system

Abstract

The system according to the embodiment aims to eliminate the digital divide and make AI technology more accessible. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, a monitoring unit, and a feedback unit. The reception unit receives a user's question. The analysis unit analyzes the information received by the reception unit. The provision unit provides a solution based on the information obtained by the analysis unit. The monitoring unit monitors the user's learning progress and level of understanding. The feedback unit provides feedback based on the information obtained by the monitoring unit.

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 have not adequately bridged the digital divide, and there is room for improvement.

[0005] The system according to the embodiment aims to eliminate the digital divide and make AI technology more accessible. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, a monitoring unit, and a feedback unit. The reception unit receives a user's question. The analysis unit analyzes the information received by the reception unit. The provision unit provides a solution based on the information obtained by the analysis unit. The monitoring unit monitors the user's learning progress and level of understanding. The feedback unit provides feedback based on the information obtained by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can bridge the digital divide and make AI technology more accessible. [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 digital divide elimination system according to an embodiment of the present invention uses AI technology to bridge generational and regional digital divides. In this system, users input their questions and concerns about digital technology. AI analyzes the input information and provides solutions tailored to the user's needs. Furthermore, AI monitors the user's learning progress and comprehension and provides appropriate feedback. This allows users to learn digital technology at their own pace. This system can help individuals and organizations with preconceived notions about digital technology embrace it. For example, it is expected to help elderly people become proficient in using smartphones and encourage local small businesses to expand their online businesses. For example, a user inputs a question or concern about digital technology. For example, they input specific questions such as "I don't know how to use my smartphone" or "I want to know how to shop online." This information is then input into AI. The AI ​​then analyzes the input information and provides solutions tailored to the user's needs. For example, if a user does not know how to use a smartphone, AI provides specific operating instructions via video or text. Similarly, if a user wants to know how to shop online, AI provides step-by-step guidance. Furthermore, the AI ​​monitors the user's learning progress and level of understanding and provides appropriate feedback. For example, if the user is struggling with a particular operation, the AI ​​provides additional hints and support. This allows users to learn digital technologies at their own pace. This system can help people and organizations with preconceived notions about digital technology to embrace it as something familiar. For example, it is expected that this will help elderly people become proficient in using smartphones and encourage small local businesses to expand their business online. In this way, the Digital Divide Elimination System can support users in learning digital technologies and eliminate preconceived notions about digital technology.

[0029] A system for eliminating the digital divide according to an embodiment includes a reception unit, an analysis unit, a provision unit, a monitoring unit, and a feedback unit. The reception unit allows a user to input questions or problems related to digital technology. The user can input questions or problems related to digital technology in, for example, text format, voice format, or a question related to a specific topic, but is not limited to these examples. The reception unit provides an interface for the user to input questions in text format. The reception unit may also include a microphone or voice recognition technology for receiving voice input. The reception unit may also provide a category selection function for receiving questions related to a specific topic. The analysis unit uses AI to analyze the information received by the reception unit and understand the user's needs. The analysis may be performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit uses natural language processing technology to analyze the intent of the user's question. The analysis unit may also use a machine learning algorithm to analyze the user's past question history and behavioral patterns. The analysis unit may also estimate the user's emotions and reflect them in the analysis results. The providing unit uses AI to provide a solution based on the information obtained by the analyzing unit. Examples of solutions include, but are not limited to, text answers, links, and videos. For example, the providing unit provides specific operating procedures in text format in response to the user's question. The providing unit can also provide links to related websites and resources. Furthermore, the providing unit can also provide videos showing the operating procedures. The monitoring unit monitors the user's learning progress and level of understanding. Monitoring is performed based on, for example, test results, study time, progress status, and the like, but is not limited to, examples. For example, the monitoring unit provides quizzes on the content the user has studied and records the percentage of correct answers. The monitoring unit can also record the user's study time and keep track of progress. Furthermore, the monitoring unit can analyze the user's learning history and evaluate the user's level of understanding. The feedback unit provides feedback based on the information obtained by the monitoring unit.The feedback may include, but is not limited to, text messages, audio messages, video messages, etc. For example, the feedback unit may provide appropriate hints or support when the user is in trouble. The feedback unit may also provide encouraging messages according to the user's learning progress. Furthermore, the feedback unit may suggest additional learning resources based on the user's level of understanding. In this way, the system for bridging the digital divide according to the embodiment may support the user's learning of digital technology and eliminate preconceptions about digital technology.

[0030] The reception unit can analyze the user's past question history and select an appropriate reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest question formats to be used during a specific time period based on the user's past question history. In this way, by analyzing the user's past question history, the optimal reception method can be provided to the user, and efficient question reception can be achieved. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past question history data into a generation AI and have the generation AI select the optimal reception method.

