System

Abstract

To provide vocational training information and scholarship information based on individual interests and skills of children.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information on a child's interest, skill, and career orientation. The analysis unit analyzes the information collected by the collection unit. The generation unit generates job training information and scholarship information based on the information analyzed by the analysis unit. The providing unit provides the information generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently provide vocational training and scholarship information based on a child's individual interests and skills.

[0005] The system according to the embodiment aims to provide vocational training information and scholarship information based on the individual interests and skills of children. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information related to a child's interests, skills, and career aspirations. The analysis unit analyzes the information collected by the collection unit. The generation unit generates vocational training information and scholarship information based on the information analyzed by the analysis unit. The provision unit provides the information generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide vocational training information and scholarship information based on the individual interests and skills of the child. [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) An information provision system according to an embodiment of the present invention generates and provides customized vocational training information and scholarship information based on a child's interests, skills, and career aspirations. The information provision system collects information about a child's interests, skills, and career aspirations, analyzes it using AI, and generates and provides vocational training information and scholarship information optimal for each child. For example, the information provision system collects information about a child's interests, skills, and career aspirations. For example, the information provision system collects information about a child's interests, skills, and career aspirations through questionnaires and interviews. Next, the information provision system uses AI to analyze the collected information. Based on the collected information, the AI ​​understands the child's interests, skills, and career aspirations and generates vocational training information and scholarship information optimal for each child. For example, if a child is interested in science, the AI ​​generates vocational training information and scholarship information related to science. Next, the information provision system provides the generated information to the child and their guardian. For example, the generated vocational training information and scholarship information is provided via email or an app. This allows children and their guardians to obtain information based on their individual interests, skills, and career aspirations. This allows the information provision system to support children in choosing their future careers and ensure that they receive appropriate vocational training and scholarships. This enables the information provision system to provide customized vocational training and scholarship information based on a child's interests, skills, and career aspirations. For example, it can support children in choosing their future careers and help them receive appropriate vocational training and scholarships.

[0029] An information provision system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information regarding a child's interests, skills, and career aspirations. The collection unit collects information, for example, through questionnaires or interviews. The collection unit can also collect information using an online platform. For example, the collection unit collects information through a website or a mobile app. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using, for example, data mining or statistical analysis. The analysis unit can also perform analysis using a machine learning algorithm. For example, the analysis unit performs analysis using a neural network or a support vector machine. The generation unit generates vocational training information or scholarship information based on the information analyzed by the analysis unit. The generation unit generates, for example, online course or internship information. The generation unit can also generate scholarship information based on application requirements and grant amounts. The provision unit provides the information generated by the generation unit. The provision unit provides the information, for example, via email or an app. The provision unit can also provide the information through a web portal. For example, the information can be provided through a web portal equipped with user authentication and search functions. As a result, the information provision system according to the embodiment can provide customized vocational training information and scholarship information based on the child's interests, skills, and career aspirations. For example, it can support the child's future career choices and enable them to receive appropriate vocational training and scholarships.

[0030] The information provision system further includes an online collection unit that collects information using an online platform. The online collection unit collects information using the online platform. The online platform includes, for example, a website or a mobile app. For example, the online collection unit conducts a survey through the website to collect information regarding children's interests, skills, and career aspirations. The online collection unit can also conduct interviews through a mobile app to collect information. For example, questions regarding children's interests and skills are asked through the app and the answers are collected. This improves the efficiency of information collection by using the online platform. Some or all of the above-mentioned processing in the online collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the online collection unit may input data collected through the website or mobile app into a generation AI and have the generation AI analyze the data.

[0031] The information provision system further includes a machine learning analysis unit that performs analysis using a machine learning algorithm. The machine learning analysis unit performs analysis using a machine learning algorithm. Examples of machine learning algorithms include neural networks and support vector machines. The machine learning analysis unit analyzes information related to children's interests, skills, and career aspirations using, for example, a neural network. The machine learning analysis unit can also analyze information using a support vector machine. For example, the support vector machine is used to classify collected data and obtain analysis results. The machine learning analysis unit can also perform analysis by combining different machine learning algorithms. For example, a neural network and a support vector machine are combined to improve the accuracy of the analysis. As a result, the use of a machine learning algorithm improves the accuracy of the analysis. Some or all of the above-described processing in the machine learning analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the machine learning analysis unit can input collected data to a generation AI and have the generation AI analyze the data.

[0032] The information provision system further includes a web provision unit that provides the generated information through a web portal. The web provision unit provides the generated information through the web portal. The web portal includes, for example, user authentication and a search function. The web provision unit, for example, performs user authentication and provides information to authenticated users. The web provision unit can also provide a search function to enable users to search for necessary information. For example, users can search for vocational training information or scholarship information and obtain the necessary information. The web provision unit also has a function to visually display the generated information. For example, the information can be visually displayed using graphs or charts and provided in a format that is easy for users to understand. This makes it easier for users to access the information by providing it through the web portal. Some or all of the above-described processing in the web provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the web provision unit may input the generated information to a generation AI and cause the generation AI to execute a method for displaying the information.

[0033] The information providing system further includes a privacy protection unit for privacy protection. The privacy protection unit provides a function for privacy protection. The privacy protection unit performs, for example, data encryption and access control. Data encryption includes encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The privacy protection unit encrypts collected data using, for example, AES. The privacy protection unit can also perform access control to ensure that only authorized users can access the data. For example, it performs user authentication and allows only authenticated users to access the data. The privacy protection unit can also anonymize data. For example, it can anonymize data by deleting personally identifiable information. Thus, the privacy protection unit protects user privacy. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit can input collected data to a generation AI and have the generation AI encrypt and anonymize the data.

[0034] Furthermore, the information providing system includes a data update unit that performs periodic data updates. The data update unit performs periodic data updates. The data update unit updates data, for example, daily or weekly, in real time. The data update unit updates data, for example, daily, to provide the latest information. The data update unit can also update data weekly. For example, it updates data every Monday to provide the latest information. Furthermore, the data update unit can also update data in real time. For example, it updates data every time new information is collected, to always provide the latest information. In this way, by performing periodic data updates, it is possible to always provide the latest information. Some or all of the above-described processing in the data update unit may be performed using, for example, AI, or may be performed without using AI. For example, the data update unit inputs collected data to a generation AI and causes the generation AI to update the data.

[0035] The collection unit can analyze the child's past activity history and select the optimal information collection method. For example, the collection unit can include related questions in the questionnaire based on the history of workshops and events that the child has previously participated in. The collection unit can also analyze data from learning apps that the child has previously used to collect information about areas of interest. Furthermore, the collection unit can customize skill-related questions by referring to projects and reports that the child has previously submitted. This enables more effective information collection by analyzing the past activity history. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past activity history data into the generation AI and cause the generation AI to select the optimal information collection method.

