System

Abstract

An object of a system according to an embodiment is to manage generated AI so that young people can appropriately use the generated data.SOLUTION: A system includes an analysis unit, a generation unit, a monitoring unit, and a feedback unit. The analysis unit analyzes a question of a user. The generation unit generates a concept or a hint based on the question analyzed by the analysis unit. The monitoring unit monitors an action of a user. The feedback unit provides feedback based on the behavior monitored by the monitoring 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately manage the appropriate use of generative AI by young people, and there is room for improvement.

[0005] The system of the embodiment aims to manage generated AI so that young people can use it appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, a monitoring unit, and a feedback unit. The analysis unit analyzes a user's question. The generation unit generates ideas and hints based on the question analyzed by the analysis unit. The monitoring unit monitors the user's behavior. The feedback unit provides feedback based on the behavior monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can manage the generation AI so that young people can use it appropriately. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses an AI filter to manage the appropriate use of generative AI by young people. In this system, a user inputs a question to the generative AI, which then analyzes the question and generates ideas and hints for arriving at the answer. Furthermore, the system monitors the user's behavior and provides appropriate feedback. For example, when a user seeks an answer to a problem, the system provides ideas and hints for arriving at the answer, thereby cultivating the user's ability to think independently. Furthermore, the AI ​​filter monitors the user's behavior and provides appropriate feedback, thereby promoting the appropriate use of the generative AI. This allows the system to provide an environment that fosters the ability to think independently when young people use generative AI and manage the appropriate use of the generative AI. For example, when a user seeks an answer to a problem, the system provides ideas and hints for arriving at the answer, thereby cultivating the user's ability to think independently. Furthermore, the AI ​​filter monitors the user's behavior and provides appropriate feedback, thereby promoting the appropriate use of the generative AI.

[0029] A management system according to an embodiment includes an analysis unit, a generation unit, a monitoring unit, and a feedback unit. The analysis unit analyzes a question entered by a user. For example, the analysis unit analyzes the user's question using natural language processing technology. The analysis unit can also analyze the intent of the question using a machine learning algorithm. The analysis unit can also analyze the user's question in text or audio format. The generation unit generates ideas and hints for arriving at an answer based on the question analyzed by the analysis unit. For example, the generation unit explains problem-solving steps and formula usage. The generation unit can also provide reference materials. The generation unit can also use a generation AI to generate ideas and hints for the user's question. The monitoring unit monitors whether the user is using the generation AI appropriately. For example, the monitoring unit collects a user's behavior log and monitors appropriate use. The monitoring unit can also detect the user's behavior if the user repeatedly enters the same question. The monitoring unit can also adjust the monitoring frequency. The feedback unit provides appropriate feedback to the user based on the behavior monitored by the monitoring unit. For example, the feedback unit provides feedback through a text message or a notification. The feedback unit can also provide an alert for the user's actions. Furthermore, the feedback unit can generate feedback for the user using the generating AI. In this way, the management system according to the embodiment can manage the generating AI so that young people can use it appropriately. For example, by analyzing the user's questions, generating ideas and hints, monitoring the user's actions, and providing appropriate feedback, the management system can manage the generating AI so that young people can use it appropriately.

[0030] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the user's past question history. For example, if the user has asked a similar question in the past, the analysis unit performs the analysis by referring to that history. The analysis unit can also extract specific patterns from the user's past question history and reflect them in the analysis. Furthermore, the analysis unit can improve the accuracy of the analysis based on the history of problems the user has solved in the past. In this way, the accuracy of the analysis is improved by referring to the user's past question history. Some 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 the user's past question history data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0031] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the question category. For example, the analysis unit can apply a mathematical expression analysis algorithm to a mathematics question. The analysis unit can also apply a text analysis algorithm to a literature question. Furthermore, the analysis unit can apply a data analysis algorithm to a science question. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the question category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input question category data to the generation AI and have the generation AI select an analysis algorithm.

[0032] When analyzing a question, the analysis unit can adjust the depth of the analysis based on the user's learning progress. For example, if the user is a beginner, the analysis unit can perform a basic analysis. If the user is an intermediate learner, the analysis unit can also perform a detailed analysis. Furthermore, if the user is an advanced learner, the analysis unit can also perform a specialized analysis. This enables appropriate analysis by adjusting the depth of the analysis according to the user's learning progress. Some 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 the user's learning progress data into the generation AI and have the generation AI adjust the depth of the analysis.

[0033] When analyzing a question, the analysis unit can prioritize analyzing highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the analysis unit can prioritize analyzing information related to that area. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing information related to the travel destination. Furthermore, if the user is in a specific city, the analysis unit can prioritize analyzing information related to that city. This allows for more appropriate information to be provided by prioritizing analysis of highly relevant information based on the user's geographical location information. Some or all of the above-described 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 the user's geographical location data into the generation AI and cause the generation AI to perform a prioritized analysis of highly relevant information.

[0034] When analyzing a question, the analysis unit can analyze the user's social media activities and analyze related information. The analysis unit performs analysis based on, for example, information shared by the user on social media. The analysis unit can also extract and analyze related information from the user's social media activities. Furthermore, the analysis unit can also perform analysis based on the activities of the user's friends on social media. This makes it possible to provide more appropriate information by analyzing related information based on the user's social media activities. Some 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 the user's social media data into the generation AI and have the generation AI analyze the related information.

