System

Abstract

To provide a system that analyzes children's facial expressions and words, and suggests appropriate responses to teachers.SOLUTION: A system according to the embodiment comprises a capture unit, an analysis unit, and a suggestion unit. The capture unit captures children's facial expressions and words. The analysis unit analyzes data captured by the capture unit. The suggestion unit suggests appropriate responses based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult for teachers to properly grasp children's facial expressions and words and respond quickly, and there is room for improvement in standardizing the quality of instruction and reducing workload.

[0005] The system according to the embodiment aims to analyze children's facial expressions and words and suggest appropriate responses to teachers. [Means for solving the problem]

[0006] The system according to the embodiment includes a capture unit, an analysis unit, and a suggestion unit. The capture unit captures the child's facial expressions and words. The analysis unit analyzes the data captured by the capture unit. The suggestion unit suggests an appropriate response based on the analysis result obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can analyze a child's facial expressions and words and suggest appropriate responses to teachers. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The teaching assistance system according to the embodiment of the present invention is a system that uses a wearable device to recognize a child's facial expression and words, and suggests the teacher the optimal response to them. This system contributes to standardizing the teaching level by presenting busy teachers with data analysis results based on various experience values, and also aims to reduce the teacher's workload. For example, a teacher wears a wearable device. This device has a built-in camera and microphone, and captures a child's facial expression and words in real time. For example, if a child has a troubled expression during class or asks a question, the device recognizes the situation. Next, an artificial intelligence analyzes the data captured by the device. The artificial intelligence analyzes the child's facial expression and words, and suggests the teacher the optimal response according to the situation. For example, if a child has a troubled expression, the artificial intelligence suggests to the teacher, "This child may be having trouble understanding. Please explain it again." Furthermore, the artificial intelligence can also suggest individual countermeasures according to the child's learning situation and personality based on past data. For example, if a particular child has asked a similar question in the past, the tool will suggest effective ways to respond to that child to the teacher. This tool not only reduces the workload of teachers, but also improves the quality of instruction. For example, if a teacher responds appropriately to each child, the child's understanding will improve and their learning will be more effective. In addition, even when a teacher is busy, the support of artificial intelligence will help to standardize the quality of instruction. In this way, instruction support tools that utilize wearable devices and artificial intelligence are an effective means of reducing the workload of teachers and improving the quality of instruction. As a result, the instruction support system can reduce the workload of teachers and improve the quality of instruction.

[0029] The teaching assistance system according to the embodiment includes a capture unit, an analysis unit, and a suggestion unit. The capture unit captures the child's facial expressions and words. The capture unit captures the child's facial expressions and words in real time using, for example, a camera and a microphone. The capture unit recognizes the situation, for example, when a child has a troubled expression during class or when a child is asking a question. The analysis unit analyzes the data captured by the capture unit. The analysis unit analyzes the child's facial expressions and words using, for example, artificial intelligence, and suggests the teacher the optimal response according to the situation. For example, when a child has a troubled expression, the analysis unit presents the teacher with a suggestion such as "This child may be having trouble understanding. Please explain it again." The suggestion unit suggests the optimal response based on the analysis result obtained by the analysis unit. For example, the suggestion unit can also suggest individual measures according to the child's learning situation and personality based on past data using artificial intelligence. For example, if a specific child has asked a similar question in the past, the suggestion unit suggests to the teacher effective measures for that child. In this way, the teaching support system according to the embodiment can reduce the workload of teachers and improve the quality of teaching.

[0030] The capture unit captures the child's facial expressions and words. The capture unit captures the child's facial expressions and words in real time, for example, using a camera and a microphone. Specifically, the camera can capture subtle changes in the child's facial expressions with high resolution, and the microphone accurately records the tone, volume, and content of the child's voice. As a result, the capture unit recognizes the situation, such as when a child has a troubled expression during class or when he or she is asking a question. In addition, the capture unit can detect the child's movements and postures. For example, it can capture when a child is trying to ask a question by raising his or her hand, or when the child is losing concentration. This allows the capture unit to comprehensively grasp the child's learning situation and emotional state. In addition, the capture unit can monitor multiple children at the same time, and can grasp the situation of the entire classroom in real time. This allows teachers to efficiently manage the learning situation of not only individual children but also the entire class. In addition, the capture unit can use encryption technology to safely store the collected data and protect privacy. This minimizes the risk of unauthorized access to children's data.

[0031] The analysis unit analyzes the data captured by the capture unit. For example, the analysis unit uses artificial intelligence to analyze the child's facial expressions and words, and suggests the teacher the optimal response according to the situation. Specifically, the artificial intelligence uses facial expression recognition technology to analyze subtle changes in the child's face and estimate the emotional state. For example, if a child has a troubled expression, the artificial intelligence presents the teacher with a suggestion such as "This child may be having trouble understanding. Please explain it again." In addition, the analysis unit can analyze the content of the child's utterance using voice recognition technology to evaluate the intention of the question and the level of understanding. For example, if a child says "I don't understand," the specific difficulty behind the statement can be identified and an appropriate response can be proposed. Furthermore, the analysis unit can analyze long-term learning trends and patterns based on the child's past learning data and behavioral history. This makes it possible to comprehensively evaluate the child's learning progress and level of understanding, and support instruction according to individual needs. The analysis unit can utilize high-performance processors and cloud computing technology to process data in real time and provide suggestions quickly. This enables the analysis unit to efficiently process large amounts of data and provide teachers with useful information in an instant.

