge in the field of social sciences.

[0069] The lecture generation unit uses AI to generate audio for lectures in the social sciences. Specifically, it can generate audio for lectures in fields such as history, geography, culture, and politics using natural language processing technology. For example, the lecture generation unit can input a prompt to its AI model such as "Generate a lecture about historical events" and output the generated audio. This prompt can include detailed information such as specific events, eras, and regions. For example, by inputting a prompt such as "Generate a lecture about major events in World War II," the AI ​​will collect relevant information and construct the lecture content. It can also convert the generated text into natural-sounding speech using speech synthesis technology. Speech synthesis technology includes functions to adjust the tone, speed, and accent of the speech, allowing for the generation of optimal speech according to the age and comprehension level of the learners. Furthermore, the lecture generation unit can regularly update the content of the generated lectures to reflect new information and research findings. This ensures that learners are always learning the latest knowledge. The lecture generation unit also supports multiple languages ​​and can generate lectures in different languages. This allows it to accommodate learners who speak different languages ​​and provides a global educational environment.

[0070] The output unit outputs the audio generated by the lecture generation unit through a speaker attached to the stuffed animal. Specifically, it provides the generated lecture audio to the learner through the speaker. The output unit has a function to adjust the tone and speed of the audio, allowing it to be adjusted according to the learner's age and level of understanding. For example, lectures for children can be output in a bright, slow tone, while lectures for adults can be output in a more professional, faster tone. The output unit also has a function to adjust the volume of the audio, allowing it to provide lectures at the optimal volume according to the learner's environment. Furthermore, by coordinating multiple speakers, the output unit can deliver audio uniformly even in large classrooms or halls. This allows learners to clearly hear the lecture content no matter where they are positioned. In addition to audio output, the output unit may also have a display to provide visual information. For example, displaying images or videos related to the lecture content on the display can deepen the learner's understanding. In this way, the output unit can provide both audio and visual information, enhancing the learner's learning effectiveness.

[0071] The reception desk uses microphones attached to stuffed animals to receive audio questions from lecture participants. Specifically, it captures participants' questions as audio data through the microphones. The reception desk can also convert participants' questions into text data using speech recognition technology. For example, if a participant asks, "Please tell me about the background of this event," the reception desk analyzes the audio and saves it as text data. The speech recognition technology includes noise cancellation and audio clearing functions, allowing it to accurately recognize participants' questions. Furthermore, the reception desk can receive questions from multiple participants simultaneously. For example, in large lectures, multiple microphones can be installed and audio data from each microphone processed simultaneously to efficiently receive multiple questions. The reception desk also has a function to classify participants' questions and group related questions. This prevents the same question from being asked multiple times, enabling efficient question answering. In addition, the reception desk can save participants' question history and refer to it later. This allows for easy searching and reuse of past questions and their answers.

[0072] The answer generation unit generates answers based on questions received by the reception unit. Specifically, it uses a generation AI to generate answers to questions. For example, the answer generation unit prompts the generation AI with "Please generate an answer to this question" and outputs the generated answer. The generation AI analyzes the content of the question, collects relevant information, and constructs the answer. For example, in response to the question "Please tell me about the background of this event," the generation AI investigates the historical background and related events and generates a detailed answer. The generation AI can refer to a large number of databases and documents, select the most appropriate information, and generate an answer. Furthermore, the generation AI can adjust the content of the answer according to the learner's level of understanding. For example, it can generate a concise and easy-to-understand answer for beginners and a detailed and specialized answer for experts. In addition, the answer generation unit can provide the generated answer in multiple formats. For example, in addition to an answer in audio format, it can also generate an answer in text format, allowing learners to choose. This enables the answer generation unit to provide quick and appropriate answers to learners' questions, thereby enhancing learning effectiveness.

[0073] The delivery unit provides the answers generated by the generation unit. Specifically, it provides the generated answers to learners audibly through speakers. The delivery unit also has a function to display the generated answers as text data. For example, it can display the generated answers as text data through a display. This allows learners to confirm the answers not only audibly but also visually. Furthermore, the delivery unit has a function to save the generated answers and refer to them later. For example, learners can easily search for and review questions and answers they have received in the past. The delivery unit also has a function to improve the content of the answers based on learner feedback. For example, if learners provide additional questions or comments on an answer, the delivery unit can collect that feedback and update the content of the answer. This allows the delivery unit to always provide the latest and most optimal answers, maximizing the learning effect for learners. Furthermore, the delivery unit can provide answers to multiple learners simultaneously. For example, in a large lecture, it can use multiple displays and speakers to provide answers to all learners simultaneously. This allows the delivery unit to provide answers efficiently and effectively, increasing learner satisfaction.

[0074] The answer generation unit can generate answers to questions using a generative AI. For example, the answer generation unit uses the generative AI to generate appropriate answers to the student's questions. For instance, the answer generation unit prompts the generative AI with "Generate an answer to this question" and outputs the generated answer. This allows for the generation of appropriate answers to questions using the generative AI. These models have been trained on large amounts of text data and possess advanced natural language processing capabilities. The generative AI can generate appropriate answers based on the content of the question. For example, the generative AI understands the context of the question and generates an answer based on relevant information. Depending on the content of the question, the generative AI can also generate answers that include detailed explanations and specific examples. This allows the answer generation unit to provide appropriate answers to the student's questions and effectively acquire knowledge in the field of social sciences.

[0075] The response generation unit can estimate the learner's emotions and generate responses based on those estimated emotions. The response generation unit estimates the learner's emotions using, for example, an emotion engine or a generation AI. For example, it can use voice analysis technology to analyze the learner's voice tone and speed and estimate their emotions. It can also use facial recognition technology to analyze the learner's facial expressions and estimate their emotions. For example, it can capture the learner's facial expressions through a camera and estimate their emotions using facial recognition technology. Furthermore, it can use text analysis technology to estimate emotions from the content of the learner's questions. For example, it can analyze the text data of the learner's questions and estimate their emotions. Based on the estimated emotions, the response generation unit generates appropriate responses. For example, if the learner is excited, the response generation unit generates responses in a calm tone. If the learner is bored, the response generation unit can generate responses that include interesting anecdotes. Furthermore, if the learner is feeling anxious, the response generation unit can generate responses in a gentle, reassuring tone. This enables the generation of appropriate responses tailored to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. As a result, the response generation unit provides appropriate responses tailored to the learner's emotions, enabling them to effectively acquire knowledge in the social sciences.