[0035] When analyzing a question, the analysis unit can customize the analysis method by reflecting the user's past feedback. The analysis unit, for example, adjusts the analysis method based on feedback provided by the user in the past. The analysis unit can also preferentially apply a specific analysis method based on the user's past feedback. Furthermore, the analysis unit can also improve the accuracy of the analysis by reflecting the user's feedback. In this way, the analysis method is customized by reflecting the user's past feedback, and the accuracy of the analysis is improved. Some 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 the user's past feedback data into the generation AI and cause the generation AI to customize the analysis method.

[0036] When generating ideas and hints, the generation unit can adjust the level of detail of the generation based on the difficulty of the problem. For example, the generation unit provides basic hints for easy problems. The generation unit can also provide detailed ideas for problems of medium difficulty. Furthermore, the generation unit can provide step-by-step guides for difficult problems. In this way, by adjusting the level of detail of the generation according to the difficulty of the problem, appropriate hints can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input difficulty data of the problem to the generation AI and cause the generation AI to adjust the level of detail of the hints.

[0037] When generating ideas and hints, the generation unit can apply different generation algorithms depending on the problem category. For example, the generation unit provides hints using mathematical formulas for mathematics problems. The generation unit can also provide hints using text analysis for literature problems. Furthermore, the generation unit can provide hints using data analysis for science problems. In this way, appropriate hints can be provided by applying an appropriate generation algorithm depending on the problem category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input problem category data into the generation AI and have the generation AI select a generation algorithm.

[0038] When generating ideas and hints, the generation unit can improve the accuracy of the generation by referring to the user's past learning history. The generation unit provides hints based on, for example, content that the user has learned in the past. The generation unit can also provide hints by extracting specific patterns from the user's past learning history. Furthermore, the generation unit can improve the accuracy of the hints based on the history of problems that the user has solved in the past. In this way, the accuracy of the generation is improved by referring to the user's past learning history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past learning history data into the generation AI and cause the generation AI to improve the accuracy of the hints.

[0039] When generating ideas and hints, the generation unit can determine the generation priority based on the time when the problem was submitted. For example, the generation unit can provide hints preferentially for problems with an approaching deadline. The generation unit can also provide detailed ideas for problems with a long submission deadline. Furthermore, the generation unit can quickly provide hints for problems with high urgency. In this way, by determining the generation priority based on the time when the problem was submitted, hints can be provided at the appropriate time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time when the problem was submitted into the generation AI and have the generation AI determine the priority of hints.

[0040] When generating ideas and hints, the generation unit can adjust the order of generation based on the relevance of the problems. For example, the generation unit can prioritize providing hints related to the problem the user is currently working on. The generation unit can also provide hints related to problems the user has solved in the past. Furthermore, the generation unit can provide hints related to fields in which the user is interested. In this way, by adjusting the order of generation based on the relevance of the problems, it is possible to provide hints in an appropriate order. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input problem relevance data into the generation AI and cause the generation AI to adjust the order of hints.

[0041] When generating ideas and hints, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit can provide hints in simple language, avoiding technical terminology, to beginners. The generation unit can also provide hints using a moderate amount of technical terminology to intermediate users. Furthermore, the generation unit can provide detailed hints using a lot of technical terminology to advanced users. In this way, appropriate hints can be provided by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0042] During monitoring, the monitoring unit can improve the accuracy of monitoring by referring to the user's past behavioral history. For example, if the user has engaged in inappropriate use in the past, the monitoring unit strengthens monitoring based on that history. The monitoring unit can also extract specific patterns from the user's past behavioral history and monitor. Furthermore, if the user has engaged in appropriate use in the past, the monitoring unit can reduce the frequency of monitoring. In this way, by referring to the user's past behavioral history, the accuracy of monitoring is improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past behavioral history data into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0043] During monitoring, the monitoring unit can adjust the monitoring frequency taking into account the user's learning progress. For example, when the user is in the early stages of learning, the monitoring unit can increase the monitoring frequency. Furthermore, when the user is in the intermediate stage, the monitoring unit can also apply normal monitoring standards. Furthermore, when the user is in the advanced stage, the monitoring unit can also decrease the monitoring frequency. This enables appropriate monitoring by adjusting the monitoring frequency according to the user's learning progress. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's learning progress data into the generation AI and have the generation AI adjust the monitoring frequency.

[0044] During monitoring, the monitoring unit can analyze the user's behavioral patterns to detect abnormal behavior. For example, the monitoring unit can detect abnormal behavior when the user repeatedly inputs the same question. The monitoring unit can also detect abnormal behavior when the user inputs a large number of questions in a short period of time. Furthermore, the monitoring unit can detect abnormal behavior when the user inputs questions at times other than normal. This makes it possible to detect abnormal behavior by analyzing the user's behavioral patterns and take appropriate action. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input user behavioral pattern data into a generation AI and cause the generation AI to detect abnormal behavior.