[0036] When collecting information, the collection unit can filter the information based on the child's current learning situation and areas of interest. For example, the collection unit can include questions related to the subjects the child is currently studying in a questionnaire. The collection unit can also prioritize collecting information related to areas in which the child is interested. Furthermore, the collection unit can collect information at an appropriate time depending on the child's learning progress. This allows more relevant information to be collected by filtering information based on the child's current learning situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the child's current learning situation and areas of interest into a generation AI and have the generation AI perform information filtering.

[0037] When collecting information, the collection unit can select the optimal collection means depending on the child's input method. For example, if the child prefers voice input, the collection unit can collect information by conducting a voice interview. Alternatively, if the child prefers text input, the collection unit can provide an online form to collect information. Furthermore, if the child is good at expressing information using images, the collection unit can provide an image upload function to collect information. This improves the efficiency of information collection by selecting the optimal collection means depending on the child's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the child's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the child's geographical location information. For example, the collection unit can prioritize collecting information about vocational training programs in the area where the child lives. The collection unit can also collect information about events held near the child's school. Furthermore, the collection unit can collect information that matches the application requirements for a scholarship based on the child's geographical location. In this way, more relevant information can be collected by taking the geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0039] When collecting information, the collection unit can analyze the child's social media activities and collect relevant information. For example, the collection unit can collect information about areas of interest based on the accounts the child follows on social media. The collection unit can also analyze the child's social media posts to collect information about relevant vocational training programs. Furthermore, the collection unit can refer to the activities of the child's friends on social media to collect relevant scholarship information. This allows for more relevant information to be collected by analyzing social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media activity data into a generation AI and cause the generation AI to collect relevant information.

[0040] When collecting information, the collection unit can customize the collection method by reflecting the child's past feedback. For example, the collection unit can adjust the content of questionnaire questions based on feedback provided by the child in the past. The collection unit can also prioritize the use of information collection methods that the child has previously preferred. Furthermore, the collection unit can also customize the interview progress method by referring to the child's past feedback. This enables more effective information collection by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. Furthermore, the analysis unit can adjust the display order of the analysis results according to the importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. 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 information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a skill matching algorithm to vocational training information. The analysis unit can also apply an application condition matching algorithm to scholarship information. Furthermore, the analysis unit can apply an algorithm that extracts highly relevant information to areas of interest. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data into the generation AI and have the generation AI apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the child's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on feedback provided by the child in the past. The analysis unit can also improve the accuracy of the analysis by referring to the child's past analysis results. Furthermore, the analysis unit can adjust the analysis priority based on the child's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. 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 past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of information submitted earlier. The analysis unit can also postpone analysis of information submitted later. Furthermore, the analysis unit can adjust the priority of analysis according to the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. 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 of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance. In this way, by adjusting the order of analysis based on the relevance of the information, more important information can be prioritized in the analysis. 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 information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the child's level of expertise. For example, if the child's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the child's level of expertise is low, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the child's level of expertise. By adjusting the use of technical terminology in the analysis according to the child's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the child's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0047] The generation unit can adjust the level of detail of the generated information based on the importance of the information during generation. For example, the generation unit generates detailed information for information with high importance. The generation unit can also generate concise information for information with low importance. Furthermore, the generation unit can adjust the display order of the generated information according to the importance. This enables efficient information generation by adjusting the level of detail of the generated information based on the importance of the information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the generated information.

[0048] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit can apply a skill matching algorithm to vocational training information. The generation unit can also apply an application condition matching algorithm to scholarship information. Furthermore, the generation unit can apply an algorithm that extracts highly relevant information to areas of interest. This improves the accuracy of generation by applying an appropriate generation algorithm depending on the category of information. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input information category data into the generation AI and have the generation AI apply the generation algorithm.

[0049] During generation, the generation unit can improve the accuracy of generation by referring to the child's past generation results. The generation unit, for example, adjusts the generation algorithm based on feedback provided by the child in the past. The generation unit can also improve the accuracy of generation by referring to the child's past generation results. Furthermore, the generation unit can adjust the generation priority based on the child's past generation results. In this way, the accuracy of generation is improved by referring to the past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0050] The generation unit can determine the generation priority based on the time of submission of information at the time of generation. For example, the generation unit prioritizes the generation of information that was submitted earlier. The generation unit can also postpone the generation of information that was submitted later. Furthermore, the generation unit can adjust the generation priority according to the submission time. This enables efficient information generation by determining the generation priority based on the time of submission of information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information submission time data into the generation AI and have the generation AI determine the generation priority.

[0051] The generation unit can adjust the order of generation based on the relevance of the information during generation. For example, the generation unit prioritizes the generation of highly relevant information. The generation unit can also postpone the generation of less relevant information. Furthermore, the generation unit can adjust the order of generation according to the relevance. In this way, by adjusting the order of generation based on the relevance of the information, more important information can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of generation.

[0052] During generation, the generation unit can adjust the use of technical terminology in the generation according to the child's level of expertise. For example, if the child's level of expertise is high, the generation unit can provide information that uses a lot of technical terminology. Furthermore, if the child's level of expertise is low, the generation unit can also provide concise, easy-to-understand information. Furthermore, the generation unit can adjust the way information is presented according to the child's level of expertise. By adjusting the use of technical terminology in the generation according to the child's level of expertise, more understandable information can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0053] When providing the data, the providing unit can select the optimal display method by referring to the child's past operation history. For example, the providing unit can preferentially use a display method that the child has previously preferred. The providing unit can also suggest the optimal display method based on the child's past operation history. Furthermore, the providing unit can also customize the display content by referring to the child's past operation history. In this way, by referring to the past operation history, a more effective display method can be provided. 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 past operation history data into the generating AI and cause the generating AI to select the optimal display method.

[0054] The providing unit can customize the display content according to the child's current task at the time of providing. For example, the providing unit prioritizes displaying information related to the task the child is currently working on. The providing unit can also adjust the display content according to the child's current learning situation. Furthermore, the providing unit can also customize the display content based on the child's current area of ​​interest. This allows for customizing the display content according to the current task, thereby providing more relevant information. 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 current task data to a generating AI and cause the generating AI to customize the display content.

[0055] The providing unit can select the optimal display method by taking into consideration the child's device information when providing the data. For example, if the child is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the child is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the child is using a smartwatch, the providing unit can also provide a simple and highly visible display method. This makes it possible to provide a more appropriate display method by taking the device information into consideration. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input device information data to the generating AI and cause the generating AI to select the optimal display method.

[0056] The providing unit can make the display content multilingual according to the child's language setting when providing the content. The providing unit automatically sets the display content based on, for example, the language setting of the child's device. The providing unit can also provide a language switching function when the child uses multiple languages. Furthermore, if the child selects a specific language, the providing unit can provide the display content in that language. This makes it possible to accommodate a larger number of users by making the display content multilingual according to the language setting. 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 language setting data to a generating AI and cause the generating AI to perform multilingual support for the display content.