[0076] The answer generation unit can analyze the learner's question history and generate answers based on the analysis results. For example, the answer generation unit uses AI to analyze the learner's question history. For instance, it analyzes the content and frequency of past questions to understand the learner's interests and level of understanding. Based on the analysis results, the answer generation unit generates appropriate answers. For example, it generates answers that include information related to questions the learner has asked in the past. It can also generate detailed answers on topics that learners frequently ask about. Furthermore, based on the learner's question history, the answer generation unit can generate answers in a format that is easy for learners to understand. This allows for the generation of more appropriate answers based on past question history. The question history includes, for example, how past questions are recorded, how long they are stored, and how they are analyzed. This enables the answer generation unit to provide appropriate answers based on the learner's question history, allowing them to effectively acquire knowledge in the social sciences.

[0077] The reception desk can generate responses based on the learner's learning status and areas of interest. For example, the reception desk can use AI to evaluate the learner's learning status. For example, the reception desk can analyze the learner's test results, study time, and progress to understand their learning status. The reception desk can also identify the learner's areas of interest based on survey results and past learning history. For example, the reception desk can analyze topics the learner has shown interest in and their learning history to understand their areas of interest. Based on the learning status and areas of interest, the reception desk generates appropriate responses. For example, the reception desk generates responses in a format that is easy for the learner to understand. The reception desk can also generate responses that include information related to the learner's areas of interest. This allows the reception desk to generate responses that are tailored to the learner's learning status and areas of interest. Learning status includes, for example, test results, study time, and progress. Areas of interest include, for example, survey results, past learning history, and topics the learner has shown interest in. This allows the reception desk to provide appropriate responses based on the learner's learning status and areas of interest, enabling them to effectively acquire knowledge in the social sciences.

[0078] The reception unit is further equipped with a determination unit that assesses the learner's level of understanding, and the lecture generation unit can adjust the difficulty level of the lecture based on the learner's level of understanding. The reception unit, for example, uses AI to determine the learner's level of understanding. For example, the reception unit analyzes the learner's test results, study time, and comprehension evaluation to understand their level of understanding. Based on the determined level of understanding, the lecture generation unit adjusts the difficulty level of the lecture. For example, the lecture generation unit can provide a simple explanation for content that the learner is learning for the first time, and a more detailed explanation for content that the learner already knows. This allows the system to provide lectures of an appropriate difficulty level according to the learner's level of understanding. Level of understanding includes, for example, test results, study time, and comprehension evaluation. Difficulty level of the lecture includes, for example, the depth of content, the frequency of use of technical terms, and the level of detail in the explanation. This allows the reception unit to assess the learner's level of understanding, and the lecture generation unit to adjust the difficulty level of the lecture based on that level of understanding, thereby improving the learner's learning effectiveness.

[0079] The lecture generation unit can estimate the emotions of the students and adjust the content and tone of the lecture based on the estimated emotions. The lecture generation unit estimates the emotions of the students using, for example, an emotion engine or generative AI. For example, the lecture generation unit can use speech analysis technology to analyze the tone and speed of the students' voices and estimate their emotions. The lecture generation unit can also use facial recognition technology to analyze the students' facial expressions and estimate their emotions. For example, the lecture generation unit can capture the students' facial expressions through a camera and estimate their emotions using facial recognition technology. Furthermore, the lecture generation unit can use text analysis technology to estimate emotions from the content of the students' questions. For example, the lecture generation unit can analyze the text data of the students' questions and estimate their emotions. Based on the estimated emotions, the lecture generation unit adjusts the content and tone of the lecture. For example, if the students are excited, the lecture generation unit will proceed with the lecture in a calm tone to increase their concentration. Also, if the students are bored, the lecture generation unit can insert interesting episodes or quizzes to liven up the lecture. Furthermore, the lecture generation unit can also conduct lectures in a gentle tone to reassure students if they are feeling anxious. This allows for the provision of appropriate lectures tailored to the students' emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This enables the lecture generation unit to provide appropriate lectures tailored to the students' emotions, allowing them to effectively acquire knowledge in the social sciences.

[0080] The lecture generation unit can analyze a student's past learning history and select appropriate lecture content. For example, the lecture generation unit uses AI to analyze a student's past learning history. For instance, it analyzes the content, study time, and test results a student has previously learned to understand their learning history. Based on the analysis, the lecture generation unit selects appropriate lecture content. For example, it selects new topics related to what the student has previously learned. It can also select lecture content that focuses on areas where the student struggles. Furthermore, it can prioritize incorporating areas of interest into the lecture content. This allows the system to provide optimal lecture content based on past learning history. Past learning history includes, for example, topics studied, study time, and test results. This enables the lecture generation unit to provide appropriate lecture content based on the student's past learning history, allowing them to effectively acquire knowledge in the social sciences.

[0081] The lecture generation unit can apply different lecture styles depending on the age and comprehension level of the students. For example, the lecture generation unit uses AI to evaluate the age and comprehension level of the students. For instance, the lecture generation unit analyzes the students' age group, comprehension test results, and past learning history to determine their age and comprehension level. Based on the evaluation results, the lecture generation unit applies an appropriate lecture style. For example, for children, the lecture generation unit applies a lecture style that uses simple language and many illustrations. For adults, the lecture generation unit can also apply a lecture style that includes detailed explanations with specialized terminology. Furthermore, for seniors, the lecture generation unit can apply a lecture style that proceeds at a slow pace and makes frequent use of repetition. This allows the lecture generation unit to provide an appropriate lecture style according to the age and comprehension level of the students. Age and comprehension level include, for example, age group, comprehension test results, and past learning history. As a result, the lecture generation unit can provide an appropriate lecture style based on the age and comprehension level of the students, enabling them to effectively acquire knowledge in the field of social sciences.