[0031] When receiving a question, the reception unit can filter the questions based on the user's current situation and areas of interest. For example, the reception unit can prioritize questions related to areas in which the user is currently interested. The reception unit can also adjust the priority of questions based on the user's current situation (e.g., at work, on a break, etc.). Furthermore, the reception unit can filter and accept highly relevant questions based on the user's past behavioral history. In this way, by filtering questions based on the user's current situation and areas of interest, highly relevant questions can be preferentially accepted. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current situation data to a generation AI and cause the generation AI to filter the questions.

[0032] When receiving a question, the reception unit can prioritize receiving highly relevant questions based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the area around the user's home. In this way, by taking the user's geographical location information into consideration, questions related to the area can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant questions.

[0033] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit can prioritize receiving questions related to topics that the user frequently mentions on social media. The reception unit can also analyze the content of the user's social media posts and suggest related questions. Furthermore, the reception unit can also accept questions related to topics that the user's social media followers and friends are interested in. In this way, by analyzing the user's social media activity, highly relevant questions can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related questions.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. For example, the analysis unit performs a detailed analysis for questions with high importance. The analysis unit can also perform a concise analysis for questions with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the question. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the question. 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 question importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the question category. For example, the analysis unit can apply a specialized analysis algorithm to technical questions. The analysis unit can also apply a simple analysis algorithm to general questions. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the question category. This makes it possible to provide highly accurate analysis results by applying the optimal analysis algorithm depending on the question category. 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 question category data to the generation AI and cause the generation AI to select the optimal analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time when the question was submitted. For example, the analysis unit prioritizes analysis of recently submitted questions. The analysis unit can also adjust the priority of analysis according to the time when the question was submitted. Furthermore, the analysis unit can postpone questions that were submitted earlier. In this way, efficient analysis can be achieved by determining the priority of analysis based on the time when the question was submitted. 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 data on the time when the question was submitted to the generation AI and have the generation AI determine the priority of analysis.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, the analysis unit prioritizes analysis of questions with high relevance. The analysis unit can also adjust the order of analysis according to the relevance of the questions. Furthermore, the analysis unit can postpone questions with low relevance. In this way, by adjusting the order of analysis based on the relevance of the questions, highly relevant questions can be analyzed with priority. 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 question relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0038] When providing a solution, the providing unit can adjust the level of detail of the solution based on the importance of the question. For example, the providing unit provides a detailed solution for a question with a high level of importance. The providing unit can also provide a concise solution for a question with a low level of importance. Furthermore, the providing unit can adjust the depth of the solution according to the importance of the question. This makes it possible to provide an efficient solution by adjusting the level of detail of the solution according to the importance of the question. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input question importance data to the generating AI and cause the generating AI to adjust the level of detail of the solution.

[0039] When providing a solution, the providing unit can apply different solution algorithms depending on the question category. For example, the providing unit can apply a specialized solution algorithm to a technical question. The providing unit can also apply a simple solution algorithm to a general question. Furthermore, the providing unit can select an optimal solution algorithm depending on the question category. This makes it possible to provide a highly accurate solution by applying the optimal solution algorithm depending on the question category. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input question category data to the generation AI and cause the generation AI to select an optimal solution algorithm.

[0040] When providing solutions, the providing unit can determine the priority of solutions based on the time when the question was submitted. For example, the providing unit can provide solutions preferentially to recently submitted questions. The providing unit can also adjust the priority of solutions depending on the time when the question was submitted. Furthermore, the providing unit can postpone questions that were submitted earlier. In this way, by determining the priority of solutions based on the time when the question was submitted, efficient solution provision can be achieved. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input question submission time data into the generating AI and have the generating AI determine the priority of solutions.

[0041] When providing solutions, the providing unit can adjust the order of solutions based on the relevance of the questions. For example, the providing unit can provide solutions preferentially to questions with high relevance. The providing unit can also adjust the order of solutions according to the relevance of the questions. Furthermore, the providing unit can postpone questions with low relevance. In this way, by adjusting the order of solutions based on the relevance of the questions, solutions can be provided preferentially to questions with high relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input question relevance data to the generating AI and cause the generating AI to adjust the order of solutions.

[0042] During monitoring, the monitoring unit can select the optimal monitoring method by referring to the user's past learning history. The monitoring unit selects the optimal monitoring method based on, for example, the content the user has learned in the past. The monitoring unit can also grasp the user's learning progress from the user's past learning history. Furthermore, the monitoring unit can analyze the user's past learning history and suggest the optimal monitoring method. In this way, the optimal monitoring method can be provided by referring to the user's past learning history. 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 past learning history data into the generation AI and have the generation AI select the optimal monitoring method.

[0043] During monitoring, the monitoring unit can customize the monitoring means based on the user's current learning situation. The monitoring unit customizes the monitoring means according to, for example, the content the user is currently learning. The monitoring unit can also adjust the monitoring frequency based on the user's current learning progress. Furthermore, the monitoring unit can grasp the user's current learning situation in real time and provide the optimal monitoring means. This allows appropriate monitoring by customizing the monitoring means based on the user's current learning situation. 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 current learning situation data into the generation AI and have the generation AI customize the monitoring means.