[0032] The suggestion unit suggests the optimal response based on the analysis results obtained by the analysis unit. For example, the suggestion unit can also use artificial intelligence to suggest individual responses based on the child's learning situation and personality based on past data. Specifically, the artificial intelligence considers the child's learning history, past questions, and personality characteristics to present the most effective teaching method to the teacher. For example, if a specific child has asked a similar question in the past, the suggestion unit suggests an effective response to that child to the teacher. Furthermore, the suggestion unit can suggest individualized learning resources and teaching materials based on the child's learning style and interests. For example, for a child who prefers visual learning, it suggests explanations using diagrams and videos, and for a child who prefers auditory learning, it suggests audio guides and podcasts. In addition, the suggestion unit can also suggest breaks at appropriate times, taking into account the child's emotional state and stress level. This allows the child to effectively progress in learning without feeling excessive stress. The suggestion unit can provide an interface that visually displays the suggested content in an easy-to-understand manner so that the teacher can easily understand and implement it. For example, by displaying the suggested content in a dashboard format and highlighting important information, teachers can quickly understand how to respond. This allows the suggestion department to reduce the workload of teachers and improve the quality of instruction.

[0033] The capture unit may include a camera and a microphone. The capture unit may, for example, use a camera to capture the child's facial expression. The camera may be, for example, a high-resolution camera, and may capture even the slightest changes in the child's facial expression. The capture unit may also use a microphone to capture the child's words. The microphone may be, for example, a high-sensitivity microphone, and may accurately pick up even the child's soft voice. This allows the child's facial expressions and words to be accurately captured using the camera and microphone. Some or all of the above-mentioned processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit may input image data acquired by the camera to the generation AI, and cause the generation AI to perform an analysis of facial expressions from the image data.

[0034] The analysis unit can perform analysis based on past data. For example, the analysis unit analyzes the child's facial expressions and words based on past data. Past data includes, for example, data from the past year and data from specific events. For example, the analysis unit uses past data to analyze the child's learning situation and personality, and suggests optimal responses based on the results. As a result, more accurate analysis results can be obtained by performing analysis based on past data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data to a generation AI and have the generation AI execute an analysis of the data.

[0035] The suggestion unit can suggest individual countermeasures. The suggestion unit suggests individual countermeasures according to, for example, the child's learning situation and personality. The individual countermeasures include, for example, individual instruction, provision of special teaching materials, counseling, and the like. For example, if a specific child has asked a similar question in the past, the suggestion unit suggests to the teacher effective countermeasures for that child. As a result, by suggesting individual countermeasures, it becomes possible to provide guidance suitable for each child. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the child's learning situation and personality data into the generation AI and cause the generation AI to execute the proposal of individual countermeasures.

[0036] The capture unit can analyze the child's past facial expressions and speech patterns and select an appropriate capture method. The capture unit, for example, analyzes the child's past facial expressions and speech patterns and selects an appropriate capture method. The capture unit, for example, analyzes patterns of embarrassed expressions made by the child in the past, and performs capture when a similar expression is seen. The capture unit can also analyze patterns of words made by the child in the past and perform capture when a similar expression is seen. Furthermore, the capture unit can also analyze facial expression patterns made by the child in the past when he or she smiled, and perform capture when a similar expression is seen. This enables more effective capture by analyzing past patterns. Part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI, or may be performed without using AI. For example, the capture unit can input past facial expression and speech pattern data to the generation AI and cause the generation AI to select an appropriate capture method.

[0037] The capture unit can perform filtering based on the child's current activities and environment at the time of capture. The capture unit performs filtering based on the child's current activities and environment at the time of capture, for example. For example, when a child is concentrating during class, the capture unit filters the degree of concentration and performs capture. In addition, when a child is relaxing during playtime, the capture unit can also filter the degree of relaxation and perform capture. Furthermore, when a child is interacting with other children during group activities, the capture unit can also filter the degree of interaction and perform capture. In this way, appropriate data can be captured by filtering based on the child's activities and environment. Part or all of the above-mentioned processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input the child's activity data to the generation AI and have the generation AI perform filtering.

[0038] The capture unit can preferentially capture highly relevant data in consideration of the seat position information of the child at the time of capture. For example, the capture unit preferentially captures highly relevant data in consideration of the seat position information of the child at the time of capture. For example, when a child is sitting in the front of the classroom, the capture unit preferentially captures the facial expression and words of the child. In addition, when a child is sitting in the back of the classroom, the capture unit can also preferentially capture the facial expression and words of the child. Furthermore, when a child is sitting in the center of the classroom, the capture unit can also preferentially capture the facial expression and words of the child. In this way, highly relevant data can be preferentially captured by considering the seat position information. Part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI or may be performed without using AI. For example, the capture unit can input the seat position information of the child to the generation AI and cause the generation AI to perform preferential capture of highly relevant data.

[0039] The capture unit can analyze the child's social interactions and capture related data at the time of capture. The capture unit can, for example, analyze the child's social interactions and capture related data at the time of capture. For example, when the child is talking to another child, the capture unit captures the content of the conversation. In addition, when the child is playing with another child, the capture unit can also capture the state of the play. Furthermore, when the child is cooperating with another child, the capture unit can also capture the state of the cooperation. In this way, by analyzing the social interactions, the related data can be effectively captured. A part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI, or may be performed without using AI. For example, the capture unit can input the child's social interaction data to the generation AI and cause the generation AI to execute the capture of related data.

[0040] The analysis unit can adjust the level of detail of the analysis based on the child's learning progress during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the child's learning progress during the analysis. For example, when the child's learning progress is delayed, the analysis unit expresses the analysis result in detail. In addition, when the child's learning progress is good, the analysis unit can also express the analysis result in a simple manner. Furthermore, when the child's learning progress is fast, the analysis unit can also express the analysis result in a high level. Thereby, by adjusting the level of detail of the analysis based on the learning progress, an appropriate analysis result can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the child's learning progress data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms according to the child's learning category during analysis. The analysis unit applies different analysis algorithms according to the child's learning category during analysis, for example. When analyzing the child's mathematics learning situation, the analysis unit applies an analysis algorithm dedicated to mathematics. When analyzing the child's Japanese language learning situation, the analysis unit can also apply an analysis algorithm dedicated to Japanese language. When analyzing the child's science learning situation, the analysis unit can also apply an analysis algorithm dedicated to science. This makes it possible to provide more accurate analysis results by applying an analysis algorithm according to the learning category. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the child's learning category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0042] The analysis unit can adjust the order of analysis based on the child's learning history during analysis. The analysis unit, for example, adjusts the order of analysis based on the child's learning history during analysis. The analysis unit determines the order of analysis based on the child's past learning history, for example. The analysis unit can also prioritize analysis of important items from the child's learning history. Furthermore, the analysis unit can analyze the child's learning history and perform analysis in the most effective order. This allows for effective analysis by adjusting the order of analysis based on the learning history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the child's learning history data to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the child's related learning materials during the analysis. The analysis unit can improve the accuracy of the analysis by referring to the child's related learning materials during the analysis. The analysis unit can improve the accuracy of the analysis result by referring to the child's learning materials, for example. The analysis unit can also complement the analysis result based on the child's past learning materials. Furthermore, the analysis unit can analyze the child's learning materials and increase the reliability of the analysis result. As a result, the accuracy of the analysis is improved by referring to the related learning materials. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input the child's learning material data to the generation AI and cause the generation AI to execute the improvement of the analysis accuracy.