[0057] When collecting online data, the online collection unit can analyze the child's past online activity history and select the optimal collection method. For example, the online collection unit can include relevant questions in a questionnaire based on the history of online events the child has previously participated in. The online collection unit can also analyze data from online learning platforms the child has previously used to collect information about areas of interest. Furthermore, the online collection unit can customize skill-related questions based on online projects and reports the child has previously submitted. This allows for more effective information collection by analyzing the past online activity history. Some or all of the above-described processing in the online collection unit can be performed using, for example, AI, or without AI. For example, the online collection unit can input past online activity history data into a generation AI and have the generation AI select the optimal collection method.

[0058] During online collection, the online collection unit can prioritize collecting highly relevant information by taking into account the child's geographical location information. For example, the online collection unit can prioritize collecting information about vocational training programs in the area where the child lives. The online collection unit can also collect information about events held near the child's school. Furthermore, the online collection unit can collect information that matches scholarship application requirements based on the child's geographical location. In this way, more relevant information can be collected by taking geographical location information into consideration. Some or all of the above-described processing in the online collection unit may be performed using, or without, AI. For example, the online collection unit can input geographical location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0059] During machine learning analysis, the machine learning analysis unit can optimize the analysis algorithm by referring to past learning data. The machine learning analysis unit, for example, adjusts the analysis algorithm based on past learning data. The machine learning analysis unit can also improve the accuracy of the analysis by referring to past learning data. Furthermore, the machine learning analysis unit can adjust the analysis priority based on past learning data. In this way, the accuracy of the analysis algorithm is improved by referring to past learning data. Some or all of the above-mentioned processing in the machine learning analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the machine learning analysis unit can input past learning data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0060] During machine learning analysis, the machine learning analysis unit can integrate information from different data sources to enrich the analysis data. For example, the machine learning analysis unit integrates and analyzes school grade data and data from an online learning platform. The machine learning analysis unit can also integrate and analyze social media activity data and survey data. Furthermore, the machine learning analysis unit can also integrate and analyze past project data and current learning progress data. In this way, by integrating information from different data sources, the accuracy of the analysis data is improved. Some or all of the above-mentioned processing in the machine learning analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the machine learning analysis unit can input data from different data sources into the generation AI and have the generation AI integrate the analysis data.

[0061] When providing the web content, the web providing unit can select the optimal display method by referring to the child's past web operation history. For example, the web providing unit can prioritize the use of a display method that the child previously preferred. The web providing unit can also suggest the optimal display method based on the child's past operation history. Furthermore, the web providing unit can also customize the display content by referring to the child's past operation history. In this way, by referring to the past web operation history, a more effective display method can be provided. Some or all of the above-described processing in the web providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the web providing unit can input past web operation history data into the generation AI and cause the generation AI to select the optimal display method.

[0062] The web providing unit can select the optimal display method by taking into account the child's device information when providing the web. For example, if the child is using a smartphone, the web providing unit can provide a display method that matches the screen size. Furthermore, if the child is using a tablet, the web providing unit can provide a display method optimized for a large screen. Furthermore, if the child is using a smartwatch, the web providing unit can provide a simple and highly visible display method. This allows for a more appropriate display method to be provided by taking device information into account. Some or all of the above-described processing in the web providing unit may be performed using, or without, AI. For example, the web providing unit can input device information data into the generation AI and cause the generation AI to select the optimal display method.

[0063] During privacy protection, the privacy protection unit can select the optimal protection method by referring to the child's past privacy setting history. For example, the privacy protection unit can suggest the optimal protection method based on the child's past privacy settings. The privacy protection unit can also customize the protection method by referring to the child's past privacy setting history. Furthermore, the privacy protection unit can adjust the priority of privacy protection based on the child's past privacy setting history. In this way, more effective privacy protection can be provided by referring to the past privacy setting history. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit can input past privacy setting history data into a generation AI and have the generation AI select the optimal protection method.

[0064] The privacy protection unit can select the optimal protection method during privacy protection, taking into account the child's geographical location information. The privacy protection unit provides the optimal protection method, for example, based on privacy laws and regulations in the area where the child lives. The privacy protection unit can also adjust the protection method based on the privacy policy of the child's school. Furthermore, the privacy protection unit can determine the priority of privacy protection based on the child's geographical location. This allows for more appropriate privacy protection by taking geographical location information into consideration. Some or all of the above-described processing in the privacy protection unit may be performed using AI, for example, or without AI. For example, the privacy protection unit can input geographical location information data into a generation AI and have the generation AI select the optimal protection method.

[0065] When updating data, the data update unit can select the optimal update method by referring to past data update history. The data update unit, for example, proposes the optimal update method based on the past data update history. The data update unit can also customize the update method by referring to the past data update history. Furthermore, the data update unit can also adjust the priority of data updates based on the past data update history. In this way, more effective data updates can be provided by referring to the past data update history. Some or all of the above-mentioned processing in the data update unit may be performed using, for example, AI, or may be performed without using AI. For example, the data update unit can input past data update history data into a generation AI and have the generation AI select the optimal update method.

[0066] When updating data, the data update unit can integrate information from different data sources to enrich the updated data. For example, the data update unit integrates and updates school grade data with data from an online learning platform. The data update unit can also integrate and update social media activity data with survey data. Furthermore, the data update unit can also integrate and update past project data with current learning progress data. This improves the accuracy of the updated data by integrating information from different data sources. Some or all of the above-described processing in the data update unit may be performed using, for example, AI, or may be performed without using AI. For example, the data update unit can input data from different data sources into a generation AI and have the generation AI integrate the updated data.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The information provision system can further include a learning style analysis unit that analyzes a child's learning style. The learning style analysis unit can, for example, determine whether a child is a visual learner or an auditory learner and provide information in an appropriate format. For example, visual learners can be provided with materials that make extensive use of graphs and diagrams, while auditory learners can be provided with audio guides. This makes it possible to provide information that suits each child's learning style.

[0069] The information provision system may further include a social skills assessment unit that assesses the child's social skills. The social skills assessment unit may assess, for example, the child's friendships and communication skills, and provide appropriate job training information and scholarship information. For example, a child with high communication skills may be provided with information about leadership training and team projects, while a child with low communication skills may be provided with information about individual instruction and mentoring programs. This makes it possible to provide information tailored to the child's social skills.

[0070] The information provision system can further include a hobby and special skill analysis unit that takes into account the child's hobbies and special skills. The hobby and special skill analysis unit analyzes, for example, whether the child is interested in sports, music, art, etc., and provides related vocational training information and scholarship information. For example, a child interested in sports can be provided with sports-related scholarship information and training programs, and a child interested in music can be provided with music school scholarship information and workshop information. This makes it possible to provide information tailored to the child's hobbies and special skills.