[0044] During monitoring, the monitoring unit can select the optimal monitoring method 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 learning content related to that area. Furthermore, if the user is traveling, the monitoring unit can also prioritize monitoring learning content related to the travel destination. Furthermore, if the user is at home, the monitoring unit can also prioritize monitoring learning content related to the area around the user's home. In this way, by taking the user's geographical location information into account, learning content related to the area can be prioritized for monitoring. 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 information data into the generation AI and cause the generation AI to select the optimal monitoring method.

[0045] During monitoring, the monitoring unit can analyze the user's social media activities and suggest monitoring methods. For example, the monitoring unit prioritizes monitoring of learning content related to topics frequently mentioned by the user on social media. The monitoring unit can also analyze the user's social media posts and suggest related learning content. Furthermore, the monitoring unit can monitor learning content related to topics of interest to the user's social media followers and friends. In this way, by analyzing the user's social media activities, it is possible to prioritize monitoring of highly relevant learning content. 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 execute the suggestion of monitoring methods.

[0046] When providing feedback, the feedback unit can select the optimal feedback method by referring to the user's past feedback history. The feedback unit selects the optimal feedback method, for example, based on feedback the user has received in the past. The feedback unit can also understand the effectiveness of feedback from the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history and suggest the optimal feedback method. In this way, the optimal feedback method can be provided by referring to the user's past feedback history. 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 history data into the generation AI and cause the generation AI to select the optimal feedback method.

[0047] When providing feedback, the feedback unit can customize the feedback means based on the user's current situation. The feedback unit customizes the feedback means according to, for example, the content the user is currently learning. The feedback unit can also adjust the frequency of feedback based on the user's current learning progress. Furthermore, the feedback unit can grasp the user's current learning situation in real time and provide the optimal feedback means. In this way, by customizing the feedback means based on the user's current situation, appropriate feedback can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or without AI. For example, the feedback unit can input the user's current situation data into the generation AI and cause the generation AI to customize the feedback means.

[0048] When providing feedback, the feedback unit can select the optimal feedback method by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback unit can prioritize providing feedback related to that area. Also, if the user is traveling, the feedback unit can prioritize providing feedback related to the travel destination. Furthermore, if the user is at home, the feedback unit can prioritize providing feedback related to the area around the user's home. In this way, by taking the user's geographical location information into account, it is possible to prioritize providing feedback related to the area. 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 information data into the generation AI and cause the generation AI to select the optimal feedback method.

[0049] When providing feedback, the feedback unit can analyze the user's social media activity and suggest a means of feedback. For example, the feedback unit can prioritize providing feedback related to topics that the user frequently mentions on social media. The feedback unit can also analyze the content of the user's social media posts and suggest related feedback. Furthermore, the feedback unit can provide feedback related to topics that the user's social media followers and friends are interested in. In this way, by analyzing the user's social media activity, it is possible to prioritize providing highly relevant feedback. 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 social media data into a generation AI and cause the generation AI to suggest a means of feedback.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When a user inputs a question or problem related to digital technology, the reception unit can refer to the user's past learning history and suggest the most appropriate question format. For example, if the user has previously input questions in text format, the reception unit can preferentially display a text format interface. Also, if the user has previously input questions in voice format, the reception unit can preferentially provide a voice input interface. Furthermore, if the user frequently asks questions on a specific topic, questions related to that topic can be automatically displayed as candidates. This makes it possible to efficiently receive questions by utilizing the user's past learning history.

[0052] The analysis unit can analyze the user's past question history and select the optimal analysis algorithm. For example, if the user has frequently asked technical questions in the past, the analysis unit can apply a specialized analysis algorithm. On the other hand, if the user has asked general questions, the analysis unit can also apply a simple analysis algorithm. Furthermore, the analysis unit can predict and suggest the analysis algorithm to be used at a specific time period based on the user's past question history. This makes it possible to provide the optimal analysis algorithm by utilizing the user's past question history.

[0053] When monitoring a user's learning progress and level of understanding, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. For example, if the user is in a specific area, learning content related to that area can be monitored preferentially. Also, if the user is traveling, learning content related to the travel destination can be monitored preferentially. Furthermore, if the user is at home, learning content related to the area around the user's home can be monitored preferentially. In this way, by taking into account the user's geographical location information, learning content related to the area can be monitored preferentially.

[0054] The reception unit can analyze the user's social media activity and receive related questions. For example, it can prioritize receiving questions related to topics that the user frequently mentions on social media. The reception unit can also analyze the content of the user's social media posts and suggest related questions. It can also accept questions related to topics that the user's social media followers and friends are interested in. In this way, by analyzing the user's social media activity, it is possible to prioritize receiving highly relevant questions.

