system

Abstract

The system according to this embodiment aims to encourage the questioner to think for themselves. [Solution] The system according to this embodiment comprises an idea extraction unit, a hint provision unit, and a feedback unit. The idea extraction unit generates questions to elicit the questioner's ideas. The hint provision unit provides hints based on the ideas extracted by the idea extraction unit. The feedback unit provides feedback on the answer that the questioner has come up with based on the hints provided by the hint provision 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 conventional technology, there is a problem that direct answers are often given to questions, and the questioner has few opportunities to think by themselves.

[0005] The system according to the embodiment aims to promote the questioner to think by themselves.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an idea extraction unit, a hint provision unit, and a feedback unit. The idea extraction unit generates questions to elicit the questioner's thoughts. The hint provision unit provides hints based on the thoughts extracted by the idea extraction unit. The feedback unit provides feedback on the answer that the questioner has come up with based on the hints provided by the hint provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can facilitate the questioner's independent thinking. [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, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The educational support system according to an embodiment of the present invention is a system for fostering the ability of questioners to think for themselves. Instead of directly answering questions, this educational support system generates questions to elicit the questioner's thoughts, provides hints to derive answers from similar problems and related information, and provides feedback on the answers the questioner has come up with. For example, when a questioner inputs a question, the educational support system returns questions to elicit the questioner's thoughts. For example, it may ask questions such as, "What do you think about this problem?" or "What solutions have you thought of?" Next, the educational support system provides similar problems and related information, giving hints to help the questioner arrive at an answer for themselves. For example, it may provide information such as, "How were similar situations resolved?" or "We will provide resources for learning knowledge related to this problem." Finally, the questioner inputs an answer they have come up with, and the educational support system provides feedback on that answer. In this way, the questioner can cultivate their ability to think for themselves.

[0029] The educational support system according to this embodiment comprises a thought-generating unit, a hint-providing unit, and a feedback unit. The thought-generating unit generates questions to elicit thoughts in response to a question entered by the questioner. For example, the thought-generating unit can ask the questioner questions such as, "What do you think about this problem?" or "What solutions have you thought of?" The thought-generating unit can also estimate the questioner's emotions and adjust the content of the questions based on the estimated emotions. For example, if the questioner is feeling anxious, it can ask in a gentle tone, "Which part did you find difficult?" The hint-providing unit provides hints and related information to help the questioner arrive at an answer on their own. For example, the hint-providing unit can provide information such as, "How were similar situations resolved?" or "We provide resources to help you learn knowledge related to this problem." The hint-providing unit can also estimate the questioner's emotions and adjust the way the hints are expressed based on the estimated emotions. For example, if the questioner is feeling anxious, it can provide hints in a gentle tone. The feedback unit provides feedback on the answer the questioner has come up with on their own. For example, the feedback unit can provide positive feedback or constructive criticism to the answer entered by the questioner. Furthermore, the feedback unit can estimate the questioner's emotions and adjust the content of the feedback based on those emotions. For instance, if the questioner is feeling anxious, the feedback can be provided in a gentle tone. This allows the educational support system according to this embodiment to cultivate the questioner's ability to think for themselves.

[0030] The thought-generating unit can analyze the questioner's past question history and generate questions to elicit their thoughts. For example, the thought-generating unit can generate questions related to topics the questioner has frequently asked about in the past. It can also generate questions to deepen the questioner's understanding of topics they have struggled with in the past. Furthermore, it can generate applied questions on topics the questioner has excelled at in the past. This allows the unit to generate optimal questions based on the past question history. The specific content and analysis methods of the past question history include, for example, the type of question, the content of the answer, and the frequency. Some or all of the above processing in the thought-generating unit may be performed using AI, for example, or without AI. For example, the thought-generating unit can input the questioner's past question data into a generating AI and have the generating AI generate optimal questions.

[0031] The thought-generating unit can adjust the difficulty level of questions designed to elicit thoughts based on the questioner's current knowledge level. For example, if the questioner is a beginner, the thought-generating unit can generate questions to confirm basic concepts. If the questioner is an intermediate learner, the thought-generating unit can also generate questions to solve applied problems. Furthermore, if the questioner is an advanced learner, the thought-generating unit can generate questions that test specialized knowledge. This allows for the generation of appropriate questions according to the questioner's knowledge level. Methods for evaluating the current knowledge level include, for example, pre-tests, self-assessments, and past learning history. Some or all of the above processing in the thought-generating unit may be performed using, for example, AI, or not using AI. For example, the thought-generating unit can input the questioner's knowledge level data into a generating AI and have the generating AI adjust the difficulty level of the questions.