[0044] The suggestion unit can adjust the level of detail of the suggestion based on the child's learning situation when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the child's learning situation when making a suggestion. For example, when the child's learning situation is behind, the suggestion unit makes detailed suggestions. In addition, when the child's learning situation is going well, the suggestion unit can also make simple suggestions. Furthermore, when the child's learning situation is fast, the suggestion unit can also make advanced suggestions. This makes it possible to provide appropriate suggestions by adjusting the level of detail of the suggestion based on the learning situation. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the child's learning situation data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0045] The suggestion unit can apply different suggestion algorithms according to the child's personality and learning style when making suggestions. For example, the suggestion unit applies different suggestion algorithms according to the child's personality and learning style when making suggestions. For example, if the child's personality is introverted, the suggestion unit makes gentle and polite suggestions. Also, if the child's personality is extroverted, the suggestion unit can make proactive suggestions. Furthermore, if the child's learning style is visual, the suggestion unit can make visually easy-to-understand suggestions. This makes it possible to make more effective suggestions by applying suggestion algorithms according to the personality and learning style. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the child's personality and learning style data to the generation AI and cause the generation AI to apply different suggestion algorithms.

[0046] The suggestion unit can adjust the order of suggestions based on the child's learning history when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the child's learning history when making suggestions. The suggestion unit determines the order of suggestions based on the child's past learning history, for example. The suggestion unit can also prioritize suggesting important items from the child's learning history. Furthermore, the suggestion unit can analyze the child's learning history and make suggestions in the most effective order. This makes it possible to make effective suggestions by adjusting the order of suggestions based on the learning history. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the child's learning history data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0047] The suggestion unit can improve the accuracy of the suggestions by referring to the child's related learning materials at the time of suggestion. The suggestion unit can improve the accuracy of the suggestions by referring to the child's related learning materials at the time of suggestion. The suggestion unit can improve the accuracy of the suggestions by referring to the child's learning materials, for example. The suggestion unit can also complement the suggestions based on the child's past learning materials. Furthermore, the suggestion unit can analyze the child's learning materials and increase the reliability of the suggestions. As a result, the accuracy of the suggestions is improved by referring to the related learning materials. A part or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or can be performed without using AI. For example, the suggestion unit can input the child's learning material data to the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The teaching assistance system may further include a feedback unit. The feedback unit collects the child's reaction to the teaching provided by the teacher and provides it to the analysis unit. For example, when a child shows an expression that indicates understanding in response to the teacher's explanation, the feedback unit collects the expression data and transmits it to the analysis unit. In addition, when a child asks a question, the feedback unit can record the content of the question and provide it to the analysis unit. Furthermore, when a child expresses his / her impressions after the lesson, the feedback unit can collect the impressions as text data and transmit it to the analysis unit. This allows the child's reaction to the teacher's teaching to be grasped in real time and used to improve teaching.

[0050] The teaching support system may further include a notification unit. The notification unit notifies the teacher of information from the analysis unit and the suggestion unit in real time. For example, if a child looks troubled, the notification unit sends a notification such as "The child is troubled" to the teacher's device. In addition, if a child asks a question, the content of the question can be notified to the teacher. Furthermore, if a child is concentrating during class, the level of concentration can be notified to the teacher. This allows the teacher to grasp the child's situation in real time and take appropriate action.

[0051] The teaching support system may further include a recording unit. The recording unit records the children's learning situation and the teacher's teaching content for later reference. For example, the recording unit saves questions asked by children during class and the answers to those questions. The recording unit may also save explanations and teaching content given by teachers as text data. Furthermore, the recording unit may record changes in the children's facial expressions and words in chronological order for later reference by the analysis unit. This allows for centralized management of the children's learning history and the teacher's teaching history, which may be useful for improving teaching.

[0052] The teaching assistance system can further include an evaluation unit. The evaluation unit evaluates the child's learning achievement and the teacher's teaching effectiveness, and provides feedback. For example, the evaluation unit confirms what the child has understood during the lesson in the form of a test. The evaluation unit can also analyze the extent to which the teacher's teaching has influenced the child's level of understanding. Furthermore, the evaluation unit can evaluate the child's learning progress and provide feedback to the teacher on areas for improvement. This makes it possible to objectively evaluate the child's learning achievement and the teacher's teaching effectiveness, and improve the quality of teaching.

[0053] The teaching assistance system may further include a customization unit. The customization unit adjusts the system settings according to the needs of the teacher and the child. For example, if a teacher wants to provide special teaching to a specific child, the customization unit makes the settings. Also, if a child wants to use teaching materials suitable for a specific learning style, the customization unit can provide the teaching materials. Furthermore, if a teacher wants to adjust the frequency or content of notifications from the system, the customization unit can make the settings. This enables flexible teaching according to the needs of the teacher and the child.

[0054] The process flow of the first embodiment will be briefly described below.