[0071] The information provision system may further include a goal setting unit that sets future goals for the child. The goal setting unit, for example, allows the child to set a future goal and provides steps for moving toward that goal. For example, a child who wants to become a doctor may be provided with medical-related vocational training information and scholarship information, and a child who wants to become an engineer may be provided with engineering-related vocational training information and scholarship information. This makes it possible to provide information according to the child's future goals.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection department collects information about the child's interests, skills, and career aspirations. The collection department can collect information through questionnaires or interviews. They can also use online platforms to collect information through websites or mobile apps. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using data mining or statistical analysis. The analysis unit can also use machine learning algorithms, such as neural networks or support vector machines. Step 3: The generation unit generates vocational training information and scholarship information based on the information analyzed by the analysis unit. The generation unit can generate online course and internship information. It can also generate scholarship information based on application requirements and grant amounts. Step 4: The providing unit provides the information generated by the generating unit. The providing unit can provide the information via email or an app. It can also provide the information through a web portal that has user authentication and search functions.

[0074] (Example 2) An information provision system according to an embodiment of the present invention generates and provides customized vocational training information and scholarship information based on a child's interests, skills, and career aspirations. The information provision system collects information about a child's interests, skills, and career aspirations, analyzes it using AI, and generates and provides vocational training information and scholarship information optimal for each child. For example, the information provision system collects information about a child's interests, skills, and career aspirations. For example, the information provision system collects information about a child's interests, skills, and career aspirations through questionnaires and interviews. Next, the information provision system uses AI to analyze the collected information. Based on the collected information, the AI ​​understands the child's interests, skills, and career aspirations and generates vocational training information and scholarship information optimal for each child. For example, if a child is interested in science, the AI ​​generates vocational training information and scholarship information related to science. Next, the information provision system provides the generated information to the child and their guardian. For example, the generated vocational training information and scholarship information is provided via email or an app. This allows children and their guardians to obtain information based on their individual interests, skills, and career aspirations. This allows the information provision system to support children in choosing their future careers and ensure that they receive appropriate vocational training and scholarships. This enables the information provision system to provide customized vocational training and scholarship information based on a child's interests, skills, and career aspirations. For example, it can support children in choosing their future careers and help them receive appropriate vocational training and scholarships.

[0075] An information provision system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information regarding a child's interests, skills, and career aspirations. The collection unit collects information, for example, through questionnaires or interviews. The collection unit can also collect information using an online platform. For example, the collection unit collects information through a website or a mobile app. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using, for example, data mining or statistical analysis. The analysis unit can also perform analysis using a machine learning algorithm. For example, the analysis unit performs analysis using a neural network or a support vector machine. The generation unit generates vocational training information or scholarship information based on the information analyzed by the analysis unit. The generation unit generates, for example, online course or internship information. The generation unit can also generate scholarship information based on application requirements and grant amounts. The provision unit provides the information generated by the generation unit. The provision unit provides the information, for example, via email or an app. The provision unit can also provide the information through a web portal. For example, the information can be provided through a web portal equipped with user authentication and search functions. As a result, the information provision system according to the embodiment can provide customized vocational training information and scholarship information based on the child's interests, skills, and career aspirations. For example, it can support the child's future career choices and enable them to receive appropriate vocational training and scholarships.

[0076] The information provision system further includes an online collection unit that collects information using an online platform. The online collection unit collects information using the online platform. The online platform includes, for example, a website or a mobile app. For example, the online collection unit conducts a survey through the website to collect information regarding children's interests, skills, and career aspirations. The online collection unit can also conduct interviews through a mobile app to collect information. For example, questions regarding children's interests and skills are asked through the app and the answers are collected. This improves the efficiency of information collection by using the online platform. Some or all of the above-mentioned processing in the online collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the online collection unit may input data collected through the website or mobile app into a generation AI and have the generation AI analyze the data.

[0077] The information provision system further includes a machine learning analysis unit that performs analysis using a machine learning algorithm. The machine learning analysis unit performs analysis using a machine learning algorithm. Examples of machine learning algorithms include neural networks and support vector machines. The machine learning analysis unit analyzes information related to children's interests, skills, and career aspirations using, for example, a neural network. The machine learning analysis unit can also analyze information using a support vector machine. For example, the support vector machine is used to classify collected data and obtain analysis results. The machine learning analysis unit can also perform analysis by combining different machine learning algorithms. For example, a neural network and a support vector machine are combined to improve the accuracy of the analysis. As a result, the use of a machine learning algorithm improves the accuracy of the analysis. Some or all of the above-described processing in the machine learning analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the machine learning analysis unit can input collected data to a generation AI and have the generation AI analyze the data.

[0078] The information provision system further includes a web provision unit that provides the generated information through a web portal. The web provision unit provides the generated information through the web portal. The web portal includes, for example, user authentication and a search function. The web provision unit, for example, performs user authentication and provides information to authenticated users. The web provision unit can also provide a search function to enable users to search for necessary information. For example, users can search for vocational training information or scholarship information and obtain the necessary information. The web provision unit also has a function to visually display the generated information. For example, the information can be visually displayed using graphs or charts and provided in a format that is easy for users to understand. This makes it easier for users to access the information by providing it through the web portal. Some or all of the above-described processing in the web provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the web provision unit may input the generated information to a generation AI and cause the generation AI to execute a method for displaying the information.

[0079] The information providing system further includes a privacy protection unit for privacy protection. The privacy protection unit provides a function for privacy protection. The privacy protection unit performs, for example, data encryption and access control. Data encryption includes encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The privacy protection unit encrypts collected data using, for example, AES. The privacy protection unit can also perform access control to ensure that only authorized users can access the data. For example, it performs user authentication and allows only authenticated users to access the data. The privacy protection unit can also anonymize data. For example, it can anonymize data by deleting personally identifiable information. Thus, the privacy protection unit protects user privacy. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit can input collected data to a generation AI and have the generation AI encrypt and anonymize the data.

[0080] Furthermore, the information providing system includes a data update unit that performs periodic data updates. The data update unit performs periodic data updates. The data update unit updates data, for example, daily or weekly, in real time. The data update unit updates data, for example, daily, to provide the latest information. The data update unit can also update data weekly. For example, it updates data every Monday to provide the latest information. Furthermore, the data update unit can also update data in real time. For example, it updates data every time new information is collected, to always provide the latest information. In this way, by performing periodic data updates, it is possible to always provide the latest information. Some or all of the above-described processing in the data update unit may be performed using, for example, AI, or may be performed without using AI. For example, the data update unit inputs collected data to a generation AI and causes the generation AI to update the data.