[0082] The lecture generation unit can estimate the emotions of the learners and adjust the length of the lecture based on the estimated emotions. The lecture generation unit estimates the emotions of the learners using, for example, an emotion engine or generative AI. For example, the lecture generation unit can use speech analysis technology to analyze the tone and speed of the learners' voices and estimate their emotions. The lecture generation unit can also use facial recognition technology to analyze the learners' facial expressions and estimate their emotions. For example, the lecture generation unit can capture the learners' facial expressions through a camera and estimate their emotions using facial recognition technology. Furthermore, the lecture generation unit can use text analysis technology to estimate emotions from the content of the learners' questions. For example, the lecture generation unit can analyze the text data of the learners' questions and estimate their emotions. Based on the estimated emotions, the lecture generation unit adjusts the length of the lecture. For example, if the learners are focused, the lecture generation unit can extend the length of the lecture to delve deeper into the topic. Conversely, if the learners are tired, the lecture generation unit can shorten the length of the lecture to focus on the key points. Furthermore, the lecture generation unit can divide the lecture into shorter sessions if the students are excited. This allows for the provision of an appropriate lecture length based on the students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the lecture generation unit to provide an appropriate lecture length based on the students' emotions, enabling them to effectively acquire knowledge in the social sciences.

[0083] The lecture generation system can incorporate relevant case studies based on the geographical background of the students. For example, the lecture generation system can use AI to evaluate the geographical background of the students. For instance, the lecture generation system can analyze the culture, history, and economic conditions of the region where the students live to understand their geographical background. Based on the evaluation results, the lecture generation system incorporates appropriate case studies into the lectures. For example, the lecture generation system can incorporate historical events from the region where the students live into the lectures. The lecture generation system can also incorporate case studies related to the culture and traditions of the students' country into the lectures. Furthermore, the lecture generation system can introduce political events that occurred in the students' region as part of the lectures. This allows the system to provide appropriate case studies tailored to the geographical background of the students. Geographical background includes, for example, the culture, history, and economic conditions of the region. As a result, the lecture generation system can incorporate appropriate case studies into the lectures based on the geographical background of the students, enabling them to effectively acquire knowledge in the field of social science.

[0084] The lecture generation unit can adjust the order of lectures based on the students' interests. For example, the lecture generation unit uses AI to evaluate students' interests. For example, the lecture generation unit analyzes survey results and past learning history to understand students' interests. Based on the evaluation results, the lecture generation unit adjusts the order of lectures appropriately. For example, the lecture generation unit will address topics that students are interested in first. The lecture generation unit can also construct the order of lectures to focus on areas of high interest to students. Furthermore, the lecture generation unit can postpone topics that students are less interested in and start lectures with content that will pique their interest. This allows the lecture generation unit to provide an appropriate order of lectures that matches the students' interests. Interests include, for example, survey results, past learning history, and topics that students have shown interest in. As a result, the lecture generation unit can provide an appropriate order of lectures based on students' interests, enabling them to effectively acquire knowledge in the field of social sciences.

[0085] The output unit can estimate the learner's emotions and adjust the tone and speed of the audio based on the estimated emotions. The output unit estimates the learner's emotions using, for example, an emotion engine or generative AI. For example, the output unit uses speech analysis technology to analyze the tone and speed of the learner's voice and estimate their emotions. The output unit can also use facial recognition technology to analyze the learner's facial expressions and estimate their emotions. For example, the output unit captures the learner's facial expressions through a camera and estimates their emotions using facial recognition technology. Furthermore, the output unit can also use text analysis technology to estimate emotions from the content of the learner's questions. For example, the output unit analyzes the text data of the learner's questions and estimates their emotions. Based on the estimated emotions, the output unit adjusts the tone and speed of the audio. For example, if the learner is relaxed, the output unit outputs audio in a relaxed tone and speed. If the learner is in a hurry, the output unit can output audio at a fast speed and to the point. Furthermore, if the learner is excited, the output unit can output audio in a calm tone. This allows for the provision of appropriate voice tone and speed that respond to the learner's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. As a result, the output unit provides appropriate voice tone and speed that responds to the learner's emotions, enabling them to effectively acquire knowledge in the social sciences.

[0086] The output unit can adjust the frequency of the audio according to the learner's auditory characteristics. For example, the output unit uses AI to evaluate the learner's auditory characteristics. For example, the output unit analyzes the learner's hearing test results and the frequency characteristics of the audio to understand their auditory characteristics. Based on the evaluation results, the output unit adjusts the frequency of the audio. For example, the output unit outputs audio with suppressed high frequencies for elderly people. The output unit can also output clear, high-frequency audio for children. Furthermore, the output unit can output audio that emphasizes specific frequency bands for learners with hearing impairments. This allows the output unit to provide appropriate audio according to the learner's auditory characteristics. Auditory characteristics include, for example, hearing test results, audio frequency characteristics, and volume preferences. As a result, the output unit can provide appropriate audio based on the learner's auditory characteristics, enabling them to effectively acquire knowledge in the social sciences.

[0087] The output unit can detect the learner's ambient sounds and automatically adjust the audio volume. For example, the output unit uses AI to evaluate the learner's ambient sounds. For instance, it analyzes the volume of surrounding sounds and background noise to understand the ambient sounds. Based on the evaluation results, the output unit adjusts the audio volume. For example, if the learner is in a noisy environment, the output unit increases the volume. Conversely, if the learner is in a quiet environment, the output unit can decrease the volume. Furthermore, if the learner is using headphones, the output unit can automatically adjust the volume to an appropriate level. This allows the system to provide an appropriate audio volume based on the learner's ambient sounds. Ambient sounds include, for example, background noise, ambient volume, and the detection of specific sounds. This enables the output unit to provide an appropriate audio volume based on the learner's ambient sounds, allowing them to effectively acquire knowledge in the social sciences.