[0045] During monitoring, the monitoring unit can prioritize monitoring of highly relevant behaviors by taking into account the user's geographical location information. For example, if the user is in a specific area, the monitoring unit can prioritize monitoring of behaviors related to that area. Furthermore, if the user is traveling, the monitoring unit can prioritize monitoring of behaviors related to the travel destination. Furthermore, if the user is in a specific city, the monitoring unit can prioritize monitoring of behaviors related to that city. This enables appropriate monitoring by prioritizing monitoring of highly relevant behaviors based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location data to the generation AI and cause the generation AI to prioritize monitoring of highly relevant behaviors.

[0046] During monitoring, the monitoring unit can analyze the user's social media activities and monitor related behavior. The monitoring unit performs monitoring based on, for example, information shared by the user on social media. The monitoring unit can also extract and monitor related behavior from the user's social media activities. Furthermore, the monitoring unit can also perform monitoring with reference to the activities of the user's friends on social media. This enables appropriate monitoring by monitoring related behavior based on the user's social media activities. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media data into a generation AI and have the generation AI monitor related behavior.

[0047] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. The monitoring unit, for example, adjusts the monitoring method based on feedback provided by the user in the past. The monitoring unit can also preferentially apply a specific monitoring method based on the user's past feedback. Furthermore, the monitoring unit can also improve the accuracy of monitoring by reflecting the user's feedback. In this way, the monitoring method is customized by reflecting the user's past feedback, and the accuracy of monitoring is improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the monitoring method.

[0048] When providing feedback, the feedback unit can provide optimal feedback by referring to the user's past behavioral history. For example, if the user has behaved similarly in the past, the feedback unit can provide feedback based on that history. The feedback unit can also extract a specific pattern from the user's past behavioral history and provide feedback. Furthermore, the feedback unit can also provide optimal feedback based on feedback provided by the user in the past. In this way, optimal feedback can be provided by referring to the user's past behavioral history. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past behavioral history data into a generation AI and cause the generation AI to provide optimal feedback.

[0049] When providing feedback, the feedback unit can adjust the depth of the feedback taking into account the user's learning progress. For example, when the user is in an early stage of learning, the feedback unit can provide basic feedback. Furthermore, when the user is in an intermediate stage, the feedback unit can provide detailed feedback. Furthermore, when the user is in an advanced stage, the feedback unit can provide specialized feedback. In this way, by adjusting the depth of the feedback according to the user's learning progress, appropriate feedback can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's learning progress data into the generation AI and cause the generation AI to adjust the depth of the feedback.

[0050] When providing feedback, the feedback unit can analyze the user's behavioral patterns and generate appropriate feedback. For example, if the user repeatedly performs the same behavior, the feedback unit can provide feedback on that behavior. Furthermore, if the user performs a large amount of behavior in a short period of time, the feedback unit can also provide feedback on that behavior. Furthermore, if the user performs an unusual behavior, the feedback unit can also provide feedback on that behavior. In this way, appropriate feedback can be provided by analyzing the user's behavioral patterns. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input user behavioral pattern data into a generation AI and cause the generation AI to generate appropriate feedback.

[0051] When providing feedback, the feedback unit can prioritize providing highly relevant feedback by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback unit can provide feedback related to that area. Furthermore, if the user is traveling, the feedback unit can provide feedback related to the travel destination. Furthermore, if the user is in a specific city, the feedback unit can provide feedback related to that city. This enables appropriate feedback by preferentially providing highly relevant feedback based on the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's geographical location data to the generation AI and cause the generation AI to provide highly relevant feedback.

[0052] When providing feedback, the feedback unit can analyze the user's social media activities and provide relevant feedback. For example, the feedback unit can provide feedback based on information shared by the user on social media. The feedback unit can also provide relevant feedback based on the user's social media activities. Furthermore, the feedback unit can provide feedback based on the activities of the user's friends on social media. This enables appropriate feedback by providing relevant feedback based on the user's social media activities. Some or all of the above-described processing by the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media data into a generation AI and cause the generation AI to provide relevant feedback.

[0053] When providing feedback, the feedback unit can customize the feedback method by reflecting the user's past feedback. The feedback unit, for example, adjusts the feedback method based on feedback provided by the user in the past. The feedback unit can also preferentially apply a specific feedback method based on the user's past feedback. Furthermore, the feedback unit can also improve the accuracy of the feedback by reflecting the user's feedback. In this way, the feedback method is customized by reflecting the user's past feedback, and the accuracy of the feedback is improved. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the feedback method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analysis unit can estimate the user's learning style and adjust the question analysis method based on the estimated learning style. For example, if the user is a visual learner, the analysis unit can perform analysis using graphs and diagrams. If the user is an auditory learner, the analysis unit can perform analysis including audio explanations. Furthermore, if the user is an experiential learner, the analysis unit can perform analysis using practical examples. This allows for more effective learning support by adjusting the analysis method according to the user's learning style.

[0056] When monitoring user behavior, the monitoring unit can improve the accuracy of monitoring by taking into account the user's device usage status. For example, if the user is using a smartphone, the monitoring unit can monitor the frequency and pattern of touch operations. Also, if the user is using a PC, the monitoring unit can monitor the keyboard input and mouse operation patterns. Furthermore, if the user is using a tablet, the monitoring unit can monitor the touch operation and pen input patterns on the screen. This allows for more accurate monitoring by adjusting the monitoring method according to the user's device usage status.