[0032] The thought-generating unit can prioritize generating highly relevant questions based on the questioner's geographical location. For example, if the questioner is in a specific region, the thought-generating unit can generate questions related to that region. Furthermore, if the questioner is traveling, the thought-generating unit can generate questions asking about knowledge related to their travel destination. Additionally, if the questioner is in a specific facility, the thought-generating unit can generate questions asking about information related to that facility. This allows for the generation of appropriate questions based on the questioner's geographical location. Methods for obtaining geographical location information include, for example, GPS data and IP addresses. Some or all of the above-described processing in the thought-generating unit may be performed using, for example, AI, or without AI. For example, the thought-generating unit can input the questioner's geographical location data into a generating AI and have the generating AI generate highly relevant questions.

[0033] The thought-generating unit can analyze the questioner's social media activity and generate relevant questions. For example, it can generate questions related to topics the questioner has recently shown interest in on social media. It can also generate questions based on the opinions of experts the questioner follows on social media. Furthermore, it can generate questions related to communities the questioner participates in on social media. This allows for the generation of appropriate questions based on the questioner's social media activity. Specific details and methods of analyzing social media activity include, for example, posts, the number of likes, and the number of followers. Some or all of the above processing in the thought-generating unit may be performed using AI, for example, or without AI. For example, the thought-generating unit can input the questioner's social media data into a generating AI and have the generating AI generate relevant questions.

[0034] The hint-providing unit can adjust the level of detail of hints based on the importance of the problem when providing hints. For example, the hint-providing unit can provide detailed hints for important problems. It can also provide concise hints for less important problems. Furthermore, it can provide hints of moderate detail for problems of moderate importance. This allows for the provision of appropriate hints according to the importance of the problem. Methods for evaluating the importance of a problem include, for example, the difficulty of the problem and the urgency of its solution. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input problem importance data into a generating AI and have the generating AI adjust the level of detail of the hints.

[0035] The hint-providing unit can apply different hint-providing algorithms depending on the category of the problem when providing hints. For example, for mathematical problems, the hint-providing unit can provide step-by-step solutions. For historical problems, it can also provide relevant events and background information. Furthermore, for scientific problems, it can provide experimental results and theoretical explanations. This allows for the provision of appropriate hints according to the category of the problem. The categories of problems can be classified in ways such as technical problems, business problems, etc. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input problem category data into a generating AI and have the generating AI apply the hint-providing algorithm.

[0036] The hint-providing unit can determine the priority of hints based on the submission timing of the problem. For example, the hint-providing unit can prioritize hints for problems with approaching deadlines. It can also postpone hints for problems with later submission deadlines. Furthermore, it can provide hints at an appropriate time for problems with moderate submission deadlines. This allows for the provision of appropriate hints according to the problem submission timing. The evaluation method for problem submission timing includes, for example, the time elapsed since submission and the frequency of submissions. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input problem submission timing data into a generating AI and have the generating AI determine the priority of hints.

[0037] The hint-providing unit can adjust the order of hints based on the relevance of the problem when providing hints. For example, the hint-providing unit can provide directly relevant hints first. It can also provide indirectly relevant hints later. Furthermore, it can provide less relevant hints last. This allows for the provision of appropriate hints according to the relevance of the problem. Methods for evaluating the relevance of the problem include, for example, topic similarity and the order of solutions. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input problem relevance data into a generating AI and have the generating AI adjust the order of hints.

[0038] The feedback unit can provide feedback by analyzing the questioner's past answer history. For example, the feedback unit can provide detailed feedback on questions the questioner has answered incorrectly in the past. It can also provide concise feedback on questions the questioner has answered correctly in the past. Furthermore, the feedback unit can evaluate the level of understanding based on the questioner's past answer history and provide feedback accordingly. This allows for the provision of appropriate feedback based on past answer history. The specific content and analysis methods of past answer history include, for example, the accuracy of answers and the frequency of answers. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the questioner's past answer data into a generating AI and have the generating AI provide the optimal feedback.

[0039] The feedback unit can adjust the level of detail in the feedback based on the questioner's current knowledge level. For example, if the questioner is a beginner, the feedback unit can provide feedback explaining basic concepts. If the questioner is an intermediate user, the feedback unit can also provide feedback including applied knowledge. Furthermore, if the questioner is an advanced user, the feedback unit can provide feedback including specialized knowledge. This allows for the provision of appropriate feedback according to the questioner's knowledge level. Methods for adjusting the level of detail in the feedback include, for example, detailed explanations and concise comments. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input the questioner's knowledge level data into a generating AI and have the generating AI perform the adjustment of the level of detail in the feedback.