[0088] The output unit can estimate the learner's emotions and adjust the intonation of the voice based on the estimated emotions. The output unit estimates the learner's emotions using, for example, an emotion engine or generative AI. For example, the output unit uses voice analysis technology to analyze the tone and speed of the learner's voice and estimate their emotions. The output unit can also use facial recognition technology to analyze the learner's facial expressions and estimate their emotions. For example, the output unit captures the learner's facial expressions through a camera and estimates their emotions using facial recognition technology. Furthermore, the output unit can also use text analysis technology to estimate emotions from the content of the learner's questions. For example, the output unit analyzes the text data of the learner's questions and estimates their emotions. Based on the estimated emotions, the output unit adjusts the intonation of the voice. For example, if the learner is relaxed, the output unit outputs the voice with a calm intonation. If the learner is excited, the output unit can output the voice with a lively intonation. Furthermore, if the learner is feeling anxious, the output unit can output the voice with a reassuring intonation. This allows for the provision of appropriate speech intonation that corresponds to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. As a result, the output unit provides appropriate speech intonation that corresponds to the learner's emotions, enabling them to effectively acquire knowledge in the social sciences.

[0089] The output unit can select the optimal audio format considering the learner's device information. For example, the output unit uses AI to evaluate the learner's device information. For instance, it analyzes the type and performance of the device the learner uses to understand the device information. Based on the evaluation results, the output unit selects the optimal audio format. For example, it selects a compressed audio format for smartphones. It can also select an uncompressed audio format for devices requiring high sound quality. Furthermore, it can select a low-bitrate audio format for environments with unstable internet connections. This allows the output unit to provide an appropriate audio format according to the learner's device information. Device information includes, for example, the type of device used, its performance, and supported formats. This enables the output unit to provide an appropriate audio format based on the learner's device information, allowing them to effectively acquire knowledge in the social sciences.

[0090] The output unit can provide multilingual audio based on the learner's language settings. For example, the output unit uses AI to evaluate the learner's language settings. For instance, it analyzes the learner's device language settings and the languages ​​they use to understand their language preferences. Based on the evaluation, the output unit provides multilingual audio. For example, it automatically selects audio based on the learner's device language settings. The output unit can also provide a language switching function if the learner uses multiple languages. Furthermore, if the learner selects a specific language, the output unit can provide audio in that language. This allows the output unit to provide appropriate audio according to the learner's language settings. Language settings include, for example, the languages ​​used, language priority, and multilingual support methods. This enables the output unit to provide appropriate audio based on the learner's language settings, allowing them to effectively acquire knowledge in the social sciences.

[0091] The reception desk can estimate the participant's emotions and adjust the question reception method based on the estimated emotions. The reception desk estimates the participant's emotions using, for example, an emotion engine or generative AI. For example, the reception desk can use voice analysis technology to analyze the tone and speed of the participant's voice and estimate their emotions. The reception desk can also use facial recognition technology to analyze the participant's facial expressions and estimate their emotions. For example, the reception desk can capture the participant's facial expressions through a camera and estimate their emotions using facial recognition technology. Furthermore, the reception desk can use text analysis technology to estimate emotions from the content of the participant's questions. For example, the reception desk can analyze the text data of the participant's questions and estimate their emotions. Based on the estimated emotions, the reception desk adjusts the question reception method. For example, if the participant is nervous, the reception desk can provide a simple question format. If the participant is relaxed, the reception desk can also provide a more detailed question format. Furthermore, if the participant is in a hurry, the reception desk can prioritize voice input to quickly receive questions. This allows for the provision of an appropriate question reception method that is tailored to the participant's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the reception department to provide appropriate questioning methods tailored to the learner's emotions, enabling them to effectively acquire knowledge in the social sciences.

[0092] The reception desk can select an appropriate question handling method by referring to the participant's past question history. For example, the reception desk may use AI to evaluate the participant's past question history. For example, the reception desk may analyze the content and frequency of past questions to understand the question history. Based on the evaluation results, the reception desk selects the optimal question handling method. For example, the reception desk may prioritize providing question formats that the participant has used in the past. The reception desk may also suggest specific question formats based on the participant's past question history. Furthermore, the reception desk may automatically select question formats that the participant frequently uses. This allows the reception desk to provide the optimal question handling method based on the past question history. The question history includes, for example, how past questions are recorded, how long they are kept, and how they are analyzed. This allows the reception desk to provide an appropriate question handling method based on the participant's past question history, enabling them to effectively acquire knowledge in the field of social sciences.

[0093] The reception desk can analyze the learner's voice characteristics and perform noise cancellation. For example, the reception desk uses AI to evaluate the learner's voice characteristics. For instance, it analyzes the learner's voice frequency characteristics, volume, and noise level to understand their voice characteristics. Based on the evaluation results, the reception desk performs noise cancellation. For example, it analyzes the learner's voice characteristics and removes background noise. The reception desk can also acquire clear audio based on the learner's voice characteristics. Furthermore, the reception desk can enhance noise cancellation if the learner is in a noisy environment. This allows for the provision of appropriate noise cancellation tailored to the learner's voice characteristics. Voice characteristics include, for example, the frequency characteristics, volume, and noise level of the voice. This enables the reception desk to provide appropriate noise cancellation based on the learner's voice characteristics, allowing them to effectively acquire knowledge in the social sciences.

[0094] The reception desk can estimate the emotions of participants and prioritize questions based on those estimated emotions. The reception desk can estimate participants' emotions using, for example, an emotion engine or generative AI. For example, the reception desk can use voice analysis technology to analyze the tone and speed of the participant's voice and estimate their emotions. The reception desk can also use facial recognition technology to analyze the participant's facial expressions and estimate their emotions. For example, the reception desk can capture the participant's facial expressions through a camera and estimate their emotions using facial recognition technology. Furthermore, the reception desk can use text analysis technology to estimate emotions from the content of the participant's questions. For example, the reception desk can analyze the text data of the participant's questions and estimate their emotions. Based on the estimated emotions, the reception desk determines the priority of questions. For example, if the participant is nervous, the reception desk will prioritize simple questions. Conversely, if the participant is relaxed, the reception desk may prioritize detailed questions. Furthermore, if the participant is in a hurry, the reception desk may prioritize questions that require a quick answer. This allows for the provision of appropriate question prioritization based on the learner's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This enables the reception department to provide appropriate question prioritization based on the learner's emotions, allowing them to effectively acquire knowledge in the social sciences.