[0055] Step 1: The capture unit captures the child's facial expressions and words. The capture unit captures the child's facial expressions and words in real time, for example, using a camera and microphone. The capture unit recognizes the situation, for example, when a child looks troubled during class or asks a question. Step 2: The analysis unit analyzes the data captured by the capture unit. For example, the analysis unit uses artificial intelligence to analyze the child's facial expressions and words, and suggests to the teacher the optimal response according to the situation. For example, if a child has a troubled expression, the analysis unit's artificial intelligence will suggest to the teacher, "This child may be having trouble understanding. Please try explaining it again." Step 3: The suggestion unit suggests the optimal response based on the analysis results obtained by the analysis unit. For example, the suggestion unit can use artificial intelligence to suggest individual responses based on the child's learning situation and personality based on past data. For example, if a specific child has asked a similar question in the past, the suggestion unit will suggest to the teacher effective responses to that child.

[0056] (Example 2) The teaching assistance system according to the embodiment of the present invention is a system that uses a wearable device to recognize a child's facial expression and words, and suggests the teacher the optimal response to them. This system contributes to standardizing the teaching level by presenting busy teachers with data analysis results based on various experience values, and also aims to reduce the teacher's workload. For example, a teacher wears a wearable device. This device has a built-in camera and microphone, and captures a child's facial expression and words in real time. For example, if a child has a troubled expression during class or asks a question, the device recognizes the situation. Next, an artificial intelligence analyzes the data captured by the device. The artificial intelligence analyzes the child's facial expression and words, and suggests the teacher the optimal response according to the situation. For example, if a child has a troubled expression, the artificial intelligence suggests to the teacher, "This child may be having trouble understanding. Please explain it again." Furthermore, the artificial intelligence can also suggest individual countermeasures according to the child's learning situation and personality based on past data. For example, if a particular child has asked a similar question in the past, the tool will suggest effective ways to respond to that child to the teacher. This tool not only reduces the workload of teachers, but also improves the quality of instruction. For example, if a teacher responds appropriately to each child, the child's understanding will improve and their learning will be more effective. In addition, even when a teacher is busy, the support of artificial intelligence will help to standardize the quality of instruction. In this way, instruction support tools that utilize wearable devices and artificial intelligence are an effective means of reducing the workload of teachers and improving the quality of instruction. As a result, the instruction support system can reduce the workload of teachers and improve the quality of instruction.

[0057] The teaching assistance system according to the embodiment includes a capture unit, an analysis unit, and a suggestion unit. The capture unit captures the child's facial expressions and words. The capture unit captures the child's facial expressions and words in real time using, for example, a camera and a microphone. The capture unit recognizes the situation, for example, when a child has a troubled expression during class or when a child is asking a question. The analysis unit analyzes the data captured by the capture unit. The analysis unit analyzes the child's facial expressions and words using, for example, artificial intelligence, and suggests the teacher the optimal response according to the situation. For example, when a child has a troubled expression, the analysis unit presents the teacher with a suggestion such as "This child may be having trouble understanding. Please explain it again." The suggestion unit suggests the optimal response based on the analysis result obtained by the analysis unit. For example, the suggestion unit can also suggest individual measures according to the child's learning situation and personality based on past data using artificial intelligence. For example, if a specific child has asked a similar question in the past, the suggestion unit suggests to the teacher effective measures for that child. In this way, the teaching support system according to the embodiment can reduce the workload of teachers and improve the quality of teaching.

[0058] The capture unit captures the child's facial expressions and words. The capture unit captures the child's facial expressions and words in real time, for example, using a camera and a microphone. Specifically, the camera can capture subtle changes in the child's facial expressions with high resolution, and the microphone accurately records the tone, volume, and content of the child's voice. As a result, the capture unit recognizes the situation, such as when a child has a troubled expression during class or when he or she is asking a question. In addition, the capture unit can detect the child's movements and postures. For example, it can capture when a child is trying to ask a question by raising his or her hand, or when the child is losing concentration. This allows the capture unit to comprehensively grasp the child's learning situation and emotional state. In addition, the capture unit can monitor multiple children at the same time, and can grasp the situation of the entire classroom in real time. This allows teachers to efficiently manage the learning situation of not only individual children but also the entire class. In addition, the capture unit can use encryption technology to safely store the collected data and protect privacy. This minimizes the risk of unauthorized access to children's data.

[0059] The analysis unit analyzes the data captured by the capture unit. For example, the analysis unit uses artificial intelligence to analyze the child's facial expressions and words, and suggests the teacher the optimal response according to the situation. Specifically, the artificial intelligence uses facial expression recognition technology to analyze subtle changes in the child's face and estimate the emotional state. For example, if a child has a troubled expression, the artificial intelligence presents the teacher with a suggestion such as "This child may be having trouble understanding. Please explain it again." In addition, the analysis unit can analyze the content of the child's utterance using voice recognition technology to evaluate the intention of the question and the level of understanding. For example, if a child says "I don't understand," the specific difficulty behind the statement can be identified and an appropriate response can be proposed. Furthermore, the analysis unit can analyze long-term learning trends and patterns based on the child's past learning data and behavioral history. This makes it possible to comprehensively evaluate the child's learning progress and level of understanding, and support instruction according to individual needs. The analysis unit can utilize high-performance processors and cloud computing technology to process data in real time and provide suggestions quickly. This enables the analysis unit to efficiently process large amounts of data and provide teachers with useful information in an instant.

[0060] The suggestion unit suggests the optimal response based on the analysis results obtained by the analysis unit. For example, the suggestion unit can also use artificial intelligence to suggest individual responses based on the child's learning situation and personality based on past data. Specifically, the artificial intelligence considers the child's learning history, past questions, and personality characteristics to present the most effective teaching method to the teacher. For example, if a specific child has asked a similar question in the past, the suggestion unit suggests an effective response to that child to the teacher. Furthermore, the suggestion unit can suggest individualized learning resources and teaching materials based on the child's learning style and interests. For example, for a child who prefers visual learning, it suggests explanations using diagrams and videos, and for a child who prefers auditory learning, it suggests audio guides and podcasts. In addition, the suggestion unit can also suggest breaks at appropriate times, taking into account the child's emotional state and stress level. This allows the child to effectively progress in learning without feeling excessive stress. The suggestion unit can provide an interface that visually displays the suggested content in an easy-to-understand manner so that the teacher can easily understand and implement it. For example, by displaying the suggested content in a dashboard format and highlighting important information, teachers can quickly understand how to respond. This allows the suggestion department to reduce the workload of teachers and improve the quality of instruction.