[0057] The analysis unit can set the user's learning goals and adjust the question analysis method based on those goals. For example, if the user's goal is to pass a specific exam, the analysis unit can perform an analysis specialized for exam preparation. Also, if the user's goal is to acquire a specific skill, the analysis unit can perform an analysis related to that skill. Furthermore, if the user's goal is self-improvement, the analysis unit can perform an analysis that provides a wide range of knowledge. In this way, by adjusting the analysis method according to the user's learning goals, more effective learning support is possible.

[0058] When monitoring a user's behavior, the monitoring unit can improve the accuracy of the monitoring by taking into account the user's Internet connection status. For example, if the user has a high-speed Internet connection, the monitoring unit can monitor in real time. Alternatively, if the user has a slow Internet connection, the monitoring unit can collect data at regular intervals and monitor. Furthermore, if the user is offline, the monitoring unit can monitor based on data stored on the device. This allows for more accurate monitoring by adjusting the monitoring method according to the user's Internet connection status.

[0059] The analysis unit can estimate the user's learning environment and adjust the question analysis method based on that environment. For example, if the user is learning in a quiet environment, the analysis unit can perform a detailed analysis. On the other hand, if the user is learning in a noisy environment, the analysis unit can perform a concise analysis. Furthermore, if the user is learning while on the move, the analysis unit can perform an analysis suitable for a mobile device. This allows for more effective learning support by adjusting the analysis method according to the user's learning environment.

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

[0061] Step 1: The analysis unit analyzes the question entered by the user. For example, the analysis unit analyzes the user's question using natural language processing technology. The analysis unit can also analyze the intent of the question using a machine learning algorithm. Furthermore, the analysis unit can analyze the user's question in text format or voice format. Step 2: The generator generates ideas and hints for arriving at an answer based on the question analyzed by the analyzer. For example, the generator explains the steps for solving the problem or how to use a formula. The generator can also provide reference materials. Furthermore, the generator can use a generation AI to generate ideas and hints for the user's question. Step 3: The monitoring unit monitors whether the user is using the generating AI appropriately. For example, the monitoring unit collects user behavior logs and monitors appropriate usage. The monitoring unit can also detect if the user repeatedly enters the same question. Furthermore, the monitoring unit can adjust the frequency of monitoring. Step 4: The feedback unit provides appropriate feedback to the user based on the behavior monitored by the monitoring unit. For example, the feedback unit provides feedback through a text message or a notification. The feedback unit can also provide an alert to the user's behavior. Furthermore, the feedback unit can generate feedback for the user using a generation AI.

[0062] (Example 2) A system according to an embodiment of the present invention uses an AI filter to manage the appropriate use of generative AI by young people. In this system, a user inputs a question to the generative AI, which then analyzes the question and generates ideas and hints for arriving at the answer. Furthermore, the system monitors the user's behavior and provides appropriate feedback. For example, when a user seeks an answer to a problem, the system provides ideas and hints for arriving at the answer, thereby cultivating the user's ability to think independently. Furthermore, the AI ​​filter monitors the user's behavior and provides appropriate feedback, thereby promoting the appropriate use of the generative AI. This allows the system to provide an environment that fosters the ability to think independently when young people use generative AI and manage the appropriate use of the generative AI. For example, when a user seeks an answer to a problem, the system provides ideas and hints for arriving at the answer, thereby cultivating the user's ability to think independently. Furthermore, the AI ​​filter monitors the user's behavior and provides appropriate feedback, thereby promoting the appropriate use of the generative AI.

[0063] A management system according to an embodiment includes an analysis unit, a generation unit, a monitoring unit, and a feedback unit. The analysis unit analyzes a question entered by a user. For example, the analysis unit analyzes the user's question using natural language processing technology. The analysis unit can also analyze the intent of the question using a machine learning algorithm. The analysis unit can also analyze the user's question in text or audio format. The generation unit generates ideas and hints for arriving at an answer based on the question analyzed by the analysis unit. For example, the generation unit explains problem-solving steps and formula usage. The generation unit can also provide reference materials. The generation unit can also use a generation AI to generate ideas and hints for the user's question. The monitoring unit monitors whether the user is using the generation AI appropriately. For example, the monitoring unit collects a user's behavior log and monitors appropriate use. The monitoring unit can also detect the user's behavior if the user repeatedly enters the same question. The monitoring unit can also adjust the monitoring frequency. The feedback unit provides appropriate feedback to the user based on the behavior monitored by the monitoring unit. For example, the feedback unit provides feedback through a text message or a notification. The feedback unit can also provide an alert for the user's actions. Furthermore, the feedback unit can generate feedback for the user using the generating AI. In this way, the management system according to the embodiment can manage the generating AI so that young people can use it appropriately. For example, by analyzing the user's questions, generating ideas and hints, monitoring the user's actions, and providing appropriate feedback, the management system can manage the generating AI so that young people can use it appropriately.