[0095] The reception desk can provide the optimal question submission method considering the participant's device information. For example, the reception desk can use AI to evaluate the participant's device information. For example, the reception desk can analyze the type and performance of the device the participant is using to understand the device information. Based on the evaluation results, the reception desk provides the optimal question submission method. For example, the reception desk can provide an interface for smartphones that allows for easy question input via touch operation. The reception desk can also provide a question submission method that prioritizes keyboard input for PCs. Furthermore, the reception desk can provide a question submission method that prioritizes voice input for smartwatches. This allows the reception desk to provide an appropriate question submission method according to the participant's device information. Device information includes, for example, the type of device used, the device's performance, and the supported formats. This allows the reception desk to provide an appropriate question submission method based on the participant's device information, enabling them to effectively acquire knowledge in the field of social sciences.

[0096] The reception desk can handle questions in multiple languages ​​based on the learner's language settings. For example, the reception desk can use AI to evaluate the learner's language settings. For instance, the reception desk can analyze the language settings of the learner's device and the languages ​​they use to understand their language preferences. Based on the evaluation results, the reception desk can handle questions in multiple languages. For example, the reception desk can automatically set the language for question acceptance based on the language settings of the learner's device. The reception desk can also provide a language switching function if the learner uses multiple languages. Furthermore, if the learner selects a specific language, the reception desk can accept questions in that language. This allows the reception desk to provide appropriate question acceptance based on the learner's language settings. Language settings include, for example, the languages ​​used, language priority, and multilingual support methods. This enables the reception desk to provide appropriate question acceptance based on the learner's language settings, allowing them to effectively acquire knowledge in the social sciences.

[0097] The response generation unit can estimate the learner's emotions and adjust the content and tone of the response based on the estimated emotions. The response generation unit estimates the learner's emotions using, for example, an emotion engine or a generation AI. For example, the response generation unit uses voice analysis technology to analyze the tone and speed of the learner's voice and estimate their emotions. The response generation unit can also use facial recognition technology to analyze the learner's facial expressions and estimate their emotions. For example, the response generation unit can capture the learner's facial expressions through a camera and estimate their emotions using facial recognition technology. Furthermore, the response generation unit can also estimate emotions from the content of the learner's questions using text analysis technology. For example, the response generation unit analyzes the text data of the learner's questions and estimates their emotions. Based on the estimated emotions, the response generation unit adjusts the content and tone of the response. For example, if the learner is excited, the response generation unit generates a response in a calm tone. Also, if the learner is bored, the response generation unit can generate a response that includes an interesting anecdote. Furthermore, the response generation unit can also generate responses in a gentle tone to provide reassurance if the learner is feeling anxious. This allows for the provision of appropriate responses tailored to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This enables the response generation unit to provide appropriate responses tailored to the learner's emotions, allowing them to effectively acquire knowledge in the social sciences.

[0098] The answer generation unit can generate optimal answers by analyzing the learner's past question history. For example, the answer generation unit uses AI to evaluate the learner's past question history. For instance, it analyzes the content and frequency of past questions to understand the question history. Based on the evaluation results, the answer generation unit generates the optimal answer. For example, it generates relevant answers based on the learner's past questions. The answer generation unit can also generate detailed answers from the learner's past question history. Furthermore, the answer generation unit can prioritize generating answers on topics that learners frequently ask about. This allows the system to provide optimal answers based on past question history. Question history includes, for example, how past questions are recorded, how long they are stored, and how they are analyzed. This enables the answer generation unit to provide appropriate answers based on the learner's past question history, allowing them to effectively acquire knowledge in the social sciences.

[0099] The answer generation unit can apply different answer styles depending on the learner's age and level of understanding. For example, the answer generation unit uses AI to evaluate the learner's age and level of understanding. For instance, it analyzes the learner's age group, comprehension test results, and past learning history to determine their age and level of understanding. Based on the evaluation results, the answer generation unit applies an appropriate answer style. For example, for children, the answer generation unit generates answers that use simple language and illustrations. For adults, the answer generation unit can also generate detailed answers that include specialized terminology. Furthermore, for seniors, the answer generation unit can generate answers that proceed at a slow pace and make frequent use of repetition. This allows the system to provide appropriate answers tailored to the learner's age and level of understanding. Age and level of understanding include, for example, age group, comprehension test results, and past learning history. This enables the answer generation unit to provide appropriate answers based on the learner's age and level of understanding, allowing them to effectively acquire knowledge in the social sciences.

[0100] The response generation unit can estimate the learner's emotions and adjust the length of the response based on the estimated emotions. The response generation unit estimates the learner's emotions using, for example, an emotion engine or a generation AI. For example, the response generation unit can use voice analysis technology to analyze the tone and speed of the learner's voice and estimate their emotions. The response generation unit can also use facial recognition technology to analyze the learner's facial expressions and estimate their emotions. For example, the response generation unit can capture the learner's facial expressions through a camera and estimate their emotions using facial recognition technology. Furthermore, the response generation unit can also use text analysis technology to estimate emotions from the content of the learner's questions. For example, the response generation unit analyzes the text data of the learner's questions and estimates their emotions. Based on the estimated emotions, the response generation unit adjusts the length of the response. For example, if the learner is in a hurry, the response generation unit will generate a short, to-the-point response. Conversely, if the learner is relaxed, the response generation unit can generate a longer response that includes detailed explanations. Furthermore, the response generation unit can also generate responses with visually stimulating effects if the learner is excited. This allows for the provision of an appropriate response length based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the response generation unit to provide an appropriate response length based on the learner's emotions, enabling them to effectively acquire knowledge in the social sciences.