[0040] The feedback unit can prioritize providing highly relevant feedback based on the questioner's geographical location information during the feedback process. For example, if the questioner is in a specific region, the feedback unit can provide feedback that includes information relevant to that region. Furthermore, if the questioner is traveling, the feedback unit can provide feedback that includes knowledge relevant to their travel destination. Additionally, if the questioner is in a specific facility, the feedback unit can provide feedback that includes information relevant to that facility. This enables the provision of appropriate feedback based on the questioner's geographical location information. Methods for obtaining geographical location information include, for example, GPS data and IP addresses. Some or all of the processing described above in the feedback unit may be performed using, for example, AI, or without AI. For example, the feedback unit can input the questioner's geographical location data into a generating AI and have the generating AI provide highly relevant feedback.

[0041] The feedback unit can analyze the questioner's social media activity and provide relevant feedback when providing feedback. For example, the feedback unit can provide feedback related to topics the questioner has recently shown interest in on social media. It can also provide feedback based on the opinions of experts the questioner follows on social media. Furthermore, the feedback unit can provide feedback related to communities the questioner participates in on social media. This allows for the provision of appropriate feedback based on the questioner's social media activity. Specific details and methods of analyzing social media activity include, for example, the content of posts, the number of likes, and the number of followers. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the questioner's social media data into a generating AI and have the generating AI provide relevant feedback.

[0042] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0043] The educational support system can also include a learning progress tracking unit. This unit can track the learner's progress in real time and provide appropriate feedback and hints based on their progress. For example, if a learner is falling behind on a particular topic, it can provide additional resources and practice problems related to that topic. Conversely, if a learner is progressing well toward their goals, it can offer more challenging problems. Furthermore, the learning progress tracking unit can analyze the learner's progress data, identify learning patterns, and propose personalized learning plans. This allows learners to learn effectively at their own pace.

[0044] The educational support system can also include a content recommendation section. This section recommends relevant learning content based on the user's learning history and interests. For example, if the user has shown interest in a particular topic, it can recommend articles and videos related to that topic. Furthermore, the content recommendation section can suggest topics for the user to learn next based on what they have previously studied. In addition, the content recommendation section can analyze the user's learning patterns and provide optimal learning content. This allows the user to learn more efficiently.

[0045] The educational support system can also include a self-assessment section. This section provides functions for the user to self-assess their learning progress. For example, it can provide a checklist for the user to self-assess a specific topic. Furthermore, the self-assessment section can provide advice for the user to revise their learning plan based on the self-assessment results. Additionally, the self-assessment section can analyze the user's self-assessment data and visualize their learning progress. This allows the user to objectively understand their learning situation and proceed with their studies effectively.

[0046] The educational support system can also include a reflection section. This section provides functions for the user to reflect on their learning after it has been completed. For example, it can generate questions to encourage the user to reflect on what they have learned. The reflection section can also provide a journal function for the user to record their learning outcomes. Furthermore, the reflection section can analyze the user's reflection data and suggest areas for improvement in their learning. This allows the user to promote self-growth through reflection on their learning.

[0047] The educational support system can also include an interactive learning materials section. This section provides interactive learning materials that encourage active participation from the learner. For example, it can offer interactive quizzes that provide real-time feedback to the learner as they solve problems. The interactive learning materials section can also provide simulation tools for the learner to conduct virtual experiments. Furthermore, the interactive learning materials section can adjust the difficulty level of the materials according to the learner's progress. This allows the learner to progress through the learning process in an enjoyable and effective way.

[0048] The following briefly describes the processing flow for example form 1.

[0049] Step 1: The thought-eliciting section generates questions to elicit thoughts in response to the question entered by the questioner. For example, it can ask questions such as, "What are your thoughts on this problem?" or "What solutions have you thought of?" It can also estimate the questioner's emotions and adjust the content of the questions based on those emotions. For example, if the questioner is feeling anxious, it can ask in a gentle tone, "What parts did you find difficult?" Step 2: The hint provider section provides hints and relevant information to help the questioner arrive at the answer themselves. For example, it can provide information such as, "How were similar situations resolved?" or "Here are some resources to help you learn about this problem." It can also estimate the questioner's emotions and adjust the way the hints are presented based on those emotions. For example, if the questioner is feeling anxious, the hints can be provided in a gentle tone. Step 3: The feedback section provides feedback on the answer the questioner has come up with. For example, it can provide positive feedback or constructive criticism on the answer the questioner has entered. It can also estimate the questioner's emotions and adjust the content of the feedback based on those emotions. For example, if the questioner is feeling anxious, the feedback can be provided in a gentle tone.