[0055] When providing a solution, the providing unit can adjust the level of detail of the solution based on the importance of the question. For example, a detailed solution can be provided for a question of high importance. A concise solution can also be provided for a question of low importance. Furthermore, the depth of the solution can be adjusted according to the importance of the question. In this way, by adjusting the level of detail of the solution according to the importance of the question, efficient solution provision can be achieved.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit allows users to input questions or problems related to digital technology. Users can input questions in text format, voice format, or on specific topics. The reception unit provides an interface for text input, a microphone for voice input, voice recognition technology, and a category selection function. Step 2: The analysis unit uses AI to analyze the information received by the reception unit and understand the user's needs. The analysis is carried out using natural language processing technology and machine learning algorithms to analyze the intent of the user's question, past question history, behavioral patterns, and emotions. Step 3: The solution provider uses AI to provide solutions based on the information obtained by the analysis provider. Solutions can include text answers, links, videos, etc. Specific operating procedures, links to related websites and resources, and videos showing the operating procedures are provided. Step 4: The monitoring unit monitors the user's learning progress and level of understanding. Monitoring is based on test results, study time, progress, etc., and evaluates the level of understanding by analyzing the quiz correct answer rate, study time, progress, and study history. Step 5: The feedback department provides feedback based on the information obtained by the monitoring department. The feedback may include text, audio, or video messages, and may provide appropriate tips, support, encouragement, or suggest additional learning resources.

[0058] (Example 2) A digital divide elimination system according to an embodiment of the present invention uses AI technology to bridge generational and regional digital divides. In this system, users input their questions and concerns about digital technology. AI analyzes the input information and provides solutions tailored to the user's needs. Furthermore, AI monitors the user's learning progress and comprehension and provides appropriate feedback. This allows users to learn digital technology at their own pace. This system can help individuals and organizations with preconceived notions about digital technology embrace it. For example, it is expected to help elderly people become proficient in using smartphones and encourage local small businesses to expand their online businesses. For example, a user inputs a question or concern about digital technology. For example, they input specific questions such as "I don't know how to use my smartphone" or "I want to know how to shop online." This information is then input into AI. The AI ​​then analyzes the input information and provides solutions tailored to the user's needs. For example, if a user does not know how to use a smartphone, AI provides specific operating instructions via video or text. Similarly, if a user wants to know how to shop online, AI provides step-by-step guidance. Furthermore, the AI ​​monitors the user's learning progress and level of understanding and provides appropriate feedback. For example, if the user is struggling with a particular operation, the AI ​​provides additional hints and support. This allows users to learn digital technologies at their own pace. This system can help people and organizations with preconceived notions about digital technology to embrace it as something familiar. For example, it is expected that this will help elderly people become proficient in using smartphones and encourage small local businesses to expand their business online. In this way, the Digital Divide Elimination System can support users in learning digital technologies and eliminate preconceived notions about digital technology.

[0059] A system for eliminating the digital divide according to an embodiment includes a reception unit, an analysis unit, a provision unit, a monitoring unit, and a feedback unit. The reception unit allows a user to input questions or problems related to digital technology. The user can input questions or problems related to digital technology in, for example, text format, voice format, or a question related to a specific topic, but is not limited to these examples. The reception unit provides an interface for the user to input questions in text format. The reception unit may also include a microphone or voice recognition technology for receiving voice input. The reception unit may also provide a category selection function for receiving questions related to a specific topic. The analysis unit uses AI to analyze the information received by the reception unit and understand the user's needs. The analysis may be performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit uses natural language processing technology to analyze the intent of the user's question. The analysis unit may also use a machine learning algorithm to analyze the user's past question history and behavioral patterns. The analysis unit may also estimate the user's emotions and reflect them in the analysis results. The providing unit uses AI to provide a solution based on the information obtained by the analyzing unit. Examples of solutions include, but are not limited to, text answers, links, and videos. For example, the providing unit provides specific operating procedures in text format in response to the user's question. The providing unit can also provide links to related websites and resources. Furthermore, the providing unit can also provide videos showing the operating procedures. The monitoring unit monitors the user's learning progress and level of understanding. Monitoring is performed based on, for example, test results, study time, progress status, and the like, but is not limited to, examples. For example, the monitoring unit provides quizzes on the content the user has studied and records the percentage of correct answers. The monitoring unit can also record the user's study time and keep track of progress. Furthermore, the monitoring unit can analyze the user's learning history and evaluate the user's level of understanding. The feedback unit provides feedback based on the information obtained by the monitoring unit.The feedback may include, but is not limited to, text messages, audio messages, video messages, etc. For example, the feedback unit may provide appropriate hints or support when the user is in trouble. The feedback unit may also provide encouraging messages according to the user's learning progress. Furthermore, the feedback unit may suggest additional learning resources based on the user's level of understanding. In this way, the system for bridging the digital divide according to the embodiment may support the user's learning of digital technology and eliminate preconceptions about digital technology.