[0064] The analysis unit can estimate the user's emotions and adjust the question analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can simplify the analysis and provide a basic approach. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis and suggest multiple approaches. Furthermore, if the user is in a hurry, the analysis unit can quickly perform the analysis and provide the most efficient approach. This enables more appropriate analysis by adjusting the question analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis method based on the emotion.

[0065] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the user's past question history. For example, if the user has asked a similar question in the past, the analysis unit performs the analysis by referring to that history. The analysis unit can also extract specific patterns from the user's past question history and reflect them in the analysis. Furthermore, the analysis unit can improve the accuracy of the analysis based on the history of problems the user has solved in the past. In this way, the accuracy of the analysis is improved by referring to the user's past question history. Some 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 the user's past question history data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0066] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the question category. For example, the analysis unit can apply a mathematical expression analysis algorithm to a mathematics question. The analysis unit can also apply a text analysis algorithm to a literature question. Furthermore, the analysis unit can apply a data analysis algorithm to a science question. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the question category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input question category data to the generation AI and have the generation AI select an analysis algorithm.

[0067] When analyzing a question, the analysis unit can adjust the depth of the analysis based on the user's learning progress. For example, if the user is a beginner, the analysis unit can perform a basic analysis. If the user is an intermediate learner, the analysis unit can also perform a detailed analysis. Furthermore, if the user is an advanced learner, the analysis unit can also perform a specialized analysis. This enables appropriate analysis by adjusting the depth of the analysis according to the user's learning progress. Some 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 the user's learning progress data into the generation AI and have the generation AI adjust the depth of the analysis.

[0068] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can provide a simple display method when the user is stressed. The analysis unit can also provide a detailed display method when the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows the display method of the analysis results to be adjusted according to the user's emotions, making it easier for the user to understand. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0069] When analyzing a question, the analysis unit can prioritize analyzing highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the analysis unit can prioritize analyzing information related to that area. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing information related to the travel destination. Furthermore, if the user is in a specific city, the analysis unit can prioritize analyzing information related to that city. This allows for more appropriate information to be provided by prioritizing analysis of highly relevant information based on the user's geographical location information. Some or all of the above-described 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 the user's geographical location data into the generation AI and cause the generation AI to perform a prioritized analysis of highly relevant information.

[0070] When analyzing a question, the analysis unit can analyze the user's social media activities and analyze related information. The analysis unit performs analysis based on, for example, information shared by the user on social media. The analysis unit can also extract and analyze related information from the user's social media activities. Furthermore, the analysis unit can also perform analysis based on the activities of the user's friends on social media. This makes it possible to provide more appropriate information by analyzing related information based on the user's social media activities. Some 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 the user's social media data into the generation AI and have the generation AI analyze the related information.

[0071] When analyzing a question, the analysis unit can customize the analysis method by reflecting the user's past feedback. The analysis unit, for example, adjusts the analysis method based on feedback provided by the user in the past. The analysis unit can also preferentially apply a specific analysis method based on the user's past feedback. Furthermore, the analysis unit can also improve the accuracy of the analysis by reflecting the user's feedback. In this way, the analysis method is customized by reflecting the user's past feedback, and the accuracy of the analysis is improved. Some 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 the user's past feedback data into the generation AI and cause the generation AI to customize the analysis method.

[0072] The generation unit can estimate the user's emotions and adjust the way of generating ideas and hints based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can provide simple hints. Furthermore, if the user is relaxed, the generation unit can provide detailed ideas. Furthermore, if the user is in a hurry, the generation unit can provide hints for quickly arriving at an answer. By adjusting the way of generating ideas and hints according to the user's emotions, more appropriate hints can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way of generating hints.

[0073] When generating ideas and hints, the generation unit can adjust the level of detail of the generation based on the difficulty of the problem. For example, the generation unit provides basic hints for easy problems. The generation unit can also provide detailed ideas for problems of medium difficulty. Furthermore, the generation unit can provide step-by-step guides for difficult problems. In this way, by adjusting the level of detail of the generation according to the difficulty of the problem, appropriate hints can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input difficulty data of the problem to the generation AI and cause the generation AI to adjust the level of detail of the hints.

[0074] When generating ideas and hints, the generation unit can apply different generation algorithms depending on the problem category. For example, the generation unit provides hints using mathematical formulas for mathematics problems. The generation unit can also provide hints using text analysis for literature problems. Furthermore, the generation unit can provide hints using data analysis for science problems. In this way, appropriate hints can be provided by applying an appropriate generation algorithm depending on the problem category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input problem category data into the generation AI and have the generation AI select a generation algorithm.

[0075] When generating ideas and hints, the generation unit can improve the accuracy of the generation by referring to the user's past learning history. The generation unit provides hints based on, for example, content that the user has learned in the past. The generation unit can also provide hints by extracting specific patterns from the user's past learning history. Furthermore, the generation unit can improve the accuracy of the hints based on the history of problems that the user has solved in the past. In this way, the accuracy of the generation is improved by referring to the user's past learning history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past learning history data into the generation AI and cause the generation AI to improve the accuracy of the hints.

[0076] The generation unit can estimate the user's emotions and adjust the length of the ideas and hints based on the estimated user emotions. For example, if the user is stressed, the generation unit can provide short, concise hints. If the user is relaxed, the generation unit can also provide longer hints with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can provide short hints to quickly arrive at the answer. This allows appropriate hints to be provided by adjusting the length of the ideas and hints according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the hints.