[0081] The collection unit can estimate the child's emotions and adjust the timing of information collection based on the estimated child's emotions. For example, the collection unit can conduct a questionnaire when the child is relaxed to collect accurate information. The collection unit can also conduct an interview when the child is excited to elicit details about activities that interest the child. Furthermore, the collection unit can submit an online form when the child is concentrating to collect information about skills. This allows for more accurate information collection by adjusting the timing of information collection according to the child'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-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the child's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0082] The collection unit can analyze the child's past activity history and select the optimal information collection method. For example, the collection unit can include related questions in the questionnaire based on the history of workshops and events that the child has previously participated in. The collection unit can also analyze data from learning apps that the child has previously used to collect information about areas of interest. Furthermore, the collection unit can customize skill-related questions by referring to projects and reports that the child has previously submitted. This enables more effective information collection by analyzing the past activity history. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past activity history data into the generation AI and cause the generation AI to select the optimal information collection method.

[0083] When collecting information, the collection unit can filter the information based on the child's current learning situation and areas of interest. For example, the collection unit can include questions related to the subjects the child is currently studying in a questionnaire. The collection unit can also prioritize collecting information related to areas in which the child is interested. Furthermore, the collection unit can collect information at an appropriate time depending on the child's learning progress. This allows more relevant information to be collected by filtering information based on the child's current learning situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the child's current learning situation and areas of interest into a generation AI and have the generation AI perform information filtering.

[0084] When collecting information, the collection unit can select the optimal collection means depending on the child's input method. For example, if the child prefers voice input, the collection unit can collect information by conducting a voice interview. Alternatively, if the child prefers text input, the collection unit can provide an online form to collect information. Furthermore, if the child is good at expressing information using images, the collection unit can provide an image upload function to collect information. This improves the efficiency of information collection by selecting the optimal collection means depending on the child's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the child's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0085] The collection unit can estimate the child's emotions and determine the priority of information to be collected based on the estimated child's emotions. For example, if the child is excited, the collection unit can prioritize collecting information related to the child's areas of interest. Furthermore, if the child is relaxed, the collection unit can also prioritize collecting detailed information related to the child's skills. Furthermore, if the child is concentrating, the collection unit can prioritize collecting information related to the child's career aspirations. Thus, by prioritizing information based on the child's emotions, more important information can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of the information.

[0086] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the child's geographical location information. For example, the collection unit can prioritize collecting information about vocational training programs in the area where the child lives. The collection unit can also collect information about events held near the child's school. Furthermore, the collection unit can collect information that matches the application requirements for a scholarship based on the child's geographical location. In this way, more relevant information can be collected by taking the geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0087] When collecting information, the collection unit can analyze the child's social media activities and collect relevant information. For example, the collection unit can collect information about areas of interest based on the accounts the child follows on social media. The collection unit can also analyze the child's social media posts to collect information about relevant vocational training programs. Furthermore, the collection unit can refer to the activities of the child's friends on social media to collect relevant scholarship information. This allows for more relevant information to be collected by analyzing social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media activity data into a generation AI and cause the generation AI to collect relevant information.

[0088] When collecting information, the collection unit can customize the collection method by reflecting the child's past feedback. For example, the collection unit can adjust the content of questionnaire questions based on feedback provided by the child in the past. The collection unit can also prioritize the use of information collection methods that the child has previously preferred. Furthermore, the collection unit can also customize the interview progress method by referring to the child's past feedback. This enables more effective information collection by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0089] The analysis unit can estimate the child's emotions and adjust the way the analysis is presented based on the estimated child's emotions. For example, if the child is relaxed, the analysis unit can provide detailed analysis results. If the child is nervous, the analysis unit can also provide concise and to-the-point analysis results. If the child is excited, the analysis unit can also provide visually appealing analysis results. By adjusting the way the analysis is presented based on the child's emotions, more understandable analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the child's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0090] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. Furthermore, the analysis unit can adjust the display order of the analysis results according to the importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. 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 information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0091] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a skill matching algorithm to vocational training information. The analysis unit can also apply an application condition matching algorithm to scholarship information. Furthermore, the analysis unit can apply an algorithm that extracts highly relevant information to areas of interest. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data into the generation AI and have the generation AI apply the analysis algorithm.

[0092] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the child's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on feedback provided by the child in the past. The analysis unit can also improve the accuracy of the analysis by referring to the child's past analysis results. Furthermore, the analysis unit can adjust the analysis priority based on the child's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. 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 past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0093] The analysis unit can estimate the child's emotions and adjust the length of the analysis based on the estimated child's emotions. For example, if the child is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the child is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the child is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis according to the child's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the child's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0094] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of information submitted earlier. The analysis unit can also postpone analysis of information submitted later. Furthermore, the analysis unit can adjust the priority of analysis according to the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. 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 of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0095] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance. In this way, by adjusting the order of analysis based on the relevance of the information, more important information can be prioritized in the analysis. 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 information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0096] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the child's level of expertise. For example, if the child's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the child's level of expertise is low, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the child's level of expertise. By adjusting the use of technical terminology in the analysis according to the child's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the child's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0097] The generation unit can estimate the child's emotions and adjust the way the information is presented based on the estimated child's emotions. For example, if the child is relaxed, the generation unit can provide detailed information. If the child is nervous, the generation unit can also provide concise, to-the-point information. If the child is excited, the generation unit can also provide visually appealing information. This allows for more appropriate information to be provided by adjusting the way the information is presented according to the child'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 generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the way the information is presented.

[0098] The generation unit can adjust the level of detail of the generated information based on the importance of the information during generation. For example, the generation unit generates detailed information for information with high importance. The generation unit can also generate concise information for information with low importance. Furthermore, the generation unit can adjust the display order of the generated information according to the importance. This enables efficient information generation by adjusting the level of detail of the generated information based on the importance of the information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the generated information.

[0099] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit can apply a skill matching algorithm to vocational training information. The generation unit can also apply an application condition matching algorithm to scholarship information. Furthermore, the generation unit can apply an algorithm that extracts highly relevant information to areas of interest. This improves the accuracy of generation by applying an appropriate generation algorithm depending on the category of information. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input information category data into the generation AI and have the generation AI apply the generation algorithm.

[0100] During generation, the generation unit can improve the accuracy of generation by referring to the child's past generation results. The generation unit, for example, adjusts the generation algorithm based on feedback provided by the child in the past. The generation unit can also improve the accuracy of generation by referring to the child's past generation results. Furthermore, the generation unit can adjust the generation priority based on the child's past generation results. In this way, the accuracy of generation is improved by referring to the past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0101] The generation unit can estimate the child's emotions and adjust the length of the information to be generated based on the estimated child's emotions. For example, if the child is in a hurry, the generation unit can provide short, concise information. Furthermore, if the child is relaxed, the generation unit can provide detailed information. Furthermore, if the child is excited, the generation unit can provide visually appealing information. By adjusting the length of the information according to the child's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the length of the information.

[0102] The generation unit can determine the generation priority based on the time of submission of information at the time of generation. For example, the generation unit prioritizes the generation of information that was submitted earlier. The generation unit can also postpone the generation of information that was submitted later. Furthermore, the generation unit can adjust the generation priority according to the submission time. This enables efficient information generation by determining the generation priority based on the time of submission of information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information submission time data into the generation AI and have the generation AI determine the generation priority.