[0060] The reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can temporarily delay the reception of the question to provide time for the user to relax. Furthermore, if the user is relaxed, the reception unit can immediately accept the question and respond quickly. Furthermore, if the user is in a hurry, the reception unit can immediately accept the question and start processing quickly. This allows the timing of question reception to be adjusted according to the user's emotions, reducing the user's stress and allowing the question to be received at an appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0061] The reception unit can analyze the user's past question history and select an appropriate reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest question formats to be used during a specific time period based on the user's past question history. In this way, by analyzing the user's past question history, the optimal reception method can be provided to the user, and efficient question reception can be achieved. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past question history data into a generation AI and have the generation AI select the optimal reception method.

[0062] When receiving a question, the reception unit can filter the questions based on the user's current situation and areas of interest. For example, the reception unit can prioritize questions related to areas in which the user is currently interested. The reception unit can also adjust the priority of questions based on the user's current situation (e.g., at work, on a break, etc.). Furthermore, the reception unit can filter and accept highly relevant questions based on the user's past behavioral history. In this way, by filtering questions based on the user's current situation and areas of interest, highly relevant questions can be preferentially accepted. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current situation data to a generation AI and cause the generation AI to filter the questions.

[0063] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit postpones questions of lower importance. Furthermore, when the user is relaxed, the reception unit can prioritize questions of higher importance. Furthermore, when the user is in a hurry, the reception unit can prioritize questions of higher urgency. This allows the user's needs to be addressed promptly by determining the priority of questions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of questions.

[0064] When receiving a question, the reception unit can prioritize receiving highly relevant questions based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the area around the user's home. In this way, by taking the user's geographical location information into consideration, questions related to the area can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant questions.

[0065] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit can prioritize receiving questions related to topics that the user frequently mentions on social media. The reception unit can also analyze the content of the user's social media posts and suggest related questions. Furthermore, the reception unit can also accept questions related to topics that the user's social media followers and friends are interested in. In this way, by analyzing the user's social media activity, highly relevant questions can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related questions.

[0066] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. Furthermore, if the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide analysis results that are easy 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-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 presentation method of the analysis.

[0067] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. For example, the analysis unit performs a detailed analysis for questions with high importance. The analysis unit can also perform a concise analysis for questions with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the question. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the question. 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 question importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0068] During analysis, the analysis unit can apply different analysis algorithms depending on the question category. For example, the analysis unit can apply a specialized analysis algorithm to technical questions. The analysis unit can also apply a simple analysis algorithm to general questions. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the question category. This makes it possible to provide highly accurate analysis results by applying the optimal analysis algorithm depending on the question category. 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 question category data to the generation AI and cause the generation AI to select the optimal analysis algorithm.

[0069] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is stressed, the analysis unit can provide a visually easy-to-understand analysis result. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis result of an appropriate length for the user. 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 length of the analysis.

[0070] During analysis, the analysis unit can determine the priority of analysis based on the time when the question was submitted. For example, the analysis unit prioritizes analysis of recently submitted questions. The analysis unit can also adjust the priority of analysis according to the time when the question was submitted. Furthermore, the analysis unit can postpone questions that were submitted earlier. In this way, efficient analysis can be achieved by determining the priority of analysis based on the time when the question was submitted. 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 data on the time when the question was submitted to the generation AI and have the generation AI determine the priority of analysis.

[0071] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, the analysis unit prioritizes analysis of questions with high relevance. The analysis unit can also adjust the order of analysis according to the relevance of the questions. Furthermore, the analysis unit can postpone questions with low relevance. In this way, by adjusting the order of analysis based on the relevance of the questions, highly relevant questions can be analyzed with priority. 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 question relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0072] The providing unit can estimate the user's emotions and adjust the way the solution is presented based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide a detailed solution. If the user is in a hurry, the providing unit can also provide a concise solution that focuses on the main points. Furthermore, if the user is stressed, the providing unit can also provide a visually easy-to-understand solution. By adjusting the way the solution is presented according to the user's emotions, a solution that is easy for the user to understand 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, 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 providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the solution is presented.

[0073] When providing a solution, the providing unit can adjust the level of detail of the solution based on the importance of the question. For example, the providing unit provides a detailed solution for a question with a high level of importance. The providing unit can also provide a concise solution for a question with a low level of importance. Furthermore, the providing unit can adjust the depth of the solution according to the importance of the question. This makes it possible to provide an efficient solution by adjusting the level of detail of the solution according to the importance of the question. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input question importance data to the generating AI and cause the generating AI to adjust the level of detail of the solution.