[0101] The answer generation unit can incorporate relevant examples considering the learner's geographical background. For example, the answer generation unit uses AI to evaluate the learner's geographical background. For instance, the answer generation unit analyzes the culture, history, and economic conditions of the area where the learner lives to understand their geographical background. Based on the evaluation results, the answer generation unit incorporates appropriate examples into the answers. For example, the answer generation unit can incorporate historical events from the area where the learner lives into the answers. The answer generation unit can also incorporate examples related to the culture and traditions of the learner's country into the answers. Furthermore, the answer generation unit can introduce political events that occurred in the learner's region as part of the answers. This allows the answer generation unit to provide appropriate examples tailored to the learner's geographical background. Geographical background includes, for example, the local culture, history, and economic conditions. As a result, the answer generation unit can incorporate appropriate examples into the answers based on the learner's geographical background, enabling them to effectively acquire knowledge in the field of social science.

[0102] The answer generation unit can adjust the order of answers based on the learner's interests. For example, the answer generation unit uses AI to evaluate the learner's interests. For example, the answer generation unit analyzes survey results and past learning history to understand the learner's interests. Based on the evaluation results, the answer generation unit adjusts the order of answers appropriately. For example, the answer generation unit will address topics that the learner is interested in first. The answer generation unit can also construct the order of answers focusing on areas of high interest to the learner. Furthermore, the answer generation unit can postpone topics of little interest to the learner and start with content that is likely to interest them. This allows the answer generation unit to provide an appropriate order of answers that matches the learner's interests. Interests include, for example, survey results, past learning history, and topics that the learner has shown interest in. As a result, the answer generation unit can provide an appropriate order of answers based on the learner's interests, enabling them to effectively acquire knowledge in the field of social sciences.

[0103] The service provider can estimate the learner's emotions and adjust the method of providing answers based on the estimated emotions. The service provider can estimate the learner's emotions using, for example, an emotion engine or generative AI. For example, the service provider can use voice analysis technology to analyze the learner's voice tone and speed to estimate emotions. Alternatively, the service provider can use facial recognition technology to analyze the learner's facial expressions and estimate emotions. For example, the service provider can capture the learner's facial expressions through a camera and estimate emotions using facial recognition technology. Furthermore, the service provider can use text analysis technology to estimate emotions from the content of the learner's questions. For example, the service provider can analyze the text data of the learner's questions to estimate emotions. Based on the estimated emotions, the service provider adjusts the method of providing answers. For example, if the learner is nervous, the service provider can provide answers with a simple interface. If the learner is relaxed, the service provider can also provide answers with a detailed interface. Furthermore, if the learner is in a hurry, the service provider can provide quick answers via voice. This allows for the provision of appropriate answers tailored to the learner's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the provider to offer appropriate responses tailored to the learner's emotions, enabling them to effectively acquire knowledge in the social sciences.

[0104] The delivery department can select the optimal delivery method by referring to the learner's past learning history. For example, the delivery department can use AI to evaluate the learner's past learning history. For example, the delivery department can analyze past learning content, study time, and test results to understand the learning history. Based on the evaluation results, the delivery department selects the optimal delivery method. For example, the delivery department can prioritize delivery methods that the learner has used in the past. The delivery department can also suggest specific delivery methods based on the learner's past learning history. Furthermore, the delivery department can automatically select delivery methods that the learner frequently uses. This allows the delivery department to provide the optimal delivery method based on past learning history. Learning history includes, for example, topics studied, study time, and test results. This allows the delivery department to provide appropriate delivery methods based on the learner's past learning history, enabling them to effectively acquire knowledge in the social sciences.

[0105] The service provider can select the optimal delivery format considering the learner's device information. For example, the service provider can use AI to evaluate the learner's device information. For example, the service provider can analyze the type and performance of the device used by the learner to understand the device information. Based on the evaluation results, the service provider selects the optimal delivery format. For example, the service provider can select a compressed delivery format for smartphones. The service provider can also select an uncompressed delivery format for devices that require high sound quality. Furthermore, the service provider can select a low-bitrate delivery format for environments with unstable internet connections. This allows the service provider to provide an appropriate delivery format according to the learner's device information. Device information includes, for example, the type of device used, the device's performance, and the supported formats. This allows the service provider to provide an appropriate delivery format based on the learner's device information, enabling them to effectively acquire knowledge in the field of social sciences.

[0106] The service provider can estimate the learner's emotions and adjust the display method of the answers based on the estimated emotions. The service provider estimates the learner's emotions using, for example, an emotion engine or generative AI. For example, the service provider can use voice analysis technology to analyze the tone and speed of the learner's voice and estimate their emotions. The service provider can also use facial recognition technology to analyze the learner's facial expressions and estimate their emotions. For example, the service provider can capture the learner's facial expressions through a camera and estimate their emotions using facial recognition technology. Furthermore, the service provider can use text analysis technology to estimate emotions from the content of the learner's questions. For example, the service provider can analyze the text data of the learner's questions and estimate their emotions. Based on the estimated emotions, the service provider adjusts the display method of the answers. For example, if the learner is nervous, the service provider provides a simple and highly visible display method. If the learner is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the learner is in a hurry, the service provider can provide a display method that gets straight to the point. This allows for the display of appropriate responses tailored to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This enables the provider to display appropriate responses tailored to the learner's emotions, allowing them to effectively acquire knowledge in the social sciences.

[0107] The service provider can provide multilingual answers based on the learner's language settings. For example, the service provider can use AI to evaluate the learner's language settings. For instance, the service provider can analyze the language settings of the learner's device and the languages ​​they use to understand their language preferences. Based on the evaluation results, the service provider can provide multilingual answers. For example, the service provider can automatically set the language of the answer based on the language settings of the learner's device. The service provider can also provide a language switching function if the learner uses multiple languages. Furthermore, the service provider can provide answers in a specific language if the learner selects that language. This allows the service provider to provide appropriate answers according to the learner's language settings. Language settings include, for example, the languages ​​used, language priority, and multilingual support methods. This enables the service provider to provide appropriate answers based on the learner's language settings and effectively acquire knowledge in the social sciences.