[0077] When generating ideas and hints, the generation unit can determine the generation priority based on the time when the problem was submitted. For example, the generation unit can provide hints preferentially for problems with an approaching deadline. The generation unit can also provide detailed ideas for problems with a long submission deadline. Furthermore, the generation unit can quickly provide hints for problems with high urgency. In this way, by determining the generation priority based on the time when the problem was submitted, hints can be provided at the appropriate time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time when the problem was submitted into the generation AI and have the generation AI determine the priority of hints.

[0078] When generating ideas and hints, the generation unit can adjust the order of generation based on the relevance of the problems. For example, the generation unit can prioritize providing hints related to the problem the user is currently working on. The generation unit can also provide hints related to problems the user has solved in the past. Furthermore, the generation unit can provide hints related to fields in which the user is interested. In this way, by adjusting the order of generation based on the relevance of the problems, it is possible to provide hints in an appropriate order. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input problem relevance data into the generation AI and cause the generation AI to adjust the order of hints.

[0079] When generating ideas and hints, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit can provide hints in simple language, avoiding technical terminology, to beginners. The generation unit can also provide hints using a moderate amount of technical terminology to intermediate users. Furthermore, the generation unit can provide detailed hints using a lot of technical terminology to advanced users. In this way, appropriate hints can be provided by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0080] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated user emotions. For example, the monitoring unit can reduce the frequency of monitoring when the user is stressed. The monitoring unit can also apply normal monitoring criteria when the user is relaxed. Furthermore, the monitoring unit can increase the frequency of monitoring when the user is in a hurry. This enables appropriate monitoring by adjusting the monitoring criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 monitoring unit can be performed using an AI, for example, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the monitoring criteria.

[0081] During monitoring, the monitoring unit can improve the accuracy of monitoring by referring to the user's past behavioral history. For example, if the user has engaged in inappropriate use in the past, the monitoring unit strengthens monitoring based on that history. The monitoring unit can also extract specific patterns from the user's past behavioral history and monitor. Furthermore, if the user has engaged in appropriate use in the past, the monitoring unit can reduce the frequency of monitoring. In this way, by referring to the user's past behavioral history, the accuracy of monitoring is improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past behavioral history data into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0082] During monitoring, the monitoring unit can adjust the monitoring frequency taking into account the user's learning progress. For example, when the user is in the early stages of learning, the monitoring unit can increase the monitoring frequency. Furthermore, when the user is in the intermediate stage, the monitoring unit can also apply normal monitoring standards. Furthermore, when the user is in the advanced stage, the monitoring unit can also decrease the monitoring frequency. This enables appropriate monitoring by adjusting the monitoring frequency according to the user's learning progress. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's learning progress data into the generation AI and have the generation AI adjust the monitoring frequency.

[0083] During monitoring, the monitoring unit can analyze the user's behavioral patterns to detect abnormal behavior. For example, the monitoring unit can detect abnormal behavior when the user repeatedly inputs the same question. The monitoring unit can also detect abnormal behavior when the user inputs a large number of questions in a short period of time. Furthermore, the monitoring unit can detect abnormal behavior when the user inputs questions at times other than normal. This makes it possible to detect abnormal behavior by analyzing the user's behavioral patterns and take appropriate action. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input user behavioral pattern data into a generation AI and cause the generation AI to detect abnormal behavior.

[0084] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can provide a simple display method. If the user is relaxed, the monitoring unit can also provide a detailed display method. If the user is in a hurry, the monitoring unit can also provide a display method that focuses on the main points. This allows the display method of the monitoring results to be adjusted according to the user's emotions, making it easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 monitoring unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the monitoring results.

[0085] During monitoring, the monitoring unit can prioritize monitoring of highly relevant behaviors by taking into account the user's geographical location information. For example, if the user is in a specific area, the monitoring unit can prioritize monitoring of behaviors related to that area. Furthermore, if the user is traveling, the monitoring unit can prioritize monitoring of behaviors related to the travel destination. Furthermore, if the user is in a specific city, the monitoring unit can prioritize monitoring of behaviors related to that city. This enables appropriate monitoring by prioritizing monitoring of highly relevant behaviors based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location data to the generation AI and cause the generation AI to prioritize monitoring of highly relevant behaviors.

[0086] During monitoring, the monitoring unit can analyze the user's social media activities and monitor related behavior. The monitoring unit performs monitoring based on, for example, information shared by the user on social media. The monitoring unit can also extract and monitor related behavior from the user's social media activities. Furthermore, the monitoring unit can also perform monitoring with reference to the activities of the user's friends on social media. This enables appropriate monitoring by monitoring related behavior based on the user's social media activities. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media data into a generation AI and have the generation AI monitor related behavior.

[0087] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. The monitoring unit, for example, adjusts the monitoring method based on feedback provided by the user in the past. The monitoring unit can also preferentially apply a specific monitoring method based on the user's past feedback. Furthermore, the monitoring unit can also improve the accuracy of monitoring by reflecting the user's feedback. In this way, the monitoring method is customized by reflecting the user's past feedback, and the accuracy of monitoring is improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the monitoring method.