[0103] The generation unit can adjust the order of generation based on the relevance of the information during generation. For example, the generation unit prioritizes the generation of highly relevant information. The generation unit can also postpone the generation of less relevant information. Furthermore, the generation unit can adjust the order of generation according to the relevance. In this way, by adjusting the order of generation based on the relevance of the information, more important information can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of generation.

[0104] During generation, the generation unit can adjust the use of technical terminology in the generation according to the child's level of expertise. For example, if the child's level of expertise is high, the generation unit can provide information that uses a lot of technical terminology. Furthermore, if the child's level of expertise is low, the generation unit can also provide concise, easy-to-understand information. Furthermore, the generation unit can adjust the way information is presented according to the child's level of expertise. By adjusting the use of technical terminology in the generation according to the child's level of expertise, more understandable information can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0105] The providing unit can estimate the child's emotions and adjust the display method of the information to be provided based on the estimated child's emotions. For example, if the child is relaxed, the providing unit can display detailed information. If the child is nervous, the providing unit can also display concise and to-the-point information. Furthermore, if the child is excited, the providing unit can display visually appealing information. This allows for adjusting the information display method according to the child's emotions, thereby providing more appropriate information. 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 providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the information display method.

[0106] When providing the data, the providing unit can select the optimal display method by referring to the child's past operation history. For example, the providing unit can preferentially use a display method that the child has previously preferred. The providing unit can also suggest the optimal display method based on the child's past operation history. Furthermore, the providing unit can also customize the display content by referring to the child's past operation history. In this way, by referring to the past operation history, a more effective display method can be provided. 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 past operation history data into the generating AI and cause the generating AI to select the optimal display method.

[0107] The providing unit can customize the display content according to the child's current task at the time of providing. For example, the providing unit prioritizes displaying information related to the task the child is currently working on. The providing unit can also adjust the display content according to the child's current learning situation. Furthermore, the providing unit can also customize the display content based on the child's current area of ​​interest. This allows for customizing the display content according to the current task, thereby providing more relevant information. 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 current task data to a generating AI and cause the generating AI to customize the display content.

[0108] The providing unit can estimate the child's emotions and adjust the operation procedures of the information to be provided based on the estimated child's emotions. For example, if the child is relaxed, the providing unit can provide detailed operation procedures. Furthermore, if the child is nervous, the providing unit can provide concise and to-the-point operation procedures. Furthermore, if the child is excited, the providing unit can provide visually appealing operation procedures. This allows for adjusting the operation procedures according to the child's emotions to provide more appropriate operation procedures. 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 providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the operation procedures.

[0109] The providing unit can select the optimal display method by taking into consideration the child's device information when providing the data. For example, if the child is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the child is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the child is using a smartwatch, the providing unit can also provide a simple and highly visible display method. This makes it possible to provide a more appropriate display method by taking the device information into consideration. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input device information data to the generating AI and cause the generating AI to select the optimal display method.

[0110] The providing unit can make the display content multilingual according to the child's language setting when providing the content. The providing unit automatically sets the display content based on, for example, the language setting of the child's device. The providing unit can also provide a language switching function when the child uses multiple languages. Furthermore, if the child selects a specific language, the providing unit can provide the display content in that language. This makes it possible to accommodate a larger number of users by making the display content multilingual according to the language setting. 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 language setting data to a generating AI and cause the generating AI to perform multilingual support for the display content.

[0111] The online collection unit can estimate the child's emotions and adjust the timing of online collection based on the estimated child's emotions. For example, the online collection unit can conduct an online questionnaire when the child is relaxed. The online collection unit can also conduct an online interview when the child is excited. Furthermore, the online collection unit can send an online form when the child is concentrating. This allows for more accurate information collection by adjusting the timing of online collection according to the child'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-mentioned processing in the online collection unit can be performed using, for example, AI, or without AI. For example, the online collection unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the timing of online collection.

[0112] When collecting online data, the online collection unit can analyze the child's past online activity history and select the optimal collection method. For example, the online collection unit can include relevant questions in a questionnaire based on the history of online events the child has previously participated in. The online collection unit can also analyze data from online learning platforms the child has previously used to collect information about areas of interest. Furthermore, the online collection unit can customize skill-related questions based on online projects and reports the child has previously submitted. This allows for more effective information collection by analyzing the past online activity history. Some or all of the above-described processing in the online collection unit can be performed using, for example, AI, or without AI. For example, the online collection unit can input past online activity history data into a generation AI and have the generation AI select the optimal collection method.

[0113] The online collection unit can estimate the child's emotions and prioritize the information to be collected online based on the estimated child's emotions. For example, if the child is excited, the online collection unit can prioritize collecting information related to the child's areas of interest. Furthermore, if the child is relaxed, the online collection unit can also prioritize collecting detailed information related to the child's skills. Furthermore, if the child is concentrating, the online collection unit can prioritize collecting information related to the child's career aspirations. By prioritizing information based on the child's emotions, more important information can be collected preferentially. The 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 online collection unit can be performed using, for example, AI, or without AI. For example, the online collection unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of the information to be collected online.

[0114] During online collection, the online collection unit can prioritize collecting highly relevant information by taking into account the child's geographical location information. For example, the online collection unit can prioritize collecting information about vocational training programs in the area where the child lives. The online collection unit can also collect information about events held near the child's school. Furthermore, the online collection unit can collect information that matches scholarship application requirements based on the child's geographical location. In this way, more relevant information can be collected by taking geographical location information into consideration. Some or all of the above-described processing in the online collection unit may be performed using, or without, AI. For example, the online collection unit can input geographical location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0115] The machine learning analysis unit can estimate the child's emotions and adjust the machine learning analysis method based on the estimated child's emotions. For example, if the child is relaxed, the machine learning analysis unit can perform a detailed analysis. If the child is nervous, the machine learning analysis unit can also perform a concise and to-the-point analysis. If the child is excited, the machine learning analysis unit can also perform a visually appealing analysis. This allows for adjusting the analysis method according to the child's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 machine learning analysis unit can be performed using, for example, an AI, or without an AI. For example, the machine learning analysis unit can input the child's emotion data into the generative AI and cause the generative AI to adjust the analysis method.

[0116] During machine learning analysis, the machine learning analysis unit can optimize the analysis algorithm by referring to past learning data. The machine learning analysis unit, for example, adjusts the analysis algorithm based on past learning data. The machine learning analysis unit can also improve the accuracy of the analysis by referring to past learning data. Furthermore, the machine learning analysis unit can adjust the analysis priority based on past learning data. In this way, the accuracy of the analysis algorithm is improved by referring to past learning data. Some or all of the above-mentioned processing in the machine learning analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the machine learning analysis unit can input past learning data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0117] The machine learning analysis unit can estimate the child's emotions and adjust the frequency of machine learning based on the estimated child's emotions. For example, the machine learning analysis unit can perform analysis more frequently when the child is relaxed. Furthermore, the machine learning analysis unit can also reduce the frequency of analysis when the child is nervous. Furthermore, the machine learning analysis unit can perform analysis at an appropriate frequency when the child is excited. This allows for adjusting the frequency of machine learning according to the child's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 machine learning analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the machine learning analysis unit can input the child's emotion data into the generative AI and cause the generative AI to adjust the analysis frequency.