[0050] (Example of form 2) The educational support system according to an embodiment of the present invention is a system for fostering the ability of questioners to think for themselves. Instead of directly answering questions, this educational support system generates questions to elicit the questioner's thoughts, provides hints to derive answers from similar problems and related information, and provides feedback on the answers the questioner has come up with. For example, when a questioner inputs a question, the educational support system returns questions to elicit the questioner's thoughts. For example, it may ask questions such as, "What do you think about this problem?" or "What solutions have you thought of?" Next, the educational support system provides similar problems and related information, giving hints to help the questioner arrive at an answer for themselves. For example, it may provide information such as, "How were similar situations resolved?" or "We will provide resources for learning knowledge related to this problem." Finally, the questioner inputs an answer they have come up with, and the educational support system provides feedback on that answer. In this way, the questioner can cultivate their ability to think for themselves.

[0051] The educational support system according to this embodiment comprises a thought-generating unit, a hint-providing unit, and a feedback unit. The thought-generating unit generates questions to elicit thoughts in response to a question entered by the questioner. For example, the thought-generating unit can ask the questioner questions such as, "What do you think about this problem?" or "What solutions have you thought of?" The thought-generating unit can also estimate the questioner's emotions and adjust the content of the questions based on the estimated emotions. For example, if the questioner is feeling anxious, it can ask in a gentle tone, "Which part did you find difficult?" The hint-providing unit provides hints and related information to help the questioner arrive at an answer on their own. For example, the hint-providing unit can provide information such as, "How were similar situations resolved?" or "We provide resources to help you learn knowledge related to this problem." The hint-providing unit can also estimate the questioner's emotions and adjust the way the hints are expressed based on the estimated emotions. For example, if the questioner is feeling anxious, it can provide hints in a gentle tone. The feedback unit provides feedback on the answer the questioner has come up with on their own. For example, the feedback unit can provide positive feedback or constructive criticism to the answer entered by the questioner. Furthermore, the feedback unit can estimate the questioner's emotions and adjust the content of the feedback based on those emotions. For instance, if the questioner is feeling anxious, the feedback can be provided in a gentle tone. This allows the educational support system according to this embodiment to cultivate the questioner's ability to think for themselves.

[0052] The thought-eliciting unit can estimate the questioner's emotions and adjust the content of the questions to elicit thoughts based on the estimated emotions. For example, if the questioner is feeling anxious, the thought-eliciting unit can ask in a gentle tone, "What part did you find difficult?" If the questioner is confident, the thought-eliciting unit can ask in a challenging tone, "What approaches have you considered to solve this problem?" Furthermore, if the questioner is excited, the thought-eliciting unit can ask in a calm tone, "What knowledge do you have about this problem?" This allows the unit to generate appropriate questions according to the questioner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the thought-eliciting unit may be performed using AI, or not using AI. For example, the thought-eliciting unit can input the questioner's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0053] The thought-generating unit can analyze the questioner's past question history and generate questions to elicit their thoughts. For example, the thought-generating unit can generate questions related to topics the questioner has frequently asked about in the past. It can also generate questions to deepen the questioner's understanding of topics they have struggled with in the past. Furthermore, it can generate applied questions on topics the questioner has excelled at in the past. This allows the unit to generate optimal questions based on the past question history. The specific content and analysis methods of the past question history include, for example, the type of question, the content of the answer, and the frequency. Some or all of the above processing in the thought-generating unit may be performed using AI, for example, or without AI. For example, the thought-generating unit can input the questioner's past question data into a generating AI and have the generating AI generate optimal questions.

[0054] The thought-generating unit can adjust the difficulty level of questions designed to elicit thoughts based on the questioner's current knowledge level. For example, if the questioner is a beginner, the thought-generating unit can generate questions to confirm basic concepts. If the questioner is an intermediate learner, the thought-generating unit can also generate questions to solve applied problems. Furthermore, if the questioner is an advanced learner, the thought-generating unit can generate questions that test specialized knowledge. This allows for the generation of appropriate questions according to the questioner's knowledge level. Methods for evaluating the current knowledge level include, for example, pre-tests, self-assessments, and past learning history. Some or all of the above processing in the thought-generating unit may be performed using, for example, AI, or not using AI. For example, the thought-generating unit can input the questioner's knowledge level data into a generating AI and have the generating AI adjust the difficulty level of the questions.