[0074] When providing a solution, the providing unit can apply different solution algorithms depending on the question category. For example, the providing unit can apply a specialized solution algorithm to a technical question. The providing unit can also apply a simple solution algorithm to a general question. Furthermore, the providing unit can select an optimal solution algorithm depending on the question category. This makes it possible to provide a highly accurate solution by applying the optimal solution algorithm depending on the question category. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input question category data to the generation AI and cause the generation AI to select an optimal solution algorithm.

[0075] The providing unit can estimate the user's emotions and adjust the length of the solution based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide a short, concise solution. Furthermore, if the user is relaxed, the providing unit can provide a detailed solution. Furthermore, if the user is stressed, the providing unit can provide a visually easy-to-understand solution. By adjusting the length of the solution according to the user's emotions, a solution of an appropriate length for the user 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, 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 providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the solution.

[0076] When providing solutions, the providing unit can determine the priority of solutions based on the time when the question was submitted. For example, the providing unit can provide solutions preferentially to recently submitted questions. The providing unit can also adjust the priority of solutions depending on the time when the question was submitted. Furthermore, the providing unit can postpone questions that were submitted earlier. In this way, by determining the priority of solutions based on the time when the question was submitted, efficient solution provision can be achieved. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input question submission time data into the generating AI and have the generating AI determine the priority of solutions.

[0077] When providing solutions, the providing unit can adjust the order of solutions based on the relevance of the questions. For example, the providing unit can provide solutions preferentially to questions with high relevance. The providing unit can also adjust the order of solutions according to the relevance of the questions. Furthermore, the providing unit can postpone questions with low relevance. In this way, by adjusting the order of solutions based on the relevance of the questions, solutions can be provided preferentially to questions with high relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input question relevance data to the generating AI and cause the generating AI to adjust the order of solutions.

[0078] The monitoring unit can estimate the user's emotions and adjust the learning progress monitoring method based on the estimated user emotions. For example, the monitoring unit can perform detailed monitoring when the user is relaxed. Furthermore, the monitoring unit can also perform brief monitoring when the user is in a hurry. Furthermore, the monitoring unit can perform visually easy-to-understand monitoring when the user is stressed. This allows for appropriate monitoring for the user by adjusting the learning progress monitoring method according to the user's emotions. 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 monitoring unit can be performed using, for example, an AI, 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 method.

[0079] During monitoring, the monitoring unit can select the optimal monitoring method by referring to the user's past learning history. The monitoring unit selects the optimal monitoring method based on, for example, the content the user has learned in the past. The monitoring unit can also grasp the user's learning progress from the user's past learning history. Furthermore, the monitoring unit can analyze the user's past learning history and suggest the optimal monitoring method. In this way, the optimal monitoring method can be provided by referring to the user's past learning history. 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 past learning history data into the generation AI and have the generation AI select the optimal monitoring method.

[0080] During monitoring, the monitoring unit can customize the monitoring means based on the user's current learning situation. The monitoring unit customizes the monitoring means according to, for example, the content the user is currently learning. The monitoring unit can also adjust the monitoring frequency based on the user's current learning progress. Furthermore, the monitoring unit can grasp the user's current learning situation in real time and provide the optimal monitoring means. This allows appropriate monitoring by customizing the monitoring means based on the user's current learning situation. 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 current learning situation data into the generation AI and have the generation AI customize the monitoring means.

[0081] The monitoring unit can estimate the user's emotions and determine the priorities of monitoring based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring unit can postpone monitoring of lesser importance. Furthermore, if the user is relaxed, the monitoring unit can prioritize monitoring of higher importance. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring of higher urgency. Thus, by determining the priorities of monitoring according to the user's emotions, important monitoring can be prioritized. The estimation of emotions 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 monitoring unit can be performed using, for example, an AI, 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 determine the priorities of monitoring.

[0082] During monitoring, the monitoring unit can select the optimal monitoring method 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 learning content related to that area. Furthermore, if the user is traveling, the monitoring unit can also prioritize monitoring learning content related to the travel destination. Furthermore, if the user is at home, the monitoring unit can also prioritize monitoring learning content related to the area around the user's home. In this way, by taking the user's geographical location information into account, learning content related to the area can be prioritized for monitoring. 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 information data into the generation AI and cause the generation AI to select the optimal monitoring method.

[0083] During monitoring, the monitoring unit can analyze the user's social media activities and suggest monitoring methods. For example, the monitoring unit prioritizes monitoring of learning content related to topics frequently mentioned by the user on social media. The monitoring unit can also analyze the user's social media posts and suggest related learning content. Furthermore, the monitoring unit can monitor learning content related to topics of interest to the user's social media followers and friends. In this way, by analyzing the user's social media activities, it is possible to prioritize monitoring of highly relevant learning content. 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 execute the suggestion of monitoring methods.