[0088] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback unit can provide feedback including encouraging words. If the user is relaxed, the feedback unit can also provide detailed feedback. If the user is in a hurry, the feedback unit can also provide concise feedback. This allows appropriate feedback to be provided by adjusting the content of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 feedback unit can be performed using an AI, for example, or without an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback content.

[0089] When providing feedback, the feedback unit can provide optimal feedback by referring to the user's past behavioral history. For example, if the user has behaved similarly in the past, the feedback unit can provide feedback based on that history. The feedback unit can also extract a specific pattern from the user's past behavioral history and provide feedback. Furthermore, the feedback unit can also provide optimal feedback based on feedback provided by the user in the past. In this way, optimal feedback can be provided by referring to the user's past behavioral history. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past behavioral history data into a generation AI and cause the generation AI to provide optimal feedback.

[0090] When providing feedback, the feedback unit can adjust the depth of the feedback taking into account the user's learning progress. For example, when the user is in an early stage of learning, the feedback unit can provide basic feedback. Furthermore, when the user is in an intermediate stage, the feedback unit can provide detailed feedback. Furthermore, when the user is in an advanced stage, the feedback unit can provide specialized feedback. In this way, by adjusting the depth of the feedback according to the user's learning progress, appropriate feedback can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's learning progress data into the generation AI and cause the generation AI to adjust the depth of the feedback.

[0091] When providing feedback, the feedback unit can analyze the user's behavioral patterns and generate appropriate feedback. For example, if the user repeatedly performs the same behavior, the feedback unit can provide feedback on that behavior. Furthermore, if the user performs a large amount of behavior in a short period of time, the feedback unit can also provide feedback on that behavior. Furthermore, if the user performs an unusual behavior, the feedback unit can also provide feedback on that behavior. In this way, appropriate feedback can be provided by analyzing the user's behavioral patterns. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input user behavioral pattern data into a generation AI and cause the generation AI to generate appropriate feedback.

[0092] The feedback unit can estimate the user's emotions and adjust the feedback display method based on the estimated user emotions. For example, the feedback unit can provide a simple display method when the user is stressed. The feedback unit can also provide a detailed display method when the user is relaxed. Furthermore, the feedback unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows the feedback display method to be adjusted according to the user's emotions, making it easier for the user to understand. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or without an AI. For example, the feedback unit can input the user's emotion data into the generation AI and have the generation AI adjust the feedback display method.

[0093] When providing feedback, the feedback unit can prioritize providing highly relevant feedback by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback unit can provide feedback related to that area. Furthermore, if the user is traveling, the feedback unit can provide feedback related to the travel destination. Furthermore, if the user is in a specific city, the feedback unit can provide feedback related to that city. This enables appropriate feedback by preferentially providing highly relevant feedback based on the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's geographical location data to the generation AI and cause the generation AI to provide highly relevant feedback.

[0094] When providing feedback, the feedback unit can analyze the user's social media activities and provide relevant feedback. For example, the feedback unit can provide feedback based on information shared by the user on social media. The feedback unit can also provide relevant feedback based on the user's social media activities. Furthermore, the feedback unit can provide feedback based on the activities of the user's friends on social media. This enables appropriate feedback by providing relevant feedback based on the user's social media activities. Some or all of the above-described processing by the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media data into a generation AI and cause the generation AI to provide relevant feedback.

[0095] When providing feedback, the feedback unit can customize the feedback method by reflecting the user's past feedback. The feedback unit, for example, adjusts the feedback method based on feedback provided by the user in the past. The feedback unit can also preferentially apply a specific feedback method based on the user's past feedback. Furthermore, the feedback unit can also improve the accuracy of the feedback by reflecting the user's feedback. In this way, the feedback method is customized by reflecting the user's past feedback, and the accuracy of the feedback is improved. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the feedback method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, monitoring unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the feedback unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, monitoring unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the feedback unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, monitoring unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the feedback unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, monitoring unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the feedback unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate the user's learning style and adjust the question analysis method based on the estimated learning style. For example, if the user is a visual learner, the analysis unit can perform analysis using graphs and diagrams. If the user is an auditory learner, the analysis unit can perform analysis including audio explanations. Furthermore, if the user is an experiential learner, the analysis unit can perform analysis using practical examples. This allows for more effective learning support by adjusting the analysis method according to the user's learning style.

[0098] The generation unit can estimate the user's emotions and adjust the format of the hint based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can provide a hint in short sentences or bullet points. If the user is relaxed, the generation unit can provide a hint in paragraph format with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can provide a concise hint that gets to the point. This allows for more appropriate assistance by adjusting the format of the hint according to the user's emotions.

[0099] When monitoring user behavior, the monitoring unit can improve the accuracy of monitoring by taking into account the user's device usage status. For example, if the user is using a smartphone, the monitoring unit can monitor the frequency and pattern of touch operations. Also, if the user is using a PC, the monitoring unit can monitor the keyboard input and mouse operation patterns. Furthermore, if the user is using a tablet, the monitoring unit can monitor the touch operation and pen input patterns on the screen. This allows for more accurate monitoring by adjusting the monitoring method according to the user's device usage status.