[0055] The thought-eliciting unit can estimate the questioner's emotions and adjust the order of questions to elicit thoughts based on the estimated emotions. For example, if the questioner is nervous, the thought-eliciting unit can start with easy questions and gradually increase the difficulty. Conversely, if the questioner is relaxed, the thought-eliciting unit can start with more difficult questions. Furthermore, if the questioner is anxious, the thought-eliciting unit can ask important questions first. This allows for setting an appropriate order of questions according to the questioner's emotions. Methods for adjusting the order of questions include, for example, increasing difficulty order and topic relevance. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the thought-eliciting unit may be performed using AI or not. For example, the thought-eliciting unit can input the questioner's emotion data into the generative AI and have the generative AI adjust the order of questions.

[0056] The thought-generating unit can prioritize generating highly relevant questions based on the questioner's geographical location. For example, if the questioner is in a specific region, the thought-generating unit can generate questions related to that region. Furthermore, if the questioner is traveling, the thought-generating unit can generate questions asking about knowledge related to their travel destination. Additionally, if the questioner is in a specific facility, the thought-generating unit can generate questions asking about information related to that facility. This allows for the generation of appropriate questions based on the questioner's geographical location. Methods for obtaining geographical location information include, for example, GPS data and IP addresses. Some or all of the above-described processing in the thought-generating unit may be performed using, for example, AI, or without AI. For example, the thought-generating unit can input the questioner's geographical location data into a generating AI and have the generating AI generate highly relevant questions.

[0057] The thought-generating unit can analyze the questioner's social media activity and generate relevant questions. For example, it can generate questions related to topics the questioner has recently shown interest in on social media. It can also generate questions based on the opinions of experts the questioner follows on social media. Furthermore, it can generate questions related to communities the questioner participates in on social media. This allows for the generation of appropriate questions based on the questioner's social media activity. Specific details and methods of analyzing social media activity include, for example, posts, the number of likes, and the number of followers. Some or all of the above processing in the thought-generating unit may be performed using AI, for example, or without AI. For example, the thought-generating unit can input the questioner's social media data into a generating AI and have the generating AI generate relevant questions.

[0058] The hint-providing unit can estimate the questioner's emotions and adjust the way hints are expressed based on the estimated emotions. For example, if the questioner is feeling anxious, the hint-providing unit can provide hints in a gentle tone. If the questioner is confident, the hint-providing unit can provide hints in a challenging tone. Furthermore, if the questioner is excited, the hint-providing unit can provide hints in a calm tone. This allows for the provision of appropriate hints according to the questioner's emotions. The methods for adjusting the way hints are expressed include, for example, the length of the text, word choice, and visual elements. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the hint-providing unit may be performed using AI, for example, or not using AI. For example, the hint-providing unit can input the questioner's emotion data into the generative AI and have the generative AI adjust the way hints are expressed.

[0059] The hint-providing unit can adjust the level of detail of hints based on the importance of the problem when providing hints. For example, the hint-providing unit can provide detailed hints for important problems. It can also provide concise hints for less important problems. Furthermore, it can provide hints of moderate detail for problems of moderate importance. This allows for the provision of appropriate hints according to the importance of the problem. Methods for evaluating the importance of a problem include, for example, the difficulty of the problem and the urgency of its solution. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input problem importance data into a generating AI and have the generating AI adjust the level of detail of the hints.

[0060] The hint-providing unit can apply different hint-providing algorithms depending on the category of the problem when providing hints. For example, for mathematical problems, the hint-providing unit can provide step-by-step solutions. For historical problems, it can also provide relevant events and background information. Furthermore, for scientific problems, it can provide experimental results and theoretical explanations. This allows for the provision of appropriate hints according to the category of the problem. The categories of problems can be classified in ways such as technical problems, business problems, etc. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input problem category data into a generating AI and have the generating AI apply the hint-providing algorithm.

[0061] The hint provider can estimate the questioner's emotions and adjust the length of the hint based on the estimated emotions. For example, if the questioner is anxious, the hint provider can provide a short, concise hint. If the questioner is relaxed, the hint provider can provide a longer hint with a more detailed explanation. Furthermore, if the questioner is excited, the hint provider can provide a hint with visually stimulating effects. This allows for the provision of an appropriate hint length according to the questioner's emotions. Methods for adjusting the hint length include, for example, the number of characters or paragraphs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the hint provider may be performed using AI, or not using AI. For example, the hint provider can input the questioner's emotion data into the generative AI and have the generative AI adjust the hint length.