[0084] The feedback unit can estimate the user's emotions and adjust the feedback expression method based on the estimated user's emotions. For example, the feedback unit can provide detailed feedback when the user is relaxed. Furthermore, the feedback unit can provide concise feedback that focuses on the main points when the user is in a hurry. Furthermore, the feedback unit can provide visually easy-to-understand feedback when the user is stressed. This allows the feedback expression method to be adjusted according to the user's emotions, making it possible to provide feedback that is easy 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 feedback unit can be performed using, for example, an AI, or can be performed without using 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 expression method.

[0085] When providing feedback, the feedback unit can select the optimal feedback method by referring to the user's past feedback history. The feedback unit selects the optimal feedback method, for example, based on feedback the user has received in the past. The feedback unit can also understand the effectiveness of feedback from the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history and suggest the optimal feedback method. In this way, the optimal feedback method can be provided by referring to the user's past feedback history. 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 history data into the generation AI and cause the generation AI to select the optimal feedback method.

[0086] When providing feedback, the feedback unit can customize the feedback means based on the user's current situation. The feedback unit customizes the feedback means according to, for example, the content the user is currently learning. The feedback unit can also adjust the frequency of feedback based on the user's current learning progress. Furthermore, the feedback unit can grasp the user's current learning situation in real time and provide the optimal feedback means. In this way, by customizing the feedback means based on the user's current situation, appropriate feedback can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or without AI. For example, the feedback unit can input the user's current situation data into the generation AI and cause the generation AI to customize the feedback means.

[0087] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback unit can postpone providing less important feedback. Furthermore, if the user is relaxed, the feedback unit can also provide more urgent feedback as a top priority when the user is in a hurry. This allows important feedback to be provided preferentially by determining the priority of feedback 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, 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 feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of feedback.

[0088] When providing feedback, the feedback unit can select the optimal feedback method by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback unit can prioritize providing feedback related to that area. Also, if the user is traveling, the feedback unit can prioritize providing feedback related to the travel destination. Furthermore, if the user is at home, the feedback unit can prioritize providing feedback related to the area around the user's home. In this way, by taking the user's geographical location information into account, it is possible to prioritize providing feedback related to the area. 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 information data into the generation AI and cause the generation AI to select the optimal feedback method.

[0089] When providing feedback, the feedback unit can analyze the user's social media activity and suggest a means of feedback. For example, the feedback unit can prioritize providing feedback related to topics that the user frequently mentions on social media. The feedback unit can also analyze the content of the user's social media posts and suggest related feedback. Furthermore, the feedback unit can provide feedback related to topics that the user's social media followers and friends are interested in. In this way, by analyzing the user's social media activity, it is possible to prioritize providing highly relevant feedback. 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 social media data into a generation AI and cause the generation AI to suggest a means of feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, monitoring unit, and feedback unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for a user to input questions or problems related to digital technology. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses AI to analyze the information received by the reception unit and understand the user's needs. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a solution based on the information obtained by the analysis unit. The monitoring unit is realized, for example, by the control unit 46A of the smart device 14 and monitors the user's learning progress and level of understanding. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback based on the information obtained by the monitoring unit. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, monitoring unit, and feedback unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for a user to input questions or problems related to digital technology. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses AI to analyze the information received by the reception unit and understand the user's needs. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a solution based on the information obtained by the analysis unit. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214 and monitors the user's learning progress and level of understanding. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback based on the information obtained by the monitoring unit. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, monitoring unit, and feedback unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface for users to input questions or problems related to digital technology. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses AI to analyze the information received by the reception unit and understand the user's needs. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides solutions based on the information obtained by the analysis unit. The monitoring unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and monitors the user's learning progress and level of understanding. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback based on the information obtained by the monitoring unit. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, monitoring unit, and feedback unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for users to input questions or problems related to digital technology. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses AI to analyze the information received by the reception unit and understand the user's needs. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides solutions based on the information obtained by the analysis unit. The monitoring unit is realized, for example, by the control unit 46A of the robot 414 and monitors the user's learning progress and level of understanding. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback based on the information obtained by the monitoring unit.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] When a user inputs a question or problem related to digital technology, the reception unit can refer to the user's past learning history and suggest the most appropriate question format. For example, if the user has previously input questions in text format, the reception unit can preferentially display a text format interface. Also, if the user has previously input questions in voice format, the reception unit can preferentially provide a voice input interface. Furthermore, if the user frequently asks questions on a specific topic, questions related to that topic can be automatically displayed as candidates. This makes it possible to efficiently receive questions by utilizing the user's past learning history.

[0092] The reception unit can estimate the user's emotions and adjust the method of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can temporarily delay the reception of questions to provide time for the user to relax. Also, if the user is relaxed, the reception unit can immediately receive questions and respond quickly. Furthermore, if the user is in a hurry, the reception unit can immediately receive questions and start processing quickly. In this way, by adjusting the method of receiving questions according to the user's emotions, it is possible to reduce the user's stress and receive questions at an appropriate time.