[0118] During machine learning analysis, the machine learning analysis unit can integrate information from different data sources to enrich the analysis data. For example, the machine learning analysis unit integrates and analyzes school grade data and data from an online learning platform. The machine learning analysis unit can also integrate and analyze social media activity data and survey data. Furthermore, the machine learning analysis unit can also integrate and analyze past project data and current learning progress data. In this way, by integrating information from different data sources, the accuracy of the analysis data is improved. Some or all of the above-mentioned processing in the machine learning analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the machine learning analysis unit can input data from different data sources into the generation AI and have the generation AI integrate the analysis data.

[0119] The web providing unit can estimate the child's emotions and adjust the display method of the web presentation based on the estimated child's emotions. For example, if the child is relaxed, the web providing unit can display detailed information. If the child is nervous, the web providing unit can also display concise and to-the-point information. If the child is excited, the web providing unit can also display visually appealing information. This allows for adjusting the display method according to the child's emotions to provide more appropriate information. 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 web providing unit can be performed using AI, for example, or without AI. For example, the web providing unit can input the child's emotion data into the generation AI and have the generation AI adjust the display method.

[0120] When providing the web content, the web providing unit can select the optimal display method by referring to the child's past web operation history. For example, the web providing unit can prioritize the use of a display method that the child previously preferred. The web providing unit can also suggest the optimal display method based on the child's past operation history. Furthermore, the web providing unit can also customize the display content by referring to the child's past operation history. In this way, by referring to the past web operation history, a more effective display method can be provided. Some or all of the above-described processing in the web providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the web providing unit can input past web operation history data into the generation AI and cause the generation AI to select the optimal display method.

[0121] The web providing unit can estimate the child's emotions and adjust the web-provided operation procedures based on the estimated child's emotions. For example, if the child is relaxed, the web providing unit can provide detailed operation procedures. Furthermore, if the child is nervous, the web providing unit can provide concise and concise operation procedures. Furthermore, if the child is excited, the web providing unit can provide visually appealing operation procedures. This allows for adjusting the operation procedures according to the child's emotions, thereby providing more appropriate operation procedures. The emotion estimation is achieved 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 web providing unit can be performed using, for example, AI, or without AI. For example, the web providing unit can input the child's emotion data into the generation AI and have the generation AI adjust the operation procedures.

[0122] The web providing unit can select the optimal display method by taking into account the child's device information when providing the web. For example, if the child is using a smartphone, the web providing unit can provide a display method that matches the screen size. Furthermore, if the child is using a tablet, the web providing unit can provide a display method optimized for a large screen. Furthermore, if the child is using a smartwatch, the web providing unit can provide a simple and highly visible display method. This allows for a more appropriate display method to be provided by taking device information into account. Some or all of the above-described processing in the web providing unit may be performed using, or without, AI. For example, the web providing unit can input device information data into the generation AI and cause the generation AI to select the optimal display method.

[0123] The privacy protection unit can estimate the child's emotions and adjust the privacy protection method based on the estimated child's emotions. For example, if the child is relaxed, the privacy protection unit can provide detailed privacy settings. If the child is nervous, the privacy protection unit can also provide concise and to-the-point privacy settings. If the child is excited, the privacy protection unit can also provide visually appealing privacy settings. This allows for more appropriate privacy protection by adjusting the privacy protection method according to the child'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 privacy protection unit can be performed using AI, for example, or without AI. For example, the privacy protection unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the privacy protection method.

[0124] During privacy protection, the privacy protection unit can select the optimal protection method by referring to the child's past privacy setting history. For example, the privacy protection unit can suggest the optimal protection method based on the child's past privacy settings. The privacy protection unit can also customize the protection method by referring to the child's past privacy setting history. Furthermore, the privacy protection unit can adjust the priority of privacy protection based on the child's past privacy setting history. In this way, more effective privacy protection can be provided by referring to the past privacy setting history. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit can input past privacy setting history data into a generation AI and have the generation AI select the optimal protection method.

[0125] The privacy protection unit can estimate the child's emotions and determine the priority of privacy protection based on the estimated child's emotions. For example, if the child is relaxed, the privacy protection unit can prioritize detailed privacy protection. If the child is nervous, the privacy protection unit can also prioritize concise and to-the-point privacy protection. If the child is excited, the privacy protection unit can also prioritize visually appealing privacy protection. Thus, by determining the priority of privacy protection based on the child's emotions, more important privacy protection can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the privacy protection unit can be performed using, for example, AI, or without AI. For example, the privacy protection unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of privacy protection.

[0126] The privacy protection unit can select the optimal protection method during privacy protection, taking into account the child's geographical location information. The privacy protection unit provides the optimal protection method, for example, based on privacy laws and regulations in the area where the child lives. The privacy protection unit can also adjust the protection method based on the privacy policy of the child's school. Furthermore, the privacy protection unit can determine the priority of privacy protection based on the child's geographical location. This allows for more appropriate privacy protection by taking geographical location information into consideration. Some or all of the above-described processing in the privacy protection unit may be performed using AI, for example, or without AI. For example, the privacy protection unit can input geographical location information data into a generation AI and have the generation AI select the optimal protection method.

[0127] The data update unit can estimate the child's emotions and adjust the frequency of data updates based on the estimated child's emotions. For example, the data update unit can perform data updates frequently when the child is relaxed. Furthermore, the data update unit can reduce the frequency of data updates when the child is nervous. Furthermore, the data update unit can perform data updates at an appropriate frequency when the child is excited. This allows for more appropriate data updates by adjusting the frequency of data updates according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the data update unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the data update unit can input the child's emotion data to the generation AI and cause the generation AI to adjust the frequency of data updates.

[0128] When updating data, the data update unit can select the optimal update method by referring to past data update history. The data update unit, for example, proposes the optimal update method based on the past data update history. The data update unit can also customize the update method by referring to the past data update history. Furthermore, the data update unit can also adjust the priority of data updates based on the past data update history. In this way, more effective data updates can be provided by referring to the past data update history. Some or all of the above-mentioned processing in the data update unit may be performed using, for example, AI, or may be performed without using AI. For example, the data update unit can input past data update history data into a generation AI and have the generation AI select the optimal update method.