[0061] The capture unit may include a camera and a microphone. The capture unit may, for example, use a camera to capture the child's facial expression. The camera may be, for example, a high-resolution camera, and may capture even the slightest changes in the child's facial expression. The capture unit may also use a microphone to capture the child's words. The microphone may be, for example, a high-sensitivity microphone, and may accurately pick up even the child's soft voice. This allows the child's facial expressions and words to be accurately captured using the camera and microphone. Some or all of the above-mentioned processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit may input image data acquired by the camera to the generation AI, and cause the generation AI to perform an analysis of facial expressions from the image data.

[0062] The analysis unit can perform analysis based on past data. For example, the analysis unit analyzes the child's facial expressions and words based on past data. Past data includes, for example, data from the past year and data from specific events. For example, the analysis unit uses past data to analyze the child's learning situation and personality, and suggests optimal responses based on the results. As a result, more accurate analysis results can be obtained by performing analysis based on past data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data to a generation AI and have the generation AI execute an analysis of the data.

[0063] The suggestion unit can suggest individual countermeasures. The suggestion unit suggests individual countermeasures according to, for example, the child's learning situation and personality. The individual countermeasures include, for example, individual instruction, provision of special teaching materials, counseling, and the like. For example, if a specific child has asked a similar question in the past, the suggestion unit suggests to the teacher effective countermeasures for that child. As a result, by suggesting individual countermeasures, it becomes possible to provide guidance suitable for each child. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the child's learning situation and personality data into the generation AI and cause the generation AI to execute the proposal of individual countermeasures.

[0064] The capture unit can estimate the child's emotion and adjust the timing of capture based on the estimated child's emotion. The capture unit, for example, estimates the child's emotion and adjusts the timing of capture based on the estimated child's emotion. In the capture unit, for example, in order to capture the moment when the child has a troubled expression during class, the emotion engine monitors the child's expression in real time and captures the moment when a sign of confusion is seen. In addition, in order to capture the expression just before the child speaks, the emotion engine can detect the movement of the child's mouth and capture the moment when a sign of speaking is seen. Furthermore, in order to capture the moment when the child smiles, the emotion engine can detect the degree to which the corners of the child's mouth are raised and capture the moment when a sign of smiling is seen. In this way, by adjusting the timing of capture based on the child's emotion, it is possible to capture important moments without missing them. The emotion estimation is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the capture unit may be performed, for example, using AI, or may be performed without using AI. For example, the capture unit may input facial expression data of a child to the generation AI and cause the generation AI to estimate emotions.

[0065] The capture unit can analyze the child's past facial expressions and speech patterns and select an appropriate capture method. The capture unit, for example, analyzes the child's past facial expressions and speech patterns and selects an appropriate capture method. The capture unit, for example, analyzes patterns of embarrassed expressions made by the child in the past, and performs capture when a similar expression is seen. The capture unit can also analyze patterns of words made by the child in the past and perform capture when a similar expression is seen. Furthermore, the capture unit can also analyze facial expression patterns made by the child in the past when he or she smiled, and perform capture when a similar expression is seen. This enables more effective capture by analyzing past patterns. Part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI, or may be performed without using AI. For example, the capture unit can input past facial expression and speech pattern data to the generation AI and cause the generation AI to select an appropriate capture method.

[0066] The capture unit can perform filtering based on the child's current activities and environment at the time of capture. The capture unit performs filtering based on the child's current activities and environment at the time of capture, for example. For example, when a child is concentrating during class, the capture unit filters the degree of concentration and performs capture. In addition, when a child is relaxing during playtime, the capture unit can also filter the degree of relaxation and perform capture. Furthermore, when a child is interacting with other children during group activities, the capture unit can also filter the degree of interaction and perform capture. In this way, appropriate data can be captured by filtering based on the child's activities and environment. Part or all of the above-mentioned processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input the child's activity data to the generation AI and have the generation AI perform filtering.

[0067] The capture unit can estimate the child's emotion and determine the priority of data to be captured based on the estimated child's emotion. The capture unit, for example, estimates the child's emotion and determines the priority of data to be captured based on the estimated child's emotion. For example, when the child is confused, the capture unit captures the child's facial expression with priority. In addition, when the child is happy, the capture unit can also capture the child's facial expression with priority. In addition, when the child is angry, the capture unit can also capture the child's facial expression with priority. In this way, important data can be captured with priority by determining the priority of data based on the child's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the capture unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the capture unit can input the child's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of data.

[0068] The capture unit can preferentially capture highly relevant data in consideration of the seat position information of the child at the time of capture. For example, the capture unit preferentially captures highly relevant data in consideration of the seat position information of the child at the time of capture. For example, when a child is sitting in the front of the classroom, the capture unit preferentially captures the facial expression and words of the child. In addition, when a child is sitting in the back of the classroom, the capture unit can also preferentially capture the facial expression and words of the child. Furthermore, when a child is sitting in the center of the classroom, the capture unit can also preferentially capture the facial expression and words of the child. In this way, highly relevant data can be preferentially captured by considering the seat position information. Part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI or may be performed without using AI. For example, the capture unit can input the seat position information of the child to the generation AI and cause the generation AI to perform preferential capture of highly relevant data.