[0062] The hint-providing unit can determine the priority of hints based on the submission timing of the problem. For example, the hint-providing unit can prioritize hints for problems with approaching deadlines. It can also postpone hints for problems with later submission deadlines. Furthermore, it can provide hints at an appropriate time for problems with moderate submission deadlines. This allows for the provision of appropriate hints according to the problem submission timing. The evaluation method for problem submission timing includes, for example, the time elapsed since submission and the frequency of submissions. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input problem submission timing data into a generating AI and have the generating AI determine the priority of hints.

[0063] The hint-providing unit can adjust the order of hints based on the relevance of the problem when providing hints. For example, the hint-providing unit can provide directly relevant hints first. It can also provide indirectly relevant hints later. Furthermore, it can provide less relevant hints last. This allows for the provision of appropriate hints according to the relevance of the problem. Methods for evaluating the relevance of the problem include, for example, topic similarity and the order of solutions. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input problem relevance data into a generating AI and have the generating AI adjust the order of hints.

[0064] The feedback unit can estimate the questioner's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the questioner is feeling anxious, the feedback unit can provide feedback in a gentle tone. If the questioner is confident, the feedback unit can provide feedback in a challenging tone. Furthermore, if the questioner is excited, the feedback unit can provide feedback in a calm tone. This allows for the provision of appropriate feedback according to the questioner's emotions. Methods for adjusting the content of the feedback include, for example, specific advice and positive comments. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the questioner's emotion data into a generative AI and have the generative AI adjust the content of the feedback.

[0065] The feedback unit can provide feedback by analyzing the questioner's past answer history. For example, the feedback unit can provide detailed feedback on questions the questioner has answered incorrectly in the past. It can also provide concise feedback on questions the questioner has answered correctly in the past. Furthermore, the feedback unit can evaluate the level of understanding based on the questioner's past answer history and provide feedback accordingly. This allows for the provision of appropriate feedback based on past answer history. The specific content and analysis methods of past answer history include, for example, the accuracy of answers and the frequency of answers. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the questioner's past answer data into a generating AI and have the generating AI provide the optimal feedback.

[0066] The feedback unit can adjust the level of detail in the feedback based on the questioner's current knowledge level. For example, if the questioner is a beginner, the feedback unit can provide feedback explaining basic concepts. If the questioner is an intermediate user, the feedback unit can also provide feedback including applied knowledge. Furthermore, if the questioner is an advanced user, the feedback unit can provide feedback including specialized knowledge. This allows for the provision of appropriate feedback according to the questioner's knowledge level. Methods for adjusting the level of detail in the feedback include, for example, detailed explanations and concise comments. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input the questioner's knowledge level data into a generating AI and have the generating AI perform the adjustment of the level of detail in the feedback.

[0067] The feedback unit can estimate the questioner's emotions and adjust the order of feedback based on the estimated emotions. For example, if the questioner is nervous, the feedback unit can start with simple feedback and gradually provide more detailed feedback. If the questioner is relaxed, the feedback unit can also start with detailed feedback. Furthermore, if the questioner is anxious, the feedback unit can provide important feedback first. This allows for setting an appropriate order of feedback according to the questioner's emotions. Methods for adjusting the order of feedback include, for example, prioritizing by importance or relevance. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input the questioner's emotion data into the generative AI and have the generative AI adjust the order of feedback.

[0068] The feedback unit can prioritize providing highly relevant feedback based on the questioner's geographical location information during the feedback process. For example, if the questioner is in a specific region, the feedback unit can provide feedback that includes information relevant to that region. Furthermore, if the questioner is traveling, the feedback unit can provide feedback that includes knowledge relevant to their travel destination. Additionally, if the questioner is in a specific facility, the feedback unit can provide feedback that includes information relevant to that facility. This enables the provision of appropriate feedback based on the questioner's geographical location information. Methods for obtaining geographical location information include, for example, GPS data and IP addresses. Some or all of the processing described above in the feedback unit may be performed using, for example, AI, or without AI. For example, the feedback unit can input the questioner's geographical location data into a generating AI and have the generating AI provide highly relevant feedback.