[0100] The feedback unit can estimate the user's emotion and adjust the timing of the feedback based on the estimated user's emotion. For example, if the user is feeling stressed, the feedback unit can provide feedback immediately. If the user is relaxed, the feedback unit can provide feedback after a certain time has passed. Furthermore, if the user is in a hurry, the feedback unit can provide feedback quickly. This allows for more effective feedback by adjusting the timing of the feedback according to the user's emotion.

[0101] The analysis unit can set the user's learning goals and adjust the question analysis method based on those goals. For example, if the user's goal is to pass a specific exam, the analysis unit can perform an analysis specialized for exam preparation. Also, if the user's goal is to acquire a specific skill, the analysis unit can perform an analysis related to that skill. Furthermore, if the user's goal is self-improvement, the analysis unit can perform an analysis that provides a wide range of knowledge. In this way, by adjusting the analysis method according to the user's learning goals, more effective learning support is possible.

[0102] The generation unit can estimate the user's emotions and adjust the difficulty of the hints based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can provide an easy hint. If the user is relaxed, the generation unit can provide a slightly more difficult hint. Furthermore, if the user is in a hurry, the generation unit can provide a hint to help the user quickly arrive at the answer. This allows for more appropriate support by adjusting the difficulty of the hints according to the user's emotions.

[0103] When monitoring a user's behavior, the monitoring unit can improve the accuracy of the monitoring by taking into account the user's Internet connection status. For example, if the user has a high-speed Internet connection, the monitoring unit can monitor in real time. Alternatively, if the user has a slow Internet connection, the monitoring unit can collect data at regular intervals and monitor. Furthermore, if the user is offline, the monitoring unit can monitor based on data stored on the device. This allows for more accurate monitoring by adjusting the monitoring method according to the user's Internet connection status.

[0104] The feedback unit can estimate the user's emotions and personalize the content of the feedback based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback unit can provide feedback including encouraging words. If the user is relaxed, the feedback unit can provide feedback including detailed advice. Furthermore, if the user is in a hurry, the feedback unit can provide concise feedback. This allows for more effective feedback by personalizing the content of the feedback according to the user's emotions.

[0105] The analysis unit can estimate the user's learning environment and adjust the question analysis method based on that environment. For example, if the user is learning in a quiet environment, the analysis unit can perform a detailed analysis. On the other hand, if the user is learning in a noisy environment, the analysis unit can perform a concise analysis. Furthermore, if the user is learning while on the move, the analysis unit can perform an analysis suitable for a mobile device. This allows for more effective learning support by adjusting the analysis method according to the user's learning environment.

[0106] The generation unit can estimate the user's emotions and adjust the frequency of providing hints based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can provide hints frequently. Also, if the user is relaxed, the generation unit can provide hints at appropriate intervals. Furthermore, if the user is in a hurry, the generation unit can provide hints quickly. In this way, by adjusting the frequency of providing hints according to the user's emotions, more appropriate support can be provided.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The analysis unit analyzes the question entered by the user. For example, the analysis unit analyzes the user's question using natural language processing technology. The analysis unit can also analyze the intent of the question using a machine learning algorithm. Furthermore, the analysis unit can analyze the user's question in text format or voice format. Step 2: The generator generates ideas and hints for arriving at an answer based on the question analyzed by the analyzer. For example, the generator explains the steps for solving the problem or how to use a formula. The generator can also provide reference materials. Furthermore, the generator can use a generation AI to generate ideas and hints for the user's question. Step 3: The monitoring unit monitors whether the user is using the generating AI appropriately. For example, the monitoring unit collects user behavior logs and monitors appropriate usage. The monitoring unit can also detect if the user repeatedly enters the same question. Furthermore, the monitoring unit can adjust the frequency of monitoring. Step 4: The feedback unit provides appropriate feedback to the user based on the behavior monitored by the monitoring unit. For example, the feedback unit provides feedback through a text message or a notification. The feedback unit can also provide an alert to the user's behavior. Furthermore, the feedback unit can generate feedback for the user using a generation AI.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] 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.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] 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.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 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. an analysis unit that analyzes a user's question; a generation unit that generates ideas and hints based on the questions analyzed by the analysis unit; a monitoring unit that monitors user behavior; a feedback unit that provides feedback based on the behavior monitored by the monitoring unit; Equipped with A system characterized by:

2. The analysis unit Inferring user sentiment and adjusting question analysis methods based on the estimated user sentiment The system of claim 1 .

3. The analysis unit When analyzing questions, the accuracy of the analysis is improved by referring to the user's past question history. The system of claim 1 .

4. The analysis unit When parsing questions, apply different parsing algorithms depending on the question category The system of claim 1 .

5. The analysis unit When parsing questions, adjust the depth of analysis based on the user's learning progress The system of claim 1 .

6. The analysis unit Inferring user emotions and adjusting the display of analysis results based on the estimated user emotions The system of claim 1 .

7. The analysis unit When parsing questions, the system takes into account the user's geographic location to prioritize relevant information. The system of claim 1 .

8. The analysis unit When parsing a question, analyze the user's social media activity and parse related information. The system of claim 1 .

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