[0069] The capture unit can analyze the child's social interactions and capture related data at the time of capture. The capture unit can, for example, analyze the child's social interactions and capture related data at the time of capture. For example, when the child is talking to another child, the capture unit captures the content of the conversation. In addition, when the child is playing with another child, the capture unit can also capture the state of the play. Furthermore, when the child is cooperating with another child, the capture unit can also capture the state of the cooperation. In this way, by analyzing the social interactions, the related data can be effectively captured. A part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI, or may be performed without using AI. For example, the capture unit can input the child's social interaction data to the generation AI and cause the generation AI to execute the capture of related data.

[0070] The analysis unit can estimate the child's emotion and adjust the expression method of the analysis based on the estimated child's emotion. The analysis unit, for example, estimates the child's emotion and adjusts the expression method of the analysis based on the estimated child's emotion. For example, when the child is confused, the analysis unit expresses the analysis result simply. Furthermore, when the child is relaxed, the analysis unit can also express the analysis result in detail. Furthermore, when the child is excited, the analysis unit can also express the analysis result in a visually stimulating manner. This makes it possible to provide a more appropriate analysis result by adjusting the expression method of the analysis based on the child's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the child's emotion data to the generation AI and cause the generation AI to adjust the expression method of the analysis.

[0071] The analysis unit can adjust the level of detail of the analysis based on the child's learning progress during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the child's learning progress during the analysis. For example, when the child's learning progress is delayed, the analysis unit expresses the analysis result in detail. In addition, when the child's learning progress is good, the analysis unit can also express the analysis result in a simple manner. Furthermore, when the child's learning progress is fast, the analysis unit can also express the analysis result in a high level. Thereby, by adjusting the level of detail of the analysis based on the learning progress, an appropriate analysis result can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the child's learning progress data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms according to the child's learning category during analysis. The analysis unit applies different analysis algorithms according to the child's learning category during analysis, for example. When analyzing the child's mathematics learning situation, the analysis unit applies an analysis algorithm dedicated to mathematics. When analyzing the child's Japanese language learning situation, the analysis unit can also apply an analysis algorithm dedicated to Japanese language. When analyzing the child's science learning situation, the analysis unit can also apply an analysis algorithm dedicated to science. This makes it possible to provide more accurate analysis results by applying an analysis algorithm according to the learning category. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the child's learning category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0073] The analysis unit can estimate the child's emotion and determine the analysis priority based on the estimated child's emotion. The analysis unit, for example, estimates the child's emotion and determines the analysis priority based on the estimated child's emotion. For example, when the child is confused, the analysis unit prioritizes the analysis based on the emotion. In addition, when the child is happy, the analysis unit can also prioritize the analysis based on the emotion. Furthermore, when the child is angry, the analysis unit can also prioritize the analysis based on the emotion. In this way, by determining the analysis priority based on the child's emotion, important analysis can be prioritized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a child's emotional data into the generation AI and have the generation AI determine the analysis priorities.

[0074] The analysis unit can adjust the order of analysis based on the child's learning history during analysis. The analysis unit, for example, adjusts the order of analysis based on the child's learning history during analysis. The analysis unit determines the order of analysis based on the child's past learning history, for example. The analysis unit can also prioritize analysis of important items from the child's learning history. Furthermore, the analysis unit can analyze the child's learning history and perform analysis in the most effective order. This allows for effective analysis by adjusting the order of analysis based on the learning history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the child's learning history data to the generation AI and cause the generation AI to adjust the order of analysis.

[0075] The analysis unit can improve the accuracy of the analysis by referring to the child's related learning materials during the analysis. The analysis unit can improve the accuracy of the analysis by referring to the child's related learning materials during the analysis. The analysis unit can improve the accuracy of the analysis result by referring to the child's learning materials, for example. The analysis unit can also complement the analysis result based on the child's past learning materials. Furthermore, the analysis unit can analyze the child's learning materials and increase the reliability of the analysis result. As a result, the accuracy of the analysis is improved by referring to the related learning materials. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input the child's learning material data to the generation AI and cause the generation AI to execute the improvement of the analysis accuracy.

[0076] The suggestion unit can estimate the child's emotion and adjust the expression method of the suggestion based on the estimated child's emotion. The suggestion unit, for example, estimates the child's emotion and adjusts the expression method of the suggestion based on the estimated child's emotion. For example, when the child is confused, the suggestion unit provides a simple and easy-to-understand suggestion. Furthermore, when the child is relaxed, the suggestion unit can also provide a detailed suggestion. Furthermore, when the child is excited, the suggestion unit can also provide a visually stimulating suggestion. This allows the suggestion unit to provide a more appropriate suggestion by adjusting the expression method of the suggestion based on the child's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input a child's emotional data into the generation AI and have the generation AI adjust the way the suggestions are expressed.

[0077] The suggestion unit can adjust the level of detail of the suggestion based on the child's learning situation when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the child's learning situation when making a suggestion. For example, when the child's learning situation is behind, the suggestion unit makes detailed suggestions. In addition, when the child's learning situation is going well, the suggestion unit can also make simple suggestions. Furthermore, when the child's learning situation is fast, the suggestion unit can also make advanced suggestions. This makes it possible to provide appropriate suggestions by adjusting the level of detail of the suggestion based on the learning situation. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the child's learning situation data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0078] The suggestion unit can apply different suggestion algorithms according to the child's personality and learning style when making suggestions. For example, the suggestion unit applies different suggestion algorithms according to the child's personality and learning style when making suggestions. For example, if the child's personality is introverted, the suggestion unit makes gentle and polite suggestions. Also, if the child's personality is extroverted, the suggestion unit can make proactive suggestions. Furthermore, if the child's learning style is visual, the suggestion unit can make visually easy-to-understand suggestions. This makes it possible to make more effective suggestions by applying suggestion algorithms according to the personality and learning style. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the child's personality and learning style data to the generation AI and cause the generation AI to apply different suggestion algorithms.