[0069] The feedback unit can analyze the questioner's social media activity and provide relevant feedback when providing feedback. For example, the feedback unit can provide feedback related to topics the questioner has recently shown interest in on social media. It can also provide feedback based on the opinions of experts the questioner follows on social media. Furthermore, the feedback unit can provide feedback related to communities the questioner participates in on social media. This allows for the provision of appropriate feedback based on the questioner's social media activity. Specific details and methods of analyzing social media activity include, for example, the content of posts, the number of likes, and the number of followers. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the questioner's social media data into a generating AI and have the generating AI provide relevant feedback. === Hard Collateral 1-1 === Each of the multiple elements described above, including the idea extraction unit, hint provision unit, and feedback unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the idea extraction unit is implemented by the control unit 46A of the smart device 14 and generates questions to elicit ideas in response to a question entered by the questioner. The hint provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides hints and related information to help the questioner arrive at an answer themselves. The feedback unit is implemented, for example, by the control unit 46A of the smart device 14 and provides feedback on the answer the questioner has come up with themselves. === Hard Collateral 1-2 === Each of the multiple elements described above, including the thought-generating unit, hint-providing unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the thought-generating unit is implemented by the control unit 46A of the smart glasses 214 and generates questions to elicit thoughts in response to a question entered by the questioner. The hint-providing unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides hints and related information to help the questioner arrive at an answer themselves. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides feedback on the answer the questioner has come up with themselves. === Hard Collateral 1-3 === Each of the multiple elements described above, including the thought-eliciting unit, hint-providing unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the thought-eliciting unit is implemented by the control unit 46A of the headset terminal 314 and generates questions to elicit thoughts in response to a question entered by the questioner. The hint-providing unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides hints and related information to help the questioner arrive at an answer themselves. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides feedback on the answer the questioner has come up with. === Hard Collateral 1-4 === Each of the multiple elements described above, including the thought extraction unit, hint provision unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the thought extraction unit is implemented by the control unit 46A of the robot 414 and generates questions to elicit thoughts in response to a question input by the questioner. The hint provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides hints and related information to help the questioner arrive at an answer themselves. The feedback unit is implemented by the control unit 46A of the robot 414 and provides feedback on the answer the questioner has come up with themselves.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The educational support system can also include a learning progress tracking unit. This unit can track the learner's progress in real time and provide appropriate feedback and hints based on their progress. For example, if a learner is falling behind on a particular topic, it can provide additional resources and practice problems related to that topic. Conversely, if a learner is progressing well toward their goals, it can offer more challenging problems. Furthermore, the learning progress tracking unit can analyze the learner's progress data, identify learning patterns, and propose personalized learning plans. This allows learners to learn effectively at their own pace.

[0072] The educational support system can also be equipped with a motivation maintenance unit. This unit provides functions to enhance the learner's motivation. For example, it can award badges or points when the learner achieves certain learning goals. It can also present encouraging messages and success stories if the learner shows low motivation. Furthermore, the motivation maintenance unit can estimate the learner's emotions and select an appropriate approach to increase motivation based on those emotions. This helps the learner maintain their motivation to learn.

[0073] The educational support system can also include a collaborative learning section. This section provides functions that enable questioners to collaborate with other learners to advance their learning. For example, it can provide a chat function for questioners to discuss specific problems with other learners. Furthermore, the collaborative learning section can provide tools for questioners to work on projects together with other learners. In addition, the collaborative learning section can estimate the questioner's emotions and support the progress of collaborative learning based on these estimated emotions. This allows questioners to effectively advance their learning in collaboration with other learners.

[0074] The educational support system can also be equipped with a learning style adaptation unit. This unit provides the optimal learning method according to the learner's learning style. For example, if the learner is a visual learner, it can provide materials that make extensive use of visual aids and infographics. If the learner is an auditory learner, it can provide audio explanations or podcast-style materials. Furthermore, the learning style adaptation unit can estimate the learner's emotions and adapt their learning style based on those emotions. This allows the learner to proceed with their learning in a way that is best suited to them.

[0075] The educational support system may also include a time management unit. This unit provides functions for effectively managing the user's study time. For example, it can create a timetable for the user to plan their studies. It can also track the user's progress towards their set learning goals and send reminders as needed. Furthermore, the unit can estimate the user's emotions and adjust study time based on those emotions. This allows the user to study more efficiently.

[0076] The educational support system can also include a content recommendation section. This section recommends relevant learning content based on the user's learning history and interests. For example, if the user has shown interest in a particular topic, it can recommend articles and videos related to that topic. Furthermore, the content recommendation section can suggest topics for the user to learn next based on what they have previously studied. In addition, the content recommendation section can analyze the user's learning patterns and provide optimal learning content. This allows the user to learn more efficiently.