[0108] The system can detect the learner's ambient sounds and automatically adjust the audio volume. For example, the system uses AI to evaluate the learner's ambient sounds. For instance, it analyzes the volume of surrounding sounds and background noise to understand the ambient noise. Based on the evaluation results, the system adjusts the audio volume. For example, if the learner is in a noisy environment, the system increases the volume. Conversely, if the learner is in a quiet environment, the system can decrease the volume. Furthermore, if the learner is using headphones, the system can automatically adjust the volume to an appropriate level. This allows the system to provide an appropriate audio volume based on the learner's ambient sounds. Ambient sounds include, for example, background noise, ambient volume, and the detection of specific sounds. This enables the system to provide an appropriate audio volume based on the learner's ambient sounds, allowing them to effectively acquire knowledge in the social sciences.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The educational system can provide customized learning plans based on the learner's learning style. For example, visual learners can be provided with lecture materials that make extensive use of diagrams and graphs. Auditory learners can be provided with lectures that incorporate audio and music. Furthermore, experiential learners can learn through actual experiences and simulations. This allows for the provision of an optimal learning plan tailored to the learner's learning style, enabling them to effectively acquire knowledge in the social sciences.

[0111] The educational system can estimate the learner's emotions and adjust the pace of learning based on those emotions. For example, if a learner is excited, the pace of learning can be increased to maintain concentration. If a learner is tired, the pace of learning can be slowed down to deepen understanding. Furthermore, if a learner is feeling anxious, the learning can proceed at a pace that provides a sense of security. This provides an appropriate learning pace that responds to the learner's emotions, enabling them to effectively acquire knowledge in the social sciences.

[0112] The educational system can analyze students' learning history and provide feedback based on their learning progress. For example, if a student is struggling with a particular topic, it can provide additional materials or practice exercises on that topic. Conversely, if a student understands a particular topic, it can provide advanced content related to that topic. Furthermore, it can evaluate learning progress and provide appropriate feedback based on the student's learning history. This allows for effective feedback based on the student's learning history, enabling them to effectively acquire knowledge in the social sciences.

[0113] Educational systems can estimate learners' emotions and provide incentives to improve their motivation to learn based on those estimated emotions. For example, if a learner is excited, a system can be provided that allows them to earn points or badges based on their learning progress. If a learner is bored, engaging episodes or quizzes can be inserted to stimulate learning. Furthermore, if a learner is feeling anxious, reassuring messages and support can be provided. This allows for the provision of appropriate incentives tailored to learners' emotions, enabling them to effectively acquire knowledge in the social sciences.

[0114] Educational systems can assess learners' learning environments and provide the optimal learning environment. For example, if a learner is in a noisy environment, noise cancellation can be provided. If a learner is in a quiet environment, music or audio can be provided to enhance concentration. Furthermore, if a learner is studying on the go, learning content optimized for mobile devices can be provided. This allows for the provision of an optimal learning environment tailored to each learner's circumstances, enabling them to effectively acquire knowledge in the social sciences.

[0115] Educational systems can estimate learners' emotions and adjust learning feedback based on those estimates. For example, if a learner is excited, positive feedback can be emphasized to increase their motivation. If a learner is bored, engaging episodes or quizzes can be inserted to stimulate learning. Furthermore, if a learner is feeling anxious, feedback can be provided in a gentle, reassuring tone. This allows for appropriate feedback tailored to the learner's emotions, enabling them to effectively acquire knowledge in the social sciences.

[0116] The educational system can analyze students' learning history and provide customized learning plans based on their progress. For example, if a student is struggling with a particular topic, it can provide additional materials and practice exercises on that topic. Conversely, if a student understands a particular topic, it can provide advanced content related to that topic. Furthermore, it can evaluate learning progress based on the student's learning history and provide appropriate learning plans. This allows for the provision of effective learning plans based on the student's learning history, enabling them to effectively acquire knowledge in the social sciences.

[0117] Educational systems can estimate learners' emotions and adjust the learning interface based on those estimates. For example, if a learner is excited, a simple and highly visible interface can be provided. If a learner is bored, a visually stimulating interface can be provided. Furthermore, if a learner is feeling anxious, a reassuringly gentle interface can be provided. This allows for the provision of an appropriate interface tailored to the learner's emotions, enabling them to effectively acquire knowledge in the social sciences.

[0118] The educational system can analyze students' learning history and provide customized tests based on their learning progress. For example, if a student is struggling with a particular topic, a test on that topic can be provided. Conversely, if a student understands a particular topic, an advanced test related to that topic can be provided. Furthermore, the system can assess learning progress based on the student's learning history and provide appropriate tests. This allows for effective testing based on the student's learning history, enabling them to effectively acquire knowledge in the social sciences.