[0079] The suggestion unit can estimate the child's emotion and determine the priority of suggestions based on the estimated child's emotion. The suggestion unit can estimate the child's emotion and determine the priority of suggestions based on the estimated child's emotion. For example, when the child is confused, the suggestion unit can give priority to suggestions based on the emotion. Furthermore, when the child is happy, the suggestion unit can also give priority to suggestions based on the emotion. Furthermore, when the child is angry, the suggestion unit can also give priority to suggestions based on the emotion. In this way, by determining the priority of suggestions based on the child's emotion, important suggestions can be given priority. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input a child's emotional data into the generation AI and have the generation AI determine the priority of suggestions.

[0080] The suggestion unit can adjust the order of suggestions based on the child's learning history when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the child's learning history when making suggestions. The suggestion unit determines the order of suggestions based on the child's past learning history, for example. The suggestion unit can also prioritize suggesting important items from the child's learning history. Furthermore, the suggestion unit can analyze the child's learning history and make suggestions in the most effective order. This makes it possible to make effective suggestions by adjusting the order of suggestions based on the learning history. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the child's learning history data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0081] The suggestion unit can improve the accuracy of the suggestions by referring to the child's related learning materials at the time of suggestion. The suggestion unit can improve the accuracy of the suggestions by referring to the child's related learning materials at the time of suggestion. The suggestion unit can improve the accuracy of the suggestions by referring to the child's learning materials, for example. The suggestion unit can also complement the suggestions based on the child's past learning materials. Furthermore, the suggestion unit can analyze the child's learning materials and increase the reliability of the suggestions. As a result, the accuracy of the suggestions is improved by referring to the related learning materials. A part or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or can be performed without using AI. For example, the suggestion unit can input the child's learning material data to the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The teaching assistance system may further include a feedback unit. The feedback unit collects the child's reaction to the teaching provided by the teacher and provides it to the analysis unit. For example, when a child shows an expression that indicates understanding in response to the teacher's explanation, the feedback unit collects the expression data and transmits it to the analysis unit. In addition, when a child asks a question, the feedback unit can record the content of the question and provide it to the analysis unit. Furthermore, when a child expresses his / her impressions after the lesson, the feedback unit can collect the impressions as text data and transmit it to the analysis unit. This allows the child's reaction to the teacher's teaching to be grasped in real time and used to improve teaching.

[0084] The teaching support system may further include a notification unit. The notification unit notifies the teacher of information from the analysis unit and the suggestion unit in real time. For example, if a child looks troubled, the notification unit sends a notification such as "The child is troubled" to the teacher's device. In addition, if a child asks a question, the content of the question can be notified to the teacher. Furthermore, if a child is concentrating during class, the level of concentration can be notified to the teacher. This allows the teacher to grasp the child's situation in real time and take appropriate action.

[0085] The teaching support system may further include a recording unit. The recording unit records the children's learning situation and the teacher's teaching content for later reference. For example, the recording unit saves questions asked by children during class and the answers to those questions. The recording unit may also save explanations and teaching content given by teachers as text data. Furthermore, the recording unit may record changes in the children's facial expressions and words in chronological order for later reference by the analysis unit. This allows for centralized management of the children's learning history and the teacher's teaching history, which may be useful for improving teaching.

[0086] The teaching assistance system can further include an evaluation unit. The evaluation unit evaluates the child's learning achievement and the teacher's teaching effectiveness, and provides feedback. For example, the evaluation unit confirms what the child has understood during the lesson in the form of a test. The evaluation unit can also analyze the extent to which the teacher's teaching has influenced the child's level of understanding. Furthermore, the evaluation unit can evaluate the child's learning progress and provide feedback to the teacher on areas for improvement. This makes it possible to objectively evaluate the child's learning achievement and the teacher's teaching effectiveness, and improve the quality of teaching.

[0087] The teaching assistance system may further include a customization unit. The customization unit adjusts the system settings according to the needs of the teacher and the child. For example, if a teacher wants to provide special teaching to a specific child, the customization unit makes the settings. Also, if a child wants to use teaching materials suitable for a specific learning style, the customization unit can provide the teaching materials. Furthermore, if a teacher wants to adjust the frequency or content of notifications from the system, the customization unit can make the settings. This enables flexible teaching according to the needs of the teacher and the child.

[0088] The teaching assistance system may further include an emotion estimation unit. The emotion estimation unit estimates the child's emotion in real time and provides the result to the analysis unit and the suggestion unit. For example, if a child has a troubled expression during class, the emotion estimation unit analyzes the expression and estimates that the child is confused. If the child is happy, the emotion estimation unit can also analyze the expression and estimate the emotion of happiness. If the child is angry, the emotion estimation unit can also analyze the expression and estimate the emotion of anger. This allows an appropriate response to be made based on the child's emotion.

[0089] The teaching assistance system may further include an emotion feedback unit. The emotion feedback unit provides feedback to the teacher based on the child's emotion. For example, if a child is confused during class, the emotion feedback unit notifies the teacher that "the child is confused." If the child is happy, the emotion can be fed back to the teacher. If the child is angry, the emotion can be fed back to the teacher. This allows the teacher to grasp the child's emotion in real time and take appropriate action.

[0090] The teaching assistance system can further include an emotion recording unit. The emotion recording unit records the child's emotional changes in chronological order, allowing the analysis unit to refer to them later. For example, if a child looks troubled during class, the emotion recording unit saves the facial expression data. If a child looks happy, the facial expression data can also be recorded. If a child looks angry, the facial expression data can also be recorded. This allows for centralized management of the child's emotional changes, which can be used to improve teaching.

[0091] The teaching assistance system may further include an emotion evaluation unit. The emotion evaluation unit evaluates the effectiveness of teaching based on the child's emotions and provides feedback. For example, if a child shows an expression of understanding during class, the emotion evaluation unit analyzes the facial expression data and evaluates that the teacher's teaching was effective. If a child is confused, the facial expression data can be analyzed and feedback can be provided on how to improve the teaching. If a child is happy, the facial expression data can be analyzed and the success of the teaching can be evaluated. This makes it possible to objectively evaluate the effectiveness of teaching based on the child's emotions and improve the quality of teaching.