[0077] The educational support system can also include a self-assessment section. This section provides functions for the user to self-assess their learning progress. For example, it can provide a checklist for the user to self-assess a specific topic. Furthermore, the self-assessment section can provide advice for the user to revise their learning plan based on the self-assessment results. Additionally, the self-assessment section can analyze the user's self-assessment data and visualize their learning progress. This allows the user to objectively understand their learning situation and proceed with their studies effectively.

[0078] The educational support system can also include a reflection section. This section provides functions for the user to reflect on their learning after it has been completed. For example, it can generate questions to encourage the user to reflect on what they have learned. The reflection section can also provide a journal function for the user to record their learning outcomes. Furthermore, the reflection section can analyze the user's reflection data and suggest areas for improvement in their learning. This allows the user to promote self-growth through reflection on their learning.

[0079] The educational support system can also include an interactive learning materials section. This section provides interactive learning materials that encourage active participation from the learner. For example, it can offer interactive quizzes that provide real-time feedback to the learner as they solve problems. The interactive learning materials section can also provide simulation tools for the learner to conduct virtual experiments. Furthermore, the interactive learning materials section can adjust the difficulty level of the materials according to the learner's progress. This allows the learner to progress through the learning process in an enjoyable and effective way.

[0080] The educational support system can also include a learning community section. This section provides functions for questioners to interact with other learners and share information. For example, it can provide a forum for questioners to discuss specific topics. The learning community section can also provide collaboration tools for questioners to work on projects together with other learners. Furthermore, the learning community section can estimate the questioner's emotions and provide support within the community based on those estimates. This allows questioners to collaborate with other learners and learn effectively.

[0081] The following briefly describes the processing flow for example form 2.

[0082] Step 1: The thought-eliciting section generates questions to elicit thoughts in response to the question entered by the questioner. For example, it can ask questions such as, "What are your thoughts on this problem?" or "What solutions have you thought of?" It can also estimate the questioner's emotions and adjust the content of the questions based on those emotions. For example, if the questioner is feeling anxious, it can ask in a gentle tone, "What parts did you find difficult?" Step 2: The hint provider section provides hints and relevant information to help the questioner arrive at the answer themselves. For example, it can provide information such as, "How were similar situations resolved?" or "Here are some resources to help you learn about this problem." It can also estimate the questioner's emotions and adjust the way the hints are presented based on those emotions. For example, if the questioner is feeling anxious, the hints can be provided in a gentle tone. Step 3: The feedback section provides feedback on the answer the questioner has come up with. For example, it can provide positive feedback or constructive criticism on the answer the questioner has entered. It can also estimate the questioner's emotions and adjust the content of the feedback based on those emotions. For example, if the questioner is feeling anxious, the feedback can be provided in a gentle tone.

[0083] 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.

[0084] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0085] 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.

[0086] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0087] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.).

[0099] 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.

[0100] 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. 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.

[0101] 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.

[0102] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0103] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0104] 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.

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

[0106] The 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.

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

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

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

[0110] Figure 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.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the 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.

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

[0115] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0117] The data processing system 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.

[0118] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0119] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0120] 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.

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

[0122] The 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.

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

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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. 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.

[0134] 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.

[0135] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] [Explanation of Symbols]

[0155] 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 thought-eliciting unit that generates questions to draw out the questioner's thoughts, A hint-providing unit that provides hints based on the ideas derived by the aforementioned idea-generating unit, The system includes a feedback unit that provides feedback to the answer that the questioner has come up with based on the hints provided by the hint-providing unit. A system characterized by the following features.

2. The aforementioned thought extraction unit is, Infer the questioner's emotions and adjust the content of the questions to elicit their thoughts based on those emotions. The system according to feature 1.

3. The aforementioned thought extraction unit is, Analyze the questioner's past question history and generate questions designed to elicit their thoughts. The system according to feature 1.

4. The aforementioned thought extraction unit is, Adjust the difficulty of the questions to elicit thoughts based on the questioner's current level of knowledge. The system according to feature 1.

5. The aforementioned thought extraction unit is, Infer the questioner's emotions and adjust the order of questions to elicit their thoughts based on those emotions. The system according to feature 1.

6. The aforementioned thought extraction unit is, Based on the questioner's geographical location, the system prioritizes generating highly relevant questions. The system according to feature 1.

7. The aforementioned thought extraction unit is, Analyze the questioner's social media activity and generate relevant questions. The system according to feature 1.

8. The aforementioned hint-providing unit, The system estimates the questioner's emotions and adjusts the way hints are presented based on those estimated emotions. The system according to feature 1.

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