[0119] Educational systems can estimate learners' emotions and provide learning support based on those estimates. For example, if a learner is excited, a system can be provided that allows them to earn points or badges based on their learning progress. If a learner is bored, engaging episodes or quizzes can be inserted to stimulate learning. Furthermore, if a learner is feeling anxious, reassuring messages and support can be provided. This allows for appropriate support tailored to the learner's emotions, enabling them to effectively acquire knowledge in the social sciences.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The lecture generation unit generates audio for lectures in the social sciences using AI. For example, it can use natural language processing technology to generate audio for lectures in fields such as history, geography, culture, and politics. It can also use speech synthesis technology to generate lecture audio. Specifically, the prompt "Generate a lecture about historical events" is input to the AI ​​model, and the generated audio is output. Step 2: The output unit outputs the audio generated by the lecture generation unit through a speaker attached to the stuffed animal. For example, the generated lecture audio is provided to the student through the speaker. It also has a function to adjust the tone and speed of the audio, allowing it to be adjusted according to the student's age and level of understanding. Step 3: The reception desk receives audio questions from lecture participants via a microphone attached to a stuffed animal. For example, the microphone captures the participant's question as audio data. Alternatively, speech recognition technology can be used to convert the participant's question into text data. Specifically, the participant's question is analyzed using speech recognition technology and saved as text data. Step 4: The answer generation unit generates answers based on the questions received by the reception unit. It uses a generation AI to generate answers to questions. For example, it prompts the generation AI with "Please generate an answer to this question" and outputs the generated answer. Step 5: The providing unit provides the answers generated by the generating unit. For example, it provides the generated answers to the learners via audio through a speaker. It also has a function to display the generated answers as text data, and displays the generated answers as text data through a display.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] For example, the lecture generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the output unit is implemented by the speaker 40B of the smart device 14. For example, the reception unit is implemented by the microphone 38B of the smart device 14. For example, the answer generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the delivery unit is implemented by the speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] For example, the lecture generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the output unit is implemented by the speaker 240 of the smart glasses 214. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214. For example, the answer generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the delivery unit is implemented by the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] For example, the lecture generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the output unit is implemented by the speaker 240 of the headset terminal 314. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314. For example, the answer generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the delivery unit is implemented by the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] For example, the lecture generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the output unit is implemented by the speaker 240 of the stuffed animal 414. For example, the reception unit is implemented by the microphone 238 of the stuffed animal 414. For example, the answer generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the delivery unit is implemented by the speaker 240 of the stuffed animal 414. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) A lecture generation unit that uses AI to generate audio for lectures in the field of social sciences, An output unit that outputs the audio generated by the lecture generation unit from a speaker attached to the stuffed animal, A microphone attached to the stuffed animal serves as a reception unit that receives audio of questions from the lecture participants, An answer generation unit that generates an answer based on a question received by the reception unit, The system includes a providing unit that provides the answers generated by the answer generation unit. A system characterized by the following features. (Note 2) The aforementioned response generation unit, Generative AI generates answers to questions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned response generation unit, It estimates the emotions of the participants and generates responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned response generation unit, Analyze the learner's question history and generate answers based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system generates responses based on the learner's learning progress and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It also includes an evaluation unit to determine the learning level of the participants. The aforementioned lecture generation unit, The difficulty level of the lecture will be adjusted based on the learning level of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned lecture generation unit, The system estimates the emotions of the participants and adjusts the content and tone of the lecture based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned lecture generation unit, Analyze the students' past learning history and select appropriate lecture content. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned lecture generation unit, Apply different teaching styles depending on the age and level of understanding of the students. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned lecture generation unit, The system estimates the emotions of the participants and adjusts the length of the lecture based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned lecture generation unit, Incorporate relevant case studies based on the geographical background of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned lecture generation unit, The order of lectures will be adjusted based on the students' interests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The output unit is, It estimates the learner's emotions and adjusts the tone and speed of the voice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The output unit is, The audio frequency is adjusted according to the auditory characteristics of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 15) The output unit is, The system detects ambient noise from the learner and automatically adjusts the audio volume. The system described in Appendix 1, characterized by the features described herein. (Note 16) The output unit is, It estimates the learner's emotions and adjusts the intonation of the voice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The output unit is, Select the appropriate audio format considering the participant's device information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The output unit is, Provides multilingual audio based on the learner's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reception unit is We estimate the emotions of the participants and adjust the question-taking method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reception unit is We will select the appropriate method for receiving questions by referring to the participant's past question history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reception unit is The system analyzes the voice characteristics of the participants and performs noise cancellation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reception unit is The system estimates the participants' emotions and prioritizes questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reception unit is We provide the optimal question submission method, taking into account the participant's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reception unit is We will provide multilingual support for questions based on the language settings of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned response generation unit, The system estimates the emotions of the participants and adjusts the content and tone of their responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned response generation unit, Analyzes the participant's past question history to generate the most suitable answer. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned response generation unit, Apply different response styles depending on the age and level of understanding of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned response generation unit, The system estimates the participant's emotions and adjusts the length of their responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned response generation unit, Take into account the geographical background of the participants and incorporate relevant case studies. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned response generation unit, The order of answers will be adjusted based on the participants' interests. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, The system estimates the participants' emotions and adjusts the way responses are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, The optimal delivery method is selected by referring to the participant's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, The optimal delivery format will be selected considering the participant's device information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates the emotions of the participants and adjusts how the responses are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, Provide multilingual responses based on the participant's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, The system detects ambient noise from the learner and automatically adjusts the audio volume. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A lecture generation unit that uses AI to generate audio for lectures in the field of social sciences, An output unit that outputs the audio generated by the lecture generation unit from a speaker attached to the stuffed animal, A microphone attached to the stuffed animal serves as a reception unit that receives audio of questions from the lecture participants, An answer generation unit that generates an answer based on a question received by the reception unit, The system includes a providing unit that provides the answers generated by the answer generation unit. A system characterized by the following features.

2. The aforementioned response generation unit, Generative AI generates answers to questions. The system according to feature 1.

3. The aforementioned response generation unit, The system estimates the emotions of the participant and generates a response based on the estimated emotions. The system according to feature 1.

4. The aforementioned response generation unit, The system analyzes the question history of the aforementioned participant and generates answers based on the analysis results. The system according to feature 1.

5. The aforementioned reception unit is The response is generated based on the learner's learning status and areas of interest. The system according to feature 1.

6. The aforementioned reception unit is The system further includes a determination unit for determining the learning level of the aforementioned student, The aforementioned lecture generation unit, The difficulty level of the lecture will be adjusted based on the learning level of the participants. The system according to feature 1.

7. The aforementioned lecture generation unit, The system estimates the emotions of the participants and adjusts the content and tone of the lecture based on those estimated emotions. The system according to feature 1.

8. The aforementioned lecture generation unit, The past learning history of the aforementioned students is analyzed, and appropriate lecture content is selected. The system according to feature 1.

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