[0092] The teaching assistance system may further include an emotion customization unit. The emotion customization unit adjusts emotion estimation settings according to the needs of the teacher and the child. For example, if the teacher wants to focus on monitoring the emotions of a particular child, the emotion customization unit sets the emotion. It is also possible to set situations in which a child is likely to express a particular emotion. Furthermore, if the teacher wants to adjust the accuracy or frequency of emotion estimation, the emotion customization unit can set the emotion. This enables flexible emotion estimation according to the needs of the teacher and the child.

[0093] The process flow of the second embodiment will be briefly described below.

[0094] Step 1: The capture unit captures the child's facial expressions and words. The capture unit captures the child's facial expressions and words in real time, for example, using a camera and microphone. The capture unit recognizes the situation, for example, when a child looks troubled during class or asks a question. Step 2: The analysis unit analyzes the data captured by the capture unit. For example, the analysis unit uses artificial intelligence to analyze the child's facial expressions and words, and suggests to the teacher the optimal response according to the situation. For example, if a child has a troubled expression, the analysis unit's artificial intelligence will suggest to the teacher, "This child may be having trouble understanding. Please try explaining it again." Step 3: The suggestion unit suggests the optimal response based on the analysis results obtained by the analysis unit. For example, the suggestion unit can use artificial intelligence to suggest individual responses based on the child's learning situation and personality based on past data. For example, if a specific child has asked a similar question in the past, the suggestion unit will suggest to the teacher effective responses to that child.

[0095] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0097] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0098] Each of the multiple elements including the above-mentioned capture unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the capture unit captures the child's facial expressions and words in real time using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured data using artificial intelligence. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests the teacher the optimal response based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0106] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0109] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned capture unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the capture unit captures the child's facial expressions and words in real time using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured data using artificial intelligence. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests the teacher the optimal response based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned capture unit, analysis unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the capture unit captures the child's facial expressions and words in real time using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured data using artificial intelligence. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests the teacher the optimal response based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] Each of the multiple elements including the above-mentioned capture unit, analysis unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the capture unit captures the child's facial expressions and words in real time using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured data using artificial intelligence. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests the teacher the optimal response based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0149] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0153] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] In addition, 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 upon request from the data processing device 12.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0160] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0164] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0166] (Appendix 1) A capture section that captures children's expressions and words, an analysis unit that analyzes the data captured by the capture unit; A suggestion unit that suggests an appropriate response based on the analysis result obtained by the analysis unit. A system characterized by: (Appendix 2) The capture unit includes: Equipped with a camera and microphone 2. The system of claim 1, (Appendix 3) The analysis unit is Conduct analysis based on past data 2. The system of claim 1, (Appendix 4) The suggestion unit is Propose individual solutions 2. The system of claim 1, (Appendix 5) The capture unit includes: Estimate the child's emotions and adjust the timing of capture based on the estimated child's emotions. 2. The system of claim 1, (Appendix 6) The capture unit includes: Analyze a child's past facial expressions and speech patterns to select the appropriate capture method 2. The system of claim 1, (Appendix 7) The capture unit includes: Filtering based on child's current activity and environment during capture 2. The system of claim 1, (Appendix 8) The capture unit includes: Infer a child's emotions and prioritize data capture based on the inferred emotions 2. The system of claim 1, (Appendix 9) The capture unit includes: When capturing, the child's seat position information is taken into consideration to prioritize capturing the most relevant data. 2. The system of claim 1, (Appendix 10) The capture unit includes: During capture, analyze the child's social interactions and capture relevant data 2. The system of claim 1, (Appendix 11) The analysis unit is Infer the child's emotions and adjust the way the analysis is expressed based on the estimated child's emotions. 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, adjust the level of detail based on the child's learning progress 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, different analysis algorithms are applied depending on the child's learning category. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer the child's emotions and determine the analysis priority based on the estimated child's emotions. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the analysis order based on the child's learning history 2. The system of claim 1, (Appendix 16) The analysis unit is When analyzing, refer to children's related learning materials to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 17) The suggestion unit is Infer the child's emotions and adjust the way suggestions are presented based on the child's emotions. 2. The system of claim 1, (Appendix 18) The suggestion unit is When making suggestions, adjust the level of detail based on the child's learning situation. 2. The system of claim 1, (Appendix 19) The suggestion unit is When making suggestions, different suggestion algorithms are applied depending on the child's personality and learning style. 2. The system of claim 1, (Appendix 20) The suggestion unit is Infer the child's emotions and prioritize suggestions based on the estimated child's emotions 2. The system of claim 1, (Appendix 21) The suggestion unit is When making suggestions, adjust the order of suggestions based on the child's learning history 2. The system of claim 1, (Appendix 22) The suggestion unit is When making suggestions, improve the accuracy of suggestions by referring to children's related learning materials 2. The system of claim 1, [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A capture section that captures children's expressions and words, an analysis unit that analyzes the data captured by the capture unit; A suggestion unit that suggests an appropriate response based on the analysis result obtained by the analysis unit. A system characterized by:

2. The capture unit includes: Equipped with a camera and microphone 2. The system of claim 1 .

3. The analysis unit is Conduct analysis based on past data 2. The system of claim 1 .

4. The suggestion unit is Propose individual solutions 2. The system of claim 1 .

5. The capture unit includes: Estimate the child's emotions and adjust the timing of capture based on the estimated child's emotions.

2. The system of claim 1 .

6. The capture unit includes: Analyze a child's past facial expressions and speech patterns to select the appropriate capture method 2. The system of claim 1 .

7. The capture unit includes: Filtering based on child's current activity and environment during capture 2. The system of claim 1 .

8. The capture unit includes: Infer a child's emotions and prioritize data capture based on the inferred emotions 2. The system of claim 1 .

9. The capture unit includes: When capturing, the child's seat position information is taken into consideration to prioritize capturing the most relevant data.

2. The system of claim 1 .

10. The capture unit includes: During capture, analyze the child's social interactions and capture relevant data 2. The system of claim 1 .

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