[0129] The data update unit can estimate the child's emotions and prioritize data updates based on the estimated child's emotions. For example, if the child is relaxed, the data update unit can prioritize detailed data updates. Furthermore, if the child is nervous, the data update unit can prioritize concise and to-the-point data updates. Furthermore, if the child is excited, the data update unit can prioritize visually appealing data updates. Thus, by prioritizing data updates based on the child's emotions, more important data updates can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data update unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the data update unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of data updates.

[0130] When updating data, the data update unit can integrate information from different data sources to enrich the updated data. For example, the data update unit integrates and updates school grade data with data from an online learning platform. The data update unit can also integrate and update social media activity data with survey data. Furthermore, the data update unit can also integrate and update past project data with current learning progress data. This improves the accuracy of the updated data by integrating information from different data sources. Some or all of the above-described processing in the data update unit may be performed using, for example, AI, or may be performed without using AI. For example, the data update unit can input data from different data sources into a generation AI and have the generation AI integrate the updated data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision 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 collection unit collects information on a child's interests, skills, and career aspirations using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates vocational training information and scholarship information based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated information to the child and their guardian. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision 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 collection unit collects information on a child's interests, skills, and career aspirations using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates vocational training information and scholarship information based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated information to the child and his or her guardian. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision 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 collection unit collects information on a child's interests, skills, and career aspirations using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates vocational training information and scholarship information based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated information to the child and their guardian. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on the child's interests, skills, and career aspirations using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates vocational training information and scholarship information based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated information to the child and his or her guardian.

[0131] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0132] The information provision system may further include a health monitoring unit that monitors the child's health condition. The health monitoring unit, for example, collects the child's heart rate and sleep patterns via a wearable device and transmits them to the analysis unit. The analysis unit can evaluate the child's stress level and fatigue level based on the collected health data and provide information at an appropriate time. For example, if the child is tired, it can provide relaxing content, and if the child is energetic, it can provide information related to learning. This makes it possible to provide information according to the child's health condition.

[0133] The information provision system can further include a learning style analysis unit that analyzes a child's learning style. The learning style analysis unit can, for example, determine whether a child is a visual learner or an auditory learner and provide information in an appropriate format. For example, visual learners can be provided with materials that make extensive use of graphs and diagrams, while auditory learners can be provided with audio guides. This makes it possible to provide information that suits each child's learning style.

[0134] The information provision system may further include a social skills assessment unit that assesses the child's social skills. The social skills assessment unit may assess, for example, the child's friendships and communication skills, and provide appropriate job training information and scholarship information. For example, a child with high communication skills may be provided with information about leadership training and team projects, while a child with low communication skills may be provided with information about individual instruction and mentoring programs. This makes it possible to provide information tailored to the child's social skills.

[0135] The information provision system can further include a hobby and special skill analysis unit that takes into account the child's hobbies and special skills. The hobby and special skill analysis unit analyzes, for example, whether the child is interested in sports, music, art, etc., and provides related vocational training information and scholarship information. For example, a child interested in sports can be provided with sports-related scholarship information and training programs, and a child interested in music can be provided with music school scholarship information and workshop information. This makes it possible to provide information tailored to the child's hobbies and special skills.

[0136] The information provision system may further include a goal setting unit that sets future goals for the child. The goal setting unit, for example, allows the child to set a future goal and provides steps for moving toward that goal. For example, a child who wants to become a doctor may be provided with medical-related vocational training information and scholarship information, and a child who wants to become an engineer may be provided with engineering-related vocational training information and scholarship information. This makes it possible to provide information according to the child's future goals.

[0137] The information provision system can further estimate the child's emotions and adjust the way information is provided based on the estimated child's emotions. For example, if the child is relaxed, detailed information can be provided, and if the child is nervous, concise and to the point information can be provided. Furthermore, if the child is excited, visually appealing information can be provided. This makes it possible to provide information according to the child's emotions.

[0138] The information provision system can further estimate the child's emotions and determine the priority of information based on the estimated child's emotions. For example, if the child is excited, information about areas of interest can be provided preferentially, and if the child is relaxed, detailed information about skills can be provided preferentially. Furthermore, if the child is concentrating, information about career aspirations can be provided preferentially. This makes it possible to provide information based on the child's emotions.

[0139] The information provision system can further estimate the child's emotions and adjust the way information is displayed based on the estimated child's emotions. For example, if the child is relaxed, detailed information can be displayed, and if the child is nervous, concise and to the point information can be displayed. Furthermore, if the child is excited, visually appealing information can be displayed. This makes it possible to display information according to the child's emotions.

[0140] The information provision system can further estimate the child's emotions and adjust the timing of providing information based on the estimated child's emotions. For example, detailed information can be provided when the child is relaxed, and concise information can be provided when the child is nervous. Furthermore, visually appealing information can be provided when the child is excited. This makes it possible to adjust the timing of providing information according to the child's emotions.

[0141] The information providing system can further estimate the child's emotions and adjust the method of information feedback based on the estimated child's emotions. For example, if the child is relaxed, detailed feedback can be provided, and if the child is nervous, brief and to the point feedback can be provided. Furthermore, if the child is excited, visually appealing feedback can be provided. This makes it possible to adjust the feedback method according to the child's emotions.

[0142] The processing flow of the second embodiment will be briefly explained below.

[0143] Step 1: The collection department collects information about the child's interests, skills, and career aspirations. The collection department can collect information through questionnaires or interviews. They can also use online platforms to collect information through websites or mobile apps. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using data mining or statistical analysis. The analysis unit can also use machine learning algorithms, such as neural networks or support vector machines. Step 3: The generation unit generates vocational training information and scholarship information based on the information analyzed by the analysis unit. The generation unit can generate online course and internship information. It can also generate scholarship information based on application requirements and grant amounts. Step 4: The providing unit provides the information generated by the generating unit. The providing unit can provide the information via email or an app. It can also provide the information through a web portal that has user authentication and search functions.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0149] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0165] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0178] 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.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0181] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0195] 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.

[0196] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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).

[0201] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0202] 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."

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Explanation of symbols]

[0216] 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 collection department that collects information about children's interests, skills, and career aspirations. an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates vocational training information and scholarship information based on the information analyzed by the analysis unit; a providing unit that provides the information generated by the generating unit. A system characterized by:

2. An online collection department will be established to collect information using online platforms.

2. The system of claim 1.

3. Equipped with a machine learning analysis unit that performs analysis using machine learning algorithms 2. The system of claim 1.

4. A web providing unit is provided that provides the generated information through a web portal.

2. The system of claim 1.

5. Equipped with a privacy protection section for privacy protection 2. The system of claim 1.

6. Equipped with a data update unit that periodically updates data 2. The system of claim 1.

7. The collecting unit Inferring the child's emotions and adjusting the timing of information gathering based on the estimated child's emotions 2. The system of claim 1.

8. The collecting unit Analyze your child's past activity history and select the best method of collecting information 2. The system of claim 1.

9. The collecting unit When collecting information, filter it based on your child's current learning status and areas of interest.

2. The system of claim 1.

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