[0093] The analysis unit can analyze the user's past question history and select the optimal analysis algorithm. For example, if the user has frequently asked technical questions in the past, the analysis unit can apply a specialized analysis algorithm. On the other hand, if the user has asked general questions, the analysis unit can also apply a simple analysis algorithm. Furthermore, the analysis unit can predict and suggest the analysis algorithm to be used at a specific time period based on the user's past question history. This makes it possible to provide the optimal analysis algorithm by utilizing the user's past question history.

[0094] The providing unit can estimate the user's emotions and adjust the way in which the solution is presented based on the estimated user's emotions. For example, if the user is relaxed, a detailed solution can be provided. If the user is in a hurry, a concise solution that focuses on the main points can be provided. Furthermore, if the user is feeling stressed, a visually easy-to-understand solution can be provided. In this way, by adjusting the way in which the solution is presented according to the user's emotions, a solution that is easy for the user to understand can be provided.

[0095] When monitoring a user's learning progress and level of understanding, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. For example, if the user is in a specific area, learning content related to that area can be monitored preferentially. Also, if the user is traveling, learning content related to the travel destination can be monitored preferentially. Furthermore, if the user is at home, learning content related to the area around the user's home can be monitored preferentially. In this way, by taking into account the user's geographical location information, learning content related to the area can be monitored preferentially.

[0096] The feedback unit can estimate the user's emotions and adjust the way in which feedback is expressed based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is in a hurry, concise feedback that focuses on the main points can be provided. Furthermore, if the user is feeling stressed, visually easy-to-understand feedback can be provided. In this way, by adjusting the way in which feedback is expressed depending on the user's emotions, it is possible to provide feedback that is easy for the user to understand.

[0097] The reception unit can analyze the user's social media activity and receive related questions. For example, it can prioritize receiving questions related to topics that the user frequently mentions on social media. The reception unit can also analyze the content of the user's social media posts and suggest related questions. It can also accept questions related to topics that the user's social media followers and friends are interested in. In this way, by analyzing the user's social media activity, it is possible to prioritize receiving highly relevant questions.

[0098] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is feeling stressed, analysis results that are visually easy to understand can be provided. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0099] When providing a solution, the providing unit can adjust the level of detail of the solution based on the importance of the question. For example, a detailed solution can be provided for a question of high importance. A concise solution can also be provided for a question of low importance. Furthermore, the depth of the solution can be adjusted according to the importance of the question. In this way, by adjusting the level of detail of the solution according to the importance of the question, efficient solution provision can be achieved.

[0100] The monitoring unit can estimate the user's emotions and adjust the method of monitoring the learning progress based on the estimated user emotions. For example, if the user is relaxed, detailed monitoring can be performed. If the user is in a hurry, brief monitoring can be performed. Furthermore, if the user is feeling stressed, visually easy-to-understand monitoring can be performed. In this way, by adjusting the method of monitoring the learning progress according to the user's emotions, it is possible to perform monitoring that is appropriate for the user.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit allows users to input questions or problems related to digital technology. Users can input questions in text format, voice format, or on specific topics. The reception unit provides an interface for text input, a microphone for voice input, voice recognition technology, and a category selection function. Step 2: The analysis unit uses AI to analyze the information received by the reception unit and understand the user's needs. The analysis is carried out using natural language processing technology and machine learning algorithms to analyze the intent of the user's question, past question history, behavioral patterns, and emotions. Step 3: The solution provider uses AI to provide solutions based on the information obtained by the analysis provider. Solutions can include text answers, links, videos, etc. Specific operating procedures, links to related websites and resources, and videos showing the operating procedures are provided. Step 4: The monitoring unit monitors the user's learning progress and level of understanding. Monitoring is based on test results, study time, progress, etc., and evaluates the level of understanding by analyzing the quiz correct answer rate, study time, progress, and study history. Step 5: The feedback department provides feedback based on the information obtained by the monitoring department. The feedback may include text, audio, or video messages, and may provide appropriate tips, support, encouragement, or suggest additional learning resources.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions from users; an analysis unit that analyzes the information received by the reception unit; a providing unit that provides a solution based on the information obtained by the analysis unit; a monitoring unit that monitors the user's learning progress and understanding; a feedback unit that provides feedback based on the information obtained by the monitoring unit. A system characterized by:

2. The reception unit Estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past question history and select the appropriate reception method 2. The system of claim 1.

4. The reception unit Filter questions based on your current situation and interests 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize the questions to be accepted based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit When accepting questions, prioritize relevant questions based on the user's geographic location.

2. The system of claim 1.

7. The reception unit When a question is received, the social media activity of the user is analyzed and related questions are received.

2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1.

9. The analysis unit During analysis, adjust the level of detail in the analysis based on the importance of the question 2. The system of claim 1.

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